Dataset Information

Expression-based network biology identifies alteration in key regulatory pathways of type 2 diabetes and associated risk/complications.

ABSTRACT: Type 2 diabetes mellitus (T2D) is a multifactorial and genetically heterogeneous disease which leads to impaired glucose homeostasis and insulin resistance. The advanced form of disease causes acute cardiovascular, renal, neurological and microvascular complications. Thus there is a constant need to discover new and efficient treatment against the disease by seeking to uncover various novel alternate signalling mechanisms that can lead to diabetes and its associated complications. The present study allows detection of molecular targets by unravelling their role in altered biological pathways during diabetes and its associated risk factors and complications. We have used an integrated functional networks concept by merging co-expression network and interaction network to detect the transcriptionally altered pathways and regulations involved in the disease. Our analysis reports four novel significant networks which could lead to the development of diabetes and other associated dysfunctions. (a) The first network illustrates the up regulation of TGFBRII facilitating oxidative stress and causing the expression of early transcription genes via MAPK pathway leading to cardiovascular and kidney related complications. (b) The second network demonstrates novel interactions between GAPDH and inflammatory and proliferation candidate genes i.e., SUMO4 and EGFR indicating a new link between obesity and diabetes. (c) The third network portrays unique interactions PTPN1 with EGFR and CAV1 which could lead to an impaired vascular function in diabetic nephropathy condition. (d) Lastly, from our fourth network we have inferred that the interaction of beta-catenin with CDH5 and TGFBR1 through Smad molecules could contribute to endothelial dysfunction. A probability of emergence of kidney complication might be suggested in T2D condition. An experimental investigation on this aspect may further provide more decisive observation in drug target identification and better understanding of the pathophysiology of T2D and its complications.

SUBMITTER: Sengupta U

PROVIDER: S-EPMC2785475 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Expression-based network biology identifies alteration in key regulatory pathways of type 2 diabetes and associated risk/complications.

Sengupta Urmi U Ukil Sanchaita S Dimitrova Nevenka N Agrawal Shipra S

PloS one 20091207 12

Type 2 diabetes mellitus (T2D) is a multifactorial and genetically heterogeneous disease which leads to impaired glucose homeostasis and insulin resistance. The advanced form of disease causes acute cardiovascular, renal, neurological and microvascular complications. Thus there is a constant need to discover new and efficient treatment against the disease by seeking to uncover various novel alternate signalling mechanisms that can lead to diabetes and its associated complications. The present st ...[more]

PMID: 19997558

Similar Datasets

Project description:Abstract AbstractBackground: Type II diabetes mellitus (T2DM), characterized by fasting hyperglycemia and impaired insulin secretion and action, is a global health burden. Despite the advances in this field, the mechanism underlying T2DM is far from clear. Objective: The present study sheds light upon a systematic evaluation of the genes, pathways, and interaction networks underlying T2DM with the aid of bioinformatics. Methods: Two Gene Expression Omnibus microarray datasets: GSE148961 and GSE26168 were selected for this study. The common differentially expressed genes (DEG) were sorted by the cutoff |logFC|≥1.0 for the first dataset and |logFC|≥0.263 for the second. Gene Ontology (GO), functional enrichment, and protein-protein interaction (PPI) network were analyzed in Search Tool for the Retrieval of Interacting Genes (STRING). The MCODE and CytoHubba plugins in Cytoscape (v3.7.2) were used to identify gene clusters and top hub genes, respectively. Top 10 nodes were ranked in CytoHubba according to MCC, DMNC, MNC, Degree, and EPC methods, and genes common in at least 3 methods were selected as top nodes. Results: 88 common DEGs were identified by Venn diagram (http://bioinformatics.psb.ugent.be/cgi-bin/liste/Venn/calculate_venn.htpl). GO analysis had 91 significantly enriched biological processes, including regulated exocytosis, secretion, vesicle mediated transport, antibacterial humoral response, and neutrophil degranulation. 4 molecular functions- fibrinogen binding, fibronectin binding, lipopolysaccharide binding, and Extracellular matrix binding; and 38 cellular components, including secretory vesicle, endomembrane system, and adherens junction were significant. The PPI network was highly significant (p-value < 0.001) at medium confidence (0.400) with 88 nodes, 140 edges, and an average node degree of 3.18. The MCODE plugin revealed two clusters, the former with 14 nodes, 73 edges, and a score of 9.733, and the latter with 6 nodes, 14 edges, and a score of 4.000. 9 candidate genes: ELANE, DEFA4, BPI, MPO, LTF, CAMP, OLFM4, LCN2, and VCL were identified, amongst which ELANE, LCN2, and MPO are associated with T2DM pathogenesis, while BPI and LTF have protective effects. OLFM4 deletion has been observed to improve glucose tolerance in mice models. Conclusion: This study provides a comprehensive analysis of genes, pathways, and functions which may be pivotal in T2DM pathogenesis and may represent potential therapeutic targets.

