plant microbe interaction research paper

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Publications
Account settings
My Bibliography
Collections
Citation manager

Save citation to file

Email citation, add to collections.

Create a new collection
Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

Search in PubMed
Search in NLM Catalog
Add to Search

Plant-Microbe Interactions Facing Environmental Challenge

Affiliations.

1 Howard Hughes Medical Institute, Michigan State University, East Lansing, MI 48824, USA; Department of Energy Plant Research Laboratory, Michigan State University, East Lansing, MI 48824, USA. Electronic address: [email protected].
2 Howard Hughes Medical Institute, Michigan State University, East Lansing, MI 48824, USA; Department of Energy Plant Research Laboratory, Michigan State University, East Lansing, MI 48824, USA. Electronic address: [email protected].
3 Howard Hughes Medical Institute, Michigan State University, East Lansing, MI 48824, USA; Department of Energy Plant Research Laboratory, Michigan State University, East Lansing, MI 48824, USA; Plant Resilient Institute, Michigan State University, East Lansing, MI 48824, USA. Electronic address: [email protected].
PMID: 31415751
PMCID: PMC6697056
DOI: 10.1016/j.chom.2019.07.009

In the past four decades, tremendous progress has been made in understanding how plants respond to microbial colonization and how microbial pathogens and symbionts reprogram plant cellular processes. In contrast, our knowledge of how environmental conditions impact plant-microbe interactions is less understood at the mechanistic level, as most molecular studies are performed under simple and static laboratory conditions. In this review, we highlight research that begins to shed light on the mechanisms by which environmental conditions influence diverse plant-pathogen, plant-symbiont, and plant-microbiota interactions. There is a great need to increase efforts in this important area of research in order to reach a systems-level understanding of plant-microbe interactions that are more reflective of what occurs in nature.

Keywords: abiotic stress; circadian clock; climate change; humidity; innate immunity; light; nutrient; plant pathogen; symbiosis; temperature.

PubMed Disclaimer

(A) An overview diagram depicting…

(A) An overview diagram depicting environmental conditions that are known to affect plant-microbe…

Schematic diagram of temperature-, circadian-…

Schematic diagram of temperature-, circadian- and humidity-mediated effects on plant immunity. (A) Effect…

Nutrient status and plant-microbe interactions.…

Nutrient status and plant-microbe interactions. (A) Phosphate status and Arabidopsis-root microbiome interaction. Under…

Publication types

Search in MeSH

Grants and funding

R01 GM109928/GM/NIGMS NIH HHS/United States

LinkOut - more resources

Full text sources.

Elsevier Science
Europe PubMed Central
PubMed Central

Miscellaneous

NCI CPTAC Assay Portal

Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

REVIEW article

Revisiting plant–microbe interactions and microbial consortia application for enhancing sustainable agriculture: a review.

$\r\nKanchan Vishwakarma*$

1 Amity Institute of Microbial Technology, Amity University, Noida, India
2 Department of Biotechnology, Periyar Maniammai Institute of Science and Technology, Thanjavur, India

The present scenario of agricultural sector is dependent hugely on the use of chemical-based fertilizers and pesticides that impact the nutritional quality, health status, and productivity of the crops. Moreover, continuous release of these chemical inputs causes toxic compounds such as metals to accumulate in the soil and move to the plants with prolonged exposure, which ultimately impact the human health. Hence, it becomes necessary to bring out the alternatives to chemical pesticides/fertilizers for improvement of agricultural outputs. The rhizosphere of plant is an important niche with abundant microorganisms residing in it. They possess the properties of plant growth promotion, disease suppression, removal of toxic compounds, and assimilating nutrients to plants. Utilizing such beneficial microbes for crop productivity presents an efficient way to modulate the crop yield and productivity by maintaining healthy status and quality of the plants through bioformulations. To understand these microbial formulation compositions, it becomes essential to understand the processes going on in the rhizosphere as well as their concrete identification for better utilization of the microbial diversity such as plant growth–promoting bacteria and arbuscular mycorrhizal fungi. Hence, with this background, the present review article highlights the plant microbiome aboveground and belowground, importance of microbial inoculants in various plant species, and their subsequent interactive mechanisms for sustainable agriculture.

Introduction

Plants have dense inhabitation of the variety of microbes both belowground and aboveground that serve for their mutualistic benefits. The microbes that colonize the plants can be categorized into epiphytes that are present on the surface, endophytes that are located inside the plant tissues, phyllospheric that grow on leaf surfaces, and rhizospheric that inhabits into the soil close to the roots. Among them, rhizosphere is considered the most dynamic to significantly impact the nutritional status of plant and its growth ( Bakker et al., 2013 ; Mendes et al., 2013 ; Lakshmanan et al., 2014 ). The term rhizosphere is defined as the narrow region of soil surrounding the roots and directly influenced by microbes and root secretions. The underground system comprises mainly soil and primary roots along with lateral developments and root hairs, which establish their interactions with countless microbial diversity in the rhizosphere, thereby significantly influencing the plant growth stages and resistance against variety of stresses ( Figure 1 ) ( Panke-Buisse et al., 2015 ; Bandyopadhyay et al., 2017 ). This whole system with plant roots interacting with the rhizomicrobiome constitutes the plant–root microbiome ( Philippot et al., 2013 ).

Figure 1. Associations in the rhizosphere between plant roots, microbes, and root exudates under biotic and abiotic influences.

Knowing the hugely diversified speciation, complexity in interactions, and structural composition of communities, the need of comprehending the root architectural biology and associated microbiome as an interactome becomes essential. The intertwining nature of host and microbes opens the possibility of numerous interactions such as plant root–root interactions and root–microbe interactions. Apart from this, root–nematode interactions also serve as an essential mode to understand the behavior of plants in response to such factors. Plant hosts and associated microbes possess inseparable ecological properties, which functions as metaorganism or holobiont ( Hacquard and Schadt, 2015 ; Hacquard, 2016 ).

With the advancements in the techniques with respect to genome and proteome identification and analysis, studies are conducted to explore the mutual association between plant and microbes and understand related mechanisms for improved crop production ( Bakker et al., 2013 ; Oldroyd, 2013 ). If the characteristics that are responsible for forming microbial community in the rhizosphere and its influence on plants are unraveled, these can be utilized for probable sustainable alternative in agroecosystem for enhanced stability and crop productivity in longer run ( Quiza et al., 2015 ; Knapp et al., 2018 ). Hence, with this background, the review focuses on belowground microbial communities that start from their establishment to their interactions in the rhizosphere and mechanistic approaches and also highlights the aboveground plant microbiome.

Aboveground Plant Microbiota

Unique environments for endophytic and epiphytic microbial diversities have been provided by different aboveground plant tissues such as vegetative foliar tissues, leaves, and floral parts, but the major differences in ecology of endospheric (endosphere is inside the environment of plant where microbes survive and may or may not be harmful to the plants; Hardoim et al., 2015 ; Compant et al., 2020 ) and phyllospheric (phyllosphere refers to the aerial region of the plant colonized by microbes) bacterial diversity exist. Systematic distribution of endophytes to different compartments such as stem, leaves, and fruits is facilitated by xylem ( Compant et al., 2010 ), but it is observed that their entry to plant tissues can also take place through aerial parts such as fruits and flowers ( Compant et al., 2011 ). Different compartments of plants possess distinct communities of endophytes, depending on source allocation of plant. The movement of phyllospheric bacteria is reportedly seen from soil environment that is driven by plant and various environmental parameters ( Vorholt, 2012 ; Wallace et al., 2018 ). This leads to subsequent distribution of various microorganisms at genus and species level in endospheric and phyllospheric regions. For example, upon analyzing the structure of phyllosphere or carposphere of the grapevine, it was observed that Pseudomonas , Sphingomonas , Frigoribacterium , Curtobacterium , Bacillus , Enterobacter , Acinetobacter , Erwinia , Citrobacter , Pantoea , and Methylobacterium are predominant genera ( Zarraonaindia et al., 2015 ; Kecskeméti et al., 2016 ), whereas when endophytes of grape berries were analyzed, the dominant genera found were Ralstonia , Burkholderia , Pseudomonas , Staphylococcus , Mesorhizobium , Propionibacterium , Dyella , and Bacillus ( Campisano et al., 2014 ).

A study conducted on microbiome of maize leaf across 300 plant cell lines showed that Sphingomonads and Methylobacteria are the predominant taxa ( Wallace et al., 2018 ). It was also established that environmental factors play a major role in deriving microbial composition of the phyllosphere. Another study done by Steven et al. (2018) on apple flowers showed the dominance of Pseudomonas and Enterobacteriaceae taxa. Moreover, Pseudomonas has been observed to be an abundant genus in numerous studies conducted on flowers of apple, grapefruit, almonds, pumpkin, and tobacco ( Aleklett et al., 2014 ). Recent studies were facilitated to assess the seed microbes, and it was observed that Firmicutes , Proteobacteria , Bacteroidetes , and Actinobacteria are the dominant ones ( Liu et al., 2012 ; Barret et al., 2015 ; Rodríguez et al., 2018 ). The relation of seed microbiota has been seen with soil microbiota, and it is also evidenced that they can also be related to those of flowers and fruits ( Compant et al., 2010 ; Glassner et al., 2018 ). The aboveground bacterial diversity originates from soil, seeds, and air followed by their inhabitation on or inside the plant tissues. Their existence on tissues is further shaped by various factors such as soil, environmental, and agricultural management practices. The strength of relationship between plant and its aboveground bacterial composition is specific to the host and the specific compartment where diversity exists; however, detailed knowledge of this relationship requires more research-based studies. These endophytes and aboveground microbiota are potentially known for promotion of plant growth, improvement of disease resistance, and alleviation of stresses ( Hardoim et al., 2015 ; Vishwakarma et al., 2020 ).

Belowground Microbial Occurrence and Interactions

Microorganisms are ubiquitously present on the surfaces of plant along with their presence in the soil and are recruited by the plant from the surroundings, which then serve as microbial reservoirs ( Hardoim et al., 2015 ). The root microbiome can be transferred in two different ways, i.e., horizontal and vertical. The dynamic communities of microbes associated with the plant roots generally undergo horizontal transfer, which means that they are enriched from the soil rich in diversified bacterial communities predominated by Acidobacteria , Bacteroidetes , Proteobacteria , Planctomycetes , and Actinobacteria ( Fierer, 2017 ). The transfer of bacterial communities can also take place in vertical direction by seeds, representing an essential source of proliferating microbes from roots of a plant to its development ( Hardoim et al., 2012 ). Distinct and interesting soil microbial niches are provided by the plant roots that allow their colonization in the rhizosphere and root, as well as aboveground areas to a certain limit ( Hartmann et al., 2009 ). The narrow layer of soil in the vicinity of the plant roots (rhizosphere) is thought to be a highly active area for microbial movement, making it one of the most intricate environments ( Hiltner, 1904 ). In a study, it was demonstrated by using culture-based technique, i.e., terminal restriction fragment length polymorphism, that abundant microbial community was present in the rhizosphere in comparison to the bulk soil in an extensive wheat cropping system ( Donn et al., 2015 ).

Root exudation is defined as the secretion of several compounds of importance by the roots into the rhizosphere, for example, organic acids, sugars, amino acids, polyphenols, flavonoids, hormones, and nutrients, which act as source of nutrients for the microorganisms surrounding the roots ( Mendes et al., 2013 ; Compant et al., 2019 ). This phenomenon is known as the rhizosphere effect. Nevertheless, the association of plant roots with microbiome involves the formation of selective niches for microbial development ( Figure 2A ). With the help of phytochemicals and root exudates, several microbial groups fail to grow in the rhizospheric niche. The population able to grow by utilizing root-secreted compounds forms a niche for themselves and also helps in recruiting other microbes by cross-feeding approach, thereby generating a new niche for rest of the microbes ( Jacoby and Kopriva, 2019 ). The niche selection process is specific for the plant species and the compounds being secreted. For example, several secondary metabolites with defense properties such as benzoxazinoids discharged from the maize roots change the structure of root microbiome and influence the group of Actinobacteria and Proteobacteria the most ( Hu et al., 2018 ). Moreover, the dynamics of structural composition of bacterial communities in the Avena barbata roots and their mechanisms were researched in a recent study ( Zhalnina et al., 2018 ). It was observed that the amalgamation of root exudate composition and substrate selectivity significantly modified the assemblage of bacterial population in rhizosphere. Fitzpatrick et al. (2018) revealed various rhizobacterial species of Pseudoxanthomonas depicting differential patterns of occurrence across 30 angiospermic species. Moreover, the niche specifications and the huge diversity of the rhizospheric microbiota are also governed by the spatiotemporal organization of the rhizosphere and changes in physicochemical conditions ( Vetterlein et al., 2020 ). On the whole, variety of plant species and related genotypes and components of root exudates affect the structure and alignment of rhizospheric microbiome ( Vishwakarma et al., 2017a , b ).

Figure 2. Interactions in the rhizosphere, (A) Plant–microbiome interactions: Plant roots secrete root exudates and phytochemicals that engage microbial populations in developing niches. Some metabolites filter out the unnecessary microbial strains occupied in niches (indicated by red cross), whereas some metabolites allow the different microbial population to coexist in same niche that may secrete compounds needed for growth of other microorganisms. (B) Root–root interactions: The neighboring plants may associate with other forming beneficial, as well as competing interactions by allelochemicals, root exudates, and volatile organic compounds. (C) Microbiome–plant interactions: Beneficial bacteria allow the promotion of plant growth by various mechanisms, such as making nutrients available by chelating them and transporting to plants (for example, siderophore-Fe transporter to carry utilizable iron); and producing phytohormones, such as indole acetic acid, secreted effectors, and antibiotics to protect plants from pathogens. AHL, N -acyl homoserine lactone; QSM, quorum-sensing molecules; VOCs, volatile organic compounds; Fe, iron; Cd, cadmium; Zn, zinc.

The internal colonization of roots also takes place by a variety of endophytic microbes. Their distribution in plants is dependent on several parameters such as the distribution of plant assets and the capability of endophytes in colonizing plants. One of the important and symbiotic root endophytes, Piriformospora indica , has been significantly used in agriculture for its function. The endophyte P. indica increases phosphorous (P) uptake and protects the crop from variety of stress factors ( Lahrmann et al., 2013 ). It has been reported that a cyclophilin A–like protein from P. indica was overexpressed for protecting the tobacco plant against salt stress ( Trivedi et al., 2013 ). It has been also observed that Azotobacter chrococcum can facilitate the modulation of P. indica physiology and helps in improving its nutrient acquisition through their synergistic action ( Bhuyan et al., 2015 ).

Many endophytic fungi have been shown to exhibit chemotaxis for root-exuded chemicals. For instance, in a non-pathogenic Fusarium oxysporum when tested for activity against root knot nematode ( Meloidogyne incognita ) in tomato plants, it was found that the tomato exudates have facilitated the colonization of F. oxysporum , whereas it reduced the occurrence of nematode ( Sikora and Dababat, 2007 ), suggesting that root exudates preferentially select the microbes in its vicinity. Nevertheless, root exudate–mediated chemotaxis also causes attraction for the pathogenic microbes. In a study by Gu et al. (2017) , fine biochar was utilized to suppress bacterial wilt disease in tomato. The mechanism that biochar followed was absorption of root exudates that exerted strong chemotactic signal toward Ralstonia solanacearum , and as a result, its activity and swarming motility were suppressed. In a recent study, this bacterial pathogen has also been shown to follow chemotaxis for tomato root–exuded secondary metabolites (hydroxylated aromatic acids) ( Hasegawa et al., 2019 ). Pratylenchus coffeae is an endoparasitic nematode that causes disruption of root tissues mechanically followed by invasion in plants ( Das and Das, 1986 ). The molecular and gene expression studies on Pratylenchus coffeae have been conducted to specify the genes (related to cell wall degrading enzyme) regulated in the presence and absence of root exudates, and it was observed that their activity changed with respect to the host-specific root exudate components provided for the assay ( Bell et al., 2019 ). The protozoan parasite Trypanosoma brucei generally displays its movement away from the other inhabited microbial groups; however, DeMarco et al. (2020) have recently observed their positive chemotactic effect toward the colonized area of Escherichia coli . It is due to the presence of attractant that is a soluble, diffusible signal dependent on actively growing E. coli .

Root–Root Interactions

Because of the coexistence of different plants in the same soil, a competition is formed in the overlapping root systems for required resources that are limited in the soil. This coexistence has been thought relative to differentiation of niche because of different rooting patterns of plant species ( Parrish and Bazzaz, 1976 ; Berendse, 1982 ). However, this theory supports competitive interactions occurring belowground. The surprising knowledge of coexistence also helps in showing the interactions that are competitive as well as facilitative between the co-occurring roots. The communication between roots of neighboring plants takes place by secretion of several signaling molecules such as root exudates and allelochemicals ( Figure 2B ) ( Mommer et al., 2016a ). Among them, allelopathy is the frequent communication process where phytotoxins such as catechin are released by plants. Catechin is capable of mediating both interspecific and intraspecific association by inhibiting growth of adjacent plant species, thereby enabling reduced competition and enhanced nutrient availability ( Mommer et al., 2016b ). Volatile organic compounds (VOCs) are also allelochemicals that mediate rhizospheric signaling by mycorrhiza networks among plants and increasing their transmission.

Apart from this, different experiments were carried out to prove different evidence in relation to interactions between plant roots with differential niches. For instance, Semchenko et al. (2018) showed that vertically distributed roots are related to competitive interactions between plants rather than integral niche. Their results have shown that there is strong competition between the plant species, which spread their roots largely leading to the suppression of neighboring species, whereas species having deeper and less branched root system are extensively able to withstand such competition. Further, using genetically transformed plants, Weidlich et al. (2018) showed facilitative interactions between the roots of legume and non-legume species belowground. These interactions are limited not only to different species but also between the genotypes. Stepping from interactions between species to interactions between genotypes, Montazeaud et al. (2018) experimented on some species and observed the productivity of rice plants ( Oryza sativa ), which was grown in pairs, and it was observed that with increase in between-genotype distance, there was increase in mixture productivity in crops, which was attributed to resource-use complementarity. Moreover, mixing of two different species of trees was performed to explore soil by their fine roots. The species used were Acacia mangium and Eucalyptus grandis , where soil was more exploited by tree species as compared to the trees that were grown in the monoculture ( Germon et al., 2018 ). These results further helped in supporting the importance of direct competition over the niche complementarity hypothesis.

Root–Microbe Interactions

The identity of plant species largely influences variety of diverse organisms living in soils and particularly those living in close region to plant ( Kowalchuk et al., 2002 ). Thus, organisms present in the soil can impact plant development and execution ( Van der Putten et al., 2013 ; Jones et al., 2019 ). For establishing symbiotic association with the plants, microbes engage in releasing many beneficial compounds in the rhizosphere for plant’s uptake. Such molecules facilitate the regulation of plant’s transcriptome. In addition to production of hormones by plants, several cytokinin, auxins, and gibberellins are secreted by microbial population residing near plant roots ( Figure 2C ) ( Fahad et al., 2015 ).

Interaction Between Root and Microbe via Root Exudates

Plant-specific root exudates display the specific selection of rhizospheric microbial communities; for instance, cucumber plant secreted citric acids from its roots, which then influenced the attraction of Bacillus amyloliquefaciens and banana root–exuded fumaric acid, which attracted B. Bacillus subtilis toward roots leading to biofilm formation ( Zhang et al., 2014 ). Some compounds have displayed the ability of inducing nodule formation in roots like flavonoids, which are the derivatives of 2-phenyl-1,4-benzopyrone, cause induction of bacterial nod genes, and lead lipochitooligosaccharides (LCOs) to initiate root nodule formation. These compounds have classified role in mimicking quorum sensing in bacteria and hence impact the bacterial metabolism ( Hassan and Mathesius, 2012 ). Apart from these, several other compounds help in synthesizing phytohormones required by bacteria for plant growth–promoting rhizobacterium (PGPR) activities like tryptophan that biosynthesizes indole acetic acid (IAA) ( Haichar et al., 2014 ). Additionally, aminocyclopropane-1-carboxylic acid (ACC) is also exuded by roots for synthesis of ethylene (ET, a stress hormone) and as carbon and nitrogen source for bacterial growth, which is evident from the expression of acd S gene in microbes inhabiting the roots and involved in root exudate assimilation ( Haichar et al., 2012 ). Through this, ACC deaminase–producing PGPRs help in utilization of ACC to decrease the level of ACC outside the plants to equalize with that of inside levels ( Glick et al., 1998 ).

Influence of Climatic and Soil Conditions on Root–Microbe Interaction

The role of plant species is dependent on the soil feedback and climatic alterations. For instance, concentrating on how climatic conditions impact plant-soil inputs, Legay et al. (2017) showed that the inheritance impact of a past dry spell supported the resistance of Lolium perenne to another dry season occasion. This beneficial outcome was then credited to the choice of microorganisms during the primary dry season. Concentrating on severely phosphorous drained soils, Zemunik et al. (2017) showed that the extent of non-mycorrhizal plant species expanded directly with phosphorous deprivation in soils. The authors recommend that in severely phosphorous-exhausted soils, retaining the phosphorous through the influx of carboxylates is supported over the broadly spread beneficial interaction between arbuscular mycorrhizae and plant roots. In another study, Gang et al. (2018) deliver the constructive outcomes of the rhizobacterium Klebsiella SGM 81 on the development and improvement of root hairs by Dianthus caryophyllus . A mutualistic connection between Klebsiella SGM 81, living and forming IAA in close region to the establishment of D. caryophyllus , was distinguished as the fundamental mechanism clarifying the improved root hair generation and plant development. Rutten and Gómez-Aparicio (2018) demonstrated that soil and plant feedback depended on different species as well as on the related soil microbial communities, by using precipitation gradient that showed climatic change.

These examinations work to translate the complex and frequently setting wide collaborations between plant roots, soil, and microbes. While they together shed light on novel components intervening these associations, a major point of view of how root-microbiome connections are adjusted by natural conditions still requires extending the scope of living organisms and thought of a more extensive board of ecological conditions, including an assortment of atmosphere and soil properties.

Mechanism of Belowground Interactions in the Rhizosphere: Beyond Plant’s Innate Immune Response

A number of characteristic traits, such as growth patterns, behavior under stress and its mitigation, etc., have been displayed by the plant species present in an ecosystem. These traits allow the plant species to occupy different niche in space and time; this leads to the reason of having a high diversity of plant species, which can exist in correlation in a provided habitat ( Kraft et al., 2015 ). For interactions of microbes with plants, it is essential to demark the previously formed barriers in plant species including defense responses and signaling cascades ( Mhlongo et al., 2018 ). The defense response of the plant’s immune system is based on the recognition of the pattern-triggered immunity (PTI) and effector-triggered immunity (ETI). The first line of defense action is thought to be the PTI that includes the protein recognition receptors (PRRs) present at the surface of the cells. The conserved patterns known as pathogen (microbe)–associated molecular patterns (MAMP) serve as the binding sites for the PRR initiating a signaling cascade mechanism of defense responses, thereby inhibiting the microbe’s (pathogen’s) growth ( Deslandes and Rivas, 2012 ; Denancé et al., 2013 ; Gao et al., 2013 ). However, some pathogens may cause the downregulation of PTI by secreting the effector proteins. This leads to the activation of second lineage of defensive actions, i.e., ETI, where intracellular resistance (R) genes having nucleotide-binding leucine-rich repeats are present. These R genes facilitate the binding of coding proteins to the effector virulence proteins of microbes triggering a signaling mechanism to cause cell death. The cascades PTI and ETI may involve sharing of certain biochemicals; however, they are often viewed as distinct in activities with more conserved evolutionary responses of PTI than that of ETI ( Zhang and Zhou, 2010 ; Dempsey and Klessig, 2012 ). It has been highlighted that the immune system of the plant involves the strict regulation of coevolving interactive responses with multitude signaling processes among which phytohormones play a significant role inducing both systemic and local effects ( Bartoli et al., 2013 ). The pathways in which the phytohormones play an active role involve induced systemic resistance (ISR) and systemic acquired resistance (SAR) ( Pieterse et al., 2012 ; Fu and Dong, 2013 ). To achieve an efficient plant and microbe symbiosis, the aforementioned innate responses and predefined restrictions need to be circumvented through chemistry of chemical cross talking between microbes and plants. Hence, the interactions between the plant roots and microbes as well as plant root–root associations must be considered beyond innate defense responses.

The advancements made in the associations of plant and microbes in the rhizosphere have enhanced the demands of developing and commercializing the microbe-based inoculants/formulations. Microbial inoculants are the agricultural amendments that can be applied to the soil or plant for enhanced crop productivity. These inoculants may be the natural diversity of a rhizosphere or synthetic composition of one or more microbes ( Johns et al., 2016 ). It may be facilitated in several ways including introducing new microbial species to the rhizosphere, manipulating the environmental parameters such as moisture, pH, temperature, etc., and growing plants that modify the microbial diversity of soil ( Finkel et al., 2017 ; Pineda et al., 2017 ).

During inoculation of bacterial formulation in the rhizosphere, sophisticated and complex interactions among plant–microbe and microbe–microbe take place, which are governed by the establishment of chemical communication in rhizosphere. The process of root exudation actively engages itself in the signaling cascades prompted in the rhizosphere due to inoculation. These associations hold a vital importance in achieving resistance to plant pathogens ( Bertin et al., 2003 ), making nutrients available to the plants, facilitation of root–root interactions ( Mommer et al., 2016a ), and inhabited microbial community regulations ( Sasse et al., 2018 ). However, there is competitive pressure with respect to nutrients selectivity, chemotaxis, and root colonization on the introduced microbial inoculant to make its place in the rhizosphere, along with native microbial communities. The discretion of root exudate compounds in nourishing specific rhizobacterial species has been investigated where key substrate driver was observed to be organic acids that facilitated the chemotaxis by attracting bacterial species to the roots ( Zhalnina et al., 2018 ). Exometabolomics was deployed to delineate the substrates specifically required by bacterial strains grown on root exudates. Root exudates, having specificity to plant genotype or species, display the ability to highlight the communication knowledge between microbes, roots, and plants ( Mommer et al., 2016b ; Sasse et al., 2018 ).

Microbial species in an assemblage secrete several signaling molecules influencing the expression of genes of host plant species. Such signaling compounds comprise VOCs, for example, ketones, alcohols, alkanes, terpenoids, etc., which serve as communication channel between microbial communities in rhizosphere ( Kanchiswamy et al., 2015 ). VOCs secreted by bacteria and plants are widely known for promoting plant growth and inducing defense responses, as well as expression of nutrient (ion) transporters ( Chung et al., 2016 ). However, for establishing symbiosis with the plants, rhizomicrobes or microbial inoculants secrete plant beneficial compounds triggering the specific alterations in plant transcriptome. Phytohormones such as auxins, cytokinins, abscisic acid (ABA), salicylic acid (SA), jasmonic acid (JA), gibberellins, etc., apart from produced from plants, are secreted by beneficial microbes ( Fahad et al., 2015 ). PGPRs, defined as the beneficial microorganisms especially bacterial species in the rhizosphere that help in plant growth promotion (PGP) by multiple means either directly or indirectly, can also produce VOCs to which certain plants respond. For instance, the consortium (two or more microbes when displaying synergism in order to improve plant growth) of B. subtilis GB03 and B. amyloliquefaciens IN937a was inoculated to Arabidopsis seeds in Petri dish and enhanced its growth by secreting the volatiles acetoin and butanediol, which were common to both the microbes ( Ryu et al., 2003 ).

Multitude of Functions of Microbial Consortia in the Rhizosphere With Emphasis on Phytohormones, Nutrients, and Microbial Defense Mechanisms

Coevolving of plants with microbes follow the symbiotic association in order to colonize the terrestrial ecological systems ( Werner et al., 2014 ). The knowledge of beneficial characteristics of natural PGPRs and their interactions could support the agriculture by decreasing the utilization of chemical-based fertilizers and enhancing the plant productivity. Among several traits displayed by PGPRs, the direct properties include the nutrient assimilation, phytohormone secretion and signaling, and biological nitrogen (N 2 ) fixation and siderophore production for making iron available to the plants ( Figure 2C ), and indirect ones include pathogen suppression, e.g., by releasing gaseous substances such as hydrogen cyanide (HCN), inducing ISR and SAR and ACC deaminase enzyme production for reducing the concentration of ET in plants.

Phytohormones

Several PGPRs as well as pathogenic bacteria are capable of producing phytohormones such as auxins, cytokinins, and gibberellins, thereby influencing the plant growth by working in conjugation with endogenous formation of these hormones in plants ( Jones and Dangl, 2006 ; Gamalero and Glick, 2011 ; Spaepen, 2015 ). Rascovan et al. (2016) noticed a variety of microorganisms in wheat and soybean roots, which included Pseudomonas , Paraburkholderia , and Pantoea with significant plant growth properties such as P solubilization, N 2 fixation, IAA, and ACC deaminase production. Auxins have a significant role in regulation of plant root growth and stress responses ( Liu et al., 2014 ). Lateral root formation and elongation of nodular meristem are essentially performed by auxins ( Oldroyd et al., 2011 ). IAA is produced by both the PGPRs and pathogens in the rhizosphere or soil, and in case of secretion by pathogens, it is associated with virulence factor. For instance, T-DNA transfer by Agrobacterium tumefaciens to constitutively encode IAA production causes tumor formation (undifferentiated tissues) in plants ( Spaepen and Vanderleyden, 2011 ).

Ethylene is a volatile hormone that influences the plant growth as evidenced in plants such as bean and oats ( Laan, 1934 ; Sukumar, 2010 ). The enhancement in ET biosynthesis in Nicotiana tabacum can indicate the importance of ET in defense response of plants at the early PTI responses ( Sharon et al., 1993 ). Subsequently, in Arabidopsis thaliana , the evidence was provided for involvement of ET signaling in expressing receptor kinases (FLS2) for binding with bacterial flagellin (flg22) to initiate the defense responses ( Mersmann et al., 2010 ). Its association with resistance to stress incidences was also reported ( Thao et al., 2015 ). The defense responses via ET are indicated not only by individual microbes but also through the regulation of microbial community that are influenced by ET ( Nascimento et al., 2018 ). Several studies have followed the mutant generation approach by using A. thaliana to determine the potential factors that affect the bacterial community structure ( Bodenhausen et al., 2014 ). The mutants with ET-disabled gene displayed shifts in bacterial communities at genus level; however, it could not be correlated that the enhancement in abundant species is due to the ET levels or its cross talk with other hormones. Further, the experiments of Doornbos et al. (2011) signified that initial composition of bacterial communities has a critical role in regulating ET for their capability to influence other microbial communities. This effect might elicit ET responses in shaping the microbial structure, which then can be manipulated to act against stress responses. The essentiality of JA in defense responses came into light with an infection-mediated wound response ( Farmer and Ryan, 1992 ). Later, it has also been observed to act under necrotrophic plant defense responses ( Plett et al., 2014 ; Wei et al., 2016 ). Some studies have suggested that root exudates display their involvement in regulation of hormone JA that shapes the microbial communities around the root ( Bertin et al., 2003 ; Sasse et al., 2018 ). For instance, in a recent study, benzoxazinoids (component of root exudates) have been regulated by JA and interestingly demonstrated the ability to modify the microbial community composition ( Hu et al., 2018 ). This benzoxazinoid when inoculated in the soil exhibited improvement in herbivore resistance with enhancement in JA levels. As it has been known that several root exudates have allelopathic and chemotactic properties, this benzoxazinoid has proven chemotactic traits toward Pseudomonas putida that cause elicitation in JA priming and provide tolerance against fungal infection ( Neal et al., 2012 ; Neal and Ton, 2013 ). However, the correlation between the JA and root exudates’ functions in order to select and modify the community structure needs further elucidation.

Another essential phytohormone involved in defense signaling is SA. Unlike JA and ET, SA is considered to be associated with SAR. The signaling of SA-JA-ET phytohormones forms the backbone of defensive response action. Its role in modulating the root microbiota has been derived using A. thaliana mutants in which knockout mutants of SA, JA, and ET were targeted ( Lebeis et al., 2015 ). The knocked-out mutants displayed lesser rate of survival, and it was observed that some endophyte species might need SA-linked pathways for colonization. The preference of SA to select microbial communities has been displayed when SA was exogenously supplemented suggesting the active involvement of SA in shaping microbial structure ( Lebeis et al., 2015 ). Several other hormones such as ABA, cytokinin, auxins, brassinosteroids, etc., might show antagonism or synergism with SA, JA, and ET pathways ( Naseem and Dandekar, 2012 ; Denancé et al., 2013 ; Uhrig et al., 2013 ). For instance, ABA essentially takes part in modulating defense responses against abiotic stresses. It implicates negative effect to SA-linked defense, whereas it displays both negative and positive correlations with JA signaling pathways and affects ET-related responses to biotic stress ( Pieterse et al., 2012 ; Takatsuji and Jiang, 2014 ). In a study by Carvalhais et al. (2014) , microbial genera such as Cellvibrio , Limnobacter , and Massilia were preferentially selected by supplementing the pot soil with exogenous ABA; however, its definite role in regulating the microbial communities is still greatly unexplored.

Nutrient Acquisition

The importance of PGPRs in rhizosphere has been marked by their ability to make nutrients such as nitrogen, phosphorous, etc., available to plants and thereby act as biofertilizers. Biofertilizers are the microbial preparations that when applied to the soil, plant, or roots provide or enhance the nutrients and increase the fertility of soil. The most highly studied feature is nitrogen (N 2 ) fixation by Rhizobia species symbiotically ( Udvardi and Poole, 2013 ). The mode of action of rhizobial N 2 fixation involves mutual symbiosis with their leguminous plant host and the nod factors (LCOs), which are derived in response to flavonoids ( Kondorosi et al., 1989 ; Oldroyd, 2013 ). It comprises chitin molecules with N-acyl moieties having varying length fatty acids, which are responsible for conferring the specificity between host and rhizobium ( Oldroyd, 2013 ). The association between bacterial LCOs and host plant relies on direct detection of bacterial signal molecules by the plants. Lysin motif-containing receptor-like kinases (LysMs) are present on the leguminous plant cells as receptors that form bond with and gives responses to MAMPs including chitin ( Antolín-Llovera et al., 2012 ; Liang et al., 2014 ). This binding of LysM with nod factors initiates several cascade signals such as cytokinin and calcium accumulation and root hair curls, developing infection thread followed by infection that happens in nodules, the place where N 2 fixation by bacteria occurs in exchange to photosynthetic carbon ( Limpens et al., 2015 ; van Zeijl et al., 2015 ). In an experiment with non-legume plant A. thaliana , exogenous LCO from Bradyrhizobium japonicum was provided to the media that significantly increased the root tip numbers, length, and surface area of roots ( Khan et al., 2011 ).

Growth and nutrition of plants are also influenced by rhizobacterial chemical secretions that alter plant physiological responses; however, their molecular mechanisms have not been completely identified, but they overlap with plant defense and symbiosis parameters. In a study by Zhang et al. (2009) , accumulation of iron was increased by B. subtilis G03 in A. thaliana by activating host plant’s defense machinery. It was identified that Arabidopsis when exposed to bacterial volatiles upregulated the Fe deficiency–induced transcription factor 1 required to induce ferric reductase FRO2 and the iron transporter IRT1 expression by B. subtilis volatiles ( Zhang et al., 2009 ). When this bacterium G03 was inoculated to other plants, the iron accumulation was observed to be triggered by enhanced transporter expression. For example, G03 supplementation to Manihot esculenta (cassava) stem parts before plantation induced increase in iron content by 400% in leaves ( Freitas et al., 2015 ). In a study by Vishwakarma et al. (2018) , the efficacies of Bacillus paramycoides KVS27, Bacillus thuringiensis KVS25, and Pseudomonas species KVS20 were tested, and they have been found to increase the growth of Brassica juncea by facilitating P solubilization, N 2 assimilation, IAA, siderophore, and HCN production. It was also examined that there exists a synergism between these strains and that they have cumulatively enhanced the B. juncea growth.

Microbial Defense Mechanisms

Microbes display role in both disease occurrence and biocontrol activity. A few microorganisms can cause infection manifestations through the generation of phytotoxic compounds. One such pathogenic microbe is Pseudomonas syringae , which is very notable for having diverse hosts such as tomato, tobacco, olive, and green bean. Similar pathogenic bacterium is Erwinia amylovora , which is known for causing fire blight disease of fruit-bearing trees and ornament plants. Banana and potato crops also face variety of diseases due to the occurrence of Xanthomonas , R. solanacearum , and Xylella fastidiosa ( Mansfield et al., 2012 ). The seriousness of plant disease relies upon several parameters, viz., size of pathogen population, favorable environment, and susceptible nature of host, as well as biotic conditions involved in collective determination of plant–pathogen associations ( Brader et al., 2017 ). The host might acquire resistance against the pathogenic interventions due to the above and belowground bacterial communities by modifying defense responses of plant ( de Vrieze et al., 2018 ).

However, the pathogenic intrusions and disease can be controlled by various biocontrol activities ( Hopkins et al., 2017 ; Berg and Koskella, 2018 ). Because use of chemicals imposed many serious concerns in the agricultural productivity, hence employing benign microbial population has gained increasing popularity for economic approach ( Rosier et al., 2018 ). This can be facilitated by the lytic enzymes, generation of antibiotics, and production of siderophores and volatile compounds, which are inhibitory to pathogens ( Verma et al., 2018 ). The biological control by the microbes against pathogenic microbes follows different mechanisms such as antagonism, competition of nutrients and niches, and defense responses. Antagonistic microbes do not allow the other microbes to grow in its vicinity and hence can limit the growth of pathogens. Further, the fast-growing microbes can utilize the nutrients for their growth and deplete for other leading to limited or no growth of the pathogenic microbes. A few microorganisms shield the plant from pathogens by regulating plant hormonal levels and inducing resistance in the plant system. The consistent utilization of agricultural soils can develop pathogenic pressure and form disease-suppressive soil that contains microbes that suppress the disease ( Durán et al., 2018 ). In a study, three essential bacterial taxa that belonged to Firmicutes , Actinobacteria , and Acidobacteria were observed to control the Fusarium wilt disease at a huge scale ( Trivedi et al., 2017 ). The significance of bacterial communities of the endosphere was observed to suppress the destructive disease ( Gaeumannomyces graminis ), and further endophytes of Serratia and Enterobacter were recognized as most encouraging competitors against G. graminis . The action of ISR happens through the involvement of phytohormones ET and JA in protecting the plant systemically when exposed to beneficial microbes ( Figure 3 ) ( Verhagen et al., 2004 ; Pieterse et al., 2014 ). The priming process of plants is typically known during ISR in which defense responses against pathogenic microbes are activated aboveground very quickly ( Conrath et al., 2006 ), and several growth-promoting rhizobacterial species have displayed plant-priming phenomena ( Martinez-Medina et al., 2016 ). In SAR, MAMP-triggered immunity is induced as a first line of defense as discussed in Mechanism of Belowground Interactions in the Rhizosphere: Beyond Plant’s Innate Immune Response , and unlike ISR, it utilizes SA to confer the systemic protection to the plants ( Figure 3 ) ( Fu and Dong, 2013 ).

Figure 3. Mechanism of SAR and ISR utilizing phytohormones for inducing defense responses upon microbial incidence. Systemic acquired resistance involves salicylic acid accumulation after perception of pathogen by plants (in red) and expression of pathogenesis-related proteins in resistant tissues (upper leaf with dark red border) for inducing defense actions, whereas in induced systemic resistance, nonpathogenic plant growth–promoting rhizobacteria enable defense responses via ethylene and jasmonic acid priming process. NPR1 is the positive regulator of salicylic acid in SAR and is also needed in downstream processes of ethylene signaling in ISR. SAR, systemic acquired resistance; ISR, induced systemic resistance; SA, salicylic acid; ET, ethylene; JA, jasmonic acid; PRs, pathogenesis related genes; PGPR, plant growth–promoting rhizobacteria; NPR1, non-expresser of PR genes.

To elicit defense responses in plants, bacteria secrete several molecules such as antibiotics, volatiles, quorum-sensing signals, and certain proteins and small compounds ( Figure 2C ). Antibiotics are generally defined as low-molecular-weight, organic molecules with diversified chemical nature formed by microbes in order to limit the growth of other microbes ( Thomashow and Weller, 1996 ). A widely known microbial antibiotic, 2,4-diacetylphloroglucinol (DAPG), promotes the plant growth by suppressing pathogenic bacteria and fungi ( Weller et al., 2012 ). The mode of action of DAPG is to induce the generation of auxins and alteration of root physiology, which further stimulates the plant growth ( Brazelton et al., 2008 ). Pseudomonas aeruginosa is widely known to produce DAPG; however, it is also known to generate other class of antibiotic, i.e., phenazines that have been shown to induce the ISR in rice infected with Magnaporthe oryzae ( Ma et al., 2016 ). Another important class of antibiotics includes cyclic lipopeptides (cLPs) that have been isolated from Bacillus and Pseudomonas species to date having unique configurations ( Raaijmakers et al., 2010 ). Among cLPs, Bacillus species produce surfactin, fengycin, and iturin, of which surfactins have been considered as potential natural surfactant ( Nihorimbere et al., 2012 ). When surfactin-producing microbe B. subtilis 499 was inoculated in tomato and bean plants, the occurrence of disease by Botrytis cinerea was significantly suppressed ( Ongena et al., 2007 ). It had induced the lipoxygenase enzyme activity (indicator of ISR induction) in tomato plants infected with Botrytis pathogen when inoculated with Bacillus species ( Ongena et al., 2007 ). Gram-negative quorum-sensing molecule, N -acyl homoserine lactone (AHL), has been observed to upregulate the plant defense responses. Inoculation of Arabidopsis by Sinorhizobium meliloti (now renamed to Ensifer meliloti ) producing 3-oxo-C14-HL imparted resistance against P. syringae pv. tomato ( Zarkani et al., 2013 ). There is also the activation of systemic tolerance by AHLs observed in fungus Golovinomyces orontii and bacterium P. syringae pv. tomato DC3000-infected A. thaliana ( Schikora et al., 2011 ).

Techniques for Microbiome Analysis

To characterize the microbial diversity from a sample, there are number of approaches available. However, the characterization of whole microbiome and single components with complete details is majorly performed by two next-generation sequencing methods, i.e., amplicon sequencing and metagenomics ( Figure 4 ).

Figure 4. Detailed flowchart-based methodology for (A) metagenomics and (B) amplicon sequencing methods.

Amplicon Sequencing

These strategies depend on the specific binding of the pair of the universal primers to the regions, which are highly conserved within the particular microbial genome of interest. Amplicon sequencing is applied in microbial ecological studies for exploring the microbial communities. It involves the sequencing of subsequent polymerase chain reaction (PCR) products encompassing taxon-specific hypervariable regions (HVRs) ( D’Amore et al., 2016 ). 16S rRNA gene of bacteria are the most widely utilized amplicon targeted for microbiome examination ( Kittelmann et al., 2013 ). Several combinations of primers have been suggested for bacterial 16S rRNA gene for amplifying various HVRs and subsequently generating PCR products varying in their lengths for sequencing platforms (such as Pacific Biosciences vs. Illumina) ( D’Amore et al., 2016 ). The varying sequences of 16S rRNA (for bacteria), 18S rRNA (for fungi) genes, and internal transcribed spacer (ITS) segments (for fungi) along with metagenomic loci possess the information regarding the phylogeny of microorganisms, which can be utilized for inferring and deducing their taxonomy. However, it should be noted that the accuracy of taxonomical identification using marker genes is dependent upon the quality and completeness of the reference databases used. In comparison of 18S rRNA gene, the ITS region was preferred because of the presence of high comprehensive and curated database as well as the higher sequence variability ( Schoch et al., 2012 ). However, it is debatable that the ITS fragments with uneven lengths may enhance preferential PCR amplification of ITS sequences with shorter lengths, which can take to a biased quantification of relative abundances of fungal taxa, and consequently, non-ITS targets can be additionally used in studies of fungi microbiota based on sequencing ( De Filippis et al., 2017 ).

