DNA-metabarcoding analyses of the grapevine wood fungal community in the Krasnodar Region and Crimea

Abstract

Grapes are an economically important agricultural plant. Studies of the Grapevine microbiota and rhizosphere have become increasingly important in the last decade. The importance of such research is also supported by the fact that grapes are a perennial, long-used crop.

In this work, we present the results of a DNA-metabarcoding analysis of the fungal community of grape wood, collected from vineyards in the Krasnodar Territory and the Southern Coast of Crimea, and considered approaches to the analysis of DNA-metabarcoding data. Classifier is Naïve base (“sklearn”) based on machine learning is more informative metagenomic data classifier than BLAST+ (local alignment) and Vsearch (global alignment). Analysis of the ITS locus revealed the largest number of taxa, which was confirmed for all types of classifiers used in the study. Primers for the ITS locus showed a high specificity of fungal DNA in comparison with the LSU and SSU loci. The most common genera in the fungal community are Acidea, Alternaria, Cladosporium and Fusarium. Significant differences were revealed in the assessment of alpha and beta diversity in the analysis of samples from different regions. This article presents an analysis of the wood grapevine fungal community and ways to ASV classification. This study is the first to describe the endophytic fungal communities of the Krasnodar Territory and the Crimea vines using the analysis of DNA metabarcoding data.

Keywords

grapes / data-analysis / metagenomic data classifier / ITS locus / diversity / SSU locus / LSU locus

References

[1] Jayawardena RS, Purahong W, Zhang W, et al. Biodiversity of fungi on Vitis vinifera L. revealed by traditional and high-resolution culture-independent approaches. Fungal Divers. 2018;90:1–84. DOI: 10.1007/s13225-018-0398-4

[2] Jayawardena R.S., Purahong W., Zhang W., et al. Biodiversity of fungi on Vitis vinifera L. revealed by traditional and high-resolution culture-independent approaches // Fungal Divers. 2018. Vol. 90. P. 1–84. DOI: 10.1007/s13225-018-0398-4

[3] Fuchs M. Grapevine viruses: a multitude of diverse species with simple but overall poorly adopted management solutions in the vineyard. J Plant Pathol. 2020;102(3):643–653. DOI: 10.1007/s42161-020-00579-2

[4] Fuchs M. Grapevine viruses: a multitude of diverse species with simple but overall poorly adopted management solutions in the vineyard // J Plant Pathol. 2020. Vol. 102, No. 3. P. 643–653. DOI: 10.1007/s42161-020-00579-2

[5] Salvetti E, Campanaro S, Campedelli I, et al. Whole-Metagenome-Sequencing-Based Community Profiles of Vitis vinifera L. cv. Corvina Berries Withered in Two Post-harvest Conditions. Front Microbiol. 2016;7:937. DOI: 10.3389/fmicb.2016.00937

[6] Salvetti E., Campanaro S., Campedelli I., et al. Whole-Metagenome-Sequencing-Based Community Profiles of Vitis vinifera L. cv. Corvina Berries Withered in Two Post-harvest Conditions // Front Microbiol. 2016. Vol. 7. ID937. DOI: 10.3389/fmicb.2016.00937

[7] Mori N, Quaglino F, Tessari F, et al. Investigation on ‘bois noir’ epidemiology in north-eastern Italian vineyards through a multidisciplinary approach. Ann Appl Biol. 2014;166(1):75–89. DOI:10.1111/aab.12165

[8] Mori N., Quaglino F., Tessari F., et al. Investigation on ‘bois noir’ epidemiology in north-eastern Italian vineyards through a multidisciplinary approach // Ann Appl Biol. 2014. Vol. 166, No. 1. P. 75–89. DOI: 10.1111/aab.12165

[9] Burovinskaya MV, Yurchenko EG. Tracheomycosis grape diseases and measures of their restrictions. Fruit growing and viticulture of South Russia. 2020;(63):270–284. (In Russ.) DOI: 10.30679/2219-5335-2020-3-63-270-284

[10] Буровинская М.В., Юрченко Ю.Г. Трахеомикозные заболевания винограда и меры их ограничения // Плодоводство и виноградарство Юга России. 2020. № 63. С. 270–284. DOI: 10.30679/2219-5335-2020-3-63-270-284

[11] Savchuk NV, Yurchenko EG, Vinogradova SV, Porotikova EV. Causative agents of Fusarium wilt of the reproductive organs of grapes. Ways of infection. Proceedings of the V International science and practice conferences: “Sovremennoe sostoyanie, problemy i perspektivy razvitiya agrarnoi nauki”; 2020 Sept 21–25. P. 95–97. (In Russ.) DOI: 10.33952/2542-0720-2020-5-9-10-45

