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

Before It Gets Started: Regulating Translation at the 5′ UTR

Translation regulation plays important roles in both normal physiological conditions and diseases states. This regulation requires cis-regulatory elements located mostly in 5′ and 3′ UTRs and trans-regulatory factors (e.g., RNA binding proteins (RBPs)) which recognize specific RNA features and interact with the translation machinery to modulate its activity. In this paper, we discuss important aspects of 5′ UTR-mediated regulation by providing an overview of the characteristics and the function of the main elements present in this region, like uORF (upstream open reading frame), secondary structures, and RBPs binding motifs and different mechanisms of translation regulation and the impact they have on gene expression and human health when deregulated

The RNA-Binding Protein Musashi1 Affects Medulloblastoma Growth via a Network of Cancer- Related Genes and Is an Indicator of Poor Prognosis

Musashi1 (Msi1) is a highly conserved RNA-binding protein that is required during the development of the nervous system. Msi1 has been characterized as a stem cell marker, controlling the balance between self-renewal and differentiation, and has also been implicated in tumorigenesis, being highly expressed in multiple tumor types. We analyzed Msi1 expression in a large cohort of medulloblastoma samples and found that Msi1 is highly expressed in tumor tissue compared with normal cerebellum. Notably, high Msi1 expression levels proved to be a sign of poor prognosis. Msi1 expression was determined to be particularly high in molecular subgroups 3 and 4 of medulloblastoma. We determined that Msi1 is required for tumorigenesis because inhibition of Msi1 expression by small-interfering RNAs reduced the growth of Daoy medulloblastoma cells in xenografts. To characterize the participation of Msi1 in medulloblastoma, we conducted different high-throughput analyses. Ribonucleoprotein immunoprecipitation followed by microarray analysis (RIP-chip) was used to identify mRNA species preferentially associated with Msi1 protein in Daoy cells. We also used cluster analysis to identify genes with similar or opposite expression patterns to Msi1 in our medulloblastoma cohort. A network study identified RAC1, CTGF, SDCBP, SRC, PRL, and SHC1 as major nodes of an Msi1-associated network. Our results suggest that Msi1 functions as a regulator of multiple processes in medulloblastoma formation and could become an important therapeutic target

Combined Gene Expression and RNAi Screening to Identify Alkylation Damage Survival Pathways from Fly to Human

<div><p>Alkylating agents are a key component of cancer chemotherapy. Several cellular mechanisms are known to be important for its survival, particularly DNA repair and xenobiotic detoxification, yet genomic screens indicate that additional cellular components may be involved. Elucidating these components has value in either identifying key processes that can be modulated to improve chemotherapeutic efficacy or may be altered in some cancers to confer chemoresistance. We therefore set out to reevaluate our prior <i>Drosophila</i> RNAi screening data by comparison to gene expression arrays in order to determine if we could identify any novel processes in alkylation damage survival. We noted a consistent conservation of alkylation survival pathways across platforms and species when the analysis was conducted on a pathway/process level rather than at an individual gene level. Better results were obtained when combining gene lists from two datasets (RNAi screen plus microarray) prior to analysis. In addition to previously identified DNA damage responses (p53 signaling and Nucleotide Excision Repair), DNA-mRNA-protein metabolism (transcription/translation) and proteasome machinery, we also noted a highly conserved cross-species requirement for NRF2, glutathione (GSH)-mediated drug detoxification and Endoplasmic Reticulum stress (ER stress)/Unfolded Protein Responses (UPR) in cells exposed to alkylation. The requirement for GSH, NRF2 and UPR in alkylation survival was validated by metabolomics, protein studies and functional cell assays. From this we conclude that RNAi/gene expression fusion is a valid strategy to rapidly identify key processes that may be extendable to other contexts beyond damage survival.</p></div

NRF2, glutathione and UPR survival responses are conserved across species.

