Draft - tspace.library.utoronto.ca · Draft 2 20 Abstract 21 An investigation was carried out on...

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Changes in rhizosphere bacterial communities associated to tree decline: grapevine esca syndrome case study.

Journal: Canadian Journal of Microbiology

Manuscript ID cjm-2019-0384.R1

Manuscript Type: Article

Date Submitted by the Author: 16-Sep-2019

Complete List of Authors: Saccà, Maria Ludovica; CREA, Agriculture and EnvironmentManici, Luisa Maria; CREA, Agriculture and EnvironmentCaputo, Francesco; CREA, Agriculture and EnvironmentFrisullo, Salvatore; Università degli Studi di Foggia

Keyword: Pseudomonas; actinomycetes; vineyards; qPCR; next generation sequencing.

Is the invited manuscript for consideration in a Special

Issue? :Not applicable (regular submission)

https://mc06.manuscriptcentral.com/cjm-pubs

Canadian Journal of Microbiology

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1 Changes in rhizosphere bacterial communities associated to tree decline:

2 grapevine esca syndrome case study.

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4

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6 Maria Ludovica Saccà1, Luisa Maria Manici*1, Francesco Caputo1, Salvatore Frisullo2

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8 1Council for Agricultural Research and Economics, Agriculture and Environment Research Center

9 (CREA-AA), Bologna, Italy.

10 2University of Foggia, Department of the Sciences of Agriculture, Food and Environment, Foggia,

11 Italy.

12

13

14 Corresponding Author’s information

15 *To whom correspondence should be addressed.

16 Address: Via di Corticella 133, 40128 Bologna, Italy

17 (Phone): +39 051 6316839

18 (E-mail): [email protected]

19

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mailto:[email protected]

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20 Abstract

21 An investigation was carried out on rhizosphere bacteria to identify if they may be associated to

22 perennial crops affected by nonspecific decline, a phenomenon that is difficult to diagnose and

23 prevent. Esca disease of grapevine was chosen for this case study due to its easy foliar symptom

24 identification. Ribosomal DNA-fingerprinting (PCR-DGGE), quantitative PCR (qPCR) and rDNA

25 amplicon sequencing (NGS) were adopted to investigate bacterial communities associated to

26 grapevines which were selected for presence and absence of external foliar symptoms in eleven

27 vineyards. According to PCR-DGGE and qPCR, bacterial communities differed in site of origin

28 (vineyards), but not between symptomatic and asymptomatic plants, whereas qPCR gave a

29 significantly higher presence of total bacteria and Pseudomonas spp. in asymptomatic plants. NGS

30 confirmed no difference between symptomatic and asymptomatic plants, apart from a few minor

31 genera (<0.5%) such as Salinibacterium, Flavobacterium, Nocardia and Janthinobacterium, which

32 were, in all cases, higher in asymptomatic plants and whose functional role should be the object of

33 further investigation. The fact that total bacteria and Pseudomonas were more abundant in

34 rhizosphere of asymptomatic grapevines and that some bacterial genera were associated to the

35 latter, represents a new element when investigating into the multiple-origin phenomenon such as

36 esca disease of grapevine.

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38 Keywords: actinomycetes; PCR-DGGE; Pseudomonas; qPCR; next generation sequencing;

39 vineyards.

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42 Introduction

43 Nonspecific multiple diseases of perennial crops, which are becoming increasingly widespread

44 natural and agro-ecosystems, are reported asone of the largest phenomenon causing crop decline

45 (Ramirez and Kallarackal 2015). This is more frequently attributed to climate change and loss of

46 soil resilience due to unsustainable land management (Winkler et al. 2002; Bréda et al. 2006;

47 Brouwers et al. 2012). At the same time, increased temperatures, drought, salinity and other abiotic

48 stresses linked to climate change can increase the rate of emerging plant pathogens alongside

49 opportunistic fungal pathogens due to more favourable conditions for plant infection or to plant

50 defence variations (Fones and Gurr 2017). Furthermore, the response of plants to combinations of

51 biotic and abiotic stresses is very difficult to extrapolate from the response of plants to each of the

52 different stresses because plant responses to combined stresses is controlled by different pathways

53 that may interact and inhibit each other (Suzuki et al. 2014). Therefore, although root and wood

54 fungal pathogens undebatably represent causal agents of esca-syndrome in grapevine (Vitis vinifera

55 L.) (Bertsch et al. 2013) along with a series of similar phenomena of orchard decline such as replant

56 disease of apple, peach and almond, olive, pistachio and stone fruits decline (Browne et al. 2006;

57 Mazzola and Manici 2012; Úrbez-Torres et al. 2013; Mohammadi et al. 2015), the complex of

58 fungal pathogens associated with those symptoms is unlikely to represent the only explanation for

59 those syndromes. Identification, therefore, of the microbial factors associated to rhizosphere of

60 plants affected by those syndromes may benefit early identification of changes in health and decline

61 trends and consequently the adoption of more suitable management strategies.

62 Evidence that abiotic stresses linked to climate change can increase the rate of plant infection

63 has already been provided for some fungal agents involved in esca syndrome and responsible for

64 grapevine decline (Ferreira et al. 1999). Esca disease which is part of the grapevine trunk disease

65 complex (Larignon and Dubos 1997; Bertsch et al. 2013), and therefore considered to contribute to

66 the general decline of vineyards (Gramaje et al. 2016), are responsible for the global decline of

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67 yield and quality in vineyards that has caused huge economic damage to the vine and grape sector

68 over the past three decades (Hofstetter et al. 2012; Bertsch et al. 2013). A largest number of

69 different fungi have been so far associated to this complex disease of grapevine and continual

70 updates are published (Úrbez-Torres et al. 2015; Gramaje et al. 2018). The fact that the primary

71 causal agents of esca disease have not yet been elucidated and esca-foliar symptoms, appearing as

72 typical foliage deterioration and interveinal chlorosis spots with further necrosis, are still the

73 principal diagnostic tool of this syndrome suggests that a number of diverse mechanisms of plant

74 response can cause the external symptoms of this disease (Bruno and Sparapano 2007).

