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Fungal endophytic communities in grapevines (Vitis vinifera L.) respond to crop management 1

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Michael Pancher, Marco Ceol, Paola Elisa Corneo, Claudia Maria Oliveira Longa, Sohail Yousaf, Ilaria 3

Pertot, Andrea Campisano∗. Sustainable Agro-ecosystems and Bioresources Department, IASMA 4

Research and Innovation Centre, Fondazione Edmund Mach, Via E. Mach 1, 38010 San Michele 5

all'Adige, (TN), Italy. 6

Running title: fungal endophytes diversity in grapevine 7

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∗ Corresponding author. Andrea Campisano, Fondazione Edmund Mach, via E.Mach, 38010, S. Michele all’Adige, (TN), Italy. Phone +39 0461615511, Fax +39 0461615500. Andrea.Campisano@iasma.it

Copyright © 2012, American Society for Microbiology. All Rights Reserved.Appl. Environ. Microbiol. doi:10.1128/AEM.07655-11 AEM Accepts, published online ahead of print on 6 April 2012

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Abstract 9

We studied the distribution of fungal endophytes of grapevine (V. vinifera L.) plants in a subalpine area 10

of Northern Italy where viticulture is of high economic relevance. We adopted both cultivation-based 11

and cultivation-independent approaches, to address how various anthropic and non-anthropic factors 12

shape microbial communities. Grapevine stems were harvested from several locations considering 13

organic and integrated pest management (IPM) and from cultivars Merlot and Chardonnay. Cultivable 14

fungi were isolated and identified by ITS sequence analysis, using a novel colony-PCR method, to 15

amplify DNA from fungal specimens. The composition of fungal communities was assessed through a 16

cultivation-independent approach: Automated Ribosomal Intergenic Spacer Analysis (ARISA). 17

Multivariate statistical analysis of both culture-dependent and culture-independent datasets was 18

convergent and indicated that fungal endophytic communities in grapevines from organically managed 19

farms were different from those from IPM farms. Fungal communities in plants of cv. Merlot and cv. 20

Chardonnay overlapped when analysed using culture-dependent approaches, but could be partially 21

resolved using ARISA fingerprinting. 22

23

Introduction 24

Microorganisms dwelling asymptomatically within plant tissues (endophytes) have been found in all 25

studied plants. Endophytes can have either a mutualistic or a parasitic lifestyle; under some conditions, 26

mutualists may switch to pathogens upon perception of plant-borne or environmental conditions (50). 27

Known endophytes include viruses, phytoplasmas, bacteria and fungi. 28

The study of plant-fungi interaction has long focused on pathogenic interaction. DNA-based 29

approaches have been extensively employed for fingerprinting, tracking and identifying plant 30

pathogenic fungi (46, 61, 63), but fewer studies address non-pathogenic fungal communities. Fungal 31

endophytes have been investigated for their role as plant growth promoters, biocontrol agents (43), 32

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enhancers of the plant’s bioremediation potential (55) and producers of novel secondary metabolites 33

(75) or enzymes (51). However, the relation between communities of endophytic fungi and host plants 34

is still poorly studied, and as of yet far from being fully understood. In grapevines, recent studies have 35

shed some light upon the bacterial endophytic communities (13, 14, 20, 47, 72), while investigations on 36

fungal communities have been rare and often limited to culture-dependent methods (20, 30, 43, 71). 37

Furthermore, research has mainly focused on subsoil plant-microbe associations (36, 65). 38

Several factors may affect plant-associated microbial communities, e.g. anthropic factors (52, 53), plant 39

physiology (34), the environment (57, 74) and pathogen infections (4, 14, 15). A shift in the 40

composition of microbial communities associated with plants can be driven by genetic and 41

physiological diversities, e.g. between different cultivated varieties (1, 42, 47). In contrast to grasses 42

and annual plants, fewer attempts have been made in woody plants to correlate fungal endophytic 43

communities with cultivar (17, 30). To our knowledge, only a handful of studies attempt to link organic 44

management or the use of antifungal treatments with modifications in the microbiota in woody plants 45

(31, 54, 60). 46

When attempting to identify a high number of isolated fungi, as in environmental studies, isolation and 47

purification of DNA is both time-consuming and expensive, but required for PCR amplification of 48

taxonomically relevant DNA regions. Direct colony-PCR of fungal isolates is usually avoided since 49

DNA availability and purity in heat-lysed fungi is frequently insufficient for the reaction (7). 50

Furthermore, growth media often contain contaminants inhibiting PCR. Fungal metabolites inhibiting 51

DNA polymerase and resilience of fungal spores or conidia to lysis are among the causes of unreliable 52

or poor amplification when PCR is performed directly on fungal colonies (28, 35). Previous studies 53

attempting to improve speed and quality of PCR amplification directly from fungi have several 54

methodological limitations. Examples include dependence on DNA extraction (28, 41, 49, 56, 68), 55

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validation over a limited range of taxa (2, 39, 70) and the use of expensive, proprietary chemicals (2, 56

