Gurkan Bebek

PathFinder: Mining signal transduction pathway segments from protein-protein interaction networks

BackgroundA Signal transduction pathway is the chain of processes by which a cell converts an extracellular signal into a response. In most unicellular organisms, the number of signal transduction pathways influences the number of ways the cell can react and respond to the environment. Discovering signal transduction pathways is an arduous problem, even with the use of systematic genomic, proteomic and metabolomic technologies. These techniques lead to an enormous amount of data and how to interpret and process this data becomes a challenging computational problem.ResultsIn this study we present a new framework for identifying signaling pathways in protein-protein interaction networks. Our goal is to find biologically significant pathway segments in a given interaction network. Currently, protein-protein interaction data has excessive amount of noise, e.g., false positive and false negative interactions. First, we eliminate false positives in the protein-protein interaction network by integrating the network with microarray expression profiles, protein subcellular localization and sequence information. In addition, protein families are used to repair false negative interactions. Then the characteristics of known signal transduction pathways and their functional annotations are extracted in the form of association rules.ConclusionGiven a pair of starting and ending proteins, our methodology returns candidate pathway segments between these two proteins with possible missing links (recovered false negatives). In our study, S. cerevisiae (yeast) data is used to demonstrate the effectiveness of our method.

DADA: Degree-Aware Algorithms for Network- Based Disease Gene Prioritization

BackgroundHigh-throughput molecular interaction data have been used effectively to prioritize candidate genes that are linked to a disease, based on the observation that the products of genes associated with similar diseases are likely to interact with each other heavily in a network of protein-protein interactions (PPIs). An important challenge for these applications, however, is the incomplete and noisy nature of PPI data. Information flow based methods alleviate these problems to a certain extent, by considering indirect interactions and multiplicity of paths.ResultsWe demonstrate that existing methods are likely to favor highly connected genes, making prioritization sensitive to the skewed degree distribution of PPI networks, as well as ascertainment bias in available interaction and disease association data. Motivated by this observation, we propose several statistical adjustment methods to account for the degree distribution of known disease and candidate genes, using a PPI network with associated confidence scores for interactions. We show that the proposed methods can detect loosely connected disease genes that are missed by existing approaches, however, this improvement might come at the price of more false negatives for highly connected genes. Consequently, we develop a suite called DA DA, which includes different uniform prioritization methods that effectively integrate existing approaches with the proposed statistical adjustment strategies. Comprehensive experimental results on the Online Mendelian Inheritance in Man (OMIM) database show that DA DA outperforms existing methods in prioritizing candidate disease genes.ConclusionsThese results demonstrate the importance of employing accurate statistical models and associated adjustment methods in network-based disease gene prioritization, as well as other network-based functional inference applications. DA DA is implemented in Matlab and is freely available at http://compbio.case.edu/dada/.

FOXA1 Represses the Molecular Phenotype of Basal Breast Cancer Cells

Breast cancer is a heterogeneous disease that comprises multiple subtypes. Luminal subtype tumors confer a more favorable patient prognosis, which is, in part, attributed to estrogen receptor (ER)-α positivity and antihormone responsiveness. Expression of the forkhead box transcription factor, FOXA1, similarly correlates with the luminal subtype and patient survival, but is also present in a subset of ER-negative tumors. FOXA1 is also consistently expressed in luminal breast cancer cell lines even in the absence of ER. In contrast, breast cancer cell lines representing the basal subtype do not express FOXA1. To delineate an ER-independent role for FOXA1 in maintaining the luminal phenotype, and hence a more favorable prognosis, we performed expression microarray analyses on FOXA1-positive and ER-positive (MCF7, T47D), or FOXA1-positive and ER-negative (MDA-MB-453, SKBR3) luminal cell lines in the presence or absence of transient FOXA1 silencing. This resulted in three FOXA1 transcriptomes: (1) a luminal signature (consistent across cell lines), (2) an ER-positive signature (restricted to MCF7 and T47D) and (3) an ER-negative signature (restricted to MDA-MB-453 and SKBR3). Gene set enrichment analyses revealed FOXA1 silencing causes a partial transcriptome shift from luminal to basal gene expression signatures. FOXA1 binds to a subset of both luminal and basal genes within luminal breast cancer cells, and loss of FOXA1 increases enhancer RNA transcription for a representative basal gene (CD58). These data suggest FOXA1 directly represses a subset of basal signature genes. Functionally, FOXA1 silencing increases migration and invasion of luminal cancer cells, both of which are characteristics of basal subtype cells. We conclude FOXA1 controls plasticity between basal and luminal breast cancer cells, not only by inducing luminal genes but also by repressing the basal phenotype, and thus aggressiveness. Although it has been proposed that FOXA1-targeting agents may be useful for treating luminal tumors, these data suggest that this approach may promote transitions toward more aggressive cancers.

