Large-Scale Replicated Field Study of Maize Rhizosphere Identifies Heritable Microbes

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Large-scale replicated field study of maize rhizosphere identifies heritable microbes

William A. Waltersa, Zhao Jinb,c, Nicholas Youngbluta, Jason G. Wallaced, Jessica Suttera, Wei Zhangb, Antonio González-Peñae, Jason Peifferf, Omry Korenb,g, Qiaojuan Shib, Rob Knightd,h,i, Tijana Glavina del Rioj, Susannah G. Tringej, Edward S. Bucklerk,l, Jeffery L. Danglm,n, and Ruth E. Leya,b,1

aDepartment of Microbiome Science, Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany; bDepartment of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853; cDepartment of Microbiology, Cornell University, Ithaca, NY 14853; dDepartment of Crop & Soil Sciences, University of Georgia, Athens, GA 30602; eDepartment of Pediatrics, University of California, San Diego, La Jolla, CA 92093; fPlant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853; gAzrieli Faculty of Medicine, Bar Ilan University, 1311502 Safed, Israel; hCenter for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093; iDepartment of Computer Science & Engineering, University of California, San Diego, La Jolla, CA 92093; jDepartment of Energy Joint Genome Institute, Walnut Creek, CA 94598; kPlant, Soil and Nutrition Research, United States Department of Agriculture – Agricultural Research Service, Ithaca, NY 14853; lInstitute for Genomic Diversity, Cornell University, Ithaca, NY 14853; mHoward Hughes Medical Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514; and nDepartment of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514

Edited by Jeffrey I. Gordon, Washington University School of Medicine in St. Louis, St. Louis, MO, and approved May 23, 2018 (received for review January 18, 2018) Soil microbes that colonize plant roots and are responsive to used for a variety of food and industrial products (16). Maize differences in plant genotype remain to be ascertained for possesses exceptional phenotypic diversity that is influenced by agronomically important crops. From a very large-scale longitudi- genotype, which has the potential to influence the rhizosphere nal field study of 27 maize inbred lines planted in three fields, with microbiome. For instance, maize lines with mutations affecting partial replication 5 y later, we identify root-associated microbiota their carbon storage patterns have been shown to harbor distinct exhibiting reproducible associations with plant genotype. Analysis rhizosphere microbiomes; mutant lines with the sugary endo- of 4,866 samples identified 143 operational taxonomic units sperm su1 gene have been associated with higher fungal and (OTUs) whose variation in relative abundances across the samples Gram-positive bacterial biomass compared with lines with the was significantly regulated by plant genotype, and included five shrunken endosperm sh2 gene, and their overall communities of seven core OTUs present in all samples. Plant genetic effects differed (7). Maize lines can also differ in their root structures, were significant amid the large effects of plant age on the with effects on the rhizosphere microbiome (17). Heritable rhizosphere microbiome, regardless of the specific community of microbiota of the maize rhizosphere remain to be identified. each field, and despite microbiome responses to climate events. The largest set of public maize germplasm used to dissect Seasonal patterns showed that the plant root microbiome is locally genetically complex traits (i.e., lines developed for nested asso- seeded, changes with plant growth, and responds to weather ciation mapping, so-called NAM lines) (18) consists of ∼5,000 events. However, against this background of variation, specific recombinant inbred lines. Analysis of genotype–phenotype re- taxa responded to differences in host genotype. If shown to have lationships using NAM lines has revealed the genetic architec- beneficial functions, microbes may be considered candidate traits ture underlying a variety of complex quantitative traits, such as for selective breeding.