18). 57

Changes in the composition of plant-associated microbial communities have often been associated with 58

plant physiology (9), health (14) and environmental perturbances (11). In this work we present a study 59

of fungal endophytic communities in grapevines using both a cultivation-based and a cultivation-60

independent approach. For the first time we applied ARISA to the study of fungal endophytic 61

communities in grapevines comparing organic and integrated pest management and investigating the 62

cultivar effect. Fungal ARISA is a community fingerprinting method based on the analysis of length 63

polymorphism of the nuclear ribosomal DNA (rDNA) region containing the two internal transcribed 64

spacers (ITS) and the 5.8S rRNA gene. It was chosen over T-RFLP for the higher accuracy in 65

describing the microbial community’s diversity (22), as well as for the ease of use and the precision 66

that capillary electrophoresis offers (21). Furthermore, we report a novel colony-PCR method for the 67

rapid identification of fungal isolates, which is both inexpensive and DNA isolation-independent. This 68

method was used to PCR amplify the ITS regions and identify all isolates obtained in this study. 69

70

Materials and Methods 71

Study sites 72

To minimise environmental variability between samples from different areas, we followed strict criteria 73

for selection. Communities were sampled only from lateral vine stocks of field grapevines in a 74

restricted geographic region (Trentino, Northern Italy), with medium sandy, calcareous soils (48) 75

characterized by humid, temperate, oceanic climate particularly in pre-alpine areas, with rainfalls 76

maxima in spring and autumn (16). Seven locations and a total of 28 vineyards were selected. In each 77

location four vineyards were sampled, representing each of the four treatments: organic and integrated 78

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pest management (IPM) and cultivars Chardonnay and Merlot. Coordinates for sampling sites are listed 79

in table 1. 80

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Sample collection and plant material 82

A total of 28 vineyards were sampled (table 1). Samples were taken during the fall of 2010, from 83

October 27th to November 11th. In each vineyard, four plants for each treatment were randomly 84

selected. One lateral vine shoot was cut from each plant using pruning scissors. After removing the 85

leaves, stems were transferred in a refrigerated basket for transportation for up to 6 h and stored at 4°C 86

for up to one day. 87

88

Isolation of endophytic fungi 89

Lateral stems (1 to 2 m long) were cut to 20 cm-long fragments in the lab. These cuttings were surface 90

disinfected by a succession of 2 min immersions, conducted under sterile laminar air flow, in 90% 91

ethanol, 2.5% sodium hypochlorite solution, 70% ethanol and sterile distilled water. To test the efficacy 92

of this method, random surface-disinfected stems were repeatedly rolled on nutrient broth (NB, Oxoid, 93

UK) or malt extract agar vegitone (MEA-V, Fluka, Sigma-Aldrich Switzerland) petri dishes and 94

incubated for two weeks at 20-25°C to confirm the absence of any microbial growth. After disinfection, 95

stems were cut into 0.5 cm sections and placed on MEA-V with the vascular vessels facing the 96

medium. The plates were incubated for 7-15 days and all morphologically different colonies were 97

isolated. Mycelium from isolated colonies was freeze-dried and stored at room temperature. 98

99

Extraction-independent PCR amplification of DNA from cultivable isolates 100

Only a minor fraction of all the morphologically different fungal isolates was identified, based on 101

microscopic analysis of hyphae and conidia morphology, using available morphological keys (8). 102

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Therefore we developed a new method for identification. The method was validated on a wide range of 103

fungal taxa chosen from the culture collection at our institute (Fondazione Edmund Mach, S. Michele 104

all’Adige, TN, Italy), from other collections, or from the isolates of this study that were already 105

identified based on morphological traits (table 2). ITS sequences of all isolates were PCR amplified 106

using either or both ITS1 (TCCGTAGGTGAACCTGCGG) -ITS4 (TCCTCCGCTTATTGATATGC) 107

(73) and nu nu-SSU-0817-59 (TTAGCATGGAATAATRRAATAGGA)-nu-SSU-1196-39 108

(TCTGGACCTGGTGAGTTTCC) (10) primer pairs (Sigma-Aldrich) and the two protocols described 109

below. Henceforth we will refer to these two protocols as Freeze-Dried Mycelium (FDM) and Actively 110

Growing Mycelium (AGM). In FDM freeze-dried fungal material was lysed mechanically using two 111

sterile stainless steel 5/32” ball bearings and shaken using a tissue lyser (Retsch, type MM200, 112

Germany) for 2 min at maximum frequency (25 Hertz) to obtain a fine powder. We stored the 113

pulverised mycelium without loss of PCR efficacy for at least 2 months (data not shown). For PCR, 114

approximately 1 mg of this powder was suspended in 1 ml of sterile distilled water and mixed by 115

vortexing for 20 s. In AGM, a 0.5 cm2 plug including fresh mycelium and the agar medium underneath 116

was frozen at -80°C. For method validation purposes, samples were harvested from either small fungal 117

colonies (0.5 – 1 cm2 diameter) or from the actively growing edge of larger colonies (3 – 6 cm2 118

diameter). Henceforth these two stages will be referred to as early and late, respectively. Lysis of the 119

frozen samples was achieved using the same procedure described for FDM, after which 1 ml of sterile 120

distilled water was added to the lysate and mixed by vortexing. The diluted AGM and FDM lysates 121

were centrifuged 5 min at full speed (16,000 rcf) on a tabletop centrifuge (5415R Eppendorf, Germany) 122

to sediment insoluble debris, including non-lysed cells, agar and cell wall fragments. One microlitre of 123

the supernatant was used as template in a 25 μl PCR reaction, including 1×Dream Taq Green PCR 124