Project description:ObjectiveThe transcription factor networks that drive parotid salivary gland progenitor cells to terminally differentiate, remain largely unknown and are vital to understanding the regeneration process.MethodologyA systems biology approach was taken to measure mRNA and microRNA expression in vivo across acinar cell terminal differentiation in the rat parotid salivary gland. Laser capture microdissection (LCM) was used to specifically isolate acinar cell RNA at times spanning the month-long period of parotid differentiation.ResultsClustering of microarray measurements suggests that expression occurs in four stages. mRNA expression patterns suggest a novel role for Pparg which is transiently increased during mid postnatal differentiation in concert with several target gene mRNAs. 79 microRNAs are significantly differentially expressed across time. Profiles of statistically significant changes of mRNA expression, combined with reciprocal correlations of microRNAs and their target mRNAs, suggest a putative network involving Klf4, a differentiation inhibiting transcription factor, which decreases as several targeting microRNAs increase late in differentiation. The network suggests a molecular switch (involving Prdm1, Sox11, Pax5, miR-200a, and miR-30a) progressively decreases repression of Xbp1 gene transcription, in concert with decreased translational repression by miR-214. The transcription factor Xbp1 mRNA is initially low, increases progressively, and may be maintained by a positive feedback loop with Atf6. Transfection studies show that Xbp1 activates the Mist1 promoter [corrected]. In addition, Xbp1 and Mist1 each activate the parotid secretory protein (Psp) gene, which encodes an abundant salivary protein, and is a marker of terminal differentiation.ConclusionThis study identifies novel expression patterns of Pparg, Klf4, and Sox11 during parotid acinar cell differentiation, as well as numerous differentially expressed microRNAs. Network analysis identifies a novel stemness arm, a genetic switch involving transcription factors and microRNAs, and transition to an Xbp1 driven differentiation network. This proposed network suggests key regulatory interactions in parotid gland terminal differentiation.

Project description:Plasmablastic lymphoma (PBL) represents a clinically heterogeneous subtype of aggressive B-cell non-Hodgkin lymphoma. Targeted-sequencing studies and a single-center whole-exome sequencing (WES) study in HIV-positive patients recently revealed several genes associated with PBL pathogenesis; however, the global mutational landscape and transcriptional profile of PBL remain elusive. To inform on disease-associated mutational drivers, mutational patterns, and perturbed pathways in HIV-positive and HIV-negative PBL, we performed WES and transcriptome sequencing (RNA-sequencing) of 33 PBL tumors. Integrative analysis of somatic mutations and gene expression profiles was performed to acquire insights into the divergent genotype-phenotype correlation in Epstein-Barr virus-positive (EBV+) and EBV- PBL. We describe a significant accumulation of mutations in the JAK signal transducer and transcription activator (OSMR, STAT3, PIM1, and SOCS1), as well as receptor tyrosine-kinase RAS (ERBB3, NRAS, PDGFRB, and NTRK) pathways. We provide further evidence of frequent perturbances of NF-κB signaling (NFKB2 and BTK). Induced pathways, identified by RNA-sequencing, closely resemble the mutational profile regarding alterations accentuated in interleukin-6/JAK/STAT signaling, NF-κB activity, and MYC signaling. Moreover, class I major histocompatibility complex-mediated antigen processing and cell cycle regulation were significantly affected by EBV status. An almost exclusive upregulation of phosphatidylinositol 3-kinase/AKT/mTOR signaling in EBV+ PBL and a significantly induced expression of NTRK3 in concert with recurrent oncogenic mutations in EBV- PBL hint at a specific therapeutically targetable mechanism in PBL subgroups. Our characterization of a mutational and transcriptomic landscape in PBL, distinct from that of diffuse large B-cell lymphoma and multiple myeloma, substantiates the pathobiological independence of PBL in the spectrum of B-cell malignancies and thereby refines the taxonomy for aggressive lymphomas.

Project description:Breast cancer is the second most prevalent malignancy affecting women. Postmenopausal women breast tumor is one of the top causes of death in women, accounting for 23% of cancer cases. Type 2 diabetes, a worldwide pandemic, has been connected to a heightened risk of several malignancies, although its association with breast cancer is still uncertain. In comparison to non-diabetic women, women with T2DM had a 23% elevated likelihood of developing breast cancer. It is difficult to determine causative or genetic susceptibility that connect T2DM and breast cancer. We created a large-scale network-based quantitative approach employing unbiased methods to discover abnormally amplified genes in both T2DM and breast cancer, to solve these issues. We performed transcriptome analysis to uncover identical genetic biomarkers and pathways to clarify the connection between T2DM and breast cancer patients. In this study, two RNA-seq datasets (GSE103001 and GSE86468) from the Gene Expression Omnibus (GEO) are used to identify mutually differentially expressed genes (DEGs) for breast cancer and T2DM, as well as common pathways and prospective medicines. Firstly, 45 shared genes (30 upregulated and 15 downregulated) between T2D and breast cancer were detected. We employed gene ontology and pathway enrichment to characterize prevalent DEGs' molecular processes and signal transduction pathways and observed that T2DM has certain connections to the progression of breast cancer. Using several computational and statistical approaches, we created a protein-protein interactions (PPI) network and revealed hub genes. These hub genes can be potential biomarkers, which may also lead to new therapeutic strategies for investigated diseases. We conducted TF-gene interactions, gene-microRNA interactions, protein-drug interactions, and gene-disease associations to find potential connections between T2DM and breast cancer pathologies. We assume that the potential drugs that emerged from this study could be useful therapeutic values. Researchers, doctors, biotechnologists, and many others may benefit from this research.

Dataset Information

Expression-based network biology identifies alteration in key regulatory pathways of type 2 diabetes and associated risk/complications.

Publications

Expression-based network biology identifies alteration in key regulatory pathways of type 2 diabetes and associated risk/complications.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets