Genome-wide Profiling of RNA splicing in prostate tumor from RNA-seq data using virtual microarrays

BACKGROUND: Second generation RNA sequencing technology (RNA-seq) offers the potential to interrogate genome-wide differential RNA splicing in cancer. However, since short RNA reads spanning spliced junctions cannot be mapped contiguously onto to the chromosomes, there is a need for methods to profile splicing from RNA-seq data. Before the invent of RNA-seq technologies, microarrays containing probe sequences representing exon-exon junctions of known genes have been used to hybridize cellular RNAs for measuring context-specific differential splicing. Here, we extend this approach to detect tumor-specific splicing in prostate cancer from a RNA-seq dataset. METHOD: A database, SPEventH, representing probe sequences of under a million non-redundant splice events in human is created with exon-exon junctions of optimized length for use as virtual microarray. SPEventH is used to map tens of millions of reads from matched tumor-normal samples from ten individuals with prostate cancer. Differential counts of reads mapped to each event from tumor and matched normal is used to identify statistically significant tumor-specific splice events in prostate. RESULTS: We find sixty-one (61) splice events that are differentially expressed with a p-value of less than 0.0001 and a fold change of greater than 1.5 in prostate tumor compared to the respective matched normal samples. Interestingly, the only evidence, EST (BF372485), in the public database for one of the tumor-specific splice event joining one of the intron in KLK3 gene to an intron in KLK2, is also derived from prostate tumor-tissue. Also, the 765 events with a p-value of less than 0.001 is shown to cluster all twenty samples in a context-specific fashion with few exceptions stemming from low coverage of samples. CONCLUSIONS: We demonstrate that virtual microarray experiments using a non-redundant database of splice events in human is both efficient and sensitive way to profile genome-wide splicing in biological samples and to detect tumor-specific splicing signatures in datasets from RNA-seq technologies. The signature from the large number of splice events that could cluster tumor and matched-normal samples into two tight separate clusters, suggests that differential splicing is yet another RNA phenotype, alongside gene expression and SNPs, that can be exploited for tumor stratification

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

<div><p>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.</p></div

Exclusively down-regulated shortest path molecules for IL-1β and TGF-β related pathways.

<p>The figure shows the completely down-regulated shortest path molecules in IL-1β and TGF-β cytokines and AP-1, SMAD and STAT1 transcription factor pairs.</p

A concise interaction map for the 12 cytokines, eight transcription factors and 24 key molecules.

<p>This map shows how the cytokines and transcription factors considered in this study interact with 24 key molecules. The key molecules, JUN, FOS and FOSB are represented with AP-1. Another key molecule, IL2RG is represented with IL-21R.</p

Hierarchical clustering of RA and control samples based on the gene expression of selected key molecules in GSE55235.

<p>The RA and control samples were clustered into separate groups.</p

Details of microarray datasets used in this study.

<p>Details of microarray datasets used in this study.</p

Up- and down-regulated shortest path molecules in IFNγ, IL-17 and TGF-β related pathways.

<p>The figure shows the mostly up-regulated shortest path molecules in IFNγ and STAT1, IL-17 and STAT3 pairs. It also shows the mostly down-regulated molecules in the shortest path between TGF-β and NF-κB pair. The blue and red bars represent the up- and down-regulated genes respectively.</p

The histograms of the four centrality measures, degree, betweenness, closeness and eigenvector.

<p>The distributions of the four centrality measures are plotted as histograms. Taking the histogram as a reference, approximately 20% of the proteins with high centrality scores (toward the right side of the histogram) are extracted for differential expression analysis.</p

Mostly down-regulated shortest path molecules in IL-1α/β and IL-33 related pathways.

<p>The figure shows mostly down-regulated shortest path molecules in IL-1α/β cytokine and IRF7, IRF3 and IRF1 transcription factor pairs, and IL-33 and NF-κB pair. The blue and red bars represent the up- and down-regulated genes respectively.</p

Centrality and differential expression of the CPPIN network proteins.

<p>The proteins that are selected in at least (a) three centrality measures and three synovial microarray datasets or (b) two centrality measures and four synovial microarray datasets are considered as the key molecules. This resulted in 24 key molecules.</p