Master Mix (Fermentas, Lithuania), and 0.2 μM of each primer. PCR was performed for 35 cycles 125

using the appropriate protocols to each primer pair (10, 73). PCR amplified DNA was purified using 126

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Exo-SAP (Euroclone S.p.A., Italy) following the manufacturer’s instructions and sequenced using Big 127

Dye terminator v3.1. Sequence analysis of the amplicons was performed by BLASTN comparison 128

using the NCBI database’s best hit (58) to confirm the identity of selected strains. Whenever possible, 129

sequences were identified to the species level. All fungal sequences were at least 98% identical to the 130

best hit in the NCBI database. This value was considered sufficiently robust for species identification 131

(66). For some isolates, when ITS sequence was not discriminant at the species level, the isolates were 132

assigned to the corresponding genus. 133

Once validated, the method was used to identify the unidentified isolates of this study. Presence or 134

absence of OTUs was scored for each plant sample. 135

136

DNA extraction, PCR and ARISA of total fungi 137

For cultivation-independent analysis of fungal communities, total DNA extraction was performed. 138

Plant stems were surface disinfected as described above. Bark was carefully removed to avoid 139

contamination with DNA from nonviable cells, which may persist on the surface after disinfection. 140

Disc sections of lateral shoots used for microorganism isolation were frozen in liquid nitrogen and 141

pulverised in sterile steel jars using a tissue lyser. DNA was isolated from 200 mg of ground specimens 142

using the CTAB method as previously reported (24). Briefly, pulverised material was incubated 30 min 143

at 65°C in pre-warmed lysis buffer and extracted using chloroform-isoamylalcohol (24:1). The gDNA 144

was precipitated using isopropanol and the pellet washed with 70% ethanol. 145

The 18S-28S ITS of the fungal rDNA was amplified using the primer set FAM (carboxy-fluorescein) 146

labelled 1406f (TGYACACACCGCCCGT) (27) and ITS2 (GCTGCGTTCTTCATCGATGC) (73). 147

The 25 μl PCR reaction mix contained 1×Dream Taq Green PCR Master Mix, 1 μl dimethyl sulfoxide 148

(DMSO), 25 μg bovine serum albumin (BSA) and 0.2 μM of each primer. PCR was performed using 149

an initial denaturation step at 95°C for 5 min, 31 cycles as follows: denaturation 95°C for 40 s, 150

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annealing 54°C for 40 s, elongation 72°C for 1 min, followed by a final elongation at 72°C for 10 min. 151

PCR reactions were performed in a PTC-200 thermal cycler (MJ Research Inc, USA). The PCR 152

product was checked on 1% agarose gel and 1 μl of the product was mixed with 8.8 μl of Hi-DiTM 153

formamide (Applied Biosystem, CA, USA) and 0.2 μl of GeneScanTM 1200 LIZ® size standard 154

(Applied Biosystems), denatured for 5 minutes at 95°C and then cooled in ice before loading. 155

Denatured amplicons were loaded on an ABI Prism 3130xl Genetic analyzer (Applied Biosystems) 156

equipped with sixteen 50 cm capillaries filled with POP 7TM polymer (Applied Biosystems). Run 157

conditions were set to 8.5 kV and 60°C, and total run time was 6700 s. 158

Electropherograms were analysed using the Gene Mapper 4.0 software, using the normalization inside 159

the experiment, fluorescence threshold was set at 50 relative fluorescence units (RFU). We found and 160

scored for analysis fragments in the size range of 100-800 bp in length. Peak binning was set to 1.5 bp 161

and manual correction was applied where peak shifts occurred. The tables of presence/absence and 162

fluorescence associated to each peak were exported in spreadsheets for subsequent analysis. 163

164

Multivariate data analysis 165

ARISA electropherograms of individual plant-associated communities were transformed in a binary 166

presence matrix (scoring 1 for presence or 0 for absence), and each peak scored. Matrices generated 167

both through identification of cultivable isolates and through ARISA peak scoring, were then 168

transformed by adding presence scores together for each of the four biological replicates. The 169

frequency score obtained thus ranged from 0 to 4. Data matrices were analysed by PCA (principal 170

component analysis) and CCA (canonical correspondence analysis, 40) using the PAST software (32). 171

PCA and CCA are similar procedures for finding variables (called components) which are linear 172

combinations of all the variation contained by the dataset. The reduction of several variables to two 173

provides the advantage of making complex dataset plottable, while preserving much of the variance in 174

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the data. In addition, it allows the generation of hypotheses regarding components and the controlled 175

variables in the dataset. As a correspondence analysis method, CCA is designed for counted data 176

(integers). In CCA, the axes are linear combinations of the environmental variables. CCA is 177

specifically suited to data where the gradient in environmental variables is known a priori and 178

abundance (or presence/absences) is considered to be a response to this gradient (32). 179