Sometimes, it becomes difficult to distinguish the natural genetic variations from the technical errors during sequencing, which even is less than 0.1% using the Illumina platform ( Schirmer et al., 2015 ). To analyze the microbiome after amplicon-based sequencing, operational taxonomical units (OTUs) clustering is utilized depending on the arbitrary definitive sequence similarity thresholds (for, e.g., 97%). Similar but somewhat variant sequences are assigned to the same taxa by OTU picking giving an assumption for sharing a biological origin. In comparison to OTU-based methodologies, the enhanced specificity and sensitivity are provided by amplicon sequence variants and also diminished the possibility of false identification of OTU sets arriving from wrongly clustered sequences, but they might bear the risk to overestimate the microbial diversities ( Kopylova et al., 2016 ).

Metagenomics

Metagenomics utilizes the entire genome shotgun method to deal with fragmentation and sequencing the complete DNA sequence of a microbial sample rather than 16S rRNA gene fragments or other targeted amplicons. Subsequently, the reads obtained have their origin from bacteria, viruses, archaea, phages, and fungi with other eukaryotes, as well as it can incorporate extrachromosomal fragments, plasmids, and host DNA. In contrast to 16S rRNA gene examination, this strategy requires essentially more information to get the depth of sequencing that is necessary to distinguish and characterize uncommon/rare members of microbiome. For robust analysis of the data, several quality control techniques are utilized to trim and filter the metagenomic reads for human, plant, and eukaryotic DNA reads by tools such as KneadDeata, QIIME, RAST, etc. ( Nayfach and Pollard, 2016 ). Web-based tools are nowadays very easily approachable and can provide the measure to compare and map the reads in the references databases. The annotated functions can be identified by various databases such as KEGG orthologs and cluster of orthologous genes.

The metagenomics-based studies improve researcher’s ability to characterize microorganisms not only at species level but also even at strain level. This contrasts with 16S rRNA–based NGS methods, which offers only limited characterization resolution because of the high sequence conservation at these taxonomic levels of the amplicons produced ( Konstantinidis and Tiedje, 2007 ). However, additional bioinformatics approach is needed to reconstruct microbial genome from mixtures of small fragments of DNA derived from several microbes and to further enhance sequencing resolution. This is mainly relevant for finding and characterizing microbes at the strain level, where assembly algorithms overcome barriers such as intergenomic repetitive elements and to accurately detect small genetic differences ( Ghurye et al., 2016 ). Lastly, functional level annotation of sequences of genes is allowed in metagenomics and hence has broader explanation of microbial characterization than targeted amplicon sequencing surveys. Generally, two steps of functional annotation are gene prediction and gene annotation. In gene prediction, sequences that may encode proteins are identified by bioinformatics tools. Then, these sequences are matched and annotated with database of protein families ( Sharpton, 2014 ). This information is further used to find new functional gene sequences ( Qin et al., 2010 ). Point to be careful about is that in metagenomics, the prediction of genes does not confirm their actual expression within the initial tested sample. Although amplicon sequencing and metagenomics are next-generation sequencing approaches, they still sometimes pose several limitations during experimentation and analysis ( Boers et al., 2019 ).

Contribution of Microbial Inoculants in Agricultural Sustainability

Albeit less information is available about the specific mechanism of microbial interaction with the plants, accelerating the use of microbes in a targeted way can contribute to sustainability. To enhance the microbial population, extensive research depicted practice of organic farming that enhances occurrence of microbes such as fungal and bacterial load in the soil, commonly known as plant probiotic ( Yadav et al., 2017 ).

The utilization of beneficial microbes has gained the pace against the chemical-based and synthetic pesticides and fertilizers in agriculture industry ( Alori et al., 2017 ). The inoculation of seeds by beneficial microbes reflects their efficiency to colonize the roots when they are placed in soil, as well as help in protection from the pathogens ( Ahmad et al., 2018 ). This process of seed inoculation by microbial consortia possesses advantage of direct delivery of microbes in the rhizosphere where they can establish association with plants ( Philippot et al., 2013 ). Inoculation of microorganisms helps in improving the nutrient availability to the plants, as well as help in effective carbon sequestration belowground ( Vishwakarma et al., 2016 ). In leguminous plants, inoculating the seeds results in high occurrence of rhizobia in the rhizosphere, which further colonizes, forms nodules, and fixes nitrogen in order to achieve maximum yield and productivity ( Deaker et al., 2004 ). Burkholderia ambifaria MCI 7 when used for seed treatment has shown growth promotion in maize seedlings, but at the same time, it has shown negative effect on plant growth when applied directly in the soil ( Ciccillo et al., 2002 ).

The rising issues of varying costs and distribution related to the P-based fertilizers led to the enhancement in microbial fertilizers that promote the P acquisition by the plants from soil ( Richardson and Simpson, 2011 ). One of the products commercialized for canola and wheat is JumpStart ® ( Monsanto BioAg, 2016 ), which contains Penicillium bilaii fungus. It displayed the high yield (66%) in one study ( Harvey et al., 2009 ); however, in some studies, it has been reported to deliver less beneficial properties ( Karamanos et al., 2010 ). The inoculation with fungus on the seeds is facilitated just before the sowing procedure. The species belonging to Pseudomonas have shown the plant growth–promoting potential and pathogen suppression; hence, different ways were applied for seed coating by Pseudomonas that delivered mixed success levels ( O’Callaghan et al., 2006 ). Two strains of P. syringae have been tested under greenhouse conditions in tomato plant in which P. syringae pv. syringae strain 260-02 promoted the growth of plants and exerted biocontrol of P. syringae pv. tomato strain DC3000 against the fungus B. cinerea and the virus Cymbidium ringspot ( Passera et al., 2019 ). Apart from being a pathogen, P. syringae can also be beneficial in some cases. This might be due to its distinct volatile emission profiles and root colonization patterns. In one of the studies, when P. putida KT2440 was supplied as root inoculant in maize plants, the induction of ISR was observed against the fungus Colletotrichum graminicola that was evident from the significantly decreased leaf necrosis and low fungal load in treated samples ( Planchamp et al., 2015 ). Other bacteria, i.e., Bacillus species, have emerged as great candidates for developing stable bioproducts against pathogens, as they are capable of producing heat-resistant and drought-resistant endospores ( Yánez-Mendizabal et al., 2012 ). In tomato plants, coinoculation of Pseudomonas and Bacillus at various stages of plant growth promoted the yield, growth, and nutritional status of plants ( He et al., 2019 ). Similarly, the coinoculation of Pseudomonas and Rhizobium sullae enhanced growth and antioxidant levels and reduced cadmium accumulation in Sulla coronaria ( Chiboub et al., 2019 ) and that of Rhizobium and Pseudomonas increased the root and shoot dry weight and overall yield of rice ( Deshwal et al., 2011 ). There are ample studies on inoculation of microbes (both single and consortia) to the plants or seeds in order to promote the growth and development of plants. Some more examples are presented in Table 1 .

Table 1. Various microbial inoculants in consortia or single application and their effect on plants for growth promotion and defense.

Mycorrhiza describes a symbiotic association between root-colonizing fungi and plants ( Sylvia et al., 2005 ). The mycorrhizal association begins with the exchange of signals between both the partners. The host root releases the signaling molecules known as “branching factors” for initiating extensive hyphal branching for arbuscular mycorrhizal (AM) fungi ( Akhtar and Panwar, 2011 ). AM fungi have long been presumed to generate signal molecules known as “myc factors” that give the molecular and cellular responses to AM fungi for successful root colonization. None of these signals had been isolated and chemically identified until the discovery of ‘branching factors” from root secretions of legume Lotus japonicus . It was identified as a strigolactone, 5-deoxy-strigol ( Akiyama and Hayashi, 2006 ). It has been widely studied that the plant immunity can be enhanced by the association between the mycorrhizae and plant.

The endophytic fungi are known for existing greatly in the plant’s tissues for maintaining health of the plant and possess an essential parameter in plant–microbe associations. The plants and endophytes at the later stage of ecological process become synergistically beneficial. One of the beneficial endophytes is P. indica that has been isolated from the roots of plants growing in the desert of Rajasthan, India ( Varma et al., 2012 ). It has been studied widely for their essential properties and tested with many plant species. This fungus enhances the uptake of nutritional elements and facilitates the survival of plants under stressed conditions such as salinity and drought; presents systemic resistance against pathogens, heavy metals, and toxic compounds; and promotes yield and crop productivity ( Varma et al., 2012 ). Many other researchers have observed high biomass delivery and improvement in plant growth when treated with this fungus ( Achatz et al., 2010 ; Gill et al., 2016 ). More than 150 species of host plants have been tested and observed to beneficially associate with P. indica with respect to their benefits in agriculture, medicinal, ornamental, and other plants ( Varma et al., 2012 ). The roots that are colonized by P. indica have shown early developmental gene expression indicating more growth at initial stages in treated in comparison to control ( Waller et al., 2005 ). Colonization of exterior root cortex of maize was observed after inoculation of P. indica to maize roots, which further significantly increased the growth responses ( Kumar et al., 2009 ). In a study on Ocimum basilicum (sweet basil), lead (Pb) uptake in shoots is restricted by combined inoculation of endophytic fungi Rhizophagus irregularis and Serendipita indica ; however, copper (Cu) uptake is limited by S. indica only ( Sabra et al., 2018 ). Useful products from Trichoderma harzianum are being produced by many countries; for example, in Poland T-22 strain is used to market a product known as Tianum-P. Many studies have reported the production of useful compounds by Trichoderma species and have found that it can produce viriden, isonitryles, gliotoxines, peptaboils, and sesquiterpenes among many other essential compounds ( Pylak et al., 2019 ). A study has shown that Trichoderma atroviride G79/11 is able to produce the enzyme cellulase, which makes it suitable candidate for biopreparation of antifungal compounds ( Oszust et al., 2017a , b ).

Talaromyces is an important fungal genus from the group of heat-resistant fungi (HRFs), among which most common is Talaromyces flavus strain. The HRFs have the ability to resist high temperature ranging from 90°C for 6 min to 95°C for 1 min in glucose tartarate–rich medium at pH 5 ( Frąc et al., 2015 ; Panek and Frąc, 2018 ). It has been reported to produce bioactive compounds such as actofunicone, deoxy-funicone, and vermistatin ( Proksa, 2010 ). These compounds help them in nutrient competition and to grow faster; therefore, this strain has the potential to be used in pathogen biocontrol ( Pylak et al., 2019 ). In production of organic fruits, many bioproducts and biopreparations are being utilized, e.g., Biosept 33 SL and Micosat F. These are dependent on various active ingredients such as plant extracts (e.g., garlic— Allium sativum ), animal-derived substances (e.g., chitosan), or microbial inoculum (e.g., Pythium oligandrum ). These biopreparations are appreciated by farmers because of their safety and effectiveness for plants themselves and animals ( Reddy et al., 2000 ; Marjanska-Cichon and Sapieha-Waszkiewicz, 2011 ).

Agricultural Management and Status of Microbial Inoculants

Numerous studies have shown that, besides the plant influence, long-term agricultural practices affect the assembly of the rhizosphere microbiota ( Chowdhury et al., 2019 ). It has been observed that recruitment of management process–specific taxa is favored by the plant hosts, which also helps in shifting the nutrient cycling in rhizospheric region ( Schmidt et al., 2019 ). The influence of agricultural management practices and modulated microbiome can subsequently affect the dependent plant characteristics and hence the performance. Apart from microbial inoculations, agricultural practices such as organic farming, crop diversification, and intercropping have been used for sustainability in agriculture. Although there is limitation in the studies that show impact of several practices on plant microbiome, fertilization, or biodiversity protection, it has been shown that utilizing low input farm practices lead to promotion of diversity and abundance of many microbes ( Postma-Blaauw et al., 2010 ). Hence, it is necessary to understand the impact of agricultural practices on plant microbiota to formulate strategies on modulation of microbiome in desired direction.

It has been shown that integrated or organic pest treatment of grapevine may cause different plant and soil microbiota build-up ( Campisano et al., 2014 ). Likewise, studies on viticulture treatment have shown different microbiota build-up in comparison to the biodynamic and organic management practices ( Longa et al., 2017 ). Vineyards were assessed for 10 years under integrated, biodynamic, and organic management practices, and it was found that soil treated with organic management practices had rich bacterial diversity in comparison to integrated management but bacterial community composition found to be similar in both ( Hendgen et al., 2018 ). Further, a study reported that soil under 20 years of organic farming exhibited rich microbial diversity in comparison to conventionally managed soil ( Hartmann et al., 2015 ). In another study, Hartman et al. (2018) analyzed the impact on microbial diversity under conventional and organic farming management types with varying tillage intensities. It was observed that primary soil microbial diversity is influenced by tillage while root microbial diversity such as fungal communities are influenced mainly by management type (conventional and organic) and somewhat due to tillage. Effects of soil management practices depend on, for instance, soil microbiota, soil type, and plant species, and approximately 10% of disparity in microbial diversity can be explained by the farming practices utilized ( Hartman et al., 2018 ). Our understanding on effects of soil management practices on microbial diversity has advanced, but the effects of complex system such as environmental factors are yet to be understood.

Process of Microbial Inoculant (Single/Consortia) Formulation

The identification and characterization of PGPRs and/or consortia involve bottom-up selection procedures, which include collecting the bacterial cultures and investigating the properties in culture-dependent screening methods ( Armanhi et al., 2018 ). The detailed outline of process is given in Figure 5 . Bacterial stress resistance to desiccation, temperature, or toxic components and promotional activities for plant growth can be assessed for the cultures grown in axenic conditions ( Suleman et al., 2018 ; Compant et al., 2019 ). These in vitro tests can be used as selection criterion to screen the PGP traits ( Syranidou et al., 2016 ; Liu et al., 2017 ). However, there is no correlation between the efficiency of PGP bacteria and their abundant molecular PGP traits ( Tiryaki et al., 2019 ).

Figure 5. Description of the process involved in screening microbial cultures for PGP traits and development of inoculant. PGP, plant growth–promoting traits; HCN, hydrogen cyanide; 2,4-DAPG, 2,4-diacetylphloroglucinol.

Laboratory screening can give only limited information. In years, the majority of the research were focused on developing strains, understanding mode of action when inoculated to plants, and assessing their effects. And now, research is being focused on conversion of science into technology by producing the inoculants ( Yadav and Chandra, 2014 ). Automation technologies can be adopted for mass and time-efficient production of inoculants such as using sterile liquid inoculants having more microbe load to enhance the shelf life and contamination-free products. According to a report produced by the National Centre of Organic Farming, India has around 225 biofertilizer production units that can produce up to 98,000 Mt per annum through installed capacities ( NCOF, 2011 , 2012 ). Initially, the inoculants of Rhizobium have gained momentum in commercialization in market followed by Azotobacter , Azospirillum , phosphate-solubilizing bacteria (PSBs), Acetobacter , Frateuria aurantia + Bacillus species, and the mixtures of Azotobacter, Azospirillum , PSB, and Pseudomonas fluorescens . The market is dominated by single-inoculant cultures; however, the trend of employing the consortia is projected to increase within coming years ( Yadav and Chandra, 2014 ). State Governments (in India) supply the majority of such inoculants and biofertilizers to the farmers through various schemes with subsidy varying from 25% to 75%. However, there is still a gap in direct marketing of the biofertilizers via dealers. Moreover, the acceptance rate of biofertilizers by the farmers is still inconsistent for utilization in fields due to temperature-sensitive nature and varying response and the fear that these inoculants are also pests ( Sahoo et al., 2013 ).

Future Prospects, Challenges, and Limitations

To ensure long-term viability of microbial cells especially during storage and deliver sufficient viable number of cells to plants grown in fields, the development microbial formulations are needed. Unfortunate scene is that there is lack of suitable formulations for many microbes, in particular, the Gram-negatives ( Berninger et al., 2018 ). Further limitation for viability in formulations is the toleration capacity of bacteria to low-humidity conditions ( Köhl et al., 2011 ). Use of several compounds on the formulations might actually help in improvement of PGP effects. Experiments conducted for addition of LCOs isolated from rhizobia in the formulation or adaptation of growth medium of inoculants help in increasing exopolysaccharides and polyhydroxybutyrate content and increased PGP activities ( Oliveira et al., 2017 ).

It has been observed that the bacterial products/additives do not have clear understanding with respect to their adhesion, but adjustments in droplet size and rheological properties can be achieved by surfactants, which might help in improvement of adhesion to hydrophobic cuticular surfaces ( Preininger et al., 2018 ). Improvement of adhesion of PGPRs to roots has been done by nanoparticles and humid environment provided by encapsulated PGPR macrobeads ( Perez et al., 2018 ; Timmusk et al., 2018 ). Generally, yield of wheat in field studies is successfully increased by inoculation techniques adopted for inoculating seed, leaf, and soil of same PGPRs ( Berger et al., 2018 ). Interference of seed inoculants with pesticides can be seen, but in such cases, seed inoculant colonizes the plants and activates microbial defense system, which include activation of plant immune response, biofilm production, etc. Development of new methods was done in addition to classical delivery approaches. Mitter et al. (2017) devised the concept of seed microbiome modulation. In this, flower spray inoculation was followed for achieving next-generation seeds colonized with endophytes and modulated seed microbiome. Colonization of germinated plants was done efficiently by inoculant strain, which displayed that the use of alternative approaches may lead to improvement of microbial inoculant performance under field conditions.

Microbial inocula, either single or consortia, have many advantages than limitations. These include their environment-friendly nature; they can restore soil fertility, improve/enhance nutrient availability, protect against biotic and abiotic stresses, increase soil microbial activity, decompose toxic substances, promote colonization of mycorrhizae and other useful microbes, help in recycling soil organic matter, increase plant defense and immunity for suppressing unwanted parasitic and pathogenic attacks, and carry out signal transduction and plant–microbe interactions. Each year, there is nearly 12% increase in demand for microbial inoculants because of the increasing cost of chemical fertilizers and demand for environment-friendly technologies in society ( Calvo et al., 2014 ). PGPRs such as Azotobacter , Bacillus , Azospirillum , Pseudomonas , Burkholderia , Serratia , and Rhizobium species are now being commercially produced at a large scale ( Parray et al., 2016 ), although different countries have their own rules for the use of these microbes based biofertilizers and biopesticides for agricultural practices ( Bashan et al., 2014 ). The main obstacles are consistency, reliability, and shelf life of microbial inoculants under field conditions. Gram-positive bacteria have longer shelf-life in comparison to non–spore-forming gram-negative bacteria. However, studies have reported super-inoculants containing all the required characteristics of a microbial inoculant ( Schoebitz et al., 2013 ). On the other hand, studies have also issued concern about some PGPRs that can be pathogenic to humans, for example, pathogenic Pseudomonas species and Burkholderia cepacia ( Kumar et al., 2013 ). These species can be harmful to human, despite the PGP activity shown by them, and therefore before their commercial production, they should be addressed properly ( Compant et al., 2010 ). More research is required before incorporating pathogenic PGPRs in sustainable agriculture. Many European and other countries such as the United States are reassessing the biosafety of PGPR-based biofertilizers. Studies have shown the effect of climate change on plant–microbe interactions; however, further studies are needed to know the full capabilities of PGPRs before their acceptance by government regulations, biofertilizer companies, and farmers. There can be the provision to make cost-effective technology of microbial consortium acceptance and utilization by the farmers in the future. There can be government-regulated outlets where biofertilizers/biopesticides with improved shelf life and stability should be provided to the farmers at subsidized rates with an opportunity to replace the old stored batch of inoculum with a fresh batch. The administrative bodies of agriculture-based towns can provide training to farmers highlighting the benefits, proper handling and usage, and their general guidelines. The schemes by the government can be launched to help farmers set up small production units in their area so as to regularize the inoculant production. It will certainly help them in overcoming shelf life, stability, and viable count problems by producing the inoculant as desired for the use.

With the increase in world population at alarming rate, there is a need to increase crop production to fulfill the global food requirements and at the same time enhance agricultural sustainability. Plant growth–promoting microbes, which are active constituents of biofertilizers and biopesticides, can be represented as a feasible alternative technology for enhancing plant yield and protecting against pathogens. The microbial inoculums possess the ability to positively impact the agriculture sector; however, plant selectivity along with organic and conventional management procedures also comes into play in shaping the rhizospheric microbiome structure, their concurrence, and subsequent effects. Since the microbial community structure in bulk and rhizosphere region frequently differs in their composition in various plant niches, it becomes necessary to reorganize the priorities of research toward isolating beneficial microbes and understanding the dynamics of their association with plants for enhanced crop productivity, quality, and agroecological sustainability. Despite some limitations of microbial consortia application, the measures to move past these limitations can be taken such as enhancement of shelf-life and viable load at the time of application, as well as developing faith in farmers for consistent utilization of inoculants in their fields. In the future, studies related to large-scale viable production of inoculant can be made using synergistic microbes proven to increase the crop productivity under conventional and organic agricultural practices.

Author Contributions

AV and KV designed the structure of the manuscript. CS, NK, SM, and KV wrote the manuscript. CS, NK, KV, and SB prepared the tables, figures and arranged the references. KV and AV critically read and organized the manuscript. All the authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

Authors would like to express thanks to ICAR-NASF, DST FIST, and DST Nano Mission for providing support.

Aboudrar, W., Schwartz, C., Morel, J. L., and Boularbah, A. (2013). Effects of nickel-resistant rhizosphere bacteria on the uptake of nickel by the hyperaccumulator Noccaea caerulescens under controlled conditions. J. Soils Sediments 13, 501–507. doi: 10.1007/s11368-012-0614-x

CrossRef Full Text | Google Scholar

Achatz, B., von Rüden, S., Andrade, D., Neumann, E., Pons-Kühnemann, J., Kogel, K. H., et al. (2010). Root colonization by Piriformospora indica enhances grain yield in barley under diverse nutrient regimes by accelerating plant development. Plant Soil 333, 59–70. doi: 10.1007/s11104-010-0319-0

Ahmad, M., Pataczek, L., Hilger, T. H., Zahir, Z. A., Hussain, A., Rasche, F., et al. (2018). Perspectives of microbial inoculation for sustainable development and environmental management. Front. Microbiol. 9:2992. doi: 10.3389/fmicb.2018.02992

PubMed Abstract | CrossRef Full Text | Google Scholar

Akhtar, M. S., and Panwar, J. (2011). Arbuscular mycorrhizal fungi and opportunistic fungi, efficient root symbionts for the management of plant parasitic nematodes. Adv. Sci. Eng. Med. 3, 165–175. doi: 10.1166/asem.2011.1109

Akiyama, K., and Hayashi, H. (2006). Strigolactones: chemical signals for fungal symbionts and parasitic weeds in plant roots. Ann. Bot. 97, 925–931. doi: 10.1093/aob/mcl063

Aleklett, K., Hart, M., and Shade, A. (2014). The microbial ecology of flowers: an emerging frontier in phyllosphere research. Botany 92, 253–266. doi: 10.1139/cjb-2013-0166

Alori, E. T., Dare, M. O., and Babalola, O. O. (2017). “Microbial inoculants for soil quality and plant fitness,” in Sustainable Agriculture Review , ed. E. Lichtfouse (Berlin: Springer), 181–308. doi: 10.1007/978-3-319-48006-0

Antolín-Llovera, M., Ried, M. K., Binder, A., and Parniske, M. (2012). Receptor kinase signaling pathways in plant-microbe interactions. Annu. Rev. Phytopathol. 50, 451–473. doi: 10.1146/annurev-phyto-081211-173002

Armanhi, J. S. L., de Souza, R. S. C., Damasceno, N. D. B., de Araújo, L. M., Imperial, J., and Arruda, P. (2018). A community-based culture collection for targeting novel plant growth promoting bacteria from the sugarcane microbiome. Front. Plant Sci. 8:2191. doi: 10.3389/fpls.2017.02191

Bakker, P. A. H. M., Berendsen, R. L., Doornbos, R. F., Wintermans, P. C. A., and Pieterse, C. M. J. (2013). The rhizosphere revisited: root microbiomics. Front. Plant Sci. 4:165. doi: 10.3389/fpls.2013.00165

Bandyopadhyay, P., Bhuyan, S. K., Yadava, P. K., Varma, A., and Tuteja, N. (2017). Emergence of plant and rhizospheric microbiota as stable interactomes. Protoplasma 254, 617–626. doi: 10.1007/s00709-016-1003-x

Barret, M., Briand, M., Bonneau, S., Préveaux, A., Valière, S., Bouchez, O., et al. (2015). Emergence shapes the structure of the seed microbiota. Appl. Environ. Microbiol. 81, 1257–1266. doi: 10.1128/aem.03722-14

Bartoli, C. C., Casalongué, C. A., Simontacchi, M., Marquez-Garcia, B., and Foyer, C. H. (2013). Interactions between hormone and redox signalling pathways in the control of growth and cross tolerance to stress. Environ. Exp. Bot. 94, 73–88. doi: 10.1016/j.envexpbot.2012.05.003

Bashan, Y., de Bashan, L. E., Prabhu, S. R., and Hernandez, J.-P. (2014). Advances in plant growth-promoting bacterial inoculant technology: formulations and practical perspectives (1998–2013). Plant Soil 378, 1–33. doi: 10.1007/s11104-013-1956-x

Bell, C. A., Lilley, C. J., McCarthy, J., Atkinson, H. J., and Urwin, P. E. (2019). Plant-parasitic nematodes respond to root exudate signals with host-specific gene expression patterns. PLoS Pathog. 15:e1007503. doi: 10.1371/journal.ppat.1007503

Berendse, F. (1982). Competition between plant populations with different rooting depths - III, Field experiments. Oecologia 53, 50–55. doi: 10.1007/BF00377135

Berg, M., and Koskella, B. (2018). Nutrient-and dose-dependent microbiome-mediated protection against a plant pathogen. Curr. Biol. 28, 2487–2492. doi: 10.1016/j.cub.2018.05.085

Berger, B., Patz, S., Ruppel, S., Dietel, K., Faetke, S., Junge, H., et al. (2018). Successful formulation and application of plant growth-promoting Kosakonia radicincitans in maize cultivation. Biomed Res. Int. 2018:6439481.

Google Scholar

Berninger, T., González López, Ó., Bejarano, A., Preininger, C., and Sessitsch, A. (2018). Maintenance and assessment of cell viability in formulation of nonsporulating bacterial inoculants. Microb. Biotechnol. 11, 277–301. doi: 10.1111/1751-7915.12880

Bertin, C., Yang, X., and Weston, L. A. (2003). The role of root exudates and allelochemicals in the rhizosphere. Plant Soil 256, 67–83. doi: 10.1023/A:1026290508166

Bhuyan, S. K., Bandyopadhyay, P., Kumar, P., Mishra, D. K., Prasad, R., Kumari, A., et al. (2015). Interaction of Piriformospora indica with Azotobacter chroococcum . Sci. Rep. 5:13911.

Bodenhausen, N., Bortfeld-Miller, M., Ackermann, M., and Vorholt, J. A. (2014). A synthetic community approach reveals plant genotypes affecting the phyllosphere microbiota. PLoS Genet. 10:e1004283. doi: 10.1371/journal.pgen.1004283

Boers, S. A., Jansen, R., and Hays, J. P. (2019). Understanding and overcoming the pitfalls and biases of next-generation sequencing (NGS) methods for use in the routine clinical microbiological diagnostic laboratory. Eur. J. Clin. Microbiol. Infect. Dis. 38, 1059–1070. doi: 10.1007/s10096-019-03520-3

Brader, G., Compant, S., Vescio, K., Mitter, B., Trognitz, F., Ma, L.-J., et al. (2017). Ecology and genomic insights into plant-pathogenic and plant-nonpathogenic endophytes. Annu. Rev. Phytopathol. 55, 61–83. doi: 10.1146/annurev-phyto-080516-035641

Brazelton, J. N., Pfeufer, E. E., Sweat, T. A., Gardener, B. B. M., and Coenen, C. (2008). 2,4-diacetylphloroglucinol alters plant root development. Mol. Plant Microbe Interact. 21, 1349–1358. doi: 10.1094/mpmi-21-10-1349

Calvo, P., Nelson, L., and Kloepper, J. W. (2014). Agricultural uses of plant biostimulants. Plant Soil 383, 3–41. doi: 10.1007/s11104-014-2131-8

Campisano, A., Antonielli, L., Pancher, M., Yousaf, S., Pindo, M., and Pertot, I. (2014). Bacterial endophytic communities in the grapevine depend on pest management. PLoS One 9:e112763. doi: 10.1371/journal.pone.0112763

Carvalhais, L. C., Dennis, P. G., and Schenk, P. M. (2014). Plant defence inducers rapidly influence the diversity of bacterial communities in a potting mix. Appl. Soil Ecol. 84, 1–5. doi: 10.1016/j.apsoil.2014.06.011

Chiboub, M., Jebara, S. H., Abid, G., and Jebara, M. (2019). Co-inoculation effects of Rhizobium sullae and Pseudomonas sp. on growth, antioxidant status, and expression pattern of genes associated with heavy metal tolerance and accumulation of cadmium in Sulla coronaria . J. Plant Growth Regul. 39, 216–228. doi: 10.1007/s00344-019-09976-z

Chowdhury, S. P., Babin, D., Sandmann, M., Jacquiod, S., Sommermann, L., Sørensen, S. J., et al. (2019). Effect of long-term organic and mineral fertilization strategies on rhizosphere microbiota assemblage and performance of lettuce. Environ. Microbiol. 21, 2426–2439. doi: 10.1111/1462-2920.14631

Chung, J., Song, G. C., and Ryu, C.-M. (2016). Sweet scents from good bacteria: case studies on bacterial volatile compounds for plant growth and immunity. Plant Mol. Biol. 90, 677–687. doi: 10.1007/s11103-015-0344-8

Ciccillo, F., Fiore, A., Bevivino, A., Dalmastri, C., Tabacchioni, S., and Chiarini, L. (2002). Effects of two different application methods of Burkholderia ambifaria MCI 7 on plant growth and rhizospheric bacterial diversity. Environ. Microbiol. 4, 238–245. doi: 10.1046/j.1462-2920.2002.00291.x

Compant, S., Cambon, M. C., Vacher, C., Mitter, B., Samad, A., and Sessitsch, A. (2020). The plant endosphere world – bacterial life within plants. Environ. Microbiol . doi: 10.1111/1462-2920.15240 [Epub ahead of print].

Compant, S., Clément, C., and Sessitsch, A. (2010). Plant growth-promoting bacteria in the rhizo- and endosphere of plants: their role, colonization, mechanisms involved and prospects for utilization. Soil Biol. Biochem. 42, 669–678. doi: 10.1016/j.soilbio.2009.11.024

Compant, S., Mitter, B., Colli-Mull, J. G., Gangl, H., and Sessitsch, A. (2011). Endophytes of grapevine flowers, berries, and seeds: identification of cultivable bacteria, comparison with other plant parts, and visualization of niches of colonization. Microb. Ecol. 62, 188–197. doi: 10.1007/s00248-011-9883-y

Compant, S., Samad, A., Faist, H., and Sessitsch, A. (2019). A review on the plant microbiome: ecology, functions and emerging trends in microbial application. J. Adv. Res. 20, 29–37. doi: 10.1016/j.jare.2019.03.004

Conrath, U., Beckers, G. J. M., Flors, V., García-Agustín, P., Jakab, G., Mauch, F., et al. (2006). Priming: getting ready for battle. Mol. Plant Microbe Interact. 19, 1062–1071. doi: 10.1094/mpmi-19-1062

D’Amore, R., Ijaz, U. Z., Schirmer, M., Kenny, J. G., Gregory, R., Darby, A. C., et al. (2016). A comprehensive benchmarking study of protocols and sequencing platforms for 16S rRNA community profiling. BMC Genomics 17:55. doi: 10.1186/s12864-015-2194-9

Das, S., and Das, S. N. (1986). Host-range of Pratylenchus coffeae . Indian J. Nematol. 16, 180–184.

De Filippis, F., Laiola, M., Blaiotta, G., and Ercolini, D. (2017). Different amplicon targets for sequencing-based studies of fungal diversity. Appl. Environ. Microbiol. 83:e00905-17.

de Vrieze, M., Germanier, F., Vuille, N., and Weisskopf, L. (2018). Combining different potato-associated Pseudomonas strains for improved biocontrol of Phytophthora infestans . Front. Microbiol. 9:2573. doi: 10.3389/fmicb.2018.02573

Deaker, R., Roughley, R. J., and Kennedy, I. R. (2004). Legume seed inoculation technology-a review. Soil Biol. Biochem. 36, 1275–1288. doi: 10.1016/j.soilbio.2004.04.009

DeMarco, S. F., Saada, E. A., Lopez, M. A., and Hill, K. L. (2020). Identification of positive chemotaxis in the protozoan pathogen Trypanosoma brucei . mSphere 5:e00685-20.

Dempsey, D. A., and Klessig, D. F. (2012). SOS-too many signals for systemic acquired resistance? Trends Plant Sci. 17, 538–545. doi: 10.1016/j.tplants.2012.05.011

Denancé, N., Sánchez-Vallet, A., Goffner, D., and Molina, A. (2013). Disease resistance or growth: the role of plant hormones in balancing immune responses and fitness costs. Front. Plant Sci. 4:155. doi: 10.3389/fpls.2013.00155

Deshwal, V. K., Vig, K., Singh, S. B., Gupta, N., Agarwal, S., and Patil, S. (2011). Influence of the Co-inoculation Rhizobium SR-9 and Pseudomonas SP-8 on growth of soybean crop. Dev. Microbiol. Mol. Biol. 2, 67–74.

Deslandes, L., and Rivas, S. (2012). Catch me if you can: bacterial effectors and plant targets. Trends Plant Sci. 17, 644–655. doi: 10.1016/j.tplants.2012.06.011

Donn, S., Kirkegaard, J. A., Perera, G., Richardson, A. E., and Watt, M. (2015). Evolution of bacterial communities in the wheat crop rhizosphere. Environ. Microbiol. 17, 610–621. doi: 10.1111/1462-2920.12452

Doornbos, R. F., Geraats, B. P., Kuramae, E. E., Van Loon, L., and Bakker, P. A. (2011). Effects of jasmonic acid, ethylene, and salicylic acid signaling on the rhizosphere bacterial community of Arabidopsis thaliana. Mol. Plant Microbe Interact. 24, 395–407. doi: 10.1094/MPMI-05-10-0115

Durán, P., Tortella, G., Viscardi, S., Barra, P. J., Carrión, V. J., De La Luz Mora, M., et al. (2018). Microbial community composition in take-All suppressive soils. Front. Microbiol. 9:2198. doi: 10.3389/fmicb.2018.02198

Etminani, F., and Harighi, B. (2018). Isolation and identification of endophytic bacteria with plant growth promoting activity and biocontrol potential from wild pistachio trees. Plant Pathol. J. 34, 208–217. doi: 10.5423/PPJ.OA.07.2017.0158

Fahad, S., Hussain, S., Bano, A., Saud, S., Hassan, S., and Shan, D. (2015). Potential role of phytohormones and plant growth-promoting rhizobacteria in abiotic stresses: consequences for changing environment. Environ. Sci. Pollut. Res. 22, 4907–4921. doi: 10.1007/s11356-014-3754-2

Farmer, E. E., and Ryan, C. A. (1992). Octadecanoid precursors of jasmonic acid activate the synthesis of wound-inducible proteinase inhibitors. Plant Cell 4, 129–134. doi: 10.1105/tpc.4.2.129

Fierer, N. (2017). Embracing the unknown: disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590. doi: 10.1038/nrmicro.2017.87

Finkel, O. M., Castrillo, G., Herrera Paredes, S., Salas González, I., and Dangl, J. L. (2017). Understanding and exploiting plant beneficial microbes. Curr. Opin. Plant Biol. 38, 155–163. doi: 10.1016/j.pbi.2017.04.018

Fituma, T., Tamado, T., and Anteneh, A. (2018). Effect of inoculating Bradyrhizobium on phosphorus use efficiency and nutrient uptake of soybean intercropped with sugarcane in calcareous soil of metahara, Central Rift Valley, Ethiopia. Adv. Crop Sci. Tech. 28, 17–32. doi: 10.4172/2329-8863.1000290

Fitzpatrick, C. R., Copeland, J., Wang, P. W., Guttman, D. S., Kotanen, P. M., and Johnson, M. T. J. (2018). Assembly and ecological function of the root microbiome across angiosperm plant species. Proc. Natl. Acad. Sci. U.S.A. 115, E1157–E1165.

Frąc, M., Jezierska-Tys, S., and Yaguchi, T. (2015). Occurrence, detection, and molecular and metabolic characterization of heat-resistant fungi in soils and plants and their risk to human health. Adv. Agron. 132, 161–204. doi: 10.1016/bs.agron.2015.02.003

Freitas, M. A., Medeiros, F. H. V., Carvalho, S. P., Guilherme, L. R. G., Teixeira, W. D., Zhang, H., et al. (2015). Augmenting iron accumulation in cassava by the beneficial soil bacterium Bacillus subtilis (GBO3). Front. Plant Sci. 6:596. doi: 10.3389/fpls.2015.00596

Fu, Z. Q., and Dong, X. (2013). Systemic acquired resistance: turning local infection into global defense. Annu. Rev. Plant Biol. 64, 839–863. doi: 10.1146/annurev-arplant-042811-105606

Gamalero, E., and Glick, B. R. (2011). “Mechanisms used by plant growth-promoting bacteria,” in Bacteria in Agrobiology: Plant Nutrient Management , ed. D. K. Maheshwari (Berlin: Springer), 17–46. doi: 10.1007/978-3-642-21061-7_2

Gang, S., Saraf, M., Waite, C. J., Buck, M., and Schumacher, J. (2018). Mutualism between Klebsiella SGM 81 and Dianthus caryophyllus in modulating root plasticity and rhizospheric bacterial density. Plant Soil 424, 273–288. doi: 10.1007/s11104-017-3440-5

Gao, X., Chen, X., Lin, W., Chen, S., Lu, D., Niu, Y., et al. (2013). Bifurcation of Arabidopsis NLR immune signaling via Ca2C-dependent protein kinases. PLoS Pathog. 9:e1003127. doi: 10.1371/journal.ppat.1003127

Germon, A., Amaral Guerrini, I., Bordron, B., Bouillet, J.-P., Nouvellon, Y., Leonardo de Moraes Gonçalves, J., et al. (2018). Consequences of mixing Acacia mangium and Eucalyptus grandis trees on soil exploration by fine-roots down to a depth of 17 m. Plant Soil 424, 203–220. doi: 10.1007/s11104-017-3428-1

Ghorchiani, M., Etesami, H., and Alikhani, H. A. (2018). Improvement of growth and yield of maize under water stress by co-inoculating an arbuscular mycorrhizal fungus and a plant growth promoting rhizobacterium together with phosphate fertilizers. Agric. Ecosyst. Environ. 258, 59–70. doi: 10.1016/j.agee.2018.02.016

Ghurye, J. S., Cepeda-Espinoza, V., and Pop, M. (2016). Metagenomic assembly: overview, challenges and applications. Yale J. Biol. Med. 89, 353–362.

Gill, S. S., Gill, R., Trivedi, D. K., Anjum, N. A., Sharma, K. K., Ansari, M. W., et al. (2016). Piriformospora indica : potential and significance in plant stress tolerance. Front. Microbiol. 7:332. doi: 10.3389/fmicb.2016.00332

Glassner, H., Zchori-Fein, E., Yaron, S., Sessitsch, A., Sauer, U., and Compant, S. (2018). Bacterial niches inside seeds of Cucumis melo L. Plant Soil 422, 101–113. doi: 10.1007/s11104-017-3175-3

Glick, B. R., Penrose, D. M., and Li, J. (1998). A model for the lowering of plant ethylene concentrations by plant growth-promoting bacteria. J. Theor. Biol. 190, 63–68. doi: 10.1006/jtbi.1997.0532

Gu, Y., Hou, Y., Huang, D., Hao, Z., Wang, X., Wei, Z., et al. (2017). Application of biochar reduces Ralstonia solanacearum infection via effects on pathogen chemotaxis, swarming motility, and root exudate adsorption. Plant Soil 415, 269–281. doi: 10.1007/s11104-016-3159-8

Hacquard, S. (2016). Disentangling the factors shaping microbiota composition across the plant holobiont. New Phytol. 209, 454–457. doi: 10.1111/nph.13760

Hacquard, S., and Schadt, C. W. (2015). Towards a holistic understanding of the beneficial interactions across the Populus microbiome. New Phytol. 205, 1424–1430. doi: 10.1111/nph.13133

Haichar, F. E. Z., Roncato, M.-A., and Achouak, W. (2012). Stable isotope probing of bacterial community structure and gene expression in the rhizosphere of Arabidopsis thaliana . FEMS Microbiol. Ecol. 81, 291–302. doi: 10.1111/j.1574-6941.2012.01345.x

Haichar, F. E. Z., Santaella, C., Heulin, T., and Achouak, W. (2014). Root exudates mediated interactions belowground. Soil Biol. Biochem. 77, 69–80. doi: 10.1016/j.soilbio.2014.06.017

Hardoim, P. R., Hardoim, C. C., van Overbeek, L. S., and van Elsas, J. D. (2012). Dynamics of seedborne rice endophytes on early plant growth stages. PLoS One 7:e30438. doi: 10.1371/journal.pone.0030438

Hardoim, P. R., van Overbeek, L. S., Berg, G., Pirttilä, A. M., Compant, S., Campisano, A., et al. (2015). The hidden world within plants: ecological and evolutionary considerations for defining functioning of microbial endophytes. Microbiol. Mol. Biol. Rev. 79, 293–320. doi: 10.1128/mmbr.00050-14

Hartman, K., van der Heijden, M. G. A., Wittwer, R. A., Banerjee, S., Walser, J.-C., and Schlaeppi, K. (2018). Cropping practices manipulate abundance patterns of root and soil microbiome members paving the way to smart farming. Microbiome 6:14.

Hartmann, A., Schmid, M., van Tuinen, D., and Berg, G. (2009). Plant-driven selection of microbes. Plant Soil 321, 235–275. doi: 10.1007/s11104-008-9814-y

Hartmann, M., Frey, B., Mayer, J., Mäder, P., and Widmer, F. (2015). Distinct soil microbial diversity under long-term organic and conventional farming. ISME J. 9, 1177–1194. doi: 10.1038/ismej.2014.210

Harvey, P. R., Warren, R. A., and Wakelin, S. (2009). Potential to improve root access to phosphorus: the role of non-symbiotic microbial inoculants in the rhizosphere. Crop Pasture Sci. 60, 144–151. doi: 10.1071/cp08084

Hasegawa, T., Kato, Y., Okabe, A., Itoi, C., Ooshiro, A., Kawaide, H., et al. (2019). Effect of secondary metabolites of tomato ( Solanum lycopersicum ) on chemotaxis of Ralstonia solanacearum , pathogen of bacterial wilt disease. J. Agric. Food Chem. 67, 1807–1813. doi: 10.1021/acs.jafc.8b06245

Hassan, S., and Mathesius, U. (2012). The role of flavonoids in root rhizosphere signalling: opportunities and challenges for improving plant microbe interactions. J. Exp. Bot. 63, 3429–3444. doi: 10.1093/jxb/err430

He, Y., Pantigoso, H. A., Wu, Z., and Vivanco, J. M. (2019). Co-inoculation of Bacillus sp. and Pseudomonas putida at different development stages acts as a biostimulant to promote growth, yield and nutrient uptake of tomato. J. Appl. Microbiol. 127, 196–207. doi: 10.1111/jam.14273

Hendgen, M., Hoppe, B., Döring, J., Friedel, M., Kauer, R., Frisch, M., et al. (2018). Effects of different management regimes on microbial biodiversity in vineyard soils. Sci. Rep. 8:9393.

Hiltner, L. (1904). Über neuere Erfahrungen und Probleme auf dem Gebiete der Bodenbakteriologie unter besonderer Berücksichtigung der Gründüngung und Brache. Soil Biol. Biochem. 98, 59–78.

Hopkins, S. R., Wojdak, J. M., and Belden, L. K. (2017). Defensive symbionts mediate host-parasite interactions at multiple scales. Trends Parasitol. 33, 53–64. doi: 10.1016/j.pt.2017.01.002

Hu, L., Robert, C. A. M., Cadot, S., Zhang, X., Ye, M., Li, B., et al. (2018). Root exudate metabolites drive plant-soil feedbacks on growth and defense by shaping the rhizosphere microbiota. Nat. Commun. 9:2738.

