In Situ Relationships Between Microbiota and Potential Pathobiota in Arabidopsis Thaliana

bioRxiv preprint doi: https://doi.org/10.1101/261602; this version posted February 7, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

1 In situ relationships between microbiota and potential pathobiota in Arabidopsis thaliana

2 Claudia Bartoli1¶, Léa Frachon1¶, Matthieu Barret2, Mylène Rigal1, Carine Huard-Chauveau1,

3 Baptiste Mayjonade1, Catherine Zanchetta3, Olivier Bouchez3, Dominique Roby1, Sébastien

4 Carrère1, Fabrice Roux1*

6 Affiliations:

7 1 LIPM, Université de Toulouse, INRA, CNRS, Castanet-Tolosan, France

8 2 IRHS, INRA, AGROCAMPUS-Ouest, Université d’Angers, SFR4207 QUASAV, 42, rue

9 Georges Morel, 49071 Beaucouzé, France

10 3 INRA, GeT-PlaGe, Genotoul, Castanet-Tolosan, France

11 ¶These authors contributed equally to this work.

13 Corresponding author: [email protected]

14 ABSTRACT

15 A current challenge in microbial pathogenesis is to identify biological control agents that may

16 prevent and/or limit host invasion by microbial pathogens. In natura, hosts are often infected

17 by multiple pathogens. However, most of the current studies have been performed under

18 laboratory controlled conditions and by taking into account the interaction between a single

19 commensal species and a single pathogenic species. The next step is therefore to explore the

20 relationships between host-microbial communities (microbiota) and microbial members with

21 potential pathogenic behavior (pathobiota) in a realistic ecological context. In the present

22 study, we investigated such relationships within root and leaf associated bacterial

23 communities of 163 ecologically contrasted Arabidopsis thaliana populations sampled across

24 two seasons in South-West of France. In agreement with the theory of the invasion paradox,

25 we observed a significant humped-back relationship between microbiota and pathobiota α-

26 diversity that was robust between both seasons and plant organs. In most populations, we also

27 observed a strong dynamics of microbiota composition between seasons. Accordingly, the

28 potential pathobiota composition was explained by combinations of season-specific

29 microbiota OTUs. This result suggests that the potential biomarkers controlling pathogen's

30 invasion are highly dynamic.

31 INTRODUCTION

32 In the last decade, a conspicuous effort has been made to characterize and understand

33 the role of host-associated microorganisms. Animals and plants are currently considered as

34 holobionts associated with the commensals that inhabit and evolve with them (Youle et al.,

35 2013; Bordenstein and Theis, 2015; Vandenkoornhuyse et al., 2015). However, how

36 commensal microbes interact with pathogens remains largely unknown. It has been recently

37 reported that the human gut microbiota can protect against the potential overgrowth of

38 indigenous opportunistic pathobionts (i.e. pathogenic species that inhabit the host at a low

39 bacterial population size) (Vayssier-Taussat et al., 2014) or pathogenic invaders (Kamada et

40 al., 2013) by niche competition and/or induction of the host immune system (Belkaid and

41 Hand, 2014). In plants, both rhizosphere- and phyllosphere-associated bacteria can directly or

42 indirectly increase host resistance to phytopathogenic microorganisms via production of

43 antimicrobial compounds or elicitation of plant defences (Mendes et al., 2013; Berg et al.,

44 2014; Bodenhausen et al., 2014; Haney et al., 2015; Santhanam et al., 2015; Ritpitakphong et

45 al., 2016). For example, plant-associated strains belonging to the Sphingomonas genus can

46 protect the model annual plant Arabidopsis thaliana from infections caused by the causal

47 agent of bacterial spot Pseudomonas syringae (Innerebner et al., 2011). Although these

48 studies support the importance of certain microbes in protecting plants against infections, they

49 mainly focused on the interaction between a single commensal species and a single

50 pathogenic strain. Nevertheless, as previously observed in animals (Singer, 2010; Short et al.,

51 2014), plants are often infected in natura by multiple pathogens (Bartoli et al., 2016;

52 Lamichhane and Venturi, 2015; Roux and Bergelson, 2016). Therefore, the current challenge

53 in plant pathology is to investigate the relationships between resident microbial communities

54 and pathogenic microbes at the community level, i.e. between microbiota (defined here as the

55 microbial communities inhabiting plants) and pathobiota (defined here as the complex of

56 microorganisms with the potential to cause disease on a given plant host; Bartoli and Roux,

57 2017).

58 Understanding how the native cortege of beneficial/commensal microbes interacts to

59 protect plants against the invasion of a cortege of pathogenic species requires testing for

60 community ecological theories (Mallon, et al., 2018). In plant community ecology, substantial

61 efforts have been made in the last decades to understand the relationships between α-diversity

62 of resident species and α-diversity of invasive species (Fridley et al. 2007) by both applying

63 spatial pattern studies and constructed community studies (Naeem et al. 2000; Tomasetto et

64 al. 2013). In a large number of studies conducted at large spatial scale, species richness of

65 both resident and invasive species were found to be positively correlated with the quality of

66 the abiotic environment, thereby leading to a positive relationship between resident species

67 diversity and invader success (Eisenhauer et al., 2013a) (Supplementary Figure S1). On the

68 other hand, as predicted by Elton's theory (1958), constructed community studies performed

69 in homogenous abiotic environments revealed a negative relationship between resident

70 species diversity and invader success (Naeem et al., 2000). This negative relationship is

71 explained by the negative impact of the increased resident species diversity on resource

72 availability, which is in turn detrimental for the establishment of the invasive species

73 (Supplementary Figure S1). These two contrasted phenomena lead to the invasion paradox

74 (humped-back relationship between the diversity of invaders and the diversity of resident

75 species, Supplementary Figure S1), which is therefore predicted to mainly result from the

76 interplay between species diversity, resource availability and niche dimensionality in

77 ecologically relevant conditions (Mallon et al., 2015a).

78 Testing for the invasion paradox between microbiota and pathobiota therefore requires

79 a characterization of the microbial communities across the range of native habitats

80 encountered by a given plant species (Roux & Bergelson 2016). Accordingly, recent studies

81 highlighted that the ecology of the habitats where both plants and microorganisms co-exist

82 and evolve is a crucial variable to take into account when investigating the relationships

83 between a host and its microbes participating in the holobiont system (Agler et al. 2016,

84 Wagner et al. 2016). In addition, in native habitats, plants are naturally exposed to pathogens

85 whose attacks are influenced by local abiotic/biotic conditions (Bartoli et al., 2016).

86 In this study, we aimed to investigate the in situ relationships between bacterial

87 communities and the potential bacterial pathobiota in 163 natural A. thaliana populations

88 collected in southwest of France and inhabiting ecologically contrasted habitats. In particular,

89 we aimed (i) to test for the invasion paradox and (ii) to identify the combinations of microbial

90 species that can prevent and/or limit pathogen invasion. Because bacterial communities

91 associated with plants can rapidly change within the host life cycle (Chaparro et al., 2014;

92 Schlaeppi et al., 2014; Copeland et al., 2015; Agler et al., 2016) and can largely differ

93 between plant compartments (Bodenhausen et al., 2013; Coleman-Derr et al., 2016; Wagner

94 et al., 2016), we described both microbiota and potential pathobiota in the leaf and root

95 compartments across two seasons within a single life cycle of A. thaliana.

96 MATERIAL AND METHODS

97 Identification of A. thaliana populations

98 In this study, we focused on 163 natural A. thaliana populations identified in May

99 2014 in the Midi-Pyrénées region (Figure 1a, Supplementary Table 1). These populations

100 were chosen to maximize the diversity of habitats (such as climate, soil type, vegetation type

101 and degree of anthropogenic perturbation) encountered by A. thaliana (Figure 1b). Because

102 the 163 populations strongly differed in their main germination cohort in autumn 2014 (early

103 November vs early December; Supplementary Text), we defined three seasonal groups

104 hereafter named (i) ‘autumn’ corresponding to 84 populations collected in

105 November/December 2014, (ii) ‘spring with autumn’ corresponding to 80 populations already

106 sampled in autumn and additionally sampled in early-spring (February/March 2015), and (iii)

107 ‘spring without autumn’, corresponding to 79 populations only sampled in early-spring

108 (February/March 2015) (Supplementary Text).

109 To avoid a confounding effect between the sampling date and geographical origin,

110 populations were randomly collected during the sampling periods in autumn 2014 and early-

111 spring 2015.

112

113 Sampling, generation of the gyrB amplicons and sequences

114 The bacterial communities were characterized through amplification of a fraction of

115 gyrB gene encoding for the bacterial gyrase β subunit . This molecular marker has a deeper

116 taxonomic resolution than other molecular markers designed on the hypervariable regions of

117 the 16S rRNA gene (Barret et al., 2015), thereby allowing to distinguish bacterial OTUs at the

118 species level. Furthermore, this single-copy gene limits the overestimation of taxa carrying

119 multiple copies of rrn operons. In this study, based on synthetic communities, we confirmed a

120 better taxonomic resolution of the gyrB gene compared with the 16S rRNA gene

121 (Supplementary Text, Supplementary Data 1).