75 To date, little has been investigated into plant-bacteria interaction in the rhizosphere when

76 plants are affected by esca syndrome. This is a clear gap in knowledge that has already been

77 highlighted by several authors, who have hypothesized that other biological components, such as

78 soil bacteria, may mediate disease (Hofstetter et al. 2012; Bruez et al. 2014; Nerva et al. 2019). The

79 functional role of root-associated bacteria in grapevine has been demonstrated for growth promotion

80 and increased resistance to drought and salinity stress (Marasco et al. 2013). Nevertheless, very few

81 works have focused on the rhizosphere microbial environment associated to grapevines (Martins et

82 al. 2013; Castañeda et al. 2015; Marasco et al. 2018; Berlanas et al. 2019), but none of them

83 concern plant health.

84 As esca syndrome can fits into the context of nonspecific crop decline and microbial

85 communities are good indicators of plant health (Kelderer et al. 2012; Lareen et al. 2016), a study

86 was performed to investigate the hypothesis that rhizosphere bacteria can be indicators of the

87 grapevine health. Therefore, qualitative and quantitative molecular analysis of rhizosphere bacterial

88 communities was performed in a network of eleven vineyards in an important vine growing area

89 located in the European Mediterranean belt. The final aim of this study was to evaluate whether

90 bacterial shifts were associated to symptomatic plants and which bacterial populations were most

91 affected by those changes.

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92 Materials and Methods

93 Sampling areas and soil sampling

94 An intensive wine producing area in the Puglia region in southern Italy was selected for this study.

95 This region has a typical Mediterranean warm temperate climate characterized by hot and dry

96 summers (Csa) according to the Köppen climate classification (Peel et al. 2007), with annual

97 average temperatures of 16.1 °C and rainfall of 567 mm. Eleven above 9- year-old vineyards were

98 selected in the Bari and Foggia provinces (latitude and longitude of the sampling area center:

99 41°16’48”N, 16°3’0”E) with a maximum distance of 30 km from each other whose texture fell

100 within the sandy-loam class according to the USDA soil texture triangle Despite plant replacement

101 is commonly adopted to overcome esca disease (OIV 2016), no plant replacement was carried in

102 those vineyards. Several plants in all of those vineyards presented typical external foliar symptoms

103 ascribable to esca disease, such as foliage deterioration and interveinal chlorosis spots evolving to

104 coalescence necrosis as described by several authors (Larignon and Dubos 1997; Bruno and

105 Sparapano 2007). The Vittoria and Italia varieties of table grape and the wine grape, Sangiovese

106 (nested on the Kober 5BB, 41B, 420A, Sélection Oppenheim 4 -SO4- and Ruggeri 140 -140 Ru-

107 rootstock), cultivated in those vineyards, were used for this study. In all cases, vineyard

108 groundcover was kept free of vegetation, with superficial periodic tillage throughout the growing

109 season.

110 Soil samples were collected in summer (July-August) 2015 at a depth of 0-25 cm under the

111 canopy in four opposite sites in relation to the trunk of two symptomatic and two asymptomatic

112 plants in each of the eleven selected vineyards. Four soil cores including roots (dia. 5 cm) per plant

113 were collected. After collection, soil subsamples adhering to the roots were obtained by vigorous

114 shaking and mixed to obtain two homogeneous samples of 500 g from each of two symptomatic

115 plants and asymptomatic plants in every vineyard. Then, forty-four soil samples (2 replicates of

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116 symptomatic and 2 of asymptomatic plants per 11 vineyards) were air-dried at room temperature,

117 then sieved, and stored in 50 ml sterile falcon per treatment at -80 °C until processed.

118 DNA extraction

119 Total genomic DNA was extracted from 0.25 g of rhizosphere soil (dry weight) using the PowerSoil

120 DNA Isolation kit according to manufacturer instructions (MoBio Laboratories, Carlsbad, CA,

121 USA). Quantification and quality control of DNA were performed using Infinite 200 NanoQuant

122 (Trading AG, Switzerland) and DNA was stored at -20 °C until use. Forty-four DNA extractions

123 were performed for PCR-DGGE and qPCR.

124 Bacterial community DGGE fingerprinting

125 Amplification of Pseudomonas spp., actinomycetes and Bacillus spp. DNA prior to DGGE analysis

126 was performed using a nested PCR approach (PCR-DGGE) with the primer pairs described in Table

127 1. A 40-nucleotide GC clamp was inserted on the 5’ end of the forward primer F968 to prevent

128 complete melting of PCR products during DGGE runs. Assays were carried out using a TGradient

129 Thermal Cycler (Biometra, Göttingen, Germany) in 25 μl reaction volumes containing 5 ng of

130 template DNA, 0.2 μM of each primer, 1.88 mM MgCl2, 1x buffer (20 mM Tris–HCl pH 8.4, 50

131 mM KCl), 200 μM dNTPs mix and 1.25 U Taq polymerase (Invitrogen, Carlsbad, CA, USA).

132 Cycling parameters for the first PCR reactions were as follows: initial denaturation at 94 °C for 5

133 min, followed by 35 cycles of 94 °C for 1 min; 65 °C for 1 min; 72 °C for 2 min, and final

134 extension at 72 °C for 10 min. Amplicons obtained from the first reactions were used as template (1

135 μl) for a second PCR reaction, consisting in an initial denaturation at 94 °C for 3 min, followed by

136 20 cycles of 94 °C for 45 s; 60 °C for 30 s; 72 °C for 1 min, followed by 10 cycles of 94 °C for 45

137 s; 55 °C for 30 s; 72 °C for 1 min, and final extension at 72 °C for 5 min. A negative control

138 without template DNA was included in every PCR run. A double gradient DGGE gel was prepared

139 by using both a 6-8% acrylamide porous gradient and a 45-65% urea/formamide denaturing

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140 gradient (Cremonesi et al. 1997). These conditions permitted to obtain optimal DGGE band

141 separation. DGGE analysis was performed with a D-code system (Bio-Rad Laboratories, Hercules,

142 CA, USA). PCR products (200–250 ng) were loaded on DGGE gel and electrophoresis was run in

143 1x TAE buffer at a constant voltage of 60 V at 60 °C for 16 h. Following electrophoresis, gel was

144 stained with GelRedTM (Biotium) at 10,000x dilution in 1x TAE for 30 min, washed in dH2O for 20

145 min, and photographed using an Alpha Image UV illuminator (Alpha Innotech, San Leandro, CA,

146 USA). DGGE analysis was repeated three times to confirm the pattern. Soil bacterial strains from

147 the Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures

148 (Braunschweig, Germany) were inserted as reference in the Bacillus, actinomycetes and

149 Pseudomonas DGGE analyses respectively. They were: B. circulans (DSMZ 11), Arthrobacter

150 sialophilus (DSMZ 7306) and P. chlororaphis (DSMZ 6508). DGGE images of bacterial

151 communities’ profiles were processed with GelCompar II software (Applied Maths, Belgium).

