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• W2896942545 abstract "There is a growing appreciation for the important roles microorganisms play in association with plants. Microorganisms are drawn to distinct plant surfaces by the nutrient-rich microenvironment, and in turn some of these colonizing microbes provide mutualistic benefits to their host. The development of plant probiotics to increase crop yield and provide plant resistance against biotic and abiotic stresses, while minimizing chemical inputs, would benefit from a deeper mechanistic understanding of plant-microbe interaction. Technological advances in molecular biology and high-throughput -omics provide stepping stones to the elucidation of critical microbiome gene functions that aid in improving plant performance. Here, we review -omics-based approaches that are propelling forward the current understanding of plant-associated bacterial gene functions, and describe how these technologies have helped unravel key bacterial genes and pathways that mediate pathogenic, beneficial, and commensal host interactions. There is a growing appreciation for the important roles microorganisms play in association with plants. Microorganisms are drawn to distinct plant surfaces by the nutrient-rich microenvironment, and in turn some of these colonizing microbes provide mutualistic benefits to their host. The development of plant probiotics to increase crop yield and provide plant resistance against biotic and abiotic stresses, while minimizing chemical inputs, would benefit from a deeper mechanistic understanding of plant-microbe interaction. Technological advances in molecular biology and high-throughput -omics provide stepping stones to the elucidation of critical microbiome gene functions that aid in improving plant performance. Here, we review -omics-based approaches that are propelling forward the current understanding of plant-associated bacterial gene functions, and describe how these technologies have helped unravel key bacterial genes and pathways that mediate pathogenic, beneficial, and commensal host interactions. All land plants host a microbiome composed of bacteria, fungi, oomycetes, viruses, archaea, and protists. These organisms inhabit primarily the root environment (rhizosphere; the area immediately adjacent to the root), the rhizoplane (the root surface), and to a lesser extent the leaf (phyllosphere), seed (spermosphere), and internal (endosphere) plant environments. Microbes are attracted to the rich nutrients provided by the plant and are sorted from the surrounding environment (soil, water, and air), presumably by the plant immune system, the exudates that the plant secretes to the soil in the immediate vicinity of the root, and the ability to outcompete other microbes. Interestingly, very different plants, such as the dicot Arabidopsis and the monocot barley, share a core root and rhizosphere microbiome composed of mainly Proteobacteria, Actinobacteria, and Bacteroidetes, and this core microbiome is distinct from the bulk soil. Plant microbiome research has rapidly expanded along with the understanding that the microbiome can have far-reaching implications on the plant's health, development, and productivity (Mayak et al., 2004Mayak S. Tirosh T. Glick B.R. Plant growth-promoting bacteria confer resistance in tomato plants to salt stress.Plant Physiol. Biochem. 2004; 42: 565-572Crossref PubMed Scopus (836) Google Scholar, Niu et al., 2017Niu B. Paulson J.N. Zheng X. Kolter R. Simplified and representative bacterial community of maize roots.Proc. Natl. Acad. Sci. USA. 2017; 114: E2450-E2459Crossref PubMed Scopus (313) Google Scholar). Nonetheless, most plant microbiome studies either use amplicon-based microbial ecology approaches to describe the community structure or focus on a limited set of model plant pathogens or beneficial microorganisms. To improve the mechanistic understanding of the interaction between plants and their microbiomes there needs to be a shift from community structure description to systematic microbial function elucidation. Common microbial ecology tools (e.g., 16S ribosomal RNA or gyrB gene amplicon sequencing) provide insights into the makeup of a