Pathway Distiller - multisource biological pathway consolidation

BACKGROUND: One method to understand and evaluate an experiment that produces a large set of genes, such as a gene expression microarray analysis, is to identify overrepresentation or enrichment for biological pathways. Because pathways are able to functionally describe the set of genes, much effort has been made to collect curated biological pathways into publicly accessible databases. When combining disparate databases, highly related or redundant pathways exist, making their consolidation into pathway concepts essential. This will facilitate unbiased, comprehensive yet streamlined analysis of experiments that result in large gene sets. METHODS: After gene set enrichment finds representative pathways for large gene sets, pathways are consolidated into representative pathway concepts. Three complementary, but different methods of pathway consolidation are explored. Enrichment Consolidation combines the set of the pathways enriched for the signature gene list through iterative combining of enriched pathways with other pathways with similar signature gene sets; Weighted Consolidation utilizes a Protein-Protein Interaction network based gene-weighting approach that finds clusters of both enriched and non-enriched pathways limited to the experiments\u27 resultant gene list; and finally the de novo Consolidation method uses several measurements of pathway similarity, that finds static pathway clusters independent of any given experiment. RESULTS: We demonstrate that the three consolidation methods provide unified yet different functional insights of a resultant gene set derived from a genome-wide profiling experiment. Results from the methods are presented, demonstrating their applications in biological studies and comparing with a pathway web-based framework that also combines several pathway databases. Additionally a web-based consolidation framework that encompasses all three methods discussed in this paper, Pathway Distiller (http://cbbiweb.uthscsa.edu/PathwayDistiller), is established to allow researchers access to the methods and example microarray data described in this manuscript, and the ability to analyze their own gene list by using our unique consolidation methods. CONCLUSIONS: By combining several pathway systems, implementing different, but complementary pathway consolidation methods, and providing a user-friendly web-accessible tool, we have enabled users the ability to extract functional explanations of their genome wide experiments

Pathway Distiller - multisource biological pathway consolidation

BACKGROUND: One method to understand and evaluate an experiment that produces a large set of genes, such as a gene expression microarray analysis, is to identify overrepresentation or enrichment for biological pathways. Because pathways are able to functionally describe the set of genes, much effort has been made to collect curated biological pathways into publicly accessible databases. When combining disparate databases, highly related or redundant pathways exist, making their consolidation into pathway concepts essential. This will facilitate unbiased, comprehensive yet streamlined analysis of experiments that result in large gene sets. METHODS: After gene set enrichment finds representative pathways for large gene sets, pathways are consolidated into representative pathway concepts. Three complementary, but different methods of pathway consolidation are explored. Enrichment Consolidation combines the set of the pathways enriched for the signature gene list through iterative combining of enriched pathways with other pathways with similar signature gene sets; Weighted Consolidation utilizes a Protein-Protein Interaction network based gene-weighting approach that finds clusters of both enriched and non-enriched pathways limited to the experiments\u27 resultant gene list; and finally the de novo Consolidation method uses several measurements of pathway similarity, that finds static pathway clusters independent of any given experiment. RESULTS: We demonstrate that the three consolidation methods provide unified yet different functional insights of a resultant gene set derived from a genome-wide profiling experiment. Results from the methods are presented, demonstrating their applications in biological studies and comparing with a pathway web-based framework that also combines several pathway databases. Additionally a web-based consolidation framework that encompasses all three methods discussed in this paper, Pathway Distiller (http://cbbiweb.uthscsa.edu/PathwayDistiller), is established to allow researchers access to the methods and example microarray data described in this manuscript, and the ability to analyze their own gene list by using our unique consolidation methods. CONCLUSIONS: By combining several pathway systems, implementing different, but complementary pathway consolidation methods, and providing a user-friendly web-accessible tool, we have enabled users the ability to extract functional explanations of their genome wide experiments

The immune checkpoint molecules PD-1, PD-L1, TIM-3 and LAG-3 in diffuse large B-cell lymphoma

Signaling through immune checkpoint receptors may lead to T-cell exhaustion and function as immune escape mechanisms in cancer. For diffuse large B-cell lymphoma (DLBCL), the mechanistic and prognostic importance of these markers on tumor cells and the tumor microenvironment remains unclear. We determined the immunohistochemical expression of PD-1, PD-L1, TIM-3, and LAG-3 on tumor cells and on tumor infiltrating lymphocytes (TILs) among 123 DLBCL patients. TIM-3 showed positive staining on tumor cells in 39% of DLBCL cases and PD-L1 expression was noted in 15% of cases. Both PD-1 and LAG-3 were positive on tumor cells in a minority of DLBCL cases (8.3% and 7.5%, respectively), but were more widely expressed on TILs in a correlated manner. With median follow-up of 44 months (n = 70, range 5-85), 4-year progression-free survival (PFS) and overall survival (OS) rates were significantly inferior among DLBCL patients with high vs low/negative TIM-3 expression (PFS: 23% [95% CI 7% to 46%] vs 60% [95% CI 43% to 74%], respectively, P = 0.008; OS: 30% [95% CI 10% to 53%] vs 74% [95% CI 58% to 85%], respectively, P = 0.006). Differences in OS remained significant when controlling for International Prognostic Index in Cox regression analyses (HR 3.49 [95% CI 1.40-6.15], P = 0.007). In addition, we observed that co-culture of DLBCL cell lines with primed T cells in the presence of anti-LAG-3 and anti-TIM-3 induced potent dose-dependent increases in in vitro cell death via AcellaTox and IL-2 ELISA assays, suggesting potent anti-tumor activity of these compounds