Proteomic Analysis of Synovial Fluid From the Osteoarthritic Knee: Comparison With Transcriptome Analyses of Joint Tissues

OBJECTIVE The pathophysiology of the most common joint disease, osteoarthritis (OA), remains poorly understood. Since synovial fluid (SF) bathes joint cartilage and synovium, we reasoned that a comparative analysis of its protein constituents in health and OA could identify pathways involved in joint damage. We undertook this study to perform a proteomic analysis of knee SF from OA patients and control subjects and to compare the results to microarray expression data from cartilage and synovium. METHODS Age-matched knee SF samples from 10 control subjects, 10 patients with early-stage OA, and 10 patients with late-stage OA were compared using 2-dimensional difference-in-gel electrophoresis and mass spectrometry (MS). MS with a multiplexed peptide selected reaction monitoring assay was used to confirm differential expression of a subset of proteins in an independent OA patient cohort. Proteomic results were analyzed by Ingenuity Pathways Analysis and compared to published synovial tissue and cartilage messenger RNA profiles. RESULTS Sixty-six proteins were differentially present in healthy and OA SF. Three major pathways were identified among these proteins: the acute-phase response signaling pathway, the complement pathway, and the coagulation pathway. Differential expression of 5 proteins was confirmed by selected reaction monitoring assay. A focused analysis of transcripts corresponding to the differentially present proteins indicated that both synovial and cartilage tissues may contribute to the OA SF proteome. CONCLUSION Proteins involved in the acute-phase response signaling pathway, the complement pathway, and the coagulation pathway are differentially regulated in SF from OA patients, suggesting that they contribute to joint damage. Validation of these pathways and their utility as biomarkers or therapeutic targets in OA is warranted.

Network biology methods integrating biological data for translational science

The explosion of biomedical data, both on the genomic and proteomic side as well as clinical data, will require complex integration and analysis to provide new molecular variables to better understand the molecular basis of phenotype. Currently, much data exist in silos and is not analyzed in frameworks where all data are brought to bear in the development of biomarkers and novel functional targets. This is beginning to change. Network biology approaches, which emphasize the interactions between genes, proteins and metabolites provide a framework for data integration such that genome, proteome, metabolome and other -omics data can be jointly analyzed to understand and predict disease phenotypes. In this review, recent advances in network biology approaches and results are identified. A common theme is the potential for network analysis to provide multiplexed and functionally connected biomarkers for analyzing the molecular basis of disease, thus changing our approaches to analyzing and modeling genome- and proteome-wide data.

Quantitative H2S-mediated protein sulfhydration reveals metabolic reprogramming during the integrated stress response.

The sulfhydration of cysteine residues in proteins is an important mechanism involved in diverse biological processes. We have developed a proteomics approach to quantitatively profile the changes of sulfhydrated cysteines in biological systems. Bioinformatics analysis revealed that sulfhydrated cysteines are part of a wide range of biological functions. In pancreatic β cells exposed to endoplasmic reticulum (ER) stress, elevated H2S promotes the sulfhydration of enzymes in energy metabolism and stimulates glycolytic flux. We propose that transcriptional and translational reprogramming by the integrated stress response (ISR) in pancreatic β cells is coupled to metabolic alternations triggered by sulfhydration of key enzymes in intermediary metabolism. DOI: http://dx.doi.org/10.7554/eLife.10067.001

Microbiomic subprofiles and MDR1 promoter methylation in head and neck squamous cell carcinoma