bacterial community. However, these techniques cannot determine if a certain microbe is harmful, neutral, or beneficial to the plant. These outcomes are dependent on the genetics of both the host and the microbiome. The presence or absence of even a small number of accessory genes in either the plant (e.g., disease resistance, or R genes) or its microbiome (e.g., virulence factors, or genes that dampen plant stress responses, modulate plant hormone levels, or mobilize nutrients) may cause a drastic change in the nature of their interaction. Moreover, samples that are different in their species diversity can still encode similar gene functions and proteomes, as shown in four tree species (Lambais et al., 2017Lambais M.R. Barrera S.E. Santos E.C. Crowley D.E. Jumpponen A. Phyllosphere metaproteomes of trees from the Brazilian Atlantic forest show high levels of functional redundancy.Microb. Ecol. 2017; 73: 123-134Crossref PubMed Scopus (27) Google Scholar) as well as in different samples of the human microbiomes (Lozupone et al., 2012Lozupone C.A. Stombaugh J.I. Gordon J.I. Jansson J.K. Knight R. Diversity, stability and resilience of the human gut microbiota.Nature. 2012; 489: 220-230Crossref PubMed Scopus (3077) Google Scholar). Research using model microorganisms, such as different root-nodulating rhizobiales and the phytopathogen Pseudomonas syringae, has identified factors contributing to mutualism or virulence, respectively (Glick, 2014Glick B.R. Bacteria with ACC deaminase can promote plant growth and help to feed the world.Microbiol. Res. 2014; 169: 30-39Crossref PubMed Scopus (1171) Google Scholar, Xin et al., 2018Xin X.-F. Kvitko B. He S.Y. Pseudomonas syringae: what it takes to be a pathogen.Nat. Rev. Microbiol. 2018; 16: 316-328Crossref PubMed Scopus (275) Google Scholar). However, mechanistic studies on model plant-associated bacterial isolates tend to ignore the effect of the extant plant microbiome during colonization and persistence of the studied strain. Methodological advances in molecular biology in multiple -omics fields, including genomics, transcriptomics, proteomics, and metabolomics, have begun to yield insights into the functions performed at the community-level by plant-associated bacterial genes and pathways. Here, we review recent developments in the elucidation of bacterial gene functions and characterization of molecular changes at the plant-bacteria interface through the application of -omics techniques. A thorough molecular understanding of plant microbiome functions will have significant agricultural implications, including the deployment of useful microbes and microbial-derived products to increase crop yields. These might include inoculating crops with a supportive and robust microbial community or engineering plants with beneficial microbial genes to confer higher productivity and resistance against plant diseases, pests, and abiotic stresses. Ultimately, these technologies will contribute to more efficient and sustainable agriculture. The striking reduction of DNA sequencing costs has led to the creation of large-scale bacterial genome collections. Currently, hundreds of public genomic datasets of plant-associated bacterial isolates, single cells, and metagenomes become available each year (Figure 1). High-quality bacterial isolate genomes can be compared to identify candidate genes and pathways that correlate with a given phenotype of interest, such as association with a specific niche, virulence, or a beneficial phenotypic trait. These genes can then be manipulated to test for the predicted function. In recent years, thousands of bacterial isolate genomes have been sequenced from different plant environments and compared to identify bacterial genes that affect general adaptation to plants (Levy et al., 2018Levy A. Salas Gonzalez I. Mittelviefhaus M. Clingenpeel S. Herrera Paredes S. Miao J. Wang K. Devescovi G. Stillman K. Monteiro F. et al.Genomic features of bacterial adaptation to plants.Nat. Genet. 2018; 50: 138-150Crossref PubMed Scopus (258) Google Scholar), adaptation to root versus shoot (Bai et al., 2015Bai Y. Muller D.B. Srinivas G. Garrido-Oter R. Potthoff E. Rott M. Dombrowski N. Munch P.C. Spaepen S. Remus-Emsermann M. et al.Functional overlap of the Arabidopsis leaf and root microbiota.Nature. 