Introduction and aim: The study of the genetic systems that are involved in regulating plant stress responses is a promising approach to uncovering the fundamental molecular mechanisms underlying their organization. The availability and intensity of sunlight are among the primary factors influencing plant growth, development, and metabolism. Long-term exposure to excessive light levels is one of the major stress factors that inhibit photosynthesis, restrict growth and developmental processes, and reduce plant productivity. Transcriptomic data can help assess the involvement of genes under different stress conditions and identify key genes and transcription factors involved. Furthermore, meta-analysis of transcriptomic data is a powerful approach for identifying key molecular genetic systems that are involved in plant stress responses.

Materials and methods: To date, a substantial amount of transcriptomic data on plants' responses to high light are only available for the model species Arabidopsis thaliana, which requires further generalization. This study builds upon our previous meta-analysis, significantly expanding it by incorporating a larger number of datasets. We collected and performed a de novo analysis of data from more than twenty individual transcriptomic experiments on the photosynthetic tissues of A. thaliana plants when exposed to various high-light conditions (moderate and severe excess illumination, ranging from several minutes to several days) for the Columbia genotype.

Results and discussion: We identified a core subset of 1019 differentially expressed genes (DEGs) that were represented in at least half of the experimental points of high-light treatments. This subset is significantly enriched with various stress-responsive genes, including light stress and oxidative stress ones, and 117 transcription factors from bHLH, ERF, MYB, bZIP, C2H2, and other families. Most antioxidant DEGs were found to be preferentially upregulated under high-light conditions (42 out of 62), and almost half of the DEGs involved in the biosynthesis of secondary metabolites were upregulated (386 out of 754); however, most DEGs of hormone signal transduction pathways were characterized by preferential downregulation (70 out of 151). We additionally revealed more than 2000 DEGs to be highly specific to the main experimental conditions (duration of treatment, intensity of high light, age of plants). Taken together, our results align with existing findings, while significantly expanding and refining them.

Future perspectives: The applied bioinformatics approach has proven effective and can be used to generalize various large sets of stress-induced plant transcriptomes.