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A cytokine protein-protein interaction network for identifying key molecules in rheumatoid arthritis.

ABSTRACT: Rheumatoid arthritis (RA) is a chronic inflammatory disease of the synovial joints. Though the current RA therapeutics such as disease-modifying antirheumatic drugs (DMARDs), nonsteroidal anti-inflammatory drugs (NSAIDs) and biologics can halt the progression of the disease, none of these would either dramatically reduce or cure RA. So, the identification of potential therapeutic targets and new therapies for RA are active areas of research. Several studies have discovered the involvement of cytokines in the pathogenesis of this disease. These cytokines induce signal transduction pathways in RA synovial fibroblasts (RASF). These pathways share many signal transducers and their interacting proteins, resulting in the formation of a signaling network. In order to understand the involvement of this network in RA pathogenesis, it is essential to identify the key transducers and their interacting proteins that are part of this network. In this study, based on a detailed literature survey, we have identified a list of 12 cytokines that induce signal transduction pathways in RASF. For these cytokines, we have built a signaling network using the protein-protein interaction (PPI) data that was obtained from public repositories such as HPRD, BioGRID, MINT, IntAct and STRING. By combining the network centrality measures with the gene expression data from the RA related microarrays that are available in the open source Gene Expression Omnibus (GEO) database, we have identified 24 key proteins of this signaling network. Two of these 24 are already drug targets for RA, and of the remaining, 12 have direct PPI links to some of the current drug targets of RA. Therefore, these key proteins seem to be crucial in the pathogenesis of RA and hence might be treated as potential drug targets.

SUBMITTER: Panga V

PROVIDER: S-EPMC6013252 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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A cytokine protein-protein interaction network for identifying key molecules in rheumatoid arthritis.

Panga Venugopal V Raghunathan Srivatsan S

PloS one 20180621 6

Rheumatoid arthritis (RA) is a chronic inflammatory disease of the synovial joints. Though the current RA therapeutics such as disease-modifying antirheumatic drugs (DMARDs), nonsteroidal anti-inflammatory drugs (NSAIDs) and biologics can halt the progression of the disease, none of these would either dramatically reduce or cure RA. So, the identification of potential therapeutic targets and new therapies for RA are active areas of research. Several studies have discovered the involvement of cyt ...[more]

PMID: 29928007

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Project description:BACKGROUND: Rheumatoid arthritis (RA) is a chronic, inflammatory and systemic autoimmune disease that leads to progressive cartilage destruction. Advances in the treatment of RA-related destruction of cartilage require profound insights into the molecular mechanisms involved in cartilage degradation. Until now, comprehensive data about the molecular RA-related dysfunction of chondrocytes have been limited. Hence, the objective of this study was to establish a standardized in vitro model to profile the key regulatory molecules of RA-related destruction of cartilage that are expressed by human chondrocytes. METHODS: Human chondrocytes were cultured three-dimensionally for 14 days in alginate beads and subsequently stimulated for 48 hours with supernatants from SV40 T-antigen immortalized human synovial fibroblasts (SF) derived from a normal donor (NDSF) and from a patient with RA (RASF), respectively. To identify RA-related factors released from SF, supernatants of RASF and NDSF were analyzed with antibody-based protein membrane arrays. Stimulated cartilage-like cultures were used for subsequent gene expression profiling with oligonucleotide microarrays. Affymetrix GeneChip Operating Software and Robust Multi-array Analysis (RMA) were used to identify differentially expressed genes. Expression of selected genes was verified by real-time RT-PCR. RESULTS: Antibody-based protein membrane arrays of synovial fibroblast supernatants identified RA-related soluble mediators (IL-6, CCL2, CXCL1-3, CXCL8) released from RASF. Genome-wide microarray analysis of RASF-stimulated chondrocytes disclosed a distinct expression profile related to cartilage destruction involving marker genes of inflammation (adenosine A2A receptor, cyclooxygenase-2), the NF-kappaB signaling pathway (toll-like receptor 2, spermine synthase, receptor-interacting serine-threonine kinase 2), cytokines/chemokines and receptors (CXCL1-3, CXCL8, CCL20, CXCR4, IL-1beta, IL-6), cartilage degradation (matrix metalloproteinase (MMP)-10, MMP-12) and suppressed matrix synthesis (cartilage oligomeric matrix protein, chondroitin sulfate proteoglycan 2). CONCLUSION: Differential transcriptome profiling of stimulated human chondrocytes revealed a disturbed catabolic-anabolic homeostasis of chondrocyte function and disclosed relevant pharmacological target genes of cartilage destruction. This study provides comprehensive insight into molecular regulatory processes induced in human chondrocytes during RA-related destruction of cartilage. The established model may serve as a human in vitro disease model of RA-related destruction of cartilage and may help to elucidate the molecular effects of anti-rheumatic drugs on human chondrocyte gene expression.

