Evidence of differential spreading events of grapevine pinot Gris virus in Italy using datamining as a tool

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Evidence of differential spreading events of grapevine pinot Gris virus in Italy using datamining as a tool

Jean-Michel Hily, Véronique Komar, Nils Poulicard, Amandine Velt, Lauriane Renault, Pierre Mustin, Emmanuelle Vigne, Anne-Sophie Spilmont, Olivier

Lemaire

To cite this version:

Jean-Michel Hily, Véronique Komar, Nils Poulicard, Amandine Velt, Lauriane Renault, et al.. Ev- idence of differential spreading events of grapevine pinot Gris virus in Italy using datamining as a tool. European Journal of Plant Pathology, Springer Verlag, In press, �10.1007/s10658-021-02343-3�.

�hal-03341480�

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Evidence of differential spreading events of grapevine pinot Gris virus in Italy using datamining as a tool

Jean-Michel Hily &Véronique Komar&Nils Poulicard &Amandine Velt&

Lauriane Renault_&Pierre Mustin_&Emmanuelle Vigne _&Anne-Sophie Spilmont_&

Olivier Lemaire

Accepted: 24 July 2021

#The Author(s) 2021

Abstract Since its identification in 2003, grapevine Pinot gris virus (GPGV, Trichovirus) has now been detected in most grape-growing countries. So far, little is known about the epidemiology of this newly emerg- ing virus. In this work, we used datamining as a tool to monitorin-silicothe sanitary status of three vineyards in Italy. All data used in the study were recovered from a work that was already published and for which data were publicly available as SRA (Sequence Read Ar- chive, NCBI) files. While incomplete, knowledge gath- ered from this work was still important, with evidence of differential accumulation of the virus in grapevine according to year, location, and variety-rootstock association. Additional data regarding GPGV genetic diversity were collected. Some advantages and pitfalls of datamining are discussed.

Keywords Grapevine . GPGV . Detection . Datamining

Since its characterization in Italy (Giampetruzzi et al., 2012), grapevine Pinot gris virus (GPGV,Trichovirus, Betaflexiviridae) has been detected in most grapevine growing regions around the world. Generally, the virus is detected using serological and/or molecular tools. In this work, we describe datamining as a potential additional method to identify grapevine infected with this virus, better estimating its distribution worldwide.

While this specific work cannot be considered as an epidemiological study per se, it still unquestionably offers valuable information on the virus (i.e.,its geo- graphic distribution and genetic composition), provid- ing a snapshot of the situation in three different vineyards in Italy at a specific time, giving new insight on GPGV accumulation, introduction and transmission.

This particular work is based on the data provided by a study on the contribution of genotype, the environment and their interaction to the berry transcriptome that was previously published (Dal Santo et al.,2018). Two cultivars, Cabernet Sauvignon and Sangiovese, were planted in three different locations: Montalcino, Bolgheri and Riccione. The former two Italian cities are located in the Tuscany hills and Tuscany coast respectively, while the latter is positioned on the Adriatic coast (Fig. 1). To minimize genetic variation, researchers used the same clonal material for each cultivar, with clones R5 and VCR23 of Cabernet Sauvignon and Sangiovese, respectively. In addition, three different rootstocks were tested in the study: Kober-5BB, 420A and 161.49 C. After uploading the 72 SRA files generated from this work, all samples were analyzed for the presence of GPGV using Workbench 12.0 software (CLC Genomics Workbench, https://doi.org/10.1007/s10658-021-02343-3

J.<M. Hily (*)

:

A.<S. Spilmont IFV, Le Grau-Du-Roi, France e-mail: [email protected]

V. Komar

:

A. Velt

:

L. Renault

:

P. Mustin

:

E. Vigne

:

O. Lemaire

Université de Strasbourg, INRAE, SVQV UMR-A 1131, F-68000 Colmar, France

N. Poulicard

PHIM Plant Health Institute, Univ Montpellier, IRD, CIRAD, INRAE, Institut Agro, Montpellier, France

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Aarhus, Denmark) as previously described (Hily et al., 2018). This was first assessed by mapping reads to a collection of curated GPGV reference sequences. For those displaying reads corresponding to GPGV, de novo assem- bly steps were performed and further extended by multiple rounds of residual reads mapping as previously described (Nourinejhad Zarghani et al.,2018). Genome sequences being produced were ascertained using very stringent mapping parameters (length of 0.95/similarity of 0.95).

