Constructing an integrated genetic and epigenetic cellular network for whole cellular mechanism using high-throughput next-generation sequencing data

Abstract

BackgroundEpigenetics has been investigated in cancer initiation, and development, especially, since the appearance of epigenomics. Epigenetics may be defined as the mechanisms that lead to heritable changes in gene function and without affecting the sequence of genome. These mechanisms explain how individuals with the same genotype produce phenotypic differences in response to environmental stimuli. Recently, with the accumulation of high-throughput next-generation sequencing (NGS) data, a key goal of systems biology is to construct networks for different cellular levels to explore whole cellular mechanisms. At present, there is no satisfactory method to construct an integrated genetic and epigenetic cellular network (IGECN), which combines NGS omics data with gene regulatory networks (GRNs), microRNAs (miRNAs) regulatory networks, protein-protein interaction networks (PPINs), and epigenetic regulatory networks of methylation using high-throughput NGS data.ResultsWe investigated different kinds of NGS omics data to develop a systems biology method to construct an integrated cellular network based on three coupling models that describe genetic regulatory networks, protein–protein interaction networks, microRNA (miRNA) regulatory networks, and methylation regulation. The proposed method was applied to construct IGECNs of gastric cancer and the human immune response to human immunodeficiency virus (HIV) infection, to elucidate human defense response mechanisms. We successfully constructed an IGECN and validated it by using evidence from literature search. The integration of NGS omics data related to transcription regulation, protein-protein interactions, and miRNA and methylation regulation has more predictive power than independent datasets. We found that dysregulation of MIR7 contributes to the initiation and progression of inflammation-induced gastric cancer; dysregulation of MIR9 contributes to HIV-1 infection to hijack CD4+ T cells through dysfunction of the immune and hormone pathways; dysregulation of MIR139-5p, MIRLET7i, and MIR10a contributes to the HIV-1 integration/replication stage; dysregulation of MIR101, MIR141, and MIR152 contributes to the HIV-1 virus assembly and budding mechanisms; dysregulation of MIR302a contributes to not only microvesicle-mediated transfer of miRNAs but also dysfunction of NF-κB signaling pathway in hepatocarcinogenesis.ConclusionThe coupling dynamic systems of the whole IGECN can allow us to investigate genetic and epigenetic cellular mechanisms via omics data and big database mining, and are useful for further experiments in the field of systems and synthetic biology.

New signatures of poor CD4 cell recovery after suppressive antiretroviral therapy in HIV-1-infected individuals: involvement of miR-192, IL-6, sCD14 and miR-144

“…infection since they can regulate viral replication and translation and might play an important role in HIV-1 pathogenesis and latency 17,30,31 . The interaction of HIV-1 with cellular miRs expressed in infected cells has been controversial.…”

“…The interaction of HIV-1 with cellular miRs expressed in infected cells has been controversial. Many groups have reported that cellular miRs bind to HIV and reduce viral gene expression by interfering virion infectivity and virion miR function, facilitating in some degree the HIV-1 latency [30][31][32] . On the contrary, a few reports have shown that miRNAs bind HIV-1 transcripts very inefficiently 19,33 .…”

Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science)

“…Chen and Li 32 presented 3 stochastic discrete dynamic equations to describe the relations among genes, proteins, miRNAs, and DNA methylations. These stochastic dynamic equations provide quantitative predictions of measurements of mRNA, miRNA, and protein expression at a specific time point.…”

Systems multiple molecule drug design with less side-effects via drug data mining and genome-wide data identification

“…(2) we then constructed candidate GENs by using candidate PPI network and candidate gene/ miRNA/lncRNA regulatory networks (in infectious diseases, candidate interspecies cross-talk GEN is necessary); (3) we identified real GEN (or real interspecies GEN) of each stage of a disease by using the genome-wide high throughput data of the disease; (4) we then applied the principal network projection (PNP) method to extract the core nodes of GENs such as core proteins, genes, miRNAs, and lncRNAs to construct core GENs in different stages of a disease. [16][17][18][19] By comparing the core GENs between two connective stages of a disease and projecting them to KEGG pathways to get core signalling pathways of the disease, we then could extract differential core signalling pathways between different stages to get insight into pathogenic mechanism of the disease. Based on pathogenic mechanism, we could select multiple biomarkers for multiple drug targets to design multiple molecule drugs by applying drug database mining to CMAP and DGIdb databases for therapeutic treatment of the disease.…”

“…A principal network projection (PNP) method is employed to extract core GENs from GENs based on the principal component analysis (PCA) of GENs in different stages of diseases. [16][17][18][19] Then we project the core GENs to KEGG pathways (i.e., denotation based on KEGG pathways) to obtain the core pathways from which we could investigate the pathogenic mechanism based the changes of core pathways of different stages of disease. We could identify some significant biomarkers for multiple drug targets based on pathogenic mechanism.…”