<p>(A) Venn diagrams showing the overlap between alkylation-induced genes expressions and pathways across MDA-MB231, fly <i>Kc167</i> and MEFs. Black and red fonts denote comparisons of human and fruitfly orthologs, respectively. The pathways overlapping across the three species are also described. Detailed PEA of MMS-induced genes in MEF and MDA-MB231 are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153970#pone.0153970.s006" target="_blank">S4 Table</a>. (B) Ingenuity canonical pathway charts showing upregulated genes with the NRF2 and ER stress/UPR pathways in MMS-treated MDA-MB231 cells. Details of the edges and nodes are as described for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153970#pone.0153970.g003" target="_blank">Fig 3</a>. (C) MMS-induced changes in NRF2 and ER stress pathway markers as determined after 8 h alkylation treatment in MDA-MB231 and MEFs. (D) Box-plot representation of MMS-induced changes in compounds of the GSH metabolism as determined by metabolomics in Kc167 and MEFs (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153970#sec002" target="_blank">methods</a>). (E) ARE luciferase assays showing the relative basal and MMS-induced NRF2 activity in MDA-MB231 and A549 cells. The effect of KEAP1 and NRF2 siRNAs is also shown as a control. (F) Cell viability assays showing the effect of NAC or BSO pre-treatments on viability of MDA-MB231, A549 and MEFs treated with ~IC50 levels of MMS for 48 h. (G)Cell viability assay showing the protective effect of KEAP1 knockdown and GRP78 chaperone overexpression upon toxicity of varying MMS levels in MDA-MB231 cells (48 h treatment). pcDNA was used as empty vector control; GRP78 overexpression (~7 fold-induction was validated by immunoblot 24 h post-transfection; data not shown); scrambled siRNA controls showed no alteration and is not shown. (H) Time course effect of MMS (40 μg/mL) on the immunocontent of NRF2, GRP78 and CHOP proteins in MDA-MB231 and MEFs as assessed by Western blot. *Different from untreated cells; ^#different from untreated and from MMS-treated cells. In (G), asterisks denote differences from MMS alone at equivalent concentrations (ANOVA-Tukey, p<0.05, n = 3).</p

Gene/Protein interaction networks of NRF2-GSH pathway in MMS treated <i>Drosophila</i> cells.

<p>(A) Ingenuity canonical pathway charts showing gene expression inductions (left), RNAi hits (center) and fusion (right) of MMS responses with the NRF2 pathway. All edges are supported by at least one reference from the literature and stored in the Ingenuity Knowledge Base. Nodes are displayed using various shapes that represent the functional classes of the gene product (square: cytokines; diamond: enzyme; circle into a circle: complex/group; trapezium: transporter; ellipse/oval shape: transcription regulator; triangle: kinase; circle: other) (B) Fusion of gene expression profiles and RNAi screening hits applied to Viacomplex functional networks shows a landscape of overexpressed and lethal components/clusters with the NRF2-GSH pathway in <i>Kc167</i> cells treated with MMS for 8 and 24h. The genes used to build NRF2-GSH interactomes, the MMS-induced changes in gene expression and their survival role (from RNAi screen) are described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153970#pone.0153970.s005" target="_blank">S3 Table</a>.</p

Microarray/RNAi data fusion.

<p>(A) Schematic representation of the fusion strategy for MMS-induced gene expression changes and RNAi survival hits followed by Pathway Enrichment Analysis (PEA). (B) Pathway level overlap of MMS-induced survival responses from analysis of microarray, RNAi survival hits and fusion (microarray+RNAi hits) gene lists. (C) Antilog p-value representation of the pathways identified by PEA. Pathways are grouped into major biological processes, and detailed results are described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153970#pone.0153970.s004" target="_blank">S2 Table</a>. (D) Protein-protein interaction networks of MMS induced genes and hits with the “transcription, translation and proteasome”, “DNA damage response” and “NRF2” and “UPR” pathways in <i>Kc167</i> cells. Networks were developed by inputting into the STRING database both genes induced by MMS to alter expression and those necessary for survival (RNAi hits; converted to human orthologs) as identified by PEA. Color legends of edges in STRING interactomes denote “experiments” (pink), “databases” (light blue), “co-expressions” (black), “textmining” (lime green) and “co-occurrence” (blue) interactions between two nodes.</p

Fusion of RNAi hits and microarray expression changes improves pathway detection.

<p>Table showing the number of genes-in-pathway associated with microarray DEG (upregulations), RNAi survival hits or fused gene lists in Kc167 cells treated with MMS. P-value of Fisher-exact test (one-tailed) comparison of the proportion of MMS altered/survival genes in each platform is also shown. “ND” (not-detected) means a given pathway was not detected or was not significantly enriched at a FDR<10%. Legend: “Total genes”: total number of genes in the reference pathway; “Microarray”: MMS-induced gene expressions; “RNAi screen”: MMS survival hits.</p

Gene/Protein interaction networks of UPR/ER stress pathway in MMS treated <i>Drosophila</i> cells.

<p>(A) Ingenuity canonical pathway charts showing gene expression inductions (left), RNAi hits (center) and fusion (right) of MMS responses with the pathway. Details of the edges and nodes are as described for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153970#pone.0153970.g003" target="_blank">Fig 3</a>. (B) Fusion of gene expression profiles and RNAi screening hits applied to Viacomplex functional networks shows a landscape of overexpressed and lethal components/clusters with the UPR pathway in <i>Kc167</i> cells treated with MMS for 8 and 24h. The genes used to build UPR interactome, the MMS-induced changes in gene expression, and their survival role (from RNAi screen) are described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153970#pone.0153970.s005" target="_blank">S3 Table</a>.</p