122 To characterize the bacterial communities, ~4 individuals per population and per

123 season were sampled in situ at the rosette stage resulting in a total number of 1,912 leaf and

124 root samples (Supplementary Text). The epiphytic and endophytic bacterial components of

125 either leaf or root samples were not separated. For each plant compartment, we therefore

126 extracted the total DNA of both epiphytic and endophytic microbes (Supplementary Text).

127 The amplicon gyrB was amplified with some modification of the protocol described in Barret

128 et al. (2015). In particular, three internal tags were added at each 5' and 3' of the original

129 primers to allow the multiplexing of three 96-well plates (Supplementary Text).

130 For each sample, PCR amplifications were repeated three times and technical

131 replicates were pooled in a unique PCR plate. PCR products were purified by using

132 Agencourt®AMPure® magnetic beads following manufacturer's instructions and purified

133 amplicons were quantified with Nanodrop and appropriately diluted to obtain an equimolar

134 concentration. Two µl of equimolar PCR purified products were used for a second PCR with

135 the Illumina adaptors. The second PCR amplicons were then purified and quantified as

136 described above to obtain a unique equimolar pool. The latter was quantified by RQ-PCR and

137 then sequenced with Illumina MiSeq 2X250 v3 (Illumina Inc., San Diego, CA, USA) in the

138 GeT-PlaGe Platform (Toulouse, France). MS-102-3003 MiSeq Reagent Kit v3 600 cycle was

139 used for this purpose.

140

141

142

143

144 Bioinformatics analysis and data curation

145 Reads were demultiplexed by considering the three internal tags. After demultiplexing,

146 the average number of sequences per sample was of 46,135 ± 19,820. Prior further analysis,

147 the negative controls – consisted by the sterilized water used to clean up the samples during

148 field sampling, the sterilized DNA free water used to elute the DNA after phenol/chloroform

149 extraction and the DNA free water used for PCR amplifications – were checked for

150 presence/absence of amplicons by blasting them against the gyrB database composed by

151 30,627 sequences (Barret et al. 2015; Supplementary Text). Because negative controls

152 showed no trace of entire gyrB sequences, they were removed before clustering. Samples

153 showing low sequence quality were also removed before clustering, resulting in a total of

154 1,903 samples. Taxonomic affiliation of gyrB sequences was performed by using a Bayesian

155 classifier (Wang et al., 2007) implemented in the classify.seqs command of mothur (Schloss

156 et al., 2009) against an in-house gyrB database containing 30,627 representative sequences

157 with an 80% bootstrap confidence score. Clustering of sequences into Operational Taxonomic

158 Units (OTUs) was performed with Swarm (Mahé et al., 2015) by using a clustering threshold

159 (d) = 1. Only the OTUs that were composed by a minimum of 5 sequences across all samples

160 were kept, resulting in a total of 278,336 OTUs. Then, we applied two steps of filtering. First,

161 to control for sampling limitation within each sample, we estimated a Good’s coverage score

162 for each sample (Schloss et al., 2009). Based on the distribution of the Good’s coverage score,

163 only samples with a score more than 0.5 were considered. Second, only OTUs showing a

164 minimum relative abundance of 1% in at least one sample were selected by using a home-

165 made perl program. The final data set corresponded to a matrix of 1,655 samples by 6,627

166 OTUs. The 6,627 OTUs obtained after the filtering of the data represented 2.4 % of the

167 totality of the OTUs but 55.6 % of the totality of the reads (Supplementary Figure S2). In

168 addition, the mean number of reads per OTU maintained after filtering was 51.3 fold higher

169 than the one of the discarded OTUs (Supplementary Figure S2). This final data set was used

170 to determine the matrix composed by the potential pathogenic species. More precisely, the

171 potential pathobiota in both leaves and roots of the 163 A. thaliana populations was

172 determined by using a list of the phytopathogenic bacteria established by the International

173 Society of Plant Pathology Committee on the Taxonomy of Plant Pathogenic Bacteria (ISPP-

174 CTPPB; Supplementary Data 2 ) (Bull et al., 2010, 2014). This potential pathobiota list

175 (Supplementary Data Set 2) was composed by 199 bacterial species that were filtered on the

176 microbiota matrix. The potential pathobiota matrix resulted in 11 bacterial species that were

177 further curated. More precisely,we only considered: i) the bacterial species already reported to

178 colonize A. thaliana (Pseudomonas syringae, Pseudomonas viridiflava, Pantoea

179 agglomerans, Sphingomonas melonis and Xanthomonas campestris) (Jakob et al., 2002,

180 Kniskern et al., 2007), ii) the bacterial species pathogenic on Brassicaceae species

181 (Pseudomonas marginalis, Streptomyces scabei and Xanthomonas perforans) (Charron &

182 Sams, 2002, Lerat et al., 2009) , iii) the causal agent of lamb’s lettuce spot Acidovorax

183 valerianellae reported to be widely distributed in France (Gardan et al., 2003). The bacterial

184 species Diaphorobacter oryzae and Janthinobacterium agaricidamnosum were removed from

185 the potential pathobiota matrix because they have been reported previously only in non-plant

186 habitats (Pham et al., 2009) and mushrooms (Lincoln et al., 1999), respectively. The resulted

187 potential pathobiota matrix was composed by 1203 samples and 29 OTUs characterizing the

188 nine bacterial species listed above (i.e. a given bacterial species can be included in more than

189 one OTU). The 29 potential pathogenic OTUs were removed from the microbiota matrix.

190 Therefore, the final microbiota matrix used for further analysis was composed by 6,598

191 OTUs.

192

193

194 Analysis of the α and β-diversity and characterization of the potential pathobiota

195 Shannon diversity and observed species richness were estimated on the final OTU

196 matrix by using the summary.single function of mothur (Schloss et al., 2009). Indexes of α-

197 diversity were also calculated by sample rarefaction of 300/600/900 iters. Results between

198 non-rarefied and rarefied samples were similar and the complete non-rarefied data set was

199 used for the calculation of microbiota diversity.

200 Due to sparsity of the OTU matrix, a Hellinger transformation (Legendre and

201 Gallagher, 2001) was performed by using the vegan R package (Jari et al., 2009) and the

202 relative Hellinger distance was inferred with the decostand command in the vegan R package

203 prior β-diversity analyses. Following Ramette (2007), the resulting Hellinger distance matrix

204 was reduced by running a Principal Coordinates Analysis (PCoA) with the ape R package

205 (Paradis et al., 2004). Because PCoA performed on Hellinger distance matrix based on

206 rarefied data and on Jaccard similarity coefficient matrix led to similar patterns of ordination

207 (Supplementary Figure S3), the PCoA coordinates from the non-rarefied Hellinger distance

208 matrix were retrieved and used for statistical analysis described below.

209 Non-metric multidimensional scaling (NMDS) was also run on the Hellinger distance

210 matrix. However, the values of stress were 0.431 and 0.293 for 2D and 3D NMDS ordination

211 space, respectively. These stress values suggest a lack of fit between the ranks on the NMDS

212 ordination configuration and the ranks in the original distance matrix (Ramette, 2007).

213

214

215

216

217 Statistical analysis

218 Natural variation for the eight descriptors of microbiota (i.e. species richness, α-diversity

219 Shannon index, first and second PCoA axes) and potential pathobiota microbiota (i.e. species

220 richness, α-diversity Shannon index, first and second PCoA axes) was explored using

221 different mixed models (Supplementary Text). A correction for the number of tests was

222 performed to control the FDR at a nominal level of 5%.

223 In order to study the relationship between microbiota α-diversity and potential

224 pathobiota α-diversity, linear and non-linear regressions were fitted using the ‘lm’ and ‘nls’

225 functions implemented in the R environment, respectively (Supplementary Text). Using a

226 paired sample t-test, model selection was performed by comparing the goodness of fit

227 between linear and non-linear models across the ‘diversity estimate x plant compartment x

228 seasonal group’ combinations. In order to confirm the significance of the humped-back curve

229 observed between diversity estimates (species richness and Shannon α-diversity) of the

230 microbiota and the potential pathobiota, the parameters of the non-linear model were

231 compared to a null distribution of those parameters obtained by creating 100 random

232 microbiota OTU matrices paired with 100 random pathobiota OTU matrices (Supplementary

233 Text).

234 Although pathogen-focused network analysis has been already used for investigating

235 the relationships between whole microbiota and microbial pathogens, this method appears

236 only suitable for studying monospecific interactions between a single pathogenic species and

237 the rest of the microbial community members (Poudel et al., 2016). In order to include higher-

238 order interactions in the study of the relationship between microbiota and potential pathobiota

239 composition, a sparse Partial Least Square Regression (sPLSR) (Lê Cao et al., 2008;

240 Carrascal et al., 2009) was therefore adopted to maximize the covariance between linear

241 combinations of relative abundances of OTUs from the microbiota (matrix X) and linear

242 combinations of relative abundances of species from the potential pathobiota (matrix Y)

243 (Supplementary Text). Significance of the OTUs included in the linear combinations was

244 estimated by a Jackknife resampling approach by leaving out 10% of the samples 1,000 times

245 (Supplementary Text).

246

247 Data availability

248 The raw FastQ reads were deposited in the Sequence Read Archive (SRA) of NCBI

249 https://trace.ncbi.nlm.nih.gov/Traces/sra/?study=SRP096011 under the study number

250 SRP096011. The filtered matrix for both microbiota and potential pathobiota are available in

251 Supplementary Data 4 and Supplementary Data 5 respectively. Raw values for both α and β-

252 diversity used for statistical analysis are available in Supplementary Data 6.