A dataset was obtained using linear combinations of the cultivated fungi and ARISA fingerprints 180

matrices (henceforth referred to as the combined dataset). Samples grouping according to treatments 181

(organic/integrated management, cv. Merlot/Chardonnay) and across the seven areas of sampling was 182

also studied by one-way ANOSIM (19) and one-way NPMANOVA (3). One-way ANOSIM (ANalysis 183

Of Similarities) and NPMANOVA (Non-Parametric MANOVA) are non-parametric tests that analyse 184

the significance of distance measures among multivariate groups (32). 185

186

Results 187

Identification of fungi by colony PCR 188

In the method validation, PCR amplification using FDM as template was successful for 36 out of 40 189

samples (90%), using either the ITS1/ITS4 or the nu-SSU-0817-59/nu-SSU-1196-39 primer pair. The 190

four fungi recalcitrant to PCR amplification using both primer pairs were Trichoderma atroviride, T. 191

aggressivum, Penicillium chrysogenum and Alternaria sp. (table 2). All yeasts lysates were 192

successfully PCR amplified. Using AGM 38 out of 40 samples were also successfully PCR amplified 193

(exceptions were P. spinulosum and Aspergillus niger) using the ITS1/ITS4 primer pair, while all 194

fungal isolates were successfully PCR amplified with the nu-SSU-0817-59/nu-SSU-1196-39 primers. 195

However PCR amplification of some isolates was partial or absent when these were harvested during 196

either early or late growth stage (table 2). Strains more efficiently amplified when sampled during early 197

growth include P. spinulosum, T. atroviride, Epicoccum nigrum, T. virens and Phaeosphaeria 198

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nodorum. A larger number of strains were more efficiently amplified during late growth, including 199

Absidia glauca, P. restrictum, Alternaria sp., Botrytis cinerea, Fusarium sp., T. reesei, Fusarium 200

oxysporum, Neurospora crassa, P. chrysogenum and Podospora anserina. After validation, either or 201

both approaches were used to amplify the ITS region of the 377 fungi isolated in this study. Using the 202

method described above, the vast majority (93%) of ITS regions were immediately amplified and 203

sequenced. Sequence analysis was successfully used to assign the isolate to a taxonomic group. For the 204

identification of a minority (7%) of isolates, a second PCR and subsequent sequencing reaction was 205

required. 206

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Isolation and identification of cultivable endophytic fungi 208

A total of 377 fungi were isolated from the 112 field samples analysed. After identification, fungal 209

isolates from the same sample that were assigned to the same OTU (by ITS sequence) were considered 210

to be a unique isolate. From the 377 fungi, we identified 254 isolates. The total number of fungi did not 211

significantly differ (p≤0.05) when considering the isolates from organic and IPM vineyards, as well as 212

from cultivars Merlot and Chardonnay. All isolates were placed in one of 14 OTUs, according to the 213

ITS DNA sequence (Figure 1); of these, two OTUs (Alternaria sp. and Epicoccum nigrum) were 214

detected in all fields. Seven OTUs (Xylaria sp., Ampelomyces humuli, Gibberella pulicaris, Truncatella 215

angustata, Neofusicoccum parvum, Phoma herbarum and Davidiella tassiana) were only isolated from 216

a single vineyard, whereas the remaining seven OTUs were present in at least three vineyards. Among 217

the OTUs found in multiple fields, Leptosphaerulina chartarum was only found in plants from organic 218

farms and Botryosphaeria sp. was only found in plants from IPM farms (figure 1b). Thirteen out of 219

fourteen OTUs could be isolated from vines of cv. Merlot (with the only exception of N. parvum), 220

while only eight OTUs were found in cv. Chardonnay vines (figure 1c). 221

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ARISA fingerprinting of total fungal endophytic communities 223

Using ARISA fingerprinting, 66 distinct markers (electrophoretic peaks) were observed. A total of 943 224

peaks were scored, with individual samples showing from 4 to 18 (on average 8.6) peaks. Grapevine-225

associated fungi did not show a significant difference in number of ARISA markers between organic 226

and IPM farms, or between cv. Chardonnay and cv. Merlot (chi-square, P ≤ 0.05). 227

228

Multivariate data analysis 229

The scatter plots obtained by multivariate analysis showed samples from organic and IPM to be 230

partially or completely separated according to the two main components, using either canonical 231

correspondence analysis (figure 2) and principal component analysis (figure 3). The separation can be 232

observed using the data from both isolated fungi and ARISA fingerprinting. Similarly, the scatter plot 233

based on the combination of ARISA fingerprints and culturable fungi showed partial separation of 234

microbial communities in grapevines of cv. Merlot and cv. Chardonnay (figure 2d). The same does not 235

apply for the cultivable fungal community. CCA analysis of this matrix produced results similar to 236

those obtained using the ARISA-derived data matrix. 237

Multivariate analysis (either one-way ANOSIM or one-way NPMANOVA) of either ARISA and 238

combined data grouped according to treatments also indicated that fungal communities from organic 239

agriculture were quantitatively different from those obtained from IPM vineyards (table 3). No 240

statistically significant difference was found between communities from grapevines of cv. Merlot and 241

cv. Chardonnay (data not shown). The same datasets were used for comparison of fungal communities 242

grouped according to the sampling area indicated that there is no significant difference among most 243

areas (table 4). Significant pairwise differences (using a value of p<0.05) were observed between the 244

areas G (Navicello) and C (Noarna), G and E (Pietramurata), and E and F (Pressano-Sorni). 245