maize | rhizosphere | soil microbiome | heritability | field study Significance

In this very large-scale longitudinal field study of the maize growing number of studies report an influence of plant ge- rhizosphere microbiome, we identify heritable taxa. These taxa notype on various facets of the rhizosphere microbiome, but A display variance in their relative abundances that can be par- the majority are conducted under greenhouse and laboratory tially explained by genetic differences between the maize conditions where environmental variability is controlled (1–5), or – lines, above and beyond the strong influences of field, plant are limited in scope (3, 6 10). Studies with small sample numbers age, and weather on the diversity of the rhizosphere micro- (e.g., in the hundreds), or that lack replication over space or biome. If these heritable taxa are associated with beneficial time, are limited in their power and may yield spurious results. traits, they may serve as phenotypesinfuturebreedingendeavors. As a result, they can both overestimate the importance of plant genotype and underestimate genotype interactions with the en- Author contributions: W.A.W., Z.J., J.G.W., J.P., O.K., E.S.B., J.L.D., and R.E.L. designed vironment (11). Furthermore, although small-scale studies can research; W.A.W., Z.J., J.S., W.Z., J.P., O.K., Q.S., T.G.d.R., S.G.T., and J.L.D. performed assess the effect of plant genotype on abstracted measures of the research; W.A.W., Z.J., N.Y., J.G.W., J.S., A.G.-P., R.K., and R.E.L. analyzed data; and W.A.W., N.Y., J.G.W., J.S., and R.E.L. wrote the paper. microbiome, such as within (alpha-diversity) and between (beta- Conflict of interest statement: J.L.D. is a cofounder, shareholder, and member of the diversity) samples, they lack the power to quantify the effect of Scientific Advisory Board of AgBiome, a corporation with the goal to use plant- plant genotype on abundances of specific taxa comprising the associated microbes to improve plant productivity. R.E.L. is a shareholder and member microbiome. Thus, large-scale studies with replication are re- of the Scientific Advisory Board of AgBiome. All other authors declare no competing quired to identify heritable taxa, i.e., those whose variance in financial interest. abundance across samples has a significant plant genotype com- This article is a PNAS Direct Submission. ponent (12). Heritable microbes of the rhizosphere (the region of This open access article is distributed under Creative Commons Attribution-NonCommercial- NoDerivatives License 4.0 (CC BY-NC-ND). soil surrounding, and chemically influenced by, plant roots) should Data deposition: The sequences reported in this paper have been deposited in the Euro- exhibit, across a plant population, differential abundances signif- pean Nucleotide Archive (accession nos. 2010 maize data: PRJEB21985; 2015 partial icantly influenced by variation in plant genotype (13, 14). Heri- replication data: PRJEB21590). table components (e.g., taxa or functions) of the soil microbiome 1To whom correspondence should be addressed. Email: [email protected]. remain to be identified for major crop species. This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. Maize (Zea mays L. subsp. mays) is a globally important crop 1073/pnas.1800918115/-/DCSupplemental. with over a billion metric tons of production in 2016 (15) and is Published online June 25, 2018.

7368–7373 | PNAS | July 10, 2018 | vol. 115 | no. 28 www.pnas.org/cgi/doi/10.1073/pnas.1800918115 Downloaded by guest on September 28, 2021 flowering time, height, stalk strength, and disease resistance (19). showed a strong patterning according to plant age (Fig. 1A). If the parents of the NAM lines, selected for maximal genetic Principal coordinate (PC) 1 explains the majority of the variance diversity while still flowering in summer in North America, show in the data, and plant age (sampling week) maps well onto this PC. variation in their microbiomes, then subsequent analysis of a far Samples form a gradient from left to right along PC1, ranging greater number of NAM inbred lines would be warranted. In a from the bulk soil and early samples to the samples taken at later previous study of the maize rhizosphere microbiome of NAM weeks (Fig. 1A). The second PC of the unweighted UniFrac PCoA parent lines, we analyzed ∼500 samples derived at flowering time shows a strong patterning of samples by field (Fig. 1B). The age from 27 NAM parental lines planted in a randomized block design patterning, but not field patterning, was stronger when we used replicated in five fields (20). This analysis was sufficient to show the weighted UniFrac metric (Fig. 1C). that plant genotype drove subtle differences in rhizosphere bac- Additionally, we analyzed the data for clustering of samples by terial diversity. However, as with other small-scale analyses, this Adonis testing with unweighted and weighted UniFrac distances, study did not have sufficient power to identify the specific taxa that which revealed that plant age is the strongest factor shaping responded to differences in maize plant genotype (20). these rhizosphere communities, followed by field, and then plant Here, we performed a longitudinal analysis of the 27 NAM genetics [R2 of 0.10363, 0.08684, and 0.00770 (unweighted parental line genotypes planted in five fields, yielding >4,800 UniFrac); 0.38644, 0.07716, and 0.00724 (weighted UniFrac), for samples collected weekly over the course of the growing season. age, field, and inbred maize line, respectively, P value < 0.001 for In addition, we partially replicated the study 5 y later with a all tests]. To further assess the relative importance of field, plant subset of the NAM parent genotypes sampled over the growing age, plant genotype, and their interactions on these patterns, we season in one field. Microbial diversity was assessed with Illu- performed variance decomposition for each principal coordinate mina sequencing of the V4 region of the 16S rRNA gene for all of the PCoAs (SI Appendix,Fig.S1). This confirmed that the samples, resulting in an extremely large assessment of rhizo- majority of variance exhibited by PC 1 in the unweighted UniFrac sphere microbial community membership. We chose 16S rRNA PCoA (Fig. 1) is attributable to plant age, and variance in PC2 gene sequencing over metagenome sequencing primarily due to was attributable to location. For PC1 of the unweighted UniFrac the very large size of the sample set and to specifically identify analysis, the second largest component of the variance is the in- heritable taxa. These data were analyzed with respect to climate teraction of plant age, genotype, and location. Together, these variables, field location, plant age, and genotype. We report well- results imply significant gene-by-environment interaction, implying

powered identification of heritable taxa of the maize rhizosphere, that plant genotype impacts the community composition differently ECOLOGY over and above the prior beta-diversity differences observed in ref. 20. depending on the plant’s age and location.