253 RESULTS

254 Characterization of the A. thaliana microbiota and potential pathobiota

255 We sampled 163 natural A. thaliana populations chosen to maximize the diversity of

256 habitats encountered by A. thaliana in the Midi-Pyrénées region (Figure 1). Due to differences

257 in germination timing in autumn, about half of the populations were sampled both in autumn

258 and in spring, whereas the other half of populations were sampled only in spring, thereby

259 leading to three seasonal groups of populations, that is ‘autumn’, ‘spring with autumn’ and

260 ‘spring without autumn’.

261 We obtained 18,610,383 high-quality reads across 1,655 samples, with on average

262 ~10,136 reads per sample. After data filtering, we identified 6,627 non-singleton bacterial

263 OTUs. A large amount of these OTUs were specific to roots or leaves, as only ~8.1% OTUs

264 (n = 540 OTUs) were shared between both plant compartments. However, the relative

265 abundance of OTUs shared between leaf and root samples was 20.2 and 16.0 higher than the

266 relative abundance of leaf and root specific OTUs. This suggests that generalists OTUs are

267 dominant members of the Arabidopsis thaliana microbiota.

268 As commonly observed in A. thaliana and other plant species (Lundberg et al., 2012;

269 Bulgarelli et al., 2013; Horton et al., 2014; Coleman-Derr et al., 2016; Wagner et al., 2016),

270 bacterial communities were largely dominated by Proteobacteria (> 80%). At the order level,

271 Burkholderiales (29.3%) and Sphingomonodales (27.9%) were dominant (Figure 2a). In

272 comparison with autumn, samples collected in spring were enriched for Burkholderiales and

273 depleted for Sphingomonodales in the root compartment (Figure 2a). Germination timing in

274 autumn did not impact the relative abundance of bacterial order in plants collected in spring

275 (Figure 2a).

276 Across all samples, we identified 9 bacterial species described as phytopathogenic by

277 the International Society of Plant Pathology Committee on the Taxonomy of Plant Pathogenic

278 Bacteria (Bull et al., 2010, 2014) (Figure 2b). Among them, the three most abundant species

279 representing more than 72% of the whole potential pathobiota were X. campestris (31.9%), P.

280 viridiflava (29.2%) and P. agglomerans (11.7%) (Figure 2b). For the seasonal groups

281 ‘autumn’ and ‘spring w/autumn’, we observed a depletion of X. campestris and an enrichment

282 of S. scabiei in the root compartment in comparison with the leaf compartment (Figure 2b). In

283 the leaf compartment, an enrichment of P. viridiflava between autumn and spring was

284 associated with a depletion of X. campestris (Figure 2b). Similarly to the microbiota, no effect

285 of germination timing in autumn was observed on the relative abundance of the potential

286 pathogenic species for plants collected in spring (Figure 2b).

287 It is noteworthy that the relationship between the total relative abundance among

288 samples and the prevalence among populations was weak for some potential pathogenic

289 bacterial species (Figure 2c). For example, although the total relative abundance of P.

290 syringae and S. scabiei among all samples ranged from 3.4% to 5.5% respectively (Figure

291 2b), these two species were present on average in more than 31% of the populations (Figure

292 2c). Visual inspection of original data suggests that this pattern is mainly explained by the

293 presence of few highly infected plants in many populations.

294 To confirm the pathogenic behaviour of the potential pathobiota, representative

295 bacterial strains of the three most abundant species were tested for their pathogenicity on their

296 natural host A. thaliana and on a non-host plant (tobacco). For the P. syringae complex

297 (including both P. syringae sensu stricto and P. viridiflava; Supplementary Text), we isolated

298 97 strains (74 P. viridiflava strains and 23 P. syringae sensu stricto strains; Supplementary

299 Text, Supplementary Data 3, Supplementary Figure S4). Their pathogenicity was assessed

300 based on in planta bacterial growth and disease symptoms in A. thaliana and a Hypersensitive

301 Response (HR) test on tobacco (Supplementary Text). We found that: i) 84 strains of the P.

302 syringae complex induced a HR on tobacco (Supplementary Text, Supplementary Data 3), ii)

303 all the four strains tested for in planta growth were able to reach a population size of 106

304 CFU.cm-2 7 days post inoculation on A. thaliana (Supplementary Figure S5a, Supplementary

305 Table S2), and iii) seven out of eight strains tested were able to induce disease on at least one

306 of the eight A. thaliana local accessions tested (Supplementary Text, Supplementary Figure

307 S5b, S6, S7, Supplementary Table S3). For X. campestris, 62 strains were isolated

308 (Supplementary Text, Supplementary Figure S8) and all of them induced disease symptoms

309 on the A. thaliana Kas-1 accession (Supplementary Figure S9), which is susceptible to most

310 X. campestris strains isolated from crops (Huard-Chauveau et al., 2013). In addition, 58 of the

311 62 X. campestris induced a HR on tobacco (Supplementary Text, Supplementary Data 3). For

312 P. agglomerans, we isolated a single strain (Supplementary Text, Supplementary Figure S10)

313 that was able to induce disease symptoms on all the 23 A. thaliana local accessions tested

314 (Supplementary Text, Supplementary Figure S11, S12, Supplementary Table S4). Taken

315 together, these results support that most of the strains identified here as part of the A. thaliana

316 potential pathobiota have a pathogenic behavior on A. thaliana. In addition, the relative

317 abundance of the potential pathobiota was significantly higher in A. thaliana individuals with

318 visible disease symptoms (4.5%) than in asymptomatic A. thaliana individuals (1.6%) when

319 sampled in situ (general linear model, F = 26.05, P < 0.001) (Supplementary Figure S13),

320 strengthening the potential pathogenic behavior of the pathobiota identified in this study.

321

322 Alpha-diversity of the A. thaliana microbiota and potential pathobiota

323 We investigated whether the level of α-diversity (species richness and Shannon index)

324 estimated for each sample was driven by the effects of season, plant compartment and

325 population. Statistical results globally led to similar biological conclusions between species

326 richness and Shannon index (Supplementary Tables S5 - S12). Bacterial communities of the

327 microbiota were on average less diverse in roots than in leaves, in particular in autumn

328 (Figure 3a, Supplementary Tables S5, S6, S7). For the potential pathobiota, a similar pattern

329 was observed in autumn but not in spring where the potential pathogenic communities were as

330 diverse in roots as in leaves (Figure 3b, Supplementary Tables S5, S6, S7). More importantly,

331 strong differences among populations in the dynamics of α-diversity between autumn and

332 spring were observed for the microbiota and to a lesser extent for the potential pathobiota

333 (Figure 4, Supplementary Tables S5, S6).

334 Whatever the seasonal group considered, variation in α-diversity of the microbiota was

335 first explained by differences among populations (~16.8% for species richness and ~26.2%

336 for Shannon index) (Figure 4, Supplementary Tables S8 -S11). In contrast, the main source of

337 variation in the α-diversity of the potential pathobiota largely differs between the two seasons

338 (Supplementary Tables S8 -S11). Variation in α-diversity of the pathobiota in autumn and

339 spring was first explained by the factor ‘plant compartment’ (~12.5%) and the factor

340 ‘population’ (~13.8%), respectively (Supplementary Table S11). At the ‘plant compartment ×

341 seasonal group’ level, the level of differentiation among populations for Shannon index was

342 on average almost twice higher for the microbiota than for the potential pathobiota (~38.3%

343 vs ~14.5% of variance explained by the factor ‘population’) (Supplementary Table S12) .

344 No effect of germination timing in autumn was observed on the microbiota and

345 pathobiota α-diversity of plants collected in spring (Supplementary Table S7, S11).

346

347 Relationships between microbiota and potential pathobiota α-diversity: testing for the

348 invasion paradox

349 By considering all samples, we observed a highly significant humped-back

350 relationship between the species richness of the potential pathobiota and the species richness

351 of the microbiota (Figure 5a, Supplementary Table S13, Supplementary Figure S14). This

352 humped-back relationship was robust whatever the ‘seasonal group × plant compartment’

353 considered (Figure 5b, 5c, Supplementary Table S13). A similar humped-back relationship

354 was observed when considering Shannon index instead of species richness, with the exception

355 of the root compartment in the seasonal group ‘spring w/ autumn’ (Figure 5d, 5e, 5f,

356 Supplementary Table S13). This humped-back relationship indicates that a poorly diversified

357 potential pathobiota was associated with either a highly or a poorly diversified microbiota,

358 whereas a highly diversified potential pathobiota was found in presence of microbiota with an

359 intermediate level of diversity. Altogether, these results are in line with theoretical

360 expectations of the invasion paradox (Supplementary Figure S1).

361

362 Composition and structure of A. thaliana microbiota and potential pathobiota

363 For the microbiota, the first two PCoA axes explained 20% of the β-diversity (Figure

364 6). The microbiota was structured according to a pattern of two perpendicular branches

365 (Figure 6a). The abundance variation of the most abundant OTU, i.e. an OTU belonging to the

366 genus Sphingomonas, explained up to 20.7% of the variation along the first branch (Figure

367 6b).The abundance variation of the second most abundant OTU (an OTU belonging to the

368 family Oxalobacteraceae), was related to the variation along the second branch (Figures 6c).