[12] Савчук Н.В., Юрченко Е.Г., Виноградова С.В., Поротикова Е.В. Способы проникновения инфекции возбудителей фузариозного усыхания генеративных органов винограда // Материалы V Международной научно-практической конференции «Современное состояние, проблемы и перспективы развития аграрной науки»; Сентябрь 21–25, 2020. С. 95–97. DOI: 10.33952/2542-0720-2020-5-9-10-45

[13] Blackwell M. The Fungi: 1, 2, 3 … 5.1 million species? Am J Bot. 2011;98(3):426–438. DOI: 10.3732/ajb.1000298

[14] Blackwell M. The Fungi: 1, 2, 3 … 5.1 million species? // Am J Bot. 2011. Vol. 98, No. 3. P. 426–438. DOI: 10.3732/ajb.1000298

[15] Berlanas C, Berbegal M, Elena G, et al. The Fungal and Bacterial Rhizosphere Microbiome Associated With Grapevine Rootstock Genotypes in Mature and Young Vineyards. Front Microbiol. 2019;10:1142. DOI: 10.3389/fmicb.2019.01142

[16] Berlanas C., Berbegal M., Elena G., et al. The Fungal and Bacterial Rhizosphere Microbiome Associated With Grapevine Rootstock Genotypes in Mature and Young Vineyards // Front Microbiol. 2019. Vol. 10. ID1142. DOI: 10.3389/fmicb.2019.01142

[17] Úrbez-Torres JR, Gubler WD. Susceptibility of grapevine pruning wounds to infection by Lasiodiplodia theobromae and Neofusicoccum parvum. Plant Pathol. 2010;60(2):261–270. DOI: 10.1111/j.1365-3059.2010.02381.x

[18] Úrbez-Torres J.R., Gubler W.D. Susceptibility of grapevine pruning wounds to infection by Lasiodiplodia theobromae and Neofusicoccum parvum // Plant Pathol. 2010. Vol. 60, No. 2. P. 261–270. DOI: 10.1111/j.1365-3059.2010.02381.x

[19] Jayawardena R, Purahong W, Zhang W, et al. Biodiversity of fungi on Vitis vinifera L. revealed by traditional and high-resolution culture-independent approaches. Fungal Divers. 2018;90(1):1–84. DOI: 10.1007/s13225-018-0398-4

[20] Jayawardena R., Purahong W., Zhang W., et al. Biodiversity of fungi on Vitis vinifera L. revealed by traditional and high-resolution culture-independent approaches // Fungal Divers. 2018. Vol. 90, No. 1. P. 1–84. DOI: 10.1007/s13225-018-0398-4

[21] Lade SB, Štraus D, Oliva J. Variation in Fungal Community in Grapevine (Vitis vinifera) Nursery Stock Depends on Nursery, Variety and Rootstock. Journal of Fungi. 2022;8(1):47. DOI: 10.3390/jof8010047

[22] Lade S.B., Štraus D., Oliva J. Variation in Fungal Community in Grapevine (Vitis vinifera) Nursery Stock Depends on Nursery, Variety and Rootstock // Journal of Fungi. 2022. Vol. 8, No. 1. ID 47. DOI: 10.3390/jof8010047

[23] Mendes R, Kruijt M, De Bruijn I, et al. Deciphering the Rhizosphere Microbiome for Disease-Suppressive Bacteria. Science. 2011;3329(6033):1097–1100. DOI: 10.1126/science.1203980

[24] Mendes R., Kruijt M., De Bruijn I., et al. Deciphering the Rhizosphere Microbiome for Disease-Suppressive Bacteria // Science. 2011. Vol. 332, No. 6033. P. 1097–1100. DOI: 10.1126/science.1203980

[25] Janssen PH. Identifying the Dominant Soil Bacterial Taxa in Libraries of 16S rRNA and 16S rRNA Genes. Appl Environ Microbiol. 2006;72(3):1719–1728. DOI: 10.1128/aem.72.3.1719-1728.2006

[26] Janssen P.H. Identifying the Dominant Soil Bacterial Taxa in Libraries of 16S rRNA and 16S rRNA Genes // Appl Environ Microbiol. 2006. Vol. 72, No. 3. P. 1719–1728. DOI: 10.1128/aem.72.3.1719-1728.2006

[27] Buffet-Bataillon S, Rizk G, Cattoir V, et al. Efficient and Quality-Optimized Metagenomic Pipeline Designed for Taxonomic Classification in Routine Microbiological Clinical Tests. Microorganisms. 2022;10(4):711. DOI: 10.3390/microorganisms10040711

