Transcription Factor Co-regulatory Networks Research Articles

Comprehensive epigenomic profiling of human alveolar epithelial differentiation identifies key epigenetic states and transcription factor co-regulatory networks for maintenance of distal lung identity

BackgroundDisruption of alveolar epithelial cell (AEC) differentiation is implicated in distal lung diseases such as chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, and lung adenocarcinoma that impact morbidity and mortality worldwide. Elucidating underlying disease pathogenesis requires a mechanistic molecular understanding of AEC differentiation. Previous studies have focused on changes of individual transcription factors, and to date no study has comprehensively characterized the dynamic, global epigenomic alterations that facilitate this critical differentiation process in humans.ResultsWe comprehensively profiled the epigenomic states of human AECs during type 2 to type 1-like cell differentiation, including the methylome and chromatin functional domains, and integrated this with transcriptome-wide RNA expression data. Enhancer regions were drastically altered during AEC differentiation. Transcription factor binding analysis within enhancer regions revealed diverse interactive networks with enrichment for many transcription factors, including NKX2–1 and FOXA family members, as well as transcription factors with less well characterized roles in AEC differentiation, such as members of the MEF2, TEAD, and AP1 families. Additionally, associations among transcription factors changed during differentiation, implicating a complex network of heterotrimeric complex switching in driving differentiation. Integration of AEC enhancer states with the catalog of enhancer elements in the Roadmap Epigenomics Mapping Consortium and Encyclopedia of DNA Elements (ENCODE) revealed that AECs have similar epigenomic structures to other profiled epithelial cell types, including human mammary epithelial cells (HMECs), with NKX2–1 serving as a distinguishing feature of distal lung differentiation.ConclusionsEnhancer regions are hotspots of epigenomic alteration that regulate AEC differentiation. Furthermore, the differentiation process is regulated by dynamic networks of transcription factors acting in concert, rather than individually. These findings provide a roadmap for understanding the relationship between disruption of the epigenetic state during AEC differentiation and development of lung diseases that may be therapeutically amenable.

Identification of putative master regulators in rheumatoid arthritis synovial fibroblasts using gene expression data and network inference

Rheumatoid arthritis (RA) is a systemic autoimmune disease that affects the synovial joints of the body. Rheumatoid arthritis fibroblast-like synoviocytes (RA FLS) are central players in the disease pathogenesis, as they are involved in the secretion of cytokines and proteolytic enzymes, exhibit invasive traits, high rate of self-proliferation and an apoptosis-resistant phenotype. We aim at characterizing transcription factors (TFs) that are master regulators in RA FLS and could potentially explain phenotypic traits. We make use of differentially expressed genes in synovial tissue from patients suffering from RA and osteoarthritis (OA) to infer a TF co-regulatory network, using dedicated software. The co-regulatory network serves as a reference to analyze microarray and single-cell RNA-seq data from isolated RA FLS. We identified five master regulators specific to RA FLS, namely BATF, POU2AF1, STAT1, LEF1 and IRF4. TF activity of the identified master regulators was also estimated with the use of two additional, independent software. The identified TFs contribute to the regulation of inflammation, proliferation and apoptosis, as indicated by the comparison of their differentially expressed target genes with hallmark molecular signatures derived from the Molecular Signatures Database (MSigDB). Our results show that TFs influence could be used to identify putative master regulators of phenotypic traits and suggest novel, druggable targets for experimental validation.

Identification of miRNA-mRNA-TFs Regulatory Network and Crucial Pathways Involved in Tetralogy of Fallot.

