Identification of Regulatory Modules That Stratify Lupus Disease Mechanism through Integrating Multi-Omics Data.
ABSTRACT: Although recent advances in genetic studies have shed light on systemic lupus erythematosus (SLE), its detailed mechanisms remain elusive. In this study, using datasets on SLE transcriptomic profiles, we identified 750 differentially expressed genes (DEGs) in T and B lymphocytes and peripheral blood cells. Using transcription factor (TF) binding data derived from chromatin immunoprecipitation sequencing (ChIP-seq) experiments from the Encyclopedia of DNA Elements (ENCODE) project, we inferred networks of co-regulated genes (NcRGs) based on binding profiles of the upregulated DEGs by significantly enriched TFs. Modularization analysis of NcRGs identified co-regulatory modules among the DEGs and master TFs vital for each module. Remarkably, the co-regulatory modules stratified the common SLE interferon (IFN) signature and revealed SLE pathogenesis pathways, including the complement cascade, cell cycle regulation, NETosis, and epigenetic regulation. By integrative analyses of disease-associated genes (DAGs), DEGs, and enriched TFs, as well as proteins interacting with them, we identified a hierarchical regulatory cascade with TFs regulated by DAGs, which in turn regulates gene expression. Integrative analysis of multi-omics data provided valuable molecular insights into the molecular mechanisms of SLE.
Project description:This study aimed to gain a better understanding of the molecular circuitry of Schmid-type metaphyseal chondrodysplasia (SMCD), and to identify more potential genes associated with the pathogenesis of SMCD. Microarray data from GSE72261 were downloaded from the NCBI GEO database, including collagen X p.Asn617Lys knock-in mutation (ColXN617K), ablated XBP1 activity (Xbp1Cart?Ex2), compound mutant (C/X), and wild-type (WT) specimens. Differentially expressed genes (DEGs) were screened in Xbp1 vs. WT, Col vs. WT and CX vs. WT, respectively. Pathway enrichment analysis of these DEGs was performed. Transcription factors (TFs) of the overlapping DEGs were identified. Weighted correlation network analysis (WGCNA) was performed to find modules of DEGs with high correlations, followed by gene function analysis and a protein-protein interaction network construction. In total, 481, 1,530 and 1,214 DEGs were identified in Xbp1 vs. WT, Col vs. WT and CX vs. WT, respectively. These DEGs were enriched in different pathways, such as extracellular matrix (ECM)-receptor interaction and metabolism-related pathways. A total of 7 TFs were found to regulate 19 common upregulated genes, and 4 TFs were identified to regulate 21 common downregulated genes. Two significant gene co-expression modules were enriched and DEGs in the 2 modules were mainly enriched in different biological processes, such as ribosome biogenesis. Moreover, Kras (downregulated), Col5a1 (upregulated) and Furin (upregulated) were both identified in the regulatory networks and protein-protein interaction (PPI) network. On the whole, our findings indicate that the Kras, Col5a1 and Furin genes may play essential roles in the molecular mechanisms of SMCD, which warrants further investigation.
Project description:Sepsis is a type of systemic inflammatory response caused by infection. The present study aimed to identify novel targets for the treatment of sepsis. We conducted bioinformatic analysis of the microarray Gene Expression Omnibus dataset GSE12624, which includes data on 34 patients with sepsis and 36 healthy individuals without sepsis. Differentially expressed genes (DEGs) in sepsis patients were identified using Bayesian methods included in the limma package in R. Correlations among the expression values of DEGs were analyzed using the weighted gene co?expression network analysis (WGCNA) to construct a co?expression network. Subsequently, the generated co?expression network was visualized using Cytoscape 3.3 software. Additionally, a protein?protein interaction (PPI) network was constructed based on all the DEGs using STRING. Finally, the integrated regulatory network was constructed based on DEGs, microRNAs (miRNAs) and transcription factors (TFs). A total of 407 DEGs were identified in the sepsis samples, including 227 upregulated DEGs and 180 downregulated DEGs. WGCNA grouped the DEGs into 13 co?expressed modules. Additionally, MAP3K8 and RPS6KA5 in the MEyellow module were enriched in the MAPK and TNF signaling pathways. In addition, the PPI network comprised 48 nodes and 112 edges, which included the pairs MAP3K8?RPS6KA5, MAP3K8?IL10, RPS6KA5?EXOSC4 and EXOSC4?EXOSC5. Lastly, the TF?miRNA?target DEG regulatory network was constructed based on eight TFs (NF??B), seven miRNAs (miR152, miR?148A/B), and 52 TF?miRNA?target gene triplets (17 upregulated genes, including MAP3K8, and 10 downregulated genes, including RPS6KA5). Our analysis showed that the members of the miR?148 family (miR?148A/B and miR?152) are candidate biomarkers for sepsis.
