Deciphering lineage-relevant gene regulatory networks during endoderm formation by InPheRNo-ChIP.

Autor:	Su C; Department of Electrical and Computer Engineering, McGill University, 845 Sherbrooke Street West, Montreal, Quebec H3A 0G4, Canada., Pastor WA; Department of Biochemistry, McGill University, 845 Sherbrooke Street West, Montreal, Quebec H3A 0G4, Canada.; The Rosalind and Morris Goodman Cancer Institute, 1160 Pine Avenue, Montreal, Quebec H3A 1A3, Canada., Emad A; Department of Electrical and Computer Engineering, McGill University, 845 Sherbrooke Street West, Montreal, Quebec H3A 0G4, Canada.; The Rosalind and Morris Goodman Cancer Institute, 1160 Pine Avenue, Montreal, Quebec H3A 1A3, Canada.; Mila, Quebec AI Institute, 6666 St-Urbain Street #200, Montreal, Quebec H2S 3H1, Canada.
Jazyk:	angličtina
Zdroj:	Briefings in bioinformatics [Brief Bioinform] 2024 Sep 23; Vol. 25 (6).
DOI:	10.1093/bib/bbae592
Abstrakt:	Deciphering the underlying gene regulatory networks (GRNs) that govern early human embryogenesis is critical for understanding developmental mechanisms yet remains challenging due to limited sample availability and the inherent complexity of the biological processes involved. To address this, we developed InPheRNo-ChIP, a computational framework that integrates multimodal data, including RNA-seq, transcription factor (TF)-specific ChIP-seq, and phenotypic labels, to reconstruct phenotype-relevant GRNs associated with endoderm development. The core of this method is a probabilistic graphical model that models the simultaneous effect of TFs on their putative target genes to influence a particular phenotypic outcome. Unlike the majority of existing GRN inference methods that are agnostic to the phenotypic outcomes, InPheRNo-ChIP directly incorporates phenotypic information during GRN inference, enabling the distinction between lineage-specific and general regulatory interactions. We integrated data from three experimental studies and applied InPheRNo-ChIP to infer the GRN governing the differentiation of human embryonic stem cells into definitive endoderm. Benchmarking against a scRNA-seq CRISPRi study demonstrated InPheRNo-ChIP's ability to identify regulatory interactions involving endoderm markers FOXA2, SMAD2, and SOX17, outperforming other methods. This highlights the importance of incorporating the phenotypic context during network inference. Furthermore, an ablation study confirms the synergistic contribution of ChIP-seq, RNA-seq, and phenotypic data, highlighting the value of multimodal integration for accurate phenotype-relevant GRN reconstruction. (© The Author(s) 2024. Published by Oxford University Press.)
Databáze:	MEDLINE
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