TRED: a Transcriptional Regulatory Element Database and a platform for in silico gene regulation studies

Study of hub nodes of transcription factor-target gene regulatory network and immune mechanism for type 2 diabetes based on chip analysis of GEO database

Identification of novel therapeutic targets for type 2 diabetes is a key area of contemporary research. In this study, we screened differentially expressed genes in type 2 diabetes through the GEO database and sought to identify the key virulence factors for type 2 diabetes through a transcription factor regulatory network. Our findings may help identify new therapeutic targets for type 2 diabetes. Data pertaining to the humoral (whole blood) gene expression profile of diabetic patients were obtained from the NCBI's GEO Datasets database and gene sets with differential expression were identified. Subsequently, the TRED transcriptional regulatory element database was integrated to build a gene regulatory network for type 2 diabetes. Functional analysis (GO-Analysis) and Pathway-analysis of differentially expressed genes were performed using the DAVID database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Finally, gene-disease correlation analysis was performed using the DAVID online annotation tool. A total of 236 pathogenic genes, four transcription factors related to the pathogenic genes, and 261 corresponding target genes were identified. A transcription factor-target gene regulatory network for type 2 diabetes was constructed. Most of the key factors of the transcription factor-target gene regulatory network for type 2 diabetes were found closely related to the immune metabolic system and the functions of cell proliferation and transformation.

Retinoic acid receptor activation reprograms senescence response and enhances anti-tumor activity of natural killer cells

Cellular senescence can exert dual effects in tumors, either suppressing or promoting tumor progression. The senescence-associated secretory phenotype (SASP), released by senescent cells, plays a crucial role in this dichotomy. Consequently, the clinical challenge lies in developing therapies that safely enhance senescence in cancer, favoring tumor-suppressive SASP factors over tumor-promoting ones. Here, we identify the retinoic-acid-receptor (RAR) agonist adapalene as an effective pro-senescence compound in prostate cancer (PCa). Reactivation of RARs triggers a robust senescence response and a tumor-suppressive SASP. In preclinical mouse models of PCa, the combination of adapalene and docetaxel promotes a tumor-suppressive SASP that enhances natural killer (NK) cell-mediated tumor clearance more effectively than either agent alone. This approach increases the efficacy of the allogenic infusion of human NK cells in mice injected with human PCa cells, suggesting an alternative therapeutic strategy to stimulate the anti-tumor immune response in "immunologically cold" tumors.

GEO dataset mining analysis reveals novel Staphylococcus aureus virulence gene regulatory networks and diagnostic targets in mice

Staphylococcus (S.) aureus infection is a serious, worldwide health concern, particularly in many communities and hospitals. Understanding the S. aureus pathogenetic regulatory network will provide significant insights into diagnostic target screening to improve clinical treatment of diseases caused by S. aureus. We screened differentially expressed genes between normal mice and S. aureus-infected mice. We used the Gene Expression Omnibus (GEO) DataSets database for functional analysis (GO-analysis) and the DAVID and KEGG databases for signaling pathway analyses. We next integrated the gene and pathway analyses with Transcriptional Regulatory Element Database (TRED) to build an antimicrobial resistance gene regulatory network of S. aureus. We performed association analysis of network genes and diseases using DAVID online annotation tools. We identified a total of 437 virulence genes and 15 transcription factors (TFs), as well as 444 corresponding target genes, in the S. aureus TF regulatory network. We screened seven key network nodes (Met, Mmp13, Il12b, Il4, Tnf, Ptgs2, and Ctsl), four key transcription factors (Jun, C3, Spil, and Il6) and an important signaling pathway (TNF). We hypothesized that the cytokine activity and growth factor activity of S. aureus are combinatorically cross-regulated by Met, Mmp13, Il12b, Il4, Tnf, Ptgs2, and Ctsl genes, the TFs Jun, C3, Spi1, and Il6, as well as the immune response, cellular response to lipopolysaccharide, and inflammatory response. Our study provides information and reference values for the molecular understanding of the S. aureus pathogenetic gene regulatory network.

Characteristics of alveolar macrophages in bronchioalveolar lavage fluids from active tuberculosis patients identified by single-cell RNA sequencing

Tuberculosis (TB), is an infectious disease caused by Mycobacterium tuberculosis (M. tuberculosis), and presents with high morbidity and mortality. Alveolar macrophages play an important role in TB pathogenesis although there is heterogeneity and functional plasticity. This study aimed to show the characteristics of alveolar macrophages from bronchioalveolar lavage fluid (BALF) in active TB patients. Single-cell RNA sequencing (scRNA-seq) was performed on BALF cells from three patients with active TB and additional scRNA-seq data from three healthy adults were established as controls. Transcriptional profiles were analyzed and compared by differential geneexpression and functional enrichment analysis. We applied pseudo-temporal trajectory analysis to investigate correlations and heterogeneity within alveolar macrophage subclusters. Alveolar macrophages from active TB patients at the single-cell resolution are described. We found that TB patients have higher cellular percentages in five macrophage subclusters. Alveolar macrophage subclusters with increased percentages were involved in inflammatory signaling pathways as well as the basic macrophage functions. The TB-increased alveolar macrophage subclusters might be derived from M1-like polarization state, before switching to an M2-like polarization state with the development ofM. tuberculosis infection. Cell-cell communications of alveolar macrophages also increased and enhanced in active TB patients. Overall, our study demonstrated the characteristics of alveolar macrophages from BALF in active TB patients by using scRNA-seq.

iDEP Web Application for RNA-Seq Data Analysis

RNA sequencing (RNA-seq) has become a routine method for transcriptomic profiling. We developed a user-friendly web app called iDEP (integrated differential expression and pathway analysis) to help biologists interpret read counts or other types of expression matrices derived from read mapping. With iDEP, users can easily conduct exploratory data analysis, identify differentially expressed genes, and perform pathway analysis. Due to its intuitive user interface and massive annotation database, iDEP is being widely adopted for interactive analysis of RNA-seq data. Using a public dataset on the effect of heat shock on mouse with and without functional Hsf1, we demonstrate how users can prepare data files and conduct in-depth analysis. We also discuss the importance of critical interpretion of results (avoid p-hacking and rationalizing) and validation of significant pathways by using different methods and independent annotation databases.

A CRISPR/Cas9-based method for targeted DNA methylation enables cancer initiation in B lymphocytes

Targeted DNA methylation is important for understanding transcriptional modulation and epigenetic diseases. Although CRISPR-Cas9 has potential for this purpose, it has not yet been successfully used to efficiently introduce DNA methylation and induce epigenetic diseases. We herein developed a new system that enables the replacement of an unmethylated promoter with a methylated promoter through microhomology-mediated end joining-based knock-in. We successfully introduced an approximately 100% DNA methylation ratio at the cancer-associated gene SP3 in HEK293 cells. Moreover, engineered SP3 promoter hypermethylation led to transcriptional suppression in human B lymphocytes and induced B-cell lymphoma. Our system provides a promising framework for targeted DNA methylation and cancer initiation through epimutations.