Project description:Prostate cancer is a global health issue. Usually, men with metastatic disease will progress to castration-resistant prostate cancer (CRPC). We aimed to identify the differentially expressed genes (DEGs) in tumor samples from non-castrated and castrated men from LNCaP Orthotopic xenograft models of prostate cancer and to study the mechanisms of CRPC.In this work, GSE46218 containing 4 samples from non-castrated men and 4 samples from castrated men was downloaded from Gene Expression Omnibus. We identified DEGs using limma Geoquery in R, the Robust Multi-array Average (RMA) method in Bioconductor, and Bias methods, followed by constructing an integrated regulatory network involving DEGs, miRNAs, and TFs using Cytoscape. Then, we analyzed network motifs of the integrated gene regulatory network using FANMOD. We selected regulatory modules corresponding to network motifs from the integrated regulatory network by Perl script. We preformed gene ontology (GO) and pathway enrichment analysis of DEGs in the regulatory modules using DAVID.We identified total 443 DEGs. We built an integrated regulatory network, found three motifs (motif 1, motif 2 and motif 3), and got two function modules (module 1 corresponded to motif 1, and module 2 corresponded to motif 2). Several GO terms (such as regulation of cell proliferation, positive regulation of macromolecule metabolic process, phosphorylation, and phosphorus metabolic process) and two pathways (pathway in cancer and Melanoma) were enriched. Furthermore, some significant DEGs (such as CAV1, LYN, FGFR3 and FGFR3) were related to CPRC development.These genes might play important roles in the development and progression of CRPC.
Project description:Site-specific transcription factors (TFs) are coordinators of developmental and physiological gene expression programs. Their binding to cis-regulatory modules of target genes mediates the precise cell- and context-specific activation and repression of genes. The expression of TFs should therefore reflect the core expression program of each cell.We studied the expression dynamics of about 750 TFs using the available genomics resources in Drosophila melanogaster. We find that 95% of these TFs are expressed at some point during embryonic development, with a peak roughly between 10 and 12 hours after egg laying, the core stages of organogenesis. We address the differential utilization of DNA-binding domains in different developmental programs systematically in a spatio-temporal context, and show that the zinc finger class of TFs is predominantly early expressed, while Homeobox TFs exhibit later expression in embryogenesis.Previous work, dissecting cis-regulatory modules during Drosophila development, suggests that TFs are deployed in groups acting in a cooperative manner. In contrast, we find that there is rapid exchange of co-expressed partners amongst the fly TFs, at rates similar to the genome-wide dynamics of co-expression clusters. This suggests there may also be a high level of combinatorial complexity of TFs at cis-regulatory modules.
Project description:The combinatorial binding of trans-acting factors (TFs) to the DNA is critical to the spatial and temporal specificity of gene regulation. For certain regulatory regions, more than one regulatory module (set of TFs that bind together) are combined to achieve context-specific gene regulation. However, previous approaches are limited to either pairwise TF co-association analysis or assuming that only one module is used in each regulatory region.We present a new computational approach that models the modular organization of TF combinatorial binding. Our method learns compact and coherent regulatory modules from in vivo binding data using a topic model. We found that the binding of 115 TFs in K562 cells can be organized into 49 interpretable modules. Furthermore, we found that tens of thousands of regulatory regions use multiple modules, a structure that cannot be observed with previous hard clustering based methods. The modules discovered recapitulate many published protein-protein physical interactions, have consistent functional annotations of chromatin states, and uncover context specific co-binding such as gene proximal binding of NFY?+?FOS?+?SP and distal binding of NFY?+?FOS?+?USF. For certain TFs, the co-binding partners of direct binding (motif present) differs from those of indirect binding (motif absent); the distinct set of co-binding partners can predict whether the TF binds directly or indirectly with up to 95% accuracy. Joint analysis across two cell types reveals both cell-type-specific and shared regulatory modules.Our results provide comprehensive cell-type-specific combinatorial binding maps and suggest a modular organization of combinatorial binding.