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Discussion 247

In our study, we have accomplished a wide comparison of the endophytic fungal communities of 248

grapevines in vineyards under IPM or organic management, and between cv Merlot and 249

cv.Chardonnay. We approached the study of grapevine endophytic fungal community composition and 250

its biomarkers across seven locations in Trentino (Italy) with similar characteristics regarding soil and 251

climate. Our results indicate that mycota in grapevines from organic farms form communities which 252

are significantly different from those in grapevines from IPM farms. We also found the DNA-253

dependent approach to be more powerful as compared to the analysis of culturable fungi. 254

To accomplish this, we established novel experimental protocols, which, after initial validation, 255

enabled us to efficiently identify fungal isolates from the communities analysed, without the need for 256

DNA extraction. 257

The need to analyse a large collection of fungal isolates has led to the development of a method for 258

colony-PCR using non purified fungal mycelia lysates. The method developed here requires little 259

hands-on labour, does not require separation of fungal mycelium from the agar medium and is 260

validated for a diverse array of fungal taxa. To the best of our knowledge , no previously known 261

protocol combines all three of these highly desirable features, and our protocol is thus a significant 262

improvement. We achieved PCR amplification of ITS regions for all 377 fungi in collection by either 263

or both the methods described here, and the sequence of PCR products placed each isolate in one of the 264

14 OTUs identified in the study. ITS PCR was highly effective both against the test panel and the 265

collection of isolates (with success rates ranging from 67.5% to 95%, table 2). Overall, taxonomically 266

relevant sequences could be PCR amplified from all tested fungi prepared using either FDM or AGM, 267

and using at least one of the two primer pairs tested in this study. The colony PCR described here 268

enables rapid screening of numerous fungal isolates and can be easily applicable to further studies of 269

fungal communities that use a culture-dependent approach. Furthermore, AGM can be applied directly 270

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to early-stage fungal colonies from any kind of environmental monitoring (be it air, plant-associated 271

microflora, or food processing surfaces), even prior to isolation of their pure cultures. 272

Most fungi isolated in the course of this study are previously known grapevine endophytes (30, 43), 273

but, to our knowledge, this is the first report of the isolation of Ampelomyces humuli and Gibberella 274

pulicaris (Fusarium sambucinum) from the grapevine endosphere. Isolates identified as Alternaria sp., 275

Epicoccum nigrum and Aureobasidium pullulans were found frequently in plants from both organic 276

and IPM farms. Fungi belonging to the genus Alternaria are among the most common fungal 277

endophytes in grapevines, and some strains may play a role in biocontrol of Plasmopara viticola (44). 278

E. nigrum is commonly considered either a saprophyte or a biocontrol agent of important grapevine 279

pathogens (26, 37). A. pullulans is often found both as epiphyte and as endophyte and is considered an 280

antagonist of grapevine disease agents (59). 281

Interestingly, we also consistently isolated Botrytis cinerea. Although the common occurrence of this 282

species as grapevine endophyte was previously reported (17, 30), it must be noted that it is considered 283

an important grapevine pathogen. B. cinerea could be latent (25), therefore behaving as an 284

asymptomatic plant endophyte and turn pathogenic only under specific physiological or environmental 285

conditions. 286

The species Botryosphaeria obtusa and Botryosphaeria dothidea, Truncatella angustata, 287

Neofusicoccum parvum, Phoma herbarum and Davidiella tassiana were isolated from apparently 288

healthy vines, but are also sometimes regarded as grapevine pathogens (23, 38, 45, 64, 67, 69). 289

However, the majority of these fungi (with the exception of D. tassiana) were isolated from IPM 290

vineyards (figure 1b), and may thus represent potential pathogens not detected in grapevines from 291

organic farms. Fungicides used in IPM may be a driving force in shaping the composition of the fungal 292

communities observed, but the level of tolerance to these fungicides among the fungi we isolated is 293

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unknown. Differences between fungi isolated from organic or IPM plants in the response to the applied 294

fungicides will be tested in future experiments. 295

Several studies have investigated microbial communities in soil and their shifts under different land 296

management practices (12, 29). Comparatively fewer attempts have been made to assess the effect of 297

agricultural management on the endophytic microbial communities present in crops (60, 62). In this 298

study, multivariate analysis of both cultivable fungi as well as a DNA-based approach concur to 299

indicate that integrated pest management (IPM) has an impact on the composition of endophytic fungal 300

communities. A likely factor behind this could be the long term use of synthetic fungicides in IPM or 301

the use of organic fertilisers in organic farming. 302

Multivariate analysis indicated that fungal community composition differed between the organic and 303

IPM vineyards, with partially distinct areas in CCA scatter plots (canonical correspondence analysis) 304

when considering datasets from cultivable fungi, total fungal DNA analysis, or both (figure 2a and 2b). 305