Our datamining study revealed that only samples from Bolgheri and Riccione were positive for GPGV. The virus was hardly detected in a few samples from Montalcino (Table1); however, no complete sequence could be recovered. These‘Low Read Count’samples were probably the result of ‘intra-lane contamination’, as previously described in other studies (Vigne et al.,2018). When using RPKM (Reads per kilo base per million) data as a proxi for virus accumulation in the samples, our analyses revealed differential accumulation of GPGV according to many variables (Fig. 2). Indeed, GPGV seems to accumulate more in berries in 2011 than in 2012 (P < 10⁻⁵) and at a later stage of fruit development, at mid-ripening rather than pre-veraison (P < 10⁻⁴). Also, the association cultivar-rootstock seems to have its importance in virus

accumulation. Indeed, GPGV seems to accumulate more in Cabernet Sauvignon cultivar grafted onto either 161–49 or Kober-5BB rootstocks, rather than in Sangiovese grafted onto 420A at either location (P ≤ 10⁻⁴). In addition, differential accumulation of GPGV was also observed according to location where grapevines were grown (P <

10⁻⁵), with GPGV accumulating more in Riccione than in Bolgheri.

When delving into the genetics of the virus, other information was revealed. Overall, 47 complete genome GPGV sequences (or near complete, covering at least all open reading frames) were assembled (Table1), all submitted to GenBank (BK011089-BK011101, and the other sequences are available upon request). After a phylogenet- ic analysis (Fig. 1), three major clades of GPGV were found to infect these grapevines, each displaying a high intra-clade nucleic acid identity percentage ≥ 98.10%.

Interestingly, GPGV sequences seemed to cluster together very well by location (Fig.1, colors) however indepen- dently from cultivar. Fixation index (FST) analyses (Fig.1) confirmed the genetic differentiation of the viral population according to location, showing a statistically significant high FSTvalue (FST= 0.608,P≤ 10⁻⁵). Such segregation by location was also highlighted for grapevines grafted

0.003

SRR5457664-GPGV2

SRR5457621-GPGV

SRR5457636-GPGV

SRR5457630-GPGV

SRR5457662-GPGV-1 SRR5457653-GPGV

SRR5457607-GPGV1 SRR5457611-GPGV SRR5457644-GPGV

SRR5457612-GPGV SRR5457622-GPGV

SRR5457641-GPGV

SRR5457655-GPGV

SRR5457645-GPGV SRR5457606-GPGV2

SRR5457654-GPGV SRR5457620-GPGV SRR5457643-GPGV

SRR5457627-GPGV

SRR5457659-GPGV

SRR5457635-GPGV

SRR5457637-GPGV

SRR5457606-GPGV1

SRR5457646-GPGV

SRR5457617-GPGV

SRR5457662-GPGV-2

SRR5457619-GPGV

SRR5457629-GPGV

SRR5457623-GPGV

SRR5457642-GPGV

SRR5457663-GPGV-2

SRR5457618-GPGV

SRR5457605-GPGV-1

0,84

0,64

1 0,99

0,98

1

0,61

1 1

0,8

1

0,89 0,64

0,97

1

0,61

0,7

culvar rootstock

99.53 / 100 %

98.10 / 100 %

98.11 / 99.72 %

96.79 / 100 %

Fig. 1 Maximum-likelyhood tree inferred from sequences (7206 nt) of grapevine Pinot gris virus genome isolated from two cultivars, Cabernet Sauvignon clone R5 (star) and Sangiovese clone VCR23 (circle). Rootstocks are also indicated with 161.49 C (square), Kober 5BB (triangle) and 420A (diamond). Only bootstraps above 0,5 are shown. Colors correspond to the location

in Italy where samples were recovered, Bolgheri (blue) and Riccione (red), see map on the upper right corner. Identity per- centages between sequences are indicated on the right of the ML- tree. Measurements of population’s differentiation (fixation index, FST) and associated statistics (Pvalue) are on the upper left corner Eur J Plant Pathol

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Table1AllinformationregardingthedatamininganalysesperformedfromthestudyfromDalSantoetal.,2018 SEA#hybridization #SampleIDCultivarRootstockDevelopmental StageLocationVintageGPGVRPKMMappedread counts*Totalread countsGenome length(nt) SRR54575934CS_MO_PV_11_ACabernet Sauvignon S04Pre-veraisonMontalcino201139,659,627 SRR54575945CS_MO_PV_11_BCabernet Sauvignon

S04Pre-veraisonMontalcino201137,953,191 SRR54575956CS_MO_PV_11_CCabernet Sauvignon S04Pre-veraisonMontalcino201145,920,500 SRR54575967CS_MO_MR_11_ACabernet Sauvignon