369 For the potential pathobiota structured according to a pattern of three branches (Figure 6d),

370 the first two PCoA axes explained 37% of the β-diversity (Figure 6d). The variation along two

371 of these branches was related strongly but independently to the abundance variations of P.

372 viridiflava and X. campestris (Figures 6e, 6f).

373 We observed strong differences among populations for the dynamics of β-diversity of

374 the microbiota between autumn and spring, with the factor ‘season × population’ explaining

375 53.8% and 43.2% of the variation along the first and second PCoA axes, respectively

376 (Supplementary Figure S15, Supplementary Tables S5,S6). The dynamics of β-diversity of

377 the potential pathobiota between autumn and spring was also dependent on the considered

378 population but to a much lesser extent than what was observed for the microbiota β-diversity

379 (i.e. 9.8% and 7.6% of the variation along the first and second PCoA axes, respectively)

380 (Supplementary Tables S5, S6).

381 Within each seasonal group, the variation of microbiota along the PCoA axes was

382 largely explained by differences among populations (up to ~81%) (Supplementary Figure

383 S16, Supplementary Tables S8 – S11), while the variation of the potential pathobiota β-

384 diversity was first explained by the factors ‘population’ (~16.2%) and ‘plant compartment’

385 (~12.6%) when considering the first and second PCoA axis, respectively (Supplementary

386 Table S11). At the ‘plant compartment × seasonal group’ level, the level of differentiation

387 among populations for β-diversity was on average higher for the microbiota than for the

388 potential pathobiota (~76.5% vs ~10.8% of the first PCoA axis variance explained by the

389 factor ‘population’) (Supplementary Table S12, Supplementary Figure S16).

390 An effect of germination timing in autumn on the β-diversity of plants collected in

391 spring was not observed on microbiota but on potential pathobiota. In the leaf compartment,

392 variation along the first PCoA axis was driven by differences among populations with early

393 autumn germinants (i.e. ‘spring with autumn’ season group), while variation along the second

394 PCoA axis was driven by differences among populations with late autumn germinants (i.e.

395 ‘spring without autumn’ season group) (Supplementary Table S12, Supplementary Figure

396 S16).

397

398 Relationship between microbiota and potential pathobiota β-diversity

399 For each ‘season group × plant compartment’ combination, a non-negligible

400 percentage of variation of the potential pathobiota composition (16.9% on average) was

401 explained by a minor fraction of the variation of the microbiota composition (3.3% on

402 average) (Figure 7). The identity of the microbiota OTUs associated with variation of the

403 potential pathobiota composition largely differs between seasons, plant compartment (in

404 particular in autumn) and germination cohorts in autumn (Figures 7 and 8). Among the 17

405 candidate OTUs of the microbiota, the fifth most abundant microbiota OTU (Pseudomonas

406 moraviensis) was the most prevalent microbiota OTU across the six ‘season group × plant

407 compartment’ combinations. However, a large fraction of the candidate microbiota OTUs

408 (58.8%) corresponds to unclassified OTUs at the order level.

409

410 DISCUSSION

411 The characterization of the A. thaliana potential pathobiota in an ecological context

412 Several evidences suggest that the potential pathobiota was well characterized in our

413 set of 163 natural populations of A. thaliana, thereby allowing investigating the bacterial

414 microbiota-potential pathobiota relationships. First, according to the gyrB community

415 profiling, X. campestris and P. viridiflava were the two most abundant species of the A.

416 thaliana potential pathobiota. These observations are congruent with a previous bacterial

417 community profiling obtained with a 16S rRNA region of 1.4kb from a field experiment on

418 wild-type and mutant lines of A. thaliana (Kniskern et al., 2007). Second, we detected in situ

419 a strong relationship between the presence of disease symptoms and the relative abundance of

420 the potential pathobiota. Third, pathogenicity tests on both host and non-host plants confirmed

421 that most of the strains belonging to the three most abundant species - composing almost three

422 quarters of the whole potential pathobiota - have a pathogenic behavior. Finally, we found a

423 significant and strong positive relationship between relative abundance and α-diversity of the

424 pathobiota (Supplementary Table S14). The latter result suggests that plants are more often

425 infected by consortia of pathogenic species than by a single pathogenic lineage, which is in

426 line with similar observations obtained in humans and animals (Faust et al., 2012). For

427 example, multiple closely related Borrelia genospecies have been found to display a positive

428 co-occurrence in ticks (Herrmann et al., 2013). Studies on plant pathogens also suggest that

429 co-infection is a frequent process mediated by niche functionality of the colonizing species

430 (Lamichhane and Ventury, 2015). More broadly, primary infecting pathogenic lineages can

431 open the route for opportunistic lineages, as previously demonstrated for brassica black rot

432 disease in which leaf infection by Xanthomonas is followed by the infection of a soft-rotting

433 bacterium responsible for the rotting disease (Williams, 1980).

434

435 Putting the characterization of bacterial communities in an ecological genomics

436 framework

437 In this study, we found that the in situ microbiota and potential pathobiota of A.

438 thaliana were affected by the combined effects of season, plant compartment and population.

439 In particular, we observed a strong dynamics in community succession of the microbiota

440 between seasons, reinforcing the need to study the dynamics of bacterial communities over

441 the entire plant life cycle (from seed to seed) across a large range of native habitats (Roux and

442 Bergelson, 2016). In addition, our study revealed that seasonal community succession of the

443 microbiota largely differed among the 163 populations. Because α- and β-diversity varied at a

444 very small geographic scale (Supplementary Figure S17), understanding among-population

445 variation of seasonal community succession will require a thorough characterization of abiotic

446 and biotic factors known to influence microbiota variation in plant species, such as soil

447 conditions (Lundberg et al., 2012; Bulgarelli et al., 2013), micro-local climatic conditions

448 (Classen et al., 2015) and plant community composition (Aleklett et al., 2015; Geremia et al.,

449 2016).

450 In contrast to the microbiota, we found that plant compartment mainly influenced the

451 composition of the potential pathobiota as well as the relative abundance of the most abundant

452 pathogenic species. Our results are in accordance with previous studies reporting that

453 pathogenic species such as P. syringae sensu lato and X. campestris evolved specific

454 strategies (e.g. entry by stomata, hydatodes and wounds) to infect the leaf compartment in a

455 wide range of crops (Mansfield et al., 2012). On the other hand, the most abundant OTUs of

456 the microbiota were shared between leaves and roots. In agreement, the use of a gnotobiotic

457 A. thaliana plant system allows to demonstrate potential reciprocal relocation between root

458 and leaf microbiota members (Bai et al., 2015). In the same study, whole-genome sequencing

459 and functional analysis of bacteria associated with both leaves and roots of A. thaliana

460 highlighted a clear taxonomy and functional overlap of the bacterial populations inhabiting

461 this plant species (Bai et al., 2015). Altogether, these results are in contrast with previous

462 studies on human microbiota demonstrating a remarkable organization of microbes into body

463 site niches (Faust et al., 2012; Belkaid and Hand, 2014). This discrepancy might originate

464 from a lack of specialized plant tissues constituting strictly specialized niches for commensal

465 bacteria as observed in human organs.

466

467 The invasion paradox is mediated by distinct microbiota composition between seasons

468 and plant organs

469 In agreement with theoretical expectations on the invasion paradox (Levine and

470 D’Antonio, 1999; Fridley et al., 2007; Tomasetto et al., 2013), we observed a humped-back

471 relationship between potential pathobiota α-diversity and microbiota α-diversity. Such a

472 pattern may have been observed due to the large range of habitats where A. thaliana plants

473 have been collected, thereby increasing the range of dimensionality of in planta ecological

474 niches available for microbes. Besides the interplay between diversity and niche

475 dimensionality, other hypotheses can contribute to the explanation of the humped-back

476 relationship. An increase in microbiota diversity can be associated with an increase of

477 antagonistic (e.g. production of anti-microbial compounds) and predator (e.g. grazing) species

478 (Mallon et al., 2015b), leading to a negative relationship between potential pathobiota α-

479 diversity and microbiota α-diversity. Concerning the positive relationship between poor

480 diversified potential pathobiota and microbiota, several non-exclusive hypotheses can be

481 advanced. Firstly, ecological disturbance can increase the random establishment of a single

482 pathogenic species with negative consequences on native species diversity (Kinnunen et al.,

483 2016). Secondly, by the producing virulence proteins, a pathogen can exclusively invade a

484 given plant compartment (Hacquard et al., 2017). Thirdly, both microbiota and potential

485 pathobiota can be poorly diversified because other microbial communities (such as fungal and

486 oomycete communities) exploit most of the resources available in the plant, leading to niche

487 inter-kingdom competition (Agler et al., 2016).

488 Interestingly, the pattern of invasion paradox was robust between compartments but

489 also between seasons, despite a strong seasonal community succession of the microbiota in

490 most populations. While this observation reinforces the importance of α-diversity in A.

491 thaliana populations to maintain a microbiota balance to prevent pathogen occurrence, it also

492 suggests that the potential pathobiota composition was associated with season-specific

493 combinations of microbiota OTUs. Accordingly, the identity of the microbiota OTUs

494 associated with variation of the potential pathobiota composition largely differs between

495 seasons. This dynamics in the potential biomarkers (i.e. specific bacteria taxa preventing

496 pathogen spread) controlling pathogen’s invasion may result from niche overlap and other

497 mechanisms regulating microbe-microbe interactions that drastically influence the bacterial

498 composition. A better understanding of the processes underlying the observed invasion

499 paradox will require the isolation of a large number of strains representative of the microbiota

500 found in the 163 A. thaliana populations. This step will especially be relevant for the

501 numerous microbiota OTUs that are unclassified at the order level but associated with

502 variation of the potential pathobiota composition.

