terclim by ICS banner
IVES 9 IVES Conference Series 9 International Congress on Grapevine and Wine Sciences 9 2ICGWS-2023 9 Predicting provenance and grapevine cultivar implementing machine learning on vineyard soil microbiome data: implications in grapevine breeding

Predicting provenance and grapevine cultivar implementing machine learning on vineyard soil microbiome data: implications in grapevine breeding

Abstract

The plant rhizosphere microbial communities are an essential component of plant microbiota, which is crucial for sustaining the production of healthy crops. The main drivers of the composition of such communities are the growing environment and the planted genotype. Recent viticulture studies focus on understanding the effects of these factors on soil microbial composition since microbial biodiversity is an important determinant of plant phenotype, and of wine’s organoleptic properties. Microbial biodiversity of different wine regions, for instance, is an important determinant of wine terroir. While conventional methods for microbiome analysis are extensively used, application of modern Artificial Intelligence (AI) based methods could unravel non-linear associations between microbial taxa and environmental/plant genetic factors. Here we compare the performance of shallow and Deep Machine Learning methods to predict the geographical provenance and the planted grape cultivar solely based on the soil microbiota. We used 885 previously published microbial amplicon-sequencing datasets (16S) collected from vineyards located in 13 countries across 4 continents and planted with 34 Vitis vinifera cultivars representing the largest collection of vineyard microbiomes analyzed to date. This research also aimed at addressing some common challenges associated with most ML-based studies such as easy availability of models to non-technical researchers which is necessary for research reproducibility. To facilitate this, the models built in this study will be available through a GUI-based containerized web platform. Also, to provide compatibility of processed data from other 16S studies, a computational step will be included that merge the features either by taxonomy or sequence identity. This study will be beneficial in several ways such as inferring lost/mislabeled samples, identifying important location-specific and cultivar-specific taxa. Ultimately, this approach could be implemented for the identification of the genes regulating host/microbe interactions, which will provide valuable targets for breeding programs aimed at producing more sustainable crops.  

Acknowledgements: This study was supported by the National Institute of Food and Agriculture, AFRI Competitive Grant Program Accession number 1018617, and the National Institute of Food and Agriculture, United States Department of Agriculture, Hatch Program accession number 1020852.

DOI:

Publication date: October 5, 2023

Issue: ICGWS 2023

Type: Article

Authors

Carlos M. Rodríguez López1*, Lakshay Anand1

1Environmental Epigenomics and Genomics Group, Department of Horticulture, College of Agriculture, Food and environment, University of Kentucky, Lexington, Kentucky, USA

Contact the author*

Keywords

rhizosphere microbiome, provenance, plant-microbiome interactions, breeding, machine learning

Tags

2ICGWS | ICGWS | ICGWS 2023 | IVES Conference Series

Citation

Related articles…

Overall conceptual characterization of aged dry white wines using a mental descriptive questionnaire

The purpose of the present study was to understand the overall concept of an aged dry white wine using a descriptive mental questionnaire. A total of 680 worldwide participants, grouped according to their involvement in the wine business, replied to an online questionnaire to characterize the sensory analytical and synthetic descriptors of an aged dry white wine. The descriptors were selected using a Check-All-That-Apply (CATA) approach concerning wine colour, aroma, taste, mouthfeel, and global appreciation.

A phylogenomic study reveals the major dissemination routes of ‘Tempranillo Tinto’ in the Iberian Peninsula

‘Tempranillo Tinto’ is a black-berried Iberian cultivar that originated from a hybridization between cvs. ‘Benedicto’ and ‘Albillo Mayor’ [1]. Today, it is the third most widely grown wine grape cultivar worldwide with more than 200,000 hectares of vineyards mostly distributed along the Iberian Peninsula, where it is also known as ‘Cencibel’, ‘Tinta de Toro’, ‘Tinta Roriz’, and ‘Aragonez’, among other synonyms. Here, we quantified the intra-varietal genomic diversity in this cultivar through the study of 35 clones or ancient vines from seven different Iberian wine-making regions. A comparative analysis after Illumina whole-genome sequencing revealed the presence of 1,120 clonal single nucleotide variants (SNVs).

Identification of loci associated with specialised metabolites in Vitis vinifera

Secondary (or specialised) metabolites such as terpenes and phenolic compounds are produced by plants for various roles which include defence against pathogens and herbivores, protection against abiotic stress, and plant signalling. Additionally, these metabolites influence grapevine quality traits such as colour, aroma, taste, and nutritional value. However, the biosynthesis of these metabolites is often complex and controlled by multiple genes which in grapevine are predominantly uncharacterised.

Aromatic characterization of Moscato Giallo by GC-MS/MS and stable isotopic ratio analysis of the major volatile compounds

Among the Moscato grapes, Moscato Giallo is a winegrape variety characterized by a high content of free and glycosylated monoterpenoids, which gives very aromatic wines. The aromatic bouquet of Moscato Giallo is strongly influenced by the high concentration of linalool, geraniol, linalool oxides, limonene, α-terpineol, citronellol, HO-trienol, HO-diols, 8-Hydroxylinalool, geranic acid and β-myrcene, that give citrus, rose, and peach notes.

Adsorption of tetraconazole by organic residues and vineyard organically-amended soils 

Spain is the country with the largest wine-producing area in the EU and its productivity is largely controlled applying fungicides. However, residues of these compounds can move and contaminate surface and groundwater. The objective of this work was to evaluate the capacity of bioadsorbents from different origin to adsorb and immobilize tetraconazole by themselves or when applied as organic soil amendment, and to prevent soil and water contamination by this fungicide. The adsorption of tetraconazole by 3 organic residues: spent mushroom substrate (SMS), green compost (GC) and vine pruning sawdust (VP), as well as by vineyard soils unamended and amended individually with these residues at 1.5% (w/w) was evaluated using the batch equilibrium technique.