152 Background noise was removed before searching the bands with a minimum profiling greater than

153 0.5%. Three 44-sample-fingerprints targeting three abovementioned bacterial groups were

154 transformed in binary code (1/0) and subjected to diversity analysis and nonparametric multivariate

155 analysis of variance (npMANOVA) for two factors (vineyard and symptomatic/asymptomatic

156 plants), as described in data analysis.

157 Quantitative PCR

158 Quantification of rhizosphere bacterial DNA (qPCR) in the vineyards under study was performed

159 by qPCR quantification assays. The DNA of the three aforementioned DSMZ bacterial reference

160 strains was amplified using the same primers and conditions described above for the first PCR-

161 DGGE reactions. Quantification of total bacteria was performed using the Bacillus reference strain.

162 Resulting amplicons were purified using the PureLink Quick PCR Purification Kit (Invitrogen) and

163 quantified by Infinite 200 NanoQuant (Trading AG, Switzerland). The gene copy number

164 calculation was obtained using the formula: gene copy/µl = DNA [ng/µl] x 6.02 x 1023/base pairs x

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165 660 x 109. Purified amplicons were serially diluted 10-fold and four replicates were used for

166 standard curve generation for quantification of unknown samples (Bustin et al. 2009). The slope of

167 the standard curves was used to calculate qPCR reaction efficiency.

168 A series of primer pairs was tested for quantification of Pseudomonas spp., Bacillus spp. and

169 actinomycetes from soil samples, before finding the optimal combinations. The following primer

170 pairs were discarded due to non-specific amplification revealed by melting curve analysis and PCR

171 product sequencing: BacF/R1378 for Bacillus spp.; Ps-f/Ps-r and PsF/518r for Pseudomonas spp.

172 Therefore, based on amplicon specificity and primer efficiency, the primer pairs described in Table

173 1 were selected for this study. qPCR assays were carried out using Rotor-Gene SYBR® Green PCR

174 Kit (Qiagen, Hilden, Germany) on a Rotor-Gene 6000 (Corbett Research), according to

175 manufacturer instructions. Two technical replicates were performed for 3 identical independent

176 runs, to assess reproducibility of the assays. Briefly, 1x Rotor-Gene SYBR® Green PCR Master

177 Mix was used in a final reaction volume of 25 μl, with a final primer concentration of 1 μM and 2.5

178 μl of template. After an initial PCR activation step at 95 °C for 5 min, cycling conditions consisted

179 in 5 sec denaturation at 95 °C, and 40 cycles of combined annealing extension at 65 °C for 10 sec.

180 Post-amplification melting curve analysis was performed to verify specificity and identity of qPCR

181 products, with a ramp from 55 °C to 99 °C, rising by 1 °C each step. Results were analyzed with the

182 Rotor-Gene 6000 Series Software 1.7 program. Sterile water was used as no-template control in

183 each run. Quantitative data were subjected to a multifactor ANOVA for the factors: vineyard and

184 symptomatic/asymptomatic plants and run replicates.

185 16S rDNA amplicon sequencing

186 Next generation sequencing (NGS) was carried out with Illumina MiSeq analysis of the V3-V4

187 region of 16S rRNA gene from soil DNA. Unlike the qPCR analysis and DGGE analysis, in this

188 case, onlyone factor of variability (symptomatic vs asymptomatic plants) was analyzed, by

189 considering each vineyard as replicate of each of two treatments. Therefore, a total of 22 samples

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190 was analyzed to compare bacterial communities associated to symptomatic vs asymptomatic plants,

191 in which each treatment consisted of 11 samples (one per vineyard).

192 The V3-V4 hypervariable region of prokaryotic 16S rDNA was amplified using universal

193 primers Pro341F (5′-CCTACGGGNBGCASCAG-3′) and Pro805R (5′-

194 GACTACNVGGGTATCTAATCC-3′) (Table 1) (Takahashi et al. 2014). Amplicon purification

195 was performed with Agencourt AMPure XP beads (Beckman Coulter, Brea, CA, USA) according to

196 the manufacturer’s instructions. A second amplification step was performed for the attachment of

197 Illumina adaptors and barcode tags using Nextera XT index kit. Samples were then pooled and

198 sequenced using Illumina MiSeq platform with a 2x 300 bp paired-end approach by the BMR

199 Genomics service (Padova, Italy). Sequences were then processed according to standard operating

200 procedures using the QIIME v. 1.9.1 pipeline. Briefly, R1 and R2 reads were joined using FLASH

201 v. 1.2.11, and low quality regions (Q<30) were trimmed (Bokulich et al. 2013). Primer trimming

202 was performed using Cutadapt included in Qiime2, quality filter, 3’ end trimming, denoising,

203 dereplication and chimera detection were performed with DADA2. Analysis was based on the

204 pick_closed_reference_otus method and taxonomy was assigned against the GreenGenes database

205 v. 13-8, at 97% sequence similarity for 16S rDNA OTUs. The resultant OTU table was filtered to

206 remove low abundance sequences (<0.005%). Data were further processed using METAGENassist

207 statistical tool for comparative metagenomics (Arndt et al. 2012), by filtering variables of very

208 small values using the median abundance value and removing variables with over 50% zeroes. The

209 purpose of data filtering by removing low abundance taxa was to obtain more consistent and

210 reliable data to improve performance of statistical procedures. The resulting taxa-abundance data

211 sets were used for downstream analysis. Rarefaction curves of the identified OTUs were generated

212 to evaluate sequence coverage. The sequence reads were deposited in the NCBI Sequence Read

213 Archive database (https://www.ncbi.nlm.nih.gov/sra) under the BioProject ID: PRJNA486612,

214 SRA: SRP158319 and accession numbers from SRR7716739 to SRR7716720.

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215 Data analysis

216 Multivariate analysis was conducted using PAST vers. 3.10 (Hammer et al. 2001). Presence–

217 absence data matrices from DGGE fingerprints were subjected to nonparametric multivariate

218 analysis of variance (npMANOVA) using Euclidean distance measure. Quantitative data from

219 qPCR (gene copies/μl) were square root transformed and subjected to three-way ANOVA, and

220 mean separation with Fisher’s Least Significant Differences (LSD) test using the Statgraphics

221 centurion 18 (Statgraphics Technologies, Inc. The Plains, Virginia USA). Taxa-abundance data set

222 from NGS was subjected to Principal Component Analysis (PCA) and Student's t-test, using

223 METAGENassist, after generalized log2 transformation. Dissimilarities between samples were

224 assessed by PERMANOVA and SIMPER (Similarity Percentage) analysis, using Bray-Curtis

225 distance using Past program. Diversity analysis of DGGE dataset were performed using PAST.