Jacoby, R. P., and Kopriva, S. (2019). Metabolic niches in the rhizosphere microbiome: new tools and approaches to analyse metabolic mechanisms of plant–microbe nutrient exchange. J. Exp. Bot. 70, 1087–1094. doi: 10.1093/jxb/ery438

Jamily, A. S., Koyama, Y., Win, T. A., Toyota, K., Chikamatsu, S., Shirai, T., et al. (2019). Effects of inoculation with a commercial microbial inoculant Bacillus subtilis C-3102 mixture on rice and barley growth and its possible mechanism in the plant growth stimulatory effect. J. Plant Prot. Res. 59, 193–205.

Joe, M. M., Islam, M. R., Karthikeyan, B., Bradeepa, K., Sivakumaar, P. K., and Sa, T. (2012). Resistance responses of rice to rice blast fungus after seed treatment with the endophytic Achromobacter xylosoxidans AUM54 strains. Crop Prot. 42, 141–148. doi: 10.1016/j.cropro.2012.07.006

Johns, N. I., Blazejewski, T., Gomes, A. L. C., and Wang, H. H. (2016). Principles for designing synthetic microbial communities. Curr. Opin. Microbiol. 31, 146–153. doi: 10.1016/j.mib.2016.03.010

Jones, J. D. G., and Dangl, J. L. (2006). The plant immune system. Nature 444, 323–329.

Jones, P., Garcia, B., Furches, A., Tuskan, G., and Jacobson, D. (2019). Plant host-associated mechanisms for microbial selection. Front. Plant Sci. 10:862. doi: 10.3389/fpls.2019.00862

Kanchiswamy, C. N., Malnoy, M., and Maffei, M. E. (2015). Chemical diversity of microbial volatiles and their potential for plant growth and productivity. Front. Plant Sci. 6:151. doi: 10.3389/fpls.2015.00151

Karamanos, R. E., Flore, N. A., and Harapiak, J. T. (2010). Re-visiting use of penicillium bilaii with phosphorus fertilization of hard red spring wheat. Can. J. Plant Sci. 90, 265–277. doi: 10.4141/CJPS09123

Karthik, C., Oves, M., Thangabalu, R., Sharma, R., Santhosh, S. B., and Arulselvi, P. I. (2016). Cellulosimicrobium funkei -like enhances the growth of Phaseolus vulgaris by modulating oxidative damage under chromium (VI) toxicity. J. Adv. Res. 7, 839–850. doi: 10.1016/j.jare.2016.08.007

Kecskeméti, E., Berkelmann-Löhnertz, B., and Reineke, A. (2016). Are epiphytic microbial communities in the carposphere of ripening grape clusters ( Vitis vinifera L.) different between conventional, organic, and biodynamic grapes? PLoS One 11:e0160852. doi: 10.1371/journal.pone.0160852

Khan, W., Costa, C., Souleimanov, A., Prithiviraj, B., and Smith, D. L. (2011). Response of Arabidopsis thaliana roots to lipochitooligosaccharide from Bradyrhizobium japonicum and other chitin-like compounds. Plant Growth Regul. 63, 243–249. doi: 10.1007/s10725-010-9521-6

Kittelmann, S., Seedorf, H., Walters, W., Clemente, J. C., Knight, R., Gordon, J. I., et al. (2013). Simultaneous amplicon sequencing to explore co-occurrence patterns of bacterial, archaeal and eukaryotic microorganisms in rumen microbial communities. PLoS One 8:e47879. doi: 10.1371/journal.pone.0047879

Knapp, D. G., Németh, J. B., Barry, K., Hainaut, M., Henrissat, B., Johnson, J., et al. (2018). Comparative genomics provides insights into the lifestyle and reveals functional heterogeneity of dark septate endophytic fungi. Sci. Rep. 8:6321. doi: 10.1038/s41598-018-24686-4

Köhl, J., Postma, J., Nicot, P., Ruocco, M., and Blum, B. (2011). Stepwise screening of microorganisms for commercial use in biological control of plant pathogenic fungi and bacteria. Biol. Control 57, 1–12. doi: 10.1016/j.biocontrol.2010.12.004

Kondorosi, E., Gyuris, J., Schmidt, J., John, M., Duda, E., Hoffmann, B., et al. (1989). Positive and negative control of nod gene expression in Rhizobium meliloti is required for optimal nodulation. EMBO J. 8, 1331–1340. doi: 10.1002/j.1460-2075.1989.tb03513.x

Konstantinidis, K. T., and Tiedje, J. M. (2007). Prokaryotic taxonomy and phylogeny in the genomic era: advancements and challenges ahead. Curr. Opin. Microbiol. 10, 504–509. doi: 10.1016/j.mib.2007.08.006

Kopylova, E., Navas-Molina, J. A., Mercier, C., Xu, Z. Z., Mahé, F., He, Y., et al. (2016). Open-source sequence clustering methods improve the state of the art. mSystems 1:e00003-15.

Kowalchuk, G. A., Buma, D. S., De Boer, W., Klinkhamer, P. G. L., and Van Veen, J. A. (2002). Effects of above-ground plant species composition and diversity on the diversity of soil-borne microorganisms. Antonie Van Leeuwenhoek 81, 509–520. doi: 10.1023/A:1020565523615

Kraft, N. J. B., Godoy, O., and Levine, J. M. (2015). Plant functional traits and the multidimensional nature of species coexistence. Proc. Natl. Acad. Sci. U.S.A. 112, 797–802. doi: 10.1073/pnas.1413650112

Kumar, A., Munder, A., Aravind, R., Eapen, S., Tümmler, B., and Raaijmakers, J. (2013). Friend or foe: genetic and functional characterization of plant endophytic Pseudomonas aeruginosa . Environ. Microbiol. 15, 764–779. doi: 10.1111/1462-2920.12031

Kumar, M., Yadav, V., Tuteja, N., and Johri, A. K. (2009). Antioxidant enzyme activities in maize plants colonized with Piriformospora indica . Microbiology 155, 780–790. doi: 10.1099/mic.0.019869-0

Laan, P. (1934). Effect of ethylene on growth hormone formation in Avena and Vicia. Rec. Trav. Botan. Neerl. 31, 691–742.

Lahrmann, U., Ding, Y., Banhara, A., Rath, M., Hajirezaei, M. R., Döhlemann, S., et al. (2013). Host-related metabolic cues affect colonization strategies of a root endophyte. Proc. Natl. Acad. Sci. U.S.A. 110, 13965–13970. doi: 10.1073/pnas.1301653110

Lakshmanan, V., Selvaraj, G., and Bais, H. P. (2014). Update on the soil microbiome functional soil microbiome: belowground solutions to an aboveground problem. Plant Physiol. 166, 689–700. doi: 10.1104/pp.114.245811

Lebeis, S. L., Paredes, S. H., Lundberg, D. S., Breakfield, N., Gehring, J., McDonald, M., et al. (2015). Salicylic acid modulates colonization of the root microbiome by specific bacterial taxa. Science 349, 860–864. doi: 10.1126/science.aaa8764

Legay, N., Piton, G., Arnoldi, C., Bernard, L., Binet, M.-N., Mouhamadou, B., et al. (2017). Soil legacy effects of climatic stress, management and plant functional composition on microbial communities influence the response of Lolium perenne to a new drought event. Plant Soil 424, 233–254. doi: 10.1007/s11104-017-3403-x

Liang, Y., Tóth, K., Cao, Y., Tanaka, K., Espinoza, C., and Stacey, G. (2014). Lipochitooligosaccharide recognition: an ancient story. New Phytol. 204, 289–296. doi: 10.1111/nph.12898

Limpens, E., van Zeijl, A., and Geurts, R. (2015). Lipochitooligosaccharides modulate plant host immunity to enable endosymbioses. Annu. Rev. Phytopathol. 53, 311–334. doi: 10.1146/annurev-phyto-080614-120149

Liu, K., Newman, M., McInroy, J. A., Hu, C. H., and Kloepper, J. W. (2017). Selection and assessment of plant growth-promoting rhizobacteria for biological control of multiple plant diseases. Phytopathology 107, 928–936. doi: 10.1094/phyto-02-17-0051-r

Liu, L., Guo, G., Wang, Z., Ji, H., Mu, F., and Li, X. (2014). “Auxin in plant growth and stress responses,” in Phytohormones: A Window to Metabolism, Signaling and Biotechnological Applications , eds L.-S. P. Tran and S. Pal (New York, NY: Springer), 1–35. doi: 10.1007/978-1-4939-0491-4_1

Liu, Y., Zuo, S., Xu, L., Zou, Y., and Song, W. (2012). Study on diversity of endophytic bacterial communities in seeds of hybrid maize and their parental lines. Arch. Microbiol. 194, 1001–1012. doi: 10.1007/s00203-012-0836-8

Longa, C. M. O., Nicola, L., Antonielli, L., Mescalchin, E., Zanzotti, R., Turco, E., et al. (2017). Soil microbiota respond to green manure in organic vineyards. J. Appl. Microbiol. 123, 1547–1560. doi: 10.1111/jam.13606

Ma, Z., Hua, G. K. H., Ongena, M., and Höfte, M. (2016). Role of phenazines and cyclic lipopeptides produced by Pseudomonas sp. CMR12a in induced systemic resistance on rice and bean. Environ. Microbiol. Rep. 8, 896–904. doi: 10.1111/1758-2229.12454

Mansfield, J., Genin, S., Magori, S., Citovsky, V., Sriariyanum, M., Ronald, P., et al. (2012). Top 10 plant pathogenic bacteria in molecular plant pathology. Mol. Plant Pathol. 13, 614–629. doi: 10.1111/j.1364-3703.2012.00804.x

Marjanska-Cichon, B., and Sapieha-Waszkiewicz, A. (2011). Efficacy of garlic extracts used to control of grey mould in strawberry. Prog. Plant Prot. 51, 413–420.

Martinez-Medina, A., Flors, V., Heil, M., Mauch-Mani, B., Pieterse, C. M. J., Pozo, M. J., et al. (2016). Recognizing plant defense priming. Trends Plant Sci. 21, 818–822. doi: 10.1016/j.tplants.2016.07.009

Mendes, R., Garbeva, P., and Raaijmakers, J. M. (2013). The rhizosphere microbiome: significance of plant beneficial, plant pathogenic, and human pathogenic microorganisms. FEMS Microbiol. Rev. 37, 634–663. doi: 10.1111/1574-6976.12028

Mersmann, S., Bourdais, G., Rietz, S., and Robatzek, S. (2010). Ethylene signalling regulates accumulation of the FLS2 receptor and is required for the oxidative burst contributing to plant immunity. Plant Physiol. 154, 391–400. doi: 10.1104/pp.110.154567

Mhlongo, M. I., Piater, L. A., Madala, N. E., Labuschagne, N., and Dubery, I. A. (2018). The chemistry of plant–microbe interactions in the rhizosphere and the potential for metabolomics to reveal signaling related to defense priming and induced systemic resistance. Front. Plant Sci. 9:112. doi: 10.3389/fpls.2018.00112

Mishra, B. K., Lal, G., Sharma, Y. K., Kant, K., Saxena, S. N., and Dubey, P. N. (2019). Effect of microbial inoculants on cumin ( Cuminum cyminum Linn.) growth and yield. Int. J. Seed Spices 9, 53–56.

Mitter, B., Pfaffenbichler, N., Flavell, R., Compant, S., Antonielli, L., Petric, A., et al. (2017). A new approach to modify plant microbiomes and traits by introducing beneficial bacteria at flowering into progeny seeds. Front. Microbiol. 8:11. doi: 10.3389/fmicb.2017.00011

Mommer, L., Kirkegaard, J., and van Ruijven, J. (2016a). Root– root interactions: towards a rhizosphere framework. Trends Plant Sci. 21, 209–217. doi: 10.1016/j.tplants.2016.01.009

Mommer, L., Hinsinger, P., Prigent-Combaret, C., and Visser, E. J. W. (2016b). Advances in the rhizosphere: stretching the interface of life. Plant Soil 407, 1–8. doi: 10.1007/s11104-016-3040-9

Monsanto BioAg (2016). Available online at: https://www.efeedlink.com/contents/12-28-2016/c5141bdd-e8a4-4f05-9502-279a7add15ff-0003.html (accessed December 10, 2020).

Montazeaud, G., Violle, C., Fréville, H., Luquet, D., Ahmadi, N., Courtois, B., et al. (2018). Crop mixtures: does niche complementarity hold for belowground resources? An experimental test using rice genotypic pairs. Plant Soil 424, 187–202. doi: 10.1007/s11104-017-3496-2

Moreira, H., Pereira, S., Vega, A., Castro, P. M., and Marques, A. P. (2020). Synergistic effects of arbuscular mycorrhizal fungi and plant growth-promoting bacteria benefit maize growth under increasing soil salinity. J. Environ. Manage. 257:109982. doi: 10.1016/j.jenvman.2019.109982

Nadeem, S. M., Imran, M., Naveed, M., Khan, M. Y., Ahmad, M., Zahir, Z. A., et al. (2017). Synergistic use of biochar, compost and plant growth-promoting rhizobacteria for enhancing cucumber growth under water deficit conditions. J. Sci. Food Agric. 97, 5139–5145. doi: 10.1002/jsfa.8393

Nafady, N., Hashem, M., Hassan, E., Ahmed, H. A., and Alamri, S. A. (2019). The combined effect of arbuscular mycorrhizae and plant-growth-promoting yeast improves sunflower defense against Macrophomina phaseolina diseases. Biol. Control 138:104049. doi: 10.1016/j.biocontrol.2019.104049

Nascimento, F. X., Rossi, M. J., and Glick, B. R. (2018). Ethylene and 1-Aminocyclopropane-1-carboxylate (ACC) in plant–bacterial interactions. Front. Plant Sci. 9:114. doi: 10.3389/fpls.2018.00114

Naseem, M., and Dandekar, T. (2012). The role of auxin-cytokinin antagonism in plant-pathogen interactions. PLoS Pathog. 8:e1003026. doi: 10.1371/journal.ppat.1003026

Nayfach, S., and Pollard, K. S. (2016). Toward accurate and quantitative comparative metagenomics. Cell 166, 1103–1116. doi: 10.1016/j.cell.2016.08.007

NCOF (2011). Annual Report 2010-11. Ghaziabad: National Centre of Organic Farming,.

NCOF (2012). Annual Report 2011-12. Ghaziabad: National Centre of Organic Farming.

Neal, A., and Ton, J. (2013). Systemic defense priming by Pseudomonas putida KT2440 in maize depends on benzoxazinoid exudation from the roots. Plant Signal. Behav. 8:e22655. doi: 10.4161/psb.22655

Neal, A. L., Ahmad, S., Gordon-Weeks, R., and Ton, J. (2012). Benzoxazinoids in root exudates of maize attract Pseudomonas putida to the rhizosphere. PLoS One 7:e35498. doi: 10.1371/journal.pone.0035498

Nihorimbere, V., Cawoy, H., Seyer, A., Brunelle, A., Thonart, P., and Ongena, M. (2012). Impact of rhizosphere factors on cyclic lipopeptide signature from the plant beneficial strain Bacillus amyloliquefaciens S499. FEMS Microbiol. Ecol. 79, 176–191. doi: 10.1111/j.1574-6941.2011.01208.x

O’Callaghan, M., Swaminathan, J., Lottmann, J., Wright, D. A., and Jackson, T. A. (2006). Seed coating with biocontrol strain Pseudomonas fluorescens F113. NZ Plant Prot. 59, 80–85. doi: 10.30843/nzpp.2006.59.4420

Oldroyd, G. E. D. (2013). Speak, friend, and enter: signalling systems that promote beneficial symbiotic associations in plants. Nat. Rev. Microbiol. 11, 252–263. doi: 10.1038/nrmicro2990

Oldroyd, G. E. D., Murray, J. D., Poole, P. S., and Downie, J. A. (2011). The rules of engagement in the legume-rhizobial symbiosis. Annu. Rev. Genet. 45, 119–144. doi: 10.1146/annurev-genet-110410-132549

Oliveira, A. L., Santos, O. J., Marcelino, P. R., Milani, K. M., Zuluaga, M. Y., Zucareli, C., et al. (2017). Maize inoculation with Azospirillum brasilense Ab-V5 cells enriched with exopolysaccharides and polyhydroxybutyrate results in high productivity under low N fertilizer input. Front. Microbiol. 8:1873. doi: 10.3389/fmicb.2017.01873

Ongena, M., Jourdan, E., Adam, A., Paquot, M., Brans, A., Joris, B., et al. (2007). Surfactin and fengycin lipopeptides of Bacillus subtilis as elicitors of induced systemic resistance in plants. Environ. Microbiol. 9, 1084–1090. doi: 10.1111/j.1462-2920.2006.01202.x

Oszust, K., Pawlik, A., Janusz, G., Ziemiński, K., Cyran, M., Siczek, A., et al. (2017a). Characterization and influence of a multi-enzymatic biopreparation for biogas yield enhancement. Bioresources 12, 6187–6206.

Oszust, K., Pawlik, A., Siczek, A., Janusz, G., Gryta, A., Bilińska-Wielgus, N., et al. (2017b). Efficient cellulases production by Trichoderma atroviride G79/11 in submerged culture based on soy flour-cellulose-lactose. Bioresources 12, 8468–8489.

Panek, J., and Frąc, M. (2018). Development of a qPCR assay for the detection of heat-resistant Talaromyces flavus . Int. J. Food Microbiol. 270, 44–51. doi: 10.1016/j.ijfoodmicro.2018.02.010

Panke-Buisse, K., Poole, A., Goodrich, J., Ley, R. E., and Kao-Kniffin, J. (2015). Selection on soil microbiomes reveals reproducible impacts on plant function. ISME J. 9, 980–989. doi: 10.1038/ismej.2014.196

Parray, J. A., Jan, S., Kamili, A. N., Qadri, R. A., Egamberdieva, D., and Ahmad, P. (2016). Current perspectives on plant growth-promoting rhizobacteria. J. Plant Growth Regul. 35, 877–902.

Parrish, J. A. D., and Bazzaz, F. A. (1976). Underground niche separation in successional plants. Ecology 57, 1281–1288. doi: 10.2307/1935052

Passera, A., Compant, S., Casati, P., Maturo, M. G., Battelli, G., Quaglino, F., et al. (2019). Not just a pathogen? Description of a plant-beneficial Pseudomonas syringae strain. Front. Microbiol. 10:1409. doi: 10.3389/fmicb.2019.01409

Perez, J. J., Francois, N. J., Maroniche, G. A., Borrajo, M. P., Pereyra, M. A., and Creus, C. M. (2018). A novel, green, low-cost chitosan-starch hydrogel as potential delivery system for plant growth-promoting bacteria. Carbohydr. Polym. 202, 409–417. doi: 10.1016/j.carbpol.2018.07.084

Philippot, L., Raaijmakers, J. M., Lemanceau, P., and Van Der Putten, W. H. (2013). Going back to the roots: the microbial ecology of the rhizosphere. Nat. Rev. Microbiol. 11, 789–799. doi: 10.1038/nrmicro3109

Pieterse, C. M., Van der Does, D., Zamioudis, C., Leon-Reyes, A., and Van Wees, S. C. (2012). Hormonal modulation of plant immunity. Annu. Rev. Cell Dev. Biol. 28, 489–521. doi: 10.1146/annurev-cellbio-092910-154055

Pieterse, C. M. J., Zamioudis, C., Berendsen, R. L., Weller, D. M., Van Wees, S. C. M., and Bakker, P. A. H. M. (2014). Induced systemic resistance by beneficial microbes. Annu. Rev. Phytopathol. 52, 347–375.

Pineda, A., Kaplan, I., and Bezemer, T. M. (2017). Steering soil microbiomes to suppress aboveground insect pests. Trends Plant Sci. 22, 770–778. doi: 10.1016/j.tplants.2017.07.002

Planchamp, C., Glauser, G., and Mauch-Mani, B. (2015). Root inoculation with Pseudomonas putida KT2440 induces transcriptional and metabolic changes and systemic resistance in maize plants. Front. Plant Sci. 5:719.

Plett, J. M., Daguerre, Y., Wittulsky, S., Vayssières, A., Deveau, A., Melton, S. J., et al. (2014). Effector MiSSP7 of the mutualistic fungus Laccaria bicolor stabilizes the Populus JAZ6 protein and represses jasmonic acid (JA) responsive genes. Proc. Natl. Acad. Sci. U.S.A. 111, 8299–8304. doi: 10.1073/pnas.1322671111

Postma-Blaauw, M. B., de Goede, R. G. M., Bloem, J., Faber, J. H., and Brussaard, L. (2010). Soil biota community structure and abundance under agricultural intensification and extensification. Ecology 91, 460–473. doi: 10.1890/09-0666.1

Preininger, C., Sauer, U., Bejarano, A., and Berninger, T. (2018). Concepts and applications of foliar spray for microbial inoculants. Appl. Microbiol. Biotechnol. 102, 7265–7282. doi: 10.1007/s00253-018-9173-4

Proksa, B. (2010). Talaromyces flavus and its metabolites. Chem. Pap. 64, 696–714.

Pylak, M., Oszust, K., and Frąc, M. (2019). Review report on the role of bioproducts, biopreparations, biostimulants and microbial inoculants in organic production of fruit. Rev. Environ. Sci. Biotechnol. 18, 597–616. doi: 10.1007/s11157-019-09500-5

Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. S., Manichanh, C., et al. (2010). A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65.

Quiza, L., St-Arnaud, M., and Yergeau, E. (2015). Harnessing phytomicrobiome signaling for rhizosphere microbiome engineering. Front. Plant Sci. 6:507. doi: 10.3389/fpls.2015.00507

Raaijmakers, J. M., De Bruijn, I., Nybroe, O., and Ongena, M. (2010). Natural functions of lipopeptides from Bacillus and Pseudomonas : more than surfactants and antibiotics. FEMS Microbiol. Rev. 34, 1037–1062. doi: 10.1111/j.1574-6976.2010.00221.x

Rascovan, N., Carbonetto, B., Perrig, D., and Díaz, M. (2016). Integrated analysis of root microbiomes of soybean and wheat from agricultural fields. Sci. Rep. 6:28084.

Reddy, M. B., Belkacemi, K., Corcuff, R., Castaigne, F., and Arul, J. (2000). Effect of pre-harvest chitosan sprays on post-harvest infection by Botrytis cinerea and quality of strawberry fruit. Postharvest Biol. Technol. 20, 39–51. doi: 10.1016/s0925-5214(00)00108-3

Richardson, A. E., and Simpson, R. J. (2011). Soil microorganisms mediating phosphorus availability. Plant Physiol. 156, 989–996. doi: 10.1104/pp.111.175448

Rodríguez, C. E., Mitter, B., Barret, M., Sessitsch, A., and Compant, S. (2018). Commentary: seed bacterial inhabitants and their routes of colonization. Plant Soil 422, 129–134. doi: 10.1007/s11104-017-3368-9

Rosier, A., Medeiros, F. H., and Bais, H. P. (2018). Defining plant growth promoting rhizobacteria molecular and biochemical networks in beneficial plant-microbe interactions. Plant Soil 428, 35–55. doi: 10.1007/s11104-018-3679-5

Rutten, G., and Gómez-Aparicio, L. (2018). Plant-soil feedbacks and root responses of two Mediterranean oaks along a precipitation gradient. Plant Soil 424, 221–231. doi: 10.1007/s11104-018-3567-z

Ryu, C.-M., Farag, M. A., Hu, C.-H., Reddy, M. S., Wei, H.-X., Pare, P. W., et al. (2003). Bacterial volatiles promote growth in Arabidopsis. Proc. Natl. Acad. Sci. U.S.A. 100, 4927–4932. doi: 10.1073/pnas.0730845100

Sabra, M., Aboulnasr, A., Franken, P., Perreca, E., Wright, L. P., and Camehl, I. (2018). Beneficial root endophytic fungi increase growth and quality parameters of sweet basil in heavy metal contaminated soil. Front. Plant Sci. 9:1726. doi: 10.3389/fpls.2018.01726

Sahoo, R. K., Bhardwaj, D., and Tuteja, N. (2013). “Biofertilizers: a sustainable eco-friendly agricultural approach to crop improvement,” in Plant Acclimation to Environmental Stress , eds N. Tuteja and S. S. Gill (New York, NY: Springer), 403–432. doi: 10.1007/978-1-4614-5001-6_15

Sasse, J., Martinoia, E., and Northen, T. (2018). Feed your friends: do plant exudates shape the root microbiome? Trends Plant Sci. 23, 25–41. doi: 10.1016/j.tplants.2017.09.003

Schikora, A., Schenk, S. T., Stein, E., Molitor, A., Zuccaro, A., and Kogel, K. H. (2011). N-acyl-homoserine lactone confers resistance toward biotrophic and hemibiotrophic pathogens via altered activation of AtMPK6. Plant Physiol. 157, 1407–1418. doi: 10.1104/pp.111.180604

Schirmer, M., Ijaz, U., D’Amore, R., Hall, N., Sloan, W. T., and Quince, C. (2015). Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform. Nucleic Acids Res. 43:e37. doi: 10.1093/nar/gku1341

Schmidt, J. E., Kent, A. D., Brisson, V. L., and Gaudin, A. C. (2019). Agricultural management and plant selection interactively affect rhizosphere microbial community structure and nitrogen cycling. Microbiome 7:146.

Schoch, C. L., Seifert, K. A., Huhndorf, S., Robert, V., Spouge, J. L., Levesque, C. A., et al. (2012). Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for fungi. Proc. Natl. Acad. Sci. U.S.A. 109, 6241–6246.

Schoebitz, M., López, M. D., and Roldán, A. (2013). Bioencapsulation of microbial inoculants for better soil–plant fertilization. A review. Agron. Sustain. Dev. 33, 751–765. doi: 10.1007/s13593-013-0142-0

Semchenko, M., Lepik, A., Abakumova, M., and Zobel, K. (2018). Different sets of belowground traits predict the ability of plant species to suppress and tolerate their competitors. Plant Soil 424, 157–169. doi: 10.1007/s11104-017-3282-1

Sharon, A., Fuchs, Y., and Anderson, J. D. (1993). The elicitation of ethylene biosynthesis by a Trichoderma xylanase is not related to the cell wall degradation activity of the enzyme. Plant Physiol. 102, 1325–1329. doi: 10.1104/pp.102.4.1325

Sharpton, T. J. (2014). An introduction to the analysis of shotgun metagenomic data. Front. Plant Sci. 5:209. doi: 10.3389/fpls.2014.00209

Sikora, R., and Dababat, A. E. F. (2007). Influence of the mutualistic endophyte Fusarium oxysporum 162 on Meloidogyne incognita attraction and invasion. Nematology 9, 771–776. doi: 10.1163/156854107782331225

Spaepen, S. (2015). “Plant hormones produced by microbes,” in Principles of Plant-Microbe Interactions , ed. B. Lugtenberg (Berlin: Springer), 247–256. doi: 10.1007/978-3-319-08575-3_26

Spaepen, S., and Vanderleyden, J. (2011). Auxin and plant-microbe interactions. Cold Spring Harb. Perspect. Biol. 3:a001438.

Srinivasan, K., and Mathivanan, N. (2011). Plant growth promoting microbial consortia mediated classical biocontrol of sunflower necrosis virus disease. J. Biopesticides 4, 65–72.

Steven, B., Huntley, R. B., and Zeng, Q. (2018). The influence of flower anatomy and apple cultivar on the apple flower phytobiome. Phytobiomes J. 2, 171–179. doi: 10.1094/pbiomes-03-18-0015-r

Sukumar, P. (2010). The Role of Auxin and Ethylene in Adventitious Root Formation in Arabidopsis and Tomato. Ph.D. thesis, Wake Forest University, Winston-Salem, NC.

Suleman, M., Yasmin, S., Rasul, M., Yahya, M., Atta, B. M., and Mirza, M. S. (2018). Phosphate solubilizing bacteria with glucose dehydrogenase gene for phosphorus uptake and beneficial effects on wheat. PLoS One 13:e0204408. doi: 10.1371/journal.pone.0204408

Sylvia, D. M., Fuhrmann, J. J., Hartel, P. G., and Zuberer, D. A. (2005). Principles and Applications of Soil Microbiology , 2nd Edn. Upper Saddle River, NJ: Pearson.

Syranidou, E., Christofilopoulos, S., Gkavrou, G., Thijs, S., Weyens, N., Vangronsveld, J., et al. (2016). Exploitation of endophytic bacteria to enhance the phytoremediation potential of the wetland helophyte Juncus acutus . Front. Microbiol. 7:1016. doi: 10.3389/fmicb.2016.01016

Takatsuji, H., and Jiang, C.-J. (2014). “Plant hormone crosstalks under biotic stresses,” in Phytohormones: A Window to Metabolism, Signaling and Biotechnological Applications , eds L.-S. Tran and S. Pal (New York, NY: Springer), 323–350. doi: 10.1007/978-1-4939-0491-4_11

Thao, N. P., Khan, M. I. R., Thu, N. B. A., Hoang, X. L. T., Asgher, M., Khan, N. A., et al. (2015). Role of ethylene and its cross talk with other signaling molecules in plant responses to heavy metal stress. Plant Physiol. 169, 73–84. doi: 10.1104/pp.15.00663

Thomashow, L. S., and Weller, D. M. (1996). “Current concepts in the use of introduced bacteria for biological disease control: mechanisms and antifungal metabolites,” in Plant-Microbe Interactions , Vol. 1, eds G. Stacey and N. T. Keen (Boston, MA: Springer), 187–235. doi: 10.1007/978-1-4613-1213-0_6

Timmusk, S., Seisenbaeva, G., and Behers, L. (2018). Titania (TiO2) nanoparticles enhance the performance of growth-promoting rhizobacteria. Sci. Rep. 8:617.

Tiryaki, D., Aydın, İ., and Atıcı, Ö. (2019). Psychrotolerant bacteria isolated from the leaf apoplast of cold-adapted wild plants improve the cold resistance of bean ( Phaseolus vulgaris L.) under low temperature. Cryobiology 86, 111–119. doi: 10.1016/j.cryobiol.2018.11.001

Trivedi, D. K., Bhatt, H., Pal, R. K., Tuteja, R., Garg, B., Johri, A. K., et al. (2013). Structure of RNA-interacting Cyclophilin A-like protein from Piriformospora indica that provides salinity-stress tolerance in plants. Sci. Rep. 3:3001.

Trivedi, P., Delgado-Baquerizo, M., Trivedi, C., Hamonts, K., Anderson, I. C., and Singh, B. K. (2017). Keystone microbial taxa regulate the invasion of a fungal pathogen in agro-ecosystems. Soil Biol. Biochem. 111, 10–14. doi: 10.1016/j.soilbio.2017.03.013

Udvardi, M., and Poole, P. S. (2013). Transport and metabolism in legume-rhizobia symbioses. Annu. Rev. Plant Biol. 64, 781–805. doi: 10.1146/annurev-arplant-050312-120235

Uhrig, R. G., Labandera, A.-M., and Moorhead, G. B. (2013). Arabidopsis PPP family of serine/threonine protein phosphatases: many targets but few engines. Trends Plant Sci. 18, 505–513. doi: 10.1016/j.tplants.2013.05.004

Van der Putten, W. H., Bardgett, R. D., Bever, J. D., Bezemer, T. M., Casper, B. B., Fukami, T., et al. (2013). Plant-soil feedbacks: the past, the present and future challenges. J. Ecol. 101, 265–276. doi: 10.1111/1365-2745.12054

van Zeijl, A., Op den Camp, R. H. M., Deinum, E. E., Charnikhov, T., Franssen, H., Op den Camp, H. J. M., et al. (2015). Rhizobium lipo-chitooligosaccharide signaling triggers accumulation of cytokinins in Medicago truncatula roots. Mol. Plant 8, 1213–1226. doi: 10.1016/j.molp.2015.03.010

Varinderpal-Singh, Sharma, S., Gosal, S. K., Choudhary, R., Singh, R., and Adholeya, A. (2020). Synergistic use of plant growth-promoting Rhizobacteria, arbuscular mycorrhizal fungi, and spectral properties for improving nutrient use efficiencies in wheat ( Triticum aestivum L.). Commun. Soil Sci. Plant Anal. 51, 14–27. doi: 10.1080/00103624.2019.1689259

Varma, A., Bakshi, M., Lou, B., Hartmann, A., and Oelmueller, R. (2012). Piriformospora indica : a novel plant growth-promoting mycorrhizal fungus. Agric. Res. 1, 117–131. doi: 10.1007/s40003-012-0019-5

Velásquez, A., Vega-Celedón, P., Fiaschi, G., Agnolucci, M., Avio, L., Giovannetti, M., et al. (2020). Responses of Vitis vinifera cv. Cabernet Sauvignon roots to the arbuscular mycorrhizal fungus Funneliformis mosseae and the plant growth-promoting rhizobacterium Ensifer meliloti include changes in volatile organic compounds. Mycorrhiza 30, 161–170. doi: 10.1007/s00572-020-00933-3

Verhagen, B. W. M., Glazebrook, J., Zhu, T., Chang, H.-S., van Loon, L. C., and Pieterse, C. M. J. (2004). The transcriptome of rhizobacteria induced systemic resistance in Arabidopsis. Mol. Plant Microbe Interact. 17, 895–908. doi: 10.1094/mpmi.2004.17.8.895

Verma, R. K., Sachan, M., Vishwakarma, K., Upadhyay, N., Mishra, R. K., Tripathi, D. K., et al. (2018). “Role of PGPR in sustainable agriculture: molecular approach toward disease suppression and growth promotion,” in Role of Rhizospheric Microbes in Soil , Vol. 2, ed. V. S. Meena (Singapore: Springer), 259–290. doi: 10.1007/978-981-13-0044-8_9

Vetterlein, D., Carminati, A., Kögel-Knabner, I., Bienert, G. P., Smalla, K., Oburger, E., et al. (2020). Rhizosphere spatiotemporal organization–a key to rhizosphere functions. Front. Agron. 2:8. doi: 10.3389/fagro.2020.00008

Vishwakarma, K., Kumar, N., Shandilya, C., and Varma, A. (2020). “Unravelling the role of endophytes in micronutrient uptake and enhanced crop productivity,” in Symbiotic Soil Microorganisms , eds N. Shrivastava, S. Mahajan, and A. Varma (Cham: Springer), 63–85. doi: 10.1007/978-3-030-51916-2_4

Vishwakarma, K., Kumar, V., Tripathi, D. K., and Sharma, S. (2018). Characterization of rhizobacterial isolates from Brassica juncea for multitrait plant growth promotion and their viability studies on carriers. Environ. Sustain. 1, 253–265. doi: 10.1007/s42398-018-0026-y

Vishwakarma, K., Mishra, M., Jain, S., Singh, J., Upadhyay, N., Verma, R. K., et al. (2017a). “Exploring the role of plant-microbe interactions in improving soil structure and function through root exudation: a key to sustainable agriculture,” in Plant-Microbe Interactions in Agro-Ecological Perspectives , eds D. P. Singh, H. B. Singh, and R. Prabha (Singapore: Springer), 467–487. doi: 10.1007/978-981-10-5813-4_23

Vishwakarma, K., Sharma, S., Kumar, N., Upadhyay, N., Devi, S., and Tiwari, A. (2016). “Contribution of microbial inoculants to soil carbon sequestration and sustainable agriculture,” in Microbial Inoculants in Sustainable Agricultural Productivity , Vol. 2, eds D. P. Singh, H. B. Singh, and R. Prabha (Singapore: Springer), 101–113. doi: 10.1007/978-81-322-2644-4_7

Vishwakarma, K., Sharma, S., Kumar, V., Upadhyay, N., Kumar, N., Mishra, R., et al. (2017b). “Current scenario of root exudate-mediated plant-microbe interaction and promotion of plant growth,” in Probiotics in Agroecosystem , eds V. Kumar, M. Kumar, S. Sharma, and R. Prasad (Singapore: Springer), 349–369. doi: 10.1007/978-981-10-4059-7_18

Vives-Peris, V., Gomez-Cadenas, A., and Perez-Clemente, R. M. (2018). Salt stress alleviation in citrus plants by plant growth-promoting rhizobacteria Pseudomonas putida and Novosphingobium sp. Plant Cell Rep. 37, 1557–1569. doi: 10.1007/s00299-018-2328-z

Vorholt, J. A. (2012). Microbial life in the phyllosphere. Nat. Rev. Microb. 10, 828–840. doi: 10.1038/nrmicro2910

Wallace, J. G., Kremling, K. A., Kovar, L. L., and Buckler, E. S. (2018). Quantitative genetics of the maize leaf microbiome. Phytobiomes J. 2, 208–224. doi: 10.1094/pbiomes-02-18-0008-r

Waller, F., Achatz, B., Baltruschat, H., Fodor, J., Becker, K., Fischer, M., et al. (2005). The endophytic fungus Piriformospora indica reprograms barley to salt-stress tolerance, disease resistance, and higher yield. Proc. Natl. Acad. Sci. U.S.A. 102, 13386–13391. doi: 10.1073/pnas.0504423102

Wang, Y., Wang, M., Li, Y., Wu, A., and Huang, J. (2018). Effects of arbuscular mycorrhizal fungi on growth and nitrogen uptake of Chrysanthemum morifolium under salt stress. PLoS One 13:e0196408. doi: 10.1371/journal.pone.0196408

Wei, L., Jian, H., Lu, K., Filardo, F., Yin, N., Liu, L., et al. (2016). Genomewide association analysis and differential expression analysis of resistance to Sclerotinia stem rot in Brassica napus. Plant Biotechnol. J. 14, 1368–1380. doi: 10.1111/pbi.12501

Weidlich, E. W. A., Temperton, V. M., and Faget, M. (2018). Neighbourhood stories: role of neighbour identity, spatial location and order of arrival in legume and non-legume initial interactions. Plant Soil 424, 171–182. doi: 10.1007/s11104-017-3398-3

Weller, D. M., Mavrodi, D. V., van Pelt, J. A., Pieterse, C. M. J., van Loon, L. C., and Bakker, P. A. H. M. (2012). Induced systemic resistance in Arabidopsis thaliana against Pseudomonas syringae pv. Tomato by 2,4-diacetylphloroglucinol-producing Pseudomonas fluorescens . Phytopathology 102, 403–412. doi: 10.1094/phyto-08-11-0222

Werner, G. D. A., Cornwell, W. K., Sprent, J. I., Kattge, J., and Kiers, E. T. (2014). A single evolutionary innovation drives the deep evolution of symbiotic N2-fixation in angiosperms. Nat. Commun. 5:4087.

Xia, Y., Sahib, M. R., Amna, A., Opiyo, S. O., Zhao, Z., and Gao, Y. G. (2019). Culturable endophytic fungal communities associated with plants in organic and conventional farming systems and their effects on plant growth. Sci. Rep. 9:669.

Yadav, A. K., and Chandra, K. (2014). Mass production and quality control of microbial inoculants. Proc. Indian Natl. Sci. Acad. 80, 483–489. doi: 10.16943/ptinsa/2014/v80i2/5

Yadav, G., Vishwakarma, K., Sharma, S., Kumar, V., Upadhyay, N., Kumar, N., et al. (2017). “Emerging significance of rhizospheric probiotics and its impact on plant health: current perspective towards sustainable agriculture,” in Probiotics and Plant Health , eds V. Kumar, M. Kumar, S. Sharma, and R. Prasad (Singapore: Springer), 233–251. doi: 10.1007/978-981-10-3473-2_10

Yánez-Mendizabal, V., Viñas, I., Usall, J., Cañamás, T., and Teixidó, N. (2012). Endospore production allows using spray-drying as a possible formulation system of the biocontrol agent Bacillus subtilis CPA-8. Biotechnol. Lett. 34, 729–735. doi: 10.1007/s10529-011-0834-y

Zarkani, A. A., Stein, E., Röhrich, C. R., Schikora, M., Evguenieva-Hackenberg, E., Degenkolb, T., et al. (2013). Homoserine lactones influence the reaction of plants to rhizobia. Int. J. Mol. Sci. 14, 17122–17146. doi: 10.3390/ijms140817122

Zarraonaindia, C., Owens, I. M., Weisenhorn, S. M., West, P., Hampton-Marcell, K., Lax, J., et al. (2015). The soil microbiome influences grapevine-associated microbiota. mBio 6:e02527-14.

Zemunik, G., Lambers, H., Turner, B. L., Laliberté, E., and Oliveira, R. S. (2017). High abundance of non-mycorrhizal plant species in severely phosphorus-impoverished Brazilian campos rupestres. Plant Soil 424, 255–271. doi: 10.1007/s11104-017-3503-7

Zhalnina, K., Louie, K., Hao, Z., Mansoori, N., da Rocha, U. N., Shi, S., et al. (2018). Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat. Microbiol. 3, 470–480. doi: 10.1038/s41564-018-0129-3

Zhang, H., Sun, Y., Xie, X., Kim, M.-S., Dowd, S. E., and Paré, P. W. (2009). A soil bacterium regulates plant acquisition of iron via deficiency-inducible mechanisms. Plant J. 58, 568–577. doi: 10.1111/j.1365-313x.2009.03803.x

Zhang, J., and Zhou, J.-M. (2010). Plant immunity triggered by microbial molecular signatures. Mol. Plant 3, 783–793. doi: 10.1093/mp/ssq035

Zhang, N., Wang, D., and Liu, Y. (2014). Effects of different plant root exudates and their organic acid components on chemotaxis, biofilm formation and colonization by beneficial rhizosphere-associated bacterial strains. Plant Soil 374, 689–700. doi: 10.1007/s11104-013-1915-6

Keywords : rhizosphere interactions, microbial inoculants, plant growth promotion, sustainable agriculture, microbial community analysis

Citation: Vishwakarma K, Kumar N, Shandilya C, Mohapatra S, Bhayana S and Varma A (2020) Revisiting Plant–Microbe Interactions and Microbial Consortia Application for Enhancing Sustainable Agriculture: A Review. Front. Microbiol. 11:560406. doi: 10.3389/fmicb.2020.560406

Received: 08 May 2020; Accepted: 23 November 2020; Published: 21 December 2020.

Reviewed by:

Copyright © 2020 Vishwakarma, Kumar, Shandilya, Mohapatra, Bhayana and Varma. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Kanchan Vishwakarma, [email protected] ; [email protected] ; Ajit Varma, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

View all journals
Explore content
About the journal
Publish with us
Sign up for alerts
Open access
Published: 07 September 2021

Network mapping of root–microbe interactions in Arabidopsis thaliana

Xiaoqing He ORCID: orcid.org/0000-0001-9098-9980 1 , 2 ,
Qi Zhang ORCID: orcid.org/0000-0001-7016-3954 2 ,
Beibei Li ORCID: orcid.org/0000-0003-2493-8792 2 ,
Yi Jin ORCID: orcid.org/0000-0001-6412-0835 2 ,
Libo Jiang ORCID: orcid.org/0000-0003-4703-9220 1 , 2 &
Rongling Wu ORCID: orcid.org/0000-0002-2334-6421 1 , 2 , 3

npj Biofilms and Microbiomes volume 7 , Article number: 72 ( 2021 ) Cite this article

5991 Accesses

21 Citations

3 Altmetric

Metrics details

Microbial ecology

Understanding how plants interact with their colonizing microbiota to determine plant phenotypes is a fundamental question in modern plant science. Existing approaches for genome-wide association studies (GWAS) are often focused on the association analysis between host genes and the abundance of individual microbes, failing to characterize the genetic bases of microbial interactions that are thought to be important for microbiota structure, organization, and function. Here, we implement a behavioral model to quantify various patterns of microbe-microbe interactions, i.e., mutualism, antagonism, aggression, and altruism, and map host genes that modulate microbial networks constituted by these interaction types. We reanalyze a root-microbiome data involving 179 accessions of Arabidopsis thaliana and find that the four networks differ structurally in the pattern of bacterial-fungal interactions and microbiome complexity. We identify several fungus and bacterial hubs that play a central role in mediating microbial community assembly surrounding A. thaliana root systems. We detect 1142 significant host genetic variants throughout the plant genome and then implement Bayesian networks (BN) to reconstruct epistatic networks involving all significant SNPs, of which 91 are identified as hub QTLs. Results from gene annotation analysis suggest that most of the hub QTLs detected are in proximity to candidate genes, executing a variety of biological functions in plant growth and development, resilience against pathogens, root development, and abiotic stress resistance. This study provides a new gateway to understand how genetic variation in host plants influences microbial communities and our results could help improve crops by harnessing soil microbes.

