Dataset Information

Wine Yeast Terroir: Separating the Wheat from the Chaff-for an Open Debate.

ABSTRACT: Wine terroir is characterized by a specific taste and style influenced by the cultivar of the fermented grapes, geographical factors such as the vineyard, mesoclimate, topoclimate, and microclimate, soil geology and pedology, and the agronomic approach used. These characteristics together define the concept of "terroir". Thus, regional distinctive flavors in wine have been the subject of many studies aimed at better understanding the link between the wine and the vineyard. Indeed, the identification of key environmental elements involved in the regional variation of grape and wine quality characteristics is a critical feature for improving wine production in terms of consumer preference and economic appreciation. Many studies have demonstrated the role of abiotic factors in grape composition and consequently in wine style. Biotic factors are also involved such as grape microbial communities. However, the occurrence and effects of region-specific microbiota in defining wine characteristics are more controversial issues. Indeed, several studies using high throughput sequencing technologies have made it possible to describe microbial communities and revealed a link between grape must and soil microbial communities, and the geography of the territory. Based on these observations, the concept of "microbial terroir" emerged. However, this concept has been subject to contradictory studies. The aim of this opinion article is to take a step back and examine in perspective the concept of microbial terroir, by comparing numerous data from different studies and providing arguments in favor of or against this concept to stimulate discussion and point out that experimental research is still needed to study the contribution of this assembly of microorganisms to the final product and to support or refute the concept.

SUBMITTER: Alexandre H

PROVIDER: S-EPMC7285325 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Wine Yeast Terroir: Separating the Wheat from the Chaff-for an Open Debate.

Alexandre Hervé H

Microorganisms 20200525 5

Wine terroir is characterized by a specific taste and style influenced by the cultivar of the fermented grapes, geographical factors such as the vineyard, mesoclimate, topoclimate, and microclimate, soil geology and pedology, and the agronomic approach used. These characteristics together define the concept of "terroir". Thus, regional distinctive flavors in wine have been the subject of many studies aimed at better understanding the link between the wine and the vineyard. Indeed, the identifica ...[more]

PMID: 32466171

Similar Datasets

Project description:Metabarcoding is by now a well-established method for biodiversity assessment in terrestrial, freshwater, and marine environments. Metabarcoding data sets are usually used for ?- and ?-diversity estimates, that is, interspecies (or inter-MOTU [molecular operational taxonomic unit]) patterns. However, the use of hypervariable metabarcoding markers may provide an enormous amount of intraspecies (intra-MOTU) information-mostly untapped so far. The use of cytochrome oxidase (COI) amplicons is gaining momentum in metabarcoding studies targeting eukaryote richness. COI has been for a long time the marker of choice in population genetics and phylogeographic studies. Therefore, COI metabarcoding data sets may be used to study intraspecies patterns and phylogeographic features for hundreds of species simultaneously, opening a new field that we suggest to name metaphylogeography. The main challenge for the implementation of this approach is the separation of erroneous sequences from true intra-MOTU variation. Here, we develop a cleaning protocol based on changes in entropy of the different codon positions of the COI sequence, together with co-occurrence patterns of sequences. Using a data set of community DNA from several benthic littoral communities in the Mediterranean and Atlantic seas, we first tested by simulation on a subset of sequences a two-step cleaning approach consisting of a denoising step followed by a minimal abundance filtering. The procedure was then applied to the whole data set. We obtained a total of 563 MOTUs that were usable for phylogeographic inference. We used semiquantitative rank data instead of read abundances to perform AMOVAs and haplotype networks. Genetic variability was mainly concentrated within samples, but with an important between seas component as well. There were intergroup differences in the amount of variability between and within communities in each sea. For two species, the results could be compared with traditional Sanger sequence data available for the same zones, giving similar patterns. Our study shows that metabarcoding data can be used to infer intra- and interpopulation genetic variability of many species at a time, providing a new method with great potential for basic biogeography, connectivity and dispersal studies, and for the more applied fields of conservation genetics, invasion genetics, and design of protected areas.

Project description:BackgroundThrough next-generation sequencing, the amount of sequence data potentially available for phylogenetic analyses has increased exponentially in recent years. Simultaneously, the risk of incorporating 'noisy' data with misleading phylogenetic signal has also increased, and may disproportionately influence the topology of weakly supported nodes and lineages featuring rapid radiations and/or elevated rates of evolution.ResultsWe investigated the influence of phylogenetic noise in large data sets by applying two fundamental strategies, variable site removal and long-branch exclusion, to the phylogenetic analysis of a full plastome alignment of 107 species of Pinus and six Pinaceae outgroups. While high overall phylogenetic resolution resulted from inclusion of all data, three historically recalcitrant nodes remained conflicted with previous analyses. Close investigation of these nodes revealed dramatically different responses to data removal. Whereas topological resolution and bootstrap support for two clades peaked with removal of highly variable sites, the third clade resolved most strongly when all sites were included. Similar trends were observed using long-branch exclusion, but patterns were neither as strong nor as clear. When compared to previous phylogenetic analyses of nuclear loci and morphological data, the most highly supported topologies seen in Pinus plastome analysis are congruent for the two clades gaining support from variable site removal and long-branch exclusion, but in conflict for the clade with highest support from the full data set.ConclusionsThese results suggest that removal of misleading signal in phylogenomic datasets can result not only in increased resolution for poorly supported nodes, but may serve as a tool for identifying erroneous yet highly supported topologies. For Pinus chloroplast genomes, removal of variable sites appears to be more effective than long-branch exclusion for clarifying phylogenetic hypotheses.

Project description:The development of DNA microarray and RNA-sequencing technology has led to an explosion in the generation of transcriptomic differential expression data under a wide range of biologic systems including those recapitulating the monogenic muscular dystrophies. Data generation has increased exponentially due in large part to new platforms, improved cost-effectiveness, and processing speed. However, reproducibility and thus reliability of data remain a central issue, particularly when resource constraints limit experiments to single replicates. This was observed firsthand in a recent rare disease drug repurposing project involving RNA-seq-based transcriptomic profiling of primary cerebrocortical cultures incubated with clinic-ready blood-brain penetrant drugs. Given the low validation rates obtained for single differential expression genes, alternative approaches to identify with greater confidence genes that were truly differentially expressed in our dataset were explored. Here we outline a method for differential expression data analysis in the context of drug repurposing for rare diseases that incorporates the statistical rigour of the multigene analysis to bring greater predictive power in assessing individual gene modulation. Ingenuity Pathway Analysis upstream regulator analysis was applied to the differentially expressed genes from the Care4Rare Neuron Drug Screen transcriptomic database to identify three distinct signaling networks each perturbed by a different drug and involving a central upstream modulating protein: levothyroxine (DIO3), hydroxyurea (FOXM1), dexamethasone (PPARD). Differential expression of upstream regulator network related genes was next assessed in in vitro and in vivo systems by qPCR, revealing 5× and 10× increases in validation rates, respectively, when compared with our previous experience with individual genes in the dataset not associated with a network. The Ingenuity Pathway Analysis based gene prioritization may increase the predictive value of drug-gene interactions, especially in the context of assessing single-gene modulation in single-replicate experiments.

Dataset Information

Wine Yeast Terroir: Separating the Wheat from the Chaff-for an Open Debate.

Publications

Wine Yeast Terroir: Separating the Wheat from the Chaff-for an Open Debate.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets