Matthieu Barret,a,b,cMartial Briand,a,b,cSophie Bonneau,a,b,cAnne Préveaux,a,b,cSophie Valière,d,eOlivier Bouchez,d,f Gilles Hunault,gPhilippe Simoneau,a,b,cMarie-Agnès Jacquesa,b,c
INRA, UMR1345 Institut de Recherche en Horticulture et Semences, Beaucouzé, Francea; Agrocampus Ouest, UMR1345 Institut de Recherche en Horticulture et Semences, Beaucouzé, Franceb; Université d’Angers, UMR1345 Institut de Recherche en Horticulture et Semences, SFR4207 QUASAV, Beaucouzé, Francec; GeT-PlaGe, Genotoul, INRA Auzeville, Castanet-Tolosan, Franced; INRA, UAR1209, Département de Génétique Animale, INRA Auzeville, Castanet Tolosan, Francee; UMR INRA/INPT ENSAT/INPT ENVT, Génétique, Physiologie et Systèmes d’Élevage, INRA Auzeville, Castanet Tolosan, Francef; Université d’Angers, Laboratoire d’Hémodynamique, Interaction Fibrose et Invasivité Tumorale Hépatique, UPRES 3859, IFR 132, Angers, Franceg
Seeds carry complex microbial communities, which may exert beneficial or deleterious effects on plant growth and plant health.
To date, the composition of microbial communities associated with seeds has been explored mainly through culture-based di- versity studies and therefore remains largely unknown. In this work, we analyzed the structures of the seed microbiotas of differ- ent plants from the family Brassicaceae and their dynamics during germination and emergence through sequencing of three mo- lecular markers: the ITS1 region of the fungal internal transcribed spacer, the V4 region of 16S rRNA gene, and a species-specific bacterial marker based on a fragment of
gyrB. Sequence analyses revealed important variations in microbial community compo-sition between seed samples. Moreover, we found that emergence strongly influences the structure of the microbiota, with a marked reduction of bacterial and fungal diversity. This shift in the microbial community composition is mostly due to an in- crease in the relative abundance of some bacterial and fungal taxa possessing fast-growing abilities. Altogether, our results pro- vide an estimation of the role of the seed as a source of inoculum for the seedling, which is crucial for practical applications in developing new strategies of inoculation for disease prevention.
S eeds are sexually derived structures of spermatophytes, which can, under appropriate conditions, germinate to produce new plants. Seeds, like other plant organs such as roots (1), leaves (2), and flowers (3), have evolved in association with a diverse micro- bial community, also known as the microbiota, which can play a role in plant growth and health. Indeed, numerous plant growth- promoting bacteria and phytopathogenic microorganisms have been isolated from a wide range of seeds using classical cultiva- tion-based methods (4,5).
The significance of seed transmission of pathogens in the emergence of diseases in new planting areas has been recognized for decades (4). In consequence, the processes involved in the transmission of microorganisms from plant to seed have been documented mainly for phytopathogenic microorganisms. Three major pathways of transmission have been described for seed- borne pathogens: (i) internal transmission through the vascular system, (ii) floral transmission by the stigma, and (iii) external transmission via contact of the seed with microorganisms present on fruits, flowers, or residues (6). According to the transmission pathway, seed-borne microorganisms can be located on the seed surface or imbedded in the tissue of the seed. While the internal transmission by the host xylem is probably restricted to pathogens or endophytes, many plant-associated microorganisms are poten- tially transmitted to the seed by the floral pathway. Indeed, the floral pathway allows the transmission of biocontrol microorgan- isms (7) and phytopathogenic bacteria in nonhost seeds (8,
9).Finally, the external pathway is probably the most permissive way of microorganism transmission from plant to seed, although very few data are currently available in the literature (10).
Although seeds are carriers of multiple microorganisms, this does not necessary imply that the seed-borne microbes will colo- nize seedlings. These seed-borne microorganisms must display great physiological adaptation capacity to the changing conditions encountered during seed germination (11). By definition, germi-
nation starts immediately after seeds imbibe water and is com- pleted when the radicle penetrate the structures that surround the embryo (11). During this physiological process, a range of nutri- ents are released in the soil surrounding the seed. The availability of these carbon compounds in the zone surrounding the germi- nating seed creates a new habitat called the spermosphere, which is a site of intense competition between seed-borne and soil micro- organisms (5).
While the transmission of microorganisms from seed to seed- ling is the primary source of inoculum for the plant, relatively few research groups have investigated the composition of the seed microbiota (12–15) and its dynamics during germination and emergence (16,
17). Pioneer studies have highlighted (i) that theendophytic bacterial communities are relatively well conserved from one generation to another (15) and (ii) that germinating seeds are mostly colonized by soil microorganisms (13,
17). How-ever, the vast majority of these results have been obtained with surface-disinfected seeds, thus ignoring the influence of seed epi- phytes as a source of inoculum. More recently, the composition of the seed-associated epiphytic microbiota of Brassica and Triticum
Received12 November 2014Accepted4 December 2014 Accepted manuscript posted online12 December 2014
CitationBarret M, Briand M, Bonneau S, Préveaux A, Valière S, Bouchez O, Hunault G, Simoneau P, Jacques M-A. 2015. Emergence shapes the structure of the seed microbiota. Appl Environ Microbiol 81:1257–1266.doi:10.1128/AEM.03722-14.