Genome wide association study reveals plant loci controlling heritability of the rhizosphere microbiome

plant microbe interaction research paper

Network analysis infers the wilt pathogen invasion associated with non-detrimental bacteria

The fungal root endophyte Serendipita vermifera displays inter-kingdom synergistic beneficial effects with the microbiota in Arabidopsis thaliana and barley

Introduction.

The microbiota has been widely thought to be an important determinant of various natural processes ranging from biogeographical cycling to human health. Many studies have characterized strong associations between the microbiota and a variety of human disorders, but research on how the microbiome impacts plant growth has not been conducted until recently 1 . Increasing evidence shows that the microbiota plays a pivotal role in promoting plants’ stress tolerance, determining plant productivity, improving the bioavailability of nutrients, and preventing invasion by bacterial pathogens 2 , 3 , 4 , 5 , 6 , 7 , 8 . Some bacteria can fix and preserve nitrogen in root nodules for plants 9 , 10 , whereas others can even modulate the timing of flowering of plants 11 , 12 and may contribute to rescuing host populations at the risk of extinction 13 . Under drought stress, root microbiomes can help crop plants maintain production 4 , 14 .

While the microbiota affects the phenotypes of the hosts they colonize, the hosts can also shape the structure and function of the microbial communities 15 , 16 , 17 , 18 . It has been widely recognized that the microbiota and their hosts form complex but well-orchestrated interaction networks 19 . There is great variability among plant species or genotypes in their ability to recruit specific microbial communities 20 , 21 . Plant genes affect root metabolism, immune system functioning, and root exudate composition, which in turn influence the activity and structure of the root microbiome 22 . Recent studies provide a ‘cry-for-help’ hypothesis to explain that stressed plants assemble health-promoting soil microbiomes by changing their root exudation chemistry 23 , 24 , 25 . The overall influence of host genetic variation on the microbiome remains an open question.

Roots of healthy plants are colonized by multi-kingdom microbial consortia 26 , 27 , 28 . The whole microbiome structure and function are determined by the pattern and strength of how the constituent microbes interact with each other through cooperation or competition 27 , 29 , 30 . Interactions between microbiota members, particularly bacterial-fungal interactions, contribute to plant health 26 , 27 . Given that fungi have a strong influence on the structure of the root microbiome, characterizing both bacteria and fungi can enhance our understanding of the root microbiome 31 . Several studies have identified highly interconnected ‘hub species’ in microbial networks that act as mediators between a host and its associated microbiome 15 , 32 . Yet, we are still unclear in which way microbes interact with each other to shape polymicrobial communities 33 . We know little about how microbiota members contribute to the establishment, stability, and resilience of microbial communities essential for the maintenance of plant health.

Understanding the fundamental questions described above requires integrated systems approaches 34 . Recently, with the application of next-generation sequencing, the microbiome data and host genetic data measured at unprecedented resolution have been increasingly available 28 , 35 , 36 , 37 . From these data, genome-wide association studies (GWAS) have been developed to systematically characterize the genetic underpinnings of microbiota-host associations in plants 15 , 31 , 38 . However, traditional GWAS models can only detect the host QTLs responsible for the abundance of individual microbes, failing to disentangle the relationships of diverse microbial species and microbe–host interactions 31 , 38 , 39 . It is becoming increasingly clear that genetic variation in plants influences not only the relative abundance of individual microbes but also their interaction network. To overcome the complexity of internal workings within the root microbiome that contains a highly dense microbial community, we introduce behavioral ecology theory to derive simple mathematical descriptors of pairwise interactions that encode microbial networks at any dimension 33 , 40 . These descriptors can discern and quantify common types of ecological interactions, including mutualism, antagonism, aggression, and altruism, which occur in biological communities. The biological relevance of these descriptors has been validated by an in vitro growth assay using diverse strains of two bacterial species 40 . We further integrated these mathematical descriptors into a GWAS setting to unveil the genetic and molecular mechanisms underlying microbial interactions in the host gut that contains a dense and highly diverse microbial community 40 .

In this article, we report the application of our ecology-based network model to root-microbiota interactions in Arabidopsis. As a model system, Arabidopsis has been extensively studied, aimed to explore the interactions between microbial communities and hosts. In a GWAS including 179 accessions of A. thaliana , Bergelson et al. 31 identified associations between the abundance of individual microbes within root microbiomes and plant genotypes. By reanalyzing this dataset, we further reveal the intricate relationship between A. thaliana and its colonizing microorganisms. We identify hub microbes within the root microbiome, characterize how microbes interact across kingdoms, and illustrate how this process is governed by the host genes.

Co-occurrence networks of the root microbiota

We developed a behavioral ecology model to define the strengths of mutualism, antagonism, aggression, and altruism between each pair of microbes, quantitatively described by Z mu , Z an , Z ag , and Z al , respectively (see Experimental Procedures). Validation of these descriptors through in vitro growth assays 40 , 41 shows their usefulness as a proxy to measure mutualism, antagonism, aggression, and altruism strengths. We use these descriptors to reconstruct mutualism, antagonism, aggression, and altruism networks for the root microbiota of the A. thaliana . To reduce the complexity of the networks, we chose the most abundant 100 OTUs in bacteria and fungi, respectively, for the reconstruction of four types of bacterial-fungal networks (OTU1-100 are listed as bacteria and OTU100-200 as fungi). We calculated node-level topological properties (i.e., degree, betweenness, closeness and eigencentrality) using the “igraph” R package. Bacterial and fungal co-occurrence network characteristics are listed in Supplementary Table 1 . We are aware of that rare microbes may have an over-proportional role in regulating the functioning of host-associated environments and including rare microbes in future investigations will improve our understanding of microbial community function 42 .

Interkingdom functional diversity among fungi and bacteria is important for maintaining ecosystem functioning 28 and microbial interkingdom interactions in roots can promote Arabidopsis survival 27 . We calculated degree-centrality parameters to determine the relative importance of bacteria and fungi in each network. It indicates that bacteria are more central to the structure of the mutualism and altruism networks than fungi (Fig. 1a, d ), as bacteria tend to have a higher number (i.e., degree) of network connections than fungi (Wilcoxon test, P = 0.00038 and P = 0.0001029 for the mutualism and altruism networks, respectively) (Supplementary Table 2 ). In contrast, fungi in the antagonism and aggression networks appear to have a higher number of network connections than bacteria (Fig. 1b, c ; P = 0.0002128 and P = 0.01961 for the antagonism and aggression networks, respectively). We also calculated and compared interkingdom microbial OTU relationships (the number of links; edges information) among bacterial and fungal taxonomic groups in four interaction networks (Fig. 1 ; Supplementary Table 3 ). Bacterial OTUs belonging to classes Betaproteobacteria, Flavobacteriia, Actinobacteria, Gammaproteobacteria, and Alphaproteobacteria displayed a strong mutualistic relationship with fungal OTUs belonging to classes Leotiomycetes, Dothideomycetes, Sordariomycetes, Agaricomycetes, and others, respectively (Fig. 1a ). Bacterial classes such as Actinobacterial, Alphaproteobacteria, Gammaproteobacteria all displayed antagnonistic relationships with fungal classes Leotiomycetes, Dothidemycetes, and Sodariomycetes (Fig. 1b ). In the aggression network, there were three bacterial classes (Betaproteobacteria, Actinobacteria, and Flavobacteriia) which were aggressive to fungal classes (Leotiomycetes, Sordariomycetes, Dothideomycetes, etc.) (Fig. 1c ). Bacterial classes including Actinobacteria, Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria were altruistic to fungal classes, Dothideomycetes, Leotiomycetes, Sordariomycetes, Mortierellomycetes, etc. (Fig. 1d ).

a Z mu -based mutualism network. b Z an -based antagonism network. c Z ag -based aggression network. d Z al -based altruism network. In each network, bacteria and fungi are distinguished by different colors. The network analysis was performed in the “igraph” R package and visualized in Cytoscape v3.7.1. The number of links between root inter-kingdom microbes was given at the right. Bacterial and fungal OTUs were grouped at the class level and sorted according to the number of edges between bacteria and fungi within each network. In boxplots, the fungal and bacterial degrees were calculated to determine the relative importance of bacteria and fungi in each network.

Hubs of the co-occurrence network identification

Hub microbes are important in shaping microbial communities due to their critical roles in maintaining network function 15 . The four networks differed structurally in the pattern of social links and the number of hub microbes. Fungal and bacterial OTUs that display the highest degree and the highest closeness centrality scores may serve as hub taxa to drive fungal-bacterial interaction equilibrium in A. thaliana roots 43 (Fig. 2a ; Supplementary Table 1 ).

a The identity of each OTU is labeled by a number, one to 100 for bacteria and 101 to 200 for fungi. In each network, hub microbes are highlighted in border colors. The distribution of ‘Hub microbes’ in four different microbial networks was based on degree and closeness centrality values. These two values of each OTU within each network were given at the right. The red dotted line represents the screening cutoffs of ‘Hub microbes’ corresponding to each network. b The abundance of hub microbes within each network. c The shared hub microbes within each microbial network. This ‘shared network’ was represented in Cytoscape. Circle shapes represented hub microbes from each microbial network and irregular shapes represented different microbial network types. The edges were connected to hub microbes and microbial network. The distribution of ‘Hub microbes’ in four different microbial networks was based on degree and closeness centrality values. The red dotted line represents the screening cutoffs of ‘Hub microbes’ corresponding to each network. Visualization was done with Gephi for four microbial networks.

We identified six hub microbes (leaders), four bacteria, and two fungi (nodes with degree >11 and closeness centrality values >0.08 in the network; P < 0.01), which dominate the mutualism network. The four hub bacteria are classes Betaproteobacteria (two OTUs), Sphingobacteriia, and Actinobacteria, and the two fungal hubs are phylum Ascomycota (2 OTUs). In the antagonism network, 26 hub microbes (‘antagonists’) that are more combative were found to act as ‘public enemies’, which were antagonistic to many more microbes than other microbes (degree >139 and closeness centrality values >0.78). The ‘agonists’ that are less combative were observed to be more abundant than the ‘antagonists’ ( P < 0.01; Fig. 2b ). In the aggression network, three OTUs belonging to the bacterial class Betaproteobacteria (two OTUs) and Actinobacteria might represent the hub taxa (degree >100 and closeness centrality values >0.60; P < 0.5). The ‘hawks’ which are considered to aggressively repress others are abundant than the ‘doves’ (those inhibited by others). The altruism network includes some ‘altruists’ (24 hub microbes; Fig. 2a ; Supplementary Table 1 ) that sacrificed their own growth by providing resources to beneficiaries (degree >90 and closeness centrality values >0.64). The hub microbes (beneficiaries) are more abundant than the altruists (Fig. 2b ; P < 0.01).

A total of 59 OTUs were identified as hub species, which were mainly from bacterial phyla proteobacteria (21 OTUs), Actinobacteria (12 OTUs), Chloroflexi (1 OTU), Bacteroidetes (3 OTUs) and fugal phyla Ascomycota (20 OTUs), Mortierellomycota (2 OTUs) (Supplementary Table 1 ). The altruism network shares 4, 3, and 2 phyla with the mutualism, antagonism, and aggression networks, respectively (Fig. 2c ).

Mapping root-microbe interactions

We calculated six centrality indices namely connectivity (Con), closeness (C(u)), betweenness (B(u)), eccentricity (E(u)), eigencentrality (G(u)), and PageRank (P(u)) (Fig. 3 ) for each network using the formulas given in Jiang et al. 40 . In the same network type, these indices exhibit differences among hosts and, also, the same index varies among network types. All indices depend on network type which provides a basis for mapping microbial network QTLs. In our previous study, we developed a statistical procedure to test and estimate how individual SNPs are associated with network properties 40 . By treating each network index as a phenotype, we performed association mapping for the interaction networks (Supplementary Fig. 1 ). The chosen significant threshold is −log 10 ( P ) ≥ 5. The population structure analysis was performed by Admixture software for 179 A.thaliana accessions. The results indicated that the 179 accessions were divided into six subgroups. We also considered population structure in Q GWAS and QK GWAS. The QQ plots results showed that the population structure and genetic relatedness among accessions have subtle impact on the results of association analysis (Supplementary Figs. 2 – 7 ). Our model identified 1142 significant host genetic variants throughout the plant genome, which are responsible for centrality indices of each network, including 225 acting through mutualism, 845 through antagonism, 49 through aggression, and 23 through altruism (19.70% for mutualism, 73.99% for antagonism, 4.29% for aggression, and 2.01% for altruism) (Supplementary Table 4 ). It appears that more variants control mutualism and antagonism than aggression and altruism. We also calculated heritability estimates for each network property as described in Li et al’s research 44 . SNP based heritability varied from 0 to 35.95% (Supplementary Table 5 ). We found that the total SNP-h 2 of mutualism (C(u)) (96%), antagonism (E(u)) (83%), mutualism (P(u)) (82%) and aggression (Con) (72%) was higher than other topological features of the networks.

a Mutualism. b Antagonism. c Aggression. d Altruism.

QTL networks

We implemented Jiang et al.’s 41 procedure to reconstruct Bayesian QTL networks among the significant SNPs detected to affect each type of microbial network and identified 91 hub QTLs (Fig. 4 ; Table 1 ). Through QTL network analysis, we can better characterize how a QTL mediates microbial cooperation or competition through its epistatic interactions with other QTLs. In the QTL network for the microbial mutualism network, we identify a hub QTL that affects connectivity QTL, annotated to gene TMK3 ( AT2G01820 ) that orchestrates plant growth by regulation of both cell expansion and cell proliferation and as a component of auxin signaling 45 . A hub QTL for the betweenness of microbial mutualism network is located in gene IBR1(AT4G05530) , encoding indole-3-butyric acid response 1(IBR1). IBR1 are involved in root hair elongation 46 . AOC4 ( AT1G13280 , an eigencentrality hub QTL) encodes allene oxide cyclase. One of four genes in Arabidopsis that encode this enzyme, which catalyzes an essential step in jasmonic acid biosynthesisis, a hormone whose role in defense responses is well established 47 . A hub QTL for the PageRank of the mutualism network represents gene NTRB (AT4G35460) encoding NADPH-dependent thioredoxin reductase. Thioredoxin is a key regulator of intracellular redox status that determine plant development in response to biotic and abiotic stress. Thioredoxin reductase (ntra ntrb) mutant alters both auxin transport and metabolism, causing a loss of apical dominance and reduced secondary root production, etc., largely regulated by auxin 48 .

a mutualism. b antagonism. c aggression. d altruism. Hub QTLs within each genetic network are highlighted in green circles. The emergent properties of each microbial network are described by connectivity (Con), closeness (C(u)), betweenness (B(u)), eccentricity (E(u)), eigencentrality (G(u)), and PageRank (P(u)).

In the QTL network for the microbial antagonism network, a closeness hub QTL acts like gene UBP22(AT5G10790) . UBP22 encodes a ubiquitin-specific protease, which plays role in regulating plant development and stress responses 49 . A hub QTL for the betweenness of the microbial antagonism network is located in gene Hrd1A which may be an important regulator of heat stress response in Arabidopsis 50 . A hub QTL for the closeness of the microbial antagonism network acts like gene PEPR2(AT1G17750) encoding PEP1 receptor 2, which is transcriptionally induced by wounding and pathogen-associated molecular patterns and contributes to defense responses in Arabidopsis 51 . CHL1(AT5G40090) is the hub QTL for the eigencentrality of the antagonism network, which encodes disease resistance protein (TIR-NBS class). TIR-NBS protein is involved in disease resistance in Arabidopsis 52 .

There are 12 pleiotropic genes including UPL4(AT5G02880) , which are detected to influence multiple types of microbial networks or properties (Table 1 ). UPL4 encodes a ubiquitin-protein ligase, function additively in the regulation of plant growth and development, and positively modulate immune hormone salicylic acid (SA)-mediated basal and induced resistance responses 53 .

Plant rhizosphere is considered as the second genome of plants. Nowadays, research on rhizosphere interactions has become one of the hottest topics in modern biology and agriculture. Potentially beneficial bacteria and fungi may serve as a valuable foundation for bio-fertilizer development in agriculture and forestry. Knowledge about how plants communicate and crosstalk with their entire microbiota will be crucial for the choice of microbes that benefit sustainable plant growth 54 , 55 . However, our understanding of the intrinsic principles underlying the assembly of the root microbial community is still limited 56 . In this article, we demonstrate the potential of a new computational model to reveal these principles behind.

Currently, network analysis has emerged as an extremely promising approach for modeling complex biological systems and can potentially provide deep and unique perspectives on microbial interactions and ecological assembly rules beyond those of simple richness and composition 17 . Properties of co-occurrence networks can reveal the intrinsic mechanisms of microbial interactions in response to environmental disturbance 35 , 57 . The connection and strength of the network even are crucial for the resistance to the pathogens 17 . In this study, we calculated four descriptors between each pair of genera and reconstructed four corresponding 200-node networks accordingly. Each described root microbiome interactions according to a different ecological interaction metric and help us to explore co-occurrence patterns of bacterial and fungal taxa. The four networks differ structurally in the pattern of bacterial-fungal interactions and microbiome complexity and the number of hub microbes. These differences provide a basis for the following microbial network mapping. In our previous study, we quantified the internal workings of microbial community within the gut by mathematical descriptors of pairwise interactions and provided a critical starting point to investigate these higher order interactions more deeply 40 .

Different members of root microbiota affect plant health through a complex network of microbial interactions. It is important to understand the mechanistic details of how ecological interactions are generated and how they are at play within the root microbiota. In our study, the phylogenetic signal measurement of network property parameters is calculated by Pagel’s lambda (Supplementary Table 6 ). In mutualism, the signals in both bacterial and fungal groups are relatively strong while in the other three networks most phylogenetic signals are close to 0. As can be seen, the values of lambda vary in different network types and network property parameters. Take aggression as an example, bacteria show a much higher signal value than fungi in Con (connectivity) but less than fungi in C(u) (closeness). We also found that bacteria have a higher degree in the mutualism and altruism networks and are more central to the structure of networks. Fungi have more connections in the antagonism and aggression networks, however, based on the directions of edges, bacteria still are more antagonistic and aggressive to fungi (Supplementary Table 3 ). Understanding the interaction among different species within a community is one of the central goals in ecology 58 . Bacterial communities aid in maintaining the microbial balance and protect host plants against the detrimental effects of filamentous eukaryotic microbes 27 . In Bergelson et al.’s research, strong and significant cross-kingdom correlations for the top taxa were observed which implied that bacteria and fungi interacted in the root microbiome and variation within the root microbiome was influenced by members of both kingdoms communities 31 . A previous study showed that microbes tend to be positively related within kingdoms but negatively related between kingdoms 15 . Besides bacteria and fungi, rhizosphere bacteriophages and protists also play roles in plant health 59 , 60 , which should be included in further research.

The identification of network hubs and their importance in microbial community structure has crucial implications for studying microbe–microbe interactions and can facilitate the design of strategies for future targeted biocontrol 15 . Hub microorganisms have a regulatory influence on the network of microbial interactions, which can exert strong effects on microbiome assembly and serve as mediators between the plant and microbiome 15 . According to centrality measurements, such as degree, closeness centrality, and betweenness centrality, hub microorganisms are tightly connected within a co-occurrence network 15 . We identified hub microbes in four types of networks, mutualism, antagonism, aggression, and altruism. The most dominant taxa as hub microbes belong to bacterial phyla Proteobacteria (21 OTUs), Actinobacteria (12 OTUs), and fugal phyla Ascomycota (20 OTUs). This is consistent with the finding that Proteobacteria, Actinobacteria, and Ascomycota are the most abundant phyla in plants and soil 43 , 61 . Actinobacteria is one of the bacteria whose dysbiosis in abundance in tomato rhizosphere causes the incidence of bacterial wilt disease 6 . Some key taxa with the highest degree and betweenness centrality for the root microbiome identified in Bergelson et al.’s research 31 such as Massilia , Actinobacteria , and Actinoplanes , are also considered as hub microbes in our study (Supplementary Table 1 ).

Plant phenotypes are inextricably shaped by their interactions with microbes 34 . In a well-designed GWAS study, Bergelson, et al. 31 found a few significant QTLs that are associated with root microbial species richness and community structure, which are involved in plant immunity, cell-wall integrity, root, and root-hair development. In this study, we used a newly developed network mapping model 40 to reanalyze Bergelson et al.’s 31 data, characterizing previously undetected QTLs that mediate microbial interactions. We found that most of the QTLs detected by the new model can be annotated to candidate genes with known biological functions including plant growth and development, resilience against pathogens, root development, and improved resistance against abiotic stress conditions (Table 1 ; Supplementary Table 7 ). We also investigated candidate genes within ~10 kb windows on each side of associated SNPs by software PLINK. The genes of R 2 > 0.8 were retained. Supplementary Table 8 lists the 135 genes within a 10 kb window around associated SNPs including genes such as PEPR2, UBP22, UPL4 which were also identified as hub genes linked to the six network property parameters.

Understanding how microbes improve plant stress resistance will enhance our understanding of how plants survive in stress conditions. In the near future, it will be crucial to unravel the complex network of genetic, microbial, and metabolic interactions, including the signaling events mediating microbe–host interactions. Scientists have also linked the phyllosphere microbiome to plant Health 62 and found host genes could affect bacterial communities in the phyllosphere 17 . Building synthetic microbiomes in plants has been proved to be useful for future research on plant–microbe interactions 30 , 63 , 64 , 65 , 66 . Studies have shown the potential of microbiome adjustment tailored to bring benefits for plant growth and resistance to biotic and abiotic challenges 67 . Bioorganic fertilizers promote indigenous soil plant-beneficial consortium to enhance plant disease suppression 68 . The design of more efficient biofertilizers to update soil function has important implications for the manipulation of crop microbiomes for sustainable agriculture. Our work provides a comprehensive exploration of microbial interkingdom interactions, hub microbes, and plant genes for the structure of the root microbiome. The results obtained could help design synthetic microbiomes beneficial for plant growth.

Root microbiome experiment

Bergelson et al. 31 , 38 conducted a genome-wide association study (GWAS) for the root microbiome in Arabidopsis thaliana . The study included 179 accessions of A. thaliana , each measured for the bacterial and fungal abundance of the root microbiota using a 16S/ITS rRNA gene sequencing technique and genotyped for Arabidopsis SNPs by a high-throughput sequencing technology (Supplementary Table 9 ).

Microbial interactions analysis

We chose the 100 most abundant bacterial OTUs and the 100 most abundant fungal OTUs (Supplementary Table 10 ) to reconstruct microbial interaction networks using a microbial behavioral network model 40 . This model is based on mathematical descriptors of four types of microbe-microbe interactions, mutualism, antagonism, aggression, and altruism, expressed as

where x u and x v ( x u > x v , u ≠ v , u , v = 1,…, m) are the abundance of two microbes u and v, and m is the number of microbes. Based on the above equations, we use the corrected microbial abundance (log10-transformed) to quantify four interactional relationships of two microbes u and v . The descriptor, Z mu , can be used to quantify a cooperative relationship (mutualism) between two microbes. The descriptor, Z an , can be used to quantify a competitive relationship (antagonism) between two microbes. The descriptor, Z ag , can represent the utilization extent (aggression) of a more abundant microbe to a less abundant microbe. The descriptor, Z al , can represent the sacrifice extent (altruism) of a more abundant microbe to a less abundant microbe. Microbial interaction relationships were quantified as interaction matrices. Each matrix was normalized to control the range of interaction relationship values within [0,1]. Next, we performed threshold filtering to obtain microbial interaction sparse matrix. The threshold is 0.95 in mutualism, antagonism, and aggression networks and that in altruism network is 0.99. In each network, the value of the interaction relationship above the threshold was retained. Then we obtained a sparse network of each interaction type.

Microbial networks analysis

By microbial interactions analysis, we can reveal internal workings within the root microbial community. The interaction networks can be visualized using Gephi ( https://gephi.org/ ). We constructed the corresponding network for mutualism, antagonism, aggression, and altruism, respectively. Emergent properties of each network can be calculated in the “igraph” R package 69 . We calculated six network indices to describe the features of various networks, including connectivity (Con), closeness (C(u)), betweenness(B(u)), eccentricity (E(u)), eigencentrality (G(u)) and Pagerank (P(u)). The specific calculation method was described by Jiang et al. 40 . The heat maps of each network index were generated by package pheatmap in R ( https://CRAN.R-project.org/package=pheatmap ). Meanwhile, microbial networks can be used to statistically identify hub taxa. We calculated the degree of each node for every network using the “igraph” R package 69 . It is generally believed that hub microbes with a high degree and closeness centrality value play crucial roles in microbial networks 15 , 43 . These hub microbes (called leaders, antagonists, hawks, and beneficiaries) in mutualism, antagonism, aggression, and altruism networks, respectively, are compared with other microbes (expressed as followers, agonists, doves, and altruists) from each network type 40 .

Mapping microbial network properties

To study how host genes influence root microbiomes, we consider six network property parameters as phenotypic traits that are associated with host SNPs (Single Nucleotide Polymorphisms). We chose those SNPs with MAF > 5%) for association analysis. A regression model of log-transformed phenotypes at a SNP is expressed as

where y i represents the phenotype of the ith host, μ is the mean of the phenotypes over all hosts, x i is the genotype indicator of the ith host which is 0 for high-frequency allele and 1 for low-frequency allele, β is the geneic effect of the SNP and e i is a random error value. Then, we used lm function in R for association analysis from which to get the P -value of each SNP. Package qqman ( https://CRAN.R-project.org/package=qqman ) was used to draw the Manhattan plot. By statistical testing, we can find significant SNPs that are associated with each network property.

Many existing approaches attempt to reveal the genetic architecture of complex traits by identifying key individual genes underlying the traits. However, epistatic interactions among different genes have been increasingly recognized to play an important role in genetic control. Several approaches have been developed to map epistatic interactions based on gene pairwise analysis, failing to systematically chart a network of epistasis involving all genes. More recently, Jiang et al. 41 proposed an analytical procedure of reconstructing epistatic networks from mapping data. This procedure was used to infer QTL networks of the significant SNPs that mediate the emergent properties of microbial networks. At each SNP, we calculated the mean value of each genotype for a network parameter and assigned this value to each Arabidopsis accession, transforming the GWAS data structure from its SNP-phenotype illustration to SNP-based genotype representation. We implemented Bayesian networks (BN) to reconstruct genetic networks involving all significant SNPs for each network parameter. The BN-based QTL networks are directed acyclic graphs, encoded by casual SNP-SNP interactions. We identified hub QTLs that play a crucial role in the genetic architecture of plant microbiomes assembly.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The data used can be downloaded at https://doi.org/10.1038/s41598-018-37208-z .

Code availability

The computer code can be freely downloaded at https://github.com/lenahe2006/npj-Biofilms-and-Microbiomes for any purpose of research. Also, all questions in data and computation can be addressed to the corresponding author.

Mitter, B., Brader, G., Pfaffenbichler, N. & Sessitsch, A. Next generation microbiome applications for crop production — limitations and the need of knowledge-based solutions. Curr. Opin. Microbiol. 49 , 59–65 (2019).

Article CAS PubMed Google Scholar

Coller, E. et al. Microbiome of vineyard soils is shaped by geography and management. Microbiome 7 , 140 (2019).

Article PubMed PubMed Central Google Scholar

Wagner, M. R. et al. Host genotype and age shape the leaf and root microbiomes of a wild perennial plant. Nat. Commun. 7 , 12151–12151 (2016).

Article CAS PubMed PubMed Central Google Scholar

de Vries, F. T., Griffiths, R. I., Knight, C. G., Nicolitch, O. & Williams, A. Harnessing rhizosphere microbiomes for drought-resilient crop production. Science 368 , 270–274 (2020).

Harbort, C. J. et al. Root-Secreted Coumarins and the Microbiota Interact to Improve Iron Nutrition in Arabidopsis. Cell Host & Microbe 28 , 825–837 (2020).

Article CAS Google Scholar

Lee, S.-M., Kong, H. G., Song, G. & Ryu, C.-M. Disruption of Firmicutes and Actinobacteria abundance in tomato rhizosphere causes the incidence of bacterial wilt disease. ISME J. 15 , 330–347 (2020).

Article PubMed PubMed Central CAS Google Scholar

Salas-González, I. et al. Coordination between microbiota and root endodermis supports plant mineral nutrient homeostasis. Science 371 , eabd0695 (2021).

Article PubMed CAS Google Scholar

Li, H., La, S., Zhang, X., Gao, L. & Tian, Y. Salt-induced recruitment of specific root-associated bacterial consortium capable of enhancing plant adaptability to salt stress. ISME J. (2021). https://doi.org/10.1038/s41396-021-00974-2 .

Zhang, J. et al. NRT1.1B is associated with root microbiota composition and nitrogen use in field-grown rice. Nat. Biotechnol. 37 , 676–684 (2019).

Zhu, B. et al. Rhizosphere priming effects on soil carbon and nitrogen mineralization. Soil Biol. Biochem. 76 , 183–192 (2014).

Lu, T. et al. Rhizosphere microorganisms can influence the timing of plant flowering. Microbiome 6 , 231 (2018).

Panke-Buisse, K., Poole, A. C., Goodrich, J. K., Ley, R. E. & Kao-Kniffin, J. Selection on soil microbiomes reveals reproducible impacts on plant function. ISME J. 9 , 980–989 (2015).

David, A. S. et al. Soil microbiomes underlie population persistence of an endangered plant species. Am. Nat. 194 , 488–494 (2019).

Article PubMed Google Scholar

Niu, X., Song, L., Xiao, Y. & Ge, W. Drought-tolerant plant growth-promoting rhizobacteria associated with foxtail millet in a semi-arid and their potential in alleviating drought stress. Front. in Microbiol. 8 , 11 (2018).

Article Google Scholar

Agler, M. T. et al. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLOS Biol. 14 , e1002352 (2016).

Brachi, B. et al. Plant genes influence microbial hubs that shape beneficial leaf communities. Preprint at https://www.biorxiv.org/content/10.1101/181198v3 (2017).

Qu, Z. et al. Bio-priming with a hypovirulent phytopathogenic fungus enhances the connection and strength of microbial interaction network in rapeseed. npj Biofilms Microbiomes 6 , 45 (2020).

Tabrett, A. & Horton, M. W. The influence of host genetics on the microbiome. F1000Research 9 , 84 (2020).

Layeghifard, M., Hwang, D. M. & Guttman, D. S. Disentangling interactions in the microbiome: a network perspective. Trends Microbiol. 25 , 217–228 (2017).

Walters, W. A. et al. Large-scale replicated field study of maize rhizosphere identifies heritable microbes. Proc. Natl Acad. Sci. 115 , 7368–7373 (2018).

Bodenhausen, N., Bortfeld-Miller, M., Ackermann, M. & Vorholt, J. A. A synthetic community approach reveals plant genotypes affecting the phyllosphere microbiota. PLoS Genet. 10 , e1004283–e1004283 (2014).

Lian, T. et al. Rice SST variation shapes the rhizosphere bacterial community, conferring tolerance to salt stress through regulating soil metabolites. mSystems 5 , e00721–20 (2020).

Rolfe, S., Griffiths, J. & Ton, J. Crying out for help with root exudates: adaptive mechanisms by which stressed plants assemble health-promoting soil microbiomes. Curr. Opin. Microbiol. 49 , 73–82 (2019).

Liu, Y. et al. Identification of root-secreted compounds involved in the communication between cucumber, the beneficial Bacillus amyloliquefaciens, and the soil-borne pathogen Fusarium oxysporum. Mol. Plant Microbe Interact. 30 , 53–62 (2017).

Mendes, R. et al. Deciphering the rhizosphere microbiome for disease-suppressive bacteria. Science 332 , 1097–1100 (2011).

Getzke, F., Thiergart, T. & Hacquard, S. Contribution of bacterial-fungal balance to plant and animal health. Curr. Opin. Microbiol. 49 , 66–72 (2019).

Durán, P. et al. Microbial interkingdom interactions in roots promote arabidopsis survival. Cell 175 , 973–983 (2018). e14.

Wagg, C., Schlaeppi, K., Banerjee, S., Kuramae, E. E. & van der Heijden, M. G. A. Fungal-bacterial diversity and microbiome complexity predict ecosystem functioning. Nat. Commun. 10 , 4841 (2019).

Cordovez, V., Dini-Andreote, F., Carrion, V. & Raaijmakers, J. Ecology and evolution of plant microbiomes. Ann. Rev. of Microbiol. 73 , 69–88 (2019).

Niu, B., Paulson, J. N., Zheng, X. & Kolter, R. Simplified and representative bacterial community of maize roots. Proc. Natl Acad. Sci. 114 , E2450–E2459 (2017).

Bergelson, J., Mittelstrass, J. & Horton, M. Characterizing both bacteria and fungi improves understanding of the Arabidopsis root microbiome. Sci. Rep. 9 , 24 (2019).

Trivedi, P., Leach, J. E., Tringe, S. G., Sa, T. & Singh, B. K. Plant-microbiome interactions: from community assembly to plant health. Nat. Rev. Microbiol. 18 , 607–621 (2020).

Wang, Q. et al. Interrogation of internal workings in microbial community assembly: play a game through a behavioral network? mSystems 4 , e00550–19 (2019).

Beilsmith, K. et al. Genome-wide association studies on the phyllosphere microbiome: embracing complexity in host-microbe interactions. Plant J. 97 , 164–181 (2018).

de Vries, F. T. et al. Soil bacterial networks are less stable under drought than fungal networks. Nat. Commun. 9 , 3033 (2018).

Ma, B. et al. Earth microbial co-occurrence network reveals interconnection pattern across microbiomes. Microbiome 8 , 82 (2020).

Regalado, J. et al. Combining whole-genome shotgun sequencing and rRNA gene amplicon analyses to improve detection of microbe-microbe interaction networks in plant leaves. ISME J . 14 , 2116–2130 (2020).

Horton, M. W. et al. Genome-wide association study of Arabidopsis thaliana leaf microbial community. Nat. Commun. 5 , 5320–5320 (2014).

Wallace, J., Kremling, K., Kovar, L. & Buckler, E. Quantitative Genetics of the Maize Leaf Microbiome. Phytobiomes 2 , 208–224 (2018).

Jiang, L. et al. A behavioral model for mapping the genetic architecture of gut-microbiota networks. Gut Microbes. 13 , 1–15 (2020).

Google Scholar

Jiang, L. et al. A Drive to Driven Model of Mapping Intraspecific Interaction Networks. iScience 22 , 109–122 (2019).

Jousset, A. et al. Where less may be more: how the rare biosphere pulls ecosystems strings. ISME J. 11 , 853–862 (2017).

Xiong, C. et al. Host selection shapes crop microbiome assembly and network complexity. New Phytologist 229 , 1091–1104 (2020).

Li, J., Das, K., Fu, G., Li, R. & Wu, R. The Bayesian LASSO for genome-wide association studies. Bioinformatics 27 , 516–523 (2011).

Dai, N., Wang, W., Patterson, S. E. & Bleecker, A. B. The TMK subfamily of receptor-like kinases in Arabidopsis display an essential role in growth and a reduced sensitivity to auxin. PloS one 8 , e60990 (2013).

Strader, L. C., Culler, A. H., Cohen, J. D. & Bartel, B. Conversion of Endogenous Indole-3-Butyric Acid to Indole-3-Acetic Acid Drives Cell Expansion in Arabidopsis Seedlings. Plant Physiol. 153 , 1577–1586 (2010).

Carvalhais, L. et al. Linking Jasmonic Acid Signaling, Root Exudates, and Rhizosphere Microbiomes. Mol. Plant Microbe Interact. 28 , 1049–1058 (2015).

Bashandy, T. et al. Interplay between the NADP-Linked Thioredoxin and Glutathione Systems in Arabidopsis Auxin Signaling. Plant Cell 22 , 376–391 (2010).

Zhou, H., Zhao, J., Cai, J. & Patil, S. B. UBIQUITIN-SPECIFIC PROTEASES function in plant development and stress responses. Plant Mol. Biol. 94 , 565–576 (2017).

Liu, P.-L., Du, L., Huang, Y., Gao, S.-M. & Yu, M. Origin and diversification of leucine-rich repeat receptor-like protein kinase (LRR-RLK) genes in plants. BMC Evolutionary Biol. 17 , 47 (2017).

Yamaguchi, Y., Huffaker, A., Bryan, A. C., Tax, F. E. & Ryan, C. A. PEPR2 Is a Second Receptor for the Pep1 and Pep2 Peptides and Contributes to Defense Responses in Arabidopsis. Plant Cell 22 , 508–522 (2010).

Meyers, B., Morgante, M. & Michelmore, R. TIR-X and TIR-NBS proteins: Two new families related to disease resistance TIR-NBS-LRR proteins encoded in Arabidopsis and other plant genomes. Plant J.: cell Mol. Biol. 32 , 77–92 (2002).

Furniss, J. J. et al. Proteasome-associated HECT-type ubiquitin ligase activity is required for plant immunity. PLOS Pathog. 14 , e1007447 (2018).

Martin, F. M., Uroz, S. & Barker, D. G. Ancestral alliances: Plant mutualistic symbioses with fungi and bacteria. Science 356 , eaad4501 (2017).

Delaux, P.-M. & Schornack, S. Plant evolution driven by interactions with symbiotic and pathogenic microbes. Science 371 , eaba6605 (2021).

Ren, Y. et al. Functional compensation dominates the assembly of plant rhizospheric bacterial community. Soil Biol. Biochem. 150 , 107968 (2020).

Yuanyuan, X., Chen, H., Yang, J., Liu, M. & Huang, B. Distinct patterns and processes of abundant and rare eukaryotic plankton communities following a reservoir cyanobacterial bloom. ISME J . 12 , 2263–2277 (2018).

Deng, Y. et al. Molecular ecological network analyses. BMC Bioinforma. 13 , 113 (2012).

Pratama, A. A., Terpstra, J., de Oliveria, A. L. M. & Salles, J. F. The Role of Rhizosphere Bacteriophages in Plant Health. Trends Microbiol. 28 , 709–718 (2020).

Xiong, W. et al. Rhizosphere protists are key determinants of plant health. Microbiome 8 , 27 (2020).

Shi, Y. et al. Abundance of kinless hubs within soil microbial networks are associated with high functional potential in agricultural ecosystems. Environ. Int. 142 , 105869 (2020).

Liu, H., Brettell, L. E. & Singh, B. Linking the Phyllosphere Microbiome to Plant Health. Trends Plant Sci. 25 , 841–844 (2020).

Lawson, C. et al. Common principles and best practices for engineering microbiomes. Nat. Rev. Microbiol. 17 , 1–17 (2019).

Bai, Y. et al. Functional overlap of the Arabidopsis leaf and root microbiota. Nature 528 , 364–369 (2015).

Lebeis, S. L. et al. Salicylic acid modulates colonization of the root microbiome by specific bacterial taxa. Science 349 , 860–864 (2015).

Edwards, J. et al. Structure, variation, and assembly of the root-associated microbiomes of rice. Proc. Natl Acad. Sci. 112 , E911–E920 (2015).

Liu, Y.-X., Qin, Y. & Bai, Y. Reductionist synthetic community approaches in root microbiome research. Curr. Opin. Microbiol. 49 , 97–102 (2019).

Tao, C. et al. Bio-organic fertilizers stimulate indigenous soil Pseudomonas populations to enhance plant disease suppression. Microbiome 8 , 137 (2020).

Csardi, G. & Nepusz, T. The Igraph Software Package for Complex Network Research. Int. J. Complex Syst. 1695 , (2005).

Download references

Acknowledgements

We thank Dr. Bergelson and Dr. Horton for supplying their Arabidopsis microbiome and SNP data to us. This work was funded by Natural Science Foundation of China (31971398, 31700633), the Fundamental Research Funds for the Central Universities (2017JC05, 2015ZCQ-SW-06), and Science and Technology Service Network Initiative (KFJ-STS-ZDTP-036).

Author information

Authors and affiliations.

Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, China

Xiaoqing He, Libo Jiang & Rongling Wu

Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China

Xiaoqing He, Qi Zhang, Beibei Li, Yi Jin, Libo Jiang & Rongling Wu

Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, The Pennsylvania State University, Hershey, PA, USA

Rongling Wu

You can also search for this author in PubMed Google Scholar

Contributions

X.H. performed data analysis and wrote the manuscript. Q.Z. and B.L. wrote the R software. L.J. and Y.J. critically revised the manuscript. R.W. conceived of the idea and formulated the model. All authors discussed results.

Corresponding author

Correspondence to Rongling Wu .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Reporting summary, supplementary information, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

He, X., Zhang, Q., Li, B. et al. Network mapping of root–microbe interactions in Arabidopsis thaliana . npj Biofilms Microbiomes 7 , 72 (2021). https://doi.org/10.1038/s41522-021-00241-4

Download citation

Received : 10 January 2021

Accepted : 04 August 2021

Published : 07 September 2021

DOI : https://doi.org/10.1038/s41522-021-00241-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Endophytic pseudomonas fluorescens promotes changes in the phenotype and secondary metabolite profile of houttuynia cordata thunb..

Kaifeng Wang
Zhannan Yang
Wenxuan Quan

Scientific Reports (2024)

The Arabidopsis holobiont: a (re)source of insights to understand the amazing world of plant–microbe interactions

M. J. Poupin
B. González

Environmental Microbiome (2023)

Pioneer Arabidopsis thaliana spans the succession gradient revealing a diverse root-associated microbiome

Wim H. van der Putten

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

Explore articles by subject
Guide to authors
Editorial policies

Plant-Microbe Interaction and Stress Management

© 2024
Puneet Singh Chauhan 0 ,
Shri Krishna Tewari 1 ,
Sankalp Misra 2

Microbial Technology Division, CSIR- National Botanical Research Institute, Lucknow, India

You can also search for this editor in PubMed Google Scholar

Plant Conservation & Agrotech Division, CSIR- National Botanical Research Institute, Lucknow, India

Faculty of Biosciences, Institute of Biosciences and Technology, Shri Ramswaroop Memorial University, Barabanki, India

Provides latest research on microbiome assisted approaches for stress management
Acquaints the readers with recent stress indicators and their management practices
Describes application of beneficial microbes in various agro-ecosystems under stress conditions

Part of the book series: Rhizosphere Biology (RHBIO)

29 Accesses

This is a preview of subscription content, log in via an institution to check access.

Access this book

Durable hardcover edition
Dispatched in 3 to 5 business days
Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

About this book

This book provides a comprehensive view for plant microbe interactions towards stress management and microbiome-assisted approaches in sustainable agriculture. It is divided into four major sections. The book gives insights into the increasing threat of abiotic and biotic stresses and the accompanying challenges to modern agriculture. Through different chapters, the book shows how various microorganisms could ameliorate abiotic and biotic stress, and contribute towards food sustainability and restore ecosystem functioning. It provides a deep understanding of soil microbiome and its interaction with plants, to enhance food security. It further talks about metagenomic approaches for methodological tool for studying the soil microbiome. Separate sections on stress, talk at length about the various abiotic and biotic stresses that plants are faced it. The book culminates with an exciting section on microbiome-assisted approaches for combating stress. It talks about the different microbiomes such as rhizosphere, soil, phyllosphere and endophytic microbiome.

The book would be beneficial to students, researchers and course instructors in microbiology, botany, plant pathology and agriculture.

plant-microbe interaction
rhizosphere
soli microbiome
abiotic stress
plant microbiology
plant pathogen

Table of contents (15 chapters)

Front matter, plant–microbe interaction: stress management for sustainable agriculture.

Siya Kamat, Suraj Kumar Modi, Smriti Gaur, Madhuree Kumari

An Outline of Plant–Microbe Interactions for Stress Management

Shalini Tiwari

Exploring the Phyllosphere: Microbial Diversity, Interactions, and Ecological Significance in Plant Health

Ariyan Manikandan, Rangasamy Anandham, Sivakumar Madhan, Rajasekaran Raghu, Ramasamy Krishnamoorthy, Murugaiyan Senthilkumar

Drought and Waterlogging Stress Responses in Crops

Priyam Vandana, Anmol Gupta, Manoj Kumar

Saline Stress Management

Plant–microbe interaction for heavy metal tolerance and detoxification.

