bioRxivPlant BiologyPreprint
Updated phylogeny and protein structure predictions revise the hypothesis on the origin of MADS-box transcription factors in land plants
Abstract
MADS-box transcription factors (TFs) are broadly present in eukaryotes. Varying by domain architecture, MADS-box TFs in land plants are categorized into Type I (M-type) and Type II (MIKC-type). For about twenty years, Type I and II genes were considered orthologous to the SRF and MEF2 genes in animals, respectively, presumably originating from a duplication before the divergence of eukaryotes. Here, we exploited the increasing eukaryotic MADS-box sequences and reassessed their evolution. While supporting the ancient duplication giving rise to SRF- and MEF2-types, we found that Type I and II genes originated from the MEF2-type genes through another duplication in the most recent common ancestor (MRCA) of land plants. Protein structures predicted by AlphaFold2 and OmegaFold support our phylogenetic analyses, with plant Type I and II TFs resembling the MEF2-type structure, rather than SRFs. We hypothesize that the ancestral SRF-type TFs got lost in the MRCA of Archaeplastida (the kingdom Plantae sensu lato). The retained MEF2-type TFs acquired a Keratin-like domain and became MIKC-type upon the evolution of Streptophyta. Subsequently in the MRCA of land plants, M-type TFs evolved from a duplicated MIKC-type precursor through loss of the Keratin-like domain, leading to the Type I clade. Both Type I and II TFs largely expanded and functionally differentiated in concert with the increasing complexity of land plant body architecture. We attribute the adaptation to the terrestrial environment partly to the divergence among MEF2-type MADS-box genes and the repetitive recruitment of these originally stress-responsive TFs into developmental programs, especially those underlying reproduction.
- organism: plant (other)
- assay: protein structure
- research focus: phylogenetics / evolution
- research focus: protein structure prediction
- approach: deep learning
- approach: statistical model
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