GSE201607
Machine learning accelerates the dissection of mitostasis as a cross-species central biological hub for leaf senescence
Leaf senescence is a tightly controlled and complex developmental process that shares many similarities across species, yet our understanding of the underlying conserved molecular mechanisms is still lacking. Here, we observed functional conservation of leaf senescence underlying pathways in A. thaliana, O. sativa, and S. lycopersicum. From machine learning-based integration of data from nearly 10 000 samples to obtain a universal regulatory network of leaf senescence, it was found that mitostasis is the cross-species central biological hub. We measure and compare changes in the transcriptome and metabolome of A. thaliana, O. sativa, and S. lycopersicum leaves under mitostress/natural senescence. In data from different species, mitostasis-related transcription factors binding site enrichment and amino acids expression changes converge on putative senescence modulators. Our study provides a cross-species, multi-omics perspective for understanding the leaf senescence conserved mechanisms. Overall design: Comparative gene expression profiling analysis of RNA-seq data of A. thaliana, O. sativa, and S. lycopersicum leaves when 10 µ g / mg doxycycline was applied to cultivated soil.