PPD: A Manually Curated Database for Experimentally Verified Prokaryotic Promoters

As a key region, promoter plays a key role in transcription regulation. A eukaryotic promoter database called EPD has been constructed to store eukaryotic POL II promoters. Although there are some promoter databases for specific prokaryotic species or specific promoter type, such as RegulonDB for Escherichia coli K-12, DBTBS for Bacillus subtilis and Pro54DB for sigma 54 promoter, because of the diversity of prokaryotes and the development of sequencing technology, huge amounts of prokaryotic promoters are scattered in numerous published articles, which is inconvenient for researchers to explore the process of gene regulation in prokaryotes. In this study, we constructed a Prokaryotic Promoter Database (PPD), which records the experimentally validated promoters in prokaryotes, from published articles. Up to now, PPD has stored 129,148 promoters across 63 prokaryotic species manually extracted from published papers. We provided a friendly interface for users to browse, search, blast, visualize, submit and download data. The PPD will provide relatively comprehensive resources of prokaryotic promoter for the study of prokaryotic gene transcription. The PPD is freely available and easy accessed at http://lin-group.cn/database/ppd/.

Topological Analysis of Regulatory Networks Reveals Functionally Key Genes and miRNAs Involved in the Differentiation of Mesenchymal Stem Cells

Background

The details of molecular mechanisms underlying the differentiation of Mesenchymal Stem Cells (MSCs) into specific lineages are not well understood.

Objectives

We aimed to construct the interactome network and topology analysis of bone marrow mesenchymal stem cell of CAGE data. Applying the enrichment results, we wanted to introduce the common genes and hub-microRNA and hub-genes of these giant network.

Materials and Methods

In this study, we constructed gene regulatory networks for each non-mesenchymal cell lineage according to their gene expression profiles obtained from FANTOM5 database. The putative interactions of TF-gene and protein-protein were determined using TRED, STRING, HPRD and GeneMANIA servers. In parallel, a regulatory network including corresponding miRNAs and total differentially expressed genes (DEGs) was constructed for each cell lineage.

Results

The results indicated that analysis of networks' topology can significantly distinguish the hub regulatory genes and miRNAs involved in the differentiation of MSCs. The functional annotation of identified hub genes and miRNAs revealed that several signal transduction pathways i.e. AKT, WNT and TGFβ and cell proliferation related pathways play a pivotal role in the regulation of MSCs differentiation. We also classified cell lineages into two groups based on their predicted miRNA profiles.

Conclusions

In conclusion, we found a number of hub genes and miRNAs which seem to have key regulatory functions during differentiation of MSCs. Our results also introduce a number of new regulatory genes and miRNAs which can be considered as the new candidates for genetic manipulation of MSCs in vitro.

LRRK2 mediates microglial neurotoxicity via NFATc2 in rodent models of synucleinopathies

Synucleinopathies are neurodegenerative disorders characterized by abnormal α-synuclein deposition that include Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy. The pathology of these conditions also includes neuronal loss and neuroinflammation. Neuron-released α-synuclein has been shown to induce neurotoxic, proinflammatory microglial responses through Toll-like receptor 2, but the molecular mechanisms involved are poorly understood. Here, we show that leucine-rich repeat kinase 2 (LRRK2) plays a critical role in the activation of microglia by extracellular α-synuclein. Exposure to α-synuclein was found to enhance LRRK2 phosphorylation and activity in mouse primary microglia. Furthermore, genetic and pharmacological inhibition of LRRK2 markedly diminished α-synuclein-mediated microglial neurotoxicity via lowering of tumor necrosis factor-α and interleukin-6 expression in mouse cultures. We determined that LRRK2 promoted a neuroinflammatory cascade by selectively phosphorylating and inducing nuclear translocation of the immune transcription factor nuclear factor of activated T cells, cytoplasmic 2 (NFATc2). NFATc2 activation was seen in patients with synucleinopathies and in a mouse model of synucleinopathy, where administration of an LRRK2 pharmacological inhibitor restored motor behavioral deficits. Our results suggest that modulation of LRRK2 and its downstream signaling mediator NFATc2 might be therapeutic targets for treating synucleinopathies.

BARX2 expression is downregulated by CpG island hypermethylation and is associated with suppressed cell proliferation and invasion of gastric cancer cells

BarH-like homeobox 2 (BARX2), a homeobox gene, is associated with several types of cancers. The present study aimed to determine whether DNA methylation downregulates BARX2 expression and whether BARX2 is associated with suppression of gastric carcinogenesis. BARX2 protein expression in normal and cancerous gastric tissues and various gastric cancer (GC) cell lines was detected using immunohistochemical and western blot assays. BARX2 mRNA levels were detected using both reverse transcription-polymerase chain reaction (RT-PCR) and quantitative PCR (qPCR). Promoter hypermethylation in GC cells was detected using methylation-specific PCR or bisulfite DNA sequencing PCR. Effects of BARX2 expression on GC cell proliferation, clonal formation, and migration were evaluated after lentivirus-BARX2 transfection. The effect of stable BARX2 transfection on tumor formation was assessed in a nude xenograft mouse model. BARX2 was strongly expressed in the normal gastric mucosa, but weakly or not expressed in GC tissues and most GC cell lines. BARX2 expression was negatively correlated with DNMT (a marker for DNA methylation) expression in the gastric tissues. The BARX2 promoter fragment was hypermethylated in the GC cell lines. Overexpression of BARX2 significantly inhibited GC cell proliferation, clonal formation, and migration. Stable BARX2 transfection inhibited tumor formation in xenograft mice, which was correlated with decreased expression of E-cadherin, proliferation markers, and matrix metalloproteinases. In conclusion, BARX2 expression is aberrantly reduced in GC, which is associated with increased DNA methylation of its promoter. BARX2 inhibits GC cell proliferation, migration, and tumor formation, suggesting that BARX2 acts as a tumor suppressor in gastric carcinogenesis.

Study on the Antibreast Cancer Mechanism and Bioactive Components of Si-Wu-Tang by Cell Type-Specific Molecular Network

Si-Wu-Tang (SWT), a traditional Chinese herbal formula, has shown an effect on antibreast cancer. However, the mechanisms and bioactive components of SWT are still unclear. Fortunately, cell type-specific molecular network has provided an effective method. This study integrated the data of formula components, all types of biomolecules in the human body, and nonexpressed protein in breast cancer cells and constructed the breast cancer cell network and the biological network that SWT acted on the breast cancer-related targets by Entity Grammar System (EGS). Biological network showed 59 bioactive components acting on 15 breast cancer-related targets. The antibreast cancer mechanisms were summarized by enrichment analysis: regulation of cell death, response to hormone stimulation, response to organic substance, regulation of phosphorylation of amino acids, regulation of cell proliferation, regulation of signal transmission, and affection of gland development. In addition, we discovered that verbascoside played the role of antibreast cancer by inhibiting cell proliferation, but there was not a report on this effect. The results of CCK8 and western blot were consistent with the antibreast cancer effect of verbascoside based on biological network. Biological network modeling by EGS and network analysis provide an effective way for uncovering the mechanism and identifying the bioactive components of SWT.