Results and Discussion Plant Genotype Distance Matrix Versus Microbiome Distance Matrices. Maize Rhizosphere Core Microbiome. The rhizosphere microbiome We used a distance matrix for the maize genotypes (24) to ask if examined here includes bacteria and archaea inhabiting the the overall genetic dissimilarity of any two maize lines was combined “rhizosphere” and “rhizoplane” (21) of maize. A total predictive of the dissimilarity of their rhizosphere microbiomes. of 4,911 samples and ∼627 million 16S rRNA reads were gen- Correlations between the maize genotype distance matrix and erated for these analyses (Materials and Methods). Across all the UniFrac distance matrices yielded weak (R2 = 0.04 and samples, we detected 55 bacterial phyla, which represent a siz- 0.03) effects with opposite (inverse and positive relationships) able fraction of the current ∼92 named phyla (22). As expected, directions for unweighted and weighted UniFrac, respectively maize rhizosphere and bulk soil microbiome differed, but all (SI Appendix). This implies that the overall genetic differences phyla were detected in both sample types. As microbes that are between the maize lines mostly fail to predict the overall di- consistently present across samples likely provide critical eco- versity differences in their rhizosphere microbiomes (i.e., the logical functions (23), we searched for a core microbiome, de- most genetically distant NAM lines of maize do not have the fined here using a stringent criterion: the set of operational largest UniFrac distances between microbiomes). Thus, any plant taxonomic units (OTUs) common to all rhizosphere microbiome genetic effects on the diversity of the rhizosphere microbiome will samples. Seven OTUs comprised the core maize microbiome in likely manifest at the level of specific taxa. 100% of samples. All seven were taxonomically assigned to the phylum Proteobacteria, including three Alphaproteobacteria Identification of Taxa with Differential Abundances Across Plant (Agrobacterium, Bradyrhizobiaceae, Devosia), two Betaproteo- Genotypes. To identify microbial taxa responsive to plant geno- bacteria (both Comamonadaceae), and two Gammaproteobac- type, we first compared the abundances of OTUs in pairwise teria (Pseudomonas and Sinobacteraceae; SI Appendix, Dataset categories of maize groups [six broad functional groups: sweet- S1). The core microbiome for OTUs shared across 95% of corn, popcorn, tropical, stiff-stalk, non-stiff-stalk, and mixed samples included 251 OTUs from a wider diversity of phyla. The (25)] using a mixed-model approach with Bonferroni correction seven OTUs present in 100% of 2010 samples were also present (Materials and Methods). Pairwise comparisons between broad in 100% of the 2015 samples. Given that we assessed the di- maize groups resulted in 83 instances of differentially abundant versity of a single plant species, a core microbiome might be OTUs. We observed that these instances involved 48 OTUs, with expected. However, the observation that seven OTUs were the same OTUs differentially abundant in several pairwise com- present in all maize rhizosphere samples, in five fields across parisons (SI Appendix, Dataset S2). three states, and across a 5-y timespan, implies that these taxa Compared with other maize groups, the sweet-corn group had are highly persistent and ubiquitous in agricultural soil. a strong tendency to enrich for taxa that have been associated with nitrogen fixing activities (e.g., Bradyrhizobiaceae, Plant Age and Field Are the Main Drivers of Rhizosphere Microbiome Burkholderia, Rhizobium, Sphingomonas, and Oxalobacteraceae). Diversity. To identify factors that shape the differences between The sweet-corn inbred lines harbor mutations that cause higher maize rhizosphere microbiomes (beta diversity), we applied the sucrose and glucose concentrations and lower starch production phylogenetically aware UniFrac metrics, which provide a mea- in the endosperm (26), which may lead to different root exudate sure of dissimilarity between any two microbiomes based on availability to the microbiome. shared membership (unweighted UniFrac), and also take into We performed similar paired tests of the 27 inbred maize line account the abundances of the lineages (weighted UniFrac). (SI Appendix, Dataset S2), and observed 255 significant instances Principal coordinates analysis (PCoA) of unweighted UniFrac involving 92 OTUs. Again, maize lines with characteristics likely distances showed that regardless of field, rhizosphere microbiomes impacting exudate profiles shaped the rhizosphere microbiome

Walters et al. PNAS | July 10, 2018 | vol. 115 | no. 28 | 7369 Downloaded by guest on September 28, 2021 A PC2 (5.99%) B PC2 (5.99%)

Week 1 2 3 4 5 Aurora Rhizosphere 6 Aurora Bulk Soil 7 Ithaca Rhizosphere 8 Ithaca Bulk Soil 9 Lansing Rhizosphere 10 11 Lansing Bulk Soil 12 Columbia Rhizosphere 13 Urbana Rhizosphere 14 15 20 10 Bulk Soil 15 PC1 (11.49%) PC1 (11.49%) 20