Editor:H. L. Drake
Address correspondence to Matthieu Barret, matthieu.barret@angers.inra.fr.
Supplemental material for this article may be found athttp://dx.doi.org/10.1128 /AEM.03722-14.
Copyright © 2015, American Society for Microbiology. All Rights Reserved.
doi:10.1128/AEM.03722-14
has been investigated by sequencing a portion of cpn60 (18). Al- though this molecular marker has provided interesting informa- tion on bacterial and fungal taxa present at the seed surface, the lack of 16S rRNA gene and internal transcribed spacer (ITS) se- quence data impaired comparison of these results with those of studies performed on other plant compartments.
The production of seeds with optimal sanitary quality often depends on application of pesticides, including fungicides and insecticides. Since reducing pesticide use is a key objective for sustainable agriculture, alternative seed treatments have been de- signed. One of these alternatives is to perform seed coating with microorganisms having plant growth-promoting or biocontrol activities (19). Application of biocontrol microorganisms may have the potential to improve seedlings’ health by protecting them against seed-borne or soilborne pathogens (20). However, the performance of these biological treatments is generally inconsis- tent. Variations of efficiency can be partly explained by the activity of the seed-borne microbial community, which can sometimes limit the installation of exogenous microorganisms (21). There- fore, it is necessary to improve our knowledge of the nature, suc- cession, and activities of seed-borne microorganisms during ger- mination and emergence.
The objective of this study was to investigate the dynamics of the seed microbiota during germination and emergence. To get insights into this dynamics, the compositions of the bacterial and fungal communities associated with 28 plants genotypes affiliated mostly to the Brassicaceae were evaluated at three physiological stages: seed, germinating seed, and seedlings. Sequencing of two molecular markers classically employed in microbial ecology, namely, the V4 region of 16S rRNA (22) and the ITS1 region of the fungal internal transcribed spacer (23), was performed on the MiSeq platform. Since 16S rRNA genes have relatively low se- quence divergence among related bacterial taxa and are discrimi- nant mostly at the genus level (24), an alternative bacterial marker was developed and employed in this work. This molecular marker is based on a portion of gyrB, a gene encoding the
subunit of the DNA gyrase, which is frequently employed as a phylogenetic marker for many bacterial genera (25,
26). The different molecu-lar markers used in this study provide valuable insights into the taxonomic composition of the seed microbiota. In addition, we identified key microbial taxa enriched during emergence, which could be promising candidates as seed inoculants.
MATERIALS AND METHODS
Experimental design.Twenty-eight seed samples (S01 to S28) were ob- tained from various plants belonging to different varieties, species, genera, and families (see Table S1 in the supplemental material). These seed sam- ples were chosen to represent a large range of members of the family Brassicaceae. The structure of the seed microbiota on approximately 1,000 seeds per seed sample, as assessed by 1,000-seed weights, was stud- ied. In parallel, 250 seeds of each sample were incubated in duplicate in sterile plastic boxes containing either crepe paper or blotter paper accord- ing to the standard germination methods of the International Seed Test- ing Association (ISTA). Plastic boxes were incubated at 20°C in obscurity for 24 and 96 h after imbibition in sterile distilled water. Seeds collected after 24 and 96 h of imbibition were defined as germinating seeds and seedlings, respectively. Although this is clearly an oversimplification of the physiological state of the seed, the 24 h time point (H24) was chosen as a proxy for germination since the radicles of most seed samples (including all the Brassicaceae) had emerged by that time. In addition, 96 h (H96)
after imbibition corresponded to the apparition of the cotyledon for most seedlings and was thus chosen as a proxy for emergence.
Sample preparation.A total of 1,000 seeds (H0), 200 germinating seeds (H24), and 100 seedlings (H96) of each sample were transferred in sterile tubes containing phosphate-buffered saline supplemented with 0.05% (vol/vol) of Tween 20. Samples were incubated either overnight at 4°C or for 2 h 30 min at room temperature according to standard proto- cols of ISTA (see Table S1 in the supplemental material). Suspensions were centrifuged (6,000⫻g, 10 min, 4°C), and pellets were resuspended in approximately 2 ml of supernatant and transferred to Eppendorf tubes.
Total genomic DNA was extracted from 84 different samples using the PowerSoil DNA isolation kit according to the manufacturer’s protocol. In addition, an artificial community sample was prepared by mixing equal amounts of genomic DNA from 15 bacterial strains (see Table S3 in the supplemental material). Bacterial strains were provided by the Collection for Plant-Associated Bacteria (CIRM-CFBP, IRHS, Beaucouzé, France).
Genomic DNA of each bacterial strain was extracted with the Wizard genomic DNA purification kit according to the manufacturer’s protocol.
All DNAs were pooled at an equimolar concentration with a final concen- tration of 20 ng ·l⫺1.