503 However, we should acknowledge that a substantial fraction of the variation of the A.

504 thaliana pathobiota was not explained by the microbiota variation. This fraction of

505 unexplained pathobiota variation may result from (i) habitat-specific combinations of

506 bacterial species controlling the potential pathobiota, (ii) the control of bacterial pathogen

507 species by fungal or oomycete microbes (Agler et al., 2016), and (iii) the genetics of A.

508 thaliana shaping natural variation in both pathogen’s abundance and prevalence (Roux &

509 Bergelson, 2016). Teasing apart the relative roles of these putative factors will require a

510 thorough complementary ecological and genomic characterization of our natural populations

511 of A. thaliana.

512

513 ACKNOWLEDGEMENTS

514 We are also grateful to the staff of the LIPM greenhouse for their assistance during the growth

515 chamber experiments. This work was funded by the Région Midi-Pyrénées (CLIMARES

516 project), the LABEX TULIP (ANR-10-LABX-41, ANR-11-IDEX-0002-02) and the

517 Métaprogramme MEM (INRA, Metabar programme).

518

519 CONFLICT OF INTEREST

520 The authors declare no conflict of interest.

521 REFERENCES

522 Agler MT, Ruhe J, Kroll S, Morhenn C, Kim S-T, Weigel D et al. (2016). Microbial hub taxa 523 link host and abiotic factors to plant microbiome wariation. PLoS Biol 14: e1002352.

524 Aleklett K, Leff JW, Fierer N, Hart M (2015). Wild plant species growing closely connected 525 in a subalpine meadow host distinct root-associated bacterial communities. Peer J 3: e804.

526 Bai Y, Müller DB, Srinivas G, Garrido-Oter R, Potthoff E, Rott M et al. (2015). Functional 527 overlap of the Arabidopsis leaf and root microbiota. Nature 528: 364–369.

528 Barret M, Briand M, Bonneau S, Préveaux A, Valière S, Bouchez O et al. (2015). Emergence 529 shapes the structure of the seed microbiota. Appl Environmen Microbiol 81: 1257–1266.

530 Bartoli C, Roux F (2017). Genome-wide association studies in plant pathosystems: toward an 531 ecological genomics approach. Front Plant Sci 8: 763.

532 Bartoli C, Roux F, Lamichhane JR (2016). Molecular mechanisms underlying the emergence 533 of bacterial pathogens: an ecological perspective. Mol Plant Pathol 17: 303-310.

534 Belkaid Y, Hand TW (2014). Role of the microbiota in immunity and inflammation. Cell 157: 535 121–141.

536 Berg G, Grube M, Schloter M, Smalla K (2014). Unraveling the plant microbiome: looking 537 back and future perspectives. Front Microbiol 5: 148.

538 Bodenhausen N, Bortfeld-Miller M, Ackermann M, Vorholt JA (2014). A synthetic 539 community approach reveals plant genotypes affecting the phyllosphere microbiota. PLoS 540 Genet 10: e1004283.

541 Bodenhausen N, Horton MW, Bergelson, J (2013). Bacterial communities associated with the 542 leaves and the roots of Arabidopsis thaliana. PloS One. 8: e56329.

543 Bordenstein SR, Theis KR (2015). Host biology in light of the microbiome: ten principles of 544 holobionts and hologenomes. PLoS Biol 13: 1–23.

545 Bulgarelli D, Schlaeppi K, Spaepen S, Ver Loren van Themaat E, Schulze-Lefert P (2013). 546 Structure and functions of the bacterial microbiota of plants. Annu Rev Plant Biol 64: 807– 547 838.

548 Bull CT, De Boer SH, Denny TP, Firrao G, Fischer-Le Saux et al. (2010). Comprehensive list 549 of names of plant pathogenic bacteria, 1980–2007. J Plant Pathol 92: 551–592.

550 Bull CT, Coutinho TA, Denny TP, Firrao G, Fischer-Le Sau M, Li X et al. (2014). List of 551 new names of plant pathogenic bacteria (2011-2012). J Plant Pathol 96: 223–226.

552 Chaparro JM, Badri DV, Vivanco JM (2014). Rhizosphere microbiome assemblage is 553 affected by development. ISME J 8: 790–803.

554 Classen AT, Sundqvist MK, Henning JA, Newman GS, Moore, JAM, Cregger MA, et al. 555 (2015). Direct and indirect effects of climate change on soil microbial and soil microbial-plant 556 interactions: what lies ahead? Ecosphere 6: 130.

557 Carrascal LM, Galván I, Gordo O (2009). Partial least squares regression as an alternative to

558 current regression methods used in ecology. Oikos 118: 681–690.

559 Charron CS, Sams CE (2002). Impact of glucosinolate content in broccoli (Brassica oleracea 560 (italic group)) on growth of Pseudomonas marginalis, a causal agent of bacterial soft rot. 561 Plant Dis 86: 629-632.

562 Coleman-Derr D, Desgarennes D, Fonseca-Garcia C, Gross S, Clingenpeel S, Woyke T et al. 563 (2016). Plant compartment and biogeography affect microbiome composition in cultivated 564 and native Agave species. New Phytol 209: 798–811.

565 Copeland JK, Yuan L, Layeghifard M, Wang PW, Guttman DS (2015). Seasonal community 566 succession of the phyllosphere microbiome. Mol Plant Microbe Interact 28: 274–285.

567 Eisenhauer N, Dobies T, Cesarz S, Hobbie SE, Meyer RJ, Worm K et al. (2013b). Plant 568 diversity effects on soil food webs are stronger than those of elevated CO2 and N deposition 569 in a long-term grassland experiment. Proc Natl Acad Sci USA 110: 6889–6894.

570 Eisenhauer N, Schulz W, Scheu S, Jousset A (2013a). Niche dimensionality links biodiversity 571 and invisibility of microbial communities. Funct Ecol 27: 282–288.

572 Faust K, Sathirapongsasuti JF, Izard J, Segata N, Gevers D, Raes J et al. (2012). Microbial co- 573 occurrence relationships in the human microbiome. Plos Computat Biol 8: e1002606.

574 Fridley JD, Stachowicz JJ, Naeem S, Sax DF, Seabloom EW, Smith MD et al. (2007). The 575 invasion paradox: reconciling pattern and process. Ecology 88: 3–17.

576 Gardan L, Stead DE, Dauga C, Gillis M (2003). Acidovorax valerianellae sp. nov., a novel 577 pathogen of lamb’s lettuce [Valerianella locusta (L.) Latter]. Int J Syst Evol Microbiol 53: 578 795-800.

579 Geremia RA, Pușcaș M, Zinger L, Bonneville J-M, Choler P (2016). Contrasting microbial 580 biogeographical patterns between anthropogenic subalpine grasslands and natural alpine 581 grasslands. New Phytol 209: 1196–1207.

582 Hacquard S, Spaepen S, Garrido-Oter R, Schulze-Lefert P (2017). Interplay between innate 583 immunity and the plant microbiota. Annu Rev Phytopathol 55: 565-589.

584 Haney CH, Samuel BS, Bush J, Ausubel FM (2015). Associations with rhizosphere bacteria 585 can confer an adaptive advantage to plants. Nature Plants 1: 15051.

586 Herrman C, Gern L, Voordouw MJ (2013). Species co-occurrence patterns among lyme 587 borreliosis pathogens in the tick vector Ixodes ricinus. Appl Environm Microbiol 79: 7273- 588 7280.

589 Horton MW, Bodenhausen N, Beilsmith K, Meng D, Muegge BD, Subramanian S et al. 590 (2014). Genome-wide association study of Arabidopsis thaliana leaf microbial community. 591 Nature Comm 5: 5320.

592 Huard-Chauveau C, Perchepied L, Debieu M, Rivas S, Kroj T, Kars I, Bergelson J, Roux F, 593 Roby D (2013). An atypical kinase under balancing selection confers broad-spectrum disease 594 resistance in Arabidopsis. Plos Genet 9: e1003766.

595 Innerebner G, Knief C, Vorholt, JA (2011). Protection of Arabidopsis thaliana against leaf- 596 pathogenic Pseudomonas syringae by Sphingomonas strains in a controlled model system.

597 Appl Environmen Microbiol 77: 3202–3210.

598 Jakob K, Goss EM, Araki H, Van T, Kreitman M, Bergelson J (2002). Pseudomonas 599 viridiflava and P. syringae – natural pathogens of Arabidopsis thaliana. Mol Plant Microb 600 Interact 15: 1195-1203.

601 Jari Oksanen F, Blanchet G, Kindt R, Legendre P, O’Hara RG, Simpson G, et al. (2009). 602 Vegan: community ecology package. R package version 1.16-0. Available at: citeulike- 603 article-id:6657659.

604 Kamada N, Chen GY, Inohara N, Nunez G (2013). Control of pathogens and pathobionts by 605 the gut microbiota. Nature Immunol 14: 685–690.

