Gene Duplication Models and Reconstruction of Gene Regulatory Network Evolution from Network Structure

AbstractMammalian pluripotent stem cells (PSCs) have distinct molecular and biological characteristics, but we lack a comprehensive understanding of regulatory network evolution in mammals. Here, we applied a comparative genetic analysis of 134 genes constituting the pluripotency gene regulatory network across 48 mammalian species covering all the major taxonomic groups. We found evolutionary conservation in JAK-STAT and PI3K-Akt signaling pathways, suggesting equivalent capabilities in self-renewal and proliferation of mammalian PSCs. On the other hand, we discovered rapid evolution of the downstream targets of the core regulatory circuit, providing insights into variations of characteristics. Our data indicate that the variations in the PSCs characteristics may be due to positive selections in the downstream targets of the core regulatory circuit. We further reveal that positively selected genes can be associated with species unique adaptation that is not dedicated to regulation of PSCs. These results provide important insight into the evolution of the pluripotency gene regulatory network underlying variations in characteristics of mammalian PSCs.

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Neural Gene Network Constructor: A Neural Based Model for Reconstructing Gene Regulatory Network

10.1101/842369 ◽

2019 ◽

Author(s):

Zhang Zhang ◽

Lifei Wang ◽

Shuo Wang ◽

Ruyi Tao ◽

Jingshu Xiao ◽

...

Keyword(s):

Gene Expression ◽

Gene Regulatory Network ◽

Gene Expression Data ◽

Gene Regulatory Networks ◽

Network Structure ◽

Regulatory Network ◽

Gene Network ◽

Regulatory Networks ◽

Expression Data ◽

Gene Regulatory

SummaryReconstructing gene regulatory networks (GRNs) and inferring the gene dynamics are important to understand the behavior and the fate of the normal and abnormal cells. Gene regulatory networks could be reconstructed by experimental methods or from gene expression data. Recent advances in Single Cell RNA sequencing technology and the computational method to reconstruct trajectory have generated huge scRNA-seq data tagged with additional time labels. Here, we present a deep learning model “Neural Gene Network Constructor” (NGNC), for inferring gene regulatory network and reconstructing the gene dynamics simultaneously from time series gene expression data. NGNC is a model-free heterogenous model, which can reconstruct any network structure and non-linear dynamics. It consists of two parts: a network generator which incorporating gumbel softmax technique to generate candidate network structure, and a dynamics learner which adopting multiple feedforward neural networks to predict the dynamics. We compare our model with other well-known frameworks on the data set generated by GeneNetWeaver, and achieve the state of the arts results both on network reconstruction and dynamics learning.

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A novel method of gene regulatory network structure inference from gene knock-out expression data

Tsinghua Science & Technology ◽

10.26599/tst.2018.9010097 ◽

2019 ◽

Vol 24 (4) ◽

pp. 446-455 ◽

Cited By ~ 6

Author(s):

Xiang Chen ◽

Min Li ◽

Ruiqing Zheng ◽

Siyu Zhao ◽

Fang-Xiang Wu ◽

...

Keyword(s):

Gene Regulatory Network ◽

Network Structure ◽

Regulatory Network ◽

Expression Data ◽

Knock Out ◽

Novel Method ◽

Gene Regulatory

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Developmental gene regulatory network evolution: Insights from comparative studies in echinoderms

genesis ◽

10.1002/dvg.22757 ◽

2014 ◽

Vol 52 (3) ◽

pp. 193-207 ◽

Cited By ~ 22

Author(s):

Veronica F. Hinman ◽

Alys M. Cheatle Jarvela

Keyword(s):

Gene Regulatory Network ◽

Comparative Studies ◽

Regulatory Network ◽

Network Evolution ◽

Developmental Gene ◽

Gene Regulatory

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Genetic Signatures of Evolution of the Pluripotency Gene Regulating Network across Mammals

Genome Biology and Evolution ◽

10.1093/gbe/evaa169 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1806-1818

Author(s):

Yoshinori Endo ◽

Ken-ichiro Kamei ◽

Miho Inoue-Murayama

Keyword(s):