Amplicon library construction and sequencing.Amplicon libraries were constructed following two rounds of PCR amplification. The first step was performed with the PCR primers 515f/806r (22) and ITS1F/ITS2 (27), which target the V4 region of 16S rRNA gene and ITS1, respectively.
In addition, primers gyrB_aF64 (5=-MGNCCNGSNATGTAYATHGG- 3=) and gyrB_aR353 (5=-ACNCCRTGNARDCCDCCNGA-3=) were de- signed to amplify a portion ofgyrB, which encodes subunit B of the bac- terial gyrase. The primers’ binding sites correspond toEscherichia coliE22 (IMG taxon ID, 638341087) nucleotide positions 64 to 353 (see “gyrB sequence collection and analysis” below for further information). For- ward and reverse primers carry the 5=-CTTTCCCTACACGACGCTCTT CCGATCT-3= and 5=-GGAGTTCAGACGTGTGCTCTTCCGATCT-3=
tails, respectively. All PCRs were performed with a high-fidelity polymer- ase (AccuPrimeTaqDNA polymerase system; Invitrogen) using the man- ufacturer’s protocol and 2l of environmental DNA (approximately 10 ng). The cycling conditions for 515f/806r and ITS1F/ITS2 were adapted from those described in references22and27. Briefly, reaction mixtures were held at 94°C for 2 min, followed by 30 cycles of amplification at 94°C (30 s), 50°C (60 s), and 68°C (90 s), with a final extension step of 10 min at 68°C. Amplification ofgyrBwas performed as follows: 94°C (2 min) fol- lowed by 35 cycles of amplification at 94°C (30 s), 55°C (60 s), and 68°C (90 s), with a final extension step of 10 min at 68°C. All amplicons were purified with the Agencourt AMPure XP system and quantified with QuantIT PicoGreen. A second round of amplification was performed with 5l of purified amplicons and primers containing the Illumina adapters and indexes. PCR cycling conditions were 94°C (2 min), followed by 12 cycles of amplification (94°C for 1 min, 55°C for 1 min, 68°C for 1 min) and a final extension step at 68°C (10 min). All amplicons were purified and quantified as previously described. The purified amplicons were then pooled in equimolar concentrations, and the final concentration of the library was determined using a quantitative PCR (qPCR) next-generation sequencing (NGS) library quantification kit. Amplicon libraries were mixed with 10% PhiX control according to Illumina’s protocols. Three sequencing runs were performed for this study (see Table S3 in the sup- plemental material) with MiSeq reagent kit v2 (500 cycles).
gyrBsequence collection and analysis.The prevalence ofgyrBwas investigated in 19,345 genomic sequences publicly available in the IMG database v4.3 (28) at the time of analysis. Coding sequences (CDSs) that exclusively belong to the protein family TIGR01059 were defined as GyrB homologs and retrieved for further analysis (18,572 hits found in 18,185 genomic sequences). These amino acid sequences were aligned with E- INS-i of MAFFT v7.012 (29). The corresponding nucleotide sequences were aligned with TranslatorX (30) using the protein alignment. Follow- ing this step, conserved nucleotide blocks were visualized with Bioedit v7.2.5, and primers gyrB_aF64 and gyrB_aR353 were designed. The tax-
onomic resolution of thegyrBregion targeted by the primers gyrB_aF64 and gyrB_aR353 was assessed as follows: nucleotide regions located be- tween nucleotide positions 64 to 353 were selected in thegyrBalignment, primer sequences were trimmed, pairwise distances between allgyrBre- gions were computed, and sequences were grouped according to different genetic distances (from 0.01 to 0.10). ThesegyrBgroups were then com- pared to cliques obtained with whole-genome-based average nucleotide identity (gANI) values, available athttp://ani.jgi-psf.org. These gANI cliques can be used as a proxy for species delineation (N. Varghese, S.
Mukherjee, N. Ivanova, K. Konstantinidis, K. Mavrommatis, N. Kyrpides, and A. Pati, unpublished data). At each genetic distance, the sensitivity, precision, and F1 score were calculated. Using these criterions, we defined the distance of 0.02 as the most representative of the species level, with an F1 score of 0.959 (see Fig. S2 in the supplemental material).
Clustering MiSeq reads into OTUs.Raw reads were assembled in quality sequences using the steps described in the standard operating pro- cedure of mothur athttp://www.mothur.org/wiki/MiSeq_SOP(31). 16S rRNA gene andgyrBsequences were aligned against the 16S rRNA gene Silva alignment and a gyrBreference alignment, respectively. All se- quences that did not align correctly were removed from the data sets.
