Human Regulatory Network Research Articles

Regulation of endogenous retrovirus-derived regulatory elements by GATA2/3 and MSX2 in human trophoblast stem cells.

The placenta is an organ with extraordinary phenotypic diversity in eutherian mammals. Recent evidence suggests that numerous human placental enhancers are evolved from lineage-specific insertions of endogenous retroviruses (ERVs), yet the transcription factors (TFs) underlying their regulation remain largely elusive. Here, by first focusing on MER41, a primate-specific ERV family previously linked to placenta and innate immunity, we uncover the binding motifs of multiple crucial trophoblast TFs (GATA2/3, MSX2, GRHL2) in addition to innate immunity TFs STAT1 and IRF1. Integration of ChIP-seq data confirms the binding of GATA2/3, MSX2, and their related factors on the majority of MER41-derived enhancers in human trophoblast stem cells (TSCs). MER41-derived enhancers that are constitutively active in human TSCs are distinct from those activated upon interferon stimulation, which is determined by the binding of relevant TFs and their subfamily compositions. We further demonstrate that GATA2/3 and MSX2 have prevalent binding to numerous other ERV families - indicating their broad impact on ERV-derived enhancers. Functionally, the derepression of many syncytiotrophoblast genes after MSX2 knockdown is likely to be mediated by regulatory elements derived from ERVs - suggesting ERVs are also important for mediating transcriptional repression. Overall, this study characterizes the regulation of ERV-derived regulatory elements by GATA2/3, MSX2, and their cofactors in human TSCs, and provides mechanistic insights into the importance of ERVs in human trophoblast regulatory network.

A data-driven optimization method for coarse-graining gene regulatory networks

One major challenge in systems biology is to understand how various genes in a gene regulatory network (GRN) collectively perform their functions and control network dynamics. This task becomes extremely hard to tackle in the case of large networks with hundreds of genes and edges, many of which have redundant regulatory roles and functions. The existing methods for model reduction usually require the detailed mathematical description of dynamical systems and their corresponding kinetic parameters, which are often not available. Here, we present a data-driven method for coarse-graining large GRNs, named SacoGraci, using ensemble-based mathematical modeling, dimensionality reduction, and gene circuit optimization by Markov Chain Monte Carlo methods. SacoGraci requires network topology as the only input and is robust against errors in GRNs. We benchmark and demonstrate its usage with synthetic, literature-based, and bioinformatics-derived GRNs. We hope SacoGraci will enhance our ability to model the gene regulation of complex biological systems.

Drug screening and biomarker gene investigation in cancer therapy through the human transcriptional regulatory network

A complex and vast biological network regulates all biological functions in the human body in a sophisticated manner, and abnormalities in this network can lead to disease and even cancer. The construction of a high-quality human molecular interaction network is possible with the development of experimental techniques that facilitate the interpretation of the mechanisms of drug treatment for cancer. We collected 11 molecular interaction databases based on experimental sources and constructed a human protein-protein interaction (PPI) network and a human transcriptional regulatory network (HTRN). A random walk-based graph embedding method was used to calculate the diffusion profiles of drugs and cancers, and a pipeline was constructed by using five similarity comparison metrics combined with a rank aggregation algorithm, which can be implemented for drug screening and biomarker gene prediction. Taking NSCLC as an example, curcumin was identified as a potentially promising anticancer drug from 5450 natural small molecules, and combined with differentially expressed genes, survival analysis, and topological ranking, we obtained BIRC5 (survivin), which is both a biomarker for NSCLC and a key target for curcumin. Finally, the binding mode of curcumin and survivin was explored using molecular docking. This work has a guiding significance for antitumor drug screening and the identification of tumor markers.

DGRanker: Cancer Driver Gene Detection in Human Transcriptional Regulatory Network.