PC3 (2.39%) PC3 (2.39%) CD PC2 (15.42%) PC2 (15.42%)

PC1 (32.62%) PC1 (32.62%) PC3 (5.83%) PC3 (5.83%)

Fig. 1. Environment and age strongly structure the rhizosphere microbial community. PCoA of unweighted (A and B) and weighted (C and D) UniFrac distances for maize rhizosphere and bulk soil samples. A and B show the same projection of the data, as do C and D. Symbols represent microbiomes and are colored by plant age (A and C; sampling week; see color gradient) by environment (B and D; by the specific field of origin). The first three PCs are plotted with the percentage of variation explained by each PC.

differentially. For instance, members of the Chloracidobacteria Identification of Heritable OTUs in the 2010 Field Study. To estimate and Pedosphaerales orders, and the Opitutaceae family, were the amount of variance in OTU abundances across all samples enriched on the non-stiff-stalk Oh43 maize line, which has a that could be attributed to plant genetics, while controlling for defective invertase for sucrose uptake in the root system (27). A environmental factors, we calculated the broad-sense heritability Paenibacillus sp. was enriched in the tropical CML247 line. This (H2) for OTUs shared by ≥80% of samples (n = 792). We genus includes free-living N2 fixers (28). Many of the OTUs identified 143 OTUs with significantly more heritability than differentially abundant between maize lines belong to the same expected by chance based on 5,000 random permutations of the bacterial families, and they might be expected to show similar data (empirical P value ≤ 0.001; Fig. 2 and SI Appendix, Fig. S2 patterns of enrichment or depletion. Indeed, we noted that for and Dataset S4). the 20 cases where OTUs had the same taxonomic classification, Overall, the broad-sense heritabilities for these OTUs were all responded in the same manner on the same maize lines. This relatively low, with a range of 0.15–0.25 (on a scale from 0 to 1, may indicate that the related OTUs perform similar functions. In where 1 means that all variance is attributed to genetics). This the 2015 dataset, no OTUs had significantly different abundance indicates that environmental factors contribute most of the var- by inbred maize line or maize group after multiple comparison iance observed in the relative proportion of OTUs across samples, corrections. This is likely due to the reduced power of the as expected given the strong environmental effects discussed ear- smaller dataset. lier. We observed partial overlap between the heritable OTUs and the 92 OTUs that showed significant differences in abundances in Microbiome Richness Across Plant Genotypes. Microbiome richness the pairwise comparisons (inbred maize lines and broad maize (alpha diversity) varied between broad maize groups and be- groups; SI Appendix, Dataset S5). tween inbred lines. Specifically, “mixed” maize lineage had lower Mapping the heritable OTUs onto a common phylogeny diversity than other groups, including the tropical and non-stiff- revealed some clusters of related taxa (Fig. 3). The heritable stalk maize. In accordance, the mixed inbred line Mo18W had OTUs are diverse, including 26 Alphaproteobacteria, 9 Betapro- lower microbiome richness compared with many other lineages teobacteria, 12 Actinobacteria, 6 Verrucomicrobia, and 8 Bacter- (SI Appendix, Dataset S3; this line was not planted in 2015). oidetes. Others belong to WS3, Beta- and Gammaproteobacteria, Differences in richness replicated 5 y later; compared with other Planctomycetes, Firmicutes, Chloroflexi, Acidobacteria, Gemmati- maize lines, the non-stiff-stalk Mo17 had higher alpha diversity, monadetes, and interestingly, the Archaeal phylum Crenarchaeota and the sweet-corn IL14H had lower diversity in both years (SI (candidatus Nitrososphaera). Five of the seven core OTUs described Appendix, Dataset S3). previously (Agrobacterium, Devosia, both Comamonadaceae, and the

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Fig. 2. Broad-sense heritability of individual OTUs for the 2010 field study. The broad-sense heritability (H ) is shown for the 100 OTUs with highest H values. ECOLOGY Circles show the actual H2 values for each OTU in decreasing order and blue distributions show the corresponding H2 values from 5,000 permutations of the data. Red circles indicate OTUs with P values ≤0.001. Taxonomies shown are most specific for each OTU.

