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      Diversity of fungal communities on Cabernet and Aglianico grapes from vineyards located in Southern Italy

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          Abstract

          Grape-associated microbial community is influenced by a combination of viticultural, climatic, pedological and anthropological factors, collectively known as terroir. Therefore, grapes of the same cultivar grown in different areas can be appreciated for their distinctive biogeographic characteristics. In our previous study, we showed that the phenotypic response of Aglianico and Cabernet grapevines from Molise and Sicily regions is significantly influenced by the prevailing pedoclimatic conditions, particularly soil physical properties. However, the scale at which microbial communities differ could be important in clarifying the concept of terroir, including whether it is linked to the grape variety present in a particular vineyard. To explore this further, in the research presented here, a comparative study on the fungal communities inhabiting the berry surfaces of Cabernet and Aglianico cultivars was conducted on different vineyards located in Southern Italy (Molise, Sicily and Campania regions, the first two of which had been involved in our previous study) by using high-throughput sequencing (HTS) and multivariate data analysis. The descriptive approach through relative abundance analysis showed the most abundant phyla (Ascomycota, Basidiomycota, and Chytridiomycota), families ( Cladosporiaceae, Saccotheciaceae, Pleosporaceae, Saccharomycodaceae, Sporidiobolaceae, Didymellaceae, Filobasidiaceae, Bulleribasidiaceae, and Saccharomycetaceae) and genera ( Cladosporium, Aureobasidium, Alternaria, Stemphylium and Filobasidium) detected on grape berries. The multivariate data analysis performed by using different packages (phyloseq, Vegan, mixOmics, microbiomeMarker and ggplot2) highlighted that the variable “vineyard location” significantly affect the fungal community, while the variable “grape variety” has no significant effect. Thus, some taxa are found to be part of specific vineyard ecosystems rather than specific grape varieties, giving additional information on the microbial contribution to wine quality, thanks to the presence of fermentative yeasts or, conversely, to the involvement in negative or detrimental roles, due to the presence of grape-deriving fungi implied in the spoilage of wine or in grapevine pathogenesis. In this connection, the main functions of core taxa fungi, whose role in the vineyard environment is still poorly understood, are also described.

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          Most cited references64

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          DADA2: High resolution sample inference from Illumina amplicon data

          We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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            QIIME allows analysis of high-throughput community sequencing data.

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              phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data

              Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.
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                Author and article information

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                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                25 April 2024
                2024
                : 15
                : 1399968
                Affiliations
                [1] 1Department of Agricultural, Environmental and Food Sciences, University of Molise , Campobasso, Italy
                [2] 2Department of Science and Technology for Sustainable Development and One Health, University Campus Bio-Medico of Rome , Rome, Italy
                Author notes

                Edited by: Rosanna Tofalo, University of Teramo, Italy

                Reviewed by: Angela Capece, University of Basilicata, Italy

                Mariagiovanna Fragasso, University of Foggia, Italy

                Rossana Sidari, Mediterranea University of Reggio Calabria, Italy

                *Correspondence: Mariantonietta Succi, succi@ 123456unimol.it
                Gianfranco Pannella, g.pannella@ 123456unicampus.it
                Article
                10.3389/fmicb.2024.1399968
                11079197
                38725687
                dd297b04-1146-42ba-8cc2-e783af895de4
                Copyright © 2024 Iorizzo, Bagnoli, Vergalito, Testa, Tremonte, Succi, Pannella, Letizia, Albanese, Lombardi and Coppola.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 12 March 2024
                : 11 April 2024
                Page count
                Figures: 8, Tables: 0, Equations: 0, References: 64, Pages: 14, Words: 8393
                Funding
                The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was carried out in the context of the scientific project ADAPT: influence of agro-climatic conditions on the microbiome and genetic expression of grapevines for the production of red wines: a multidisciplinary approach. PRIN: Research Projects of National Interest; funded by the Italian Ministry for Universities and Research (MUR), grant number 2017M83XFJ-(CUP H34I19000590001).
                Categories
                Microbiology
                Original Research
                Custom metadata
                Food Microbiology

                Microbiology & Virology
                aglianico,cabernet,biogeography,fungi,grape microbiota,ngs,terroir,metataxonomic
                Microbiology & Virology
                aglianico, cabernet, biogeography, fungi, grape microbiota, ngs, terroir, metataxonomic

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