Recent advances in functional research in Giardia intestinalis

This review considers current advances in tools to investigate the functional biology of Giardia, it's coding and non-coding genes, features and cellular and molecular biology. We consider major gaps in current knowledge of the parasite and discuss the present state-of-the-art in its in vivo and in vitro cultivation. Advances in in silico tools, including for the modelling non-coding RNAs and genomic elements, as well as detailed exploration of coding genes through inferred homology to model organisms, have provided significant, primary level insight. Improved methods to model the three-dimensional structure of proteins offer new insights into their function, and binding interactions with ligands, other proteins or precursor drugs, and offer substantial opportunities to prioritise proteins for further study and experimentation. These approaches can be supplemented by the growing and highly accessible arsenal of systems-based methods now being applied to Giardia, led by genomic, transcriptomic and proteomic methods, but rapidly incorporating advanced tools for detection of real-time transcription, evaluation of chromatin states and direct measurement of macromolecular complexes. Methods to directly interrogate and perturb gene function have made major leaps in recent years, with CRISPr-interference now available. These approaches, coupled with protein over-expression, fluorescent labelling and in vitro and in vivo imaging, are set to revolutionize the field and herald an exciting time during which the field may finally realise Giardia's long proposed potential as a model parasite and eukaryote.

Reprogramming of macrophages employing gene regulatory and metabolic network models

Upon exposure to different stimuli, resting macrophages undergo classical or alternative polarization into distinct phenotypes that can cause fatal dysfunction in a large range of diseases, such as systemic infection leading to sepsis or the generation of an immunosuppressive tumor microenvironment. Investigating gene regulatory and metabolic networks, we observed two metabolic switches during polarization. Most prominently, anaerobic glycolysis was utilized by M1-polarized macrophages, while the biosynthesis of inosine monophosphate was upregulated in M2-polarized macrophages. Moreover, we observed a switch in the urea cycle. Gene regulatory network models revealed E2F1, MYC, PPARγ and STAT6 to be the major players in the distinct signatures of these polarization events. Employing functional assays targeting these regulators, we observed the repolarization of M2-like cells into M1-like cells, as evidenced by their specific gene expression signatures and cytokine secretion profiles. The predicted regulators are essential to maintaining the M2-like phenotype and function and thus represent potential targets for the therapeutic reprogramming of immunosuppressive M2-like macrophages.

MicroRNA regulatory pattern in spinal cord ischemia-reperfusion injury

After spinal cord injury, dysregulated miRNAs appear and can participate in inflammatory responses, as well as the inhibition of apoptosis and axon regeneration through multiple pathways. However, the functions of miRNAs in spinal cord ischemia-reperfusion injury progression remain unclear. miRCURY LNATM Arrays were used to analyze miRNA expression profiles of rats after 90 minutes of ischemia followed by reperfusion for 24 and 48 hours. Furthermore, subsequent construction of aberrantly expressed miRNA regulatory patterns involved cell survival, proliferation, and apoptosis. Remarkably, the mitogen-activated protein kinase (MAPK) signaling pathway was the most significantly enriched pathway among 24- and 48-hour groups. Bioinformatics analysis and quantitative reverse transcription polymerase chain reaction confirmed the persistent overexpression of miR-22-3p in both groups. These results suggest that the aberrant miRNA regulatory network is possibly regulated MAPK signaling and continuously affects the physiological and biochemical status of cells, thus participating in the regulation of spinal cord ischemia-reperfusion injury. As such, miR-22-3p may play sustained regulatory roles in spinal cord ischemia-reperfusion injury. All experimental procedures were approved by the Animal Ethics Committee of Jilin University, China [approval No. 2020 (Research) 01].

Modelling TERT regulation across 19 different cancer types based on the MIPRIP 2.0 gene regulatory network approach

Background

Reactivation of the telomerase reverse transcriptase gene TERT is a central feature for unlimited proliferation of the majority of cancers. However, the underlying regulatory processes are only partly understood.

Results

We assembled regulator binding information from serveral sources to construct a generic human and mouse gene regulatory network. Advancing our “Mixed Integer linear Programming based Regulatory Interaction Predictor” (MIPRIP) approach, we identified the most common and cancer-type specific regulators of TERT across 19 different human cancers. The results were validated by using the well-known TERT regulation by the ETS1 transcription factor in a subset of melanomas with mutations in the TERT promoter.

Our improved MIPRIP2 R-package and the associated generic regulatory networks are freely available at https://github.com/KoenigLabNM/MIPRIP.

Conclusion

MIPRIP 2.0 identified common as well as tumor type specific regulators of TERT. The software can be easily applied to transcriptome datasets to predict gene regulation for any gene and disease/condition under investigation.

CRNET: an efficient sampling approach to infer functional regulatory networks by integrating large-scale ChIP-seq and time-course RNA-seq data

Motivation

NGS techniques have been widely applied in genetic and epigenetic studies. Multiple ChIP-seq and RNA-seq profiles can now be jointly used to infer functional regulatory networks (FRNs). However, existing methods suffer from either oversimplified assumption on transcription factor (TF) regulation or slow convergence of sampling for FRN inference from large-scale ChIP-seq and time-course RNA-seq data.

Results

We developed an efficient Bayesian integration method (CRNET) for FRN inference using a two-stage Gibbs sampler to estimate iteratively hidden TF activities and the posterior probabilities of binding events. A novel statistic measure that jointly considers regulation strength and regression error enables the sampling process of CRNET to converge quickly, thus making CRNET very efficient for large-scale FRN inference. Experiments on synthetic and benchmark data showed a significantly improved performance of CRNET when compared with existing methods. CRNET was applied to breast cancer data to identify FRNs functional at promoter or enhancer regions in breast cancer MCF-7 cells. Transcription factor MYC is predicted as a key functional factor in both promoter and enhancer FRNs. We experimentally validated the regulation effects of MYC on CRNET-predicted target genes using appropriate RNAi approaches in MCF-7 cells.

Availability and implementation

R scripts of CRNET are available at http://www.cbil.ece.vt.edu/software.htm.

Contact

xuan@vt.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

iOmicsPASS: network-based integration of multiomics data for predictive subnetwork discovery

Computational tools for multiomics data integration have usually been designed for unsupervised detection of multiomics features explaining large phenotypic variations. To achieve this, some approaches extract latent signals in heterogeneous data sets from a joint statistical error model, while others use biological networks to propagate differential expression signals and find consensus signatures. However, few approaches directly consider molecular interaction as a data feature, the essential linker between different omics data sets. The increasing availability of genome-scale interactome data connecting different molecular levels motivates a new class of methods to extract interactive signals from multiomics data. Here we developed iOmicsPASS, a tool to search for predictive subnetworks consisting of molecular interactions within and between related omics data types in a supervised analysis setting. Based on user-provided network data and relevant omics data sets, iOmicsPASS computes a score for each molecular interaction, and applies a modified nearest shrunken centroid algorithm to the scores to select densely connected subnetworks that can accurately predict each phenotypic group. iOmicsPASS detects a sparse set of predictive molecular interactions without loss of prediction accuracy compared to alternative methods, and the selected network signature immediately provides mechanistic interpretation of the multiomics profile representing each sample group. Extensive simulation studies demonstrate clear benefit of interaction-level modeling. iOmicsPASS analysis of TCGA/CPTAC breast cancer data also highlights new transcriptional regulatory network underlying the basal-like subtype as positive protein markers, a result not seen through analysis of individual omics data.