Chimeric sequences were detected with Uchime (32) and subsequently removed from the data set. Moreover, errors in coding sequences were assessed by translation ofgyrBin amino acid sequences. Any sequence possessing a stop codon was discarded. Taxonomic affiliation of 16S rRNA gene andgyrBsequences was performed with a Bayesian classifier (33) (80% bootstrap confidence score) against the 16S rRNA gene train- ing set (v9) of the Ribosomal Database Project (34) or against thegyrB database created with sequences retrieved from the IMG database (see
“gyrBsequence collection and analysis” above). Unclassified sequences or sequences belonging toEukaryotaorArchaea, chloroplasts, or mitochon- dria were discarded. Sequences were divided into groups according to their taxonomic rank (level of order) and then assigned to operational taxonomic units (OTUs) at a 97% identity cutoff for 16S rRNA gene and 98% identity forgyrBsequences. ITS read pairs were initially combined in contigs with the command “make.contigs” of mothur v1.33, using the same parameters as those described for 16S rRNA gene andgyrBsequences. The variable ITS1 regions of ITS sequences were extracted with the Perl-based software ITSx (35). Then, sequences were processed using the Quantitative Insight Into Microbial Ecology (QIIME v1.7.0) software (36) according to the procedure of the Fungal ITS analysis tutorial (GitHub, Inc.). Briefly, se- quences were clustered at a 97% identity cutoff using Uclust (37) and taxo- nomic affiliation was performed with a Bayesian classifier (33) (80% boot- strap confidence score) against the Unite database (38).
Definition of a minimum threshold of 1‰ relative abundance for reproducible detection of OTUs.Incorporation of an artificial commu- nity sample in each sequencing run is useful to assess sequencing error rates (39). The artificial microbial community sequenced in this study is composed of genomic DNA from 15 bacterial isolates belonging to 13 distinct families (see Table S2 in the supplemental material). Therefore, in theory, the number of bacterial OTUs observed in this sample should be 13 after filtering of noisy sequences, removal of chimeras, and OTU pick- ing. However, 183 and 281 OTUs were detected in the artificial commu- nity samples after analyses of sequencing runs 1 and 2, respectively (see Fig. S1 in the supplemental material). While the majority of these super- numerary OTUs were represented by few quality sequences in the artificial community samples, they were composed of numerous quality sequences in other sequenced samples. Thus, the additional OTUs observed in our artificial community sample are probably due to false assignation of index reads (40). Consequently, we used the artificial community sequence data sets obtained in sequencing runs 1 and 2 to define a threshold at which OTUs were considered of low abundance and then removed from the sample studied. Due to differences in the number of quality sequences per sample, the number of quality sequences per OTU was divided by the total size of the library (41). At a threshold ofⱖ1 ‰ of the library size, OTUs were defined as abundant OTUs (aOTUs) and conserved for further anal-
yses. Following this procedure, 14 and 13 aOTUs were observed in the artificial community samples sequenced in runs 1 and 2, respectively (see Fig.
S1A and B in the supplemental material). Therefore, subsequent analyses of 16S rRNA gene,gyrB, and ITS were systematically performed on aOTUs.
Microbial community analyses.A recent study has highlighted that the rarefying procedure classically performed in microbial ecology to nor- malize library size could undermine the performance of sample clustering (41). Since “omitted read counts added noise from the random sampling step” (41), we decided to present in this paper the data obtained with the normalization procedure related to the proportion. Both␣anddiversity indexes were calculated with mothur v1.33 (42). Richness was assessed with the number of observed aOTUs, and diversity was assessed with Simp- son’s inverse index. Beta diversity was assessed using the Bray-Curtis dissim- ilarity matrix (43). Nonmetric multidimensional scaling (NMDS) plots were generated for-diversity analyses. Analysis of molecular variance (AMOVA) was performed to assess the effects of the different factors on the microbial community structure (P⬍0.001). Differences in taxonomic abundance were highlighted with Krona radial space-filling displays (44).
Differences in the relative abundance of aOTUs between the different factors were assessed with the R package edgeR (45). This type of analysis is usually employed to detect differential expressions of genes in whole- transcriptome shotgun sequencing (RNA-seq) data sets but has been re- cently proven to be effective for detection of enriched or depleted OTUs between treatments (41). Sequence counts were first normalized with the relative log expression (RLE) method (46), which is implemented in edgeR. Exact binomial tests corrected for multiples inferences with the Benjamini-Hochberg method (47) were then performed to detect differ- ences in relative aOTU abundance between factors. aOTUs were defined as significantly enriched or depleted in one treatment with a correctedP value of⬍0.001 and a log2-fold change in magnitude ofⱖ2. Correlations between aOTUs were calculated with the Sparse Correlations for Compo- sitional data algorithm (SparCC) (48) implemented in mothur. The effect of uneven sampling was corrected by dividing sequence counts by total library size (proportion). Only correlations with values less than⫺0.30 or larger than 0.30 were represented in the network using the R package qgraph (49).
Nucleotide sequence accession number.All sequences have been de- posited in the ENA database under the accession numberERP006367.