Govind Gupta, Riddhi Shrivastava, Naveen Kumar Singh

Nature’s Protectors: A Biofilm Perspective on Bacterial Disease Control in Plants

Daniel Gómez-Pérez, Leonie M. Zott, Monja Schmid, Vasvi Chaudhry

Rhizosphere Microbiome-Assisted Approaches for Biotic Stress Management

Tina Roy, Pooja Yadav, Anjali Chaudhary, Kanchan Yadav, Kunal Singh

Sustainable Management of Economically Important Plant Viral Pathogens by Plant Growth-Promoting Rhizobacteria

Archana Rathore, Tanu Vishwakarma, Dilip Kumar Maurya, Prabhat Kumar Maurya, S. Vijay Anand Raj, Puneet Singh Chauhan et al.

Insects as Biotic Stress in Agriculture

Vinay Kumar Dhiman, Devendra Singh, Vivek Kumar Dhiman, Minakshi Pandey, Avinash Sharma, Himanshu Pandey et al.

Phytopathogenic Fungi: Stress and Mitigation Approaches

Pooja Yadav, Rupali Katoch, Indu, Namo Dubey, Kunal Singh

Advance Techniques Used for Decoding the Hidden Language Used During Plant–Microbe Interaction

Garima Gupta, Durgesh Singh, Kamna Madheshiya, Updesh Chauhan, Puneet Singh Chauhan

Sodic-Tolerant Plant Growth-Promoting Rhizobacteria Mediated Sodic Stress Alleviation in Plants

Gunasekaran Yazhini, Subramanium Thiyageshwari, Ariyan Manikandan, Duraisamy Selvi, Rangasamy Anandham

Endophytic Microbiome: An Insight into the Hidden World of Microorganisms Within Plants

Padinjarakavil Soumya, K. Jayachandran, E. K. Radhakrishnan

Biotic Stress to Plants: Fungal Pathogen as a Major Biotic Stress

Udit Yadav, Poonam C. Singh

Editors and Affiliations

Puneet Singh Chauhan

Plant Conservation & Agrotech Division, CSIR- National Botanical Research Institute, Lucknow, India

Shri Krishna Tewari

Sankalp Misra

About the editors

Dr. Puneet Singh Chauhan is a Senior Principal Scientist in Microbial Technology Division at CSIR-National Botanical Research Institute, Lucknow, India. He completed his Ph.D. from CSIR-NBRI, Lucknow and was awarded with Postdoctoral Research fellowship from Brain Korea 21 project, Chungbuk National University, Cheongju, South Korea. His main research interests include Plant Microbe Interactions, Microbial Ecology, and Soil Metagenomics. Dr. Chauhan is a member of the editorial board of several leading scientific journals, has published more than 100 SCI research and review articles, authored 20 book chapters. He has been awarded with Biotech Research Excellence Award from Biotech Research Society, and The Society of Life Sciences, and Innovative Young Scientist Award from Asian PGPR Society for Sustainable Agriculture, Hyderabad, India. He has undertaken many research projects funded by different agencies like Council of Scientific & Industrial Research, Department of Science and Technology, Department of Biotechnology, Rashtriya Krishi Vikas Yojana, etc.

Dr. Shri Krishna Tewari is Chief Scientist and Professor, Academy of Scientific and Innovative Research (AcSIR) at CSIR-National Botanical Research Institute, Lucknow, India. Dr. Tewari is Head of Botanic Garden & Plant Conservation & Agrotechnology Divisions of the institute. Having completed his Ph.D. from G.B. Pant University of Agriculture and Technology, Pantnagar, he joined CSIR-NBRI and worked in the research areas of conservation, propagation and agro-technology of economically important plants. His research interests include amelioration and management of sodic soil, non-traditional economic plants, bio-composting, and extension of green technologies for rural development. He has received several national awards, including the “CAIRD-2011” award from CSIR. Dr. Tewari has published over 100 research papers in reputed journals, 10 books, 11 review articles, 25 chapters in books, 22 extension bulletins and >100 popular articles.

Dr. Sankalp Misra is an Assistant Professor at Shri Ramswaroop Memorial University (SRMU), Uttar Pradesh, India. He completed his Ph.D. in Biological Sciences from CSIR-National Botanical Research Institute (NBRI), Lucknow, and was awarded by the Academy of Scientific and Innovative Research (AcSIR). He has qualified for CSIR-UGC NET and has been awarded a Senior Research Fellowship (SRF) and Research Associateship (RA) from ICMR (Indian Council of Medical Research), New Delhi, India. He has been felicitated with the Young Researcher Award from the Institute of Scholars and SRMU. Dr. Misra has also received Best Poster Awards at international and national conferences. He holds a Life membership of “The Association of Microbiologists of India (AMI)”. He has published more than 15 SCI research articles and authored more than 10 book chapters. He has received research grants from different funding agencies including ICMR, UPCST, and SRMU. His research interests include Sustainable Agriculture, Abiotic Stress Resilient Agriculture, and Biofertilizer-based technologies for improving soil fertility and crop productivity.

Bibliographic Information

Book Title : Plant-Microbe Interaction and Stress Management

Editors : Puneet Singh Chauhan, Shri Krishna Tewari, Sankalp Misra

Series Title : Rhizosphere Biology

DOI : https://doi.org/10.1007/978-981-97-4239-4

Publisher : Springer Singapore

eBook Packages : Biomedical and Life Sciences , Biomedical and Life Sciences (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024

Hardcover ISBN : 978-981-97-4238-7 Published: 01 September 2024

Softcover ISBN : 978-981-97-4241-7 Due: 15 September 2025

eBook ISBN : 978-981-97-4239-4 Published: 31 August 2024

Series ISSN : 2523-8442

Series E-ISSN : 2523-8450

Edition Number : 1

Number of Pages : XV, 305

Number of Illustrations : 1 b/w illustrations, 18 illustrations in colour

Topics : Microbial Ecology , Microbiology , Microbial Genetics and Genomics , Plant Pathology , Biotechnology

Publish with us

Policies and ethics

Find a journal
Track your research

Search Menu
Sign in through your institution
Advance articles
Editor's Choice
Research Articles
Perspectives
Short communications
Commentaries
Method papers
Data papers
Collections
Why Publish with JPE?
Author Guidelines
Submission Site
Open Access
About Journal of Plant Ecology
Editorial Board
Self-Archiving Policy
Advertising and Corporate Services
Journals Career Network
Dispatch Dates
Journals on Oxford Academic
Books on Oxford Academic

Article Contents

Introduction to phylosymbiosis, prevalence of phylosymbiosis across the plant kingdom, drivers of phylosymbiosis, summarizing methods for detecting phylosymbiosis, significance and future directions of phylosymbiosis research, supplementary material, acknowledgements, prevalence and underlying mechanisms of phylosymbiosis in land plants.

Article contents
Figures & tables
Supplementary Data

Li-Qun Lin, Luke R Tembrock, Li Wang, Prevalence and underlying mechanisms of phylosymbiosis in land plants, Journal of Plant Ecology , Volume 17, Issue 6, December 2024, rtae051, https://doi.org/10.1093/jpe/rtae051

Permissions Icon Permissions

Phylosymbiosis, the congruence of microbiome composition with host phylogeny, is a valuable framework for investigating plant–microbe associations and their evolutionary ecology. This review assesses the prevalence of phylosymbiosis across the plant kingdom, elucidates the fundamental ecological and evolutionary processes contributing to its occurrence based on previous research and explores commonly used methods for identifying phylosymbiosis. We find that the presence of phylosymbiosis may be influenced by both phylogenetic distance and the taxonomic level at which host plants are examined, with the strength of associations potentially decreasing as the taxonomic scale becomes finer. Notably, the endophytic microbiome exhibits a stronger phylosymbiosis signal compared with the epiphytic or rhizosphere-associated microbiomes. Microorganisms such as fungi and bacteria can yield highly variable evidence for phylosymbiosis due to differences in colonization, transmission or functional characteristics. We also outline how the four community assembly processes (dispersal, selection, diversification and drift) contribute to the establishment and maintenance of host–microbe phylosymbiosis. Furthermore, we highlight the diversity of methods employed to detect phylosymbiosis, which involves three key processes: constructing host phylogenies, assessing microbial data and statistically evaluating the correlation between host phylogeny and microbial composition. Remarkably different methodologies across studies make comparisons between findings challenging. To advance our understanding, future research is expected to explore phylosymbiosis at lower taxonomic levels and investigate different microbial communities coexisting synergistically within the same host. Understanding the relative importance of community assembly processes in driving phylosymbiosis will be critical for gaining deeper insights into the ecology and evolution of host–microbe interactions.

系统共生是指微生物群落组成与宿主系统发育一致的一种现象，是研究植物-微生物关系及其进化生态学的有利框架。本文综述了植物界中系统共生现象的普遍性，基于以往研究阐明了促成其发生的基本生态和进化过程，并探讨了识别系统共生的常用方法。研究发现，系统共生现象的存在可能受宿主植物的系统发育距离和分类水平的影响，随着分类尺度的精细化，关联强度可能减弱。值得注意的是，内生微生物群落比附生或根际相关微生物群落表现出更强的系统共生信号。不同类型的微生物(如真菌和细菌)由于在定殖、传播或功能特性上的差异，可能产生高度可变的系统共生证据。本文还概述了4种基本群落组装过程(扩散、选择、多样化、漂变)如何促进宿主-微生物系统共生现象的建立与维持。此外，本文强调了检测系统共生现象所采用方法的多样性，共涉及3个关键过程：构建宿主系统发育树、评估微生物数据以及统计评估宿主系统发育与微生物组成之间的相关性。不同研究中使用显著不同的方法使得结果之间的比较变得具有挑战性。为了推进我们的理解，未来的研究有望探索更低分类水平上的系统共生现象，并研究在同一宿主内共存的不同微生物群落的协同作用。理解不同群落组装过程在驱动系统共生现象中的相对重要性，将对深入理解宿主-微生物相互作用的生态和进化具有重要意义。

Plants are associated with numerous microorganisms (bacteria, fungi, archaea, viruses, oomycetes, microalgae etc.), showing diversity in species distribution and abundance ( Berg et al. 2016 ; Harrison and Griffin 2020 ; Vandenkoornhuyse et al. 2015 ; Zhu et al. 2022 ). The plant microbiome, often regarded as the host's secondary genome, represents a substantial reservoir of genes that contribute to the host's metabolic capacity and adaptability ( Bordenstein and Theis 2015 ). It is well known that root microbial communities mediate plant adaptation to nutrient starvation, such as limited supply of nitrogen, phosphorus and iron. For instance, plants lack the ability to convert atmospheric nitrogen into usable forms, while plant-associated bacteria encode genes for nitrogen fixation ( Wang and Haney 2020 ). Microbes inhabiting the phyllosphere also hold crucial functions in plant productivity and fitness ( Liu et al. 2023 ; Stone et al. 2018 ; Thapa et al. 2021 ). For example, inoculation of epiphytic phyllosphere bacteria alleviates drought stress in rice ( Arun et al. 2020 ). Additionally, instances of horizontal gene transfer from microbiome to host plant, such as the frequent identification of microbial terpene synthase-like genes in nonseed land plants, further support the horizontal gene transfer from microbiome impact on the genomic landscape of host plants ( Jia et al. 2016 ). With the global human population growing, arable land decreasing and the effects of climate change becoming more prevalent, harnessing the plant microbiome to improve crop yield, enhance disease resistance and boost nutrient acquisition without additional inputs or an increase in cultivated land is ever more essential in maintaining food security and reducing environmental impact ( Busby et al. 2017 ; Qiu et al. 2019 ; Ray et al. 2020 ; Tosi et al. 2020 ). One of the prerequisites for the development of such an approach is to improve our fundamental understanding of the ecological and evolutionary processes underlying plant–microbiome associations ( Lajoie and Kembel 2021 ).

Recent studies have emphasized that plant microbiomes are shaped by both inheritance and environmental context, i.e. the vertical transmission of microbiomes among plant generations via seeds, and the influence of host genotypes on the assembly of plant microbiota acquired from the environment ( Mallott and Amato 2021 ). Thus, assemblages of plant-associated microbiotas often show a degree of correlation with host phylogeny, such that species with close phylogenetic relationships harbor more similar microbiomes than species with distant phylogenetic relationships, regardless of the abiotic environment where they are growing ( Brooks et al. 2016 , 2017 ; Sarver et al. 2022 ). Indeed, several studies have observed the eco-evolutionary pattern known as 'phylosymbiosis' where more closely related plants tend to harbor more similar microbial communities, especially in controlled environments ( Groussin et al. 2020 ; Liu et al. 2019b ). For instance, Abdelfattah et al. (2022) recapitulated Malus species' phylogeny through hierarchical clustering based on differences in host-associated microbiome composition. The nonrandom distribution of microbial associations with respect to plant phylogeny suggests that host genetic divergence can shape the diversity and composition of associated microbiomes.

It is important to note that phylosymbiosis does not necessarily imply coevolution of hosts and their microbiota. By definition, coevolution strictly refers to reciprocal genetic changes in interacting species resulting from selection pressure, promoting continuous coadaptation ( Occhipinti 2013 ). A classic example of coevolution is the symbiosis between plants and arbuscular mycorrhizal fungi. This mutualistic relationship allows plants to efficiently acquire nutrients like phosphates and nitrates, while the fungi obtain carbon sources like sugars for their growth. The symbiosis relationship has made both species evolve in response to each other, enhancing their survival and reproductive success. In contrast, phylosymbiosis considers the correlation between host phylogeny and microbial community compositions as a whole, rather than specific microorganisms ( Fig. 1 ). The driving mechanisms are not restricted to coevolution, and they also include environmental filtering, horizontal transmission and founder effects, among others (more details in the section 'Drivers of Phylosymbiosis'). Furthermore, the time scales, over which phylosymbiosis and coevolution are observed, are also different. Coevolution is a long-term process that involves reciprocal genetic changes in interacting species over numerous generations ( De Vienne et al. 2013 ), while phylosymbiosis can be a relatively short-term phenomenon that may be influenced by climatic and environmental factors in addition to host genetics ( Lim and Bordenstein 2020 ). That said, phylosymbiosis can be context-dependent, dynamic and vary over time and across different environments.

A schematic diagram illustrating phylosymbiosis and non-phylosymbiosis. (a) In phylosymbiosis, the host phylogeny (left) aligns with the dendrogram based on microbiome similarity (right) and the plant microbiome exhibits host specificity (indicated by corresponding colors). (b and c) Whereas in non-phylosymbiosis, there is a lack of congruence, and the plant microbiome may or may not possess host specificity.

Phylosymbiosis has emerged as a useful framework for studying the interactions between microbiota and their host plants, and has the potential to be employed in the improvement of plant germplasm for crop breeding ( Bouffaud et al. 2012 ; Cordovez et al. 2019 ; Lajoie and Kembel 2021 ). First, phylosymbiosis exemplifies the link between host evolution and microbial community composition, providing insights into host–microbe dynamics across diverse ecological settings. A comprehensive understanding of when and why phylosymbiosis occurs, as well as the factors that influence its strength, enables the assessment of the significance of eco-evolutionary forces shaping observed microbial assemblages ( Lim and Bordenstein 2020 ). Moreover, phylosymbiosis aids in identifying naturally associated microbial communities with specific plant lineages, offering novel genetic sources for breeding dynamic germplasms. Historically, breeders selected genetic variants controlling desired morphological and physiological traits in crops, often neglecting the impact of domestication on the plant microbiome. Previous research indicates that domestication disrupts plant–microbe symbiosis, diminishing the interaction between domesticated plants and microorganisms ( Genre et al. 2020 ; Martin-Robles et al. 2018 ). The observed patterns of phylosymbiosis suggest predictability in microbiome changes during crop domestication, supporting the feasibility of microbiome-based breeding strategies ( Adam et al. 2018 ; Chen et al. 2020 ; Escudero-Martinez and Bulgarelli 2019 ; Favela et al. 2021 ). Considering the evolution of plant–microbe relationships over time, breeders can make strategies of reintroducing beneficial microbes, restoring disrupted symbiotic associations and ultimately cultivating more resilient, productive and adaptable crop varieties.

Although host–symbiont interactions have been widely observed in various plant groups, research on phylosymbiosis in plants remains in its nascent stages, especially in terms of comprehensive studies on the prevalence of phylosymbiosis in plants. Knowledge gaps are present in several aspects. First, identifying the specific contextual environments, in which phylosymbiosis occurs and does not occur, is necessary, as it will greatly deepen our understanding of the ecological factors that influence the occurrence of phylosymbiosis. Furthermore, there is a dearth of clarity regarding the methodologies commonly used in plant phylosymbiosis research, which hinders the reproducibility and comparison of findings among different studies. Moreover, determining the mechanisms that contribute to phylosymbiosis is also challenging due to the complex interplay of ecological drivers. Filling these knowledge gaps is crucial for understanding the eco-evolutionary forces shaping microbial assemblages associated with plants, thereby facilitating both basic and applied research endeavors ( Lim and Bordenstein 2020 ). So, we conducted a comprehensive literature search in the Institute of Scientific Information Web of Science (WOS) database ( https://www.webofscience.com/wos ). Research targeting plant pathogenic microorganisms and that used only host taxonomy rather than explicit phylogenetic relationships were excluded. As of January 2023, we collected 35 articles assessing the correlation between host phylogeny and microbiotic composition ( Supplementary Table S1 ), with 25 of the 46 performed tests detected phylosymbiosis. These studies covered a wide range of plant lineages (mainly angiosperms and gymnosperms, as well as some ferns) and a variety of host compartments (leaves, roots and endosphere). Most of the studied microbiotas consisted of either bacteria or fungi, with some studies focusing on both. To the best our knowledge, other microorganisms such as viruses and archaea have not yet been examined for phylosysmbiosis. Our goal is to provide a broad overview of phylosymbiosis research, such that these findings can be utilized in an applied setting for improved crop yield and health as well as in the study and restoration of functional ecosystems.

Since introduction of the concept by Brucker and Bordenstein (2012) , phylosymbiosis has been studied in various plants and their associated microbiota in both natural and controlled environments, including different plant phyla along tropical soil chronosequences ( Yeoh et al. 2017 ), coastal halophytes ( Sun et al. 2021 ) and trees in various regions such as Japan, Estonia, Panama, New Guinea and southern China ( Ishida et al. 2007 ; Kembel and Mueller 2014 ; Tedersoo et al. 2013 ; Vincent et al. 2016 ; Wang et al. 2019 ). However, the depth of phylosymbiosis researches varies considerably in different plant lineages. Of the 35 articles we assessed, only 4 studies involved pteridophytes (62 species in 7 orders), and the phylosymbiosis pattern was only confirmed in the order Lycopodiales. It seems that phylosymbiosis is not universal in pteridophytes, consistent with previous studies, which showed that microbiotas of ferns rarely exhibit host specificity compared with gymnosperms and angiosperms. Although 10 studies involved gymnosperms (79 species in 8 orders), they were mainly concentrated in the order Pinales. More specifically, the family Pinaceae, with relatively extensive studies, appears to exhibit phylosymbiosis more consistently than others. Overall, data for pteridophytes and gymnosperms are limited to only a few host taxa, limiting our interpretation of phylosymbiosis patterns in these lineages ( Fig. 2 ).

Current research status of phylosymbiosis in angiosperms and gymnosperms. The phylogenetic tree depicts the relationships among all orders in seed plants. The bars indicate the number of published studies conducted to detect phylosymbiosis in the corresponding orders. The diagrams at the four corners show the representative plants examined for phylosymbiosis in gymnosperms and angiosperms (including monocots, superrosids and superasterids).

Phylosymbiosis has been investigated in a wide range of groups among angiosperms (39 studies including 1128 species in 47 orders), with a focus on maize and trees such as willow, poplar, banyan and apple ( Fig. 2 ) ( Abdelfattah et al. 2022 ; Bouffaud et al. 2012 , 2014 ; Emmett et al. 2017 ; Hardoim et al. 2011 ; Liu et al. 2019a ; Peiffer et al. 2013 ). The basal angiosperm lineages, including Amborellales, Nymphaeales, Austrobaileyales and Chloranthales, are the least studied groups and have been covered in only one study ( Zhu et al. 2021 ). Poales was by far the most well-studied order (18 studies covering 123 species), followed by Fabales (14 studies enclosing 29 species), Malpighiales (13 studies encompassing 85 species), Rosales (12 studies circumscribing 103 species) and Asterales (11 studies totaling 69 species). A challenging but intriguing situation is that evidence for phylosymbiosis in plants is highly variable, with no plant order involved in more than two studies showing consistent phylosymbiosis patterns ( Supplementary Table S2 ).

This variability can be attributed to several factors. First, macroevolutionary patterns can be divergent in phylogenetic trees, and in the case of phylosymbiosis, such differences are likely to be further amplified. For instance, some host species may have unique genotypes or physiological traits that interact with their microbiota, but the traits are not phylogenetically conserved, leading to the absence of phylosymbiosis. Such a case is observed in the Asteraceae family, where the dissimilarity in foliar endophyte fungi between hosts does not show a significant correlation with the phylogenetic distance between plant species ( Whitaker et al. 2020 ). This observation was partially attributed to the abundant presence of secondary metabolites, especially terpenoids, in Asteraceae leaves, influencing microbial colonization and potentially playing a pivotal role in shaping specific microbiomes ( Huang et al. 2019 ; Karamanoli et al. 2000 ; Xue et al. 2019 ). Due to the rapid evolution of Asteraceae ( Calabria et al. 2007 ), secondary metabolites in Asteraceae leaves exhibit substantial variation and do not consistently align with phylogenetic relationships, which may result in the loss of phylosymbiosis signal. Second, environmental factors, such as soil type, climate and geographic locations, significantly impact the composition of the microbiota, which may override the effect of host phylogeny. For instance, Schlaeppi et al. (2014) reported discordance between microbiota composition and phylogenetic distance of hosts within the Arabidopsis genus, and showed that the root microbiomes of Arabidopsis thaliana , Arabidopsis lyrata and Arabidopsis halleri were shaped more by environmental factors than by host phylogeny. In addition, such inconsistencies may, to some extent, stem from methodological alterations and inherent differences among various study subjects. For example, the utilization of different microbial taxonomic thresholds, microbial β-diversity metrics or phylosymbiosis indicators, along with the inherent differences in the physiological and ecological characteristics of microbial taxonomies across different studies, can all contribute to discrepancies in the results.

Based on the above considerations, it is not appropriate to summarize phylosymbiosis pattern within a specific plant clade from existing studies, as disparate methodologies have been employed. This limitation also precluded comparison of phylosymbiosis across different host compartments or microbial taxonomy. Consequently, we adopted a straightforward vote-counting approach, independently assessing each phylosymbiosis test. We ranked the taxonomic breadth of host plants, the microbial habitats examined (phyllosphere, rhizosphere and endosphere) and the type of microbiome (e.g. fungal and bacterial) based on the number of studies reporting phylosymbiosis.

Phylosymbiosis at different taxonomic levels

Detection of phylosymbiosis signal may vary with the taxonomic level of the host plants being studied. At broader taxonomic levels, such as plant phyla, orders or families, phylosymbiosis may be more easily detected because the host effects on microbial communities are more obvious over longer periods of evolutionary time. For example, Yeoh et al. (2017) found evidence for phylosymbiosis when studying species from different phyla growing in close proximity. Fitzpatrick et al. (2018) found a significant correlation between endophyte community dissimilarity and phylogenetic distance among angiosperm plant species. At finer taxonomic levels, such as plant genera and species, detectable phylosymbiosis patterns have also been observed. Studies have found phylosymbiosis signals in a single plant genus such as Salix ( Bell et al. 2014 ), Ficus ( Liu et al. 2019b ), Oryza ( Kim et al. 2020 ) and Malus ( Abdelfattah et al. 2022 ). However, some researchers have posited that phylosymbiosis may not manifest within different individuals of the same species, with phylosymbiosis signals observable only at coarser taxonomic levels (i.e. Poaceae family) but not within species (i.e. different inbred lines of maize and different accessions of rice) ( Bouffaud et al. 2012 , 2014 ; Kim et al. 2020 ; Peiffer et al. 2013 ). Furthermore, while numerous studies have highlighted significant variations in the diversity and composition of plant-associated microbiomes based on host genotypes, and some even have identified specific microbial taxa linked to specific host single-nucleotide polymorphisms, these instances represent examples of the broader concept known as 'host specificity' ( Fig. 1 ), which describes the significant correlation between microbiota composition and host species, usually irrespective of host phylogeny ( Mina et al. 2020 ; Sutherland et al. 2022 ).

Notably, although numerous studies have demonstrated the existence of phylosymbiosis at different taxonomic levels of the host plants, studies have also reported dissimilarities in microbial composition that cannot be explained solely by phylogenetic distance. For example, Zheng and Lin (2020) found no significant congruence between microbiome composition and host phylogeny within the bamboo subfamily (Bambusoideae). Some of the explanations for these dissimilarities provided by previous studies are summarized above. Overall, the presence of phylosymbiosis may depend on both host phylogenetic distance and environmental factors, and it is possible that the strength of the phylosymbiosis signal decreases as the taxonomic scale becomes finer. More research is needed to fully understand the patterns and mechanisms of phylosymbiosis at lower taxonomic levels.

Phylosymbiosis in different plant compartments

Plants offer specific habitats for microbial communities, which can be broadly classified as the phyllosphere ( Zhu et al. 2022 ), rhizosphere ( Berendsen et al. 2012 ; Qu et al. 2020 ) and endosphere ( Shade et al. 2017 ), representing the aboveground exterior parts of the plant, the belowground part of the plant and all inner parts of the plant, respectively. Exploring phylosymbiosis patterns across different plant compartments can shed light on the mechanisms driving phylosymbiosis. Plant microbiomes are derived through a combination of inheritance and environmental acquisition ( Mallott and Amato 2021 ). The relative importance of these two assembly mechanisms in different plant compartments exhibits fine-scale variation ( Scherer and Mast 2023 ). Specifically, the phyllosphere and rhizosphere are open systems that are susceptible to environmental disturbances which may affect the interaction between plants and their microbiome ( Zhu et al. 2022 ). Conversely, the endosphere interacts less with environmental microorganisms and is more heavily influenced by the plant environment ( Compant et al. 2021 ). In both monocotyledons and dicotyledons, the selection pressure of host plants on microbial communities increased in a gradient from soils to epiphytes to endophytes ( Wieland et al. 2001 ; Xiong et al. 2021 ), and from below to aboveground niches ( Trivedi et al. 2020 ).

Of the 35 articles we assessed, 46 phylosymbiosis tests were performed between host plant phylogeny and microbiotic composition, as some studies included multiple phylosymbiosis tests (e.g. one test for bacteria and one for fungi with the same host taxa; see Supplementary Table S1 ). The rhizosphere has received the most attention due to its essential role in plant nutrition acquisition ( Fig. 3 ) ( Berg et al. 2016 ). In contrast, little is known regarding the phyllosphere. Although phylosymbiosis varied across different plant compartments, we found that endophytic microbiotas tend to harbor phylosymbiosis patterns more frequently than microbiotas inhabiting rhizosphere or phyllosphere across 26 independent studies (61.11%, 53.57% and 33.33%, respectively). In particular, Fitzpatrick et al. (2018) studied the composition of the rhizosphere and endosphere microbiomes in 30 distantly related angiosperms and found that only the endosphere microbiome was strongly affected by host plant phylogeny, while no such correlation was observed between the rhizosphere microbiome and host plant phylogeny. This finding is consistent with a meta-analysis of 36 phylosymbiosis literature, covering hosts from plants, mammals, insects, sponges and birds ( Mazel et al. 2023 ). However, it should be noted that there may be an inherent bias in this finding due to variations in the number of studies conducted in different compartments and the methods used in different research.

A mosaic plot shows the diversity of phylosymbioses across different plant compartments and microbiome types. (a) Different habitats that plants provide for microorganisms. Red dots indicate microbes living in the rhizosphere (soil region affected by the roots exudates and the root surface), yellow dots indicate microbes living in the endosphere (inner tissues of plants, i.e. roots, branches, leaves and seeds) and blue dots indicate microbes living in phyllosphere (the aboveground part surface of plants). (b) Blue mosaics and orange mosaics indicate the percentage of plant phylosymbiosis studies that targeted fungi or bacteria, respectively. The percentage of studies where phylosymbiosis is observed is annotated with a darker color than the percentage of studies indicating that no phylosymbiosis occurs. Microorganisms detected in plant tissues that have not been surface disinfected are included in ‘Endo/Rhizosphere’ or ‘Endo/Phyllosphere’.

Phylosymbiosis among different microbiome types

Phylosymbiosis studies among different groups of microbes present a complex and diverse picture. When all collected studies were considered, more evidence of phylosymbiosis signals were detected in fungi (60.0% of fungal studies and 47.7% of bacterial studies) ( Fig. 3 ). However, the situation is different when focusing on the six studies that test phylosymbiosis of both fungal and bacterial communities in the same flora (25% and 50%, respectively). Thus, it remains challenging to ascertain whether phylosymbiosis is more likely to occur in fungi or bacteria due to variations in the number of studies conducted on bacteria and fungi, the range of examined host species and discrepancies in research methodologies.

However, there are undeniable differences in phylosymbiosis patterns between plant fungal and bacterial communities, which can be attributed to several factors, including their colonization and transmission modes, community structure patterns and functional roles within the plant microbiome. Bacteria, with their smaller body size and greater dispersal ability, can easily colonize various plant surfaces or enter internal plant tissues through natural openings or wounds, which are predominantly horizontally transmitted ( Larsen et al. 2022 ). Bacterial communities often exhibit a greater capacity to adapt to variable environmental conditions, demonstrate metabolic functional flexibility and play crucial roles in nutrient cycling, plant growth promotion and defense against pathogens ( Voolstra and Ziegler 2020 ). Bacterial community versatility is strongly influenced by host conditions, such as plant developmental stage and nutritional status, contributing to non-phylogenetic associations with hosts ( Dastogeer et al. 2020 ). These attributes contribute to more dynamic associations between bacterial communities and their hosts, and thus may produce weaker phylosymbiosis signals. Fungi, on the other hand, may have lower dispersal potential compared with bacteria ( Vannette et al. 2021 ). Although horizontal transmission is common, many fungi (e.g. the clavicipitaceous endophytes) utilize both living and dead plant tissues as dispersal routes, making opportunities to pass from one generation to the next through host seeds or other reproductive structures more likely ( Golan and Pringle 2017 ; Gundel et al. 2020 ; Rodriguez et al. 2009 ). Moreover, with their more complex cell structures than that of bacteria, fungi are capable of establishing intimate and specialized associations with hosts, exhibiting stronger host specificity. Unlike rhizobia, which mainly form symbiosis with legumes, mycorrhizal fungi take several forms and involve different plant and fungal lineages ( van Der Heijden et al. 2015 ). More than 90% of plants in nature can form mycorrhizal symbioses, among which angiosperms account for the largest and most diverse group. During symbiosis, plant roots and mycorrhizal fungal hyphae will work together to recruit diverse microbial partners into the rhizosphere. So, the coevolution of mycorrhizal symbioses is likely to contribute to phylosymbiosis.

In conclusion, further research is necessary to unravel the factors contributing to the inconsistent results observed in the detection of phylosymbiosis signals among coinhabiting bacteria and fungi within the same host. In addition, other key microorganisms including archaea, viruses, oomycetes and microalgae have been neglected in most phylosymbiosis studies. The study of different types of microorganisms associated with plants is crucial for a comprehensive understanding of the plant microbiome and phylosymbiosis, as they may all play crucial and interconnected roles in plant adaptation and growth. Expanding our knowledge in this field will enhance our understanding of the coevolutionary dynamics between hosts and their microbial partners, contributing to various applications in agriculture, ecology and environmental management.

Understanding the drivers of general patterns of host-associated microbial community assembly, including special cases of phylosymbiosis, can shed light on the coevolutionary dynamics between plants and their microbial partners, as well as the functional significance of microbiome assembly patterns. Previous research has highlighted the influence of host, microbial factors and the environment on microbial community assembly. While traditional niche-based theory emphasizes deterministic processes, such as species traits, interspecific interactions (e.g. competition, predation, mutualisms and trade-offs) and environmental conditions (e.g. pH, temperature, salt and moisture), the neutral theory posits that stochastic processes, including birth, death, colonization, extinction and speciation, govern microbial community structures ( Matthews and Whittaker 2014 ; Vannette and Fukami 2014 ). However, recent consensus supports the notion that microbial community assembly is shaped by a combination of deterministic and stochastic processes, although the relative importance of these processes remains unresolved ( Zhou and Ning 2017 ). Vellend (2016) divided these processes into four fundamental categories: dispersal and diversification, which introduce new organisms into communities, as well as selection and drift, which influence microbial species’ relative abundances. Among these, selection represents a deterministic process, while drift is stochastic. Dispersal and diversification are typically considered stochastic, yet they can display deterministic characteristics in specific cases ( Luan et al. 2020 ). In this section, we provide an overview of the current knowledge concerning the mechanisms of phylosymbiosis in plants and highlight the contributions of the four basic processes (diversification, dispersal, selection and drift) to the formation and maintenance of plant-associated microbiomes.

Dispersal , the movement of microorganisms from one microhabitat to another, plays a significant role in shaping patterns of phylosymbiosis ( Vacher et al. 2016 ). Microorganisms can be transmitted to host plants through both vertical (inherited from parents) and horizontal (acquired from the environment) dispersal routes ( Mallott and Amato 2021 ). Vertical dispersal primarily relies on plant diaspores, such as seeds, fruits or propagules. Research has shown that a conserved set of microbial communities in seeds can be inherited across generations ( Golan and Pringle 2017 ; Rodriguez et al. 2009 ), maintaining symbiotic relationships between plants and microorganisms through subsequent evolutionary divergence ( Abdelfattah et al. 2023 ; Kim et al. 2020 ), thus favoring the establishment of phylosymbiotic patterns. However, once seeds germinate, microbiome assembly is likely to be driven by horizontal transfer, leading to higher diversity in plant-associated microbial communities. Similar patterns are observed in plants via vegetative propagation, such as red mangroves, where propagule-associated microbial communities are more influenced by local environmental acquisition than inheritance across generations ( Scherer and Mast 2023 ). The process of microbial horizontal dispersal to hosts can be influenced by specific microbial characteristics. Typical rhizosphere microorganisms, such as bacteria, protists and some rapidly growing fungi ( Boer et al. 2005 ) possess high ‘rhizosphere competence’, allowing for rapid colonization and intense competition for colonization sites ( Schreiter et al. 2014 ). Additionally, various microbial traits that influence dispersal can dictate the interactions between a single microbial lineage and potential hosts. For instance, the dominance of the genera Methylobacterium and Sphingomonas in foliar microbiota may be linked to their adaptation to extreme environments, such as sustained ultraviolet radiation exposure, low water and nutrient availability, and large temperature fluctuations throughout the day ( Chaudhry et al. 2021 ; Redford et al. 2010 ). These specific adaptations may explain why certain host plants exhibit phylosymbiosis with particular microbial types but not with others.

Selection , or host filtering of microorganisms, is currently the leading hypothesis to explain patterns of phylosymbiosis ( Kohl 2020 ). This process occurs after microorganisms are exposed to host plants through vertical or horizontal transmission, resulting in colonization by certain microbial populations while excluding others ( Mallott and Amato 2021 ). Host filtering involves both physiological traits and direct host–microorganism signaling. Distinct leaf traits, including size, age, chemical composition and cuticle structure, are key factors responsible for variability in leaf microbial communities. These host factors of the leaf have been noted in both gymnosperms and angiosperms ( Kembel and Mueller 2014 ; Redford et al. 2010 ). Root diameter, root dry matter content and root C/N ratio were found to be significant predictors of variation in root microbial communities ( Szoboszlay et al. 2015 ; Wehner et al. 2014 ). Similar physiological characteristics of closely related hosts may bring about the tendency to harbor similar microbial communities. If these factors that shape microbiota structures are conserved over host phylogeny, they may bring about phylosymbiosis. Another important component of host filtering involves the array of chemical exudates released by roots. While some of these substances act as nourishment for specific microorganisms, they can be detrimental to others. For instance, scopoletin, a simple coumarin, deters pathogenic microbes and facilitates the proliferation of beneficial microorganisms in the same niche ( Stringlis et al. 2019 ). This is a strong selection of root microbiome that helps to maintain host species-specific microbial communities, which then give rise to phylosymbiosis. More interestingly, studies have indicated that microorganisms may also play a role in the selection process. For example, growth-promoting rhizobacteria, such as Pseudomonas simiae WCS417 and Paenibacillus polymyxa BFKC01, influence the production of iron-mobilizing coumarins via the induction of transcription factor MYB72 in host plants, thereby enhancing their own competitive advantage against other microbes ( Stringlis et al. 2018 ; Zhou et al. 2016 ). In fact, the selection between microorganisms and host plants is bidirectional in some cases. For example, the specificity of plant–rhizobium mutualisms is driven by the coordination of host-specific secretion of chemical signals and microbial-specific recognition of these chemical signals ( Kohl 2020 ). Disentangling the directions and interactions associated with selection (hosts selecting microbes, microbes choosing hosts or a combination) represents a challenging yet essential gap in our understanding of phylosymbiosis and host–microbe interactions ( Kohl 2020 ).

Diversification , under some concepts, is synonymous with speciation and is a process in which microorganisms generate novel genetic variants and diverge from sister lineages. Once a population of microorganisms is established in a host plant, the host's physiology may exert selective pressures on the microbial community over time, resulting in significant changes in microbial community compositions and with time divergence ( Mallott and Amato 2021 ). This process often occurs via multiple mechanisms, such as mutation, recombination and horizontal gene transfer, and involves individual microbial lineages ( Dini-Andreote and Raaijmakers 2018 ). Compared with plants and animals, diversification in microbial communities generally occurs on much shorter time scales which is due to the short generation time, rapid growth and frequent genetic mutations exhibited by microorganisms ( Nemergut et al. 2013 ; Zhou and Ning 2017 ). Existing research suggests that within-host microbial evolution is often a result of microbial adaptation during a host's lifetime. For example, during experimental evolution, Bacillus subtilis adapted to Arabidopsis roots and diversified into three distinct morphotypes through molecular evolution ( Blake et al. 2021 ). Microbial diversification may also occur more synchronously with host generations in those microbial taxa strictly inhabiting the plant endosphere, owing to coevolutionary forces ( Cordovez et al. 2019 ). For instance, in both Primulaceae and Rubiaceae, bacterial communities are permanently maintained in the shoot tip of the host plant, where the endosymbiont inoculates newly developing leaves and flowers and passes from generation to generation by seed ( Pinto-Carbó et al. 2018 ). Over evolutionary time, plants and their microbiomes can undergo co-diversification and co-speciation in such long-term and intimate host–microbe associations, potentially resulting in phylosymbiosis ( Frank et al. 2017 ). However, limited data make it difficult to fully understand whether coinheritance and diversification of a small number of microbial lineages will affect the overall plant microbial community structure. Furthermore, most genetic mutations are largely random at the molecular level and generally have little substantial impact on species traits, thereby potentially having minimal effect on microbial community composition ( Zhou and Ning 2017 ). Additionally, horizontal gene transfer and recombination act as both diversifying and homogenizing forces depending on their rate of occurrence and demographic factors ( Nemergut et al. 2013 ), potentially influencing microbial diversification and community assembly, with no evidence of necessarily contributing to phylosymbiosis ( Cordovez et al. 2019 ; Nemergut et al. 2013 ). Thus, it is difficult to precisely assess the relative importance of neutral diversification in shaping microbial community structure and inferring functional phylosymbiosis therefrom. Nevertheless, it is evident, as described above, that diversification alone is unlikely to entirely account for the observed patterns of phylosymbiosis.

Drift in the context of phylosymbiosis refers to the random fluctuations in species' relative abundances within a community over time, resulting from inherent stochastic processes of birth, death and reproduction ( Adair and Douglas 2017 ). Theoretical studies suggest that in microhabitats like the phyllosphere and endosphere, where cell densities and microbial diversity are relatively low, drift is likely to play a key role in shaping microbial community structure due to even minor fluctuations may result in the loss of niches for low-abundance microorganisms ( Nemergut et al. 2013 ). Under controlled greenhouse conditions, stochastic processes, including ecological drift, have been shown to play an important role in shaping Arabidopsis leaf-associated microbiota ( Maignien et al. 2014 ). In addition, functional redundancy, where different lineages of microbes share similar or identical functions, appears to be more prevalent in microbial communities compared with plant and animal communities ( Louca et al. 2018 ). This increases neutrality and makes functionally redundant lineages more vulnerable to the influence of drift. However, considering that plants typically have diverse and abundant microbial communities throughout their growth cycle, ecological drift in isolation is unlikely to substantially contribute to phylosymbiosis across host species.

Overall, the mechanisms underlying phylosymbiosis in plants are likely to be complex and multifaceted, involving both deterministic and stochastic processes, as well as host–microbe coevolution and selective pressures. Understanding the interplay of these processes in different microbial lineages is essential for fully comprehending the relative contribution of each to the observed patterns of phylosymbiosis in land plants.

Methods employed to identify phylosymbiosis signals encompass three primary steps: generating a host phylogeny, compiling microbial data from the host and applying statistical techniques to quantify the correlation between these two ( Lim and Bordenstein 2020 ). However, the methods vary greatly among studies, which have a significant impact on the detection of phylosymbiosis. For instance, host phylogeny reconstructed by morphological traits, DNA sequences or fossils, may each yield different results, complicating the reliability of phylosymbiosis assessments ( Liu et al. 2022 ). Additionally, different approaches to microbial DNA extraction, sequencing and analysis affect the accuracy and resolution of microbial data, which can influence the identification of phylosymbiosis signals ( Fadiji and Babalola 2020 ; Joos et al. 2020 ). Furthermore, variations in methodology and statistics utilized to evaluate the correlation between host phylogeny and microbial composition make comparisons among studies more challenging. Therefore, it is essential to carefully consider the methodology used in each study and to interpret the results with caution. In this section, we aim to provide a general overview of the most common methods used in plant phylosymbiosis research, with a focus on the abovementioned three processes to help guide future studies.

Host phylogeny

A crucial step in evaluating phylosymbiosis is accurately determining the topology of the host phylogeny. For example, Heil et al. (2022) refrained from evaluating phylosymbiosis in the host plant Sarracenia due to discrepancies in the current Sarracenia phylogeny. Yang et al. (2019) utilized two different approaches to generate phylogenetic trees to compare and evaluate the robustness of their results in evaluating the mycobiome with woody forest plants. As the methods used to reconstruct host phylogeny from DNA sequences have been extensively reviewed ( Álvarez and Wendel 2003 ; Delsuc et al. 2005 ; Kapli et al. 2020 ), we will not repeat them in this article.

Microbiome sampling

As noted earlier, phylosymbiosis is influenced by both environmental variables and host characteristics, making it challenging to determine a non-artifactual signal. One commonly used approach to mitigate environmental effects is by conducting greenhouse experiments, which offer a controlled environment for testing multiple host species under the same conditions. This is particularly useful for studying fungi, given their varying dispersal capacities and sensitivity to the external environment ( Larsen et al. 2022 ). However, for large-scale studies on long-lived perennial plants, greenhouse experiments may not be feasible. In such cases, homogenous common-garden experiments can be a useful method. Growing plants in common gardens or selecting host plants that grow in a relatively homogeneous environment, such as the same forest, grassland or wetland, can reduce the bias caused by environmental variability and help to evaluate the contribution of host phylogeny ( Erlandson et al. 2018 ). Furthermore, repeated sampling within a species can help ensure the reproducibility of host-associated microbial communities and reduce environmental noise. Considering that researchers wish to investigate the relationship between plant-associated microbial community dissimilarity and phylogenetic distances among host plants, it is essential that they sample a large number of species across different host clades to establish a gradient of phylogenetic distances ( Wehner et al. 2014 ). Additionally, it is important to note that host physiological traits and genotypes can confound phylosymbiosis analyses when they deviate strongly from phylogenetic patterning. The structural, chemical and physiological characteristics of plant tissues (e.g. leaves and roots) can vary widely over the developmental stages of host plants. These development-dependent variations can influence the composition of plant microbial communities. Therefore, precise awareness of the developmental stage during sampling is imperative for accurate comparison and detection of phylosymbiosis ( Bouffaud et al. 2014 ).