[28] Buffet-Bataillon S., Rizk G., Cattoir V., et al. Efficient and Quality-Optimized Metagenomic Pipeline Designed for Taxonomic Classification in Routine Microbiological Clinical Tests // Microorganisms. 2022. Vol. 10, No. 4. ID 711. DOI: 10.3390/microorganisms10040711

[29] Zarraonaindia I, Owens SM, Weisenhorn P, et al. The Soil Microbiome Influences Grapevine-Associated Microbiota. mBio. 2015;6(2): e02527–14. DOI: 10.1128/mbio.02527-14

[30] Zarraonaindia I., Owens S.M., Weisenhorn P., et al. The Soil Microbiome Influences Grapevine-Associated Microbiota // mBio. 2015. Vol. 6, No. 2. ID e02527–14. DOI: 10.1128/mbio.02527-14

[31] Niem JM, Billones-Baaijens R, Stodart B, Savocchia S. Diversity Profiling of Grapevine Microbial Endosphere and Antagonistic Potential of Endophytic Pseudomonas Against Grapevine Trunk Diseases. Front Microbiol. 2020;11:477. DOI: 10.3389/fmicb.2020.00477

[32] Niem J.M., Billones-Baaijens R., Stodart B., Savocchia S. Diversity Profiling of Grapevine Microbial Endosphere and Antagonistic Potential of Endophytic Pseudomonas Against Grapevine Trunk Diseases // Front Microbiol. 2020. Vol. 11. ID 477. DOI: 10.3389/fmicb.2020.00477

[33] Schoch CL, Seifert KA, Huhndorf S, et al. Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi. PNAS. 2012;109(16):6241–6246. DOI: 10.1073/pnas.1117018109

[34] Schoch C.L., Seifert K.A., Huhndorf S., et al. Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi // PNAS. 2012. Vol. 109, No. 16. P. 6241–6246. DOI: 10.1073/pnas.1117018109

[35] Ishii N, Ishida S, Kagami M. PCR primers for assessing community structure of aquatic fungi including Chytridiomycota and Cryptomycota. Fungal Ecol. 2015;13:33–43. DOI: 10.1016/j.funeco.2014.08.004

[36] Ishii N., Ishida S., Kagami M. PCR primers for assessing community structure of aquatic fungi including Chytridiomycota and Cryptomycota // Fungal Ecol. 2015. Vol. 13. P. 33–43. DOI: 10.1016/j.funeco.2014.08.004

[37] Jones EBG, Devadatha B, Abdel-Wahab MA, et al. Phylogeny of new marine Dothideomycetes and Sordariomycetes from mangroves and deep-sea sediments. Botanica Marina. 2020;63(2):155–181. DOI: 10.1515/bot-2019-0014

[38] Jones E.B.G., Devadatha B., Abdel-Wahab M.A., et al. Phylogeny of new marine Dothideomycetes and Sordariomycetes from mangroves and deep-sea sediments // Botanica Marina. 2020. Vol. 63, No. 2. P. 155–181. DOI: 10.1515/bot-2019-0014

[39] Tedersoo L, Anslan S, Bahram M, et al. Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys. 2015;10:1–43. DOI: 10.3897/mycokeys.10.4852

[40] Tedersoo L., Anslan S., Bahram M., et al. Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi // MycoKeys. 2015. Vol. 10. P. 1–43. DOI: 10.3897/mycokeys.10.4852

[41] Bioinformatics.babraham.ac.uk [Internet]. Babraham Bioinformatics [cited: 2021 Dec 11]. Available from: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/

[42] Bioinformatics.babraham.ac.uk [Интернет]. Babraham Bioinformatics [дата обращения: 11.12.2021]. Доступ по ссылке: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/

[43] Multiqc.info [Internet]. MultiQC [cited: 2021 Dec 11]. Available from: https://www.multiqc.info/

[44] Multiqc.info [Интернет]. MultiQC [дата обращения: 11.12.2021]. Доступ по ссылке: https://www.multiqc.info/

[45] Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–2120. DOI: 10.1093/bioinformatics/btu170

[46] Bolger A.M., Lohse M., Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data // Bioinformatics. 2014. Vol. 30, No. 15. P. 2114–2120. DOI: 10.1093/bioinformatics/btu170

[47] Bolyen E, Rideout JR, Dillon MR, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME2. Nat Biotechnol. 2019;37(8):852–857. DOI: 10.1038/s41587-019-0209-9

[48] Bolyen E., Rideout J.R., Dillon M.R., et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME2 // Nat Biotechnol. 2019. Vol. 37, No. 8. P. 852–857. DOI: 10.1038/s41587-019-0209-9