226 Chao 2 diversity and standard deviation were obtained with bootstrapping procedures (1000

227 bootstrap replicates) and used to perform graphical comparison of Chao 2 diversity. Whereas, taxa

228 abundance data from MiSeq analysis, were previously subjected to diversity profiling to check

229 whether they were comparable (Tóthmérész 1995), then, when applicable, diversity between

230 symptomatic and asymptomatic plants was compared using Shannon t test.

231 Results

232 Bacterial community DGGE fingerprinting

233 According to two-way npMANOVA. Pseudomonas spp., Bacillus spp. and actinomycetes differed

234 significantly (P<0.001) between vineyards, but not between symptomatic and asymptomatic plants;

235 therefore, DGGE fingerprinting gave a high site-dependent variability of bacterial communities.

236 Overall, bacterial communities did not differ in the Chao2 index between symptomatic and

237 asymptomatic plants. Whereas, when comparing diversity between symptomatic and asymptomatic

238 plants in each vineyard, Pseudomonas spp. overall showed a higher diversity index in asymptomatic

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239 plants, although this difference was significant in only 3 out of 11 vineyards, whilst actinomycetes

240 and Bacillus spp. did not show any differences.

241 Quantitative PCR

242 The standard curves obtained by diluting PCR products from the reference bacterial isolates,

243 showed a linear dynamic range (R2 between 0.97 and 0.99) and a reaction efficiency (E) between

244 93% and 104%, calculated as E = 10(1/slope)−1 (Suppl. Mat . Fig. S1). The multifactor ANOVA of

245 bacterial gene concentrations (gene copies/μl) for vineyard, symptomatic vs. asymptomatic

246 (Sympt/Asympt) and qPCR replicate runs, showed that actinomycetes, Bacillus spp. and

247 Pseudomonas spp. differed significantly between vineyards (Table 3). Total bacteria and

248 Pseudomonas spp differ significantly (P<0.05). between symptomatic and asymptomatic plants;

249 actinomycetes differ only at P=0.066 (P<0.1), whilst Bacillus did not differ at all (Table 2). The

250 qPCR run replicates resulted significantly different in all cases (Table 2). The latter result was

251 consistent with an inherent instrumentation error, as reported by several authors (Hellemans et al.

252 2007; Bustin et al. 2009). Mean DNA concentration (gene copies μl-1) of total bacteria,

253 Pseudomonas and actinomycetes was higher in asymptomatic plants compared to symptomatic

254 plants, while Bacillus showed the opposite trend, albeit with no significant differences (Table 2).

255 Pseudomonas, were higher in asymptomatic plants quite in all vineyards (Fig. 1).

256 16S rDNA amplicon sequencing

257 Illumina MiSeq analysis of the V3-V4 region of 16S rRNA gene from soil DNA of the 22 samples

258 appeared in a total of 1,913,873 reads. After denoising and quality control 735,193 filtered reads

259 were obtained, the average number of filtered reads per sample was 33,418 ranging from 17,170 to

260 50,429 per sample. A total of 42,555 OTUs was identified ranging from 1,720 to 2,079 per sample.

261 After further filtering with METAGENassist, a total of 18 phyla, 139 genera and 66 species were

262 considered for data analysis. Rarefaction curves of total OTUs showed that all libraries reached

263 saturation and covered the whole bacterial diversity within the samples (Suppl. Mat. Fig. S2). No

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264 significant differences were observed between symptomatic and asymptomatic plants at any of the

265 analyzed taxonomic levels. The PCA analysis (of which PC1 and PC2 explained more than 90% of

266 total variance) further confirmed the lack of difference between rhizobacteria communities in

267 symptomatic and asymptomatic grapevine plants at both phylum (Fig. 2 A) and genus (Fig. 2 B)

268 level, as shown by the overlapping of the corresponding 95% confidence ellipses in the PCA

269 biplots.

270 The most abundant bacterial phyla (above 10% of total phyla) were Proteobacteria (29%),

271 Actinobacteria (20%), Acidobacteria (19%), Bacteroidetes (11%) (Fig. 3). Phylum-specific

272 dissimilarities calculated by SIMPER analysis indicated that the observed differences between

273 symptomatic and asymptomatic plants were mainly ascribable to the most abundant

274 abovementioned phyla, namely Proteobacteria, Acidobacteria, Actinobacteria and Bacteroidetes,

275 (Suppl. Mat. Table S1). Nevertheless, none of the phyla differed significantly between symptomatic

276 and asymptomatic plants, according to the t-test.

277 Among the sequences identified at the genus level, the main genera in the vineyard soils (above

278 5% of total genera) were Skermanella (11%) and Arthrobacter (10%), followed by Bacillus (8%),

279 Rubrobacter (7%), Candidatus Nitrososphaera (7%) and Steroidobacter (5%). Other 133 genera

280 were found in percentages below 5. The genera that most discriminated between symptomatic and

281 asymptomatic plants, as indicated by SIMPER analysis, were in decreasing order: Rubrobacter,

282 Skermanella, Arthrobacter, Bacillus, Candidatus Nitrososphaera, Steroidobacter, (Table 3). Apart

283 from Arthrobacter, all these genera were more abundant in the rhizosphere of asymptomatic plants,

284 though not in a statistically significant way (t-test).

285 Among all identified genera, the few whose abundance was significantly different between

286 symptomatic and asymptomatic plants were among the lesser represented genera (between 0.2 and

287 0.3% of total genera). All these genera were higher in asymptomatic plants, they were

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288 Salinibacterium (Actinobacteria) (P<0.01), Flavobacterium (Bacteroidetes) (P<0.05), Nocardia

289 (Actinobacteria) (P<0.05) and Janthinobacterium (Proteobacteria) (P<0.05) (Fig. 4).