2015; 528: 364-369Crossref PubMed Scopus (614) Google Scholar), nodulation and nitrogen fixation (Seshadri et al., 2015Seshadri R. Reeve W.G. Ardley J.K. Tennessen K. Woyke T. Kyrpides N.C. Ivanova N.N. Discovery of novel plant interaction determinants from the genomes of 163 root nodule bacteria.Sci. Rep. 2015; 5: 16825Crossref PubMed Scopus (25) Google Scholar), biocontrol activity (Hossain et al., 2015Hossain M.J. Ran C. Liu K. Ryu C.-M. Rasmussen-Ivey C.R. Williams M.A. Hassan M.K. Choi S.-K. Jeong H. Newman M. et al.Deciphering the conserved genetic loci implicated in plant disease control through comparative genomics of Bacillus amyloliquefaciens subsp. plantarum.Front. Plant Sci. 2015; 6: 631Crossref PubMed Scopus (39) Google Scholar), and quorum sensing (Schaefer et al., 2013Schaefer A.L. Lappala C.R. Morlen R.P. Pelletier D.A. Lu T.-Y.S. Lankford P.K. Harwood C.S. Greenberg E.P. LuxR- and luxI-type quorum-sensing circuits are prevalent in members of the Populus deltoides microbiome.Appl. Environ. Microbiol. 2013; 79: 5745-5752Crossref PubMed Scopus (45) Google Scholar). An alternative to sequencing bacterial isolates is to sequence a plant microbiome metagenome (“shotgun metagenomics”). The “meta” prefix used here, as with other -omics techniques, indicates that the data represent measurements captured from the entire microbial community and not from a single isolate. In metagenomics, genetic sequence information is captured for the many species across a microbiome that cannot be represented by cultivation. Metagenome sequencing projects revealed genes that are enriched in the endosphere (Sessitsch et al., 2012Sessitsch A. Hardoim P. Döring J. Weilharter A. Krause A. Woyke T. Mitter B. Hauberg-Lotte L. Friedrich F. Rahalkar M. et al.Functional characteristics of an endophyte community colonizing rice roots as revealed by metagenomic analysis.Mol. Plant Microbe Interact. 2012; 25: 28-36Crossref PubMed Scopus (456) Google Scholar) and rhizospheres of different plants (Bulgarelli et al., 2015Bulgarelli D. Garrido-Oter R. Munch P.C. Weiman A. Droge J. Pan Y. McHardy A.C. Schulze-Lefert P. Structure and function of the bacterial root microbiota in wild and domesticated barley.Cell Host Microbe. 2015; 17: 392-403Abstract Full Text Full Text PDF PubMed Scopus (705) Google Scholar, Ofek-Lalzar et al., 2014Ofek-Lalzar M. Sela N. Goldman-Voronov M. Green S.J. Hadar Y. Minz D. Niche and host-associated functional signatures of the root surface microbiome.Nat. Commun. 2014; 5: 4950Crossref PubMed Scopus (223) Google Scholar), elucidated genes that are correlated with biocontrol activity (Mendes et al., 2018Mendes L.W. Raaijmakers J.M. de Hollander M. Mendes R. Tsai S.M. Influence of resistance breeding in common bean on rhizosphere microbiome composition and function.ISME J. 2018; 12: 212-224Crossref PubMed Scopus (185) Google Scholar), and even led to the discovery of novel metabolic enzymes (Campos et al., 2016Campos B.M. Liberato M.V. Alvarez T.M. Zanphorlin L.M. Ematsu G.C. Barud H. Polikarpov I. Ruller R. Gilbert H.J. Zeri A.C.d.M. et al.A novel carbohydrate-binding module from sugar cane soil metagenome featuring unique structural and carbohydrate affinity properties.J. Biol. Chem. 2016; 291: 23734-23743Crossref PubMed Scopus (14) Google Scholar). A major challenge in metagenomics is to assemble the sequencing reads into high-quality metagenome-assembled genomes where all genes in a genome are captured and the assembled contigs are assigned to the correct organisms. This may be particularly challenging for rare organisms. Other hurdles include proper taxonomic assignment of the assembled genomes and differentiation between related strains in samples containing a high degree of strain heterogeneity. In endosphere microbiomes, large amounts of host DNA masking the microbial DNA can further complicate shotgun metagenomic approaches. Functional metagenomics provides an approach to systematically test the effect of gene gain of function. Here, novel genes discovered in metagenomes can be expressed in a heterologous host microbe or in vitro, which enables