Intracellular and Intercellular Gene Regulatory Network Inference From Time-Course Individual RNA-Seq

Gene regulatory network (GRN) inference is an effective approach to understand the molecular mechanisms underlying biological events. Generally, GRN inference mainly targets intracellular regulatory relationships such as transcription factors and their associated targets. In multicellular organisms,...

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Autores principales:	Makoto Kashima, Yuki Shida, Takashi Yamashiro, Hiromi Hirata, Hiroshi Kurosaka
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	gene regulatory network bulk RNA-seq intracellular time course mouse Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/f44036938d2c4274b372ecc4c5f1c125
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spelling	oai:doaj.org-article:f44036938d2c4274b372ecc4c5f1c1252021-11-11T16:20:44ZIntracellular and Intercellular Gene Regulatory Network Inference From Time-Course Individual RNA-Seq2673-764710.3389/fbinf.2021.777299https://doaj.org/article/f44036938d2c4274b372ecc4c5f1c1252021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fbinf.2021.777299/fullhttps://doaj.org/toc/2673-7647Gene regulatory network (GRN) inference is an effective approach to understand the molecular mechanisms underlying biological events. Generally, GRN inference mainly targets intracellular regulatory relationships such as transcription factors and their associated targets. In multicellular organisms, there are both intracellular and intercellular regulatory mechanisms. Thus, we hypothesize that GRNs inferred from time-course individual (whole embryo) RNA-Seq during development can reveal intercellular regulatory relationships (signaling pathways) underlying the development. Here, we conducted time-course bulk RNA-Seq of individual mouse embryos during early development, followed by pseudo-time analysis and GRN inference. The results demonstrated that GRN inference from RNA-Seq with pseudo-time can be applied for individual bulk RNA-Seq similar to scRNA-Seq. Validation using an experimental-source-based database showed that our approach could significantly infer GRN for all transcription factors in the database. Furthermore, the inferred ligand-related and receptor-related downstream genes were significantly overlapped. Thus, the inferred GRN based on whole organism could include intercellular regulatory relationships, which cannot be inferred from scRNA-Seq based only on gene expression data. Overall, inferring GRN from time-course bulk RNA-Seq is an effective approach to understand the regulatory relationships underlying biological events in multicellular organisms.Makoto KashimaYuki ShidaTakashi YamashiroHiromi HirataHiroshi KurosakaFrontiers Media S.A.articlegene regulatory networkbulk RNA-seqintracellulartime coursemouseComputer applications to medicine. Medical informaticsR858-859.7ENFrontiers in Bioinformatics, Vol 1 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	gene regulatory network bulk RNA-seq intracellular time course mouse Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	gene regulatory network bulk RNA-seq intracellular time course mouse Computer applications to medicine. Medical informatics R858-859.7 Makoto Kashima Yuki Shida Takashi Yamashiro Hiromi Hirata Hiroshi Kurosaka Intracellular and Intercellular Gene Regulatory Network Inference From Time-Course Individual RNA-Seq
description	Gene regulatory network (GRN) inference is an effective approach to understand the molecular mechanisms underlying biological events. Generally, GRN inference mainly targets intracellular regulatory relationships such as transcription factors and their associated targets. In multicellular organisms, there are both intracellular and intercellular regulatory mechanisms. Thus, we hypothesize that GRNs inferred from time-course individual (whole embryo) RNA-Seq during development can reveal intercellular regulatory relationships (signaling pathways) underlying the development. Here, we conducted time-course bulk RNA-Seq of individual mouse embryos during early development, followed by pseudo-time analysis and GRN inference. The results demonstrated that GRN inference from RNA-Seq with pseudo-time can be applied for individual bulk RNA-Seq similar to scRNA-Seq. Validation using an experimental-source-based database showed that our approach could significantly infer GRN for all transcription factors in the database. Furthermore, the inferred ligand-related and receptor-related downstream genes were significantly overlapped. Thus, the inferred GRN based on whole organism could include intercellular regulatory relationships, which cannot be inferred from scRNA-Seq based only on gene expression data. Overall, inferring GRN from time-course bulk RNA-Seq is an effective approach to understand the regulatory relationships underlying biological events in multicellular organisms.
format	article
author	Makoto Kashima Yuki Shida Takashi Yamashiro Hiromi Hirata Hiroshi Kurosaka
author_facet	Makoto Kashima Yuki Shida Takashi Yamashiro Hiromi Hirata Hiroshi Kurosaka
author_sort	Makoto Kashima
title	Intracellular and Intercellular Gene Regulatory Network Inference From Time-Course Individual RNA-Seq
title_short	Intracellular and Intercellular Gene Regulatory Network Inference From Time-Course Individual RNA-Seq
title_full	Intracellular and Intercellular Gene Regulatory Network Inference From Time-Course Individual RNA-Seq
title_fullStr	Intracellular and Intercellular Gene Regulatory Network Inference From Time-Course Individual RNA-Seq
title_full_unstemmed	Intracellular and Intercellular Gene Regulatory Network Inference From Time-Course Individual RNA-Seq
title_sort	intracellular and intercellular gene regulatory network inference from time-course individual rna-seq
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/f44036938d2c4274b372ecc4c5f1c125
work_keys_str_mv	AT makotokashima intracellularandintercellulargeneregulatorynetworkinferencefromtimecourseindividualrnaseq AT yukishida intracellularandintercellulargeneregulatorynetworkinferencefromtimecourseindividualrnaseq AT takashiyamashiro intracellularandintercellulargeneregulatorynetworkinferencefromtimecourseindividualrnaseq AT hiromihirata intracellularandintercellulargeneregulatorynetworkinferencefromtimecourseindividualrnaseq AT hiroshikurosaka intracellularandintercellulargeneregulatorynetworkinferencefromtimecourseindividualrnaseq
_version_	1718432391927693312

Intracellular and Intercellular Gene Regulatory Network Inference From Time-Course Individual RNA-Seq

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