Tetralogy of Fallot (TOF) is the most common cyanotic congenital heart disease. However, its pathogenesis remains unknown. To explore key regulatory connections and crucial pathways underlying the TOF, gene or microRNA expression profile datasets of human TOF were obtained from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. The differentially expressed mRNAs (DEmRNAs) and microRNAs (DEmiRs) between TOF and healthy groups were identified after data preprocessing, followed by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Then, we further constructed protein–protein interaction (PPI) network and subnetwork of modules. Ultimately, to investigate the regulatory network underlying TOF, a global triple network including miRNAs, mRNAs, and transcription factors (TFs) was constructed based on the integrated data. In the present study, a total of 529 DEmRNAs, including 115 downregulated and 414 upregulated DEmRNAs, and 7 significantly upregulated DemiRs, including miR-499, miR-23b, miR-222, miR-1275, miR-93, miR-155, and miR-187, were found between TOF and control groups. Furthermore, 22 hub genes ranked by top 5% genes with high connectivity and six TFs, including SRF, CNOT4, SIX6, SRRM3, NELFA, and ONECUT3, were identified and might play crucial roles in the molecular pathogenesis of TOF. Additionally, an miRNA–mRNA–TF co-regulatory network was established and indicated ubiquitin-mediated proteolysis, energy metabolism associated pathways, neurodevelopmental disorder associated pathways, and ribosomes might be involved in the pathogenesis of TOF. The current research provides a comprehensive perspective of regulatory mechanism networks underlying TOF and also identifies potential molecule targets of genetic counseling and prenatal diagnosis for TOF.

MicroRNA and transcription factor co-regulatory networks and subtype classification of seminoma and non-seminoma in testicular germ cell tumors

Recent studies have revealed that feed-forward loops (FFLs) as regulatory motifs have synergistic roles in cellular systems and their disruption may cause diseases including cancer. FFLs may include two regulators such as transcription factors (TFs) and microRNAs (miRNAs). In this study, we extensively investigated TF and miRNA regulation pairs, their FFLs, and TF-miRNA mediated regulatory networks in two major types of testicular germ cell tumors (TGCT): seminoma (SE) and non-seminoma (NSE). Specifically, we identified differentially expressed mRNA genes and miRNAs in 103 tumors using the transcriptomic data from The Cancer Genome Atlas. Next, we determined significantly correlated TF-gene/miRNA and miRNA-gene/TF pairs with regulation direction. Subsequently, we determined 288 and 664 dysregulated TF-miRNA-gene FFLs in SE and NSE, respectively. By constructing dysregulated FFL networks, we found that many hub nodes (12 out of 30 for SE and 8 out of 32 for NSE) in the top ranked FFLs could predict subtype-classification (Random Forest classifier, average accuracy ≥90%). These hub molecules were validated by an independent dataset. Our network analysis pinpointed several SE-specific dysregulated miRNAs (miR-200c-3p, miR-25-3p, and miR-302a-3p) and genes (EPHA2, JUN, KLF4, PLXDC2, RND3, SPI1, and TIMP3) and NSE-specific dysregulated miRNAs (miR-367-3p, miR-519d-3p, and miR-96-5p) and genes (NR2F1 and NR2F2). This study is the first systematic investigation of TF and miRNA regulation and their co-regulation in two major TGCT subtypes.

Exploration of prognosis-related microRNA and transcription factor co-regulatory networks across cancer types

ABSTRACTThe study of cancer prognosis serves as an important part of cancer research. Large-scale cancer studies have identified numerous genes and microRNAs (miRNAs) associated with prognosis. These informative genes and miRNAs represent potential biomarkers to predict survival and to elucidate the molecular mechanism of tumour progression. MiRNAs and transcription factors (TFs) can work cooperatively as essential mediators of gene expression, and their dysregulation affects cancer prognosis. A panoramic view of cancer prognosis at the system level, considering the co-regulation roles of miRNA and TF, remains elusive. Here, we establish 12 prognosis-related miRNA-TF co-regulatory networks. The characteristics of prognostic target genes and their regulators in the network are depicted. Although the target genes and co-regulatory patterns exhibit cancer-specific properties, some miRNAs and TFs are highly conserved across cancers. We illustrate and interpret the roles of these conserved regulators by building a model associated with cancer hallmarks, functional enrichment analysis, network community detection, and exhaustive literature research. The elaborated system-level prognostic miRNA-TF co-regulation landscape, including the highlighted roles of conserved regulators, provides a novel and powerful insights into further biological and medical discoveries.