Sinobacteraceae OTUs) were among the heritable taxa. Although Pseudomonas Bloom. The age gradient allowed a weekly view of the Alpha, Beta, Delta, and Gammaproteobacteria were well rep- rhizosphere microbiome development over the growing season. resented among the heritable OTUs, the genus Pseudomonas was Averaging the abundances of OTUs across fields and maize in- not (except one OTU, classified as Pseudomonas viridiflava). Of bred lines, we observed that bacteria belonging to the Pseudo- note, only eight OTUs did not match the Greengenes reference monas genus bloomed from week 8 onward (average increased ∼ – ∼ database, indicating that a majority of heritable OTUs have pre- from 2.7% during weeks 1 7to 44.5% thereafter; SI Appen- dix, Fig. S4A). This bloom was apparent across all fields. Pseu- viously been observed, at least by sequencing. Indeed, the OTUs domonas was not, however, dominant in bulk soil sampled across with the greatest heritability values (>0.2) could be classified at least the season, indicating the bloom was plant-driven (SI Appendix, to the family level, and many to genus and species. Fig. S4B). Of note, Pseudomonas has previously been shown to The heritability of individual rhizosphere OTUs was lower – – dominate the maize rhizosphere microbiome (29 31). than for most traditional agronomic traits (0.15 0.25, whereas After week 8, 43.1% of the total microbial community belonged the heritability of plant yield is around 0.3 and flowering time can to just three Pseudomonas OTUs. The top 10 most abundant be 0.9 or higher). Nonetheless, there is a noticeable effect of unique sequences within these OTUs accounted for 78.6% of the plant genetics on the relative abundances of specific taxa. Since 2015 and 73.1% of the 2010 Pseudomonas sequence data. Bray– plant genotype selects on microbial phenotypes, heritable taxa Curtis dissimilarity clustering revealed that the Pseudomonas likely encode functions that are phylogenetically restricted, with community structure differed by field (SI Appendix,Fig.S5), and function manifested at the taxon level. that this field-specific structure remained constant over time. Thus, each field harbored a distinct Pseudomonas population structure Heritable OTUs in the 2015 Field Study. Analysis of the 2015 dataset that was stable over the growing season. identified five OTUs that were significantly heritable (P < 0.001; The three Pseudomonas 97% OTUs that dominated in the SI Appendix, Fig. S3 and Dataset S4). One OTU, an Ellin517 latter half of the 2010 sampling season were also present in the taxon, was overlapping between the 2 y, and another was taxo- 2015 dataset, albeit at lower abundance. Moreover, the three nomically related to OTUs heritable in 2010 (Erythrobacter- Pseudomonas 100% identity OTUs dominant in 2010 were also aceae). The OTUs that did not overlap between years were the most abundant in 2015, although at lower relative abundance detected in both years; however, they were present in a smaller to all other taxa. In 2015, Pseudomonas were slightly more number of samples (55–70%) than the Ellin517 OTU in 2010 abundant in rhizosphere compared with bulk soil samples (2.0% versus 0.3%; SI Appendix, Fig. S6), but we did not observe the (>95%). These zero counts limit variance in abundance, and same seasonal bloom. Intriguingly, when we correlated the rel- hence heritability. Note that the heritability estimates were ative abundances of all unique 100% identity Pseudomonas higher in 2015 than in 2010, likely because of the small 2015 OTUs between the 2010 and 2015 datasets, we observed a strong sample size. Moreover, the 2015 data stem from just one field, so 2 2 2 relationship (R of 0.91 for the 10 most abundant OTUs and R that environmental variability is reduced. Indeed, higher H es- of 0.94 for all data), even though the 2010 data included three timates emerged when the 2010 data were subsetted to equivalent geographically distinct fields and the 2015 data only one. Thus, sample size (SI Appendix). These findings underscore the impor- the most abundant 100% identity Pseudomonas OTUs in 2010 tance of large datasets and multiple environments in heritability were also the most abundant in 2015. These data suggest that the analyses. field-specific Pseudomonas community structure persists across