[49] Callahan BJ, McMurdie PJ, Rosen MJ, et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13(7):581–583. DOI: 10.1038/nmeth.3869

[50] Callahan B.J., McMurdie P.J., Rosen M.J., et al. DADA2: High-resolution sample inference from Illumina amplicon data // Nat Methods. 2016. Vol. 13, No. 7. P. 581–583. DOI: 10.1038/nmeth.3869

[51] Camacho C, Coulouris G, Avagyan V, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10(1):421. DOI: 10.1186/1471-2105-10-421

[52] Camacho C., Coulouris G., Avagyan V., et al. BLAST+: architecture and applications // BMC Bioinformatics. 2009. Vol. 10, No. 1. ID 421. DOI: 10.1186/1471-2105-10-421

[53] Rognes T, Flouri T, Nichols B, et al. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4: e2584. DOI: 10.7717/peerj.2584

[54] Rognes T., Flouri T., Nichols B., et al. VSEARCH: a versatile open source tool for metagenomics // PeerJ. 2016. Vol. 4. ID e2584. DOI: 10.7717/peerj.2584

[55] Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: Machine Learning in Python. J Mach Learn Res. 2012;12:2825–2830. DOI: 10.48550/arXiv.1201.0490

[56] Pedregosa F., Varoquaux G., Gramfort A., et al. Scikit-learn: Machine Learning in Python // J Mach Learn Res. 2012. Vol. 12. P. 2825–2830. DOI: 10.48550/arXiv.1201.0490

[57] Nilsson RH, Larsson K-H, Taylor AFS, et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 2018;47(D1): D259–D264. DOI: 10.1093/nar/gky1022

[58] Nilsson R.H., Larsson K.-H., Taylor A.F.S., et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications // Nucleic Acids Res. 2018. Vol. 47, No. D1. P. D259–D264. DOI: 10.1093/nar/gky1022

[59] Robeson MS II, O’Rourke DR, Kaehler BD, et al. RESCRIPt: Reproducible sequence taxonomy reference database management for the masses. bioRxiv. 2020: ID326504. DOI: 10.1101/2020.10.05.326504

[60] Robeson M.S. II, O’Rourke D.R., Kaehler B.D., et al. RESCRIPt: Reproducible sequence taxonomy reference database management for the masses // bioRxiv. 2020. ID 326504. DOI: 10.1101/2020.10.05.326504

[61] McMurdie PJ, Holmes S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS One. 2013;8(4): e61217. DOI: 10.1371/journal.pone.0061217

[62] McMurdie P.J., Holmes S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data // PLoS One. 2013. Vol. 8, No. 4. ID e61217. DOI: 10.1371/journal.pone.0061217

[63] Wickham H. ggplot2: Elegant Graphics for Data Analysis. In: Gentleman R, Hornik K, Parmigiani G, editors. Use R! New York: Springer-Verlag, 2016. P. 189–201. DOI: 10.1007/978-3-319-24277-4_9

[64] Wickham H. ggplot2: Elegant Graphics for Data Analysis. In: Gentleman R., Hornik K., Parmigiani G., editors. Use R! New York: Springer-Verlag, 2016. P. 189–201. DOI: 10.1007/978-3-319-24277-4_9

[65] González V, Tello ML. The endophytic mycota associated with Vitis vinifera in central Spain. Fungal Divers. 2010;47(1):29–42. DOI: 10.1007/s13225-010-0073-x

[66] González V., Tello M.L. The endophytic mycota associated with Vitis vinifera in central Spain // Fungal Divers. 2010. Vol. 47, No. 1. P. 29–42. DOI: 10.1007/s13225-010-0073-x

[67] Aćimović SG, Harmon CL, Bec S, et al. First Report of Diplodia corticola Causing Decline of Red Oak (Quercus rubra) Trees in Maine. Plant Dis. 2016;100(3):649. DOI: 10.1094/pdis-09-15-0994-pdn

[68] Aćimović S.G., Harmon C.L., Bec S., et al. First Report of Diplodia corticola Causing Decline of Red Oak (Quercus rubra) Trees in Maine // Plant Dis. 2016. Vol. 100, No. 3. ID649. DOI: 10.1094/pdis-09-15-0994-pdn

[69] Phillips AJL, Alves A, Abdollahzadeh J, et al. The Botryosphaeriaceae: genera and species known from culture. Stud Mycol. 2013;76(1):51–167. DOI: 10.3114/sim0021

[70] Phillips A.J.L., Alves A., Abdollahzadeh J., et al. The Botryosphaeriaceae: genera and species known from culture // Stud Mycol. 2013. Vol. 76, No. 1. P. 51–167. DOI: 10.3114/sim0021