functional assays to be employed to test for novel activities. However, cloning and heterologous expression of some genes may be intractable, or expression in a heterologous host microbe may yield a different phenotype. This approach is often used to discover novel antibiotic biosynthesis or resistance genes within soil metagenomes, but it has not yet been systematically applied to plant metagenomes. One potential application of functional metagenomics could be the systematic identification of novel plant growth-promoting genes by heterologous expression in a root colonizer. A complementary approach to metagenomics is single-cell sequencing. Here, prior to sequencing, single cells are first isolated and lysed, and the DNA is amplified through a multiple displacement amplification reaction. Single-cell sequencing allows genome sequencing of bacteria that cannot be cultivated, provides access to the genetic makeup of rare taxa, and overcomes the challenge of assigning a DNA sequence to a certain cell, thereby facilitating linkage of plasmids and viruses to their bacterial host. The main limitation of single-cell sequencing is that the resulting genomes are generally less complete, more fragmented, and more susceptible to contamination as compared with sequencing of clonal cultured bacterial isolates. Genetically tractable microorganisms can be tested for gene function using systematic gene loss-of-function approaches. One powerful approach is transposon sequencing (TnSeq), in which all genes in a genome are mutated by transposon insertion to test their involvement in a given biological process. A variant of TnSeq is randomly barcoded transposon mutagenesis sequencing (RB-TnSeq), in which TnSeq is coupled with random DNA barcoding of each mutant to identify genes that affect microbial fitness under specific growth conditions (Price et al., 2018Price M.N. Wetmore K.M. Waters R.J. Callaghan M. Ray J. Liu H. Kuehl J.V. Melnyk R.A. Lamson J.S. Suh Y. et al.Mutant phenotypes for thousands of bacterial genes of unknown function.Nature. 2018; 557: 503-509Crossref PubMed Scopus (236) Google Scholar). This approach was used to mutate the genomes of 33 bacterial strains, some of which are plant-associated, and provided a remarkable repository that includes the mutant phenotypes of 100,000 bacterial genes (Price et al., 2018Price M.N. Wetmore K.M. Waters R.J. Callaghan M. Ray J. Liu H. Kuehl J.V. Melnyk R.A. Lamson J.S. Suh Y. et al.Mutant phenotypes for thousands of bacterial genes of unknown function.Nature. 2018; 557: 503-509Crossref PubMed Scopus (236) Google Scholar). TnSeq-based approaches were recently applied to identify bacterial genes involved in Arabidopsis and legume root colonization (Cole et al., 2017Cole B.J. Feltcher M.E. Waters R.J. Wetmore K.M. Mucyn T.S. Ryan E.M. Wang G. Ul-Hasan S. McDonald M. Yoshikuni Y. et al.Genome-wide identification of bacterial plant colonization genes.PLoS Biol. 2017; 15: e2002860Crossref PubMed Scopus (107) Google Scholar, Salas et al., 2017Salas M.E. Lozano M.J. Lopez J.L. Draghi W.O. Serrania J. Torres Tejerizo G.A. Albicoro F.J. Nilsson J.F. Pistorio M. Del Papa M.F. et al.Specificity traits consistent with legume-rhizobia coevolution displayed by Ensifer meliloti rhizosphere colonization.Environ. Microbiol. 2017; 19: 3423-3438Crossref PubMed Scopus (26) Google Scholar), in bacterial persistence in tomatoes (de Moraes et al., 2017de Moraes M.H. Desai P. Porwollik S. Canals R. Perez D.R. Chu W. McClelland M. Teplitski M. Salmonella persistence in tomatoes requires a distinct set of metabolic functions identified by transposon insertion sequencing.Appl. Environ. Microbiol. 2017; 83 (e03028)Crossref PubMed Scopus (46) Google Scholar), and in xylose metabolism (Price et al., 2018Price M.N. Wetmore K.M. Waters R.J. Callaghan M. Ray J. Liu H. Kuehl J.V. Melnyk R.A. Lamson J.S. Suh Y. et al.Mutant phenotypes for thousands of bacterial genes of unknown function.Nature. 