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Gemmatimonadetes Actinosynnemataceae Gemmatimonadales Ellin5301 Microbacteriaceae Gemmatimonadales Ellin5301 Microbacteriaceae Gemmatimonadales Ellin5301 Microbacteriaceae Gemmatimonadaceae Microbacteriaceae Gemmatimonadetes Ellin5290 Firmicutes Microbacteriaceae Gemmatimonadetes Ellin5290 Gemmatimonadetes N1423WL Microbacteriaceae Gemmatimonadetes N1423WL Microbacteriaceae Cellulomonas xylanilytica Gemmatimonadetes N1423WL Gemmatimonadetes N1423WL Promicromonosporaceae Gemmatimonadetes N1423WL Micrococcaceae Chlorobi Micrococcaceae Cryomorphaceae Micrococcaceae Flavobacteriaceae Micrococcaceae Flavobacteriaceae Micrococcaceae Flavobacterium succinicans Intrasporangiaceae Sphingobacteriales Intrasporangiaceae Sphingobacteriales Intrasporangiaceae Sphingobacteriales Acidobacteria Intrasporangiaceae Sphingobacteriales Actinomycetales Sphingobacteriales Kineosporiaceae Sphingobacteriales Sphingobacteriales Nocardiaceae Sphingobacteriales Nocardiaceae Sphingobacteriales Nocardiaceae Sphingobacteriaceae Mycobacteriaceae Sphingobacteriaceae Mycobacteriaceae Sphingobacteriaceae Williamsiaceae Sphingobacteriaceae Streptomycetaceae Sphingobacteriaceae Streptomycetaceae Sphingobacteriaceae Streptomycetaceae Sphingobacteriaceae Streptomycetaceae Sphingobacteriaceae Chloroflexi Streptomycetaceae Sphingobacteriaceae Sphingobacteriaceae Streptomycetaceae Sphingobacteriaceae Streptomycetaceae Sphingobacteriaceae Streptomycetaceae Sphingobacteriaceae Streptomycetaceae Cytophagaceae Actinomycetales Cytophagales Actinomycetales Cytophagaceae Nakamurellaceae Cytophagaceae Cytophagaceae Geodermatophilaceae Cytophagaceae Geodermatophilaceae Cytophagaceae Actinobacteria MB-A2-108 Cytophagaceae Actinobacteria MB-A2-108 Cytophagaceae Verrucomicrobia Actinobacteria MB-A2-108 Cytophagaceae Cytophagaceae ActinobacteriaRubrobacteraceaeMicrococcales MB-A2-108 Cytophagaceae Acidimicrobiales Cytophagaceae Acidimicrobiales Cytophagaceae Cytophagaceae Acidimicrobiales Cytophagaceae Acidimicrobiales Cytophagaceae Acidimicrobiales Cytophagaceae Acidimicrobiales Cytophagaceae AcidimicrobialesIamiaceae11 Cytophagaceae 111 Cytophagaceae Acidimicrobiales111 Saprospiraceae Saprospiraceae Acidimicrobiales C1 Saprospiraceae Planctomycetes Acidimicrobiales C Saprospiraceae AcidimicrobialesAcidimicrobiales C Saprospiraceae Chitinophagaceae Chitinophagaceae Acidimicrobiales EB1017 Chitinophagaceae Acidimicrobiales EB1017 Chitinophagaceae Chitinophagaceae AcidimicrobialesGaiellaceae EB1017 Chitinophagaceae AcidimicrobialesGaiellaceae EB1017 Chitinophagaceae AcidimicrobialesGaiellaceae EB1017 Chitinophagaceae Gaiellaceae Chitinophagaceae Gaiellaceae Chitinophagaceae Gaiellaceae Chitinophagaceae Gaiellaceae Chitinophagaceae Gaiellaceae Chitinophagaceae Gaiellaceae Chitinophagaceae Actinobacteria Gaiellaceae Chitinophagaceae Gaiellaceae Chitinophagaceae Gaiellaceae Chitinophagaceae Gaiellaceae Chitinophagaceae Chitinophagaceae Gaiellaceae Chitinophagaceae Gaiellaceae Chitinophagaceae Gaiellaceae Chitinophagaceae Gaiellaceae Chitinophagaceae Gaiellaceae Chitinophagaceae Gaiellaceae Chitinophagaceae Gaiellaceae Chitinophagaceae Gaiellaceae Chitinophagaceae Chitinophagaceae Chitinophagaceae Chitinophagaceae Solirubrobacterales Chitinophagaceae Solirubrobacterales Chitinophagaceae Solirubrobacterales Chitinophagaceae Solirubrobacterales Chitinophagaceae Solirubrobacterales Chitinophagaceae Solirubrobacterales Chitinophagaceae Chitinophagaceae Solirubrobacterales Chitinophagaceae Solirubrobacterales Chitinophagaceae Solirubrobacterales Chitinophagaceae Solirubrobacterales Chitinophagaceae Solirubrobacterales Chitinophagaceae Solirubrobacterales Chitinophagaceae Chitinophagaceae Solirubrobacterales Chitinophagaceae Chitinophagaceae Solirubrobacteraceae Chitinophagaceae Solirubrobacteraceae Chitinophagaceae SolirubrobacteraceaePatulibacteraceae Chitinophagaceae Patulibacteraceae A17 Alicyclobacillaceae Paenibacillaceae ConexibacteraceaePirellulaceae Paenibacillus chondroitinus SolirubrobacteralesPirellulaceae Bacillaceae Fimbriimonadaceae Planococcaceae Bacillus flexus PirellulaceaePirellulaceae A17 Bacillaceae PirellulaceaePirellulaceae Planococcaceae Pirellulaceae Bacillaceae Pirellulaceae Bacillaceae Pirellulaceae Bacillaceae Geobacteraceae Entotheonellaceae Gemmataceae Nitrospirales 0319-6A21 Gemmataceae Nitrospirales 0319-6A21 Nitrospiraceae Nitrospiraceae Nitrospiraceae Acidobacteria Sva0725 Planctomycetia B97 Acidobacteria iii1-8 Planctomycetaceae Acidobacteria iii1-8 Acidobacteria iii1-8 Acidobacteria RB25 Acidobacteria RB25 Chloracidobacteria 1 Phycisphaerae WD2101 Chloracidobacteria RB41 Phycisphaerae WD2101 Chloracidobacteria RB41 Phycisphaerae WD2101 Chloracidobacteria RB41 Pedosphaerales Chloracidobacteria RB41 Pedosphaerales Ellin515 Chloracidobacteria RB41 Pedosphaerales Chloracidobacteria RB41 Pedosphaerales Ellin515 Pedosphaerales Chloracidobacteria RB41 Pedosphaerales Ellin515 Chloracidobacteria RB41 Chloracidobacteria RB41 Pedosphaerales Ellin515 Pedosphaerales Chloracidobacteria RB41 Pedosphaerales Ellin515 Pedosphaerales Chloracidobacteria RB41 Pedosphaerales Acidobacteria Acidobacteria Pedosphaerales Acidobacteria-5 Pedosphaerales Acidobacteria-5 Pedosphaerales Acidobacteria-5 Pedosphaerales Ellin515 Acidobacteria-5 Acidobacteria-5 Pedosphaerales Acidobacteria-5 Pedosphaerales Acidobacteria-5 1-24 Koribacteraceae Pedosphaerales Solibacterales Solibacterales Pedosphaerales Solibacterales Solibacterales Solibacteraceae Solibacteraceae Solibacteraceae Solibacteraceae Opitutaceae Solibacteraceae Opitutaceae Acidobacteria-6 CCU21 P Acidobacteria-6 CCU21 P Opitutaceae Acidobacteria-6 CCU21 AUC37f Opitutaceae Acidobacteria iii1-15 AUC37f Pedosphaerales auto67_4W Acidobacteria-6 iii1-15 Opitutaceae Acidobacteria-6 iii1-15 Opitutaceae Acidobacteria-6 iii1-15 Pedosphaerales OPB35 Acidobacteria-6 iii1-15 Opitutaceae Acidobacteria-6 iii1-15 Acidobacteria-6 iii1-15 Opitutaceae Acidobacteria-6 iii1-15 Pedosphaerales Ellin517 Acidobacteria-6 iii1-15 Opitutaceae Acidobacteria-6 iii1-15 Acidobacteria-6 iii1-15 Pedosphaerales Ellin517 Acidobacteria-6 iii1-15 Opitutaceae Acidobacteria-6 iii1-15 Acidobacteria-6 iii1-15 Pedosphaerales Ellin517 Acidobacteria-6 iii1-15 Acidobacteria-6 iii1-15 Acidobacteria-6 iii1-15 Pedosphaerales Ellin517 Acidobacteria-6 iii1-15 Acidobacteria-6 iii1-15 Acidobacteria-6 iii1-15 Acidobacteria-6 iii1-15 A A A Pedosphaerales Ellin517 A A