A combination of the two datasets under the same analysis showed a more marked separation of the 306

samples from organic and IPM farms (figure 2c). Interestingly, when the same combined data were 307

projected across the 2nd and 3rd axis, communities from Merlot and Chardonnay grapevines were 308

partially distinct (figure 2d). This suggests that differences between grapevine cultivars may drive a 309

minor shift in endophyte composition, as seen for other plant species (42), but that the extent of this 310

diversity is secondary, compared to the effect of crop management. Previous literature has shown that 311

the composition of the culturable fungal endophytic community may be influenced by the cultivar. 312

Casieri et al. (17) investigated the fungi in the endosphere of five grapevine cultivars in Switzerland, 313

finding that the community composition across cultivars differed both when considering the phyla and 314

the species of isolated fungi. Others analysed the endophytic mycota associated with grapevine in 315

Spain, finding the composition across cultivars differed when the order of isolated fungal taxa was 316

considered (30). Finally some information is available on the cultivar influence on microbial 317

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communities associated with the roots (47). Unfortunately none of these previous reports present a 318

statistical analysis of their interesting findings, making it difficult to compare the studies regarding the 319

extent of these differences in microbial communities. 320

PCA analysis of the ARISA fingerprinting dataset (i.e. cultivation-independent communities) allows 321

identification of the loadings of the principal components. The two main components’ contributors are 322

the 371 bp (peak 43) and 354 bp (peak 29) fragments for the main axis and 358 bp (peak 34) and 360 323

bp (peak 35) fragments for the secondary axis (figure 3a). Linking ARISA peaks to fungal species or 324

OTUs is a ticklish operation. As pointed out previously (5), different species may have ITS regions of 325

identical size (33), while some species may display multiple and different (i.e. polymorphic) ITS copies 326

(6). For this reason the taxonomically ambiguous entities (sometimes referred to as ribotypes) produced 327

by ARISA fingerprinting are not reliable indicators of species richness. We thus refrained from 328

formulating any hypotheses on the correspondence of relevant ARISA peaks to taxonomical units. 329

The PCA analysis of the cultured fungi dataset (figure 3c) indicated that the variance of the main axis, 330

roughly dividing samples from organic and integrated management, is mainly due to the OTUs A. 331

pullulans, Alternaria sp. and to a lesser extent by Epicoccum nigrum. In contrast with previous findings 332

(60) we found A. pullulans more frequently in vineyards employing IPM than in organic vineyards. 333

Schmid et al. in 2011 (60) have found A pullulans to be more abundant in plants from organic 334

vineyards, whereas they observed the opposite for the yeast Sporidiobolus pararoseus, which was 335

never isolated from grapevines in our study. Possible factors, related to these differences, are the 336

different cultivars, the terroir differences, the environmental dissimilarities and the methodology used. 337

In the former study, the abundance of A. pullulans in the samples was measured by quantitative 338

amplification of ITS sequences. while we estimate the distribution of each OTU making no 339

assumptions regarding the quantitative assessment of each microorganism. Remarkably both studies 340

indicate that A. pullulans is significantly affected by plant protection strategies. 341

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Noticeably, when PCA analysis is applied to the combined dataset, data points representing microbial 342

communities from IPM vineyards (both with cv. Merlot and cv. Chardonnay) span a smaller range on 343

the principal component than those from organic vineyards (figure 3b). This observation suggests that 344

the variability across fungal endophytic communities from IPM farms may be smaller than that from 345

organic farms. 346

With respect to marker counts (ARISA peaks or isolated fungi) across the seven sampled areas we 347

noted that in most cases data points relative to fungal communities from cv. Merlot and cv. Chardonnay 348

grapevines in the same area were grouped together (figure 3d). The same could not be observed when 349

comparing organic and integrated vineyards. This suggests that the difference between fungi from 350

organic and integrated management is larger than the difference between the plant cultivars. 351

The quantitative analysis by one-way ANOSIM and one-way NPMANOVA supported the conclusions 352

deduced by visual analysis of PCA and CCA scatter plots, pointing out that crop management (IPM 353

and organic) modifies the structure of fungal endophytic communities (table 3). One can hypothesise 354

that the use of synthetic systemic fungicides may have a role in these differences. Organic fertilizers 355

may also be a source of microorganisms, which may establish as endophytes. Further research on the 356

role of systemic fungicide or introduction by the application of organic fertilizers will be crucial to 357

prove these two hypotheses. The analysis tools employed in this study suggest that grapevine cultivar, 358

and cultivar-dependent plant physiology, may also play a role in shaping endophytic communities, but 359

to a lesser extent, as compared to crop management or sampling sites. 360

361

Acknowledgements: This research was funded by Provincia Autonoma di Trento, progetto PAT - Call 362

2 Team 2009 - Incoming – Mecagrafic. 363

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The authors acknowledge the help of Enzo Mescalchin, Matteo Secchi and Roberto Zanzotti for the 364

identification of the sampling areas ,Valerio Mazzoni and Ron Wehrens for the useful insights on 365

statistical analysis and Jonas Bengtsson for the help with critically reviewing the manuscript. 366