2018; 557: 503-509Crossref PubMed Scopus (236) Google Scholar). Transcriptomic analysis of plant-associated bacteria using RNA sequencing (RNA-seq) technology, or gene expression microarray approaches, reveals genes that are differentially expressed under certain conditions. To date, most of the plant-associated bacterial transcriptomic studies have been performed by culturing bacteria separate from the plant host. RNA-seq was used, for example, to detect genes responding to the presence of plant extract (Coutinho et al., 2015Coutinho B.G. Licastro D. Mendonca-Previato L. Camara M. Venturi V. Plant-influenced gene expression in the rice endophyte Burkholderia kururiensis M130.Mol. Plant Microbe Interact. 2015; 28: 10-21Crossref PubMed Scopus (72) Google Scholar). The challenge for the study of bacterial transcriptomes in planta is that plant transcripts significantly outnumber bacterial transcripts and most bacterial transcripts are housekeeping ribosomal RNAs. Hence, achieving a sufficient concentration of bacterial mRNA transcripts for sequencing and differential expression analysis is difficult. Several in planta bacterial isolate transcriptome studies report simultaneous plant and bacterial gene expression (termed “dual RNA-seq”) (Pankievicz et al., 2016Pankievicz V.C.S. Camilios-Neto D. Bonato P. Balsanelli E. Tadra-Sfeir M.Z. Faoro H. Chubatsu L.S. Donatti L. Wajnberg G. Passetti F. et al.RNA-seq transcriptional profiling of Herbaspirillum seropedicae colonizing wheat (Triticum aestivum) roots.Plant Mol. Biol. 2016; 90: 589-603Crossref PubMed Scopus (41) Google Scholar, Paungfoo-Lonhienne et al., 2016Paungfoo-Lonhienne C. Lonhienne T.G.A. Yeoh Y.K. Donose B.C. Webb R.I. Parsons J. Liao W. Sagulenko E. Lakshmanan P. Hugenholtz P. et al.Crosstalk between sugarcane and a plant-growth promoting Burkholderia species.Sci. Rep. 2016; 6: 37389Crossref PubMed Scopus (61) Google Scholar, Roux et al., 2014Roux B. Rodde N. Jardinaud M.-F. Timmers T. Sauviac L. Cottret L. Carrère S. Sallet E. Courcelle E. Moreau S. et al.An integrated analysis of plant and bacterial gene expression in symbiotic root nodules using laser-capture microdissection coupled to RNA sequencing.Plant J. 2014; 77: 817-837Crossref PubMed Scopus (314) Google Scholar). Recently, Nobori et al., 2018Nobori T. Velásquez A.C. Wu J. Kvitko B.H. Kremer J.M. Wang Y. He S.Y. Tsuda K. Transcriptome landscape of a bacterial pathogen under plant immunity.Proc. Natl. Acad. Sci. USA. 2018; 115: E3055-E3064Crossref PubMed Scopus (98) Google Scholar developed two highly correlated approaches to significantly enrich for the transcriptome of P. syringae in an Arabidopsis leaf infection model. In the first, a new isolation buffer that stabilizes the bacterial RNA was used during leaf grinding. This was followed by filtration and centrifugation to separate bacterial cells from plant cells prior to RNA isolation. The second approach used selective depletion of plant-derived transcripts with customized probes. It remains to be seen if these approaches can be applied to root-dwelling bacteria. RNA-seq technology also enables detection of intricate transcriptome regulation such as gene operons, small noncoding RNA, antisense RNA, and riboswitches (Filiatrault et al., 2010Filiatrault M.J. Stodghill P.V. Bronstein P.A. Moll S. Lindeberg M. Grills G. Schweitzer P. Wang W. Schroth G.P. Luo S. et al.Transcriptome analysis of Pseudomonas syringae identifies new genes, noncoding RNAs, and antisense activity.J. Bacteriol. 2010; 192: 2359-2372Crossref PubMed Scopus (110) Google Scholar). In metatranscriptomics, transcripts of the entire community are directly sequenced from environmental samples. This allows insight into the transcriptional state of many microorganisms simultaneously. Metatranscriptomics were used, for example, to identify bacterial genes from the rhizosphere that are differentially expressed during Arabidopsis development (Chaparro et al., 2014Chaparro J.M. Badri D.V. Vivanco J.M. Rhizosphere microbiome assemblage is affected by plant development.ISME J. 2014; 8: 790-803Crossref PubMed Scopus (743) Google Scholar) and invasion by a fungal pathogen (Chapelle et al., 2016Chapelle E. Mendes R. Bakker P.A.H. Raaijmakers J.M. Fungal invasion of the rhizosphere microbiome.ISME J. 2016; 10: 265-268Crossref PubMed Scopus (181) Google Scholar). Decreasing sequencing costs have enabled the increased use of transcriptomics and metatranscriptomics (Figure 1) to gain