While the above initiatives may to some extent reduce the confounding effects present in natural systems, such as environmental conditions, host traits and biological interactions, they do not eliminate them. Given the abundance of overlapping factors that can simultaneously affect plant phylosymbiosis, it is challenging to untangle their individual effects. Therefore, it is essential to fully consider all potential confounding factors and control them as much as possible when conducting phylosymbiosis research.

Microbial data and beta diversity measures

Phylosymbiosis analysis necessitates microbial diversity data from each host lineage to measure dissimilarities in microbial composition and structure. Previous work has relied on traditional culture-based methods to describe the plant microbiota, which inherently underestimated the microbial diversity of host samples ( Vincent et al. 2016 ). In recent years, culture-independent high-throughput marker gene sequencing has significantly advanced phylosymbiosis research due to its speed, ubiquity and affordability, providing a high-level but low-resolution overview of microbial community structure ( Apigo and Oono 2022 ). Although shotgun metagenomics has been used in a few animal studies to explore phylosymbiosis ( Doane et al. 2020 ; Weinstein et al. 2022 ), this approach has not been adopted in plants. In general, marker gene sequencing is still the most commonly used technique in phylosymbiosis studies.

Beta diversity, often used to quantify microbial community dissimilarity among host species, plays a key role in phylosymbiosis studies ( Lozupone and Knight 2008 ). The choice of beta diversity metrics may have an impact on the results of phylosymbiosis studies. Common metrics of beta diversity include Jaccard distance, Bray–Curtis dissimilarity and UniFrac distance. Jaccard distance is a qualitative indicator that measures the presence/absence of shared species between two samples; Bray–Curtis dissimilarity is a quantitative indicator that measures the differences in the abundance of shared species ( Kers and Saccenti 2022 ); and UniFrac distance measures the evolutionary distance between communities based on the shared and unique branches in a phylogenetic tree ( Lozupone et al. 2011 ). Because UniFrac distances take into account phylogenetic relationships among microbial communities, they may be more appropriate for investigating the role of host phylogeny in shaping the composition of microbial communities ( Lozupone et al. 2006 ). However, these metrics require a phylogenetic tree and therefore cannot be used for direct comparison of species lacking resolvable phylogenetic relationships. In some cases, applying qualitative and quantitative measures to the same dataset may lead to very different conclusions about phylosymbiosis ( Chen et al. 2012 ). For example, Mazel et al. (2023) found that the use of a weighted UniFrac metric, which considers microbial abundance, yielded a slightly stronger phylosymbiosis signal than unweighted UniFrac analysis, which does not consider abundance.

Overall, the choice of beta diversity metrics should be based on the research question and the type of data being analyzed. It is advisable to use multiple metrics (including abundance-based and presence/absence metrics) to better assess the robustness of the results.

Detection of phylosymbiosis signals

Phylosymbiosis, the nonrandom association of host phylogeny with the composition of associated microbial communities, has been observed in a wide range of host taxa. To investigate the presence and strength of phylosymbiosis, two common methods are used: the dendrogram-based approach and the Mantel approach.

The dendrogram-based approach involves constructing a dendrogram from the raw microbial beta diversity by hierarchical clustering such as single linkage (known as the nearest neighbor method), complete linkage (known as the farthest neighbor method) and average linkage (known as the Unweighted Pair Group Method with Arithmetic Mean, UPGMA) ( Saxena et al. 2017 ). The resulting tree is then compared with the host phylogenetic tree qualitatively or quantitatively (such as by the Robinson–Foulds metric, matching cluster or using a Procrustes analysis) ( Lim and Bordenstein 2020 ; Llabrés et al. 2021 ). While this method can provide an intuitive visual assessment of congruence between the dendrograms, there is a risk of data loss during the clustering process, leading to reduced power in measuring phylosymbiosis.

The Mantel approach, on the other hand, measures the correlation between the host phylogenetic divergence matrices and the microbial community dissimilarity matrices, which can be quantified using metrics such as Pearson’s r . This method is more quantitative and less prone to data loss than the dendrogram-based approach ( Ramette 2007 ). Besides, it can consider multiple factors such as host traits and environmental factors simultaneously by using regression on multiple distance matrices. A recent comparative study suggests that the Mantel approach is preferable to the dendrogram-based approach in measuring phylosymbiosis ( Mazel et al. 2018 ).

Despite the advantages of the Mantel approach over the dendrogram-based approach in detecting phylosymbiosis, it is worth noting that both methods have their strengths and limitations. For example, the Mantel approach can be sensitive to differences in the choice of distance metrics, while the dendrogram-based approach may be influenced by the choice of clustering method and threshold for defining clusters ( Clark et al. 2021 ). Moreover, the Mantel approach assumes a linear relationship between host phylogeny and microbial community dissimilarities, which may not always be the case ( Lim and Bordenstein 2020 ). Therefore, it is recommended to combine both approaches to validate the presence and strength of phylosymbiosis and to account for their respective limitations. Additionally, the development of new methods to detect phylosymbiosis is expected for future studies to overcome the limitations of current approaches.

The concept of phylosymbiosis is gaining increasing attention in the field of microbial ecology, owing to its significant implications for advancing knowledge of eco-evolutionary processes that have profound effects on host–microbiome interactions as well as future applications of microbiology to crop improvement and ecological restoration.

Phylosymbiosis highlights the coevolutionary history of plants and their associated microbial communities. By examining how patterns of microbial diversity and composition vary across different plant lineages, researchers can gain insights into the ecological and evolutionary processes that have shaped these interactions over time. Phylosymbiosis can also promote our understanding of plant–microbe interactions and inform plant breeding projects to improve microbial associations and tailor agricultural practices to enhance beneficial microbes. The plant-associated microbiome has been shown to play important roles in plant growth and development, nutrient acquisition and resistance to biotic and abiotic stresses. Some research suggests that plant domestication may have unintentionally selected for or altered the abundance and diversity of fungal and bacterial communities, thus influencing their phylogenetic associations with host plants and obscured patterns of previous phylosymbiosis ( Abdelfattah et al. 2022 ; Kim et al. 2020 ). By studying phylosymbiosis, researchers can gain a better understanding of which microbial taxa tend to be associated with specific plant lineages. This identification of microbial targets could then inform inoculation strategies and/or host genotype selection to aid in bolstering plant health to counteract environmental challenges like drought, extreme temperatures and disease outbreaks. This strategy has important implications for sustainable agriculture and ecosystem conservation since it can enhance plant growth and productivity without intensive synthetic inputs.

However, substantial work remains before we can translate these ideas into applied strategies. First, many plant lineages still lack basic information about the presence or absence of phylosymbiosis, especially at low taxonomic levels. In our examined studies, few families of gymnosperms and pteridophytes have been investigated for the presence or absence of phylosymbiosis. Even among angiosperms, more than half (56.97%) of the plant families listed in the Angiosperm Phylogeny Group (APG) IV have not been surveyed for such a pattern ( Supplementary Table S2 ). Future studies are expected to prioritize the exploration of phylosymbiosis in these unexplored plant lineages to reveal a more comprehensive understanding of plant–microbe interactions and their ecological and evolutionary implications. Moreover, current research has focused primarily on bacteria and fungi, whereas an enhanced understanding of phylosymbiosis in understudied microbial groups, such as archaea and viruses, is imperative for creating a comprehensive landscape of host–microbe symbioses. Additionally, a standardized methodology for effectively comparing microbial composition and diversity across diverse host species and environments is currently lacking. This absence may impede us from formulating overarching conclusions about the factors that drive phylosymbiosis and limits our ability to make accurate predictions about how plant–microbe interactions will respond to environmental and genetic changes.

Looking ahead, we propose the utilization of metagenomic sequencing data for the in-depth exploration of phylosymbiosis, given its ability to provide finer-scale taxonomic and functional profiling. On the one hand, current approaches are often limited to taxonomic descriptions of microbiome composition that may obscure patterns of phylosymbiosis. This may occur when evolutionarily and functionally similar microbial lineages are incorrectly separated or clustered at higher levels of classification, leading to overly conserved patterns of association with hosts. Increasing microbial taxonomic resolution of plant microbiome may contribute to uncovering taxonomic range restrictions of phylosymbiosis, i.e. we can gain a deeper understanding of its prevalence across different individuals of the same species. On the other hand, given the study that within a generalized plant host microbiome, it is the functional genes, rather than the taxonomic composition, that exhibit host specificity, gene function may play a pivotal role in the interaction between hosts and microbiomes, which should be taken into account when investigating phylosymbiosis. Metagenomic sequencing data offer a powerful tool to comprehensively explore the functional gene composition of microbial communities. Exploring whether patterns predicted by phylosymbiosis are apparent in the composition of potential gene functions is crucial for advancing our knowledge of the ecology and evolution of host–microbe interactions, thereby providing new insights and strategies for addressing future challenges and questions.

Supplementary material is available at Journal of Plant Ecology online.

Table S1: Plant phylosymbiosis research status.

Table S2: Sample collection of 36 plant phylosymbiosis literatures.

Table S3: Plant samples compiled according to orders.

This work was supported by the National Key Research and Development Program of China (2023YFA0915800); National Natural Science Foundation of China (32300223, 32070242 and 82373837); Shenzhen Fundamental Research Program (20220817165436004); Shenzhen Science and Technology Program (KQTD2016113010482651); Key Project at Central Government Level (The ability establishment of sustainable use for valuable Chinese medicine resources) (2060302); Special Funds for Science Technology Innovation and Industrial Development of Shenzhen Dapeng New District (RC201901-05 and PT201901-19); Basic and Applied Basic Research Fund of Guangdong (2020A1515110912); Science, Technology, and Innovation Commission of Shenzhen Municipality of China (ZDSYS20200811142605017).

We would like to acknowledge Dr. Ko-Hsuan Chen and two anonymous referees whose comments greatly improved the quality of this manuscript.

Conflict of interest statement . The authors declare that they have no conflict of interest.

Abdelfattah A , Tack AJM , Lobato C , et al. . ( 2023 ) From seed to seed: the role of microbial inheritance in the assembly of the plant microbiome . Trends Microbiol 31 : 346 – 355 . https://doi.org/10.1016/j.tim.2022.10.009

Google Scholar

Abdelfattah A , Tack AJM , Wasserman B , et al. . ( 2022 ) Evidence for host-microbiome co-evolution in apple . New Phytol 234 : 2088 – 2100 . https://doi.org/10.1111/nph.17820

Adair KL , Douglas AE ( 2017 ) Making a microbiome: the many determinants of host-associated microbial community composition . Curr Opin Microbiol 35 : 23 – 29 . https://doi.org/10.1016/j.mib.2016.11.002

Adam E , Bernhart M , Müller H , et al. . ( 2018 ) The Cucurbita pepo seed microbiome: genotype-specific composition and implications for breeding . Plant Soil 422 : 35 – 49 . https://doi.org/10.1007/s11104-016-3113-9

Álvarez I , Wendel JF ( 2003 ) Ribosomal ITS sequences and plant phylogenetic inference . Mol Phylogenet Evol 29 : 417 – 434 . https://doi.org/10.1016/s1055-7903(03)00208-2

Apigo A , Oono R ( 2022 ) Plant abundance, but not plant evolutionary history, shapes patterns of host specificity in foliar fungal endophytes . Ecosphere 13 : e03879 . https://doi.org/10.1002/ecs2.3879

Arun KD , Sabarinathan KG , Gomathy M , et al. . ( 2020 ) Mitigation of drought stress in rice crop with plant growth-promoting abiotic stress-tolerant rice phyllosphere bacteria . J Basic Microbiol 60 : 768 – 786 . https://doi.org/10.1002/jobm.202000011

Bell TH , El-Din Hassan S , Lauron-Moreau A , et al. . ( 2014 ) Linkage between bacterial and fungal rhizosphere communities in hydrocarbon-contaminated soils is related to plant phylogeny . ISME J 8 : 331 – 343 . https://doi.org/10.1038/ismej.2013.149

Berendsen RL , Pieterse CM , Bakker PA ( 2012 ) The rhizosphere microbiome and plant health . Trends Plant Sci 17 : 478 – 486 . https://doi.org/10.1016/j.tplants.2012.04.001

Berg G , Rybakova D , Grube M , et al. . ( 2016 ) The plant microbiome explored: implications for experimental botany . J Exp Bot 67 : 995 – 1002 . https://doi.org/10.1093/jxb/erv466

Blake C , Nordgaard M , Maroti G , et al. . ( 2021 ) Diversification of Bacillus subtilis during experimental evolution on Arabidopsis thaliana and the complementarity in root colonization of evolved subpopulations . Environ Microbiol 23 : 6122 – 6136 . https://doi.org/10.1111/1462-2920.15680

Boer W , Folman LB , Summerbell RC , et al. . ( 2005 ) Living in a fungal world: impact of fungi on soil bacterial niche development . FEMS Microbiol Rev 29 : 795 – 811 . https://doi.org/10.1016/j.femsre.2004.11.005

Bordenstein SR , Theis KR ( 2015 ) Host biology in light of the microbiome: ten principles of holobionts and hologenomes . PLoS Biol 13 : e1002226 . https://doi.org/10.1371/journal.pbio.1002226

Bouffaud ML , Kyselkova M , Gouesnard B , et al. . ( 2012 ) Is diversification history of maize influencing selection of soil bacteria by roots ? Mol Ecol 21 : 195 – 206 . https://doi.org/10.1111/j.1365-294X.2011.05359.x

Bouffaud ML , Poirier MA , Muller D , et al. . ( 2014 ) Root microbiome relates to plant host evolution in maize and other Poaceae . Environ Microbiol 16 : 2804 – 2814 . https://doi.org/10.1111/1462-2920.12442

Brooks AW , Kohl KD , Brucker RM , et al. . ( 2016 ) Phylosymbiosis: relationships and functional effects of microbial communities across host evolutionary history . PLoS Biol 14 : e2000225 . https://doi.org/10.1371/journal.pbio.2000225

Brooks AW , Kohl KD , Brucker RM , et al. . ( 2017 ) Correction: phylosymbiosis: relationships and functional effects of microbial communities across host evolutionary history . PLoS Biol 15 : e1002587 . https://doi.org/10.1371/journal.pbio.1002587

Brucker RM , Bordenstein SR ( 2012 ) Speciation by symbiosis . Trends Ecol Evol 27 : 443 – 451 . https://doi.org/10.1016/j.tree.2012.03.011

Busby PE , Soman C , Wagner MR , et al. . ( 2017 ) Research priorities for harnessing plant microbiomes in sustainable agriculture . PLoS Biol 15 : e2001793 . https://doi.org/10.1371/journal.pbio.2001793

Calabria LM , Emerenciano VP , Ferreira MJ , et al. . ( 2007 ) A phylogenetic analysis of tribes of the Asteraceae based on phytochemical data . Nat Prod Commun 2 : 277 – 285 . http://dx.doi.org/10.1177/1934578X0700200310

Chaudhry V , Runge P , Sengupta P , et al. . ( 2021 ) Shaping the leaf microbiota: plant–microbe–microbe interactions . J Exp Bot 72 : 36 – 56 . https://doi.org/10.1093/jxb/eraa417

Chen J , Bittinger K , Charlson ES , et al. . ( 2012 ) Associating microbiome composition with environmental covariates using generalized UniFrac distances . Bioinformatics 28 : 2106 – 2113 . https://doi.org/10.1093/bioinformatics/bts342

Chen X , Krug L , Yang H , et al. . ( 2020 ) Nicotiana tabacum seed endophytic communities share a common core structure and genotype-specific signatures in diverging cultivars . Comput Struct Biotechnol J 18 : 287 – 295 . https://doi.org/10.1016/j.csbj.2020.01.004

Clark DR , Underwood GJ , McGenity TJ , et al. . ( 2021 ) What drives study-dependent differences in distance–decay relationships of microbial communities ? Glob Ecol Biogeogr 30 : 811 – 825 . https://doi.org/10.1111/geb.13266

Compant S , Cambon MC , Vacher C , et al. . ( 2021 ) The plant endosphere world—bacterial life within plants . Environ Microbiol 23 : 1812 – 1829 . https://doi.org/10.1111/1462-2920.15240

Cordovez V , Dini-Andreote F , Carrion VJ , et al. . ( 2019 ) Ecology and evolution of plant microbiomes . Annu Rev Microbiol 73 : 69 – 88 . https://doi.org/10.1146/annurev-micro-090817-062524

Dastogeer KM , Tumpa FH , Sultana A , et al. . ( 2020 ) Plant microbiome—an account of the factors that shape community composition and diversity . Curr Plant Biol 23 : 100161 . https://doi.org/10.1016/j.cpb.2020.100161

De Vienne D , Refrégier G , López-Villavicencio M , et al. . ( 2013 ) Cospeciation vs host-shift speciation: methods for testing, evidence from natural associations and relation to coevolution . New Phytol 198 : 347 – 385 . https://doi.org/10.1111/nph.12150

Delsuc F , Brinkmann H , Philippe H ( 2005 ) Phylogenomics and the reconstruction of the tree of life . Nat Rev Genet 6 : 361 – 375 . https://doi.org/10.1038/nrg1603

Dini-Andreote F , Raaijmakers JM ( 2018 ) Embracing community ecology in plant microbiome research . Trends Plant Sci 23 : 467 – 469 . https://doi.org/10.1016/j.tplants.2018.03.013

Doane MP , Morris MM , Papudeshi B , et al. . ( 2020 ) The skin microbiome of elasmobranchs follows phylosymbiosis, but in teleost fishes, the microbiomes converge . Microbiome 8 : 93 . https://doi.org/10.1186/s40168-020-00840-x

Emmett BD , Youngblut ND , Buckley DH , et al. . ( 2017 ) Plant phylogeny and life history shape rhizosphere bacterial microbiome of summer annuals in an agricultural field . Front Microbiol 8 : 2414 . https://doi.org/10.3389/fmicb.2017.02414

Erlandson S , Wei X , Savage J , et al. . ( 2018 ) Soil abiotic variables are more important than Salicaceae phylogeny or habitat specialization in determining soil microbial community structure . Mol Ecol 27 : 2007 – 2024 . https://doi.org/10.1111/mec.14576

Escudero-Martinez C , Bulgarelli D ( 2019 ) Tracing the evolutionary routes of plant–microbiota interactions . Curr Opin Microbiol 49 : 34 – 40 . https://doi.org/10.1016/j.mib.2019.09.013

Fadiji AE , Babalola OO ( 2020 ) Metagenomics methods for the study of plant-associated microbial communities: a review . J Microbiol Methods 170 : 105860 . https://doi.org/10.1016/j.mimet.2020.105860

Favela A , Bohn MO , Kent AD ( 2021 ) Maize germplasm chronosequence shows crop breeding history impacts recruitment of the rhizosphere microbiome . ISME J 15 : 2454 – 2464 . http://dx.doi.org/10.1038/s41396-021-00923-z

Fitzpatrick CR , Copeland J , Wang PW , et al. . ( 2018 ) Assembly and ecological function of the root microbiome across angiosperm plant species . Proc Natl Acad Sci USA 115 : E1157 – E1165 . https://doi.org/10.1073/pnas.1717617115

Frank AC , Saldierna Guzmán JP , Shay JE ( 2017 ) Transmission of bacterial endophytes . Microorganisms 5 : 70 . https://doi.org/10.3390/microorganisms5040070

Genre A , Lanfranco L , Perotto S , et al. . ( 2020 ) Unique and common traits in mycorrhizal symbioses . Nat Rev Microbiol 18 : 649 – 660 . https://doi.org/10.1038/s41579-020-0402-3

Golan JJ , Pringle A ( 2017 ) Long-distance dispersal of fungi . Microbiol Spectr 5 . https://doi.org/10.1128/microbiolspec.funk-0047-2016

Groussin M , Mazel F , Alm EJ ( 2020 ) Co-evolution and co-speciation of host-gut bacteria systems . Cell Host Microbe 28 : 12 – 22 . https://doi.org/10.1016/j.chom.2020.06.013

Gundel PE , Sun P , Charlton ND , et al. . ( 2020 ) Simulated folivory increases vertical transmission of fungal endophytes that deter herbivores and alter tolerance to herbivory in Poa autumnalis . Ann Bot 125 : 981 – 991 . https://doi.org/10.1093/aob/mcaa021

Hardoim PR , Andreote FD , Reinhold-Hurek B , et al. . ( 2011 ) Rice root-associated bacteria: insights into community structures across 10 cultivars . FEMS Microbiol Ecol 77 : 154 – 164 . https://doi.org/10.1111/j.1574-6941.2011.01092.x

Harrison JG , Griffin EA ( 2020 ) The diversity and distribution of endophytes across biomes, plant phylogeny and host tissues: how far have we come and where do we go from here ? Environ Microbiol 22 : 2107 – 2123 . https://doi.org/10.1111/1462-2920.14968

Heil JA , Wolock CJ , Pierce NE , et al. . ( 2022 ) Sarracenia pitcher plant-associated microbial communities differ primarily by host species across a longitudinal gradient . Environ Microbiol 24 : 3500 – 3516 . https://doi.org/10.1111/1462-2920.15993

Huang AC , Jiang T , Liu Y-X , et al. . ( 2019 ) A specialized metabolic network selectively modulates Arabidopsis root microbiota . Science 364 : eaau6389 . https://doi.org/10.1126/science.aau6389

Ishida TA , Nara K , Hogetsu T ( 2007 ) Host effects on ectomycorrhizal fungal communities: insight from eight host species in mixed conifer-broadleaf forests . New Phytol 174 : 430 – 440 . https://doi.org/10.1111/j.1469-8137.2007.02016.x

Jia Q , Li G , Köllner TG , et al. . ( 2016 ) Microbial-type terpene synthase genes occur widely in nonseed land plants, but not in seed plants . Proc Natl Acad Sci USA 113 : 12328 – 12333 . https://doi.org/10.1073/pnas.1607973113

Joos L , Beirinckx S , Haegeman A , et al. . ( 2020 ) Daring to be differential: metabarcoding analysis of soil and plant-related microbial communities using amplicon sequence variants and operational taxonomical units . BMC Genomics 21 : 1 – 17 . https://doi.org/10.1186/s12864-020-07126-4

Kapli P , Yang Z , Telford MJ ( 2020 ) Phylogenetic tree building in the genomic age . Nat Rev Genet 21 : 428 – 444 . https://doi.org/10.1038/s41576-020-0233-0

Karamanoli K , Vokou D , Menkissoglu U , et al. . ( 2000 ) Bacterial colonization of phyllosphere of Mediterranean aromatic plants . J Chem Ecol 26 : 2035 – 2048 . https://doi.org/10.1023/A:1005556013314

Kembel SW , Mueller RC ( 2014 ) Plant traits and taxonomy drive host associations in tropical phyllosphere fungal communities . Botany 92 : 303 – 311 . https://doi.org/10.1139/cjb-2013-0194

Kers JG , Saccenti E ( 2022 ) The power of microbiome studies: some considerations on which alpha and beta metrics to use and how to report results . Front Microbiol 12 : 796025 . https://doi.org/10.3389/fmicb.2021.796025

Kim H , Lee KK , Jeon J , et al. . ( 2020 ) Domestication of Oryza species eco-evolutionarily shapes bacterial and fungal communities in rice seed . Microbiome 8 : 20 . https://doi.org/10.1186/s40168-020-00805-0

Kohl KD ( 2020 ) Ecological and evolutionary mechanisms underlying patterns of phylosymbiosis in host-associated microbial communities . Philos Trans R Soc Lond B Biol Sci 375 : 20190251 . https://doi.org/10.1098/rstb.2019.0251

Lajoie G , Kembel SW ( 2021 ) Plant-bacteria associations are phylogenetically structured in the phyllosphere . Mol Ecol 30 : 5572 – 5587 . https://doi.org/10.1111/mec.16131

Larsen S , Albanese D , Stegen J , et al. . ( 2022 ) Distinct and temporally stable assembly mechanisms shape bacterial and fungal communities in vineyard soils . Microb Ecol 86 : 337 – 349 . https://doi.org/10.1007/s00248-022-02065-x

Lim SJ , Bordenstein SR ( 2020 ) An introduction to phylosymbiosis . Proc Biol Sci 287 : 20192900 . https://doi.org/10.1098/rspb.2019.2900

Liu G-Q , Lian L , Wang W ( 2022 ) The molecular phylogeny of land plants: progress and future prospects . Diversity 14 : 782 . https://doi.org/10.3390/d14100782

Liu J , Meng Z , Liu X , et al. . ( 2019a ) Microbial assembly, interaction, functioning, activity and diversification: a review derived from community compositional data . Mar Life Sci Technol 1 : 112 – 128 . https://doi.org/10.1007/s42995-019-00004-3

Liu J , Zhao J , Wang G , et al. . ( 2019b ) Host identity and phylogeny shape the foliar endophytic fungal assemblages of Ficus . Ecol Evol 9 : 10472 – 10482 . https://doi.org/10.1002/ece3.5568

Liu X , Matsumoto H , Lv T , et al. . ( 2023 ) Phyllosphere microbiome induces host metabolic defence against rice false-smut disease . Nat Microbiol 8 : 1419 – 1433 . https://doi.org/10.1038/s41564-023-01379-x

Llabrés M , Rosselló F , Valiente G ( 2021 ) The generalized Robinson-Foulds distance for phylogenetic trees . J Comput Biol 28 : 1181 – 1195 . https://doi.org/10.1089/cmb.2021.0342

Louca S , Polz MF , Mazel F , et al. . ( 2018 ) Function and functional redundancy in microbial systems . Nat Ecol Evol 2 : 936 – 943 . https://doi.org/10.1038/s41559-018-0519-1

Lozupone C , Hamady M , Knight R ( 2006 ) UniFrac—an online tool for comparing microbial community diversity in a phylogenetic context . BMC Bioinf 7 : 371 . https://doi.org/10.1186/1471-2105-7-371

Lozupone CA , Knight R ( 2008 ) Species divergence and the measurement of microbial diversity . FEMS Microbiol Rev 32 : 557 – 578 . https://doi.org/10.1111/j.1574-6976.2008.00111.x

Lozupone C , Lladser ME , Knights D , et al. . ( 2011 ) UniFrac: an effective distance metric for microbial community comparison . ISME J 5 : 169 – 172 . https://doi.org/10.1038/ismej.2010.133

Luan L , Jiang Y , Cheng M , et al. . ( 2020 ) Organism body size structures the soil microbial and nematode community assembly at a continental and global scale . Nat Commun 11 : 6406 . https://doi.org/10.1038/s41467-020-20271-4

Maignien L , DeForce EA , Chafee ME , et al. . ( 2014 ) Ecological succession and stochastic variation in the assembly of Arabidopsis thaliana phyllosphere communities . mBio 5 : e00682-13 . https://doi.org/10.1128/mBio.00682-13

Mallott EK , Amato KR ( 2021 ) Host specificity of the gut microbiome . Nat Rev Microbiol 19 : 639 – 653 . https://doi.org/10.1038/s41579-021-00562-3

Martin-Robles N , Lehmann A , Seco E , et al. . ( 2018 ) Impacts of domestication on the arbuscular mycorrhizal symbiosis of 27 crop species . New Phytol 218 : 322 – 334 . https://doi.org/10.1111/nph.14962

Matthews TJ , Whittaker RJ ( 2014 ) Neutral theory and the species abundance distribution: recent developments and prospects for unifying niche and neutral perspectives . Ecol Evol 4 : 2263 – 2277 . https://doi.org/10.1002/ece3.1092

Mazel F , Davis KM , Loudon A , et al. . ( 2018 ) Is host filtering the main driver of phylosymbiosis across the tree of life ? mSystems 3 : e00097-18 . https://doi.org/10.1128/mSystems.00097-18

Mazel F , Guisan A , Parfrey LW ( 2023 ) Transmission mode and dispersal traits correlate with host specificity in mammalian gut microbes . Mol Ecol 33 :e16862. https://doi.org/10.1111/mec.16862

Mina D , Pereira JA , Lino-Neto T , et al. . ( 2020 ) Epiphytic and endophytic bacteria on olive tree phyllosphere: exploring tissue and cultivar effect . Microb Ecol 80 : 145 – 157 . https://doi.org/10.1007/s00248-020-01488-8

Nemergut DR , Schmidt SK , Fukami T , et al. . ( 2013 ) Patterns and processes of microbial community assembly . Microbiol Mol Biol Rev 77 : 342 – 356 . https://doi.org/10.1128/MMBR.00051-12

Occhipinti A ( 2013 ) Plant coevolution: evidences and new challenges . J Plant Interact 8 : 188 – 196 . https://doi.org/10.1080/17429145.2013.816881

Peiffer JA , Spor A , Koren O , et al. . ( 2013 ) Diversity and heritability of the maize rhizosphere microbiome under field conditions . Proc Natl Acad Sci USA 110 : 6548 – 6553 . https://doi.org/10.1073/pnas.1302837110

Pinto-Carbó M , Gademann K , Eberl L , et al. . ( 2018 ) Leaf nodule symbiosis: function and transmission of obligate bacterial endophytes . Curr Opin Plant Biol 44 : 23 – 31 . https://doi.org/10.1016/j.pbi.2018.01.001

Qiu Z , Egidi E , Liu H , et al. . ( 2019 ) New frontiers in agriculture productivity: optimised microbial inoculants and in situ microbiome engineering . Biotechnol Adv 37 : 107371 . https://doi.org/10.1016/j.biotechadv.2019.03.010

Qu Q , Zhang Z , Peijnenburg W , et al. . ( 2020 ) Rhizosphere microbiome assembly and its impact on plant growth . J Agric Food Chem 68 : 5024 – 5038 . https://doi.org/10.1021/acs.jafc.0c00073

Ramette A ( 2007 ) Multivariate analyses in microbial ecology . FEMS Microbiol Ecol 62 : 142 – 160 . https://doi.org/10.1111/j.1574-6941.2007.00375.x

Ray P , Lakshmanan V , Labbé JL , et al. . ( 2020 ) Microbe to microbiome: a paradigm shift in the application of microorganisms for sustainable agriculture . Front Microbiol 11 : 622926 . https://doi.org/10.3389/fmicb.2020.622926

Redford AJ , Bowers RM , Knight R , et al. . ( 2010 ) The ecology of the phyllosphere: geographic and phylogenetic variability in the distribution of bacteria on tree leaves . Environ Microbiol 12 : 2885 – 2893 . https://doi.org/10.1111/j.1462-2920.2010.02258.x

Rodriguez R , White J Jr , Arnold A , et al. . ( 2009 ) Fungal endophytes: diversity and functional roles . New Phytol 182 : 314 – 330 . https://doi.org/10.1111/j.1469-8137.2009.02773.x

Sarver J , Schultz E , Apigo A , et al. . ( 2022 ) Deep sequencing across multiple host species tests pine-endophyte specificity . Am J Bot 109 : 83 – 98 . https://doi.org/10.1002/ajb2.1792

Saxena A , Prasad M , Gupta A , et al. . ( 2017 ) A review of clustering techniques and developments . Neurocomputing 267 : 664 – 681 . https://doi.org/10.1016/j.neucom.2017.06.053

Scherer BP , Mast A ( 2023 ) Red mangrove propagule bacterial communities vary with geographic, but not genetic distance . Microb Ecol 86 : 1010 – 1022 . https://doi.org/10.1007/s00248-022-02147-w

Schlaeppi K , Dombrowski N , Oter RG , et al. . ( 2014 ) Quantitative divergence of the bacterial root microbiota in Arabidopsis thaliana relatives . Proc Natl Acad Sci USA 111 : 585 – 592 . https://doi.org/10.1073/pnas.1321597111

Schreiter S , Ding G-C , Grosch R , et al. . ( 2014 ) Soil type-dependent effects of a potential biocontrol inoculant on indigenous bacterial communities in the rhizosphere of field-grown lettuce . FEMS Microbiol Ecol 90 : 718 – 730 . https://doi.org/10.1111/1574-6941.12430

Shade A , Jacques M-A , Barret M ( 2017 ) Ecological patterns of seed microbiome diversity, transmission, and assembly . Curr Opin Microbiol 37 : 15 – 22 . https://doi.org/10.1016/j.mib.2017.03.010

Stone BW , Weingarten EA , Jackson CR ( 2018 ) The role of the phyllosphere microbiome in plant health and function . Ann Plant Rev Online : 533 – 556 . https://doi.org/10.1002/9781119312994.apr0614

Stringlis IA , De Jonge R , Pieterse CM ( 2019 ) The age of coumarins in plant–microbe interactions . Plant Cell Physiol 60 : 1405 – 1419 . https://doi.org/10.1093/pcp/pcz076

Stringlis IA , Yu K , Feussner K , et al. . ( 2018 ) MYB72-dependent coumarin exudation shapes root microbiome assembly to promote plant health . Proc Natl Acad Sci USA 115 : E5213 – E5222 . https://doi.org/10.1073/pnas.1722335115

Sun X , Li J-L , He C , et al. . ( 2021 ) Specific network and phylosymbiosis pattern in endophyte community of coastal halophytes . Fungal Ecol 53 : 101088 . https://doi.org/10.1016/j.funeco.2021.101088

Sutherland J , Bell T , Trexler RV , et al. . ( 2022 ) Host genomic influence on bacterial composition in the switchgrass rhizosphere . Mol Ecol 31 : 3934 – 3950 . https://doi.org/10.1111/mec.16549

Szoboszlay M , Lambers J , Chappell J , et al. . ( 2015 ) Comparison of root system architecture and rhizosphere microbial communities of Balsas teosinte and domesticated corn cultivars . Soil Biol Biochem 80 : 34 – 44 . https://doi.org/10.1016/j.soilbio.2014.09.001

Tedersoo L , Mett M , Ishida TA , et al. . ( 2013 ) Phylogenetic relationships among host plants explain differences in fungal species richness and community composition in ectomycorrhizal symbiosis . New Phytol 199 : 822 – 831 . https://doi.org/10.1111/nph.12328

Thapa S , Prasanna R , Ramakrishnan B , et al. . ( 2021 ) Microbial inoculation elicited changes in phyllosphere microbial communities and host immunity suppress Magnaporthe oryzae in a susceptible rice cultivar . Physiol Mol Plant Pathol 114 : 101625 . https://doi.org/10.1016/j.pmpp.2021.101625

Tosi M , Mitter EK , Gaiero J , et al. . ( 2020 ) It takes three to tango: the importance of microbes, host plant, and soil management to elucidate manipulation strategies for the plant microbiome . Can J Microbiol 66 : 413 – 433 . https://doi.org/10.1139/cjm-2020-0085

Trivedi P , Leach JE , Tringe SG , et al. . ( 2020 ) Plant–microbiome interactions: from community assembly to plant health . Nat Rev Microbiol 18 : 607 – 621 . https://doi.org/10.1038/s41579-020-0412-1

Vacher C , Hampe A , Porté AJ , et al. . ( 2016 ) The phyllosphere: microbial jungle at the plant–climate interface . Annu Rev Ecol Evol Syst 47 : 1 – 24 . https://doi.org/10.1146/annurev-ecolsys-121415-032238

van Der Heijden MG , Martin FM , Selosse MA , et al. . ( 2015 ) Mycorrhizal ecology and evolution: the past, the present, and the future . New Phytol 205 : 1406 – 1423 . https://doi.org/10.1111/nph.13288

Vandenkoornhuyse P , Quaiser A , Duhamel M , et al. . ( 2015 ) The importance of the microbiome of the plant holobiont . New Phytol 206 : 1196 – 1206 . https://doi.org/10.1111/nph.13312

Vannette RL , Fukami T ( 2014 ) Historical contingency in species interactions: towards niche-based predictions . Ecol Lett 17 : 115 – 124 . https://doi.org/10.1111/ele.12204

Vannette RL , McMunn MS , Hall GW , et al. . ( 2021 ) Culturable bacteria are more common than fungi in floral nectar and are more easily dispersed by thrips, a ubiquitous flower visitor . FEMS Microbiol Ecol 97 : fiab150 . https://doi.org/10.1093/femsec/fiab150

Vellend M ( 2016 ) The Theory of Ecological Communities (MPB-57) . Princeton, NJ, USA : Princeton University Press .

Google Preview

Vincent JB , Weiblen GD , May G ( 2016 ) Host associations and beta diversity of fungal endophyte communities in New Guinea rainforest trees . Mol Ecol 25 : 825 – 841 . https://doi.org/10.1111/mec.13510

Voolstra CR , Ziegler M ( 2020 ) Adapting with microbial help: microbiome flexibility facilitates rapid responses to environmental change . Bioessays 42 : 2000004 . https://api.semanticscholar.org/CorpusID:219458486

Wang NR , Haney CH ( 2020 ) Harnessing the genetic potential of the plant microbiome . Biochemist 42 : 20 – 25 . https://doi.org/10.1042/bio20200042

Wang Z , Jiang Y , Deane DC , et al. . ( 2019 ) Effects of host phylogeny, habitat and spatial proximity on host specificity and diversity of pathogenic and mycorrhizal fungi in a subtropical forest . New Phytol 223 : 462 – 474 . https://doi.org/10.1111/nph.15786

Wehner J , Powell JR , Muller LAH , et al. . ( 2014 ) Determinants of root-associated fungal communities within Asteraceae in a semi-arid grassland . J Ecol 102 : 425 – 436 . https://doi.org/10.1111/1365-2745.12197

Weinstein SB , Stephens WZ , Greenhalgh R , et al. . ( 2022 ) Wild herbivorous mammals (genus Neotoma ) host a diverse but transient assemblage of fungi . Symbiosis 87 : 45 – 58 . https://doi.org/10.1007/s13199-022-00853-0

Whitaker BK , Christian N , Chai Q , et al. . ( 2020 ) Foliar fungal endophyte community structure is independent of phylogenetic relatedness in an Asteraceae common garden . Ecol Evol 10 : 13895 – 13912 . https://doi.org/10.1002/ece3.6983

Wieland G , Neumann R , Backhaus H ( 2001 ) Variation of microbial communities in soil, rhizosphere, and rhizoplane in response to crop species, soil type, and crop development . Appl Environ Microbiol 67 : 5849 – 5854 . https://doi.org/10.1128/AEM.67.12.5849-5854.2001

Xiong C , Zhu YG , Wang JT , et al. . ( 2021 ) Host selection shapes crop microbiome assembly and network complexity . New Phytol 229 : 1091 – 1104 . https://doi.org/10.1111/nph.16890

Xue H , Jiang Y , Zhao H , et al. . ( 2019 ) Characterization of composition and antifungal properties of leaf secondary metabolites from thirteen cultivars of Chrysanthemum morifolium Ramat . Molecules 24 : 4202 . https://doi.org/10.3390/molecules24234202

Yang T , Tedersoo L , Soltis PS , et al. . ( 2019 ) Phylogenetic imprint of woody plants on the soil mycobiome in natural mountain forests of eastern China . ISME J 13 : 686 – 697 . https://doi.org/10.1038/s41396-018-0303-x

Yeoh YK , Dennis PG , Paungfoo-Lonhienne C , et al. . ( 2017 ) Evolutionary conservation of a core root microbiome across plant phyla along a tropical soil chronosequence . Nat Commun 8 : 215 . https://doi.org/10.1038/s41467-017-00262-8

Zheng Y , Lin X ( 2020 ) Niche specialization and functional overlap of bamboo leaf and root microbiota . Front Microbiol 11 : 571159 . https://doi.org/10.3389/fmicb.2020.571159

Zhou C , Guo J , Zhu L , et al. . ( 2016 ) Paenibacillus polymyxa BFKC01 enhances plant iron absorption via improved root systems and activated iron acquisition mechanisms . Plant Physiol Biochem 105 : 162 – 173 . https://doi.org/10.1016/j.plaphy.2016.04.025

Zhou J , Ning D ( 2017 ) Stochastic community assembly: does it matter in microbial ecology ? Microbiol Mol Biol Rev 81 : 10 – 1128 . https://doi.org/10.1128/mmbr.00002-00017

Zhu T , Yao J , Liu H , et al. . ( 2021 ) Cross-phytogroup assessment of foliar epiphytic mycobiomes . Environ Microbiol 23 : 6210 – 6222 . https://doi.org/10.1111/1462-2920.15703

Zhu YG , Xiong C , Wei Z , et al. . ( 2022 ) Impacts of global change on the phyllosphere microbiome . New Phytol 234 : 1977 – 1986 . https://doi.org/10.1111/nph.17928

Supplementary data

Month:	Total Views:
June 2024	59
July 2024	24
August 2024	19

Email alerts

Citing articles via.

Recommend to your Library

Affiliations

Online ISSN 1752-993X
About Oxford Academic
Publish journals with us
University press partners
What we publish
New features
Open access
Institutional account management
Rights and permissions
Get help with access
Accessibility
Advertising
Media enquiries
Oxford University Press
Oxford Languages
University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

Cookie settings
Cookie policy
Privacy policy
Legal notice

This Feature Is Available To Subscribers Only

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Publications
Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

Advanced Search
Journal List
Curr Genomics
v.21(5); 2020 Aug

Deciphering the Omics of Plant-Microbe Interaction: Perspectives and New Insights

Minaxi sharma, surya sudheer, zeba usmani, pratishtha gupta, introduction.

Plants do not grow in isolation, rather they are hosts to a variety of microbes in their natural environments. While, few thrive in the plants for their own benefit, others may have a direct impact on plants in a symbiotic manner. Unraveling plant-microbe interactions is a critical component in recognizing the positive and negative impacts of microbes on plants. Also, by affecting the environment around plants, microbes may indirectly influence plants. The progress in sequencing technologies in the genomics era and several omics tools has accelerated in biological science. Studying the complex nature of plant-microbe interactions can offer several strategies to increase the productivity of plants in an environmentally friendly manner by providing better insights. This review brings forward the recent works performed in building omics strategies that decipher the interactions between plant-microbiome. At the same time, it further explores other associated mutually beneficial aspects of plant-microbe interactions such as plant growth promotion, nitrogen fixation, stress suppressions in crops and bioremediation; as well as provides better insights on metabolic interactions between microbes and plants through omics approaches. It also aims to explore advances in the study of Arabidopsis as an important avenue to serve as a baseline tool to create models that help in scrutinizing various factors that contribute to the elaborate relationship between plants and microbes. Causal relationships between plants and microbes can be established through systematic gnotobiotic experimental studies to test hypotheses on biologically derived interactions.

This review will cover recent advances in the study of plant-microbe interactions keeping in view the advantages of these interactions in improving nutrient uptake and plant health.

1. Introduction

Microbial interactions play an important role in sustaining several ecosystems. Description and knowledge about these interactions between microorganisms and other biotic factors is an important step towards understanding the association and functions of different microbial communities. Plant-microbe interaction is of utmost importance in sustaining the balance in an ecosystem compared to the other microbial interactions. Several inorganic and organic compounds are produced by plants, which leads to the development of a nutrient-enriched environment that is beneficial for the profound colonization of a variety of microbes. Plant related microbial communities depend on the microenvironment provided by crops. Classification of plants can be done based on microenvironments like carposphere, spermosphere, phyllosphere and endorhiza. Microenvironments can be preserved by several environmental parameters for specified organisms. The survival mechanism of microbial populations determines their impact on the plant system [ 1 ]. This datum was discovered by scientists through dormant pathogenic bacteria that usually get colonized despite their capacity to be a good incubator for seed microbiome. However, plants interact with different microbes existing in water or wind and can colonize the phyllosphere [ 2 , 3 ]. Microbial communities can have a positive or negative impact on the physiology of plants indirectly or directly through several interactions like amensalism, commensalism, mutualism and pathogenesis. Based on their impact, microbes may be classified as endophytic or epiphytic. Almost all plants on earth house endophytes, which is one instance of plant-microbe interactions. In this scenario, bacteria live in a non-competitive environment of the host plant tissue without causing any damage to the host cells [ 4 ]. The microflora of endophytes including bacteria and fungi are present beyond surface sterilization of several plant parts like shoot, root, seed or nodules. These endophytes may originate from the seed, nodules, root or shoot, rhizosphere or aerial parts of the plant [ 5 , 6 ]. Rhizospheric region is a good source of root endophytes [ 5 - 7 ]. Endophytic microorganisms take entry into the plant tissue by the degradation of cellulose or local fractures in the root system. The endophytes colonize the intracellular spaces and thus get isolated from all the other compartments. Endophytic bacteria may be gram-negative or gram-positive and can be isolated from various tissues of different types of plant species. Several facultative endophytes are found in Arabidopsis , maize, wheat, sorghum, potato and cotton. Various species of microbes have been isolated from each plant. Very few studies have been performed that elucidate the interactions between plants and microbes through computational approaches, molecular variations, avirulence protein (Avr) and virulent gene interactions [ 7 - 9 ]. A number of laboratory-scale studies including pot experiments and growth assays have been performed to study the different interactions between plants and microbes [ 10 - 12 ]. Genomics and Bioinformatics are efficient tools to study and predict the interactions between microbes and plants for experimental validation [ 13 - 15 ]. The predictions will be based on various kinds of informational data, involving measurement of the abundance of species from high throughput sequencing or re-constructed metabolic models for several species’ communities. More reports associated with genome engineering, gene editing, and advanced technologies related to plant-microbe interactions have been discussed [ 14 , 15 ]. The present review gives insights into the types of plant-microbe interactions in the environment and envisages the concept of plant-microbe interaction with respect to genomics, metatranscriptomics and metabolomics.