PropaNet: Time-Varying Condition-Specific Transcriptional Network Construction by Network Propagation

Transcription factor (TF) has a significant influence on the state of a cell by regulating multiple down-stream genes. Thus, experimental and computational biologists have made great efforts to construct TF gene networks for regulatory interactions between TFs and their target genes. Now, an important research question is how to utilize TF networks to investigate the response of a plant to stress at the transcription control level using time-series transcriptome data. In this article, we present a new computational network, PropaNet, to investigate dynamics of TF networks from time-series transcriptome data using two state-of-the-art network analysis techniques, influence maximization and network propagation. PropaNet uses the influence maximization technique to produce a ranked list of TFs, in the order of TF that explains differentially expressed genes (DEGs) better at each time point. Then, a network propagation technique is used to select a group of TFs that explains DEGs best as a whole. For the analysis of Arabidopsis time series datasets from AtGenExpress, we used PlantRegMap as a template TF network and performed PropaNet analysis to investigate transcriptional dynamics of Arabidopsis under cold and heat stress. The time varying TF networks showed that Arabidopsis responded to cold and heat stress quite differently. For cold stress, bHLH and bZIP type TFs were the first responding TFs and the cold signal influenced histone variants, various genes involved in cell architecture, osmosis and restructuring of cells. However, the consequences of plants under heat stress were up-regulation of genes related to accelerating differentiation and starting re-differentiation. In terms of energy metabolism, plants under heat stress show elevated metabolic process and resulting in an exhausted status. We believe that PropaNet will be useful for the construction of condition-specific time-varying TF network for time-series data analysis in response to stress. PropaNet is available at http://biohealth.snu.ac.kr/software/PropaNet.

Label-Free Multi Parameter Optical Interrogation of Endothelial Activation in Single Cells using a Lab on a Disc Platform

Cellular activation and inflammation leading to endothelial dysfunction is associated with cardiovascular disease (CVD). We investigated whether a single cell label-free multi parameter optical interrogation system can detect endothelial cell and endothelial progenitor cell (EPC) activation in vitro and ex vivo, respectively. Cultured human endothelial cells were exposed to increasing concentrations of tumour necrosis factor alpha (TNF-α) or lipopolysaccharide (LPS) before endothelial activation was validated using fluorescence-activated cell sorting (FACS) analysis of inflammatory marker expression (PECAM-1, E-selectin and ICAM-1). A centrifugal microfluidic system and V-cup array was used to capture individual cells before optical measurement of light scattering, immunocytofluorescence, auto-fluorescence (AF) and cell morphology was determined. In vitro, TNF-α promoted specific changes to the refractive index and cell morphology of individual cells concomitant with enhanced photon activity of fluorescently labelled inflammatory markers and increased auto-fluorescence (AF) intensity at three different wavelengths, an effect blocked by inhibition of downstream signalling with Iκβ. Ex vivo, there was a significant increase in EPC number and AF intensity of individual EPCs from CVD patients concomitant with enhanced PECAM-1 expression when compared to normal controls. This novel label-free 'lab on a disc' (LoaD) platform can successfully detect endothelial activation in response to inflammatory stimuli in vitro and ex vivo.

Transcriptomic Data Identified Key Transcription Factors for Osteoporosis in Caucasian Women

Osteoporosis is a prevalent bone metabolic disease, mainly caused by excessive bone resorption (by osteoclasts) over bone formation (by osteoblasts). Identifying the key transcription factors and understanding the regulatory network influencing osteoclastogenesis will be helpful to explore the potential biological mechanism for osteoporosis. In our study, peripheral blood monocyte (PBM) was used as a cell model for bone mineral density (BMD) research. PBMs serve as progenitors of osteoclasts and produce important cytokines for osteoclastogenesis. In our study, via exon arrays, gene expression profiles of PBMs were analyzed between high versus low hip BMD groups. Transcription factors for differentially expressed genes were then predicted based on the enrichment analysis. We found that 591 genes were differentially expressed between the two BMD groups (nominally significant, raw p value < 0.05). For high BMD subjects, 482 genes were up-regulated and 109 genes were down-regulated. We then found 29 potential transcription factors for up-regulated genes and nine transcription factors for down-regulated genes. Among these transcription factors, HMGA1 and NFKB2 were differentially expressed between high versus low BMD groups. In addition, their regulation types with their target genes were consistent with the information from public databases. Our findings of key transcription factors and their target genes for osteoporosis were further validated by GWAS analysis. Overall, we predicted important transcription factors for osteoporosis. We were also able to infer the regulatory mechanism that exists between transcription factors and target genes in bone metabolism.

Analysis of Transcription Factor-Related Regulatory Networks Based on Bioinformatics Analysis and Validation in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) accounts for a significant proportion of liver cancer, which has become the second most common cause of cancer-related mortality worldwide. To investigate the potential mechanisms of invasion and progression of HCC, bioinformatics analysis and validation by qRT-PCR were performed. We found 237 differentially expressed genes (DEGs) including EGR1, FOS, and FOSB, which were three cancer-related transcription factors. Subsequently, we constructed TF-gene network and miRNA-TF-mRNA network based on data obtained from mRNA and miRNA expression profiles for analysis of HCC. We found that 42 key genes from the TF-gene network including EGR1, FOS, and FOSB were most enriched in the p53 signaling pathway. The qRT-PCR data confirmed that mRNA levels of EGR1, FOS, and FOSB all were decreased in HCC tissues. In addition, we confirmed that the mRNA levels of CCNB1, CCNB2, and CHEK1, three key markers of the p53 signaling pathway, were all increased in HCC tissues by bioinformatics analysis and qRT-PCR validation. Therefore, we speculated that miR-181a-5p, which was upregulated in HCC tissues, could regulate FOS and EGR1 to promote the invasion and progression of HCC by p53 signaling pathway. Overall, the study provides support for the possible mechanisms of progression in HCC.

Cancerin: A computational pipeline to infer cancer-associated ceRNA interaction networks

MicroRNAs (miRNAs) inhibit expression of target genes by binding to their RNA transcripts. It has been recently shown that RNA transcripts targeted by the same miRNA could “compete” for the miRNA molecules and thereby indirectly regulate each other. Experimental evidence has suggested that the aberration of such miRNA-mediated interaction between RNAs—called competing endogenous RNA (ceRNA) interaction—can play important roles in tumorigenesis. Given the difficulty of deciphering context-specific miRNA binding, and the existence of various gene regulatory factors such as DNA methylation and copy number alteration, inferring context-specific ceRNA interactions accurately is a computationally challenging task. Here we propose a computational method called Cancerin to identify cancer-associated ceRNA interactions. Cancerin incorporates DNA methylation, copy number alteration, gene and miRNA expression datasets to construct cancer-specific ceRNA networks. We applied Cancerin to three cancer datasets from the Cancer Genome Atlas (TCGA) project. Our results indicated that ceRNAs were enriched with cancer-related genes, and ceRNA modules in the inferred ceRNA networks were involved in cancer-associated biological processes. Using LINCS-L1000 shRNA-mediated gene knockdown experiment in breast cancer cell line to assess accuracy, Cancerin was able to predict expression outcome of ceRNA genes with high accuracy.