S04Mid-ripeningMontalcino201130,131,817 SRR54575978CS_MO_MR_11_BCabernet Sauvignon S04Mid-ripeningMontalcino201125,466,144 SRR.54575989CS_MO_MR_11_CCabernet Sauvignon

S04Mid-ripeningMontalcino201129,627,432 SRR545759916SG_MO_PV_11_ASangiovese420APre-veraisonMontalcino201130,253,594 SRR545760017SG_MO_PV_11_BSangiovese420APre-veraisonMontalcino201127,619,510 SRR545760118SG_MO_PV_11_CSangiovese420APre-veraisonMontalcino201124,825,638 SRR545760219SG _MO_MR_11_A

Sangiovese420AMid-ripeningMontalcino201131,261,949 SRR545760320SG _MO_MR_11_B Sangiovese420AMid-ripeningMontalcino201137,850.541 SRR545760421SG _MO_MR_11_C

Sangiovese420AMid-ripeningMontalcino201133,319,419 SRR545760528CS_BO_PV_11_ACabernet Sauvignon 161–49Pre-veraisonPolgheri2011291,4319,93430,211,3997287,7287 SRR545760629CS_BO_PV_11_BCabernet Sauvignon

161–49Pre-veraisonPolgheri2011244,3210,11331,519,6527254,7254 SRR545760730CS_BO_PV_11_CCabernet Sauvignon 161–49Pre-veraisonPolgheri20111100,9625,00134,310,8247247 SRR545760531CS_BO_MR_11_ACabernet Sauvignon

161–49Mid-ripeningPolgheri20111173,4542,99334,345,1147247 SRR545760932CS_BO_MR_11_BCabernet Sauvignon 161–49Mid-ripeningPolgheri201132,004,939 SRR545761033CS_BO_MR_11_CCabernet Sauvignon

161–49Mid-ripeningPolgheri201132,253,343

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Table1(continued) SEA#hybridization #SampleIDCultivarRootstockDevelopmental StageLocationVintageGPGVRPKMMappedread counts*Totalread countsGenome length(nt) SRR545761140SG_BO_PV_11_ASangiovese420APre-veraisonPolgheri2011110,43242532,216,4547243 SRR545761241SG_BO_PV_11_BSangiovese420APre-veraisonPolgheri201119,64209230,065,1987240 SRR545761342SG_BO_PV_11_CSangiovese420APre-veraisonPolgheri201114,5292225,270,2847213 SRR545761443SG_BO_MR_11_ASangiovese420AMid-ripeningPolgheri2011124,92636035,361,6027307 SRR545761544SG_BO_MR_11_BSangiovese420AMid-ripeningPolgheri201130,185,292 SRR545761645SG_BO_MR_11_CSangiovese420AMid-ripeningPolgheri2011168,1215.58931,708,9327290 SRR545761752CS_RI_PV_11_ACabernet Sauvignon Kober-5BBPre-veraisonRiccione20111128,0328,44030,778,5127254 SRR545761853CS_RI_PV_11_BCabernet Sauvignon

Kober-5BBPre-veraisonRiccione20111128,0027,08029,314,93572.54 SRR545761954CS_RI_PV_11_CCabernet Sauvignon Kober-5BBPre-veraisonRiccione20111111,4428,41635,330,7557254 SRR545762055CS_RI_MR_11_ACabernet Sauvignon

Kober-5BBMid-ripeningRiccione201112258,3548.544929,784,8347254 SRR545762156CS_RI_MR_11_BCabernet Sauvignon Kober-5BBMid-ripeningRiccione201112225,20455,93528,390,73772.54 SRR545762257CS_RI_MR_11_CCabernet Sauvignon

Kober-5BBMid-ripeningRiccione201111565,76284,49325,176,18072.54 SRR545762364SG_RI_PV_11_ASangiovese420APre-veraisonRiccione2011189,4817,87127,673,2917258 SRR545762465SG_RI_PV_11_BSangiovese420APre-veraisonRiccione2011158,2311,62127,651,8967254 SRR545762566SG_RI_PV_11_CSangiovese420APre-veraisonRiccione20111135,8827,40327,943,5887254 SRR545762667SG_RI_MR_11_ASangiovese420AMid-ripeningRiccione20111299,5948,00622,202,8537289 SRR545762768SG_RI_MR_11_BSangiovese420AMid-ripeningRiccione20111414,3389,28929,860,0687254 SRR545762859SG_RI_MR_11_CSangiovese420AMid-ripeningRiccione20111300,7461,37728,278,9387254 SRR545762991SG_BO_PV_12_ASangiovese420APre-veraisonBolgheri201213,9988430,685,7377214 SRR545763092SG_BO_PV_12_BSangiovese420APre-veraisonBolgheri201213,2968428,765,5417250 SRR545763193SG_BO_PV_12_CSangiovese420APre-veraisonBolgheri2012✓1,8745533,797,6177131 SRR545763294SG_MO_PV_12_ASangiovese420APre-veraisonMontalcino2012✓0,6914328,565,0194800 SRR545763395SG_MO_PV_12_BSangiovese420APre-veraisonMontalcino2012✓2,9967531,322,8397201 Eur J Plant Pathol