Investigating the Role of MicroRNA and Transcription Factor Co-regulatory Networks in Multiple Sclerosis Pathogenesis.

MicroRNAs (miRNAs) and transcription factors (TFs) play key roles in complex multifactorial diseases like multiple sclerosis (MS). Starting from the miRNomic profile previously associated with a cohort of pediatric MS (PedMS) patients, we applied a combined molecular and computational approach in order to verify published data in patients with adult-onset MS (AOMS). Six out of the 13 selected miRNAs (miR-320a, miR-125a-5p, miR-652-3p, miR-185-5p, miR-942-5p, miR-25-3p) were significantly upregulated in PedMS and AOMS patients, suggesting that they may be considered circulating biomarkers distinctive of the disease independently from age. A computational and unbiased miRNA-based screening of target genes not necessarily associated to MS was then performed in order to provide an extensive view of the genetic mechanisms underlying the disease. A comprehensive MS-specific miRNA-TF co-regulatory network was hypothesized; among others, SP1, RELA, NF-κB, TP53, AR, MYC, HDAC1, and STAT3 regulated the transcription of 61 targets. Interestingly, NF-κB and STAT3 cooperatively regulate the expression of immune response genes and control the cross-talk between inflammatory and immune cells. Further functional analysis will be performed on the identified critical hubs. Above all, in our view, this approach supports the need of multidisciplinary strategies for shedding light into the pathogenesis of MS.

CMTCN: a web tool for investigating cancer-specific microRNA and transcription factor co-regulatory networks

Transcription factors (TFs) and microRNAs (miRNAs) are well-characterized trans-acting essential players in gene expression regulation. Growing evidence indicates that TFs and miRNAs can work cooperatively, and their dysregulation has been associated with many diseases including cancer. A unified picture of regulatory interactions of these regulators and their joint target genes would shed light on cancer studies. Although online resources developed to support probing of TF-gene and miRNA-gene interactions are available, online applications for miRNA-TF co-regulatory analysis, especially with a focus on cancers, are lacking. In light of this, we developed a web tool, namely CMTCN (freely available at http://www.cbportal.org/CMTCN), which constructs miRNA-TF co-regulatory networks and conducts comprehensive analyses within the context of particular cancer types. With its user-friendly provision of topological and functional analyses, CMTCN promises to be a reliable and indispensable web tool for biomedical studies.

Investigating MicroRNA and transcription factor co-regulatory networks in colorectal cancer

BackgroundColorectal cancer (CRC) is one of the most common malignancies worldwide with poor prognosis. Studies have showed that abnormal microRNA (miRNA) expression can affect CRC pathogenesis and development through targeting critical genes in cellular system. However, it is unclear about which miRNAs play central roles in CRC’s pathogenesis and how they interact with transcription factors (TFs) to regulate the cancer-related genes.ResultsTo address this issue, we systematically explored the major regulation motifs, namely feed-forward loops (FFLs), that consist of miRNAs, TFs and CRC-related genes through the construction of a miRNA-TF regulatory network in CRC. First, we compiled CRC-related miRNAs, CRC-related genes, and human TFs from multiple data sources. Second, we identified 13,123 3-node FFLs including 25 miRNA-FFLs, 13,005 TF-FFLs and 93 composite-FFLs, and merged the 3-node FFLs to construct a CRC-related regulatory network. The network consists of three types of regulatory subnetworks (SNWs): miRNA-SNW, TF-SNW, and composite-SNW. To enhance the accuracy of the network, the results were filtered by using The Cancer Genome Atlas (TCGA) expression data in CRC, whereby we generated a core regulatory network consisting of 58 significant FFLs. We then applied a hub identification strategy to the significant FFLs and found 5 significant components, including two miRNAs (hsa-miR-25 and hsa-miR-31), two genes (ADAMTSL3 and AXIN1) and one TF (BRCA1). The follow up prognosis analysis indicated all of the 5 significant components having good prediction of overall survival of CRC patients.ConclusionsIn summary, we generated a CRC-specific miRNA-TF regulatory network, which is helpful to understand the complex CRC regulatory mechanisms and guide clinical treatment. The discovered 5 regulators might have critical roles in CRC pathogenesis and warrant future investigation.