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574 575

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Picture legends 576

Figure 1. a: distribution of fungi isolated in this study by OTU; b, c: distribution of isolated fungi 577 according to the source of isolation, considering phytosanitay regime (b) and cultivar (c). 578

579 Figure 2. CCA scatter plots. a: ARISA data, organic (filled diamond) and IPM (diamond) farms. b: 580

cultured fungi data, organic (circle) and IPM (star) farms. c combined data, organic (triangle) 581 and IPM (filled triangle) farms. d: combined data, cv. Chardonnay (square) and cv. Merlot 582 (filled square). Ellipses are designed using a confidence threshold of 75%. 583

584 Figure 3. PCA scatter plots. a: combined data, organic (filled circle) and IPM (circle) farms. b: 585

combined data, organic Merlot (filled square), IPM Merlot (square), organic Chardonnay (filled 586 circle), IPM Chardonnay (circle). c: cultured fungi data, organic (circle) and IPM (star) farms. 587 d: combined data by area, A (X), B (cross), C (square), D (triangle), E (ellipse), F (star), G 588 (diamond). Ellipses are designed using a confidence threshold of 75%. 589

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3,2 2,4 1,6 0,8 0 0,8 1,6 2,4Axis 1

d

MI13A

MI19B

MO8C0,36

0,48

0,6

CI21F

CO4B

CI19B

CO11F CO12G

MO10E

MI15D

MI20E

MO11FMI16C

MI2

MO9D

MO4B

MO2A

-0 12

0

0,12

0,24

Axi

s 3

CI13ACO2A

CI20E

CI21F

CO9D

CO8CCI17C

CO10E

CI18G

CI14D

MI15D

MI18G MO12G

-0,8 -0,64 -0,48 -0,32 -0,16 0 0,16 0,32 0,48Axis 2

-0,48

-0,36

-0,24

0,12

(filled diamond) and IPM (diamond) farms. b: rms. c combined data, organic (triangle) and IPM

nnay (square) and cv. Merlot (filled square). Ellipse

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aCO10E

CI19B

MO2A

CO9D

MO4B MO8C

CO2A

MO9D

CO4B

CO11F

CO8C

MO10E

CI14D

MI18G

CI18G

MI22F

CI13ACI21F

CI17C

MI20ECI20E

MI15DMI19B

MI13A

MO12G

CO12G

MO11FMI16C

-3,6 -2,4 -1,2 0 1,2 2,4 3,6 4,8 6

c

3,6 2,4 1,2 0 1,2 2,4 3,6 4,8 6

Component 1

MO9D

CI17CMI20E

1,8

2,4

MO2A

MO4B

CO10ECO2A

CO4B

CO8CCI14D

CI19B

CI18G

MI22F MI16C

CI21F

CI20E

MI19BMI13A

-2,4 -1,6 -0,8 0,8 1,6 2,4 3,2

0 6

0,6

1,2

MO12GCO9D

MO8CCO12G

MO11F

CO11F

MO10E

MI18G

MI15D

-2,4

-1,8

-1,2

-0,6

CI13AComponent 1

ure 3. PCA scatter plots. a: combined data, organia, organic Merlot (filled square), IPM Merlot (squar

d ( i l ) lt d f i d t i (ardonnay (circle). c: cultured fungi data, organic (ca, A (X), B (cross), C (square), D (triangle), E (ellipng a confidence threshold of 75%.

b

CO8C

CO10ECI19B

3,2

4,8

CO4B

CO9D

CO2A

CO8C

CO11FMI15D

MI19B

MI20E

MI16C

MI22F

MI18G

MI13ACI14D

CI20E

CI17CCI21F

CI13A

CI18G

MO10E

MO11F

MO8C

MO9D

MO4B

MO2A

-4,8 -3,6 -2,4 -1,2 1,2 2,4 3,6 4,8

-1,6

1,6

Com

pone

nt 2

CO12G

MO12G

-6,4

-4,8

-3,2

d

Component 1

CO12

MO12G

2

2,5

MI13ACI13A

CO4B

MO4B

MI16CMI15DMO10E

CI20E

CO11F

CI21FMO11FMI22F

CI18G

MI18G

0

0,5

1

1,5

ompo

nent

2

CO2AMI19B

CI19B

CO4BMI16C

CI17C

CO8C

CI14D

MI15D

M

MI20E

CO10E

-2

-1,5

-1

-0,5

Co

-2 -1,6 -1,2 -0,8 -0,4 0 0,4 0,8 1,2Component 1

2

c (filled circle) and IPM (circle) farms. b: combinedre), organic Chardonnay (filled circle), IPM i l ) d IPM ( t ) f d bi d d t bcircle) and IPM (star) farms. d: combined data by

pse), F (star), G (diamond). Ellipses are designed

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Area Vineyard # Cultivar Pest Management Sample name