Cancer is a group of diseases that have received much attention in biological research because of its high mortality rate and the lack of accurate identification of its root causes. In such studies, researchers usually try to identify cancer driver genes (CDGs) that start cancer in a cell. The majority of the methods that have ever been proposed for the identification of CDGs are based on gene expression data and the concept of mutation in genomic data. Recently, using networking techniques and the concept of influence maximization, some models have been proposed to identify these genes. We aimed to construct the cancer transcriptional regulatory network and identify cancer driver genes using a network science approach without the use of mutation and genomic data. In this study, we will employ the social influence network theory to identify CDGs in the human gene regulatory network (GRN) that is based on the concept of influence and power of webpages. First, we will create GRN Networks using gene expression data and Existing nodes and edges. Next, we will implement the modified algorithm on GRN networks being studied by weighting the regulatory interaction edges using the influence spread concept. Nodes with the highest ratings will be selected as the CDGs. The results show our proposed method outperforms most of the other computational and network-based methods and show its superiority in identifying CDGs compared to many other methods. In addition, the proposed method canidentifymany CDGs that are overlooked by all previously published methods. Our study demonstrated that the Google's PageRank algorithm can be utilized and modified as a network-based method for identifying cancer driver gene in transcriptional regulatory network. Furthermore, the proposed method can be considered as a complementary method to the computational-based cancer driver gene identification tools.

Insights into the SARS-CoV-2-Mediated Alteration in the Stress Granule Protein Regulatory Networks in Humans.

The rapidly and constantly evolving coronavirus, SARS-CoV-2, imposes a great threat to human health causing severe lung disease and significant mortality. Cytoplasmic stress granules (SGs) exert anti-viral activities due to their involvement in translation inhibition and innate immune signaling. SARS-CoV-2 sequesters important SG nucleator proteins and impairs SG formation, thus evading the host response for efficient viral replication. However, the significance of SGs in COVID-19 infection remains elusive. In this study, we utilize a protein-protein interaction network approach to systematically dissect the crosstalk of human post-translational regulatory networks governed by SG proteins due to SARS-CoV-2 infection. We uncovered that 116 human SG proteins directly interact with SARS-CoV-2 proteins and are involved in 430 different brain disorders including COVID-19. Further, we performed gene set enrichment analysis to identify the drugs against three important key SG proteins (DYNC1H1, DCTN1, and LMNA) and also looked for potential microRNAs (miRNAs) targeting these proteins. We identified bexarotene as a potential drug molecule and miRNAs, hsa-miR-615-3p, hsa-miR-221-3p, and hsa-miR-124-3p as potential candidates for the treatment of COVID-19 and associated manifestations.

Integrating microRNA expression, miRNA-mRNA regulation network and signal pathway: a novel strategy for lung cancer biomarker discovery.

BackgroundSince there are inextricably connections among molecules in the biological networks, it would be a more efficient and accurate research strategy to screen microRNA (miRNA) markers combining with miRNA-mRNA regulatory networks. The independent regulation mode is more “fragile” and “influential” than the co-regulation mode. miRNAs can be used as biomarkers if they can independently regulate hub genes with important roles in the PPI network, simultaneously the expression products of the regulated hub genes play important roles in the signaling pathways of related tissue diseases.MethodsWe collected miRNA expression of non-small cell lung cancer (NSCLC) from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Volcano plot and signal-to-noise ratio (SNR) methods were used to obtain significant differentially expressed (SDE) miRNAs from the TCGA database and GEO database, respectively. A human miRNA-mRNA regulatory network was constructed and the number of genes uniquely targeted (NOG) by a certain miRNA was calculated. The area under the curve (AUC) values were used to screen for clinical sensitivity and specificity. The candidate markers were obtained using the criteria of the top five maximum AUC values and NOG ≥ 3. The protein–protein interaction (PPI) network was constructed and independently regulated hub genes were obtained. Gene Ontology (GO) analysis and KEGG pathway analysis were used to identify genes involved in cancer-related pathways. Finally, the miRNA which can independently regulate a hub gene and the hub gene can participate in an important cancer-related pathway was considered as a biomarker. The AUC values and gene expression profile analysis from two external GEO datasets as well as literature validation were used to verify the screening capability and reliability of this marker.ResultsFifteen SDE miRNAs in lung cancer were obtained from the intersection of volcano plot and SNR based on the GEO database and the TCGA database. Five miRNAs with the top five maximum AUC values and NOG ≥ 3 were screened out. A total of 61 hub genes were obtained from the PPI network. It was found that the hub gene GTF2F2 was independently regulated by miR-708-5p. Further pathway analysis indicated that GTF2F2 participates in protein expression by binding with polymerase II, and it can regulate transcription and accelerate tumor growth. Hence, miR-708-5p could be used as a biomarker. The good screening capability and reliability of miR-708-5p as a lung cancer marker were confirmed by AUC values and gene expression profiling of external datasets, and experimental literature. The potential mechanism of miR-708-5p was proposed.ConclusionsThis study proposes a new idea for lung cancer marker screening by integrating microRNA expression, regulation network and signal pathway. miR-708-5p was identified as a biomarker using this novel strategy. This study may provide some help for cancer marker screening.