Building and analyzing protein interactome networks by cross-species comparisons

<p>Abstract</p> <p>Background</p> <p>A genomic catalogue of protein-protein interactions is a rich source of information, particularly for exploring the relationships between proteins. Numerous systems-wide and small-scale experiments have been conducted to identify interactions; however, our knowledge of all interactions for any one species is incomplete, and alternative means to expand these network maps is needed. We therefore took a comparative biology approach to predict protein-protein interactions across five species (human, mouse, fly, worm, and yeast) and developed InterologFinder for research biologists to easily navigate this data. We also developed a confidence score for interactions based on available experimental evidence and conservation across species.</p> <p>Results</p> <p>The connectivity of the resultant networks was determined to have scale-free distribution, small-world properties, and increased local modularity, indicating that the added interactions do not disrupt our current understanding of protein network structures. We show examples of how these improved interactomes can be used to analyze a genome-scale dataset (RNAi screen) and to assign new function to proteins. Predicted interactions within this dataset were tested by co-immunoprecipitation, resulting in a high rate of validation, suggesting the high quality of networks produced.</p> <p>Conclusions</p> <p>Protein-protein interactions were predicted in five species, based on orthology. An InteroScore, a score accounting for homology, number of orthologues with evidence of interactions, and number of unique observations of interactions, is given to each known and predicted interaction. Our website <url>http://www.interologfinder.org</url> provides research biologists intuitive access to this data.</p

14-3-3 σ Expression Effects G2/M Response to Oxygen and Correlates with Ovarian Cancer Metastasis

In vitro cell culture experiments with primary cells have reported that cell proliferation is retarded in the presence of ambient compared to physiological O₂ levels. Cancer is primarily a disease of aberrant cell proliferation, therefore, studying cancer cells grown under ambient O₂ may be undesirable. To understand better the impact of O₂ on the propagation of cancer cells in vitro, we compared the growth potential of a panel of ovarian cancer cell lines under ambient (21%) or physiological (3%) O₂.Our observations demonstrate that similar to primary cells, many cancer cells maintain an inherent sensitivity to O₂, but some display insensitivity to changes in O₂ concentration. Further analysis revealed an association between defective G2/M cell cycle transition regulation and O₂ insensitivity resultant from overexpression of 14-3-3 σ. Targeting 14-3-3 σ overexpression with RNAi restored O₂ sensitivity in these cell lines. Additionally, we found that metastatic ovarian tumors frequently overexpress 14-3-3 σ, which in conjunction with phosphorylated RB, results in poor prognosis.Cancer cells show differential proliferative sensitivity to changes in O₂ concentration. Although a direct link between O₂ insensitivity and metastasis was not determined, this investigation showed that an O₂ insensitive phenotype in cancer cells to correlate with metastatic tumor progression

A Network of Conserved Damage Survival Pathways Revealed by a Genomic RNAi Screen

Damage initiates a pleiotropic cellular response aimed at cellular survival when appropriate. To identify genes required for damage survival, we used a cell-based RNAi screen against the Drosophila genome and the alkylating agent methyl methanesulphonate (MMS). Similar studies performed in other model organisms report that damage response may involve pleiotropic cellular processes other than the central DNA repair components, yet an intuitive systems level view of the cellular components required for damage survival, their interrelationship, and contextual importance has been lacking. Further, by comparing data from different model organisms, identification of conserved and presumably core survival components should be forthcoming. We identified 307 genes, representing 13 signaling, metabolic, or enzymatic pathways, affecting cellular survival of MMS–induced damage. As expected, the majority of these pathways are involved in DNA repair; however, several pathways with more diverse biological functions were also identified, including the TOR pathway, transcription, translation, proteasome, glutathione synthesis, ATP synthesis, and Notch signaling, and these were equally important in damage survival. Comparison with genomic screen data from Saccharomyces cerevisiae revealed no overlap enrichment of individual genes between the species, but a conservation of the pathways. To demonstrate the functional conservation of pathways, five were tested in Drosophila and mouse cells, with each pathway responding to alkylation damage in both species. Using the protein interactome, a significant level of connectivity was observed between Drosophila MMS survival proteins, suggesting a higher order relationship. This connectivity was dramatically improved by incorporating the components of the 13 identified pathways within the network. Grouping proteins into “pathway nodes” qualitatively improved the interactome organization, revealing a highly organized “MMS survival network.” We conclude that identification of pathways can facilitate comparative biology analysis when direct gene/orthologue comparisons fail. A biologically intuitive, highly interconnected MMS survival network was revealed after we incorporated pathway data in our interactome analysis