1.1. Plant Microbiome

Microbiome refers to the microbial community related to a variety of unique environments [ 16 , 17 ]. Parallel sequencing emerged as a key enabler to study plant-microbe interactions as it enabled and promoted the research on microbial communities associated with plants. There has been a strong interest to try describing the make-up of microbial communities residing in the plants. Identifying and understanding the plant microbiomes has provided significant value to the advancement of agricultural practices and the creation of new biotechnological techniques for therapeutic and diagnostic purposes. In agricultural sectors, microbes are employed for improving plant health [ 18 ]. The plants are holobionts comprising of associated microbes and host. Although research related to microbes impacting plant health is not new, there has been a surge of research into this area to identify new pathways of impact and symbiotic benefits. The development in these technologies has enhanced the interest in plant microbiota as shown by various reviews [ 6 , 14 , 19 - 21 ]. Amplicon-depended profiling approaches of the community give a better understanding of the structure of the community and phylogenetic variations in plant-related microbes and are appropriate for examining key biological and environmental conditions that shape its configuration [ 19 ]. But, detailed information regarding plant microbiome remains to be explored. Further advancements in computational and sequencing techniques will drive functional research on plant-microbiome interactions.

Plant microbiome exists in three diverse habitats when considering plants as the host of the microbial population. These include phyllosphere - above the ground stem and leaf surfaces, endosphere - tissues present within the plant and rhizosphere - root surfaces and marginal growth layer around the roots (Fig. 1 1 ). The noticeably different atmosphere generated by the plant anatomy presents a unique microbial constitution within each area [ 22 ]. Interactions between plant and host microbiota cover a whole spectrum, which extends from mutualistic to parasitic and commensalism relationships (Fig. 1 1 ). Different studies conducted on the microbes from the leaf or root endosphere, phyllosphere and rhizosphere have revealed several traits of microbes from the host point of view [ 11 , 12 , 23 - 25 ]. Interactions between plant and microbes can be pathogenic (disease-causing) or beneficial such as nitrogen fixation, plant growth promotion, bioremediation or stress combating in plants (Table 1 1 ) [ 26 - 33 ].

An external file that holds a picture, illustration, etc.
Object name is CG-21-343_F1.jpg

Illustration depicting various kinds of interactions between plant-pathogen and plant and beneficial organisms in the environment. The figure depicts interactions between plants, mycorrhizal fungi and bacteria. There are different types of interactions between plants and microbes ranging from beneficial interactions (nitrogen fixation, allocation of nutrients and plant defense) to plant-pathogen interactions (inducing bacterial, fungal and viral infections in plants). ( A higher resolution / colour version of this figure is available in the electronic copy of the article ).

Different types of interaction between plant and microbes.


i.	Plant growth-promoting microorganisms (PGPMs)	Microbes which benefit plants majorly through improved acquisition of nutrients by nitrogen fixation [ , ], solubilizing inorganic phosphate [ , ], organic phosphorous mineralization and production of siderophores for the uptake of iron [ ]. Plant growth-promoting bacteria (PGPM) can enhance the accessibility of water and minerals by synthesizing growth regulators such as gibberellins [ ] and auxin.
ii.	Plant disease suppression by rhizobacteria	Rhizobacteria can prevent plant diseases by producing incompatible compounds against phytopathogens (antibiotics competition, siderophores production) through priming. Priming helps in enhancing the defense system of the plants and inducing the resistance against pathogens. Various bacterial traits were discovered as triggers of induced systemic resistance [ ] like cell envelope components, flagellae, phenolic compounds, antibiotics and quorum sensing.
iii.	Mycorrhizae	Arbuscular mycorrhizal fungi have the ability to spread the plant root systems and enhance the accessibility of the roots to nutrients with low mobility in plants. The roots of the plant are interconnected by hyphal networks of mycorrhizae. This network assists in resource exchange and also supplies about 90% of nitrogen [ ].
iv.	Rhizobia	Nitrogen entry into soil can be identified by interactions between legume and rhizobium. Plants metabolize the ammonium that is produced by the reduction of nitrogenase from atmospheric dinitrogen by nitrogen-fixing bacteria.
v.	Plant-Pathogen Interaction	(a) Biotrophic Pathogens: This type of interaction requires living plant tissue. Fungal haustoria are invaginations in the cell membrane of the living host for the extraction of nutrients.
		(b) Necrotrophic Pathogens: Production of cellulolytic enzymes or toxins causes necrosis in the infected tissues taking up nutrients from the dead spots.
		(c) Hemibiotrophic pathogens: These pathogens include both the biotrophic and necrotrophic pathogens. An early biotrophic infection phase is followed by the necrotrophic spreading phase.

2. Plant-Microbe Beneficial Interaction

Plants exist with a complex network of interactions with microbes where some are beneficial while others are detrimental. Various sorts of plant-microbe interactions are present such as mutualism, commensalism and parasitism [ 34 , 35 ]. Interaction between plants and various microbes such as bacteria and fungi is known to be beneficial for both and thus draws the attention of various researchers due to its potential application in agriculture. Several studies have been carried out related to interactions between plants and various other organisms such as arbuscular mycorrhizal fungi (AMF), diazotrophs, bacilli , Pseudomonas , phosphate solubilizing fungi, cellulose-degrading bacteria and plant growth-promoting rhizobacteria (PGPR) [ 11 , 12 , 36 ]. Advantages delivered by microbes vary depending on the plant species. In general, microbes support the growth of plants by fixing nitrogen in legumes thus enhancing the supply of nutrients such as iron, copper, sulphur, phosphorous. They also promote the production of plant hormones for checking bacterial and fungal diseases and assisting in bioremediation of polluted soil. Novel techniques for the protection of crops are dependent on the utilization of advantageous organisms, applied as biocontrol agents and biofertilizers [ 6 , 18 ]. This is an important technique to control plant diseases and might result in a substantial decrease in the use of chemical fertilisers that contribute to environmental pollution. Microbial inoculants have been widely used in modern agriculture as biocontrol agents and as biofertilizers. Linking plant phenotype to the expression of gene and proteins and for the production of metabolites and accrual is one of the major impediments for enhancing the global agricultural production.

2.1. Nitrogen Fixation

A good example of an interaction between plant and microbes is nitrogen fixation [ 37 - 39 ]. This process is the natural form of fertilization and it promotes the research in the field of sustainable agriculture [ 39 - 41 ]. Transcriptome analysis of Azoarcus sp. strain has been carried out, which is an obligate nitrogen-fixing endophyte nitrogenase (nif) gene inside the roots of rice. Proteomics and metabolomics are ideal methods to observe the symbiotic interaction between the root nodules and the nitrogen-fixing bacteria. These studies provide a wide spectrum of metabolites/ proteins released by the symbionts. During the initial phase, both symbiotic partners release signals into the soil and several secondary

metabolites such as isoprenoids, phenolic acids, alkaloids and flavonoids that are sensed by microbial receptors and roots leading to consequent morphological and physiological changes. The best example of a symbiotic relationship is nitrogen fixation which results in effective communication between both symbionts. Flavonoids serve as primary signals and are released by the host plants that act as chemoattractants [ 42 ], activating the expression of rhizobia node genes. The function of flavonoids in the nodulation process has been studied by several researchers [ 42 - 44 ]. Van Noorden et al. [ 45 ] observed the interaction of Sinorhizobium meliloti with 131 identified proteins in Medicago truncatula . They further reported the auxin treatment performed by various redox-associated proteins and late embryogenesis proteins during the nodule formation and isoflavone reductase. Lery et al. [ 46 ] studied the proteomic profile of interaction between Gluconacetobacter diazotrophicus and sugarcane using mass spectrometry. Enhanced nitrogen fixation can be attributed to the overexpression of signal proteins by SP70-1143 genotype of sugarcane. The presence of G. diazotrophicus along with glutamate ammonia lyase in SP70-1143 plant roots demonstrated the efficiency of nitrogen metabolism and induction of bacterial proteins. The symbiosis between plant and bacteria during nodule formation was brought forward by Salavati et al. [ 47 ]. They used MALDI/mass spectrometric imaging (MSI) to study the distribution of root nodules in Medicago trunculata and its role in nitrogen fixation when associated with Sinorhizobium meliloti . Variations in metabolite profiling between nodules and the root along with differences in nitrogen-fixing and non-fixing nodules were also reported. Moreover, metabolite profile of M. truncatula extracts identified in the first hour of the metabolite suppression exhibited resemblance to oxylipin in planta nod factor treatment. Jasmonic acid and oxylipin metabolite inhibited the signaling of nod factor [ 48 ]. In Bradyrhizobium japonicum , 2610 metabolites present in the nitrogen-fixing bacterium were inoculated inside soya bean root hairs and were further studied using GC-MS.

2.2. Growth Promotion

The growth of rice is promoted by Pseudomonas fluorescens . MS-analysis revealed that P. fluorescens strain KH-1 induces proteins such as P 23 co-chaperone, ribulose bisphosphate carboxylase, thioredoxin H, and glutathione S-transferase [ 49 ]. Gel-dependent proteomic approach revealed that inoculation with Sinorhizonium meliloti resulted in a higher level of photosynthetic proteins [ 50 ]. Transcriptomic analysis of the interaction between Petunia hybrida and arbuscular mycorrhiza showed a novel function of phosphate solubilization in the host’s repressed essential symbiotic genes. This result brings forth the beneficial impact of symbiosis on plant growth and crop yield through the modification of phosphorus concentrations. Secondary metabolites protect against environmental stressors while enhancing plant development and growth. In M. truncatula , analysis of the metabolites in mycorrhizal and non-mycorrhizal roots depicted a rise in the quantity of certain amino acids (Glutamine, Aspartate and Arginine), isoflavonoids, fatty acids (oleic acids, palmitic acid) and accretion of cell wall-bound tyrosol and apocarotenoids, particularly in the roots. Schliemann et al. [ 51 ] observed a contrast between the normal and symbiosis dependent secondary metabolism in M. trunculata . Inoculation by mycorrhiza in Anadenanthera colubrina seedlings enhanced the concentrations of metabolites such as phenols, flavonoids, and total tannins [ 52 ]. The roots of Lotus japonicus roots showed synergetic association to Mesorhizobium loti , and inoculation of secondary metabolites in its roots resulted in fourteen different phenolic acids in comparison with non-inoculated plants [ 53 ]. Popovici et al. [ 54 ] reported the changes in root phenolics when Actinobacterium frankia interacts with the Myriacaceae plant species as observed through HPLC analysis. These studies bring forward strain dependant changes in the secondary metabolism of host plants.

2.3. Bioremediation

Bioremediation using microbes is an important topic in agriculture as it helps reduce the impact on the environment. Trace metal pollution poses a foremost risk to agriculture, animal and human health. Phytoremediation of heavy metals from polluted soil can be attained using plant growth-promoting bacteria [ 12 , 55 , 56 ]. Cheng et al. [ 57 ] observed the behaviour of bacteria Pseudomonas putida UW4 towards nickel contamination and reported various mechanisms involving stresses due to the adaptation and efflux of heavy metals proteins. 130 leaf proteins were expressed in the interaction between arsenic-treated AMF such as Gigaspora margarita and Glomus mosseae and Pteris vittata . Bona et al. [ 58 ] observed and identified the major functions of glycolytic enzymes and arsenic (As) transporter ( e.g . PgPOR29) during As metabolism. Aloui et al. [ 59 ] performed 2-DE/MALDI-TOF-dependent proteomic analysis which revealed that mitigation of cadmium toxicity through M. trunculata shoot on exposure to mycorrhiza was supported by a rise in the level of chaperones and photosynthetic proteins. Considerable reduction in concentrations of Zn and Cu in mycorrhizal-inoculated poplar plants was observed by Lingua et al. [ 60 ] supporting the phytoremediation characteristics of mycorrhiza. They used mass spectroscopy to find that the growth of mycorrhizal plants on metal polluted soils led to the upregulation of a small Hsp, Hsp70, large subunit of RuBisCO and downregulation of 43 spots mostly associated with oxidative stress and carbohydrate metabolism. Farinati et al. [ 61 ] observed a decrease in concentrations of Cd and Zn and modification of shoot proteome of the Arabidopsis helleri plants when cultivated in metal-polluted areas along with the bacterial strains (isolated from the rhizospheric region of polluted soil). These results show that strain screening for various functions can help develop effective usage of biocontrol mediators.

2.4. Stress Suppression in Crops

There are several stress influences such as drought, salinity, deficit of nutrients, pests, diseases and contamination which can bring a change in the interactions of plants and microbes in the rhizosphere. These stress factors trigger the signaling molecules in which phytohormones have a major function. Signal processing is used to trace the signal input that allows plants to retort towards environmental restraints. Plants are subjected to various stresses concurrently, thus a meta-analysis reveals a composite regulation of plant immunity and growth. The way phytohormones interact with the signaling network is essential to understand interactions between plant-microbiome systems during stressed conditions. This interpretation is essential to develop biotechnological approaches for optimizing plant acclimatisation techniques and to enhance the microbial activity of soil for assuagement in crops [ 62 ]. Gomes et al. [ 63 ] observed a promotion in the growth of the host due to elevated photosynthetic proteins, transport proteins and chaperones such as GroEL and DnaK during temperature stress, drought and metal toxicity. Recent researches have shown that changes in plant morphology, physiology, transporter action, exudation profiles of roots and vicissitudes might trigger the plant to employ microbes with stress-reducing abilities and potentially enhancing the crop under stress conditions. The stress factors lead to negative effects on the productivity and functioning of agricultural systems and rhizospheric microbes are important in leading plants to sustain in hostile conditions [ 64 ].

3. Molecular Techniques for Decoding Plant-microbiome interactions

In the past, the absence of proper methods led to limited knowledge on mechanisms behind plant-microbe interactions in the rhizosphere. There were problems associated with the profiling of a wide array of methods in which a large variety of microbial communities involved were unculturable [ 65 ]. An abundance of culture-neutral molecular techniques have been popularized over time and are presently being used for deciphering the diversity of microbes and rhizospheric microenvironments and to gather more knowledge related to the molecular basis of the plant-microbe associations (Fig. 2 2 ). These modern molecular techniques are important for evaluating the effects of perturbations activated by the biotic and abiotic stress on the diversity of soil microbiome and plant-microbial interfaces in the current worldwide change.

An external file that holds a picture, illustration, etc.
Object name is CG-21-343_F2.jpg

Omics approaches and strategies for studying Plant-Microbe Interactions. ( A higher resolution / colour version of this figure is available in the electronic copy of the article ).

A major advancement related to the molecular ecology technique for analysis of microbial diversity of soil is based on the isolation of microbes. The entire microbiome of soil can be analysed directly for the extraction of DNA/RNA and the biochemical markers. DNA exemplifies the phylogenetic identity and the functional ability of microbes. Hirsch and Mauchline [ 66 ] reported about the extraction of phospholipid fatty acids from the cell membrane and stated them to be biochemical markers acting as indicators of the microbial community structure of the soil. Culture-based studies are generally utilized to determine the genetic and functional variations in the microbial communities of the soil or the rhizosphere and were undertaken by a number of researchers [ 67 , 68 ].

Molecular approach first involves the process of DNA and RNA extraction from the soil. Labeling of nucleic acids can be performed before their abstraction from environmental samples for scrutiny. The collective microbial genome constituting isolation of DNA from microbial communities is referred to as a metagenome. Metagenomics involves isolation and cloning of large fragments of DNA that comprises of various operons and genes. The total DNA extraction from the environmental samples can be analysed by various methods depending on the cloning procedures, amplification of PCR, high-throughput sequencing and microarray hybridization. Closing dependent techniques result in building of metagenomic libraries which can be used for screening of functional and structural genes or for observing phenotypic characters associated with proteins such as enzymes and profiling of secondary metabolites. Bioinformatics software is mostly used in soil metagenomic studies. Microbial diversity can be quantitatively determined using the PCR technique. Small ribosomal subunit sequences act as molecular markers of the target microbial groups. Predominantly, a comparative gene examination of 18S or 16S ribosomal rRNA is the most common molecular technique for recognising microbes. Several microbial genomes have rRNA gene as a general gene marker.

Well protected segments of rRNA sequence in the gene can be utilized to build general primers for amplifying the gene obtained from DNA extraction of environmental samples. Analysis of 18S and 16S rRNA gene sequences is the foundation for comparing richness, evenness, composition and assembly of the microbial groups. PCR products share comparable or indistinguishable variable regions and are known as operational taxonomic units (OTUs). Diversity of the PCR products is defined by distinguished molecular processes that allow a molecular impression for determining the make-up of a specific microbial group. Diversity of the amplicon is determined by sequencing and cloning methods. Sequence evaluation of 16S or 18S rDNA amplicons can be done using high throughput next-generation sequencing. The 3rd generation sequencing technology is dependent on a single DNA molecule. Techniques other than fingerprinting used for monitoring the abundance of specific taxonomic groups in microbial communities are functional gene microarray dependent PhytoChip and Geochip techniques [ 67 ]. The functional variations in the microbial community may be analysed by amplification of certain functional genes important for specific metabolic processes. Strategies depending on the abundance of transcripts were employed during studies of functional diversity and were monitored by the qRT-PCR. The functional gene arrays evaluate the expression of the transcripts from various genes and are used to determine the activity of some functional microbial actions. The shotgun sequencing involves microarrays of DNA constituting environmental cDNA and helps differentiate the response of soil microbiome towards external effects at the level of transcription. High-throughput phenotyping of plants helps to analyse plant functions affected by microbial community activities. However, the effects of plant genotype on the functioning and variability of microbial communities can be assessed by molecular ecology techniques. Thus, the important molecular techniques such as next-generation sequencing, transcriptomics, proteomics, metabolomics to study interactions between plant and microbes are discussed in detail (Fig. 2 2 ).

3.1. Deciphering the Plant Microbiome through Next-generation Sequencing

Recent developments in the plant-microbiota researches validate that plant PGPM is an essential member of the plant microbiota. Though, information related to PGPM is quite sparse with respect to reports associated with separate isolates under laboratory conditions [ 19 ] as well as how microbial communities contribute to the growth of plants. Microbial mechanisms which promote the growth of plants include increase in nutrient requirement from soil, tolerance to abiotic stress, protection from pathogen indirectly and increase in nutrient enhancement from soil. These organisms are referred to as plant growth-promoting microorganisms. It was clearly observed through investigations that PGPM forms an integral part of the plant microbiota. The better knowledge and understanding related to plant-microbe interaction can be established by several approaches discussed in the following sections.

The natural habitat of plants constitutes a variety of microorganisms. A good correlation between the upper and lower layers of the earth constitutes a huge number of microorganisms [ 69 ]. Some microbes grow well under stressed conditions and benefit the plants while natural ecosystems are better managed by mosses and PGPM. Thus, via NGS, plants can be studied with respect to taxome, ecological function and interactome involving transcriptomics, genomics, and metabolomics studies of microbes which will eventually lead to the identification of the mechanism for their survival and interactions [ 22 ].

Many NGS studies have been conducted to decipher the bacterial communities that support plant growth. Endophyte-promoting activity can be concluded by the presence of compounds such as IAA (indole-3-acetic acid), phosphatases, ACC (1-aminocyclopropane1-carboxylate) deaminase and siderophores. Analysis of tissues from various parts of the plants (flowers, leaves, stem and roots) and rhizospheric soil samples for plant growth-promoting bacteria [ 70 - 73 ] concluded that the microbiota composition varies from tissue to tissue [ 74 , 75 ]. In general, these studies have displayed the presence of Proteobacteria in larger amounts in plant-related ecosystems, accompanied by significant amounts of Firmicutes, Bacteroidetes, Actinobacteria and Acidobacteria . But, these methods to identify the bacterial communities have limitations that need to be taken into account: (i) The ease of DNA extraction is dependent on spores and membranes of the bacteria present in the community, thus making community profiling a function of the extracted DNA rather than real quantity of various bacteria present in the microbiome [ 76 ]. (ii) The 16S rRNA sequencing is a good method to identify phyla but not very suitable to classify species or genus [ 77 ]. (iii) Bias could creep in based on the selection of sample, recovery method and treatment [ 78 , 79 ].

3.2. The Meta-omics Approaches

The meta-omics study not only provides information about expressed gene levels via genomics and transcriptomics but also about post-translational changes through proteomics (Fig. 2 2 ; Table 2 2 ) [ 80 - 84 ]. Metabolites are a result of cellular processes which can be obtained by applying the metabolomics method. Thus, there exists a combination of omics methods that can connect all the aspects of variations at the cellular level. These studies provide a complete view of the complex dynamics of cellular systems involved in plants and microbe interactions. For instance, the bacteroid, Bradyrhizobium japonicum found in the soya bean root nodules was examined by accumulating datasets of transcriptomics and proteomics [ 85 ]. Several proteins were discovered depending on the dataset of various kinds of bacterial metabolism occurring during symbiosis. Ali et al. [ 86 ] used a combination of proteomic and transcriptomic methods to analyse the compatible and incompatible associations and interactions of Solanum tuberosum with Phytophthora infestans . Modifications in the range of 1700 transcripts and 1000 expressed proteins were identified through the integration of these techniques.

Molecular biology techniques and their pros and cons for studying plant-microbe interactions.


Genomics and Metagenomics	Assembly: Meta Velvet and Ray Meta, IDBA-UD Profiling: AMPHORA, MetaPhlAn mOTU Function Analysis: IMG/M, MG-RAST And CAMERA	Profiling is unbiased; Uncultured microbial studies can be performed; Correlation can be observed between genes of diverse organisms from similar environment	Less information related to sequencing of the marker genes; Less abundance anticipating functions of the gene are not equivalent to protein content expressed	New species along with their taxonomic profile will be discovered; Potential, metabolic, functional and evolutionary relationships will be derived; Helps in genome reconstruction	[ - ]
Transcriptomics and Metatranscriptomics	Mapping: BWA-SW; Bowtie2 de Novo Assembly: IDBA-MT Function Analysis: CAMERA and MG-RAST	Novel transcripts determination and sensitive methods for detection; Aids in detecting simultaneous expression of the host and microbial gene; Simple to perform; Metatranscriptomics allows capturing of transcriptomes in case of un-cultured bacteria	Low concentration of rRNA presence in the samples; Difficult to assign transcripts to specific organisms; High complexity of the community and host transcriptome leads to low sensitivity; Extensive genome is needed for mapping	Analysis of Pathways and active function study
Proteogenomics	Mascot: Protein Identification; MG-RAST and camera	Proper estimation of functional activities in comparison to transcriptomics; Results in Semi-quantification of the proteins in the environment; More accurate to perform qualitative protein analysis	Reference genes needed for identification of proteins; Difficulty in sample preparation process; Quantity of various proteins cannot be compared; Difficult for performing in plants due to host contamination	Determination of functions and analysis of pathways
Metabolomics	Metabolite study	Metabolites produced due to plant-microbe interactions can be identified; Nominal bias analysis of various compounds	Size of reference public databases is limited; Different metabolites with their functions give similar signals during Mass spectrometry; Similarity between primary metabolites of microbes and plants results in their difficult determination
Marker gene	Amplicon Noise, mothur, QIIME	Classification of new and rare species	Problems during amplification in PCR	Novel species with taxonomic profiling can be discovered

3.2.1. Genomics and Metagenomics

Genomics involves the study of the complete genome of organisms and helps to incorporate elements from genetics. The process utilizes a combination of DNA sequencing techniques, recombinant DNA and bioinformatics approach for sequencing, assembling and analysing the structural and functional characteristics of genomes. Metagenomics helps in real-time characterization of complex DNA mixtures obtained from the habitat of microbes [ 87 ]. Metagenomics establishes the base for various other techniques to investigate RNA such as metaproteomics, metatranscriptomics, and

metabolomics, all delivering new insights on the metabolic capacity of certain microbiota. The phyllosphere microbiota composition along with the expression of microbial traits in the habitat was studied by Delmotte et al. [ 88 ]. They collectively used metaproteogenomics in which metagenomic information aided the annotation of the metaproteomic data. They further researched the phyllosphere of soya bean, Arabidopsis and clover and found that despite 130 million years of divergence among the families of Brassicaceae and Fabaceae , about 70% of the phyllosphere metaproteome was conserved. Their outcome suggested the presence of a sizeable core microbiome along with slight host-specific roles of the microbiome. The metagenome of root endosphere was studied through the bacterial cells attained from surface-sterilization of rice roots, giving information about the metabolic potential of the microbial community residing inside the plant roots [ 89 ]. On coalescing the transcriptional examination of some microbial genes with metagenomics, it was established that bacterial microbiota within the plant roots can perform primary steps of the nitrogen cycle in plants. Knief et al. [ 90 ] compared the traits between the rhizosphere environment of rice plants grown in the paddy field and the microbiota using metaproteogenomics. The presence of di-nitrogenase reductase genes and proteins depicted habitat-specific microbial traits caused by nitrogen fixation in the rhizosphere. However, the technique has some limitations due to the presence of rRNA in the samples.

Rascovan et al. [ 91 ] studied and explored root microbial communities associated with wheat and soybean in field conditions using 16S rRNA pyrosequencing. Fig. ( 3 3 ) gives a comparative overview of the metagenomic and cultured bacteria datasets. They extracted bacterial isolates from rhizospheres, to test the in-vitro presence of PGP traits. They compared the dominant bacterial communities linked with roots from both crops and found that a high proportion of them (60-70%) exhibit >97% resemblance to bacteria from the rhizospheric isolated samples. Pseudomonas and Bacillus genera represented the highest proportion of the full dataset (~20%). Considering only a Blast best hit of ≥ 90% similarity, they observed a total of 100 different genera of bacterial communities. This study brought forward the fact that microbiome composition is a function of its soil environment. The root-associated microbiome in wheat plants had much more variability when compared with soybean. This may be attributed to the architecture of wheat roots which can grow up to five primary roots in order to capture water and nutrients, as opposed to soybean plant which has a simpler root structure.

An external file that holds a picture, illustration, etc.
Object name is CG-21-343_F3.jpg

Metagenomics study to identify microbial community associated with wheat and soybean plant roots. Readings from root-associated (RA) pyrotag were compared with Sanger 16 rRNA sequences from rhizospheric isolates using BlastN. Adopted and Modified from Rascovan et al. [ 91 ]. Open Access under a Creative Commons Attribution 4.0 International License. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4911569/pdf/srep28084.pdf . ( A higher resolution / colour version of this figure is available in the electronic copy of the article ).

Thus, plant microbiome deciphering needs large scale sequencing coverage along with a replication of experiments for robust analysis, and substantial computational and financial support [ 92 , 93 ]. Moreover, the interpretation of data on RNA, microbial DNA, and protein sequence gets hampered due to reduced rates in the high-confidence annotation. Thomas et al. [ 74 ] reported that only a few sequences can be retrieved from a non-human environment and about 50% can be annotated functionally with available data. Despite these limitations, it is important to underline that meta investigations deliver initial inferences and supplementary experiments are required to disclose if the practical diversification of plant microbiota is a reason or an outcome of studied plant phenotype [ 22 ].

3.2.2. Transcriptomics to Analyse Plant-microbe Interactions

Various studies related to patterns of gene expression of microbial symbionts have been performed [ 94 - 96 ] with respect to a single partner and only some have discussed the method of transcriptional profiling [ 97 ]. It has been hypothesized that beneficial fungi exhibit less impact on expression profiles of host gene in the absence of pathogens ( Pseudomonas syringae or Blumeria graminis sp.) [ 98 ] in Piriformospora indica -barley-powdery mildew. Breuillin et al. [ 99 ] researched the transcriptional profiling of AMF in Petunia hybrida and observed a novel function for phosphate requiring symbiotic genes in the host. The study was important in terms of phosphorous management under field set-up to enhance yield and plant growth by leveraging both phosphorous supply and useful effects of symbiotic interaction. Plants can interact with saprophytic fungi which breaks down the organic material and releases minerals that can be taken up by plants [ 100 ]. Fungi further help in organic acids production and extrusion of proton causing a drop in pH of the soil. This enables the phosphate precipitation into soil solution making it available for root uptake [ 101 ]. Also, there are many studies related to transcriptional profiling of beneficial microorganisms like Pseudomonas on plants under lab conditions [ 102 , 103 ]. Substantial variations were observed in the transcript levels of the Arabidopsis shoots when inoculated with pathogen Pseudomonas syringae in the leaves and Pseudomonas fluorescens WCS417r-mediated ISR inoculated in the roots [ 104 ]. Studies have reported that variations in the Arabidopsis transcriptome occur as a result of different traits [FPT9601-T5; MLG45] of Pseudomonas fluorescens. Moreover, more studies associated with multiple interactions between plants and microbes are being promoted due to the advancement of these techniques.

3.2.2.1. Transcriptome Analysis of Plant-pathogen Interactions

Microarrays have been used to study the plant-microbe associations and interactions using transcriptome analysis [ 105 , 106 ]. Xu et al. [ 107 ] performed a study using RNA-sequencing to observe plants' ability to produce genes mediating defense reactions against Verticillium dahlia, a root blighting pathogen. They uncovered a network of signal transduction pathways using microarray-based plant transcriptomics, which gets triggered due to elicitors constituting pathogen-associated molecular patterns (PAMPs) and signaling compounds such as SA, ABA, JA, and ET [ 108 , 109 ]. These studies have resulted in the discovery of key regulatory and resistance genes [ 110 ].

Plants have an efficient defense response towards microbes. A multilayer defense response triggers in the microbes and plants on the entry of a microbe into a plant. This response relies on the reserves required to sustain the defense. The induced responses need fewer resources compared to the constitutive response. Mostly the environmental surroundings stimulate plants to deliver the constitutive response. The major consequence of plant-microbe interaction is guided by genotypes of host and microbes and environmental circumstances. Interaction of plants with pathogenic microbes such as bacteria, viruses and fungi result in infectious ailments impacting only the plant kingdom. The benefits related to such interactions aid in understanding a natural phenomenon which affects daily life and could result in their application in sustainable resources, environmental clean-up, the effect on atmospheric gases, and reducing environmental impact. Plant defense mechanisms comprise a mixture of inducible and constitutive responses. Constitutive responses comprise biochemical defenses and barriers, whereas inducible responses are local in nature and advance with systematic action mode initiating from recognizing the pathogen to the defense expression of genes. The prevailing biochemical defenses include pathogen identification by the host plant, signal transduction and gene expression. Plant tissues act against the pathogen by programmed cell death in a localised response. However, in systemic defense, the signal spreads from the interaction site, facilitated by various molecules that work as plant messengers (for example, ethylene and nitric oxide). The messenger molecules are important in increased expression of profiling information for different stresses under in vitro conditions. Various plant-pathogen interactions studied through the omics approach are presented in Table 3 3 .

Study of plant-pathogen interactions by omics techniques.



		[ ]
		[ ]
		[ ]
		[ ]
		[ ]
		[ ]
		[ ]
		[ ]
		[ ]
		[ ]
		[ ]
		[ ]
	Rose Plant	[ ]
	Rose Plant	[ ]
	Zea mays	[ ]

		[ ]
		[ ]
		[ ]
		[ ]
		[ ]
	Apple rootstocks	[ ]
		[ ]
		[ ]
		[ ]
	Tomato Plants	[ ]
	Tomato Plants	[ ]

		[ ]
		[ ]
		[ ]
pv. tomato		[ ]
		[ ]
		[ ]
		[ ]
	Grapevine	[ ]
		[ ]
		[ ]
	Rice	[ ]
	Soybean Hypocotyls	[ ]

Kawahara et al. [ 147 ] reported on the transcriptome investigation of blast fungus and rice in infected plant tissue. 240 transcripts of secreted proteins, encoding fungi such as glycosyl hydrolases, LysM domain-constituting protein and cutinases were identified which behave as effector genes in instigating the early infection processes. The pathogenesis associated proteins and phytoalexin biosynthetic genes showed upregulation in rice. Transcriptome characterization of Sclerotina sclerotiorum and pea interaction discovered 142 ESTs with encoded secretory peptides and 93ESTs responsible for virulence, and 277 pea ESTs important for biotic and abiotic stresses [ 94 ]. De Cremer et al. [ 116 ] observed the downregulation and upregulation of terpenoid and phenylpropanoid pathway genes and photosynthetic genes on incubation of B. cinerea with lettuce for 48 h. Liao et al. [ 114 ] studied the involvement of genes in energy metabolism and in the redox reaction transcripts encrypting glutathione S-transferase (GST) which were considerably amassed in Arabidopsis on interacting with Botryosphaeria dothidea . Aragona et al. [ 148 ] performed functional characterization of RNA sequence data for analysing effectors and virulent mechanisms of the pathogen. Zhao et al. [ 121 ] identified prime regulatory genes associated with resistance response towards Cladosporium fulvum in the tomato plants having Cf-19. Transcriptomics was used to analyse variations between the resistant response of plants constituting Cf-19 gene (CGN18423) and the susceptible plants carrying Cf-0 gene at intervals of 0, 7 and 20 days after the inoculation. Neu et al. [ 122 ] observed the interactions between roses and biotrophic and hemibiotrophic leaf pathogen. They observed the transcriptomic changes using next-generation sequencing supported by an extensive cDNA analysis which was further validated by high-throughput qPCR.

Transcriptomics has played a key role in interpreting the concept of fungi related diseases in plants. The studies conducted by Hane et al. [ 149 ] helped in identifying genes among the pathogens (such as AG8, AG3 and AG1-1A) with functions that are unique to R. solani . Transcriptomic analysis of plant-related bacteria with the help of gene expression microarray approach using RNA sequencing technology reveals that genes are expressed differently under some conditions. Transcriptomics studies related to plant and bacteria studies were performed on separate bacteria cultures from the host plant. RNA sequencing is used to detect genes responding to the plant extract [ 150 ]. However, the plant transcriptomes considerably outnumber the bacterial transcriptomes that are basically house-keeping ribosomal RNAs. Thus, attaining proper strength of bacterial mRNA transcripts for the sequencing and differential gene expression is quite problematic. Various bacterial isolates transcriptomics studies denoting bacterial gene and plant gene expression were performed by Roux et al. [ 151 ], Pankievicz et al. [ 152 ] and Paungfoo-Lonhienne et al. [ 153 ]. Two closely related methods for enriching the P. syringae transcriptome in Arabidopsis leaf infection model were employed by Nobori et al. [ 154 ]. They used a novel isolation buffer which facilitates the stabilization of the bacterial RNA during leaf grinding.

The technique involved filtration and centrifugation to separate the bacterial cells from the plant cells before RNA isolation. Other methods involved depleting the plant-derived transcripts by customized probes. RNA sequencing technique helps in detecting transcriptome regulations like antisense RNA, gene operons, riboswitches, small non-coding RNA, and antisense RNA [ 155 , 156 ].

Metatranscriptomics involves sequencing of whole bacterial community from the environmental samples. It results in an understanding of the transcriptional state of several microbes. In Arabidopsis, metatranscriptomics was used for identifying the bacterial genes in the rhizosphere which gets expressed differently during the development [ 157 , 158 ]. Metatranscriptomics was performed on willow rhizosphere to discover the influence of microbial communities in the field of phytoremediation, presenting the earliest examples of such a discovery in plant science [ 159 ]. The main advantage of using this approach was to recognize the possibility of microbial traits occurring in a plant microbiome without the cultivation of their members. However, there are few limitations related to this approach such as the complexity of plant microbiome and inherent traits [ 19 ] due to the contamination of plant-associated microbiota specimens with DNA, RNA or proteins of the host. Around 90% of plant sequences were attained from DNA preparations of root samples from Arabidopsis [ 19 ].

With the cost of sequencing declining, the use of transcriptomics and metatranscriptomics has increased to gather knowledge about gene expression of bacteria. Transcriptomic investigation aids in the changing aspects and the regulation of actively transcribed genes for detection, thus posing an advantage over genomic analysis. Metatranscriptomics though has some limitations such as transcripts can seldom be allocated to specific microbes constituting good quality reference genomes. Hybridization based nano string technology is an alternative to sequencing-based transcriptomic approaches which allows improvement in bacterial transcript detection in mixed plant microbiota samples of a transcript. Thus, improvement in the methods for enrichment and bacterial transcripts detection is relevant to a wide range of plant-bacteria systems, and in future these techniques will enhance the knowledge of plant-related bacterial functions.

3.2.3. Proteomics

Proteomics approaches are mostly related to liquid chromatography and tandem mass spectrometry techniques and are used to study plant-pathogen interactions. Proteomics plays an important role in protein identification and their changes upon infection. It reveals a variety of bacterial proteins in the environment using semi-quantitative methodology. These methods include analysis of collected samples, protein isolation, extraction and fractionation through mass spectroscopy and further assessment against a proteome database. Proteomics determines the constituents of functional protein produced by the cell rather than identifying the possibility to make them. It also provides information on the exact number of active pathways in the sample. Some studies related to proteomics have brought forward plant-pathogen interaction. Rph15 gene is an important strain in the development of resistance breeding. It is resistant to about 350 isolates of Puccinia hordei , a pathogen causing leaf rust foliar disease in barley. Bernardo et al. [ 125 ] used LC-MS/MS analysis to profile the protein and observe the resistant and susceptible isogenic lines and study the Rph 15-based defense response. Various pathogen-associated proteins were discovered in Rph 15 resistant line at 4 dpi which are associated with carbohydrates metabolism, protein degradation, defense mechanism and photosynthesis.

Margaria et al. [ 126 ] performed a proteomic study through 2D gel analysis of Flavescence doree (a grapevine disease occurring due to phytoplasma) infecting the grapevine. This study identified 48 proteins that were expressed differently. Isocitrate dehydrogenase and glutathione S-transferase proteins hold an important antioxidant function in infected plants. The response of plants towards pathogenic fungus was studied by several authors [ 160 , 161 ]. Kundu et al. [ 127 ] observed the proteomic profiling of Vigna mungo , when mung bean interacted with the yellow mosaic virus. They detected the expression of 109 different proteins. They observed that electron transports of the photosystem II were main targets during the pathogenesis process and during the downregulation of photosynthetic proteins in some genotypes. Infection of tomato by Pseudomonas syringae results in bacterial speck disease and was observed by iTRAQ proteomic methods which further identified 2,369 proteins in the leaves of tomato, among which 477 proteins are Pst responsive.

Parker et al. [ 124 ] observed the major upregulated proteins such as glutathione S-transferase, thioredoxin and superoxide dismutase. Several literature reviews based on the proteomic applications to study interactions between plant and pathogens were studied by Delaunois et al. [ 162 ], Ashwin et al. [ 163 ] and Wright [ 164 ]. Li et al. [ 165 ] observed 38 proteins expressed differently in banana infected with Fusarium oxysporum (Cubense tropical race 4, Foc4). They found that antifungal protein production, PR proteins, cell-wall strengthening proteins were basically concerned with resistant genotypes. Proteins which are involved in scavenging of ROS, PCD and photosynthesis were highly impacted. Broad-spectrum analysis and screening of proteins in various strains and genotypes would result in the selection of resistant plants and beneficial strains for future utilization in agricultural sectors. Li et al. [ 131 ] performed proteomic studies to compare the plant-pathogen interactions between susceptible and the resistant ecotypes of poplar infected by Botryosphaeria dothidea . They identified the resistant proteins of B. dothidea by observing the molecular mechanisms of poplar and pathogen interactions. The susceptible and resistant ecotypes of poplar towards B. dothidea were studied by nanoflow liquid chromatography and tandem MS using label-free quantitative method. They identified 588 proteins categorized into 21 biological processes involving 80 metabolic enzymes, 72 types of hydrolytic enzymes and 29 proteins of unknown function. The interactions between Cacao genotypes and pathogen Moniliophthora perniciosa were studied by Santos and his co-workers [ 132 ] using proteomics. They compared the proteomic changes between the two genotypes of cacao by observing resistance and susceptibility towards witches’ broom disease on inoculation of M. perniciosa at intervals of 72 hrs (early stages) and 45 days of biotrophic and necrotrophic stages of the plant-pathogen interaction.

Metaproteomics can be used for measuring the metaproteome of phyllosphere in forest trees [ 166 ] to observe different proteins produced by PGP bacterial strains as a response to root exudates [ 167 ]. It also recognizes the proteins and organisms important for oxidation and nitrogen fixation in paddy fields [ 168 ]. Proteomics might be restricted due to low protein value, less concentration and low sensitivity owing to host proteins and complicated microbes. Metaproteogenomics was used in the Vorholt lab where proteins occurring in the microbial communities were determined from the metagenomes generated by plant microbiota. This technique increased the number of proteins which can be identified by publicly available databases [ 90 ]. Use of proteomics for describing plant-related communities of bacteria has limitations owing to several factors like comparatively low bacterial protein, lower expression levels in complex plant-related samples and subsequent detection limits and the requirement for a complete reference database. More proteomics studies should be carried out concerning plant-bacteria interactions.

3.2.4. Metabolomics

Metabolomics is also a novel technique for studying plant-microbe interactions. Several bacterial genes like nodulation genes produce Nod factors during nodulation of roots which affect the host plant or the metabolism by microbes. The variations in levels of a metabolite can be observed by specific treatments. This technique was used for demonstrating the chemical exudation process from grassroots during development, thus affecting the assembly of the rhizospheric community. This chemical succession by bacterial enrichment in the substrate preferred the consumption of exuded metabolites, primarily the aromatic organic acids [ 169 ]. Negrel et al. [ 142 ] identified the lipid markers of Plasmopara viticola infection in grapevines through metabolomics studies (non-targeted). P. viticola results in downy mildew in grapevines causing a lot of damage. They used MS-based metabolomic non-targeted approach for identifying the potential of specific metabolites of Plasmopara . The infection of Phytophthora sojae caused in soybean hypocotyls was studied by Zhu et al. [ 146 ] using metabolomics. They examined the metabolic variations between two hypocotyl lines of soybean, i.e . susceptible line (S), 06-070583 and resistant line (R), Nannong 10-1at points of 12 hpi and 36 hpi on inoculation of P. sojae and further observed the metabolic variations between S and R line. They observed 90 different accumulated metabolites and variations between S and R lines. Thus, metabolomics has been used to study a lot of plant-pathogen interactions [ 135 - 146 ].

However, there are many limitations related to metabolomic analysis in plant-microbe systems which have not been widely accepted. Moreover, the cost involved, equipment and technical expertise important for performing metabolic studies make it a less viable approach. Also, there are only a few public metabolomic reference databases which make it difficult to associate a given metabolite to a particular organism. Still, metabolomics is an influential tool for detecting and quantifying small molecules and the molecular variations at the interface of plant-microbe interaction.

3.2.5. Interaction Transcriptomics between Plants and Associated Microorganisms

Reports concerning the transcriptional profiling of plants and microbes are still rare thus, more research should be done to study the close association between plants and microbes. Nodule formation in interactions between legume and rhizobium is well documented by gene expression and developmental changes in the roots [ 170 ]. Despite dual genome symbiosis chips development, the analysis of gene expression of the host and symbiotic microbe was mostly concerned with the study of nitrogen-fixing nodules. Brechenmacher et al. [ 171 ] reported that the interactions between the root of the plants and AMF are characterized for expression of the gene in the plant roots, however few genes are involved in the mycorrhiza constituents. Thus, DNA microarrays containing both microbial and host genes have been developed. Analysis of microarrays was carried out by an Affymetrix Gene Chip comprising 38000 genes of soybean and 15800 genes of Phytophthora sojae [ 172 ]. Analysis of transcriptome was further performed between fungal pathogen of the rice blast, Magnaporthe oryzae and rice which resulted in the determination of biotrophy-related effector proteins which might help the plant during hyphal invasion [ 173 ]. Such analysis should be supplemented with a proper bioinformatics approach involving databases generated by plant-related Gene Ontology Consortium [ 174 ]. Few examples include genomes of Pseudomonas syringae , i.e ., Dickeya dadantii 3937, pv tomato DC3000, Magnaporthe oryzae fungus, few oomycete and Agrobacterium tumefaciens .