RESULTS
The structure of the seed microbiota was assessed in a total of 28 seed samples (S01 to S28) harvested from plants belonging to different varieties, species, genera, and families (see Table S1 in the supplemental material). Samples of 1,000 seeds (H0), 200 germi- nating seeds (after 24 h of water imbibition [H24]), and 100 seed- lings (after 96 h of water imbibition [H96]) were used to study the dynamics of the composition of the seed microbiota during ger- mination and emergence. Amplicon libraries of (i) the V4 region of 16S rRNA gene (22), (ii) the ITS1 region of the fungal ITS (23), and (iii) a portion of gyrB were sequenced in three independent runs using the Illumina MiSeq platform. Overall, 25,371,440 pairs of reads (see Table S2 in the supplemental material) from 85 samples corresponding to H0, H24, H96, and an artificial microbial commu- nity control (see Table S3 in the supplemental material) were ob- tained. Sequences corresponding to 16S and ITS amplicons were clustered into abundant operational taxonomic units (aOTUs) at
ⱖ97% sequence identity, whilegyrB amplicons were grouped at
ⱖ98% sequence identity, which corresponded approximately to the bacterial species level (see Materials and Methods).
Microbial
␣diversity decreased during the transition from
germinating seed to seedling. Bacterial and fungal richness levels
were first assessed in seed samples. Median bacterial richness lev-
els of 55 and 72 aOTUs were observed for 16S rRNA gene and gyrB
sequences in the different seed samples (H0). However, important variations of bacterial richness were observed between seed sam- ples (4 to 196 aOTUs for 16S rRNA gene sequences and 9 to 240 aOTUs for gyrB sequences) (Fig. 1). In comparison, fungal rich- ness was less dispersed, ranging from 14 to 74 aOTUs with a me- dian richness of 40 (Fig. 1). To assess if the observed variation was due to the plant genotype, the location of the seed production region, or the harvesting year, one-way analysis of variance (ANOVA) tests were performed. According to the analysis, none of these factors explained the variability of bacterial and fungal richness (P
⬎0.01) (see Fig. S1 in the supplemental material).
Therefore, it is tempting to conclude that the variability of aOTU richness probably reflects heterogeneity between seed lots.
The influence of germination and emergence on microbial diver- sity on germinating seed (H24) and seedling (H96), respectively, was then investigated. According to 16S rRNA gene sequences, bacterial richness significantly decreases during germination and emergence (one-way ANOVA, P
⬍0.001) (Fig. 1). However, gyrB sequences highlighted a significant decrease in aOTU richness only during the transition from germinating seed to seedling. The discrepancies ob- served with the two bacterial molecular markers employed in this work are probably due to differences in the level of taxonomic res- olution obtained with OTU clustering. Indeed, at a cutoff of 98%
identity, gyrB is species specific for more than 95% of the genomic sequences examined (see Materials and Methods and Fig. S2 in the supplemental material), while at a 97% identity cutoff, numerous 16S rRNA gene sequences are grouped at the genus level (24). A significant decline of bacterial diversity was also observed on seed- lings with both bacterial molecular markers, although this de- crease was observed earlier (H24) with 16S rRNA gene sequences (P
⬍0.001) (Fig. 1). Regarding fungal diversity, results obtained with ITS1 indicated a significant reduction in fungal richness and evenness during emergence (P
⬍0.001) (Fig. 1). Altogether, these
results suggest that bacterial and fungal diversity decreases mainly during emergence.
Emergence influences microbial
diversity. Microbial com- munity diversity between samples was estimated with Bray-Curtis dissimilarity. Nonmetric multidimensional scaling ordinations were used to plot a Bray-Curtis dissimilarity matrix obtained with 16S rRNA gene, gyrB, and ITS aOTUs (Fig. 2). NMDS plots indi- cate a spatial separation between the microbial communities as- sociated with seedlings and the microbial communities associated with seeds and germinating seeds (Fig. 2A,
B, and F). To testwhether this observed clustering was statistically significant, AMOVA tests (50) were performed on each factor present in our sample collection (see Table S4 in the supplemental material).
According to the magnitude of the F test statistic (Fs) values, the spatial separation observed between the fungal communities could be primarily explained by emergence (H96) and plant ge- notypes (at the family and genus level) (see Table S4 in the sup- plemental material). Regarding bacterial diversity, conclusions are less straightforward. Indeed, when bacterial community sim- ilarity was examined at the genus level with 16S rRNA gene se- quences, samples were significantly clustered (P
⬍0.001) by phys- iological stage (H96) and location of the seed production region.
However, samples were significantly clustered neither by emer- gence nor by production region at the bacterial species level with gyrB sequences (P
⬎0.001) (see Table S4 in the supplemental material).
The seed microbiota is composed mainly of
Gammaproteo- bacteriaand
Dothideomycetes.The composition of the microbial community was initially studied on seeds (H0). According to 16S rRNA gene sequences, bacterial aOTUs belonged mainly to Pro- teobacteria (13.1%, 5.8%, and 56.1% in the Alpha-, Beta- and Gammaproteobacteria classes, respectively), Firmicutes (11.3%), and Actinobacteria (9.1%) (Fig. 3). Despite differences in copy
1550500
H0 H24 H96
16S rRNA gene
Time
aOTUs 1550500
H0 H24 H96
gyrB
Time
aOTUs 1550500
H0 H24 H96
ITS1
Time
aOTUs
1252050
H0 H24 H96
Time
invsimpson 1252050
H0 H24 H96
Time
invsimpson 1252050
H0 H24 H96
Time
invsimpson
FIG 1Estimation of bacterial and fungal diversity. Richness (aOTUs) and diversity (Simpson’s inverse index [invsimpson]) were estimated in seeds (H0), germinating seeds (H24), and seedlings with 16S rRNA gene,gyrB, and ITS sequences. Each sample is represented by a green line, while the gray area represents the estimation of the distribution (created via Beanplot [71]).