Potential biomarkers for lung adenocarcinoma identified by integrative transcriptomics analysis

Lung cancer is one of the most occurring and death-causing cancers worldwide. Despite the progress, survival rate is still low due to the late diagnosis. The aim of this study is to develop a computational framework to identify potential prognostic biomarkers for lung adenocarcinoma (LUAD). Gene expression profiles obtained from three independent studies were analyzed to find differentially expressed genes (DEGs) in LUAD. Disease-specific protein-protein interaction (PPI) network was constructed among common DEGs and hub proteins were identified. Gene expression data was integrated with the human transcriptional regulatory network (TRN) to identify key regulatory elements and construct disease-specific TRN. Hub proteins that were also present in TRN of LUAD were considered as potential biomarkers and assessed by survival analysis. AURKA, CAV1, CLU, ENO1, FHL1, FHL2, LMO2, MYH11, NME1 and SFN were discovered as biomarkers for LUAD and survival analysis not only indicated their significant prognostic performance as a group but also revealed their contribution to the discrimination of risk groups. Our findings suggested that identified biomarkers could be valuable in LUAD progression and they should be considered for further experimentation.

HReg-CNCC reconstructs a regulatory network in human cranial neural crest cells and annotates variants in a developmental context

Cranial Neural Crest Cells (CNCC) originate at the cephalic region from forebrain, midbrain and hindbrain, migrate into the developing craniofacial region, and subsequently differentiate into multiple cell types. The entire specification, delamination, migration, and differentiation process is highly regulated and abnormalities during this craniofacial development cause birth defects. To better understand the molecular networks underlying CNCC, we integrate paired gene expression & chromatin accessibility data and reconstruct the genome-wide human Regulatory network of CNCC (hReg-CNCC). Consensus optimization predicts high-quality regulations and reveals the architecture of upstream, core, and downstream transcription factors that are associated with functions of neural plate border, specification, and migration. hReg-CNCC allows us to annotate genetic variants of human facial GWAS and disease traits with associated cis-regulatory modules, transcription factors, and target genes. For example, we reveal the distal and combinatorial regulation of multiple SNPs to core TF ALX1 and associations to facial distances and cranial rare disease. In addition, hReg-CNCC connects the DNA sequence differences in evolution, such as ultra-conserved elements and human accelerated regions, with gene expression and phenotype. hReg-CNCC provides a valuable resource to interpret genetic variants as early as gastrulation during embryonic development. The network resources are available at https://github.com/AMSSwanglab/hReg-CNCC.

GenHITS: A network science approach to driver gene detection in human regulatory network using gene’s influence evaluation

Cancer is among the diseases causing death, in which, cells uncontrollably grow and reproduce beyond the cell regulatory mechanism. In this disease, some genes are initiators of abnormalities and then transmit them to other genes through protein interactions. Accordingly, these genes are known as cancer driver genes (CDGs). In this regard, several methods have been previously developed for identifying cancer driver genes. Most of these methods are computational-based, which use the concept of mutation to predict CDGs. In this research, a method has been proposed for identifying CDGs in the transcription regulatory network using the concept of influence diffusion and by modifying the Hyperlink-Induced Topic Search algorithm based on the diffusion concept. Due to the type of these networks and the processes of abnormality progression in cells and the formation of cancerous tumors, high-influence genes can be the most likely considered as the driver genes. Therefore, we can use the influence diffusion concept as an acceptable theory to identify these genes. Recently, a method has been proposed to detect CDGs with the concept of the influence maximization. One of the challenges in these types of networks is finding the power of regulatory interaction between genes. Moreover, we have proposed a novel method to calculate the weight of regulatory interactions, based on the concept of diffusion. The performance of the proposed method was compared with other seventeen computational and network tools. Correspondingly, three cancer types were used as benchmarks as follows: breast invasive carcinoma (BRCA), Colon adenocarcinoma (COAD), and lung squamous cell carcinoma (LUSC). In addition, to determine the accuracy of the detected drivers using each method, CGC (Cancer Gene Census) and Mut-driver gene lists were utilized as gold standard. The results show that GenHITS performs better compared to the most of the other computational and network methods. Besides, it is also able to identify genes that have been identified by none of the other methods yet.