Reproducible combinatorial regulatory networks elucidate novel oncogenic microRNAs in non-small cell lung cancer.

While previous studies reported aberrant expression of microRNAs (miRNAs) in non-small cell lung cancer (NSCLC), little is known about which miRNAs play central roles in NSCLC's pathogenesis and its regulatory mechanisms. To address this issue, we presented a robust computational framework that integrated matched miRNA and mRNA expression profiles in NSCLC using feed-forward loops. The network consists of miRNAs, transcription factors (TFs), and their common predicted target genes. To discern the biological meaning of their associations, we introduced the direction of regulation. A network edge validation strategy using three independent NSCLC expression profiling data sets pinpointed reproducible biological regulations. Reproducible regulation, which may reflect the true molecular interaction, has not been applied to miRNA-TF co-regulatory network analyses in cancer or other diseases yet. We revealed eight hub miRNAs that connected to a higher proportion of targets validated by independent data sets. Network analyses showed that these miRNAs might have strong oncogenic characteristics. Furthermore, we identified a novel miRNA-TF co-regulatory module that potentially suppresses the tumor suppressor activity of the TGF-β pathway by targeting a core pathway molecule (TGFBR2). Follow-up experiments showed two miRNAs (miR-9-5p and miR-130b-3p) in this module had increased expression while their target gene TGFBR2 had decreased expression in a cohort of human NSCLC. Moreover, we demonstrated these two miRNAs directly bind to the 3' untranslated region of TGFBR2. This study enhanced our understanding of miRNA-TF co-regulatory mechanisms in NSCLC. The combined bioinformatics and validation approach we described can be applied to study other types of diseases.

How MicroRNA and Transcription Factor Co-regulatory Networks Affect Osteosarcoma Cell Proliferation

Osteosarcomas (OS) are complex bone tumors with various genomic alterations. These alterations affect the expression and function of several genes due to drastic changes in the underlying gene regulatory network. However, we know little about critical gene regulators and their functional consequences on the pathogenesis of OS. Therefore, we aimed to determine microRNA and transcription factor (TF) co-regulatory networks in OS cell proliferation. Cell proliferation is an essential part in the pathogenesis of OS and deeper understanding of its regulation might help to identify potential therapeutic targets. Based on expression data of OS cell lines divided according to their proliferative activity, we obtained 12 proliferation-related microRNAs and corresponding target genes. Therewith, microRNA and TF co-regulatory networks were generated and analyzed regarding their structure and functional influence. We identified key co-regulators comprising the microRNAs miR-9-5p, miR-138, and miR-214 and the TFs SP1 and MYC in the derived networks. These regulators are implicated in NFKB- and RB1-signaling and focal adhesion processes based on their common or interacting target genes (e.g., CDK6, CTNNB1, E2F4, HES1, ITGA6, NFKB1, NOTCH1, and SIN3A). Thus, we proposed a model of OS cell proliferation which is primarily co-regulated through the interactions of the mentioned microRNA and TF combinations. This study illustrates the benefit of systems biological approaches in the analysis of complex diseases. We integrated experimental data with publicly available information to unravel the coordinated (post)-transcriptional control of microRNAs and TFs to identify potential therapeutic targets in OS. The resulting microRNA and TF co-regulatory networks are publicly available for further exploration to generate or evaluate own hypotheses of the pathogenesis of OS (http://www.complex-systems.uni-muenster.de/co_networks.html).