insights into the dynamics of bacterial gene expression. Transcriptomic analysis enables the dynamics and regulation of actively transcribed genes to be detected, thereby presenting an advantage over genomic analysis. Metatranscriptomics, however, is limited by the fact that transcripts can rarely be assigned to specific microorganisms without high-quality reference genomes. Alternatives to sequencing-based transcriptomic approaches, such as the hybridization-based NanoString technology, may allow improved bacterial transcript detection in mixed plant microbiome transcript samples. As techniques for the enrichment and detection of bacterial transcripts further improve and become applicable to a broad array of plant-bacteria systems, we expect that transcriptomic approaches will transform our understanding of plant-associated bacterial functions. Proteomics and metaproteomics approaches, mostly based on liquid chromatography-tandem mass spectrometry technology, reveal the diversity of bacterial proteins within an environment in a semi-quantitative manner. These techniques involve sample collection, protein extraction, isolation and fractionation, mass spectroscopy analysis, and comparison with a proteome database. Unlike genomics, and to a lesser extent transcriptomics, proteomics measures the functional protein components produced by a cell rather than identifying the potential to make them. Therefore, proteomics approaches provide a more precise snapshot of the active pathways within a sample. (Meta)proteomics has been used to measure the phyllosphere metaproteome of forest trees (Lambais et al., 2017Lambais M.R. Barrera S.E. Santos E.C. Crowley D.E. Jumpponen A. Phyllosphere metaproteomes of trees from the Brazilian Atlantic forest show high levels of functional redundancy.Microb. Ecol. 2017; 73: 123-134Crossref PubMed Scopus (27) Google Scholar), to detect proteins differentially secreted by plant growth-promoting bacterial (PGPB) strains in response to root exudates (Kierul et al., 2015Kierul K. Chen X.-H. Voigt B. Carvalhais L.C. Albrecht D. Borriss R. Influence of root exudates on the extracellular proteome of the plant growth-promoting bacterium Bacillus amyloliquefaciens FZB42.Microbiology. 2015; 161: 131-147Crossref PubMed Scopus (40) Google Scholar), and identify the organisms and proteins responsible for nitrogen fixation and methane oxidation in rice fields (Bao et al., 2014Bao Z. Okubo T. Kubota K. Kasahara Y. Tsurumaru H. Anda M. Ikeda S. Minamisawa K. Metaproteomic identification of diazotrophic methanotrophs and their localization in root tissues of field-grown rice plants.Appl. Environ. Microbiol. 2014; 80: 5043-5052Crossref PubMed Scopus (76) Google Scholar). Proteomics can be limited by low protein quality and concentration, low sensitivity due to host proteins and microbial complexity, and de novo protein identification if a (meta)genome reference sequence is lacking. The Vorholt lab pioneered the use of metaproteogenomics, in which proteins present in complex microbial communities are identified based on metagenomes generated from plant microbiomes. The approach doubled the number of proteins that could be identified compared with protein identification using public databases alone (Delmotte et al., 2009Delmotte N. Knief C. Chaffron S. Innerebner G. Roschitzki B. Schlapbach R. von Mering C. Vorholt J.A. Community proteogenomics reveals insights into the physiology of phyllosphere bacteria.Proc. Natl. Acad. Sci. USA. 2009; 106: 16428-16433Crossref PubMed Scopus (580) Google Scholar, Knief et al., 2012Knief C. Delmotte N. Chaffron S. Stark M. Innerebner G. Wassmann R. von Mering C. Vorholt J.A. Metaproteogenomic analysis of microbial communities in the phyllosphere and rhizosphere of rice.ISME J. 2012; 6: 1378-1390Crossref PubMed Scopus (427) Google Scholar). Unfortunately, the current application of proteomics to describe plant-associated bacterial communities is limited (Figure 1) due to various factors, including relatively low bacterial protein expression levels in complex plant-associated samples and consequent detection limits, and the need for a comprehensive peptide reference database. We hope to see higher use of proteomics