Gene Regulatory Network ◽

Regulatory Network ◽

Mammalian Species ◽

Rapid Evolution ◽

Regulatory Elements ◽

Network Evolution ◽

Pluripotency Gene ◽

Fundamental Biological Process ◽

Gene Regulatory ◽

Taxonomic Groups

Abstract Mammalian pluripotent stem cells (PSCs) have distinct molecular and biological characteristics among species, but to date we lack a comprehensive understanding of regulatory network evolution in mammals. Here, we carried out a comparative genetic analysis of 134 genes constituting the pluripotency gene regulatory network across 48 mammalian species covering all the major taxonomic groups. We report that mammalian genes in the pluripotency regulatory network show a remarkably high degree of evolutionary stasis, suggesting the conservation of fundamental biological process of mammalian PSCs across species. Nevertheless, despite the overall conservation of the regulatory network, we discovered rapid evolution of the downstream targets of the core regulatory elements and specific amino acid residues that have undergone positive selection. Our data indicate development of lineage-specific pluripotency regulating networks that may explain observed variations in some characteristics of mammalian PSCs. We further revealed that positively selected genes could be associated with species’ unique adaptive characteristics that were not dedicated to regulation of PSCs. These results provide important insight into the evolution of the pluripotency gene regulatory network underlying variations in characteristics of mammalian PSCs.

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Genes related to differentiation are correlated with the gene regulatory network structure

Bioinformatics ◽

10.1093/bioinformatics/btt685 ◽

2013 ◽

Vol 30 (3) ◽

pp. 406-413 ◽

Cited By ~ 2

Author(s):

Matan Bodaker ◽

Eran Meshorer ◽

Eduardo Mitrani ◽

Yoram Louzoun

Keyword(s):

Gene Regulatory Network ◽

Network Structure ◽

Regulatory Network ◽

Gene Regulatory

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Perspectives on Gene Regulatory Network Evolution

Trends in Genetics ◽

10.1016/j.tig.2017.04.005 ◽

2017 ◽

Vol 33 (7) ◽

pp. 436-447 ◽

Cited By ~ 23

Author(s):

Marc S. Halfon

Keyword(s):

Gene Regulatory Network ◽

Regulatory Network ◽

Network Evolution ◽

Gene Regulatory

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DeltaNeTS+: Elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks

10.1101/788968 ◽

2019 ◽

Author(s):

Heeju Noh ◽

Ziyi Hua ◽

Panagiotis Chrysinas ◽

Jason E. Shoemaker ◽

Rudiyanto Gunawan

Keyword(s):

Gene Expression ◽

Gene Regulatory Network ◽

Network Structure ◽

Regulatory Network ◽

Gene Network ◽

Time Course ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Direct Gene ◽

Gene Regulatory

AbstractBackgroundKnowledge on the molecular targets of diseases and drugs is crucial for elucidating disease pathogenesis and mechanism of action of drugs, and for driving drug discovery and treatment formulation. In this regard, high-throughput gene transcriptional profiling has become a leading technology, generating whole-genome data on the transcriptional alterations caused by diseases or drug compounds. However, identifying direct gene targets, especially in the background of indirect (downstream) effects, based on differential gene expressions is difficult due to the complexity of gene regulatory network governing the gene transcriptional processes.ResultsIn this work, we developed a network analysis method, called DeltaNeTS+, for inferring direct gene targets of drugs and diseases from gene transcriptional profiles. DeltaNeTS+ relies on a gene regulatory network model to identify direct perturbations to the transcription of genes. Importantly, DeltaNeTS+ is able to combine both steady-state and time-course gene expression profiles, as well as to leverage information on the gene network structure that is increasingly becoming available for a multitude of organisms, including human. We demonstrated the power of DeltaNeTS+ in predicting gene targets using gene expression data in complex organisms, including Caenorhabditis elegans and human cell lines (T-cell and Calu-3). More specifically, in an application to time-course gene expression profiles of influenza A H1N1 (swine flu) and H5N1 (avian flu) infection, DeltaNeTS+ shed light on the key differences of dynamic cellular perturbations caused by the two influenza strains.ConclusionDeltaNeTS+ is an enabling tool to infer gene transcriptional perturbations caused by diseases and drugs from gene transcriptional profiles. By incorporating available information on gene network structure, DeltaNeTS+ produces accurate predictions of direct gene targets from a small sample size (~10s). DeltaNeTS+ can freely downloaded from http://www.github.com/cabsel/deltanetsplus.

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