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…

Bioprotection of grape must by Metschnikowia sp.: genericity and mechanism

The market trend heads to food products with less chemical inputs, including in oenology. During the winemaking process, sulfites are commonly use to avoid microbiological contamination and stabilization of the wine thanks to its antimicrobial and antioxidant activities. Nevertheless, this use is not without consequences on human health and environment, leading for example to allergic reaction and pollution. A biological alternative to these sulfites has emerges: the bioprotection.

Optimization of the acquisition of NIR spectrum in grape must and wine 

The characterization of chemical compounds related with quality of grape must and wine is relevant for the viticulture and enology fields. Analytical methods used for these analyses require expensive instrumentation as well as a long sample preparation processes and the use of chemical solvents. On the other hand, near-infrared (NIR) spectroscopy technique is a simple, fast and non-destructive method for the detection of chemical composition showing a fingerprint of the sample. It has been reported the potential of NIR spectroscopy to measure some enological parameters such as alcohol content, pH, organic acids, glycerol, reducing sugars and phenolic compounds.

Exploring intra-vineyard variability with sensor- and molecular-based approaches 

The application of remote and proximal sensing is a fast and efficient method to monitor grapevine vegetative and physiological parameters and is considered valuable to derive information on associated yield and quality traits in the vineyard. Further details can be obtained by the application of molecular analysis at the gene expression level aiming at elucidating how pathways controlling the formation of different grape quality traits are influenced by spatial variability. This work aims at evaluating intra-vineyard variability in grape composition at harvest and at comparing this with remotely sensed canopy vegetation data and molecular-based approaches.

Reconstructing ancient microbial fermentation genomes from the wine residues of Herod, Roman king of Judea

The fortress of the Herodium, built towards the end of the first century BCE/ante Cristo, on the orders of Herod the Great, Roman client king of Judea, attests the expansion of Roman influence in the eastern Mediterranean. During archaeological excavations of the Herodium in 2017[1], a winery was discovered on the ground floor of the palace, with an assortment of clay vessels in situ, including large dolia – clay fermentation vessels each capable of fermenting up to 300-400 L of wine. Thanks to the recent progresses in the field of paleogenomics[2], we could analyse the organic material consistent with grape pomace at the bottom of these vessels, by extracting and sequencing the DNA using shotgun metagenomics and targeted capture, aiming for enrichment of DNA from fermentation associated microbes.

Effect of drought on grapevine wood fungal pathogen communities using a metatranscriptomics approach

Crops are facing increasing biotic and abiotic stress pressures due to global changes. However, trade-off mechanisms between these stresses and the underlying physiological processes are still poorly understood, especially in perennial crop species. To better understand these trade-offs, we studied the effect of drought on grapevine (Vitis vinifera) physiology and esca-related wood fungal communities. Esca is a vascular disease caused by a community of wood-infecting pathogenic fungi, and characterized by trunk necrosis, leaf scorch symptoms, yield losses, and mortality.