Role of SARS-CoV-2 in Altering the RNA-Binding Protein and miRNA-Directed Post-Transcriptional Regulatory Networks in Humans.

The outbreak of a novel coronavirus SARS-CoV-2 responsible for the COVID-19 pandemic has caused a worldwide public health emergency. Due to the constantly evolving nature of the coronaviruses, SARS-CoV-2-mediated alterations on post-transcriptional gene regulations across human tissues remain elusive. In this study, we analyzed publicly available genomic datasets to systematically dissect the crosstalk and dysregulation of the human post-transcriptional regulatory networks governed by RNA-binding proteins (RBPs) and micro-RNAs (miRs) due to SARS-CoV-2 infection. We uncovered that 13 out of 29 SARS-CoV-2-encoded proteins directly interacted with 51 human RBPs, of which the majority of them were abundantly expressed in gonadal tissues and immune cells. We further performed a functional analysis of differentially expressed genes in mock-treated versus SARS-CoV-2-infected lung cells that revealed enrichment for the immune response, cytokine-mediated signaling, and metabolism-associated genes. This study also characterized the alternative splicing events in SARS-CoV-2-infected cells compared to the control, demonstrating that skipped exons and mutually exclusive exons were the most abundant events that potentially contributed to differential outcomes in response to the viral infection. A motif enrichment analysis on the RNA genomic sequence of SARS-CoV-2 clearly revealed the enrichment for RBPs such as SRSFs, PCBPs, ELAVs, and HNRNPs, suggesting the sponging of RBPs by the SARS-CoV-2 genome. A similar analysis to study the interactions of miRs with SARS-CoV-2 revealed functionally important miRs that were highly expressed in immune cells, suggesting that these interactions may contribute to the progression of the viral infection and modulate the host immune response across other human tissues. Given the need to understand the interactions of SARS-CoV-2 with key post-transcriptional regulators in the human genome, this study provided a systematic computational analysis to dissect the role of dysregulated post-transcriptional regulatory networks controlled by RBPs and miRs across tissue types during a SARS-CoV-2 infection.

Cancer driver gene discovery in transcriptional regulatory networks using influence maximization approach

Cancer driver genes (CDGs) are the genes whose mutations cause tumor growth. Several computational methods have been previously developed for finding CDGs. Most of these methods are sequence-based, that is, they rely on finding key mutations in genomic data to predict CDGs. In the present work, we propose iMaxDriver as a network-based tool for predicting driver genes by application of influence maximization algorithm on human transcriptional regulatory network (TRN). In the first step of this approach, the TRN is pruned and weighted by exploiting tumor-specific gene expression (GE) data. Then, influence maximization approach is used to find the influence of each gene. The top genes with the highest influence rate are selected as the potential driver genes. We compared the performance of our CDG prediction method with fifteen other computational tools, based on a benchmark of three different cancer types. Our results show that iMaxDriver outperforms most of the state-of-the-art algorithms for CDG prediction. Furthermore, iMaxDriver is able to correctly predict many CDGs that are overlooked by all previously published tools. Due to this relative orthogonality, iMaxDriver can be considered as a complementary approach to the sequence-based CDG prediction methods.

Deciphering human ribonucleoprotein regulatory networks.

RNA-binding proteins (RBPs) control and coordinate each stage in the life cycle of RNAs. Although in vivo binding sites of RBPs can now be determined genome-wide, most studies typically focused on individual RBPs. Here, we examined a large compendium of 114 high-quality transcriptome-wide in vivo RBP–RNA cross-linking interaction datasets generated by the same protocol in the same cell line and representing 64 distinct RBPs. Comparative analysis of categories of target RNA binding preference, sequence preference, and transcript region specificity was performed, and identified potential posttranscriptional regulatory modules, i.e. specific combinations of RBPs that bind to specific sets of RNAs and targeted regions. These regulatory modules represented functionally related proteins and exhibited distinct differences in RNA metabolism, expression variance, as well as subcellular localization. This integrative investigation of experimental RBP–RNA interaction evidence and RBP regulatory function in a human cell line will be a valuable resource for understanding the complexity of post-transcriptional regulation.

Water Intake, Water Balance, and the Elusive Daily Water Requirement.

Water is essential for metabolism, substrate transport across membranes, cellular homeostasis, temperature regulation, and circulatory function. Although nutritional and physiological research teams and professional organizations have described the daily total water intakes (TWI, L/24h) and Adequate Intakes (AI) of children, women, and men, there is no widespread consensus regarding the human water requirements of different demographic groups. These requirements remain undefined because of the dynamic complexity inherent in the human water regulatory network, which involves the central nervous system and several organ systems, as well as large inter-individual differences. The present review analyzes published evidence that is relevant to these issues and presents a novel approach to assessing the daily water requirements of individuals in all sex and life-stage groups, as an alternative to AI values based on survey data. This empirical method focuses on the intensity of a specific neuroendocrine response (e.g., plasma arginine vasopressin (AVP) concentration) employed by the brain to regulate total body water volume and concentration. We consider this autonomically-controlled neuroendocrine response to be an inherent hydration biomarker and one means by which the brain maintains good health and optimal function. We also propose that this individualized method defines the elusive state of euhydration (i.e., water balance) and distinguishes it from hypohydration. Using plasma AVP concentration to analyze multiple published data sets that included both men and women, we determined that a mild neuroendocrine defense of body water commences when TWI is ˂1.8 L/24h, that 19–71% of adults in various countries consume less than this TWI each day, and consuming less than the 24-h water AI may influence the risk of dysfunctional metabolism and chronic diseases.

Uncovering association networks through an eQTL analysis involving human miRNAs and lincRNAs

Non-coding RNAs (ncRNA) have an essential role in the complex landscape of human genetic regulatory networks. One area that is poorly explored is the effect of genetic variations on the interaction between ncRNA and their targets. By integrating a significant amount of public data, the present study cataloged the vast landscape of the regulatory effect of microRNAs (miRNA) and long intergenic noncoding RNAs (lincRNA) in the human genome. An expression quantitative trait loci (eQTL) analysis was used to identify genetic variants associated with miRNA and lincRNA and whose genotypes affect gene expression. Association networks were built for eQTL associated to traits of clinical and/or pharmacological relevance.

Evolutionary Rewiring of Human Regulatory Networks by Waves of Genome Expansion

Genome expansion is believed to be an important driver of the evolution of gene regulation. To investigate the role of a newly arising sequence in rewiring regulatory networks, we estimated the age of each region of the human genome by applying maximum parsimony to genome-wide alignments with 100 vertebrates. We then studied the age distribution of several types of functional regions, with a focus on regulatory elements. The age distribution of regulatory elements reveals the extensive use of newly formed genomic sequence in the evolution of regulatory interactions. Many transcription factors have expanded their repertoire of targets through waves of genomic expansions that can be traced to specific evolutionary times. Repeated elements contributed a major part of such expansion: many classes of such elements are enriched in binding sites of one or a few specific transcription factors, whose binding sites are localized in specific portions of the element and characterized by distinctive motif words. These features suggest that the binding sites were available as soon as the new sequence entered the genome, rather than being created later by accumulation of point mutations. By comparing the age of regulatory regions to the evolutionary shift in expression of nearby genes, we show that rewiring through genome expansion played an important role in shaping human regulatory networks.

Decomposition Approach for Learning Large Gene Regulatory Network

Gene Regulatory Network (GRN) represents the complex interaction between Transcription Factors (TFs) and other genes with time delays. They are important in the working of the cell. Learning GRN is an important first step towards understanding the working of the cell and consequently curing diseases related to malfunctioning of the cell. One significant problem in learning GRN is that the available time series expression data is still limited compared to the network size. To alleviate this problem, besides using multiple expression replicates, we propose to decompose large network into small subnetwork without prior knowledge. Our algorithm first infers an initial GRN using CLINDE, then decomposes it into possibly overlapping subnetworks, then infers each subnetwork by either CLINDE or DD-lasso and finally merges the subnetworks. We have tested this algorithm on synthetic data of many networks with 500 and 1000 genes. We have also tested on real data on 41 human TF regulatory networks. Results show that our proposed algorithm does improve the GRN learning performance of using either CLINDE or DD-lasso alone on the large network.