606 Kniskern JM, Traw MB, Bergelson J (2007). Salicylic acid and jasmonic acid signalling 607 defense pathways reduce natural bacterial diversity on Arabidopsis thaliana. Mol Plant 608 Microb Inter 12: 1512-1522.

609 Kinnunen M, Dechesne A, Proctor C, Hammes F, Johnson D, Quintela-Baluja M, et al. 610 (2016). A conceptual framework for invasion in microbial communities. ISME J 10: 2773– 611 2775.

612 Lamichhane JR, Venturi V (2015). Synergisms between microbial pathogens in plant disease 613 complexes: a growing trend. Front Plant Sci 6: 385.

614 Lê Cao KA, Rossouw D, Robert-Granié C, Besse P (2008). Sparse PLS: variable selection 615 when integrating omics data. Stat Appl Gen Mol Biol 7: 35.

616 Legendre P, Gallagher ED (2001). Ecologically meaningful transformations for ordination of 617 species data. Oecologia 129: 271–280.

618 Lerat S, Simao-Beaunoir AM, Beaulieu C (2009). Genetic and physiological determinants of 619 Stremptomyces scabei pathogenicity. Mol Plant Pathol 10: 579-55.

620 Levine JM, D’Antonio CM (1999). Elton revisited: a review of evidence linking diversity and 621 invasibility. Oikos 87: 15–26.

622 Lincoln SP, Fermor TR, Tindall BJ (1999). Janthinobacterium agaracidamnosum sp. nov., a 623 soft rot pathogen of Agaricus bisporus. Int J Syst Bacteriol 4: 1577-1589.

624 Lundberg DS, Lebeis SL, Paredes SH, Yourstone S, Gehring J, Malfatti S, et al. (2012). 625 Defining the core Arabidopsis thaliana root microbiome. Nature 488: 86–90.

626 Mahé F, Rognes T, Quince C, De Vargas C, Dunthorn M (2015). Swarm v2: highly-scalable 627 and high-resolution amplicon clustering. Peer J 3: e1420.

628 Mallon CA, van Elsas JD, Salles JF (2015a). Microbial invasions: the process, patterns, and 629 mechanisms. Trends Microbiol. 23: 719–729.

630 Mallon CA, Poly F, Le Roux X, Marring I, van Elsas JD, Salles JF (2015b). Resource pulses 631 can alleviate the biodiversity–invasion relationship in soil microbial communities. Ecology, 632 96: 915–926.

633 Mallon CA, Le Roux X, van Doorn GS, Dini-Andreote F, Poly F, Salles JF (2018). The 634 impact of failure: unsuccessful bacterial invasions steer the soil microbial community away 635 from the invader’s niche. ISME J (doi.org/10.1038/s41396-017-0003-y).

636 Mansfield J, Genin S, Magori S, Citovsky V, Sriariyanum M, Ronald et al. (2012) Top 10 637 plant pathogenic bacteria in molecular plant pathology. Mol Plant Pathol 13: 614–629.

638 Mendes R, Garbeva P, Raaijmakers JM (2013). The rhizosphere microbiome: significance of 639 plant beneficial, plant pathogenic, and human pathogenic microorganisms. FEMS Microbiol 640 37: 634–663.

641 Naeem S, Knops JMH, Tilman D, Howe KM, Kennedy T, Gale S (2000). Plant diversity 642 increases resistance to invasion in the absence of covarying extrinsic factors. Oikos 91: 97– 643 108.

644 Paradis E, Claude J, Strimmer K (2004). APE: Analyses of phylogenetics and evolution in R 645 language. Bioinformatics 20: 289–290.

646 Pham VH, Park SJ, Roh Y, Roh DH, Rhee SK (2009). Diaphorobacter oryzae sp. nov., 647 isolated from a thiosulfate-oxidizing enrichment culture. Int J Syst Evol Microbiol 59: 218- 648 221.

649 Poudel R, Jumpponen A, Schlatter DC, Paulitz TC, Gardener BB, Kinkel LL et al. (2016). 650 Microbiome networks: a systems framework for identifying candidate microbial assemblages 651 for disease management. Phytopathology 106: 1083–1096.

652 Ramette A (2007). Multivariate analyses in microbial ecology. FEMS Microbiol Ecol 62: 653 142–160.

654 Ritpitakphong U, Falquet L, Vimoltust A, Berger A, Métraux J-P, L’Haridon F (2016). The 655 microbiome of the leaf surface of Arabidopsis protects against a fungal pathogen. New Phytol 656 210: 1033–1043.

657 Roux F, Bergelson J (2016). The Genetics underlying natural variation in the biotic 658 interactions of Arabidopsis thaliana: the challenges of linking evolutionary genetics and 659 community ecology. Curr Top Dev Biol 119: 111–156.

660 Santhanam R, Luu VT, Weinhold A, Goldberg J, Oh Y, Baldwin IT (2015). Native root- 661 associated bacteria rescue a plant from a sudden-wilt disease that emerged during continuous 662 cropping. Proc Natl Acad Sci USA 112: 5013-5020.

663 Schlaeppi K, Dombrowski N, Oter RG, Ver Loren van Themaat E, Schulze-Lefert P (2014). 664 Quantitative divergence of the bacterial root microbiota in Arabidopsis thaliana relatives. 665 Proc Natl Acad Sci USA 111: 585–592.

666 Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., et al. 667 (2009). Introducing mothur: open-source, platform-independent, community-supported 668 software for describing and comparing microbial communities. Appl Environ Microbiol 75: 669 7537–7541.

670 Short FL, Murdoch SL, Ryan RP (2014). Polybacterial human disease: the ills of social 671 networking. Trends Microbiol 22: 508–516.

672 Singer M (2010). Pathogen-pathogen interaction. Virulence. 1: 10–18.

673 Tomasetto F, Duncan RP, Hulme PE (2013). Environmental gradients shift the direction of 674 the relationship between native and alien plant species richness. Divers Distrib 19: 49–59.

675 Vandenkoornhuyse P, Quaiser A, Duhamel M, Le Van A, Dufresne A (2015). The importance 676 of the microbiome of the plant holobiont. New Phytol 206: 1196–1206. doi: 677 10.1111/nph.13312.

678 Vayssier-Taussat M, Albina E, Citti C, Cosson JF, Jacques MA, Lebrun MH et al. (2014). 679 Shifting the paradigm from pathogens to pathobiome: new concepts in the light of meta- 680 omics. Front Cell Infect Microbiol 4: 29.

681 Wagner MR, Lundberg DS, del Rio TG, Tringe SG, Dangl JL, Mitchell-Olds T (2016). Host 682 genotype and age shape the leaf and root microbiomes of a wild perennial plant. Nature 683 Comm 7: 12151.

684 Wang Q, Garrity GM, Tiedje JM, Cole JR (2007). Naïve Bayesian classifier for rapid 685 assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73: 686 5261–5267.

687 Williams PH (1980). Black rot: a continuing. Plant Disease 64: 736-742.

688 Youle M, Knowlton N, Rohwer F, Gordon J, Relman DA (2013). Superorganisms and 689 holobionts. Microbe Mag 8: 152–153.

690

691 FIGURE LEGENDS

692

693 Figure 1. Plant material. (a) Location of the 163 A. thaliana populations across the Midi-

694 Pyrénées region (southwest of France). The average distance among populations was 99.9 km

695 (median = 92.6 km, SD = 55.4 km). Blue dots represent the 80 populations collected in both

696 autumn and spring, red dots represent the 79 populations collected in spring only and orange

697 dots represent the 4 populations collected in autumn only. The map clearly shows that the

698 range of sampling across the Midi-Pyrénées region was homogeneous during the two

699 sampling seasons. (b) Diversity of habitats encountered by A. thaliana in the Midi-Pyrénées

700 region.

701 Figure 2. Stacked bar plots of the relative abundances of the major bacterial phyla and

702 bacterial species for both microbiota and potential pathobiota of the A. thaliana populations

703 collected in the Midi-Pyrénées region. (a) Stacked barplots representing the relative

704 abundance for the ten most abundant bacterial orders of the microbiota. ‘All samples’ n

705 =1655; Leaf: ‘autumn’ n= 314, ‘spring w/ autumn’ n = 245, ‘spring wo/ autumn’ n = 262;

706 Root: ‘autumn’ n= 309, ‘spring w/ autumn’ n = 267, ‘spring wo/ autumn’ n = 258. In

707 comparison with autumn, samples in spring were enriched for Burkholderiales (χ² = 7.93, P <

708 0.01) and depleted for Sphingomonodales (χ² = 18.18, P < 0.001) but only in the root

709 compartment. (b) Stacked barplots representing the relative abundance of the nine bacterial

710 species representing the potential pathobiota. ‘All samples’ n = 1203; Leaf: ‘autumn’ n = 273,

711 ‘spring w/ autumn ‘ n = 209, ‘spring wo/ autumn’ n = 213; Root: ‘autumn’ n= 180, spring w/

712 autumn’ n = 171, ‘spring wo/autumn’ n = 157. For the seasonal groups ‘autumn’ and ‘spring

713 w/autumn’, we observed a depletion of X. campestris (‘autumn’, χ² = 15.67, P < 0.01; ‘spring

714 with autumn’, χ² = 8.26, P < 0.05) and an enrichment of S. scabiei (‘autumn’, χ² = 18.61 P <

715 0.001; ‘spring with autumn’, χ² = 9.31, P < 0.05) in the root compartment in comparison with

716 the leaf compartment. In the leaf compartment, an enrichment of P. viridiflava between

717 autumn and spring (χ² =15.20, P < 0.01) was associated with a depletion of X. campestris (χ²

718 = 11.50, P < 0.01). A correction for the number of tests was performed to control the False

719 Discovery Rate at a nominal level of 5%. c) Prevalence (number of populations in which the

720 potential pathogen species was detected) of the nine bacterial species characterizing the

721 potential pathobiota.