CeRNA interaction is a post-transcriptional gene regulation that involves interactions between RNAs competing for common miRNA regulators. Dysregulation of ceRNA interactions have been implicated in multiple diseases including cancer. Here we propose a computational pipeline called Cancerin that infers genome-wide ceRNA interactions in cancer. Unlike existing ceRNA inference tools that consider miRNAs as the only factor that regulate gene expression, Cancerin considers other types of gene regulators besides miRNAs, namely transcription factors, copy number alteration, and DNA methylation. To identify miRNA regulators for each gene, Cancerin incorporates a LASSO-based variable selection procedure that leverages both sequence-based and gene expression information. Then multiple expression-based filtering conditions are employed to select ceRNA interactions. Cancerin was applied to three cancer datasets from TCGA. Functional analysis indicated that the inferred ceRNAs were enriched with cancer-related genes, and ceRNAs within ceRNA modules (densely-connected ceRNAs) were involved in cancer-associated biological processes. Survival analysis showed that compared to non-ceRNAs, ceRNAs hold better prognostic power to predict survival outcomes. Our results showed that Cancerin can be used to identify genome-wide and functionally important ceRNA interactions, which makes it a valuable tool to better understand this recently discovered gene regulation mechanism and its role in cancer biology.

Arsenic-induced apoptosis in the p53-proficient and p53-deficient cells through differential modulation of NFkB pathway

Arsenic is a well-known environmental carcinogen and an effective chemotherapeutic agent. The underlying mechanism of this dual-effect, however, is not fully understood. In this study, we applied mouse p53^+/+ and p53^-/- cells to examine the NFκB pathway and proinflammatory cytokines after arsenic treatment. Arsenic reduced cell viability and increased more apoptosis in the p53-/- cells as compared to p53+/+ cells, which was correlated with activation of SAPK/JNK, p38 MAPK, and AKT pathways. A transcriptional regulatory network analysis revealed that arsenic activated transcription regulatory elements E2F, Egr1, Trp53, Stat6, Bcl6, Creb2 and ATF4 in the p53+/+ cells, while in the p53-/- cells, arsenic treatment altered transcription factors NFκB, Pparg, Creb2, ATF4, and Egr1. We observed dynamic changes in phosphorylated NFκB p65 (p-NFκB p65) and phosphorylated IKKαβ (p-IKKαβ) in both genotypes from 4 h to 24 h after treatment, significant decreases of p-NFκB p65 and p-IKKαβ in the p53-/- cells, whereas increases of p-NFκB p65 and p-IKKαβ were observed in the p53+/+ cells. Our study confirmed the differential modulation of NFκB pathway by arsenic in the p53+/+ or p53-/- cells and this observation of the differential mechanism of cell death between the p53+/+ and p53-/- cells might be linked to the unique ability of arsenic to act as both a carcinogen and a chemotherapeutic agent.

Landscape of transcriptional deregulation in lung cancer

BACKGROUND

Lung cancer is a very heterogeneous disease that can be pathologically classified into different subtypes including small-cell lung carcinoma (SCLC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and large-cell carcinoma (LCC). Although much progress has been made towards the oncogenic mechanism of each subtype, transcriptional circuits mediating the upstream signaling pathways and downstream functional consequences remain to be systematically studied.

RESULTS

Here we trained a one-class support vector machine (OC-SVM) model to establish a general transcription factor (TF) regulatory network containing 325 TFs and 18724 target genes. We then applied this network to lung cancer subtypes and identified those deregulated TFs and downstream targets. We found that the TP63/SOX2/DMRT3 module was specific to LUSC, corresponding to squamous epithelial differentiation and/or survival. Moreover, the LEF1/MSC module was specifically activated in LUAD and likely to confer epithelial-to-mesenchymal transition, known important for cancer malignant progression and metastasis. The proneural factor, ASCL1, was specifically up-regulated in SCLC which is known to have a neuroendocrine phenotype. Also, ID2 was differentially regulated between SCLC and LUSC, with its up-regulation in SCLC linking to energy supply for fast mitosis and its down-regulation in LUSC linking to the attenuation of immune response. We further described the landscape of TF regulation among the three major subtypes of lung cancer, highlighting their functional commonalities and specificities.

CONCLUSIONS

Our approach uncovered the landscape of transcriptional deregulation in lung cancer, and provided a useful resource of TF regulatory network for future studies.

Data Mining Mycobacterium tuberculosis Pathogenic Gene Transcription Factors and Their Regulatory Network Nodes

Tuberculosis (TB) is one of the deadliest infectious diseases worldwide. In Mycobacterium tuberculosis, changes in gene expression are highly variable and involve many genes, so traditional single-gene screening of M. tuberculosis targets has been unable to meet the needs of clinical diagnosis. In this study, using the National Center for Biotechnology Information (NCBI) GEO Datasets, whole blood gene expression profile data were obtained in patients with active pulmonary tuberculosis. Linear model-experience Bayesian statistics using the Limma package in R combined with t-tests were applied for nonspecific filtration of the expression profile data, and the differentially expressed human genes were determined. Using DAVID and KEGG, the functional analysis of differentially expressed genes (GO analysis) and the analysis of signaling pathways were performed. Based on the differentially expressed gene, the transcriptional regulatory element databases (TRED) were integrated to construct the M. tuberculosis pathogenic gene regulatory network, and the correlation of the network genes with disease was analyzed with the DAVID online annotation tool. It was predicted that IL-6, JUN, and TP53, along with transcription factors SRC, TNF, and MAPK14, could regulate the immune response, with their function being extracellular region activity and protein binding during infection with M. tuberculosis.

mRNA chip-based analysis on transcription factor regulatory network central nodes of protection targets of Deproteinized Extract of Calf Blood on acute liver injury in mice

Our previous study found that Deproteinized Extract of Calf Blood (DECB) could protect the acute liver injury induced by carbon tetrachloride in mice, but the target-related transcription factors and their regulatory networks were not comprehensively studied. Based on the mRNA expression microarray data obtained in the previous study, the mRNA transcription factor regulatory networks were constructed by screening the transcription factors of differentially expressed genes and their corresponding target proteins, and the analysis on the functions and pathways of the regulatory network central nodes was performed. Eight genes Ltf, Tnf, Il6, Jun, Il12b, Stat3, Rel and Crem could regulate the inflammatory factors, and TNF signaling pathway and Jak-STAT signaling pathway might play an important role in the mechanism through which DECB protected the liver of mice. DECB can not only inhibit the apoptosis of hepatocytes, but also inhibit the inflammatory cytokines.

TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions

Transcription factors (TFs) are major trans-acting factors in transcriptional regulation. Therefore, elucidating TF-target interactions is a key step toward understanding the regulatory circuitry underlying complex traits such as human diseases. We previously published a reference TF-target interaction database for humans-TRRUST (Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining)-which was constructed using sentence-based text mining, followed by manual curation. Here, we present TRRUST v2 (www.grnpedia.org/trrust) with a significant improvement from the previous version, including a significantly increased size of the database consisting of 8444 regulatory interactions for 800 TFs in humans. More importantly, TRRUST v2 also contains a database for TF-target interactions in mice, including 6552 TF-target interactions for 828 mouse TFs. TRRUST v2 is also substantially more comprehensive and less biased than other TF-target interaction databases. We also improved the web interface, which now enables prioritization of key TFs for a physiological condition depicted by a set of user-input transcriptional responsive genes. With the significant expansion in the database size and inclusion of the new web tool for TF prioritization, we believe that TRRUST v2 will be a versatile database for the study of the transcriptional regulation involved in human diseases.

A causal mediation model of ischemia reperfusion injury in the retina

The goal of this study is to develop a model that explains the relationship between microRNAs, transcription factors, and their co-target genes. This relationship was previously reported in gene regulatory loops associated with 24 hour (24h) and 7 day (7d) time periods following ischemia-reperfusion injury in a rat's retina. Using a model system of retinal ischemia-reperfusion injury, we propose that microRNAs first influence transcription factors, which in turn act as mediators to influence transcription of genes via triadic regulatory loops. Analysis of the relative contributions of direct and indirect regulatory influences on genes revealed that a substantial fraction of the regulatory loops (69% for 24 hours and 77% for 7 days) could be explained by causal mediation. Over 40% of the mediated loops in both time points were regulated by transcription factors only, while about 20% of the loops were regulated entirely by microRNAs. The remaining fractions of the mediated regulatory loops were cooperatively mediated by both microRNAs and transcription factors. The results from these analyses were supported by the patterns of expression of the genes, transcription factors, and microRNAs involved in the mediated loops in both post-ischemic time points. Additionally, network motif detection for the mediated loops showed a handful of time specific motifs related to ischemia-reperfusion injury in a rat's retina. In summary, the effects of microRNAs on genes are mediated, in large part, via transcription factors.

Eukaryotic and prokaryotic promoter databases as valuable tools in exploring the regulation of gene transcription: a comprehensive overview

The complete exploration of the regulation of gene expression remains one of the top-priority goals for researchers. As the regulation is mainly controlled at the level of transcription by promoters, study on promoters and findings are of great importance. This review summarizes forty selected databases that centralize experimental and theoretical knowledge regarding the organization of promoters, interacting transcription factors (TFs) and microRNAs (miRNAs) in many eukaryotic and prokaryotic species. The presented databases offer researchers valuable support in elucidating the regulation of gene transcription.

Transcriptome analysis of inflammation-related gene expression in endothelial cells activated by complement MASP-1

Mannan-binding lectin-associated serine protease 1 (MASP-1), the most abundant enzyme of the complement lectin pathway, is able to stimulate human umbilical vein endothelial cells (HUVECs) to alter the expression of several cytokines and adhesion molecules. This study has assessed to what extent MASP-1 is able to modify the transcriptional pattern of inflammation-related (IR) genes in HUVECs. We utilized Agilent microarray to analyse the effects of recombinant MASP-1 (rMASP-1) in HUVECs, on a set of 884 IR genes. Gene Set Enrichment Analysis showed an overall activation of inflammation-related genes in response to rMASP-1. rMASP-1 treatment up- and down-regulated 19 and 11 IR genes, respectively. Most of them were previously unidentified, such as genes of chemokines (CXCL1, CXCL2, CXCL3), inflammatory receptors (TLR2, BDKRB2) and other inflammatory factors (F3, LBP). Expression of IR genes changed early, during the first 2 hours of activation. Both p38-MAPK inhibitor and NFκB inhibitor efficiently suppressed the effect of rMASP-1. We delineated 12 transcriptional factors as possible regulators of rMASP-1-induced IR genes. Our microarray-based data are in line with the hypothesis that complement lectin pathway activation, generating active MASP-1, directly regulates inflammatory processes by shifting the phenotype of endothelial cells towards a more pro-inflammatory type.

Hypermethylated CpG sites in the MTR gene promoter in preterm placenta

AIM

Altered maternal one-carbon metabolism influences placental DNA methylation patterns and 'programs' the fetus for noncommunicable diseases in adult life.

EXPERIMENTAL PROCEDURES

Levels of plasma folate, vitamin B₁₂, homocysteine, mRNA and protein levels of MTHFR and MTR enzymes in placenta were compared among women delivering preterm (n = 83) and term (n = 75). MTR promoter CpG methylation was undertaken.

RESULTS

MTHFR and MTR mRNA levels were higher while protein levels were lower, and MTR CpG sites were hypermethylated in the preterm group, as compared with the term group. Methylated CpG sites were negatively associated with maternal plasma vitamin B₁₂ levels.

CONCLUSION

Study suggests a dysregulation of enzyme genes in remethylation arm of the one-carbon metabolism in placenta of women delivering preterm.

In silico analyses and global transcriptional profiling reveal novel putative targets for Pea3 transcription factor related to its function in neurons

Pea3 transcription factor belongs to the PEA3 subfamily within the ETS domain transcription factor superfamily, and has been largely studied in relation to its role in breast cancer metastasis. Nonetheless, Pea3 plays a role not only in breast tumor, but also in other tissues with branching morphogenesis, including kidneys, blood vasculature, bronchi and the developing nervous system. Identification of Pea3 target promoters in these systems are important for a thorough understanding of how Pea3 functions. Present study particularly focuses on the identification of novel neuronal targets of Pea3 in a combinatorial approach, through curation, computational analysis and microarray studies in a neuronal model system, SH-SY5Y neuroblastoma cells. We not only show that quite a number of genes in cancer, immune system and cell cycle pathways, among many others, are either up- or down-regulated by Pea3, but also identify novel targets including ephrins and ephrin receptors, semaphorins, cell adhesion molecules, as well as metalloproteases such as kallikreins, to be among potential target promoters in neuronal systems. Our overall results indicate that rather than early stages of neurite extension and axonal guidance, Pea3 is more involved in target identification and synaptic maturation.

System analysis identifies distinct and common functional networks governed by transcription factor ASCL1, in glioma and small cell lung cancer

Systematic functional network analysis of ASCL1 revealed that it regulates mitosis and cell proliferation pathways and has distinct functions in glioma and SCLC.

An Integrative Analysis of Preeclampsia Based on the Construction of an Extended Composite Network Featuring Protein-Protein Physical Interactions and Transcriptional Relationships

Preeclampsia (PE) is a pregnancy disorder defined by hypertension and proteinuria. This disease remains a major cause of maternal and fetal morbidity and mortality. Defective placentation is generally described as being at the root of the disease. The characterization of the transcriptome signature of the preeclamptic placenta has allowed to identify differentially expressed genes (DEGs). However, we still lack a detailed knowledge on how these DEGs impact the function of the placenta. The tools of network biology offer a methodology to explore complex diseases at a systems level. In this study we performed a cross-platform meta-analysis of seven publically available gene expression datasets comparing non-pathological and preeclamptic placentas. Using the rank product algorithm we identified a total of 369 DEGs consistently modified in PE. The DEGs were used as seeds to build both an extended physical protein-protein interactions network and a transcription factors regulatory network. Topological and clustering analysis was conducted to analyze the connectivity properties of the networks. Finally both networks were merged into a composite network which presents an integrated view of the regulatory pathways involved in preeclampsia and the crosstalk between them. This network is a useful tool to explore the relationship between the DEGs and enable hypothesis generation for functional experimentation.