in studies examining plant-bacteria interactions in the future, complementing the large number of genomics and transcriptomics studies. Various bacterial genes, such as the Nodulation (Nod) genes that synthesize the Nod factors as part of root nodulation, directly affect the host plant or microbial metabolism. Using targeted or untargeted metabolomics, changes in specific metabolite levels can be measured in response to a given treatment. Recently, metabolomics was used to demonstrate how the chemical exudation from grass (Avena barbata) roots over the course of development affects rhizosphere community assembly and succession by enriching for bacteria with substrate preference for the exuded metabolites, mostly aromatic organic acids (Zhalnina et al., 2018Zhalnina K. Louie K.B. Hao Z. Mansoori N. da Rocha U.N. Shi S. Cho H. Karaoz U. Loqué D. Bowen B.P. et al.Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly.Nat. Microbiol. 2018; 3: 470-480Crossref PubMed Scopus (773) Google Scholar). There are several challenges associated with metabolome analysis in plant-microbe systems such that they have not been widely adopted (Figure 1). Similar to proteomics, the costs, equipment, and technical expertise necessary to perform metabolite studies make them less accessible than DNA sequencing. Further, the sizes of public metabolite reference databases are limited, and it can be difficult to assign a measured metabolite to a specific organism. Nevertheless, metabolomics offers a powerful tool to detect and quantify small molecules and molecular changes at the plant-bacteria interface. Discovery of microbial small molecules that significantly boost plant health, growth, and resilience to stress remains a high priority target for achieving sustainable agriculture. To further our understanding of bacterial gene functions, it is critical to combine multiple -omics approaches to overcome the different limitations and biases introduced by each technique (Table 1). For example, by combining whole-genome sequencing of a large isolate collection with metatranscriptome sequencing, one can map a higher number of the transcriptome reads to individual isolate reference genomes. This results in a more sensitive differential expression analysis and improved inference about the role of specific isolates and their genes. Combining transcriptomics and/or proteomics with a relevant (meta)genome reference can provide improved insights into the coordinated expression of genomically co-located gene operons, pathogenicity islands, or biosynthetic gene clusters producing secondary metabolites. Metagenomics, metatranscriptomics, and metaproteomics recently provided an elaborate functional landscape of the polymicrobial-host disease interaction during acute oak decline disease (Broberg et al., 2018Broberg M. Doonan J. Mundt F. Denman S. McDonald J.E. Integrated multi-omic analysis of host-microbiota interactions in acute oak decline.Microbiome. 2018; 6: 21Crossref PubMed Scopus (37) Google Scholar). The combination of -omics approaches has also led to the identification of the genes and proteins active in legume-rhizobia symbiosis (Delmotte et al., 2010Delmotte N. 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Wang Y. et al.Drought delays development of the sorghum root microbiome and enriches for monoderm bacteria.Proc. Natl. Acad. Sci. USA. 2018; 115: E4284-E4293Crossref PubMed Scopus (240) Google Scholar), and genes that are present and expressed in the root environment of two plant species (Ofek-Lalzar et al., 2014Ofek-Lalzar M. Sela N. Goldman-Voronov M. Green S.J. Hadar Y. Minz D. Niche and host-associated functional signatures of the root surface microbiome.Nat. Commun. 2014; 5: 4950Crossref PubMed Scopus (223) Google Scholar). In the last" @default.
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• W2896942545 date "2018-10-01" @default.
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• W2896942545 title "Elucidating Bacterial Gene Functions in the Plant Microbiome" @default.
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• W2896942545 doi "https://doi.org/10.1016/j.chom.2018.09.005" @default.
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