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Table1(continued) SEA#hybridization #SampleIDCultivarRootstockDevelopmental StageLocationVintageGPGVRPKMMappedread counts*Totalread countsGenome length(nt) SRR545763496SG_MO_PV_12_CSangiovese420APre-veraisonMontalcino2012✓1.4331230,193,4566686 SRR545763597SG_RI_PV_12_ASangiovese420APre-veraisonRiccione2012146,2910,70532,044,7527257 SRR545763698SG_RI_PV_12_BSangiovese420APre-veraisonRiccione2012141,83776925,735,5887253 SRR545763799SG_RI_PV_12_CSangiovese420APre-veraisonRiccione2012138,53824529,653,4807271 SRR5457539100CS_MO_PV_12_ASangiovese420APre-veraisonMontalcino201228,374,413 SRR5457639101CS_MO_PV_12_BCabernet Sauvignon S04Pre-veraisonMontalcino201239,038,471 SRR5457640102CS_MO_PV_12_CCabernet Sauvignon

S04Pre-veraisonMontalcino201229,599,165 SRR5457641103CS_RI_PV_12_ACabernet Sauvignon Kober-5BBPre-veraisonRiccione2012155,1910,48826,329,3537253 SRR5457642104CS_RI_PV_12_BCabernet Sauvignon

Kober-5BBPre-veraisonRiccione2012150,2611,04530,452,5567253 SRR5457643105CS_RI_PV_12_CCabernet Sauvignon Kober-5BBPre-veraisonRiccione2012163,5214,93732,582,1177253 SRR5457644106CS_BO_PV_12_ACabernet Sauvignon

161–49Pre-veraisonBolgheri2012120,46229515,541,0927277 SRR5457645107CS_BO_PV_12_BCabernet Sauvignon 161–49Pre-veraisonBolgheri2012114,68299628,275,9627223 SRR5457646108CS_BO_PV_12_CCabernet Sauvignon

161–49Pre-veraisonBolgheri2012126,8412,93466,769,9687282 SRR5457647109SG_BO_MR_12_ASangiovese420AMid-ripeningBolgheri2012✓0,9721630,804,9116126 SRR5457648110SG_BO_MR_12_BSangiovese420AMid-ripeningBolgheri201216,31146332,129,3147219 SRR5457649111SG_BO_MR_12_CSangiovese420AMid-ripeningBolgheri2012✓2.1538825,018,4446948 SRR5457650112SG_MO_MR_12_ASangiovese420AMid-ripeningMontalcino201224,003,382 SRR5457651113SG_MO_MR_12_BSangiovese420AMid-ripeningMontalcino201237,168,759 SRR5457652114SG_MO_MR_12_CSangiovese420AMid-ripeningMontalcino201229,938,586 SRR5457653115SG_RI_MR_12_ASangiovese420AMid-ripeningRiccione2012128,48704134,255,5437250 SRR5457654116SG_RI_MR_12_BSangiovese420AMid-ripeningRiccione2012131,09679030,258,1557250 SRR5457655117SG_RI_MR_12_CSangiovese420AMid-ripeningRiccione201217,48152428,230,5677255

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Table1(continued) SEA#hybridization #SampleIDCultivarRootstockDevelopmental StageLocationVintageGPGVRPKMMappedread counts*Totalread countsGenome length(nt) SRR5457656118CS_MO_MR_12_ASangiovese420AMid-ripeningMontalcino2012✓1,0522429,549,0336431 SRR5457657119CS_MO_MR_12_BCabernet Sauvignon S04Mid-ripen− ngMontalcino201222,749,636 SRR5457658120CS_MO_MR_12_CCabernet Sauvignon

S04Mid-ripeningMontalcino201229,723,920 SRR5457659121CS_RI_MR_12_ACabernet Sauvignon Kober-5BBMid-ripeningRiccione20121408,0282,39927,982,5237250 SRR5457660122CS_RI_MR_12_BCabernet Sauvignon

Kober-5BBMid-ripeningRiccione20121923,80235,63635,343,1457284 SRR5457661123CS_RI_MR_12_CCabernet Sauvignon Kober-5BBMid-ripeningRiccione201211336,91318,33132,992,9107277 SRR5457662124CS_BO_MR_12_ACabernet Sauvignon