A comparative analysis reveals the dosage sensitivity and regulatory patterns of lncRNA in prostate cancer

Although the key roles of long non-coding RNAs (lncRNAs) in multiple diseases are well documented, the relationship between the lncRNA copy number and expression is unknown. Here, we present a comprehensive study that demonstrates the impact of miRNA-TF co-regulatory motifs on the dosage effects of protein-coding genes (PCGs) and lncRNAs in prostate cancer. By integrating copy number profiles, expression profiles and regulatory relationships with miRNAs and transcription factors, we reveal that lncRNAs and PCGs correlate with distinct dosage sensitivity and regulatory pattern characteristics. We also show that lncRNAs from different genomic regions have different features. Using a custom-built framework, we identified 24 candidate prostate cancer-related lncRNAs based on the known properties of established prostate-related lncRNAs. Our method will enable the identification of cancer-related lncRNAs, which will provide new insights into cancer lncRNA biology.

ORTI: An Open-Access Repository of Transcriptional Interactions for Interrogating Mammalian Gene Expression Data

Transcription factors (TFs) play a fundamental role in coordinating biological processes in response to stimuli. Consequently, we often seek to determine the key TFs and their regulated target genes (TGs) amidst gene expression data. This requires a knowledge-base of TF-TG interactions, which would enable us to determine the topology of the transcriptional network and predict novel regulatory interactions. To address this, we generated an Open-access Repository of Transcriptional Interactions, ORTI, by integrating available TF-TG interaction databases. These databases rely on different types of experimental evidence, including low-throughput assays, high-throughput screens, and bioinformatics predictions. We have subsequently categorised TF-TG interactions in ORTI according to the quality of this evidence. To demonstrate its capabilities, we applied ORTI to gene expression data and identified modulated TFs using an enrichment analysis. Combining this with pairwise TF-TG interactions enabled us to visualise temporal regulation of a transcriptional network. Additionally, ORTI enables the prediction of novel TF-TG interactions, based on how well candidate genes co-express with known TGs of the target TF. By filtering out known TF-TG interactions that are unlikely to occur within the experimental context, this analysis predicts context-specific TF-TG interactions. We show that this can be applied to experimental designs of varying complexities. In conclusion, ORTI is a rich and publicly available database of experimentally validated mammalian transcriptional interactions which is accompanied with tools that can identify and predict transcriptional interactions, serving as a useful resource for unravelling the topology of transcriptional networks.

Multi-omics analysis reveals regulators of the response to PDGF-BB treatment in pulmonary artery smooth muscle cells

Background

Pulmonary arterial hypertension (PAH) is a lethal disease with pronounced narrowing of pulmonary vessels due to abnormal cell proliferation. The platelet-derived growth factor BB (PDGF-BB) is well known as a potent mitogen for smooth muscle cell proliferation. To better understand how this growth factor regulates pulmonary arterial smooth muscle cells (PASMCs) proliferation, we sought to characterize the response to PDGF-BB stimulation at system-wide levels, including the transcriptome and proteome.

Results

In this study, we identified 1611 mRNAs (transcriptome), 207 proteins (proteome) differentially expressed in response to PDGF-BB stimulation in PASMCs based on RNA-sequencing and isobaric tags for relative and absolute quantification (iTRAQ) assay. Transcription factor (TF)-target network analysis revealed that PDGF-BB regulated gene expression potentially via TFs including HIF1A, JUN, EST1, ETS1, SMAD1, FOS, SP1, STAT1, LEF1 and CEBPB. Among them, SMAD1-involved BMPR2/SMADs axis plays a significant role in PAH development. Interestingly, we observed that the expression of BMPR2 was decreased in both mRNA and protein level in response to PDGF-BB. Further study revealed that BMPR2 is the direct target of miR-376b that is up-regulated upon PDGF-BB treatment. Finally, EdU incorporation assay showed that miR-376b promoted proliferation of PASMCs.

Conclusion

This integrated analysis of PDGF-BB-regulated transcriptome and proteome was performed for the first time in normal PASMCs, which revealed a crosstalk between PDGF signaling and BMPR2/SMADs axis. Further study demonstrated that PDGF-BB-induced miR-376b upregulation mediated the downregulation of BMPR2, which led to expression change of its downstream targets and promoted proliferation of PASMCs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3122-3) contains supplementary material, which is available to authorized users.

Integrative analysis of cancer genes in a functional interactome

The post-genomic era has resulted in the accumulation of high-throughput cancer data from a vast array of genomic technologies including next-generation sequencing and microarray. As such, the large amounts of germline variant and somatic mutation data that have been generated from GWAS and sequencing projects, respectively, show great promise in providing a systems-level view of these genetic aberrations. In this study, we analyze publicly available GWAS, somatic mutation, and drug target data derived from large databanks using a network-based approach that incorporates directed edge information under a randomized network hypothesis testing procedure. We show that these three classes of disease-associated nodes exhibit non-random topological characteristics in the context of a functional interactome. Specifically, we show that drug targets tend to lie upstream of somatic mutations and disease susceptibility germline variants. In addition, we introduce a new approach to measuring hierarchy between drug targets, somatic mutants, and disease susceptibility genes by utilizing directionality and path length information. Overall, our results provide new insight into the intrinsic relationships between these node classes that broaden our understanding of cancer. In addition, our results align with current knowledge on the therapeutic actionability of GWAS and somatic mutant nodes, while demonstrating relationships between node classes from a global network perspective.

Identifying network-based biomarkers of complex diseases from high-throughput data

In this work, we review the main available computational methods of identifying biomarkers of complex diseases from high-throughput data. The emerging omics techniques provide powerful alternatives to measure thousands of molecules in cells in parallel manners. The generated genomic, transcriptomic, proteomic, metabolomic and phenomic data provide comprehensive molecular and cellular information for detecting critical signals served as biomarkers by classifying disease phenotypic states. Networks are often employed to organize these profiles in the identification of biomarkers to deal with complex diseases in diagnosis, prognosis and therapy as well as mechanism deciphering from systematic perspectives. Here, we summarize some representative network-based bioinformatics methods in order to highlight the importance of computational strategies in biomarker discovery.

WT1 targets Gas1 to maintain nephron progenitor cells by modulating FGF signals

Development of the metanephric kidney depends on tightly regulated interplay between self-renewal and differentiation of a nephron progenitor cell (NPC) pool. Several key factors required for the survival of NPCs have been identified, including fibroblast growth factor (FGF) signaling and the transcription factor Wilms' tumor suppressor 1 (WT1). Here, we present evidence that WT1 modulates FGF signaling by activating the expression of growth arrest-specific 1 (Gas1), a novel WT1 target gene and novel modulator of FGF signaling. We show that WT1 directly binds to a conserved DNA binding motif within the Gas1 promoter and activates Gas1 mRNA transcription in NPCs. We confirm that WT1 is required for Gas1 expression in kidneys in vivo. Loss of function of GAS1 in vivo results in hypoplastic kidneys with reduced nephron mass due to premature depletion of NPCs. Although kidney development in Gas1 knockout mice progresses normally until E15.5, NPCs show decreased rates of proliferation at this stage and are depleted as of E17.5. Lastly, we show that Gas1 is selectively required for FGF-stimulated AKT signaling in vitro. In summary, our data suggest a model in which WT1 modulates receptor tyrosine kinase signaling in NPCs by directing the expression of Gas1.