number per genome, the bacterial community composition ob- tained with gyrB sequences is overall very similar to results ob- tained with 16S rRNA gene sequences, with the exception of Fir- micutes (21.5%) (Fig. 3). The fungal community associated with seeds contained four main classes related to the Ascomycota phy- lum, i.e., Dothideomycetes (60.7%), Eurotiomycetes (5.5%), Leotio- mycetes (6.3%), and Sordariomycetes (4.9%), and the Tremellomy- cetes class (15.5%), belonging to the Basidiomycota (Fig. 3).
Few bacterial and fungal aOTUs were systematically detected in all seed samples (see Table S5 in the supplemental material). For instance, 3 aOTUs belonging to Pantoea (Otu00002), Pseudomo- nas (Otu00001), and Xanthomonas (Otu00007) were obtained with 16S rRNA gene sequences. This value dropped to one bacte- rial aOTU (Pantoea agglomerans) and one fungal aOTU (unclas- sified member of Mycosphaerellaceae) with species-specific mark- ers (gyrB and ITS). Even the core community associated with 9 seed samples harvested from the same plant variety, namely, Bras- sica oleracea var. Capitata, was composed of only 7 fungal aOTUs and 6 bacterial aOTUs or 1 bacterial aOTU depending on the molecular marker employed (see Table S5 in the supplemental material).
Enrichment of specific aOTUs during germination and emergence. Changes in the microbial community composition were observed mainly during emergence (H96). For instance, the relative abundance of Gammaproteobacteria increased to 85% and 83.2% with 16S rRNA gene and gyrB sequences, respectively (Fig.
3). This increase in relative abundance of
Gammaproteobacteria was mainly due to the dominance of aOTUs related to the Pseu- domonas genus, which represented approximately 57% and 49%
of the total number of 16S rRNA gene and gyrB sequences, respec- tively (Fig. 3). Variations of the fungal community composition were also observed during emergence, with a simultaneous de- crease of the relative abundance of Dothideomycetes (46.8%) and an increase in the relative abundance of Eurotiomycetes (17.7%).
This shift in fungal community composition was mostly due to an increase in the relative abundance of aOTUs related to the Peni- cillium genus (Fig. 3).
To gain more insights into the dynamics of the microbial com- munity during emergence, we investigated changes in the relative abundance of aOTUs between samples. Differences in the relative abundance of aOTUs were assessed with the R package edgeR (45) using exact binomial tests corrected for multiple inferences with the Benjamini-Hochberg method (47). Pairwise comparisons were performed for each physiological stage (H0, H24, and H96).
aOTUs were defined as significantly enriched or depleted in one treatment at a corrected P value of
⬍0.001 and a log2-fold change magnitude of
ⱖ2. Changes in the relative abundance of aOTUs were detected mostly in the comparison of H96 versus H0 results.
Indeed, the relative abundances of 199 (16S rRNA gene), 106 (gyrB), and 167 (ITS) aOTUs were decreased during the transition from seeds to seedlings, confirming the decrease in bacterial and fungal richness previously observed (Fig. 1). In contrast, 13 (16S rRNA gene), 47 (gyrB), and 50 (ITS) aOTUs were significantly enriched in seedlings (see Table S6 in the supplemental material).
Interestingly, bacterial aOTUs significantly enriched in the com- parison of H96 with H0 were related to bacterial taxa (e.g., Massilia, Pantoea, or Pseudomonas) frequently found in plant- associated environments. In the same vein, several fungal aOTUs significantly enriched in seedlings compared to seeds correspond to ubiquitous cosmopolitan taxa (e.g., Penicillium, Chaetomium globosum, Rhizopus oryzae) often found in soil and on seed sur- faces. These bacterial and fungal taxa have in common a fast- growing ability (51,
52,53,54,55,56) that may explain their rapidcolonization of seedlings.
Cooperation between microbial taxa is frequent within the
seed microbiota. Cooperation and competition between aOTUs
were monitored by generating correlations networks with SparCC
(48) on 16S rRNA gene, gyrB, and ITS sequences. Considering
FIG 2Emergence influences the microbial-diversity NMDS ordination of Bray-Curtis dissimilarity matrix obtained with 16S (A, B),gyrB(C, D), and ITS (E, F) aOTUs. Each dot represents a microbial community observed in samples derived from H0 (red), H24 (blue), and H96 (green). Stress values and total variance are indicated for each NMDS ordination.only correlations with values less than
⫺0.30 or greater than 0.30, we identified 236, 652, and 98 associations between 41 (16S rRNA gene), 146 (gyrB), and 33 (ITS) aOTUs. Positive associations be- tween aOTUs were mostly observed (Fig. 4A,
D, andG), suggest-ing weak competition and frequent cooperation between micro- organisms. Overall, the cooccurrence of aOTUs across networks was not driven by the physiological stage of the sample, as aOTUs enriched during emergence were not systematically grouped to- gether (Fig. 4B,
E, andH). In contrast, clustering was observed foraOTUs affiliated to the same taxonomic class (Fig. 4C,
F, andI).For instance, bacterial aOTUs affiliated to the class Bacilli were clus- tered in the networks drawn with 16S rRNA gene and gyrB sequences (Fig. 4C and
F). Moreover, fungal aOTUs belonging to theDothideo- mycetes were also grouped in a module of network (Fig. 4I). Taken together, these results suggest that cooperation occurs between aOTUs that belong to phylogenetically related taxa.