161–49Mid-ripeningBolgheri20122117,9624,44728,741,1967217,7217 SRR5457663125CS_130_MR_12_BCabernet Sauvignon 161–49Mid-ripeningBolgheri2012274,492094138.9510347217,7217 SRR5457664126CS_130_MR_12_CCabernet Sauvignon

161–49Mid-ripeningBolgheri2012263,5315,22133,195,5777217,7217 The‘number’intheGPGVcolumncorrespondtothenumberofcompletegenomeassembledindenovoineachsample.✓indicatesthatreadshavemappedontoGPGVgenome,asshown intheMappedreadcountscolumnswouldindicate,howevernocompletegenomefromcontiguoussequencecouldbeobtainedandRPKM(ReadperKilobaseMillion)werealwaysbelow 3whennogenomewereassembled.ThisworkwasperformedusingCLC-Workbenchusingverystringentmappingparameters*(0,95/0,95)

Eur J Plant Pathol

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onto rootstock 161.49 C used exclusively in Bolgheri and grapevines onto Kober 5BB exclusively used in Riccione (FST = 0.564, P ≤ 10⁻⁵). Comparison of sequences obtained from the 420A rootstock also displayed statistically significant FSTvalues; however, the values were lower than the ones mentioned above. This is most likely because 420A was used as a rootstock in both locations.

Furthermore, the genetic background of the grapevine cultivar, which was also present in both locations, had no statistically significant impact on viral populations (FST = 0.018,P = 0.185).

In addition to the presence/absence of GPGV in the samples, this work highlights two distinct situations at the viral genomic level. Indeed, one vineyard is infected by a single variant, identity percentage ≥ 99%, as previously defined for GPGV (Hily et al.,2020), repre- sented here by samples from the Bolgheri region, while the other vineyard (Riccione) is infected by at least two (or more) variants. These results indirectly, but strongly, suggest probable independent introduction/transmission events of GPGV in two out of the three locations spe- cifically looked at, in this transcriptomic study. These situations are probably the result of transmission events through grafting (Saldarelli et al.,2014) and movement of infected material as previously suggested (Al Rwahnih et al., 2016; Fajardo et al., 2017; Wu &

Habili,2017). They may also have occurred horizontal- ly by vectors either in the nursery or in the vineyard,

with distinct variants of the virus being detected at each location, regardless of the clonal background of the grapevine. In addition, the detection of these different variants according to location, each displaying probable differences in fitness, may results in differential virus accumulation as observed above. Overall, this in silico work add onto the so-far limited knowledge on the natural transmission of GPGV in vineyards (Bertazzon et al.,2020; Hily et al.,in press).

Lately, datamining is becoming a very important and powerful tool to identify new pathogens, as well as new variants of known viruses, such as from the now well-known Coronaviridae family for example (https://virological.org/t/serratus-the-ultra-deep-search-to- discover-novel-coronaviruses/516) (last visited 04/2021).

Datamining can be also utilized to increase the number of complete genome sequences for downstream studies on the evolutionary history of specific viruses for example (Hily et al.,2020). In this work, datamining can be considered as anin-silicotool to monitorpost factothe sanitary status of any vineyards around the world from which data have already been collected, published and made publicly available. There are a few pitfalls regarding datamining as a tool. Indeed, we do not have always all the details regarding the samples (i.e. metadata about the samples such as the exact origin and location of collection). We do not have the choice of the technology with which data were obtained nor the quality of the sample. However, the

2011 2012

Vintage

MR PV

Developmental Stage

161.49 C

Rootstock

420A Kober 5BB Bolgheri Riccione

Locaon

Bolgheri Riccione

Sangiovese/420A

Sangiovese 420A

CS Kober 5BB Bolgheri

Sangiovese 420A CS 161.49 C

Riccione

Fig. 2 Box plot diagrams of RPKM in function of different variables. From left to right: year, developmental stage (MR:

mid-ripening, PV: pre-veraison), rootstock, overall location, San- giovese grafted onto 420A, grapevine cultivated in Bolgheri and in Riccione (CS: Cabernet Sauvignon). On each box, the central line is the median, the edges of the boxes are the 25th and 75th percentiles, the whiskers extend to the most extreme data and the

dots refer to the outliers. Since RPKM values did not follow a normal distribution, a generalized linear model (GLM) with Poisson link function was used. The significance of the considered effect was tested using Wald chi2 test and thepvalues smaller than 0.05 threshold were considered statistically significant. All analyses and graphic representations were made with the R software version 4.0.2 (R core Team 2012)