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Fig. 3. Phylogeny of heritable OTUs. The tree shows the phylogenetic relationships of OTUs found in 80% of all rhizosphere samples. Heritable OTUs have branches colored in red. Major phyla are color-coded in the outer circle, and the deepest named taxonomy is given for each OTU. The tree is a subset of the Aug 2013 Greengenes tree. (Scale bar indicates inferred mutation rate per site.)

long time periods, even though the relative abundance of the and 3-d sums. Members of the Pseudomonadaceae responded Pseudomonas as a broader category can vary substantially. negatively to long-term precipitation, indicating that wetter and colder conditions disfavor this group. The weather-responsive Impact of Climate, 2010 and 2015. A subset of the rhizosphere subset of the rhizosphere microbiome adds variability to the microbiome is responsive to weather: their relative abundances age-gradient trends and underscores the importance of multiple were associated with climate variables. We modeled the effects time measurements in field studies. of weather, as recorded from the National Centers for Envi- Similar patterns of responsiveness to weather were replicated ronmental Information, on the rhizosphere microbiome across 5 y later (SI Appendix, Dataset S6). Since there is limited overlap the 2010 and 2015 season (SI Appendix, Dataset S6). Families of between the specific 97% OTUs detected in 2010 and 2015, and Bacteria and Archaea tended to respond in the same positive or because we had observed that OTUs belonging to the same negative pattern for given weather conditions. For instance, bacterial family usually had similar responses to weather events Nocardioidaceae, Caulobacteraceae, and Sphingobacteriaceae in the 2010 data, we compared the 2010 and 2015 weather re- responded positively (across more than 80% of OTUs) to tem- sponses by examining if OTUs of the same family responded to perature, whereas members of the Ellin515 and Opitutaceae, weather similarly in both years. Of note, members of the family Solibacteraceae, and Oxalobacteraceae responded negatively. Hyphomicrobiaceae responded negatively to temperature in both For same-day precipitation, Sphingobacteriaceae and Cytopha- years. In addition, several taxa showed a positive response to pre- gaceae families responded positively 100% of the time, while the cipitation in both years (2- and 3-d precipitation sums), including Verrucomicrobia Ellin517 and Ellin515, Hyphomicrobiaceae, Saprospiraceae, Hyphomicrobiaceae, Verrucomicrobia, A4b Chthoniobacteraceae, and Chitinophagaceae responded nega- (Anaerolineae), Ellin6075 (Chloracidobacteria), Sphingomonadaceae, tively to precipitation at least 80% of the time. When cumulative and Chitinophagaceae. precipitation for the prior 2 or 3 d was tested, the OTUs that Interestingly, while many of the heritable OTUs belonged to responded in a positive manner changed. Chitinophagaceae, the same families, these also included taxa responsive to weather Hyphomicrobiaceae, Sphingomonadaceae, Comamonadaceae, events (e.g., members of the Oxalobacteraceae, Nocardioidaceae, Gaiellaceae, Nocardioidaceae, Microbacteriaceae, and Opituta- Sphingomonadaceae, and Chitinophagaceae). The observation ceae responded positively to long-term precipitation for both 2- that the heritable set of OTUs and the weather-responsive set are

