Altered Transcriptional Regulation of Glycolysis in Circulating CD8+ T Cells of Rheumatoid Arthritis Patients.

Peripheral T lymphocytes of rheumatoid arthritis (RA) patients show pathological changes in their metabolic pathways, especially glycolysis. These changes may drive the increased proliferation and tissue invasiveness of RA T cells. In order to study the transcriptional regulation underlying these alterations, we analysed publicly available RNA sequencing data from circulating T lymphocyte subsets of healthy individuals, untreated RA patients, and patients undergoing treatment for RA. Differential co-expression networks were created using sample-wise edge weights from an analysis called "linear interpolation to obtain network estimates for single sample" (lionessR), and annotated using the Gene Transcription Regulation Database (GTRD). Genes with high centrality scores were identified. CD8+ effector memory cells (Tem) and CD8+CD45RA+ effector memory cells (Temra) showed large changes in the transcriptional regulation of glycolysis in untreated RA. PFKFB3 and GAPDH were differentially regulated and had high centrality scores in CD8+ Tem cells. PFKFB3 downregulation may be due to HIF1A post transcriptional inhibition. Tocilizumab treatment partially reversed the RA-associated differential expression of several metabolic and regulatory genes. MYC was upregulated and had high centrality scores in RA CD8+ Temra cells; however, its glycolysis targets were unaltered. The upregulation of the PI3K-AKT and mTOR pathways may explain MYC upregulation.

Mitochondrial dysfunction in rheumatoid arthritis: A comprehensive analysis by integrating gene expression, protein-protein interactions and gene ontology data.

Several studies have reported mitochondrial dysfunction in rheumatoid arthritis (RA). Many nuclear DNA (nDNA) encoded proteins translocate to mitochondria, but their participation in the dysfunction of this cell organelle during RA is quite unclear. In this study, we have carried out an integrative analysis of gene expression, protein-protein interactions (PPI) and gene ontology data. The analysis has identified potential implications of the nDNA encoded proteins in RA mitochondrial dysfunction. Firstly, by analysing six synovial microarray datasets of RA patients and healthy controls obtained from the gene expression omnibus (GEO) database, we found differentially expressed nDNA genes that encode mitochondrial proteins. We uncovered some of the roles of these genes in RA mitochondrial dysfunction using literature search and gene ontology analysis. Secondly, by employing gene co-expression from microarrays and collating reliable PPI from seven databases, we created the first mitochondrial PPI network that is specific to the RA synovial joint tissue. Further, we identified hubs of this network, and moreover, by integrating gene expression and network analysis, we found differentially expressed neighbours of the hub proteins. The results demonstrate that nDNA encoded proteins are (i) crucial for the elevation of mitochondrial reactive oxygen species (ROS) and (ii) involved in membrane potential, transport processes, metabolism and intrinsic apoptosis during RA. Additionally, we proposed a model relating to mitochondrial dysfunction and inflammation in the disease. Our analysis presents a novel perspective on the roles of nDNA encoded proteins in mitochondrial dysfunction, especially in apoptosis, oxidative stress-related processes and their relation to inflammation in RA. These findings provide a plethora of information for further research.

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

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.

A Cytokine Signalling Network for the Regulation of Inducible Nitric Oxide Synthase Expression in Rheumatoid Arthritis.

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-ß) 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.

A systems biology based integrative framework to enhance the predictivity of in vitro methods for drug-induced liver injury.

BACKGROUND: Liver injury is the most common cause of postmarketing withdrawal of drugs. Traditional animal toxicity testing methods have proved to be imperfect tools for predicting toxicity observed in the clinic. OBJECTIVE: Predictive methods that integrate data and insights from several in vitro methods to provide a deeper understanding of the impact of a drug on the liver are the need of the hour. METHOD: A systems approach based on mathematical modelling using the kinetics of biochemical pathways involved in liver homeostasis coupled with in vitro measurements to quantify drug-induced perturbations is described here. CONCLUSIONS: Integrating in silico and in vitro methods provides a powerful platform that allows reasonably accurate and mechanistic-level prediction of drug-induced liver injury. The method demonstrates that several physiological situations can be accurately modelled as can the effect of perturbations induced by drugs. It can also be used along with high-throughput 'omic' data to generate testable hypotheses leading to informed decision-making.