Project description:BACKGROUND Many heart failure (HF) cases are caused by idiopathic dilated cardiomyopathy (iDCM). This study explored the mechanisms of the development and progression of HF caused by iDCM. MATERIAL AND METHODS The gene expression profiles of 102 samples were downloaded from the GEO database (GSE5406). Differentially expressed genes (DEGs) were identified through GO analysis and a KEGG pathway analysis, respectively. A protein-protein interaction (PPI) network was constructed and analyzed to screen potential regulatory proteins. In addition, MCODE and a cytoHubba plugin were used to identify the module and hub genes of DEGs. Finally, transcription factors (TFs) were predicted using PASTAA. We did not perform whole-exome sequencing (WES) for detecting mitochondrial DNA (mtDNA). RESULTS A total of 197 DEGs were screened, and 3 modules, and 4 upregulated and 11 downregulated hub genes were screened. The GO analysis focused on the terms and 12 KEGG pathways were enriched. The FOS, TIMP1, and SERPINE1 hub genes, as well as some key TFs, demonstrated important roles in the progression of HF caused by iDCM. CEBPD, CEBOB, CDC37L1, and SRGN may be new targets for HF in iDCM patients. CONCLUSIONS The identified DEGs and their enriched pathways provide references for exploring the mechanisms of the development and progression of HF patients with iDCM. Moreover, modules, hub genes, and TFs may be useful in the treatment and diagnosis of HF patients with iDCM. However, mtDNA was not investigated.
Project description:This study aimed to explore significant genes and pathways involved in the pathogenesis of supratentorial primitive neuroectodermal tumor (sPNET).Gene expression profile of GSE14295 was downloaded from publicly available Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened out in primary sPNET samples compared with normal fetal and adult brain reference samples (sPNET vs fetal brain and sPNET vs adult brain). Pathway enrichment analysis of these DEGs was conducted, followed by protein-protein interaction (PPI) network construction and significant module selection. Additionally, transcription factors (TFs) regulating the common DEGs in the two comparison groups were identified, and the regulatory network was constructed.In total, 526 DEGs (99 up- and 427 downregulated) in sPNET vs fetal brain and 815 DEGs (200 up- and 615 downregulated) in sPNET vs adult brain were identified. DEGs in sPNET vs fetal brain and sPNET vs adult brain were associated with calcium signaling pathway, cell cycle, and p53 signaling pathway. CDK1, CDC20, BUB1B, and BUB1 were hub nodes in the PPI networks of DEGs in sPNET vs fetal brain and sPNET vs adult brain. Significant modules were extracted from the PPI networks. In addition, 64 upregulated and 200 downregulated overlapping DEGs were identified in both sPNET vs fetal brain and sPNET vs adult brain. The genes involved in the regulatory network upon overlapping DEGs and the TFs were correlated with calcium signaling pathway.Calcium signaling pathway and several genes (CDK1, CDC20, BUB1B, and BUB1) may play important roles in the pathogenesis of sPNET.
Project description:Autism spectrum disorder (ASD) is a complex neurodevelopmental disease in early childhood, and growing up to be a major cause of disability in children. However, the underlying molecular mechanism of ASD remains elusive. Hence, we represented integrated multifactor analysis exploring dysfunctional modules based on RNA-Seq data from corpus callosum in 6 patients with ASD and 6 normal individuals. According to protein-protein interactions (PPIs) and WGCNA, we performed co-expression modules analysis for ASD-associated genes, and identified 25 modules with differentially expressed genes (DEGs), observing that genes in these modules were significantly involved in various biological processes in nervous system, sensory system, phylogenetic system and variety of signaling pathways. Then, based on transcriptional and post-transcriptional regulations, integrating transcription factor (TF)-target and RNA-associated interactions, significant regulators of co-expression modules were identified as pivot regulators, including 67 pivot TFs, 13 pivot miRNAs and 6 pivot lncRNAs. GO and KEGG pathway enrichment analysis demonstrated that the pivot miRNAs significantly enriched in neural or mental-associated biological progresses. The pivot TFs were mainly involved in various regulation of transcription, immune system and organs development. Finally, our work deciphered a multifactor dysfunctional co-expression subnetwork involved in ASD, helps uncover core dysfunctional modules for this disease and improves our understanding of its underlying molecular mechanism.