DISCUSSION
Seeds harbor a complex microbial community, which may exert
beneficial or deleterious effects on plant growth and health. To
FIG 3Dynamics of microbiota composition during germination-emergence. Krona radial space-filling (44) charts show the mean relative abundances of bacterial and fungal taxa in seeds (H0), germinating seeds (H24), and seedlings (H96).date, the structure of the seed microbiota has been explored mainly through culture-based diversity studies and therefore re- mains largely unknown. In this work, a comprehensive analysis of the seed microbiota and its dynamics during germination and emergence was performed through an amplicon sequencing ap- proach.
The amplicon sequencing approach relies on sequencing of a genomic region (also known as molecular markers) to identify the different species present in environmental samples. In microbial ecology, the most frequently employed markers are located in ri- bosomal genes (16S rRNA gene for Archaea and Bacteria) or in flanking regions (ITS for Fungi) since these regions are amplified with universal primers in a wide range of microorganisms (23,
57).However, variability of rRNA copy number per genome along with intragenomic polymorphisms within these regions may lead
to an overestimation of species diversity (23,
24). In addition, 16SrRNA gene sequences often fail to resolve bacterial species and are frequently limited to taxonomic affiliation at the genus level (24).
Therefore, methods using alternative molecular markers (i.e., cpn60 and rpoB) present in all bacterial genomes as a single-copy gene have been successfully developed (58,
59). In this work, wehave designed another universal bacterial marker based on a por- tion of gyrB, a gene encoding the
subunit of the DNA gyrase that is frequently employed as a phylogenetic marker for numerous bacterial genera (25,
26). In comparison tocpn60 and rpoB mark- ers, the region of gyrB can be successfully amplified with a single primer set in multiple bacterial phyla. Although some PCR bias probably affected the representation of the bacterial community composition, the results obtained at the phylum level with gyrB sequences are similar to those obtained with 16S rRNA gene se-
FIG 4Correlation networks observed between aOTUs. Correlation networks obtained with 16S rRNA gene (A, B, C),gyrB(D, E, F), and ITS (G, H, I) sequences.Nodes correspond to aOTUs, and connecting edges indicate correlations between them. Correlations between aOTUs were calculated with the Sparse Correla- tions for Compositional data algorithm (SparCC) (48) implemented in mothur. The effect of uneven sampling was corrected by dividing sequence counts by total library size. Only correlations with values less than⫺0.30 (blue) or larger than 0.30 (orange) were represented in the network using the R package qgraph (49).
While graphics in panels A, D, and G represent all the aOTUs, graphics in panels B, C, E, F, H, and I are restricted to aOTUs with negative or positive correlations.
Blue and orange nodes (B, E, and F) represent aOTUs with decrease and increase in relative abundance during transition from seeds to seedlings. Node colors (C, F, and I) represent the different bacterial and fungal classes.
quences (Fig. 1). Moreover, the gyrB marker developed allows the affiliation of 18,185 genomic sequences to the species level with a sensitivity and precision of
⬎0.95 (see Materials and Methods; see also Fig. S2 in the supplemental material), which outperformed the taxonomic classification achieved with 16S rRNA markers (24,
57). In consequence,gyrB is useful to monitor changes in the relative abundance of bacterial species and affords an alternative tool to 16S rRNA gene for description of bacterial community composition.
The microbial communities associated with seeds examined in this study are composed of approximately 70 bacterial and 50 fungal species. Although fungal richness is relatively constant across seed samples, strong variations in bacterial richness are observed with both molecular markers. Direct comparison of di- versity indexes between studies is not straightforward since differ- ent molecular markers, sequencing technologies, and analysis workflows are employed. For instance, in order to decrease the false-assignation rate of index reads in MiSeq experiments, we have considered only aOTUs in our analysis. Despite discrepan- cies between different studies, the range of bacterial richness ob- served in our study seems to be in accordance with previous re- sults obtained on Brassica, Spinacia oleracea, and Triticum seeds (16,
18). In addition, it is tempting to conclude that the seed mi-crobiota contains on average fewer bacterial and fungal taxa than the microbial community associated with the rhizosphere (1,
60, 61) and a comparable level of diversity in comparison to the phyl-losphere (2,
61).Differences in seed microbiota composition could be ex- plained by the plant genotype itself (e.g., seed size, seed anatomy) but also by abiotic factors, such as field management practices, harvesting methods, seed processing, and storage (62). Based on our samples, the structure of the seed microbiota seems to be indeed driven by abiotic factors, such as the geographic location of the production region and the harvesting year. Similarly, the com- position of the seed endophyte community of Zea and Oryza sa- tiva is influenced mostly by soil types and water regime treatments (13,
15). In contrast, the effect of the host species is significant onlyon fungal community composition but does not seem to impact the structure of the bacterial community associated with seeds.
