Back

Molecular signatures and associated regulators of the pea leaf response to sulfur deficiency and water deficit as revealed by multi-omics analyses

Bonnot, T.; Henriet, C.; Aime, D.; Kreplak, J.; Terezol, M.; Balliau, T.; Ourry, A.; Zivy, M.; Vernoud, V.; Gallardo, K.

2024-03-14 plant biology
10.1101/2024.03.13.582463 bioRxiv
Show abstract

Sulfur availability in soils affects both yield and seed quality in major crops, and the plant capacity to tolerate environmental constraints. Under stress combination, plants often show specific responses at the molecular level. To dissect the molecular responses to sulfur deficiency in interaction or not with water deficit, a multi-omics approach was used focusing on the leaves of pea (Pisum sativum), at several days during the early reproductive phase. Using ionomics, transcriptomics, proteomics and gene network analyses, we identified a module of genes strongly driven by sulfur availability. This includes known and putative new players of plant responses to sulfur-deprived conditions. Conserved profiles between proteins and mRNAs were specifically observed within this module, suggesting transcriptional regulation. While moderate water deficit had little impact when occurring alone, it thoroughly perturbed plant growth and the leaf transcriptome and proteome when combined with sulfur deficiency. Under this stress combination, molecular responses were amplified, notably at the transcriptome level, in a time-specific manner. Genes with specific or greater responses under this condition were identified, and transcriptional regulators of the highlighted genes and pathways were predicted, which may represent interesting targets to develop crops tolerant to multi-stress conditions.

Published in Journal of Experimental Botany (predicted rank #3) · training set

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.