AUTHOR=Konecny Tomas , Nikoghosyan Maria , Binder Hans 

TITLE=Machine learning extracts marks of thiamine’s role in cold acclimation in the transcriptome of Vitis vinifera

JOURNAL=Frontiers in Plant Science

VOLUME=14

YEAR=2023

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2023.1303542

DOI=10.3389/fpls.2023.1303542

ISSN=1664-462X

ABSTRACT=<sec><title>Introduction</title><p>The escalating challenge of climate change has underscored the critical need to understand cold defense mechanisms in cultivated grapevine <italic>Vitis vinifera</italic>. Temperature variations can affect the growth and overall health of vine.</p></sec><sec><title>Methods</title><p>We used Self Organizing Maps machine learning method to analyze gene expression data from leaves of five <italic>Vitis vinifera</italic> 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.</p></sec><sec><title>Results</title><p>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.</p></sec><sec><title>Discussion</title><p>Application of Self Organizing Maps portrayal to vine transcriptomics identified modules of coregulated genes triggered under cold stress. Our machine learning approach provides a promising option for transcriptomics studies in plants.</p></sec>