290 The most abundant prokaryotic species (above 10% of total identified species) identified in the

291 vineyard soils were Bacillus muralis (matching with the GeneBank accession n KX504252)

292 (14.4%) and B. flexus (13.6%), Candidatus nitrososphaera SCA1170 (13.1%) (Table 4). Among

293 Actinobacteria, the most abundant species (above 1%) were: Streptomyces mirabilis (5.6%),

294 Lentzea albidocapillata (4.8%), Arthrobacter nitroguajacolicus (2.7%), Virgisporangium

295 ochraceum (2.6%), Cellulomonas xylanilytica (1.3%) and Actinomadura vinacea (1.1%) (Table 4).

296 Identification of Pseudomonas species gave P. umsongensis, P. alcaligenes and P. fragi in

297 percentages below 0.5.

298 Bacterial diversity did not overall differ between symptomatic and asymptomatic plants. Based

299 on a diversity profiling test, genus diversity was not comparable between pooled data of

300 symptomatic and asymptomatic plants. Therefore, it is possible to conclude that, based on 16S

301 rDNA amplicon sequencing data, no diversity difference was found between symptomatic and

302 asymptomatic grapevines.

303 Discussion

304 Eleven vineyards subjected to similar agro-management systems were selected for this study. Based

305 on PCR-DGGE targeting three diverse bacterial groups, bacterial composition was shown to be

306 highly site dependent. This result was in agreement with other studies performed on a large-scale in

307 other vine growing areas (Burns et al. 2016; Likar et al. 2017; Manici et al. 2017; Karimi et al.

308 2018). These findings were consistent with the theory that environment is a primary factor

309 (biogeography) affecting the soil composition of bacterial communities (Fierer and Jackson 2006;

310 Karimi et al. 2018). The higher abundance of Pseudomonas spp. in asymptomatic plants, together

311 with that of total bacteria and to a lesser extent actinobacteria, suggested a differential root-derived

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312 carbon assimilation of those bacteria in rhizosphere of healthy plants as compared to symptomatic

313 ones. Root growth dynamics and photosynthesis intensity are the most important plant‐mediated

314 factors affecting soil organic matter decomposition and consequently provide increased organic

315 substrates for microbial communities (Yakov 2002; Haichar et al. 2008). Therefore, plants with a

316 well-developed and branched root system, such as healthy asymptomatic plants may accelerate that

317 process. The higher diversity of Pseudomonas in asymptomatic plants (Fig. 1) further supported the

318 hypothesis that, in complex environments, Pseudomonas lineages evolve as imperfect generalists

319 that differentiate to assimilate a certain range of substrates (Barrett et al. 2005). On the other hand,

320 it has been hypothesized that the ability possessed by several species of this genus to use various

321 nutrients and compete for limited carbon sources in the rhizosphere may play an important role in

322 plant root colonization (Somers et al. 2004). The implication of specific bacterial populations will

323 be further discussed in the light of findings of next-generation sequencing analysis (NGS) which

324 was carried out with the precise purpose of investigating microbiome differences between bacterial

325 communities associated with symptomatic and asymptomatic plants. Choosing to analyze only one

326 factor of variability with NGS was based on results of the previous analysis (qPCR and PCR-

327 DGGE) and taking into account that vineyards belonged to the same growing area and they were

328 characterized by similar soil texture.

329 Deep sequencing did not discriminate symptomatic and asymptomatic plants at any of the

330 analyzed bacterial taxonomic levels. The only genera which significantly differed between

331 symptomatic and asymptomatic grapevines were Salinibacterium, Flavobacterium, Nocardia and

332 Janthinobacterium. However, considering that they did not exceed 0.2-0.3% of total recorded

333 genera and that great spatial variability was gathered from PCR-DGGE and qPCR findings, their

334 functional role in apparently healthy plants should be further investigated. Indeed, their association

335 with asymptomatic plants should be consistent with other surveys on rhizosphere microbiome in

336 vineyards; moreover, mechanisms linking those genera to asymptomatic plants should be clarified

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337 with bio-assays aimed at elucidating plant response mechanisms to those genera. Once the

338 abovementioned conditions are reached, it will be possible to consider these genera as indicators of

339 plant health in contexts of crop decline. Two of these genera (Salinibacterium and Nocardia)

340 belong to actinomycetes, thus supporting the association of this group to plant health as already

341 highlighted by the qPCR findings. The Salinibacterium genus contains halotolerant Actinobacteria

342 that have been associated with the metabolism of a variety of plant-derived carbon sources,

343 including sucrose, glucose, cellobiose, mannose, melibiose, maltose, galactose, arabinose, and

344 fructose (Verastegui et al. 2014). Sequences of this genus have been retrieved from various

345 environments such as frozen soils from glaciers (Zhang et al. 2008), Antarctic permafrost

346 (KY476596) (Shin et al. 2012), soil from a tobacco plantation (KR831850) and forest soil

347 (KC256519). Nevertheless, very little information is available for members of this genus, and

348 interestingly, sequences of Salinibacterium have yet to be reported in vineyard soils. Nocardia

349 includes filamentous-growing soil saprophytes that are known to show high specificity toward

350 grapevine roots in an interesting study on weed- and grapevine-associated microbiomes in vineyard

351 soils (Samad et al. 2017). The higher abundance in asymptomatic plants may be linked to multiple

352 biological activities (siderophore, phytohormone production, and biological activities useful to

353 bioremediation) reported for the Nocardia genus (Satyanarayana and Johri 2005). Flavobacterium

354 was one of the most abundant genera associated to grapevine roots and rhizosphere soil in Austrian

355 and Chinese vineyards (Samad et al. 2017; Zhao et al. 2018), thus suggesting it may occur in

356 vineyard soils worldwide. This genus has been associated to an improved ability to resist soil

357 pathogens that cause replant problems in adult vineyards of China (Guo et al. 2011; Subramanian et

358 al. 2016); moreover, Flavobacterium isolates have shown plant growth-promoting traits such as

359 siderophore production, phosphate solubilization, indole acetic acid production, ACC deaminase

360 activity and antifungal activity in grapevine as well other crops (Subramanian et al. 2016; Samad et

361 al. 2017). Finally, Janthinobacteria includes fast-growing β-proteobacteria that are well adapted to

362 many environmental stresses. They indeed possess the ability to grow by forming extended biofilms

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363 (Pantanella et al. 2006) along with antifungal properties (Haack et al. 2016); therefore, this genus

364 may prove to be beneficial in overcoming the multifactorial phenomenon of tree decline.

365 The major phyla (Proteobacteria, Actinobacteria, Acidobacteria and Bacteroidetes) detected

366 by NGS in this study, did not differ considerably from those reported in previous studies on the soil

367 microbiome associated to grapevine (Zarraonaindia et al. 2015; Novello et al. 2017; Samad et al.

368 2017; Marasco et al. 2018). Proteobacteria were also found to dominate in a similar concentration

369 (around 30%) in vineyard soils in other vine growing areas (Zarraonaindia et al. 2015). The high

370 abundance of Actinobacteria (20%) confirms their association with a nutritionally rich environment

371 such as the rhizosphere. Species belonging to this phylum have been found to be enriched in

372 grapevine roots, probably driven by chemoattraction via photoassimilates secreted by roots

373 (Zarraonaindia et al. 2015). Amongst the most abundant species of Actinobacteria identified in this

374 study (< 5% of total identified species) Streptomyces mirabilis and Arthrobacter nitroguajacolicus

375 were found. Both these species were more abundant in the rhizosphere of asymptomatic plants; this

376 may be consistent with the findings of a recent study in one vineyard located in the subalpine area

377 (Italy), which reported higher Actinobacteria abundance in soil associated with asymptomatic

378 plants (Nerva et al. 2019). In grapevine, Actinobacteria strains (i.e. Streptomyces) were shown to

379 significantly reduce infection rates produced by fungal agents of grapevine trunk diseases such as

380 Dactylonectria sp., Ilyonectria sp., Phaeomoniella chlamydospora and Phaeoacremonium

381 minimum (Álvarez-Pérez et al. 2017). Arthrobacter nitroguajacolicus, which has previously been

382 isolated in forest soil, is capable of degradation or transformation of nitroaromatic compounds;

383 indeed, several species of this genus are characterized by the ability to metabolize xenobiotics

384 (Kotouckova 2004). The third most abundant phylum found in the vineyards under study was

385 Acidobacteria (19%), which has already been found to be one of the most densely represented in

386 vineyard soils and roots (Burns et al. 2015; Zarraonaindia et al. 2015; Calleja-Cervantes et al.

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387 2015). It has also been reported to prevail in bacterial communities of phyllosphere, flowers and

388 grape berry surface (Morgan et al. 2017), thus becoming endemic in vineyards.

389 Where genus is concerned, in the vineyards under study, the dominance of Skermanella (11%)

390 and Arthobacter (10%), belonging respectively to the Proteobacteria and Actinobacteria phyla, was

391 consistent with that previously reported for these two genera in vineyard soils in Austria (Samad et

392 al. 2017), Italy (Novello et al. 2017) and China (Wei et al. 2018). Skermanella has been found to be

393 amongst the most abundantly represented genera in vineyard soil (Wei et al. 2018). It was also

394 reported as positively correlated with phytochemicals in root exudates such as gamma-aminobutyric

395 acid (Badri et al. 2013), which probably represent preferential carbon and nitrogen sources for these

396 microorganisms. Dominance of Arthrobacter in vineyard rhizosphere has previously been attributed

397 to their nutritional versatility and high resistance to desiccation and starvation (Samad et al. 2017).

398 The latter functional feature may be responsible for the high occurrence of Arthrobacter in soils in

399 dry agro-environments such as that under study in the Apulia region, where water availability is one

400 of the limiting factors for summer agricultural productions such as grape.

401 Bacillus was one of the most widespread genera (8%) in vineyard soils under study. This genus

402 has previously been isolated with high frequency in all types of grapevine tissues (Bruez et al.

403 2015). Although, this genus did not significantly predominate in asymptomatic plants, the top two

404 most abundant species identified with sequencing analysis were B. muralis and B, flexus. Bacillus

405 muralis has previously been isolated from vine tissues (Samad et al. 2017). Multiple biological

406 activities and growth promotion of both these bacterial species are reported in literature (Singh et al.

407 2015; Yadav et al. 2016). Therefore, their large occurrence in intensively cultivated vineyard soils

408 may be of interest when investigating the potentiality of Bacillus spp. Indeed, this genus has been

409 reported to support plant growth and health through antagonistic activity towards fungi involved in

410 grapevine trunk disease such as Pheomoniella chlamydospora or by induction of systemic

411 resistance in grapevine (Haidar et al. 2016).

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412 The inclusion of several vineyards across an intensive grape producing area, along with the

413 combination of qualitative (PCR-DGGE) and quantitative (qPCR) molecular techniques for

414 investigating rhizosphere bacterial communities, has permitted identification of some key

415 differences between symptomatic and asymptomatic grapevines. The further analysis with NGS

416 enabled identification of the most represented genera and species, though it was not capable of

417 discriminating between symptomatic and asymptomatic plants. These findings suggest that the use

418 of multiple techniques with different degrees of accuracy in DNA investigation may, in fact, be a

419 successful approach.

420 This study suggests that bacterial microbiome associated with esca symptomatic and

421 asymptomatic plants does not overall differ in composition, but rather in relative abundance of

422 some bacterial groups.. The highest occurrence of Pseudomonas and to a lesser extent

423 actinomycetes in asymptomatic plants do not suggest a direct involvement of these bacteria in the

424 disease expression, but these differences are consistent with the progressive reduction of vigor (crop

425 decline) commonly observed in esca symptomatic grapevines. The fact that hard pruning to induce

426 re-vegetation of grapevines showing initial esca symptoms is one of the practices applied to

427 counteract grapevine trunk diseases (OIV 2016), supports the above reported hypothesis. Finally,

428 further specific studies may be oriented toward involvement on grapevine tolerance to drought or to

429 diseases by the bacterial populations, which differed in abundance between symptomatic and

430 asymptomatic plants.

431

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650 DNA Macroarray for the Detection and Identification of Fungal Pathogens Causing Decline of

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653 and branch dieback: etiology, incidence, and distribution in California. Plant Dis. 97(2): 231–

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658 Active Soil Bacterial Communities. MBio 5(4): e01157-14-e01157-14.

659 doi:10.1128/mBio.01157-14.