7372 | www.pnas.org/cgi/doi/10.1073/pnas.1800918115 Walters et al. Downloaded by guest on September 28, 2021 related suggests that many of the heritable taxa are r-selected: they rhizoplane or endosphere), but then the impact of the plant on the likely react to short-term changes in carbon availability that are soil may be lost. Another possibility is to incorporate the rhizo- modulated by the plant (32). sphere microbiome as a factor in determining desirable traits in maize, such as yield or drought tolerance, and to assess how plant Prospectus. This well-powered study revealed the heritable com- genotype interacts with the rhizosphere microbiome to shape host ponents of the maize rhizosphere under field conditions. Despite phenotype. strong environmental patterning, we identified close to 150 OTUs with significant heritability. These were highly diverse, Materials and Methods including Archaea. Many are related to taxa with putative ben- In 2010, we sampled replicate NAM parental lines in three fields in New York eficial functions. While our 2015 replication study was limited in State (USA) weekly, and two more fields (Illinois and Missouri) were sampled scope, it nevertheless partially recovered the 2010 findings con- once at flowering time. Combined with bulk soil from each field, this yielded cerning heritability and weather responses. The maize inbred 4,866 samples. From these samples, we generated ∼627,000,000 sequences of lines studied here were selected because they are the parents of the V4 region of the 16S rRNA gene using Illumina MiSeq sequencing. We the recombinant inbred lines (RILs) that comprise the largest set later partially replicated the field sampling in 2015 in one location (New of public maize germplasm used to dissect genetically complex York) with a subset of the maize lines and sampling times. From the repli- traits (18). The present work was conducted in part to ascertain if cate field study, we obtained 45 rhizosphere samples, which we processed the NAM population might exhibit enough variance in the rhi- similarly to the 2010 set. Beta diversity, Adonis testing, core microbiome, and zosphere microbiome to warrant an expanded study using the raw alpha-diversity values were calculated with the QIIME (34) software package. Differential abundance, alpha-diversity significance tests, microbial NAM RILs, aimed at mapping plant genes that drive aspects of response to weather, and heritability results were generated using the lme4 the rhizosphere microbiome. Our results suggest that there is (35) mixed-model package. More specific details for sampling handling and indeed a variable selection of NAM RILs upon specific microbial data analyses are described in SI Appendix. abundances. A next step is to discern the plant genes that associate with ACKNOWLEDGMENTS. We thank Sherry Flint-Garcia, Stephen Moose, Ayme these taxa and to better characterize their functions. GWAS in Spor, and Lynn Marie Johnson for assistance. This work was supported by mammals highlight an effect of immune-related genes on the NSF Inspire Track II Grants IOS-1343020 (to R.E.L. and J.L.D.), IOS-0958184 (to microbiome, and similarly, studies in Arabidopsis point to effects R.E.L.), and IOS-0958245 (to J.L.D.). Support was provided by the Howard Hughes Medical Institute and the Gordon and Betty Moore Foundation (J.L.D.),

of plant immune-related genes on microbiome composition (4). NSF Grants IOS-0820619 and IOS-1238014 (to J.P., J.G.W., and E.S.B.), the ECOLOGY It remains to be seen if immune-related phenotypes (33) are as United States Department of Agriculture Agricultural Research Service (E.S.B.), important in shaping the maize rhizosphere microbiome. the University of Georgia (J.G.W.), the David and Lucile Packard Foundation, Future studies may address other aspects of the microbiome and the Max Planck Society (R.E.L. and W.A.W.). This work was funded by the Joint Genome Institute (JGI) Director’s Discretionary Grand Challenge Program. that are more directly related to function, using approaches such Work conducted by the US Department of Energy (DOE) JGI, a DOE Office of as metagenomics and metabolomics. The rhizosphere microbiome Science User Facility, is supported by the Office of Science of the US DOE under phenotypes could also be defined closer to the root surface (e.g., Contract DE-AC02-05CH11231.

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