722 Figure 3. Violin plots (i.e. box-and-whisker plot overlaid with a kernel density plot)

723 representing the seasonal variation of the α-diversity inferred as Shannon's index for both (a)

724 microbiota and (b) potential pathobiota. Leaf and root samples are represented with a red and

725 blue color scale, respectively. Microbiota: ‘Autumn – Leaf’ n= 314 , ‘Autumn – Root’ n =

726 309, ‘Spring w/ Autumn - Leaf’ n= 245, ‘Spring w/ Autumn - Root’ n= 267,‘Spring w/o

727 Autumn - Leaf’ n= 262, ‘Spring w/o Autumn - Root’ n= 258. Pathobiota: ‘Autumn – Leaf’ n=

728 273 , ‘Autumn – Root’ n = 180, ‘Spring w/ Autumn - Leaf’ n= 209, ‘Spring w/ Autumn -

729 Root’ n= 171,‘Spring w/o Autumn - Leaf’ n= 213, ‘Spring w/o Autumn - Root’ n= 157.

730 Figure 4. Variation among populations in the dynamics of α-diversity between autumn and

731 spring. Each dot corresponds to the mean α-diversity (estimated as BLUPs) of a population.

732 ‘leaf’ n = 74 populations, ‘root’ n = 62 populations.

733 Figure 5. Humped-back relationships between potential pathobiota α-diversity and microbiota

734 α-diversity. (a) Species richness when considering all samples. (b) Species richness when

735 considering samples from the leaf compartment. (c) Species richness when considering

736 samples from the root compartment. (d) Shannon’s index when considering all samples. (e)

737 Shannon’s index when considering samples from the leaf compartment. (f) Shannon’s index

738 when considering samples from the root compartment. The red lines indicate a significant

739 quadratic relationship, according to the following non-linear model: pathobiota’s diversity ~

740 k*microbiota’s diversity – q*microbiota’s diversity*microbiota’s diversity.

741 Figure 6. Bacterial composition and structure of A. thaliana illustrated by Principal

742 coordinates (PCoA) plots of Hellinger dissimilarity matrices for microbiota (left panels) and

743 potential pathobiota (right panels). n = 1655 samples for the microbiota matrix and n = 1203

744 for the potential pathobiota matrix. (a) PCoA plot of microbiota for all samples indicates that

745 bacterial composition is mainly structured along two axes. The (b) and (c) plots indicate the

746 relationships between the microbiota composition and the abundance of the two most

747 abundant OTUs, i.e. Sphingomona ssp., and Oxalobacteraceae unclassified sp., respectively.

748 (d) PCoA plot of potential pathobiota for all samples indicates that bacterial composition is

749 mainly structured along three axes. The (e) and (f) plots indicate the relationships between the

750 potential pathobiota composition and the abundance of the two most abundant potential

751 pathogenic OTUs, i.e. Pseudomonas viridiflava and Xanthomonas campestris, respectively.

752 Red-to-blue color gradient indicates high-to-low OTU abundance.

753 Figure 7. Relationships between microbiota β-diversity and potential pathobiota β-diversity

754 based on a Sparse Partial Least Square Regression (sPLSR). Only OTUs with a loading value

755 above 0.2 in more than 75% of the 1,000 Jackknife resampled matrices were considered as

756 significant. The color gradient indicates the strength of the loading values for both microbiota

757 OTUs and potential pathobiota OTUs (yellow: 0.2 < loadings < 0.35, orange: 0.35 < loadings

758 < 0.5, red: loadings > 0.5). ‘All’, ‘Aut’, ‘Sw/ A’ and ‘Sw/o A’ stand for all samples and

759 samples from the three seasonal groups (i.e. ‘autumn’ populations, ‘spring w/ autumn’

760 populations and ‘spring w/o autumn’ populations), ‘L’ and ‘R’ stand for leaf and root,

761 respectively. ‘All’ n = 1655; ‘Aut - L’ n= 314, ‘Sw/ A - L’ n = 245, ‘Sw/o A - L’ n = 262;

762 ‘Aut – R’ n= 309, ‘Sw/ A - R’ n = 267, ‘Sw/o A – R’ n = 258.

763 Figure 8. 2D density plots illustrating the relationships between the relative abundance of the

764 two most abundant bacterial species of the potential pathobiota and the total relative

765 abundance of the microbiota OTUs identified by sPLSR. (a) Example with X. campestris in

766 the seasonal group ‘spring with autumn’ in the leaf compartment (Pearson’s r = -0.15, P =

767 0.0187). (b) Example with P. viridiflava in the seasonal group ‘spring with autumn’ in the

768 root compartment (Pearson’s r = -0.17, P = 0.0006). Red-to-blue color gradient represents

769 high-to-low density gradient.

770

771 SUPPLEMENTARY FILES 772 773 Supplementary Text: supplementary material and methods and supplementary results. 774 Supplementary Figure S1. The invasion paradox. (A) Species richness of both native and 775 alien species is positive correlated with the quality of the environment. Abiotic factors often 776 have similar effects on biodiversity and invader success. (B) The increasing quality of the 777 environment leads to a positive relationship between species richness of alien species and 778 species richness of native species. (C) Elton’s hypothesis describing a negative association 779 between the increasing of the biodiversity and the success of the invaders. The interplay 780 between resource availability and diversity in determining invasion resistance leads to the 781 well-known phenomenon called ‘invasion paradox’. (D), (E) and (F) indicate application of 782 the theories to microbial communities.

783 Supplementary Figure S2. Distribution of the number of reads (expressed in log10) for the 784 6,627 OTUs obtained after data filtering (top panel) and for the remaining 271,706 OTUs 785 discarded with the filtering (bottom panel). The mean number of reads per OTU maintained 786 after filtering was 51.3 fold higher than the one of the discarded OTUs. 787 Supplementary Figure S3. PCoA perfomed on a Hellinger distance matrix based on rarefied 788 data (top panel) and on a Jaccard similarity coefficient matrix distance (bottom panel). 789 Supplementary Figure S4. Phylogenetic tree inferred with a Neighbor Joining (NJ) model 790 (3000 bootstrap repetitions) and based on the cts sequences (350bp) of the 97 P. syringae 791 strains isolated from the 163 A. thaliana populations. Bootstrap values are shown at each node 792 and names of the strain at each branch. All the strains belong to the P. syringae complex and 793 are mainly distributed in the phylogroups 7, 9, 1, 2, 11 and 13. Reference P. syringae strains 794 representative of the 13 phylogroups are labeled in green. Phylogroup affiliation was based on 795 the previous work from Berge et al. (2014). 796 Supplementary Figure S5. Pathogenicity of Pseudomonas sp. strains isolated from the 163 797 A. thaliana populations of the region Midi-Pyrénées. (A) Mean bacterial growth across eight 798 accessions collected in the region Midi-Pyrénées for two strains of P. viridiflava (0114-Psy- 799 NAUV-BL and 0124-Psy-SAUB-AL) and two strains of P. syringae sensu stricto (0132-Psy- 800 BAZI-AL and 0143-Psy-THOM-AL). (B) Illustration of symptoms observed three days after 801 inoculation for two strains of P. syringae. Presence and absence of symptoms was observed 802 on each of the eight accessions collected in the region Midi-Pyrénées for the strains 0111-Psy- 803 RAYR-BL and 0117-Psy-NAZA-AL, respectively. D0, D3, D5 and D7 indicate the number 804 of days after inoculation.

805 Supplementary Figure S6. Genetic variation among five accessions from the Midi-Pyrénées 806 region for the response to the P. viridiflava strain 0124-Psy-SAUB-AL, three days after 807 infiltration. Mock: infiltration with water. 808 Supplementary Figure S7. Heatmap for the symptoms observed three days after inoculation 809 with P. syringae strains. The heatmap shows the interactions between eight natural accessions 810 of A. thaliana from the region Midi-Pyrénées and eight natural strains belonging to the P. 811 syringae complex collected in the same populations than the eight natural accessions. 812 Supplementary Figure S8. Phylogenetic tree inferred with a Neighbor Joining (NJ) model 813 (3000 bootstrap repetitions) and based on the gyrB sequences (280 bp) of the 62 Xanthomonas 814 campestris strains isolated from the 163 A. thaliana populations. Bootstrap values are shown 815 at each node and names of the strain at each branch. All the strains belong to the X. 816 campestris group. Reference Xanthomonas strains are labeled in blue. Sequences for the 817 reference strains were downloaded on GenBank.

818 Supplementary Figure S9. Distribution of disease symptoms observed on the accession Kas- 819 1 among the 62 Xanthomonas campestris strains isolated from the 163 A. thaliana 820 populations. The arrows indicate disease symptoms for the accessions Col-0 and Kas-1 and 821 the mutant rks1-1 with the X. campestris control strain Xcc568 (Huard-Chauveau et al., 2013).

822 Supplementary Figure S10. Phylogenetic tree inferred with a Neighbor Joining (NJ) model 823 (3000 bootstrap repetitions) and based on the gyrB sequences (290 bp) of the Pantoea 824 agglomerans strain (labeled in green) isolated from the MONTI-DL population. Bootstrap 825 values are shown at each node and names of the strain at each branch. All the strains belong to 826 the X. campestris group. Reference Pantoea sequences were obtained from JGI.

827 Supplementary Figure S11. Pathogenicity of a strain of P. agglomerans (0001-Pag-MONTI- 828 DL) isolated from the MONTI-D population of A. thaliana population located in the Midi- 829 Pyrénées region. Left panel: Illustration of symptoms observed five days after inoculation on 830 the reference accession Col-0. The two leaves on the left were inoculated on both sides of the 831 midrib while only one side of the midrib has been inoculated for the three leaves on the right. 832 Right panel: genetic variation observed among natural accessions collected in the Midi- 833 Pyrénées region. Symptoms showed in the picture were obtained five days after inoculation of 834 the P. agglomerans strain on three A. thaliana accessions of the Midi-Pyrénées region 835 (MONTI-A-17, NAUV-B-14 and VILLEM-A-12). As illustrated, the 0001-Pag-MONTI-DL 836 was able to induce disease symptoms but with different severity among the three accessions.

837 Supplementary Figure S12. Dynamics of disease symptoms across 23 accessions of the 838 region Midi-Pyrénées inoculated with the strain 0001-Pag-MONTI-DL of P. agglomerans. 839 dpi: days post-inoculation.

840 Supplementary Figure S13. Box-and-whisker plot illustrating the significant difference of 841 the relative abundance of the pathobiota in the leaf compartment between plants with visible 842 disease symptoms - represented by the blue box (n = 424 samples) - and asymptomatic plants 843 - represented by the red box (n = 396 samples) - when sampled in situ.

844 Supplementary Figure S14. Comparison of the parameters ‘k’ and ‘q’ of the non-linear model 845 (pathobiota’s diversity ~ k*microbiota’s diversity – q*microbiota’s diversity*microbiota’s 846 diversity) run on the raw data (red arrows) to a null distribution of those parameters obtained by 847 creating 100 random microbiota OTU matrices paired with 100 random pathobiota OTU 848 matrices.

849 Supplementary Figure S15. Variation among populations in the dynamics of β-diversity 850 (PCoA, first axis) between autumn and spring. Each dot corresponds to the mean β-diversity 851 (estimated as BLUPs) of a population. ‘leaf’ n = 74 populations, ‘root’ n = 78 populations..

852 Supplementary Figure S16. Percentage of variance of the β-diversity among populations. 853 Red and blue bars correspond to microbiota and potential pathobiota, respectively. A 854 correction for the number of tests was performed to control the FDR at a nominal level of 5%: 855 ns non-significant, ** 0.01>P> 0.001, *** P < 0.001. 856 Supplementary Figure S17. Geographic variation of α-diversity (inferred as Shannon's 857 index) and β -diversity (when considering the first axis of the PCoA) for the microbiota. For 858 each ‘seasonal group x plant compartment’ combination, blue, green, yellow and red dots 859 correspond to populations from the 1st, 2nd, 3rd and 4th quartiles of either the α-diversity of the 860 β -diversity distribution (based on population BLUPs). Values indicate the percentage of α- 861 diversity and β -diversity variation among populations. Significance after a FDR correction at 862 a nominal level of 5%: ** 0.01 > P > 0.001, *** P < 0.001. Number of populations: ‘Autumn 863 – Leaf’ n= 82, ‘Autumn – Root’ n = 82, ‘Spring w/ Autumn - Leaf’ n= 76, ‘Spring w/ Autumn 864 - Root’ n= 79,‘Spring w/o Autumn - Leaf’ n= 80, ‘Spring w/o Autumn - Root’ n= 80.

865 Supplementary Table S1. Names and GPS coordinates (expressed in degrees) of the 163 866 populations.

867 Supplementary Table S2. In planta bacterial growth of four natural Pseudomonas strains in 868 four corresponding local natural populations of A. thaliana, each represented by two 869 accessions. 870 Supplementary Table S3. Natural interactions between eight local natural A. thaliana 871 accessions and eight corresponding local natural Pseudomonas syringae strains for disease 872 symptom evolution. 873 Supplementary Table S4. Genetic variation among 23 accessions of the region Midi- 874 Pyrénées for the response to the strain 0001-Pag-MONTI-DL of P. agglomerans at four time 875 points after inoculation (dpi, days post-inoculation). H²: broad-sense heritability. 876 Supplementary Table S5. Natural variation of α-diversity and β-diversity estimators of 877 microbiota and potential pathobiota in natural populations of A. thaliana collected in both 878 autumn and spring. 879 Supplementary Table S6. Percentage of variance of α-diversity and β-diversity estimators of 880 microbiota and potential pathobiota explained by the factors ‘seasons’, ‘plant compartment’, 881 ‘population’ and their interactions in natural populations of A. thaliana collected both in 882 autumn and spring. 883 Supplementary Table S7. Natural variation of α-diversity and β-diversity estimators of 884 microbiota and potential pathobiota in all the natural populations of A. thaliana collected in 885 spring. 886 Supplementary Table S8. Natural variation of α-diversity and β-diversity estimators of 887 microbiota and potential pathobiota in the natural populations of A. thaliana collected in the 888 seasonal group ‘autumn’. 889 Supplementary Table S9. Natural variation of α-diversity and β-diversity estimators of 890 microbiota and potential pathobiota in the natural populations of A. thaliana collected in the 891 seasonal group ‘spring with autumn’. 892 Supplementary Table S10. Natural variation of α-diversity and β-diversity estimators of 893 microbiota and potential pathobiota in the natural populations of A. thaliana collected in the 894 seasonal group ‘spring without autumn’ 895 Supplementary Table S11. Percentage of variance of α-diversity and β-diversity estimators 896 of microbiota and pathobiota explained by the factors ‘population’, ‘plant compartment’ and 897 their interactions in natural populations of A. thaliana collected either in autumn or ins spring. 898 Supplementary Table S12. Natural variation of α-diversity and β-diversity estimators of 899 microbiota and potential pathobiota for each ‘seasonal group x plant compartment’ 900 combination.

901 Supplementary Table S13. Comparison of the fit between a linear model and a non-linear 902 model on the relationship between the α-diversity estimators (species richness and Shannon 903 index) of the microbiota and the α-diversity estimators (species richness and Shannon index) 904 of the potential pathobiota. 905 Supplementary Table S14. Relationships between the relative abundance of pathobiota and 906 α-diversity estimators (species richness and Shannon index) of the microbiota and potential 907 pathobiota. 908 909 Supplementary Data 1. Validation of the gyrB gene marker. 910 Supplementary Data 2. List of pathogenic bacteria used to determine the potential 911 pathobiota of the 163 A. thaliana populations. 912 Supplementary Data 3. Strains belonging to the P. syringae complex, X. campestris and P. 913 agglomerans isolated from the 163 A. thaliana populations for the validation of the potential 914 pathobiota. Housekeeping gene sequences used for phylogeny are available in the data set. 915 Supplementary Data 4. Abundance matrix obtained after data filtering for the whole 916 microbiota. 917 Supplementary Data 5. Abundance matrix for the potential pathobiota. 918 Supplementary Data 6. Raw values for both α and β diversity used in the statistical analysis 919 of natural variation of microbiota and potential pathobiota.

920 921

922 923 924 925 926 Figure 1 927 928 929 930 931 932

933

934 935 936 937 938 939 Figure 2

940 941

942 943 944 945 946 Figure 3 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961

962

963 964 965 Figure 4 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983

984 985 986 Figure 5 987 988 989 990 991 992

993 994 995 Figure 6

996

a. microbiota Aut S w/ A S w/o A All Order Family OTU L R L R L R Pseudomonadales Pseudomonadaceae Otu0000005 unclassified unclassified Otu0000006 unclassified unclassified Otu0000009 unclassified unclassified Otu0000011 unclassified unclassified Otu0000016 unclassified unclassified Otu0000017 unclassified unclassified Otu0000027 Pseudomonadales Pseudomonadaceae Otu0000028 unclassified unclassified Otu0000051 unclassified unclassified Otu0000063 Actinomycetales Geodermatophilaceae Otu0000083 Burkholderiales Comamonadaceae Otu0000093 unclassified unclassified Otu0000100 Nitrospirales Nitrospiraceae Otu0000225 Burkholderiales unclassified Otu0000346 unclassified unclassified Otu0000399 Burkholderiales Oxalobacteraceae Otu0000757 Portion of variance explained (%) 5.88 1.64 1.44 3.97 4.44 3.49 4.95

b. potential pathobiota Aut S w/ A S w/o A All Species name L R L R L R Acidovorax valerianellae Pantoea agglomerans Pseudomonas syringae Pseudomonas viridiflava Sphingomonas melonis Streptomyces scabiei Xanthomonas campestris 997 Portion of variance explained (%) 17.11 14.90 15.40 17.60 19.34 17.31 16.93 998 999 1000 Figure 7 1001 1002 1003 1004

1005

1006 1007 1008 Figure 8