Project description:Vernalization and the transition from vegetative to reproductive growth involve multiple pathways, vital for controlling floral organ formation and flowering time. However, little transcription information is available about the mechanisms behind environmental adaption and growth regulation. Here, we used high-throughput sequencing to analyze the comprehensive transcriptome of Dactylis glomerata L. during six different growth periods.During vernalization, 4689 differentially expressed genes (DEGs) significantly increased in abundance, while 3841 decreased. Furthermore, 12,967 DEGs were identified during booting stage and flowering stage, including 7750 up-regulated and 5219 down-regulated DEGs. Pathway analysis indicated that transcripts related to circadian rhythm, photoperiod, photosynthesis, flavonoid biosynthesis, starch, and sucrose metabolism changed significantly at different stages. Coexpression and weighted correlation network analysis (WGCNA) analysis linked different stages to transcriptional changes and provided evidence of inner relation modules associated with signal transduction, stress responses, cell division, and hormonal transport.We found enrichment in transcription factors (TFs) related to WRKY, NAC, AP2/EREBP, AUX/IAA, MADS-BOX, ABI3/VP1, bHLH, and the CCAAT family during vernalization and floral bud development. TFs expression patterns revealed intricate temporal variations, suggesting relatively separate regulatory programs of TF modules. Further study will unlock insights into the ability of the circadian rhythm and photoperiod to regulate vernalization and flowering time in perennial grass.
Project description:BackgroundLung adenocarcinoma (LUAD) is one of the most common cancers worldwide. The etiology and pathophysiology of LUAD remain unclear. The aim of the present study was to identify the key genes, miRNAs and transcription factors (TFs) associated with the pathogenesis and prognosis of LUAD.MethodsThree gene expression profiles (GSE43458, GSE32863, GSE74706) of LUAD were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by GEO2R.The Gene Ontology (GO) terms, pathways, and protein-protein interactions (PPIs) of these DEGs were analyzed. Bases on DEGs, the miRNAs and TFs were predicted. Furthermore, TF-gene-miRNA co-expression network was constructed to identify key genes, miRNAs and TFs by bioinformatic methods. The expressions and prognostic values of key genes, miRNAs and TFs were carried out through The Cancer Genome Atlas (TCGA) database and Kaplan Meier-plotter (KM) online dataset.ResultsA total of 337 overlapped DEGs (75 upregulated and 262 downregulated) of LUAD were identified from the three GSE datasets. Moreover, 851 miRNAs and 29 TFs were identified to be associated with these DEGs. In total, 10 hub genes, 10 key miRNAs and 10 key TFs were located in the central hub of the TF-gene-miRNA co-expression network, and validated using The Cancer Genome Atlas (TCGA) database. Specifically, seven genes (PHACTR2, MSRB3, GHR, PLSCR4, EPB41L2, NPNT, FBXO32), two miRNAs (hsa-let-7e-5p, hsa-miR-17-5p) and four TFs (STAT6, E2F1, ETS1, JUN) were identified to be associated with prognosis of LUAD, which have significantly different expressions between LUAD and normal lung tissue. Additionally, the miRNA/gene co-expression analysis also revealed that hsa-miR-17-5p and PLSCR4 have a significant negative co-expression relationship (r=?0.33, P=1.67e-14) in LUAD.ConclusionsOur study constructed a regulatory network of TF-gene-miRNA in LUAD, which may provide new insights about the interaction between genes, miRNAs and TFs in the pathogenesis of LUAD, and identify potential biomarkers or therapeutic targets for LUAD.