Evolutionary network analysis based on the transcriptome atlas of Marchantia polymorpha (EvoNet)

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Gene regulatory networks control the expression of all genes in plants via interacting transcription factors and therefore ultimately control the phenotypes plants display. The network is highly complex, frequently contains redundant elements, and generally expands in complexity from alga over mosses towards seed plants since genomic complexity expands. The liverwort Marchantia polymorpha presents us with a unique window into early land plant evolution and the evolutionary history of transcription factors in land plants. Marchantia lacks whole genome duplications and therefore retains a fairly simple regulatory circuitry with only 374 transcription factors in total. This simplicity will facilitate the study of transcriptional control for metabolic pathways, for cellular differentiation, and for responses to internal and external signals. We will construct a transcriptome atlas of the liverwort including developmental and stress and signal induced time courses which aims at capturing the majority of transcriptional changes that can occur. Supervised machine learning approaches make use of this transcriptional atlas to predict transcription factor-target gene interactions and construct a gene regulatory network. The presumably much simpler network of a liverwort can then be compared them with those in seed plant networks and the mechanisms for expansion studied.We intend to (i) provide a comprehensive, open, easily accessible gene expression resource for the Marchantia community and ourselves and (ii) leverage the resource to build and analyze gene regulatory networks in the liverwort. We will test to which degree sub- and neo-functionalization have contributed to increased complexity of regulatory networks in seed plants compared to liverworts. Experimental validation of function will test the hypotheses which were computationally generated.