Machine learning extracts marks of thiamine’s role in cold acclimation in the transcriptome of Vitis vinifera - Research Article Summary

Aug 3, 2026

Climate change increases the need to clarify how cultivated grapevine Vitis vinifera defends against cold. This study applies Self Organizing Maps to leaf gene-expression profiles from five cultivars exposed to four temperature regimes, producing temperature-specific patterns in normalized expression data. Results connect vitamin B1 (thiamine) biosynthesis with temperature regulation and thiamine-linked stress responses. Findings also indicate epigenetic regulation of stress-responsive gene expression under low temperatures, highlighting cold-stress co-regulated modules.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Introduction

Methods

Results

Discussion

Keywords and Abbreviations

TYPE Original Research PUBLISHED 06 December 2023 DOI 10.3389/fpls.2023.1303542 OPEN ACCESS EDITED BY Yuriy L Orlov, I.M.Sechenov First Moscow State Medical University, Russia REVIEWED BY Samuel Coulbourn Flores, Stockholm University, Sweden Vladimir Aleksandrovich Ivanisenko, Russian Academy of Sciences (RAS), Russia Massimo Guazzini, University of Udine, Italy *CORRESPONDENCE Tomas Konecny [tomas.konecny@abi.am](tomas.konecny@abi.am) RECEIVED 28 September 2023 ACCEPTED 14 November 2023 PUBLISHED 06 December 2023 CITATION Konecny T, Nikoghosyan M and Binder H (2023) Machine learning extracts marks of thiamine’s role in cold acclimation in the transcriptome of Vitis vinifera. Front. Plant Sci. 14:1303542 . doi: 10.3389/fpls.2023.1303542 COPYRIGHT © 2023 Konecny, Nikoghosyan and Binder. 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. Machine learning extracts marks of thiamine’s role in cold acclimation in the transcriptome of Vitis vinifera Tomas Konecny 1,2*, Maria Nikoghosyan 1,3 and Hans Binder 1,2 1Armenian Bioinformatics Institute, Yerevan, Armenia, 2 Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany, 3 Bioinformatics Group, Institute of Molecular Biology Institute of National Academy of Sciences RA, Yerevan, Armenia Introduction: The escalating challenge of climate change has underscored the critical need to understand cold defense mechanisms in cultivated grapevine Vitis vinifera. Temperature variations can affect the growth and overall health of vine. Methods: We used Self Organizing Maps machine learning method to analyze gene expression data from leaves of ﬁve Vitis vinifera cultivars each treated by four different temperature conditions. The algorithm generated sample-speciﬁc“portraits” of the normalized gene expression data, revealing distinct patterns related to the temperature conditions applied. Results: Our analysis unveiled a connection with vitamin B1 (thiamine) biosynthesis, suggesting a link between temperature regulation and thiamine metabolism, in agreement with thiamine related stress response established in Arabidopsis before. Furthermore, we found that epigenetic mechanisms play a crucial role in regulating the expression of stress-responsive genes at low temperatures in grapevines. Discussion: Application of Self Organizing Maps portrayal to vine transcriptomicsidentiﬁed modules of coregulated genes triggered under cold stress. Our machine learning approach provides a promising option for transcriptomics studies in plants. KEYWORDS Self organizing maps, grapevine, temperature stress, vitamin B1, epigenetics, climate change Abbreviations: SOM, self-organizing map; CPM, counts per million; GO, gene ontology; PANTHER, protein analysis through evolutionary relationships; REVIGO, reduce + visualize gene ontology; CIRGO, circular gene ontology; KEGG, kyoto encyclopedia of genes and genomes; BRITE, biomolecular relationship information transfer encyclopedia; AP2, apetala 2; ICE1, inducer of cbf expression 1; CBF, c-repeat binding factors; FLC, ﬂowering locus c; PRMT10, protein arginine n-methyltransferase 10; CHR10, chromatin remodeling 10; THI1-1, thiamine thiazole synthase 1; THI1-2, thiamine thiazole synthase 2; THI1, cysteine-dependent adenosine diphosphate thiazole synthase; THIC, phosphomethylpyrimidine synthase; TPK1/THIN, thiamine pyrophosphokinase; DXS, 1-deoxy-d-xylulose-5-phosphate synthase; TH1, thiamine biosynthetic bifunctional enzyme; TH2, thiamine phosphate phosphatase/aminohmp aminohydrolase. Frontiers

FAQs

What machine learning method was used to analyze grapevine transcriptome data?

Self Organizing Maps (SOM) was used to analyze normalized gene expression data from grapevine leaves under different temperature conditions.

How does the study relate temperature stress to thiamine in Vitis vinifera?

The analysis revealed a connection with vitamin B1 (thiamine) biosynthesis, suggesting a link between temperature regulation and thiamine metabolism during cold acclimation.

What role do epigenetic mechanisms play according to the results?

Epigenetic mechanisms are proposed to play a crucial role in regulating the expression of stress-responsive genes under low-temperature conditions in grapevines.

Aug 3, 2026

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