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