Machine learning extracts marks of thiamine's role in cold acclimation in the transcriptome of Vitis vinifera

doi: 10.3389/fpls.2023.1303542.

eCollection 2023.

Affiliations

Free PMC article

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Tomas Konecny et al.

Front Plant Sci.

2023.

Free PMC article

doi: 10.3389/fpls.2023.1303542.

eCollection 2023.

Affiliations

¹ Armenian 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.

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Abstract

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 transcriptomics identified 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; climate change; epigenetics; grapevine; temperature stress; vitamin B1.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

**Figure 1**

SOM portrayal and sample similarity analysis reveal clearly distinct patterns among the different temperatures. **(A)** Individual SOM portraits of all replicates (red color = overexpressed gene, blue color = underexpressed gene). **(B)** Replicate-averaged SOM portraits. **(C)** The neighbor joining tree splits into four major branches referring to the four temperature-based clusters. Out-grouped samples are in black rectangles.

**Figure 2**

Description of metagenes. **(A)** Group overexpression spots (A-K) and four portraits merged by temperature; Gray arrow indicates spot assignment to the temperature portraits; Numbers inside spots (for H = 151) represent numbers of genes. **(B)** Spot correlation identified by weighted topological overlap algorithm. **(C)** Group overexpression spots patterns among samples with the top three gene sets characteristic for each spot.

**Figure 3**

Chill shock-stressed plants involve thiamine metabolism in their response to temperature change. **(A)** Top 20 overexpressed genes description (left) and Z-scores (right) of all overexpressed genes in the spot J (indicated by arrow). **(B)** The most enriched GO terms in a set of 518 genes from the spot J; arrow indicates enrichment by the thiamine biosynthesis GO. **(C)** Z-scores of the thiamine metabolism gene set (22 genes) in each sample.

**Figure 4**

Thiamine metabolic genes and biosynthesis pathway. **(A)** Clustering of genes (by CPM values across samples) involved in thiamine metabolism; highlighted genes code for known enzymes of thiamine biosynthesis. **(B)** Positions of the thiamine biosynthetic genes in the SOM portrait; gray squares = Group Overexpression Spots; gray arrows = direction of the thiamine biosynthetic pathway. **(C)** Distribution of the normalized expression of the thiamine metabolism genes; the three most expressed biosynthetic genes have their names written on top of each violin plot. **(D)** Simplified scheme of the thiamine biosynthesis pathway (including the side branch with DXS); heatmaps display normalized gene expression; legend and color bar are at the bottom right corner; color scaling is normalized to all heatmaps.

**Figure 5**

Expression topology of genes related to epigenetics. **(A)** The abundance of genes encoding epigenetic factors from different spots related to different temperatures. Spots that do not contain any overexpressed epigenetic genes are not shown. **(B)** Distribution of genes encoding epigenetic factors involved in chromatin remodeling, chromosome condensation, gene silencing, heterochromatin formation, histone modifications, or nucleosome assembly across the spots in the SOM hatch indicates overlapping epigenetic processes at the same position. A full list of epigenetic factors across the spots is provided in
**Supplementary Table S2**
.

References

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Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Foundation of Armenian Science and Technology (FAST) in the frame of the ADVANCE Research Grants Program project Vine Bioinformatics – grape genomics for Innovative viticulture funded by Joe Barnes. We acknowledge financial support from the German Research Foundation (DFG) and Universität Leipzig within the program of Open Access Publishing.