Detecting Co-Regulatory Modules from Human Regulatory Network by Randomly Walking Between Regulator and Gene Modules

Gene expression is jointly regulated by microRNAs and transcriptional factors. As such, constructing a regulatory network for microRNAs and transcriptional factors and analyzing their combinatorial effects are vital to understand living organisms. Co-regulatory modules, including functional homogeneous microRNAs, transcriptional factors, and genes, provide insights into coordinate regulation. In this paper, we propose a random walk with restart between regulator and gene modules (RWRRGM) method to detect co-regulatory modules from a human regulatory network. The network integrates large, heterogeneous data, including transcriptional regulation, post-transcriptional regulation, and gene-gene interaction. RWRRGM first identifies regulator and gene modules by greedily expanding seed nodes and then walks on the identified modules randomly. Finally, functional homogeneous regulator and gene modules are integrated to form co-regulatory modules. RWRRGM-based modules exhibit higher enrichment in gene ontology terms and known pathways than modules predicted by other methods.

Genome-Scale Transcriptional Regulatory Network Models of Psychiatric Disorders

Non-coding genetic variants with predicted regulatory functions contribute strongly to risk for psychiatric disorders, and psychiatric disorders are associated with changes in the expression of hundreds of genes in the brain. Understanding brain gene regulation may therefore provide insights into the mechanisms of psychiatric disorders, yet mechanistically and quantitatively accurate models of human transcriptional regulatory networks remain elusive. I will describe TRENA, a new strategy to predict tissue-specific binding sites and functional targets genes for hundreds of transcription factors (TFs) by integrating large epigenomic and transcriptomics datasets. We used TRENA to model TF-target gene interactions in the human brain, combining brain-specific ENCODE data with 2,756 gene expression profiles from the Allen Brain Atlas. This model made quantitatively accurate predictions (r2 > 0.5) about the expression patterns of 11,093 genes based on the binding sites and expression patterns of 778 TFs. Integrating this TRN model with post-mortem gene expression profiles from the prefrontal cortex of psychiatric cases and controls led to the identification of 66 master regulator TFs that are predicted to drive expression changes in both bipolar disorder and schizophrenia. Our model also predicted regulatory functions for SNPs associated with genetic risk for bipolar disorder and schizophrenia. We experimentally validated our model’s prediction that a risk-associated SNP alters the activity of the VRK2 promoter and occupancy of a master regulator TF, POU3F2.

Interactive cooperation and hierarchical operation of microRNA and transcription factor crosstalk in human transcriptional regulatory network.

Transcriptional regulation of gene expression is an essential cellular process that is arranged by transcription factors (TFs), microRNAs (miRNA) and their target genes through a variety of mechanisms. Here, we set out to reconstruct a comprehensive transcriptional regulatory network of Homo sapiens consisting of experimentally verified regulatory information on miRNAs, TFs and their target genes. We have performed topological analyses to elucidate the transcriptional regulatory roles of miRNAs and TFs. When we thoroughly investigated the network motifs, different gene regulatory scenarios were observed; whereas, mutual TF-miRNA regulation (interactive cooperation) and hierarchical operation where miRNAs were the upstream regulators of TFs came into prominence. Otherwise, biological process specific subnetworks were also constructed and integration of gene and miRNA expression data on ovarian cancer was achieved as a case study to observe dynamic patterns of the gene expression. Meanwhile, both co-operation and hierarchical operation types were determined in active ovarian cancer and process-specific subnetworks. In addition, the analysis showed that multiple signals from miRNAs were integrated by TFs. Our results demonstrate new insights on the architecture of the human transcriptional regulatory network, and here we present some lessons we gained from deciphering the reciprocal interplay between miRNAs, TFs and their target genes.

Understanding tissue-specificity with human tissue-specific regulatory networks

Tissue-specificity is important for the function of human body. However, it is still not clear how the functional diversity of different tissues is achieved. Here we construct gene regulatory networks in 13 human tissues by integrating large-scale transcription factor (TF)-gene regulations with gene and protein expression data. By comparing these regulatory networks, we find many tissue-specific regulations that are important for tissue identity. In particular, the tissue-specific TFs are found to regulate more genes than those expressed in multiple tissues, and the processes regulated by these tissue-specific TFs are closely related to tissue functions. Moreover, the regulations that are present in certain tissue are found to be enriched in the tissue associated disease genes, and these networks provide the molecular context of disease genes. Therefore, recognizing tissuespecific regulatory networks can help better understand the molecular mechanisms underlying diseases and identify new disease genes.