Additional studies with experimental design dedicated to the eval- uation of the effect of the host genotype, soil types, and anthropo- genic activities on the structure of the seed microbiota will be necessary to further support these observations.
According to our DNA profiling results, germination does not seem to affect the bacterial and fungal diversity. This is quite surprising since chemical properties drastically changed within germinating seeds as a result of intense exudation (11) and consequently one might expect a selection of copiotrophic micro- organisms on germinating seeds. These results have now to be confirmed by RNA profiling approaches to specifically monitor active microbial populations and avoid the amplification of extra- cellular DNA. On the other hand, a major decrease of bacterial and fungal diversity is observed during transition from germinating seeds to seedlings, which is probably indicative of a strong selec- tive force exerted by the young plant on seed-borne microorgan- isms. The decrease of diversity observed in our study is undoubt- edly counterbalanced by the acquisition of additional microbial members when seeds are sown in soils (62). Indeed, the spermo- sphere of various germinating seeds is frequently colonized by soil bacteria (5,
12,63). This soil inoculum along with airborne mi-croorganisms (64) will then compete with seed-borne microor- ganisms for colonization of different plant compartments.
Overall, the seed microbiota is composed mainly of three bac- terial phyla, namely, Actinobacteria, Firmicutes, and Proteobacte- ria, as well as two fungal classes, the Dothideomycetes and Tremel- lomycetes. Interestingly, these taxa are also frequently associated with the rhizosphere and the phyllosphere of various plant species (60,
61,65), which suggests that the seed might provide an impor-tant source of microbial inoculum for other plant compartments, as already highlighted by Van Overbeek et al. (66). Based on gyrB and ITS sequences, one fungal aOTU affiliated to the Mycospha- erellaceae and one bacterial aOTU affiliated to Pantoea agglomer- ans (Otu00001) were ubiquitous on seeds examined in this study.
Some strains of P. agglomerans possess plant growth-promoting activities and are therefore of interest for seed treatment (18).
In order to have further insights on seed-borne bacterial and fungal taxa selected during germination and emergence, we ana- lyzed changes in relative abundance of aOTUs through edgeR, a statistical method employed in RNA-seq analysis (45). We de- tected some aOTUs significantly enriched on seedlings that belong to Bacillus, Massilia, Pantoea, and Pseudomonas. These bacterial taxa are frequently encountered in several plant compartments, including seeds, leaves, and roots (18,
60, 65, 67). In addition,fungal aOTUs enriched during emergence, such as Trichoderma viride (68) and Chaetomium globosum (69), corresponded to bio- control fungal species, which have the ability to colonize plant tissues both as epiphytes and as endophytes. All these microbial taxa enriched in seedlings are capable of rapid growth in response to increase in nutrient availability (52,
54, 55, 56), which mayindicate that r-strategists (70) are selected during emergence. En- hanced colonization of seedlings by copiotrophic taxa suggests that competitive exclusion probably occurs between functional equivalent species. However, this hypothesis is not supported by analysis of correlation between aOTUs. Indeed, negative correla- tions between aOTUs were sparse, while frequent cooccurrences of aOTUs between microbial taxa were highlighted (Fig. 4).
In summary, our detailed analysis of epiphytic and endophytic microbial communities associated with seeds through amplicon sequencing approaches revealed that the plant genotype had a strong effect on the dynamics of the seed microbiota during ger- mination and emergence and selected key microbial taxa fre- quently associated with other plant compartments. Through the design of an alternative bacterial taxonomic marker based on a portion of gyrB, we were able to monitor changes in the relative abundance of bacterial species. This marker is a valuable alterna- tive tool to 16S rRNA gene for the description of bacterial com- munity composition.
ACKNOWLEDGMENTS
This research was supported in parts by grants awarded by the Region des Pays de la Loire (Qualisem, 2009 05369 and metaSEED, 2013 10080) and the European Commission (TESTA, FP7-KBBE-2012-6, 311875).
We thank Steven Lindow for valuable comments on the manuscript, Thomas Baldwin (Vilmorin), Julia Buitink (IRHS), Régine Delourme (IGEPP), and Hubert Lybeert (HM-Clause) for providing seed samples, Amrita Pati for providing access to whole-genome-based average nucle- otide identity values, Geraldine Taghouti, Perrine Portier, and CIRM- CFBP (IRHS, UMR 1345 INRA-ACO-UA) for providing bacterial strains, and Muriel Bahut and Laurence Hibrand-Saint Oyant from the platform ANAN of SFR Quasav and the Genotoul Get-PlaGe sequencing facilities for their help on the MiSeq experiments.
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