Testis-Specific Y-Centric Protein-Protein Interaction Network Provides Clues to the Etiology of Severe Spermatogenic Failure

Pinpointing causal genes for spermatogenic failure (SpF) on the Y chromosome has been an ever daunting challenge with setbacks during the past decade. Since complex diseases result from the interaction of multiple genes and also display considerable missing heritability, network analysis is more likely to explicate an etiological molecular basis. We therefore took a network medicine approach by integrating interactome (protein-protein interaction (PPI)) and transcriptome data to reconstruct a Y-centric SpF network. Two sets of seed genes (Y genes and SpF-implicated genes (SIGs)) were used for network reconstruction. Since no PPI was observed among Y genes, we identified their common immediate interactors. Interestingly, 81% (N = 175) of these interactors not only interacted directly with SIGs, but also they were enriched for differentially expressed genes (89.6%; N = 43). The SpF network, formed mainly by the dys-regulated interactors and the two seed gene sets, comprised three modules enriched for ribosomal proteins and nuclear receptors for sex hormones. Ribosomal proteins generally showed significant dys-regulation with RPL39L, thought to be expressed at the onset of spermatogenesis, strongly down-regulated. This network is the first global PPI network pertaining to severe SpF and if experimentally validated on independent data sets can lead to more accurate diagnosis and potential fertility recovery of patients.

A canonical correlation analysis-based dynamic bayesian network prior to infer gene regulatory networks from multiple types of biological data

One of the challenging and important computational problems in systems biology is to infer gene regulatory networks (GRNs) of biological systems. Several methods that exploit gene expression data have been developed to tackle this problem. In this study, we propose the use of copy number and DNA methylation data to infer GRNs. We developed an algorithm that scores regulatory interactions between genes based on canonical correlation analysis. In this algorithm, copy number or DNA methylation variables are treated as potential regulator variables, and expression variables are treated as potential target variables. We first validated that the canonical correlation analysis method is able to infer true interactions in high accuracy. We showed that the use of DNA methylation or copy number datasets leads to improved inference over steady-state expression. Our results also showed that epigenetic and structural information could be used to infer directionality of regulatory interactions. Additional improvements in GRN inference can be gleaned from incorporating the result in an informative prior in a dynamic Bayesian algorithm. This is the first study that incorporates copy number and DNA methylation into an informative prior in dynamic Bayesian framework. By closely examining top-scoring interactions with different sources of epigenetic or structural information, we also identified potential novel regulatory interactions.

Long Noncoding RNA MEG3 Interacts with p53 Protein and Regulates Partial p53 Target Genes in Hepatoma Cells

Maternally Expressed Gene 3 (MEG3) encodes a lncRNA which is suggested to function as a tumor suppressor. Previous studies suggested that MEG3 functioned through activation of p53, however, the functional properties of MEG3 remain obscure and their relevance to human diseases is under continuous investigation. Here, we try to illuminate the relationship of MEG3 and p53, and the consequence in hepatoma cells. We find that transfection of expression construct of MEG3 enhances stability and transcriptional activity of p53. Deletion analysis of MEG3 confirms that full length and intact structure of MEG3 are critical for it to activate p53-mediated transactivation. Interestingly, our results demonstrate for the first time that MEG3 can interact with p53 DNA binding domain and various p53 target genes are deregulated after overexpression of MEG3 in hepatoma cells. Furthermore, results of qRT-PCR have shown that MEG3 RNA is lost or reduced in the majority of HCC samples compared with adjacent non-tumorous samples. Ectopic expression of MEG3 in hepatoma cells significantly inhibits proliferation and induces apoptosis. In conclusion, our data demonstrates that MEG3 functions as a tumor suppressor in hepatoma cells through interacting with p53 protein to activate p53-mediated transcriptional activity and influence the expression of partial p53 target genes.

RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse

Transcriptional and post-transcriptional regulation of gene expression is of fundamental importance to numerous biological processes. Nowadays, an increasing amount of gene regulatory relationships have been documented in various databases and literature. However, to more efficiently exploit such knowledge for biomedical research and applications, it is necessary to construct a genome-wide regulatory network database to integrate the information on gene regulatory relationships that are widely scattered in many different places. Therefore, in this work, we build a knowledge-based database, named ‘RegNetwork’, of gene regulatory networks for human and mouse by collecting and integrating the documented regulatory interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes from 25 selected databases. Moreover, we also inferred and incorporated potential regulatory relationships based on transcription factor binding site (TFBS) motifs into RegNetwork. As a result, RegNetwork contains a comprehensive set of experimentally observed or predicted transcriptional and post-transcriptional regulatory relationships, and the database framework is flexibly designed for potential extensions to include gene regulatory networks for other organisms in the future. Based on RegNetwork, we characterized the statistical and topological properties of genome-wide regulatory networks for human and mouse, we also extracted and interpreted simple yet important network motifs that involve the interplays between TF-miRNA and their targets. In summary, RegNetwork provides an integrated resource on the prior information for gene regulatory relationships, and it enables us to further investigate context-specific transcriptional and post-transcriptional regulatory interactions based on domain-specific experimental data.

Database URL: http://www.regnetworkweb.org

Identification of Biomarker and Co-Regulatory Motifs in Lung Adenocarcinoma Based on Differential Interactions

Changes in intermolecular interactions (differential interactions) may influence the progression of cancer. Specific genes and their regulatory networks may be more closely associated with cancer when taking their transcriptional and post-transcriptional levels and dynamic and static interactions into account simultaneously. In this paper, a differential interaction analysis was performed to detect lung adenocarcinoma-related genes. Furthermore, a miRNA-TF (transcription factor) synergistic regulation network was constructed to identify three kinds of co-regulated motifs, namely, triplet, crosstalk and joint. Not only were the known cancer-related miRNAs and TFs (let-7, miR-15a, miR-17, TP53, ETS1, and so on) were detected in the motifs, but also the miR-15, let-7 and miR-17 families showed a tendency to regulate the triplet, crosstalk and joint motifs, respectively. Moreover, several biological functions (i.e., cell cycle, signaling pathways and hemopoiesis) associated with the three motifs were found to be frequently targeted by the drugs for lung adenocarcinoma. Specifically, the two 4-node motifs (crosstalk and joint) based on co-expression and interaction had a closer relationship to lung adenocarcinoma, and so further research was performed on them. A 10-gene biomarker (UBC, SRC, SP1, MYC, STAT3, JUN, NR3C1, RB1, GRB2 and MAPK1) was selected from the joint motif, and a survival analysis indicated its significant association with survival. Among the ten genes, JUN, NR3C1 and GRB2 are our newly detected candidate lung adenocarcinoma-related genes. The genes, regulators and regulatory motifs detected in this work will provide potential drug targets and new strategies for individual therapy.