bioRxivEvolutionary BiologyPreprint
CroCoNet: a novel framework for cross-species network analysis reveals POU5F1 (OCT4) rewiring
Anita TérmegiD, Vladyslav StorozhukiD, Zane KliesmeteiD, Fiona C. EdenhoferiD, Johanna GeuderiD, Beate ViethiD, Philipp JansseniD, Ines HellmanniD
Abstract
BACKGROUNDGene regulatory changes play a central role in shaping cellular phenotypes across species. To understand how these phenotypes evolve, it is essential to investigate the underlying gene regulatory networks (GRNs). However, most comparative analyses of GRNs remain qualitative and are therefore sensitive to false positives and false negatives. RESULTSTo address this limitation, we introduce CroCoNet (Cross-species Comparison of Networks), an R package for the quantitative comparison of GRNs across species. CroCoNet constructs comparable network modules centered on known transcriptional regulators and quantifies the variability in module topology within and between species. By contrasting these levels of variability, CroCoNet can distinguish true evolutionary divergence from technical and biological confounders. Applying CroCoNet to scRNA-seq data from the early neural differentiation of human, gorilla, and cynomolgus macaque, we identified 20 conserved and 24 diverged modules. Despite the conserved expression pattern of the pluripotency factor POU5F1 (OCT4), its associated module was among the most diverged. This result was independently confirmed through cross-species CRISPRi perturbations coupled with single-cell RNA-seq as a readout. Moreover, we found that great ape- and human-specific LTR7 elements are enriched near POU5F1 module genes, potentially contributing to the cross-species differences in network topology. CONCLUSIONSThese findings demonstrate that CroCoNet can resolve regulatory rewiring and provides a robust framework for studying GRN evolution across closely related species. CroCoNet is available as an open-source R package at https://hellmann-lab.github.io/CroCoNet/.
- organism: human
- organism: chicken / avian
- organism: non-human primate
- assay: scRNA-seq
- research focus: gene regulatory network inference
- research focus: clustering
- approach: deep learning
- approach: statistical model
- Citations
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