2

Chardonnay Organic CO2A

A Merlot Organic MO2A

Avio-Ala 13

Chardonnay Integrated CI13A

Merlot Integrated MI13A

4

Chardonnay Organic CO4B

B Merlot Organic MO4B

Pergolese 19

Chardonnay Integrated CI19B

Merlot Integrated MI19B

8

Chardonnay Organic CO8C

C Merlot Organic MO8C

Noarna 17 Chardonnay Integrated CI17C

16 Merlot Integrated MI16C

9

Chardonnay Organic CO9D

D Merlot Organic MO9D

Isera 14 Chardonnay Integrated CI14D

15 Merlot Integrated MI15D

10

Chardonnay Organic CO10E

E Merlot Organic MO10E

Pietramurata 20

Chardonnay Integrated CI20E

Merlot Integrated MI20E

11

Chardonnay Organic CO11F

F Merlot Organic MO11F

Pressano- 21 Chardonnay Integrated CI21F

Sorni 22 Merlot Integrated MI22F

12

Chardonnay Organic CO12G

G Merlot Organic MO12G

Navicello 18

Chardonnay Integrated CI18G

Merlot Integrated MI18G

Table 1: Sample names and their characteristics.

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Fungal isolate FDM AGM

Source ITS1/ITS4 nu-SSU-0817-59/nu-SSU-1196-39 ITS1/ITS4 nu-SSU-0817-59/nu-SSU-1196-39

Early Late Early Late

Absidia glauca 1B3C Our collection

Alternaria sp. 2.1.Ca Our collection

Alternaria sp. AL2 Our collection

Aspergillus niger Our collection

Aspergillus niger CBS 513.88

Botrytis cinerea Our collection

Botrytis cinerea 9.4.Md Our collection

Cladosporium oxysporum CBS 125.88

Cladosporium sp. 10.4.Mb Our collection

Cladosporium sp. 4.2.Mb Our collection

Epicoccum nigrum 2.1.Cb Our collection

Fusarium graminearum PH1 ATCC MYA4620

Fusarium oxysporum NRRL34936

Fusarium sp. 53F Our collection

Mortierella vertici lata F2(VR) Our collection

Mucor hiemalis 1B2C Our collection

Neurospora crassa OR74A FGSC9013

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Penicillium chrysogenum Our collection

Penicillium chrysogenum 54-1255 NRRL1951

Penicillium restrictum VR31 Our collection

Penicillium spinulosum VR14 Our collection

Phaeosphaeria nodorum SN15 FGSC10173

Pithomyces chartarum 9.2.Mb Our collection

Podospora anserina FGSC10383

Rhizopus stolonifer 2948 Our collection

Sclerotinia sclerotiorum 1980 ATCC18683

Trichoderma aggressivum CBS 115901

Trichoderma atroviride MT8 Our collection

Trichoderma reesei QMA DSM 768

Trichoderma virens PGSC 10516

Umbelopsis ramanniana F13 Our collection

Zygorhyncus moelleri F11(VR) Our collection

Aureobasidium pullulans 4.3.Cc Our collection

Debaryomyces hansenii CBS 767

Hansenula polymorpha CBS 4732

Pichia stipitis CBS6054

Saccharomyces cerevisiae S288C ATCC204508

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Schizosaccaromyces pombe 972h ATCC24843

Yarrowia lipolytica CBS 7504

Zygosaccaromyces rouxii CBS 732

Table 2: Strains used for validation of the colony PCR method and PCR results. PCR was performed using the primer pairs ITS1/ITS4 and nu-SSU-0817-59/nu-SSU-1196-3. Both FDM and AGM results are shown. AGM results are reported both for early and late stages. The signs and indicate successful and unsuccessful PCR amplification,

respectively. Yeast strains were tested in one stage only.

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ANOSIM

O I

O 0,0012

I 0,0012

NPMANOVA

O I

O 0,0005

I 0,0005

Table 3: p values of ANOSIM and NPMANOVA comparison of populations from organic (O) and IPM (I) farms using the combined dataset. Highlighted cells values are <0.05

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ANOSIM

A B C D E F G

A 0,7974 0,3788 0,9156 0,6802 0,4903 0,3717

B 0,7974 0,1979 0,2844 0,3158 0,0555 0,2903

C 0,3788 0,1979 0,4277 0,107 0,3432 0,0246

D 0,9156 0,2844 0,4277 0,1977 0,6229 0,2516

E 0,6802 0,3158 0,107 0,1977 0,0273 0,0281

F 0,4903 0,0555 0,3432 0,6229 0,0273 0,1775

G 0,3717 0,2903 0,0246 0,2516 0,0281 0,1775

NPMANOVA

A B C D E F G

A 0,652 0,2854 0,9454 0,3057 0,6313 0,4561

B 0,652 0,2016 0,2055 0,2013 0,0842 0,1987

C 0,2854 0,2016 0,4046 0,057 0,1988 0,0283

D 0,9454 0,2055 0,4046 0,2965 0,6341 0,2846

E 0,3057 0,2013 0,057 0,2965 0,0284 0,0304

F 0,6313 0,0842 0,1988 0,6341 0,0284 0,1711

G 0,4561 0,1987 0,0283 0,2846 0,0304 0,1711

Table 4: p values of ANOSIM and NPMANOVA comparison of populations from seven locations in this study (A-G) using the combined dataset. Highlighted cells values are <0.05

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