Project description:In rheumatoid arthritis (RA), nitric oxide (NO) is implicated in inflammation, angiogenesis and tissue destruction. The enzyme inducible nitric oxide synthase (iNOS) is responsible for the localised over-production of NO in the synovial joints affected by RA. The pro- and anti-inflammatory cytokines stimulate the synovial macrophages and the fibroblast-like synoviocytes to express iNOS. Therefore, the cytokine signalling network underlying the regulation of iNOS is essential to understand the pathophysiology of the disease. By using information from the literature, we have constructed, for the first time, the cytokine signalling network involved in the regulation of iNOS expression. Using the differential expression patterns obtained by re-analysing the microarray data on the RA synovium and the synovial macrophages available in the Gene Expression Omnibus (GEO) database, we aimed to establish the role played by the network genes towards iNOS regulation in the RA synovium. Our analysis reveals that the network genes belonging to interferon (IFN) and interleukin-10 (IL-10) pathways are always up-regulated in the RA synovium whereas the genes which are part of the anti-inflammatory transforming growth factor-beta (TGF-β) signalling pathway are mostly down-regulated. We observed a consistent up-regulation of the transcription factor signal transducers and activators of transcription 1 (STAT1) in the RA synovium and the macrophages. Interestingly, we found a consistent up-regulation of the iNOS interacting protein ras-related C3 botulinum toxin substrate 2 (RAC2) in the RA synovium as well as the macrophages. Importantly, we have constructed a model to explain the impact of IFN and IL-10 pathways on Rac2-iNOS interaction leading to over-production of NO and thereby causing chronic inflammation in the RA synovium. The interplay between STAT1 and RAC2 in the regulation of NO could have implications for the identification of therapeutic targets for RA.

Project description:BackgroundIdentifying protein complexes is crucial to understanding principles of cellular organization and functional mechanisms. As many evidences have indicated that the subgraphs with high density or with high modularity in PPI network usually correspond to protein complexes, protein complexes detection methods based on PPI network focused on subgraph's density or its modularity in PPI network. However, dense subgraphs may have low modularity and subgraph with high modularity may have low density, which results that protein complexes may be subgraphs with low modularity or with low density in the PPI network. As the density-based methods are difficult to mine protein complexes with low density, and the modularity-based methods are difficult to mine protein complexes with low modularity, both two methods have limitation for identifying protein complexes with various density and modularity.ResultsTo identify protein complexes with various density and modularity, including those have low density but high modularity and those have low modularity but high density, we define a novel subgraph's fitness, fρ, as fρ= (density)(ρ*)(modularity)(1-ρ), and propose a novel algorithm, named LF_PIN, to identify protein complexes by expanding seed edges to subgraphs with the local maximum fitness value. Experimental results of LF-PIN in S.cerevisiae show that compared with the results of fitness equal to density (ρ = 1) or equal to modularity (ρ = 0), the LF-PIN identifies known protein complexes more effectively when the fitness value is decided by both density and modularity (0<ρ<1). Compared with the results of seven competing protein complex detection methods (CMC, Core-Attachment, CPM, DPClus, HC-PIN, MCL, and NFC) in S.cerevisiae and E.coli, LF-PIN outperforms other seven methods in terms of matching with known complexes and functional enrichment. Moreover, LF-PIN has better performance in identifying protein complexes with low density or with low modularity.ConclusionsBy considering both the density and the modularity, LF-PIN outperforms other protein complexes detection methods that only consider density or modularity, especially in identifying known protein complexes with low density or low modularity.

Dataset Information

A cytokine protein-protein interaction network for identifying key molecules in rheumatoid arthritis.

Publications

A cytokine protein-protein interaction network for identifying key molecules in rheumatoid arthritis.

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