660 Wei, Y., Wu, Y., Yan, Y., Zou, W., Xue, J., Ma, W., Wang, W., Tian, G., and Wang, L. 2018.

661 High-throughput sequencing of microbial community diversity in soil, grapes, leaves, grape

662 juice and wine of grapevine from China. PLoS One 13(3): e0193097. Public Library of

663 Science. Available from https://doi.org/10.1371/journal.pone.0193097.

664 Widmer, F., Seidler, R.J., Gillevet, P.M., Watrud, L.S., and Di Giovanni, G.D. 1998. A highly

665 selective PCR protocol for detecting 16S rRNA genes of the genus Pseudomonas (sensu

666 stricto) in environmental samples. Appl Env. Microbiol 64(7): 2545–2553. National Research

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668 Winkler, J.A., Andresen, J.A., Guentchev, G., and Kriegel, R.D. 2002. Possible Impacts of

669 Projected Temperature Change on Commercial Fruit Production in the Great Lakes Region. J.

670 Great Lakes Res. 28(4): 608–625. Elsevier. doi:10.1016/S0380-1330(02)70609-6.

671 Yadav, A.N., Sachan, S.G., Verma, P., and Saxena, A.K. 2016. Bioprospecting of plant growth

672 promoting psychrotrophic Bacilli from the cold desert of north western Indian Himalayas.

673 Indian J. Exp. Biol. 54(2): 142–50. Available from

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685 Zhao, P., Xia, W., Wang, J., Zhang, X., Zhuang, Y., Lu, Q., and Chen, S. 2018. Bacterial diversity

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687 vineyards in China. J. Biobased Mater. Bioenergy 12(2): 194–202.

688 doi:10.1166/jbmb.2018.1773.

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689 Tables

690 Table 1. Description of primer pairs.

Target Primers 16S rDNA region Amplicon (bp) References Sequences 5’-3’Pseudomonas spp. Ps-f

Ps-r 760 (Widmer et al. 1998) GGTCTGAGAGGATGATCAGTTTAGCTCCACCTCGCGGC

F968-GC Ps-r V6-V7 290 (Nübel et al. 1996)

(Widmer et al. 1998)AACGCGAAGAACCTTACTTAGCTCCACCTCGCGGC

Actinomycetes F243R1378 1175 (Heuer et al. 1997) GGATGAGCCCGCGGCCTA

CGGTGTGTACAAGGCCCGGGAACG

F968-GCR1378 V6-V8 443 (Nübel et al. 1996)

(Heuer et al. 1997)AACGCGAAGAACCTTAC

CGGTGTGTACAAGGCCCGGGAACGBacillus spp. BacF

R1378 1300 (Garbeva et al. 2003)(Heuer et al. 1997)

GGGAAACCGGGGCTAATACCGGATCGGTGTGTACAAGGCCCGGGAACG

PCR

-DG

GE

F968-GCR1378 V6-V8 410 (Nübel et al. 1996)

(Heuer et al. 1997)AACGCGAAGAACCTTAC

CGGTGTGTACAAGGCCCGGGAACGAll Bacteria 338

518r V2-V3 180 (Lane 1991)(Muyzer et al. 1993)

ACTCCTACGGGAGGCAGCAG ATTACCGCGGCTGCTGG

Pseudomonas spp. F968 Ps-r V6-V7 314 (Nübel et al. 1996) AACGCGAAGAACCTTAC

TTAGCTCCACCTCGCGGCActinomycetes F243

518r V2-V3 289 (Heuer et al. 1997)(Muyzer et al. 1993)

GGATGAGCCCGCGGCCTAATTACCGCGGCTGCTGG

qPC

R

Bacillus spp. BacF518r V2-V3 388 (Garbeva et al. 2003)

(Muyzer et al. 1993)GGGAAACCGGGGCTAATACCGGAT

ATTACCGCGGCTGCTGGMiSeq Bacteria and

Archaea Pro341FPro805R V3-V4 460 (Takahashi et al.

2014)CCTACGGGNBGCASCAG

GACTACNVGGGTATCTAATCC

691

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692

693 Table 2 Three-way ANOVA inferred from qPCR analysis for the factors vineyard,

694 symptomatic/asymptomatic plant and qPCR independent run. Mean separation test using 95% Fisher’s least

695 significant difference (LSD) procedure for the factor symptomatic/asymptomatic plant. ANOVA was

696 performed in square root-transformed data;

697

Total Bacteria Actinomycetes Bacillus spp. Pseudomonas spp.

Factors Counts

A. Vineyard 11 ns ** *** **

B. Sympt/Asympt 2 * ns ns *

C. qPCR run 3 *** ** ** ***

Interactions

AxB ns ns ns *

AxC ns ns ns ns

BxC ns ns ns ns

Mean separation test

Symptomatic 464,727a bb 39,323 a 40,201 a 32,265 b

Asymptomatic 535,030 a 45,988 a 44,393 a 45,306 a

698

699 * P<0.05; ** P<0.01; *** P<0.001; ns: not significant.

700 a Means are reported as gene copies μl-1.

701 b: numbers with different letters differ significantly according to the LSD test.

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703

704 Table 3 Genus-specific dissimilarities showing the genera responsible for the observed differences between

705 symptomatic and asymptomatic plants across the 11 vineyards. Genera explaining up to 70% of cumulative

706 contribution are shown (overall average dissimilarity was 29.05). Data from SIMPER (Similarity

707 Percentage) analysis inferred from 16S rDNA amplicon sequencing (Illumina MiSeq analysis), calculated

708 using Bray-Curtis distance measure.

709

Phylum GenusAv.

dissim

Cumulati

ve

Mean

abund.

Symptomat

ic

Mean

abund.

Asymptomat

ic

% OTU OTU

Actinobacteria Rubrobacter 3.186 10.97 649 968

Proteobacteria Skermanella 2.480 19.51 1170 1360

Actinobacteria Arthrobacter 2.441 27.91 1080 1050

Firmicutes Bacillus 2.151 35.32 827 944

Crenarchaeota Candidatus

Nitrososphaera1.581 40.76 733 762

Proteobacteria Steroidobacter 1.343 45.38 569 624

Proteobacteria Kaistobacter 0.8407 48.28 395 479

Proteobacteria Rhodoplanes 0.7684 50.92 353 381

Bacteroidetes Flavisolibacter 0.7492 53.50 250 307

Bacteroidetes Adhaeribacter 0.6677 55.80 170 245

Nitrospirae Nitrospira 0.6428 58.01 330 364

Verrucomicrobia Opitutus 0.5438 59.89 181 226

Actinobacteria Nocardioides 0.5138 61.66 139 206

Proteobacteria Pedomicrobium 0.4632 63.25 91 131

Proteobacteria Pseudomonas 0.458 64.83 132 175

Actinobacteria Streptomyces 0.4321 66.31 189 212

Actinobacteria Mycobacterium 0.4027 67.70 196 229

Actinobacteria Modestobacter 0.3599 68.94 166 180

Proteobacteria Bradyrhizobium 0.3581 70.17 131 157

710

711 Av. dissim. = genus-specific dissimilarities calculated using Bray-Curtis distance measure. Cumulative %

712 = cumulative genus-specific contribution to the overall average dissimilarity. Mean abund. = OTU mean

713 abundance of each genus in the rhizosphere of symptomatic and asymptomatic plants.