Zhang et al. [ 175 ] performed a transcriptome analysis of Halomonas sp. strain MC1(an endophyte) to study its plant growth-promoting potential on Mesembryanthemum crystallinum (Ice Plant) and identify the genes involved in providing salt tolerance to the plant. The BLAST analysis of 16S rDNA sequencing of strain MC1 followed by the construction of its phylogenetic tree revealed its close relationship with genus Halomonas sp. (Fig. 4 4 ). They were able to demonstrate that under salt stress, the bacteria strain MC1 itself was able to adapt and resist salt stress, thus enabling Ice Plant to survive under these conditions. This was achieved by MC1’s capability to regulate metabolic and cellular processes as well as regulating carboxylic and organic acid catabolic activities. This indicates a shift from other studies performed [ 176 , 177 ] that highlight the capability of bacteria to induce salt stress resistance in plants.

An external file that holds a picture, illustration, etc.
Object name is CG-21-343_F4.jpg

Genome-wide assessment. ( a ) Circular plot of reads mapped to the Halomonas sp. MC1 genome. ( b ) Phylogenetic tree constructed on the basis of 16S rDNA sequences of neighboring species using the neighbor-joining method. The bars represent 0.02 substitutions per nucleotide position [ 175 ]. Open Access under a Creative Commons Attribution License. No changes or alterations were made in the figure. https://www.mdpi.com/2076-2607/8/1/88/htm . ( A higher resolution / colour version of this figure is available in the electronic copy of the article ).

3.2.6. Multispecies Transcriptomics for Deciphering Plant-Microbe Interactions

A better understanding of the relationship between plant and microbial genes observed through multi-species transcriptomics has helped in developing new approaches towards disease resistance. A multidisciplinary technique to get an inclusive knowledge about the approaches of systems biology is clearly required. Datasets from transcriptomics, genomics, proteomics and metabolomics studies lead to identification and integration of prime biological processes and making predictions through modeling. This method helps in developing an understanding of ectomycorrhizal interactions among the roots of Populus tremuloides (Aspen) and Laccaria bicolor . Larsen and his co-workers [ 178 ]

reported short read next-generation transcriptomic sequencing data obtained from ectomycorrhiza to identify well expressed genes responsible for fixed pathways of metabolism. Ectomycorrhizal metabolome model was developed for predicting different metabolites (glutamate, allantoin and glycine) synthesized from fungus L. bicolor and may be utilized by aspen. Consequently, aspen provides sugars such as glucose and fructose to the fungus. These results suggest the involvement of transcriptomic data from these complex systems [ 178 ]. Thus, this will help in the identification of both RNA molecules and the functions of individual genes and will lead to the proteins and metabolites production during the interactions between plant and microbes. A simplified form of this method can be applied using genome-based models which are applicable for metabolic flux analysis. Here, the microbial communities act as a closely associated superorganism where the genome-based models for the interacting plants integrate compartmentalization levels and differentiate metabolic processes in mitochondria, peroxisomes, cytoplasm, vacuole and chloroplasts. The genome-based models were developed based on the key metabolites of C4 plants, Arabidopsis and 25 species of bacteria [ 179 ]. Quantitative data from metabolite profiling and expression of gene (metabolomics) can be involved in both types of models.

4. Information gaps and future perspectives

Plants constitute an enormous treasure of microbiome, which remains to be explored for the identification of associated bacterial communities. This may be interesting for plants that exist under unique ecosystems or with exceptional lifestyles (carnivores, parasites). Though there has been a huge progress in understanding plant microbiome, there is a need to develop further understanding of the processes that surmount to community formation and function in plants. Metagenomic examination and comparison of plant-related communities result in novel functional and phylogenetic views. Proteomes, transcriptomes and initial metagenomes have been extensively studied [ 90 ]. A fascinating example of plant-microbe interaction is the coexistence of photosynthesis in plant and microbes on the leaves of Tamarix plant [ 180 ]. Benefits of a microbial community can be assessed through functional analysis. Other useful information that can be derived through this analysis unravels induction pathways and activation patterns between strains and ecotypes. Amplicon sequencing of 16SrRNA gene segments reveals important information related to dominant colonizers. The quantities of gene amplicon depend on the primer efficiency, extraction methods and their variation in copy-number.

The study of plant microbiome can be an effective tool to formulate strategies for sustainable agriculture, e.g . biocontrol, developing biofertilizers using microbial inoculants, products for stress protection. The future holds great prospects for plant microbiome in plant biotechnology and breeding. The extension of the plant microbiome as an integrated biomarker should be considered to maximize the benefits of the entire microbiome. Better plant microbiome cataloguing may serve as a key to prevent the outbreak of diseases in plants as well as human pathogens transgression in plants. Recent studies have proven the involvement of the human microbiome in diseases and the association of pathogen outbreak with support from pathogenic species. While human pathogens have been extensively studied, plant pathogens have remained a mystery [ 2 ]. Plant microbiome may also hold the key to improving other microbiomes. Comparing microbiome structures may yield phylogenetic diversity identification among similar/related microbiomes e.g . microbiomes between human and plant habitats. Food not only delivers essential nutrients, but also microorganisms into the human microbiome. Studies have revealed the heavy influence of surrounding vegetation and human inhabitants on the domestic microbiome. These complex natured interactions and connections among microbiomes need further exploration.

The methods involving omics have strengthened our knowledge in studying interactions between plant and microbes in the field of bioremediation, stress tolerance and nitrogen fixation. Intensive research to discover the methods and impacts of plant and pathogen interaction to increase defense response of the host have been observed in several crop systems. Changes detected under abiotic and biotic stress levied on the host correspond to the modification of the reactive oxygen species (ROS) sifting molecular constituents. Early research focused on unraveling nitrogen fixation methodology and functions of flavonoids. Observations are underway to identify the most efficient strains and possible influences at play in the process. Omics has proven effective in characterizing known constituents such as nod factors, inositol monophosphatase, nifH , and fixA . Omics studies have also been carried out to identify unknown proteins that help in fixing nitrogen using genistein (a known isoflavone for nodulation regulation). During interactions between plant and pathogen, ROS-scavenging and PR constituents’ synthesis are primarily improved. The profile of biological constituents and their microbial interactions can be studied using the omics technique. These researches should be formulated because of the close linkage between gene expression in parasitism and symbiosis between microbes and plants. These studies help to identify resistant genes and their respective Avr proteins during the interaction. The analysis of end products of toxins produced by the pathogen helps in the evolution of strategies to increase the productivity of plants. Omics technology helps in analysing the complex cellular mechanism during the interactions of plant and microbes in various ways. Genomics helps in comparing different hosts, microbe and interaction between host and microbe. The evolution of new strains and their interactions with plants can be researched by applying comparative and integrated genomics which can make significant progress in developing sustainable agriculture strategies.

Acknowledgements

Authors would like to thank the Department of Chemistry and Biotechnology, Tallinn University of Technology, for providing research facilities. The authors would like to acknowledge the English editing of the manuscript by Dr. Urvashi Kuhad, Department of English, University of Delhi, New Delhi, India. The authors would also like to acknowledge Alex Prigojine, a native English speaker for revising the language content and grammar of the manuscript.

Consent for Publication

Not applicable.

CONFLICT OF INTEREST

The authors declare no conflict of interest, financial or otherwise.

Information

Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

Active Journals
Find a Journal
Proceedings Series
For Authors
For Reviewers
For Editors
For Librarians
For Publishers
For Societies
For Conference Organizers
Open Access Policy
Institutional Open Access Program
Special Issues Guidelines
Editorial Process
Research and Publication Ethics
Article Processing Charges
Testimonials
Preprints.org
SciProfiles
Encyclopedia

Article Menu

Subscribe SciFeed
Recommended Articles
Google Scholar
on Google Scholar
Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

A review of discrete element method applications in soil–plant interactions: challenges and opportunities.

1. Introduction

2. seed–soil interaction, 2.1. challenges, 2.2. applications and case studies, 2.3. opportunities, 2.4. summary, 3. root–soil interaction, 3.1. challenges, 3.2. applications and case studies, 3.3. opportunities, 3.4. summary, 4. residue–soil interaction, 4.1. challenges, 4.2. applications and case studies, 4.3. opportunities, 4.4. summary, 5. opportunities and challenges, 5.1. emerging fields, 5.2. future challenges, 6. conclusions, author contributions, institutional review board statement, data availability statement, acknowledgments, conflicts of interest.

Zuo, Q.; Kuai, J.; Zhao, L.; Hu, Z.; Wu, J.; Zhou, G. The effect of sowing depth and soil compaction on the growth and yield of rapeseed in rice straw returning field. Field Crop. Res. 2017 , 203 , 47–54. [ Google Scholar ] [ CrossRef ]
Zhou, H.; Chen, Y.; Sadek, M.A. Modelling of soil–seed contact using the Discrete Element Method (DEM). Biosyst. Eng. 2014 , 121 , 56–66. [ Google Scholar ] [ CrossRef ]
Acquah, K.; Chen, Y. Discrete Element Modelling of Soil Compaction of a Press-Wheel. AgriEngineering 2021 , 3 , 278–293. [ Google Scholar ] [ CrossRef ]
Brown, A.; Dexter, A.; Chamen, W.; Spoor, G. Effect of soil macroporosity and aggregate size on seed-soil contact. Soil Tillage Res. 1996 , 38 , 203–216. [ Google Scholar ] [ CrossRef ]
Fan, C.C.; Lu, J.Z.; Chen, H.H. The pullout resistance of plant roots in the field at different soil water conditions and root geometries. Catena 2021 , 207 , 105593. [ Google Scholar ] [ CrossRef ]
Cundall, P.A. A computer model for simulating progressive, large-scale movement in blocky rock system. Proc. Int. Symp. Rock Mech. 1971 , 8 , 129–136. [ Google Scholar ]
Mao, Z.; Yang, M.; Bourrier, F.; Fourcaud, T. Evaluation of root reinforcement models using numerical modelling approaches. Plant Soil. 2014 , 381 , 249–270. [ Google Scholar ] [ CrossRef ]
Gong, H.; Zeng, Z.; Qi, L. A discrete element model of seed-soil dynamics in soybean emergence. Plant Soil 2019 , 437 , 439–454. [ Google Scholar ] [ CrossRef ]
Xu, T.; Zhang, R.; Wang, Y.; Jiang, X.; Feng, W.; Wang, J. Simulation and analysis of the working process of soil covering and compacting of precision seeding units based on the coupling model of DEM with MBD. Processes 2022 , 10 , 1103. [ Google Scholar ] [ CrossRef ]
Potyondy, D.O.; Cundall, P.A. A bonded-particle model for rock. Int. J. Rock Mech. Min. Sci. 2004 , 41 , 1329–1364. [ Google Scholar ] [ CrossRef ]
Sadrmanesh, V.; Chen, Y. Simulation of tensile behavior of plant fibers using the Discrete Element Method (DEM). Compos. Part A Appl. Sci. Manuf. 2018 , 114 , 196–203. [ Google Scholar ] [ CrossRef ]
Liu, W.; Zhang, G.; Zhou, Y.; Liu, H.; Tang, N.; Kang, Q.; Zhao, Z. Establishment of discrete element flexible model of the tiller taro plant and clamping and pulling experiment. Front. Plant Sci. 2022 , 13 , 1019017. [ Google Scholar ]
Zhang, S.; Zhao, H.; Wang, X.; Dong, J.; Zhao, P.; Yang, F.; Chen, X.; Liu, F.; Huang, Y. Discrete element modeling and shear properties of the maize stubble-soil complex. Comput. Electron. Agric. 2023 , 204 , 107519. [ Google Scholar ] [ CrossRef ]
Chen, Y.; Tessier, S.; Irvine, B. Drill and crop performances as affected by different drill configurations for no-till seeding. Soil Tillage Res. 2004 , 7 , 147–155. [ Google Scholar ] [ CrossRef ]
Gong, H.; Chen, Y.; Wu, S.; Tang, Z.; Liu, C.; Wang, Z.; Fu, D.; Zhou, Y.; Qi, L. Simulation of canola seedling emergence dynamics under different soil compaction levels using the discrete element method (DEM). Soil Tillage Res. 2022 , 223 , 105461. [ Google Scholar ] [ CrossRef ]
Lysych, M.N.; Bukhtoyarov, L.D.; Shabanov, M.L. Investigation of the impact interaction of pelleted seeds with the soil environment. IOP Conf. Ser. Earth Environ. Sci. 2021 , 875 , 012023. [ Google Scholar ] [ CrossRef ]
Yan, D.; Yu, J.; Zhang, N.; Tian, Y.; Wang, L. Test and Simulation Analysis of Soybean Seed Throwing Process. Processes 2022 , 10 , 1731. [ Google Scholar ] [ CrossRef ]
Yan, D.; Xu, T.; Yu, J.; Wang, Y.; Guan, W.; Tian, Y.; Zhang, N. Test and Simulation Analysis of the Working Process of Soybean Seeding Monomer. Agriculture 2022 , 12 , 1464. [ Google Scholar ] [ CrossRef ]
Zeng, Z.; Chen, Y.; Qi, L. Simulation of cotyledon-soil dynamics using the discrete element method (DEM). Comput. Electron. Agric. 2020 , 174 , 105505. [ Google Scholar ] [ CrossRef ]
Gong, H.; Zeng, Z.; Tessier, L.; Guzman, L.; Yuan, Z.; Li, S.; Zheng, W.; Chen, Y.; Qi, L. Survival on land: A dark-grown seedling searching for path. Front. Plant Sci. 2023 , 14 , 1110521. [ Google Scholar ] [ CrossRef ]
Sun, X.; Sakai, M.; Yamada, Y. Three-dimensional Simulation of a Solid-liquid Flow by the DEM-SPH Method. J. Comput. Phys. 2013 , 248 , 147–176. [ Google Scholar ] [ CrossRef ]
Thakur, S.C.; Morrissey, J.P.; Sun, J.; Chen, J.; Ooi, J.Y. Micromechanical analysis of cohesive granular materials using the discrete element method with an adhesive elasto-plastic contact model. Granul. Matter 2014 , 16 , 383–400. [ Google Scholar ] [ CrossRef ]
Xu, T.; Yu, J.; Yu, Y.; Wang, Y. A modelling and verification approach for soybean seed particles using the discrete element method. Adv. Powder Technol. 2018 , 29 , 3274–3290. [ Google Scholar ] [ CrossRef ]
Tekeste, M.Z.; Balvanz, L.R.; Hatfield, J.L.; Ghorbani, S. Discrete element modeling of cultivator sweep-to-soil interaction: Worn and hardened edges effects on soil-tool forces and soil flow. J. Terramechanics 2019 , 82 , 1–11. [ Google Scholar ] [ CrossRef ]
Lei, X.; Hu, H.; Wu, W.; Liu, H.; Liu, L.; Yang, W.; Zhou, Z.; Ren, W. Seed motion characteristics and seeding performance of a centralised seed metering system for rapeseed investigated by DEM simulation and bench testing. Biosyst. Eng. 2021 , 203 , 22–33. [ Google Scholar ] [ CrossRef ]
Tang, Z.; Gong, H.; Wu, S.; Zeng, Z.; Wang, Z.; Zhou, Y.; Fu, D.; Liu, C.; Cai, Y.; Qi, L. Modelling of paddy soil using the CFD-DEM coupling method. Soil Tillage Res. 2023 , 226 , 105591. [ Google Scholar ] [ CrossRef ]
Le, T.; Liu, C.; Tang, C.; Zhang, X.; Shi, B. Numerical Simulation of Desiccation Cracking in Clayey Soil Using a Multifield Coupling Discrete-Element Model. J. Geotech. Geoenvironmental Eng. 2022 , 148 , 04021183. [ Google Scholar ] [ CrossRef ]
Younes, N.; Wautire, A.; Wan, R.; Millet, O.; Nicot, F.; Bouchard, R. DEM-LBM Coupling for Partially Saturated Granular Assemblies. Comput. Geotech. 2023 , 162 , 105677. [ Google Scholar ] [ CrossRef ]
Karunasena, H.C.P.; Senadeera, W.; Gu, Y.T.; Brown, R.J. A coupled SPH-DEM model for micro-scale structural deformations of plant cells during drying. Appl. Math. Model. 2014 , 38 , 3781–3801. [ Google Scholar ] [ CrossRef ]
Kolb, E.; Legué, V.; Bogeat-Triboulot, M. Physical root–soil interactions. Phys. Biol. 2017 , 14 , 065004. [ Google Scholar ] [ CrossRef ]
Colombi, T.; Walter, A. Root responses of triticale and soybean to soil compaction in the field are reproducible under controlled conditions. Funct. Plant Biol. 2016 , 43 , 114–128. [ Google Scholar ] [ CrossRef ]
Starovoitov, V.I.; Starovoitova, O.A.; Manokhina, A.A. Physical and mechanical parameters of the soil and yield of tubers of food potato depending on the spacing width. IOP Conf. Ser. Earth Environ. Sci. 2022 , 949 , 012001. [ Google Scholar ] [ CrossRef ]
Bordoloi, S.; Ng, C.W.W. The effects of vegetation traits and their stability functions in bio-engineered slopes: A perspective review. Eng. Geol. 2020 , 275 , 105742. [ Google Scholar ] [ CrossRef ]
Das, G.K.; Hazra, B.; Garg, A.; Ng, C.W.W. Stochastic hydro-mechanical stability of vegetated slopes: An integrated copula based framework. Catena 2018 , 160 , 124–133. [ Google Scholar ] [ CrossRef ]
Meng, S.; Zhao, G.; Yang, Y.; Ye, X. Impact of Plant Root Morphology on Rooted-Soil Shear Resistance Using Triaxial Testing. Adv. Civ. Eng. 2020 , 2020 , 8825828. [ Google Scholar ] [ CrossRef ]
Kokutse, N.K.; Temgoua, A.G.T.; Kavazović, Z. Slope stability and vegetation: Conceptual and numerical investigation of mechanical effects. Ecol. Eng. 2016 , 86 , 146–153. [ Google Scholar ] [ CrossRef ]
Li, J.; Liu, X.; Zou, L.; Yuan, J.; Du, S. Analysis of the interaction between end-effectors, soil and asparagus during a harvesting process based on discrete element method. Biosyst. Eng. 2020 , 196 , 127–144. [ Google Scholar ] [ CrossRef ]
Zhao, Z.; Wang, D.; Shang, S.; Hou, J.; He, X.; Gao, Z.; Xu, N.; Chang, Z.; Guo, P.; Zheng, X. Analysis of Cyperus esculentus–Soil Dynamic Behavior during Rotary Tillage Based on Discrete Element Method. Agriculture 2023 , 13 , 358. [ Google Scholar ] [ CrossRef ]
Nakashima, H.; Fujita, Y.; Tanaka, H.; Miyasaka, J. A model of root elongation by dynamic contact interaction. Plant Root 2008 , 2 , 58–66. [ Google Scholar ] [ CrossRef ]
Bourrier, F.; Kneib, F.; Chareyre, B.; Fourcaud, T. Discrete modeling of granular soils reinforcement by plant roots. Ecol. Eng. 2013 , 61 , 646–657. [ Google Scholar ] [ CrossRef ]
Bai, H.; Li, R.; Wang, W.; Xie, K.; Wang, X.; Zhang, W. Investigation on Parameter Calibration Method and Mechanical Properties of Root-Reinforced Soil by DEM. Math. Probl. Eng. 2021 , 2021 , 6623489. [ Google Scholar ] [ CrossRef ]
Li, J.; Xie, S.; Liu, F.; Guo, Y.; Liu, C.; Shang, Z.; Zhao, X. Calibration and Testing of Discrete Element Simulation Parameters for Sandy Soils in Potato Growing Areas. Appl. Sci. 2022 , 12 , 10125. [ Google Scholar ] [ CrossRef ]
Hao, J.; Long, S.; Li, H.; Jia, Y.; Ma, Z.; Zhao, J. Development of discrete element model and calibration of simulation parameters for mechanically-harvested yam. Trans. Chin. Soc. Agric. Eng. 2019 , 35 , 34–42. [ Google Scholar ]
Liu, Y.; Zhao, J.; Yin, B.; Ma, Z.; Hao, J.; Yang, X.; Feng, X.; Ma, Y. Discrete element modelling of the yam root–soil complex and its verification. Biosyst. Eng. 2022 , 220 , 55–72. [ Google Scholar ] [ CrossRef ]
Wei, Z.; Su, G.; Li, X.; Wang, F.; Sun, C.; Meng, P. Parameter optimization and test of potato harvester wavy sieve based on EDEM. Nongye Jixie Xuebao/Trans. Chin. Soc. Agric. Mach. 2020 , 51 , 109–122. [ Google Scholar ]
Colombi, T.; Keller, T. Developing strategies to recover crop productivity after soil compaction—A plant eco-physiological perspective. Soil Tillage Res. 2019 , 191 , 156–161. [ Google Scholar ] [ CrossRef ]
Yuan, J.; Li, J.; Zou, L.; Liu, X. Optimal design of spinach root-cutting shovel based on discrete element method. Trans. CSAM 2020 , 51 , 85–98. [ Google Scholar ]
Turmel, M.S.; Speratti, A.; Baudron, F.; Verhulst, N.; Govaerts, B. Crop residue management and soil health: A systems analysis. Agric. Syst. 2015 , 134 , 6–16. [ Google Scholar ] [ CrossRef ]
Vyn, T.J.; Janovicek, K.; Carter, M. Tillage requirements for annual crop production in eastern Canada. Conserv. Tillage Temp. Agroecosystems 2017 , 3 , 47–71. [ Google Scholar ]
Ahuja, L.R.; Ma, L.; Timlin, D.J. Trans-Disciplinary Soil Physics Research Critical to Synthesis and Modeling of Agricultural Systems. Soil Sci. Soc. Am. J. 2006 , 70 , 311–326. [ Google Scholar ] [ CrossRef ]
Zeng, Z.; Ma, X.; Chen, Y.; Qi, L. Modelling residue incorporation of selected chisel ploughing tools using the discrete element method (DEM). Soil Tillage Res. 2020 , 197 , 104505. [ Google Scholar ] [ CrossRef ]
Adajar, J.B.; Alfaro, M.; Chen, Y.; Zeng, Z. Calibration of discrete element parameters of crop residues and their interfaces with soil. Comput. Electron. Agric. 2021 , 188 , 106349. [ Google Scholar ] [ CrossRef ]
Gao, Z.; Shang, S.; Nan, X.; Wang, D. Parameter calibration of discrete element simulation model of wheat straw-soil mixture in Huang Huai Hai production area. INMATEH-Agric. Eng. 2022 , 66 , 201–210. [ Google Scholar ] [ CrossRef ]
Zhang, S.; Yang, F.; Dong, J.; Chen, X.; Liu, Y.; Mi, G.; Wang, T.; Jia, X.; Huang, Y.; Wang, X. Calibration of Discrete Element Parameters of Maize Root and Its Mixture with Soil. Processes 2022 , 10 , 2433. [ Google Scholar ] [ CrossRef ]
Pásthy, L.; Tamás, K. Modeling the Soil-Tool-Root or-Stem Interaction with Coupled Discrete Element and Mass-Spring Methods. 2023. Available online: https://real.mtak.hu/174614/1/ECMS_2023_Pasthy_Tamas.pdf (accessed on 25 September 2023).
Yuan, B.; Liu, C.; Qin, Y.; Zhang, T.; Ma, X. A discrete element modeling of rock and soil material based on the machine learning. IOP Conf. Ser. Earth Environ. Sci. 2021 , 861 , 032015. [ Google Scholar ] [ CrossRef ]
Cui, H.; Zhao, H.; Ji, S.; Zhang, X.; Awadalseed, W.; Tang, H. A machine learning model of liquid bridge force and its application in discrete element method. Constr. Build. Mater. 2024 , 411 , 134174. [ Google Scholar ] [ CrossRef ]
Lu, L.; Gao, X.; Dietiker, J.F.; Shahnam, M.; Rogers, W.A. Machine learning accelerated discrete element modeling of granular flows. Chem. Eng. Sci. 2021 , 245 , 116832. [ Google Scholar ] [ CrossRef ]
Liao, Z.; Yang, Y.; Sun, C.; Wu, R.; Duan, Z.; Wang, Y.; Li, X.; Xu, J. Image-based prediction of granular flow behaviors in a wedge-shaped hopper by combing DEM and deep learning methods. Powder Technol. 2021 , 383 , 159–166. [ Google Scholar ] [ CrossRef ]
Tamás, K.; Bernon, L. Role of particle shape and plant roots in the discrete element model of soil–sweep interaction. Biosyst. Eng. 2021 , 211 , 77–96. [ Google Scholar ] [ CrossRef ]
Poppa, L.; Frerichs, L.; Liu, J.; Böl, M. Development and Implementation of a Damage Model for Potato Tuber Blackspot in Discrete Element Method to Analyze Harvesting and Handling Processes. J. ASABE 2024 , 67 , 517–524. [ Google Scholar ] [ CrossRef ]
Colombi, T.; Braun, S.; Keller, T.; Walter, A. Artificial macropores attract crop roots and enhance plant productivity on compacted soils. Sci. Total Environ. 2017 , 574 , 1283–1293. [ Google Scholar ] [ CrossRef ] [ PubMed ]
Liu, W.; Zhang, G.; Wang, H.; Liu, H.; Kang, Q.; Zhao, Z.; Pei, L.; Li, Z. Microscopic Deformation and Fragmentation Energy Consumption Characteristics of Soils with Various Moisture Contents Using Discrete Element Method. Soil Tillage Res. 2024 , 241 , 106131. [ Google Scholar ] [ CrossRef ]
Aikins, K.A.; Ucgul, M.; Barr, J.B.; Awuah, E.; Antille, D.L.; Jensen, T.A.; Desbiolles, J.M.A. Review of Discrete Element Method Simulations of Soil Tillage and Furrow Opening. Agriculture 2023 , 13 , 541. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Reference	Software	Soil Feature	Contact Model	Seed Type	Seed Model Feature
Yan et al., 2022 [ ]	EDEM (2018)	Compressible and sticky soil	The Edinburgh Elasto-Plastic Adhesion Model	Soybean seed	13-sphere model
Xu et al., 2022 [ ]	EDEM (2018)	Sandy loam	Hertz–Mindlin with JKR	Soybean seed	5-sphere model
Zhou et al., 2014 [ ]	PFC (4.0)	Different soil density between bottom and upper soil	Stiffness model; slip model; contact bond model	Oilseed rape; wheat; soybean; pea; chickpea; maize; Canavalia ensiform;	Single sphere
Gong et al., 2019 [ ]	EDEM (2018)	Silt clay with moisture content of 15%	Hertz–Mindlin with JKR	Soybean seed transform to cotyledon	Six ellipsoidal particles
Zeng et al., 2020 [ ]	PFC (6.0)	Sand:70% Silt: 16% Clay: 14% Water: 26%	Linear parallel-bond model	Soybean seed (cotyledon)	Irregular shape
Gong et al., 2022 [ ]	EDEM (2021)	Sand: 70% Silt: 16% Clay: 14% Water: 15.96%	Hertz–Mindlin with bond	Canola seed (seedling)	Single sphere
Gong et al., 2023 [ ]	PFC (6.0)	Earth and lunar soil	Linear parallel-bond model	Cotyledon (soybean seed)	A clump of spherical particles

Reference	Software	Soil Feature	Contact Model of Soil	Root Feature	Contact Model of Root	Research Objective
Nakashima et al., 2008 [ ]	-	-	The Voigt model	Root elongates using internally accumulated energy	Compressed virtual spring	Simulate the root-growing process
Bourrier et al., 2013 [ ]	Yade-DEM (1st ed.)	-	-	Roots with the same diameter	Law2 ScGeom6D CohFrictPhys CohesionMoment and ScGeom6D	The reinforcement mechanism of root on soil
Bai et al., 2021 [ ]	PFC (-)	-	-	Single straight root	The parallel bond	The root can enhance the soil shear strength
Li et al., 2020 [ ]	EDEM (-)	-	The Hertz–Mindlin (no slip)	-	The Hertz–Mindlin with bonding	Select the best harvesting scheme and the suitable range of driving forces
Liu et al., 2022 [ ]	EDEM (-)	Sandy loam	Hertz–Mindlin with JKR	-	The Hertz–Mindlin with bonding	Investigate the taro harvesting process
Yuan et al., 2020 [ ]	EDEM (-)	Granular soil;	The Hertz–Mindlin (no slip)	-	The Hertz–Mindlin with bonding	Investigate the spinach harvesting using a cutting shovel
Li et al., 2022 [ ]	EDEM (2020)	Slabby soil agglomerates; granular soil;	The Hertz–Mindlin with bonding The Hertz–Mindlin with JKR	Rigid body	-	Simulate the potato separation process
Hao et al., 2019 [ ]	EDEM (-)	Sandy loam soil	The Hertz–Mindlin with JKR	Homogenous and isotropic	The Hertz–Mindlin with bonding	Calibrate the contact parameters of the mixture model
Liu et al., 2022 [ ]	EDEM (-)	Sand: 62% Silt: 24% Clay: 14% Moisture: 22%	The Hertz–Mindlin with bonding	-	The Hertz–Mindlin with bonding	Construct a yam–soil complex model for future research
Wei et al., 2020 [ ]	EDEM (-)	lumpy soil	The Hertz–Mindlin with bonding	Rigid body	-	Simulate the potato separation process

Reference	Software	Soil Feature	Contact Model of Soil	Residue Feature	Contact Model of Residue	Research Objective
Zeng et al., 2020 [ ]	PFC (6.0)	Sandy loam	The parallel bond	Rigid body	The multi-particle	Investigate the working performance of different tools
Adajar et al., 2021 [ ]	PFC (6.0)	Sandy loam	The parallel bond	Different crop residues	The built-in linear contact model (single sphere)	To determine the simulation parameters
Gao et al., 2022 [ ]	EDEM (-)	High moisture content	The Hertz–Mindlin with JKR	Rigid body	The multi-particle	Calibrate the model of the wheat straw–soil mixture
Zhang et al., 2022 [ ]	EDEM (2020)	-	The Hertz–Mindlin with JKR	Rigid body	The Hertz–Mindlin (no slip)	Calibrate the model of the maize root–soil mixture
Zhang et al., 2023 [ ]	EDEM (2020)	Loamy soil	The Hertz–Mindlin with JKR	Dividing the root into four layers	The Hertz–Mindlin with bonding	The mechanical properties of maize residue–soil complex
Pasthy and Tamas, 2023 [ ]	PFC (-)	-	The Hertz–Mindlin	-	The mass-spring method	Explore the soil–residue–tool interaction

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

Tian, Y.; Zeng, Z.; Xing, Y. A Review of Discrete Element Method Applications in Soil–Plant Interactions: Challenges and Opportunities. Agriculture 2024 , 14 , 1486. https://doi.org/10.3390/agriculture14091486

Tian Y, Zeng Z, Xing Y. A Review of Discrete Element Method Applications in Soil–Plant Interactions: Challenges and Opportunities. Agriculture . 2024; 14(9):1486. https://doi.org/10.3390/agriculture14091486

Tian, Yuyuan, Zhiwei Zeng, and Yuan Xing. 2024. "A Review of Discrete Element Method Applications in Soil–Plant Interactions: Challenges and Opportunities" Agriculture 14, no. 9: 1486. https://doi.org/10.3390/agriculture14091486

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

IMAGES

(PDF) Plant -Microbe Interaction with Enhanced Bioremediation
Plant-Microbe Interactions.
| Schematic representation of plant and microbes interaction. Various
(PDF) Signaling in plant-microbe interactions. Science 276: 726-733
(PDF) Toward a systems understanding of plant–microbe interactions
Ménage à Trois: Unraveling the Mechanisms Regulating Plant–Microbe

VIDEO

Plant-microbes interaction
Advanced School of Microbiology and Host-Microbe Interaction
Plant-Microbe Interactions group Utrecht University
PhD presentation
Flavonoids mediate plant-microbe interactions
Plant

COMMENTS

Plant-microbiome interactions: from community assembly to plant health
A Correction to this paper has been ... involved in plant-microbe interactions. Chem. ... opportunities of synthetic communities for plant microbiome research. Cell Host Microbe 22, ...
(PDF) Plant-Microbe Interaction
[email protected]. Plant-microbe interaction is a complex, dynamic and continuous process that is as. old as plant colonization on Earth. Millions of years' association of plants ...
Plant-Microbe Interaction
Plant-microbe interaction is a complex, dynamic and continuous process that is as old as plant colonization on Earth. Millions of years' association of plants with microbes has formed an assemblage of host and non-host species, forming a discrete ecological unit called "holobiont". In both natural and agricultural ecosystems, plants are ...
Molecular Plant-Microbe Interactions Journal
Molecular Plant-Microbe Interactions® (MPMI) is a gold open access journal that publishes fundamental and advanced applied research on the genetics, genomics, molecular biology, biochemistry, and biophysics of pathological, symbiotic, and associative interactions of microbes, insects, nematodes, or parasitic plants with plants.
Plant-microbe interactions through a lens: tales from the
All plant-microbial interactions are dependent on mutual recognition through effectors (e.g. fungal polysaccharides, microbe-associated molecular patterns, chitin) or via plant/hyphal exudates (Fig. 1, panel 3). Importantly, the composition of plant-exuded chemicals can be altered during plant-microbe interactions.
Plant-microbiome interactions for sustainable agriculture: a review
Plant-microbiome interactions are significant determinant for plant growth, fitness and productivity. Depending upon the specific habitat, plants' microbial communities are classified as the rhizo-, phyllo-, and endospheric regions. Understanding the plant microbiome interactions could provide an opportunity to develop strategies for sustainable agricultural practices. There is a necessity ...
Plant-Microbe Interaction 2017—The Good, the Bad and the Diverse
In this issue of Plant-Microbe Interactions 2017, two interesting reviews and twelve research articles highlight three aspects of plant-microbe interaction. Studying the beneficial interactions can enable us to increase plant fitness without the application of plant protection chemicals. These discoveries have a direct influence on ...
Plant-microbe interactions in the phyllosphere: facing ...
In short, we highlight promising research findings hinting at roles of plant-microbe interactions in (1) contributing to the remediation of urban pollution, (2) impacting plant species range ...
Plant-Microbe Interactions Facing Environmental Challenge
There is a great need to increase efforts in this important area of research in order to reach a systems-level understanding of plant-microbe interactions that are more reflective of what occurs in nature. Keywords: abiotic stress; circadian clock; climate change; humidity; innate immunity; light; nutrient; plant pathogen; symbiosis; temperature.
Revisiting Plant-Microbe Interactions and Microbial Consortia
Figure 2. Interactions in the rhizosphere, (A) Plant-microbiome interactions: Plant roots secrete root exudates and phytochemicals that engage microbial populations in developing niches. Some metabolites filter out the unnecessary microbial strains occupied in niches (indicated by red cross), whereas some metabolites allow the different microbial population to coexist in same niche that may ...
Advances and New Perspectives in Plant-Microbe Interactions
As a call to action for the plant microbiome research community, a comprehensive review by Abdul Hamid and Nadarajah (2022) outlines the chemical signaling between bacteria-bacteria, bacteria-fungi, and plant-microbe and the diverse roles played by these compounds in plant-microbe interactions, and provides recommendations for ...
Plant-Microbe Interaction: Aboveground to Belowground, from the Good to
Shows the plant-microbe interactions aboveground and belowground observed in a plant's natural environment. ... and geographic dispersion of microbial populations in the environment, and remains a popular method in plant microbiome research . ... This paper was funded by the Ministry of Education Malaysia through grants awarded to Kalaivani ...
Plant-microbiome interactions under a changing world: responses
New Phytologist is an international journal owned by the New Phytologist Foundation publishing original research in plant science and its applications. Summary Climate change is increasing global temperatures and the frequency and severity of droughts in many regions. ... Although making a direct link between plant-microbe interactions and ...
Plant Microbe Interactions
Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications. ... Plant-Microbe Interaction State ...
Network mapping of root-microbe interactions in
Building synthetic microbiomes in plants has been proved to be useful for future research on plant-microbe interactions 30,63,64,65,66. ... Calls for Papers Article Processing Charges ...
Plant-Microbe Interaction: Current Developments and Future Challenges
Plant species are in continuous association and interaction with diverse group of microorganisms. The magnitude of such associations is highly diverse in the vicinity, surface areas and within the live plant tissues (Bulgarelli et al. 2013; Verma et al. 2016; Vorholt 2012).From the agricultural point of view, plants and microbes interact in many ways resulting in manifestation of mutualistic ...
(PDF) Plant-Microbe Interactions
Rodriguez et al. 2019). 1.3 Driving Factors fo r Interactions among Plant-Micro be. The microbial habitat b elow the ground is made of rhizosphere (the soil adjacent to sur face of root ...
Plant-Microbe Interaction and Stress Management
This book provides a comprehensive view for plant microbe interactions towards stress management and microbiome-assisted approaches in sustainable agriculture. It is divided into four major sections. ... including the "CAIRD-2011" award from CSIR. Dr. Tewari has published over 100 research papers in reputed journals, 10 books, 11 review ...
Tool and techniques study to plant microbiome current understanding and
Plant-microbe interactions are important for plant growth and yield, and they have gotten a lot of attention recently . This type of interaction is seen in all regions of plants, according to microbiologists and ecologists, but it is called a plant microbiome when it occurs in a specific portion of the plant [ 39 ].
A review on the plant microbiome: Ecology, functions, and emerging
Revealing the functionality of plant-microbe interactions and factors involved in community assembly can lead to a better understanding of the plant as a meta-organism and how plants can benefit from their microbial partners [3], [6]. Nowadays, crop production is facing many challenges such as climate change, the demographic development, and ...
Molecular Plant-Microbe Interactions
Overview. Molecular Plant-Microbe Interactions® (MPMI) publishes peer-reviewed fundamental and advanced applied research on the genetics, genomics, molecular biology, biochemistry, and biophysics of pathological, symbiotic, and associative interactions of microbes, insects, nematodes, or parasitic plants with plants.
(PDF) Principles of Plant-Microbe Interactions
ity is that in the rhizosphere and in the phyllospher e. microbes also interact with each other. Plant recognition by microbes. Recognition is considered to b e the initial key event. in the ...
New Aspects of the Effects of Climate Change on Interactions Between
In this review, we have covered the effects of temperature, precipitation, drought, and CO 2 on plant-microbe interactions, as well as some physiological implications of these changes. Additionally, this paper highlights the ways in which bacteria in plants' rhizosphere react to the dominant climatic conditions in the soil environment.
The interactive effect of tree mycorrhizal type, mycorrhizal type
The underlying processes of plant-microbe associations particularly their interactions with their mycorrhizal fungal partners have been extensively studied. However, considerably less is known about the consequences of tree-tree interactions on rooting zone soil microbiota when tree species of different mycorrhizal type (myco-type) grow ...
Current Techniques to Study Beneficial Plant-Microbe Interactions
Abstract. Many different experimental approaches have been applied to elaborate and study the beneficial interactions between soil bacteria and plants. Some of these methods focus on changes to the plant and others are directed towards assessing the physiology and biochemistry of the beneficial plant growth-promoting bacteria (PGPB).
Prevalence and underlying mechanisms of phylosymbiosis in land plants
Historically, breeders selected genetic variants controlling desired morphological and physiological traits in crops, often neglecting the impact of domestication on the plant microbiome. Previous research indicates that domestication disrupts plant-microbe symbiosis, diminishing the interaction between domesticated plants and microorganisms ...
Impacts of microbial interactions on underground hydrogen storage in
Therefore, this paper provides a comprehensive review of experimental, numerical, and field studies on microbial interactions in UHS within porous media, aiming to capture research progress and elucidate microbial effects. ... respectively. In Section 4, various experimental approaches for studying gas-water-rock-microbe (GWRM) interactions and ...
Deciphering the Omics of Plant-Microbe Interaction: Perspectives and
Plant-microbe interaction is of utmost importance in sustaining the balance in an ecosystem compared to the other microbial interactions. Several inorganic and organic compounds are produced by plants, which leads to the development of a nutrient-enriched environment that is beneficial for the profound colonization of a variety of microbes.
A Review of Discrete Element Method Applications in Soil-Plant ...
The discrete-element method (DEM) has become a pivotal tool for investigating soil-plant interactions in agricultural and environmental engineering. This review examines recent advancements in DEM applications, focusing on both the challenges and opportunities that shape future research in this field. This paper first explores the effectiveness of DEM in simulating soil and plant materials ...

Save citation to file

Add to My Bibliography

Plant-Microbe Interactions Facing Environmental Challenge

Similar articles

Publication types

Grants and funding

LinkOut - more resources

Miscellaneous

REVIEW article

Introduction

Aboveground Plant Microbiota

Belowground Microbial Occurrence and Interactions

Root–Root Interactions

Root–Microbe Interactions

Interaction Between Root and Microbe via Root Exudates

Influence of Climatic and Soil Conditions on Root–Microbe Interaction

Mechanism of Belowground Interactions in the Rhizosphere: Beyond Plant’s Innate Immune Response

Multitude of Functions of Microbial Consortia in the Rhizosphere With Emphasis on Phytohormones, Nutrients, and Microbial Defense Mechanisms

Phytohormones

Nutrient Acquisition

Microbial Defense Mechanisms

Techniques for Microbiome Analysis

Amplicon Sequencing

Metagenomics

Contribution of Microbial Inoculants in Agricultural Sustainability

Agricultural Management and Status of Microbial Inoculants

Process of Microbial Inoculant (Single/Consortia) Formulation

Future Prospects, Challenges, and Limitations

Author Contributions

Conflict of Interest

Acknowledgments

Network mapping of root–microbe interactions in Arabidopsis thaliana

Similar content being viewed by others

Genome wide association study reveals plant loci controlling heritability of the rhizosphere microbiome

Network analysis infers the wilt pathogen invasion associated with non-detrimental bacteria

The fungal root endophyte Serendipita vermifera displays inter-kingdom synergistic beneficial effects with the microbiota in Arabidopsis thaliana and barley

Co-occurrence networks of the root microbiota

Hubs of the co-occurrence network identification

Mapping root-microbe interactions

QTL networks

Root microbiome experiment

Microbial interactions analysis

Microbial networks analysis

Mapping microbial network properties

Reporting summary

Data availability

Code availability

Acknowledgements

Author information

Contributions

Corresponding author

Ethics declarations

Additional information

Supplementary information

About this article

Share this article

This article is cited by

The Arabidopsis holobiont: a (re)source of insights to understand the amazing world of plant–microbe interactions

Pioneer Arabidopsis thaliana spans the succession gradient revealing a diverse root-associated microbiome

Quick links

Plant-Microbe Interaction and Stress Management

Microbial Technology Division, CSIR- National Botanical Research Institute, Lucknow, India

Faculty of Biosciences, Institute of Biosciences and Technology, Shri Ramswaroop Memorial University, Barabanki, India

Access this book

Other ways to access

About this book

Table of contents (15 chapters)

An Outline of Plant–Microbe Interactions for Stress Management

Exploring the Phyllosphere: Microbial Diversity, Interactions, and Ecological Significance in Plant Health

Drought and Waterlogging Stress Responses in Crops

Saline Stress Management

Nature’s Protectors: A Biofilm Perspective on Bacterial Disease Control in Plants

Rhizosphere Microbiome-Assisted Approaches for Biotic Stress Management

Sustainable Management of Economically Important Plant Viral Pathogens by Plant Growth-Promoting Rhizobacteria

Insects as Biotic Stress in Agriculture

Phytopathogenic Fungi: Stress and Mitigation Approaches

Advance Techniques Used for Decoding the Hidden Language Used During Plant–Microbe Interaction

Sodic-Tolerant Plant Growth-Promoting Rhizobacteria Mediated Sodic Stress Alleviation in Plants

Endophytic Microbiome: An Insight into the Hidden World of Microorganisms Within Plants

Biotic Stress to Plants: Fungal Pathogen as a Major Biotic Stress