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714

715 Table 4 Most abundant prokaryotic species identified in the vineyard soils by 16S amplicon sequencing

716 (above 0.5% of total species). Data analysis inferred from 16S rDNA amplicon sequencing (Illumina MiSeq

717 analysis),

718

Kingdom Phylum Species% total

species

Bacteria Firmicutes Bacillus muralis 14.4

Bacteria Firmicutes Bacillus flexus 13.6

Archaea Crenarchaeota Candidatus Nitrososphaera SCA1170 13.1

Archaea Crenarchaeota Candidatus Nitrososphaera SCA1145 6.5

Bacteria Actinobacteria Streptomyces mirabilis 5.6

Bacteria Proteobacteria Nitrosovibrio tenuis 5.4

Bacteria Actinobacteria Lentzea albidocapillata 4.8

Archaea Crenarchaeota Candidatus Nitrososphaera gargensis 4.0

Bacteria Actinobacteria Arthrobacter nitroguajacolicus 2.7

Bacteria Actinobacteria Virgisporangium ochraceum 2.6

Bacteria Firmicutes Bacillus badius 2.5

Bacteria Proteobacteria Methylobacterium organophilum 1.6

Bacteria Actinobacteria Cellulomonas xylanilytica 1.3

Bacteria Firmicutes Bacillus selenatarsenatis 1.3

Bacteria Proteobacteria Bosea genosp. 1.2

Bacteria Proteobacteria Aetherobacter fasciculatus 1.1

Bacteria Actinobacteria Actinomadura vinacea 1.1

Bacteria Proteobacteria Syntrichia ruralis 1.0

Bacteria Proteobacteria Variovorax paradoxus 1.0

Bacteria Bacteroidetes Flavobacterium succinicans 0.8

Bacteria Proteobacteria Janthinobacterium lividum 0.8

Bacteria Proteobacteria Polyangium fumosum 0.7

Bacteria Proteobacteria Methylotenera mobilis 0.7

Bacteria Proteobacteria Xylophilus ampelinus 0.6

Bacteria Proteobacteria Nannocystis exedens 0.6

Bacteria Bacteroidetes Algoriphagus terrigena 0.6

Bacteria Actinobacteria Actinoplanes toevensis 0.5

Bacteria Actinobacteria Geodermatophilus obscurus 0.5

719

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721

722 Figure legend

723 Fig. 1. Pseudomonas 16S rRNA gene quantity in the rhizosphere of 11 vineyards. Data from qPCR. Bars

724 represent standard deviation of the mean.

725 Fig. 2 A and 2 B. PCA score plot inferred from taxon-abundance bacteria data set of 16S rDNA sequences

726 (Illumina MiSeq analysis) identified in the rhizosphere of symptomatic and asymptomatic plants, at phylum

727 (3 A) and genus (3 B) taxonomic level. PC1 and PC2 with 95% confidence ellipses are shown.

728 Fig. 3. Relative abundance of prokaryotic phyla identified by 16S rDNA amplicon sequencing (Illumina

729 MiSeq) in the rhizosphere of symptomatic (S) and asymptomatic (A) plants in the 11 vineyards, calculated as

730 percentage of total OTUs in each sample.

731 Fig. 4. Student’s t-test plot between symptomatic and asymptomatic plants inferred from bacterial genera

732 identified by 16S rDNA amplicon sequencing (Illumina MiSeq). Pink dots indicate the significant genera

733 scoring above the P=0.05 threshold (horizontal dotted line).

734

735

736

737

738

739

740

741

742

743

744

745

746

747

748

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749 Supplementary material 750

751 Figure S1. Quantitative PCR (qPCR) standard curves obtained by 10-fold serial dilutions of known amounts

752 of 16S rRNA gene amplicons of total bacteria (A), actinomycetes (B), Bacillus spp. (C), and Pseudomonas

753 spp. (D). The mean of four replicates of CT values is plotted against the logarithm of amplicon

754 concentrations. Bars represent standard deviation of the mean.

755 Figure S2 Rarefaction curves of bacterial OTUs identified by 16S rDNA amplicon sequencing in the 22

756 vineyard soil samples.

757 Table S1 Phylum-specific dissimilarities calculated using Bray-Curtis distance measure showing phyla

758 primarily responsible for the observed differences between symptomatic and asymptomatic plants in the 11

759 vineyards, assessed by SIMPER (Similarity Percentage) analysis inferred from 16S rDNA amplicon

760 sequencing.

761

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Draft0,E+00

2,E+04

4,E+04

6,E+04

8,E+04

1,E+05

1,E+05

1,E+05

2,E+05

1 2 3 4 5 6 7 8 9 10 11

16

S D

NA

gen

e c

op

ies

/μl

Vineyards

Pseudomonas

Symptomatic

Asymptomatic

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A. B.

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0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

S A S A S A S A S A S A S A S A S A S A S A

1 2 3 4 5 6 7 8 9 10 11

Re

lati

ve

ab

un

da

nc

e

Vineyards

Tenericutes (0.01%)

Deinococcus-Thermus (0.01%)

Elusimicrobia (0.02%)

Fibrobacteres (0.03%)

Chlorobi (0.07%)

Armatimonadetes (0.14%)

Planctomycetes (0.69%)

Nitrospirae (1.35%)

Cyanobacteria (1.47%)

Thaumarchaeota (2.25%)

Firmicutes (3.40%)

Gemmatimonadetes (3.69%)

Chloroflexi (4.26%)

Verrucomicrobia (4.35%)

Bacteroidetes (10.72%)

Acidobacteria (18.71%)

Actinobacteria (20.08%)

Proteobacteria (28.76%)

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Genera

Salinibacterium

Flavobacterium

Nocardia Janthinobacterium

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