Clinical observations and epidemiologic studies suggest that the incidence of head and neck squamous cell carcinoma (HNSCC) correlates with dental hygiene, implying a role for bacteria-induced inflammation in its pathogenesis. Here we begin to explore the pilot hypothesis that specific microbial populations may contribute to HNSCC pathogenesis via epigenetic modifications in inflammatory- and HNSCC-associated genes. Microbiomic profiling by 16S rRNA sequencing of matched tumor and adjacent normal tissue specimens in 42 individuals with HNSCC demonstrate a significant association of specific bacterial subpopulations with HNSCC over normal tissue (P < 0.01). Furthermore, microbial populations can separate tumors by tobacco status (P < 0.008), but not by alcohol status (P = 0.41). If our subhypothesis regarding a mechanistic link from microorganism to carcinogenesis via inflammation and consequent aberrant DNA methylation is correct, then we should see hypermethylation of relevant genes associate with specific microbiomic profiles. Methylation analysis in four genes (MDR1, IL8, RARB, TGFBR2) previously linked to HNSCC or inflammation shows significantly increased methylation in tumor samples compared with normal oral mucosa. Of these, MDR1 promoter methylation associates with specific microbiomic profiles in tumor over normal mucosa. Additionally, we report that MDR1 methylation correlates with regional nodal metastases in the context of two specific bacterial subpopulations, Enterobacteriaceae and Tenericutes (P < 0.001 for each). These associations may lead to a different, and potentially more comprehensive, perspective on the pathogenesis of HNSCC, and support further exploration of mechanistic linkage and, if so, novel therapeutic strategies such as demethylating agents and probiotic adjuncts, particularly for patients with advanced or refractory disease.

Phylogenetic analysis of modularity in protein interaction networks

BackgroundIn systems biology, comparative analyses of molecular interactions across diverse species indicate that conservation and divergence of networks can be used to understand functional evolution from a systems perspective. A key characteristic of these networks is their modularity, which contributes significantly to their robustness, as well as adaptability. Consequently, analysis of modular network structures from a phylogenetic perspective may be useful in understanding the emergence, conservation, and diversification of functional modularity.ResultsIn this paper, we propose a phylogenetic framework for analyzing network modules, with applications that extend well beyond network-based phylogeny reconstruction. Our approach is based on identification of modular network components from each network separately, followed by projection of these modules onto the networks of other species to compare different networks. Subsequently, we use the conservation of various modules in each network to assess the similarity between different networks. Compared to traditional methods that rely on topological comparisons, our approach has key advantages in (i) avoiding intractable graph comparison problems in comparative network analysis, (ii) accounting for noise and missing data through flexible treatment of network conservation, and (iii) providing insights on the evolution of biological systems through investigation of the evolutionary trajectories of network modules. We test our method, MOPHY, on synthetic data generated by simulation of network evolution, as well as existing protein-protein interaction data for seven diverse species. Comprehensive experimental results show that MOPHY is promising in reconstructing evolutionary histories of extant networks based on conservation of modularity, it is highly robust to noise, and outperforms existing methods that quantify network similarity in terms of conservation of network topology.ConclusionThese results establish modularity and network proximity as useful features in comparative network analysis and motivate detailed studies of the evolutionary histories of network modules.

Integrated Analysis Reveals Critical Genomic Regions in Prostate Tumor Microenvironment Associated with Clinicopathologic Phenotypes

Purpose: Recent studies suggest that tumor microenvironment (stroma) is important in carcinogenesis and progression. We sought to integrate global genomic structural and expressional alterations in prostate cancer epithelium and stroma and their association with clinicopathologic features. Experimental Design: We conducted a genome-wide LOH/allelic imbalance (AI) scan of DNA from epithelium and stroma of 116 prostate cancers. LOH/AI hot or cold spots were defined as the markers with significantly higher or lower LOH/AI frequencies compared with the average frequency for markers along the same chromosome. These data were then integrated with publicly available transcriptome data sets and our experimentally derived data. Immunohistochemistry on an independent series was used for validation. Results: Overall, we identified 43 LOH/AI hot/cold spots, 17 in epithelium and stroma (P < 0.001), 18 only in epithelium (P < 0.001), and eight only in stroma (P < 0.001). Hierarchical clustering of expression data supervised by genes within LOH/AI hot/cold spots in both epithelium and stroma accurately separated samples into normal epithelium, primary cancer, and metastatic cancer groups, which could not be achieved with data from only epithelium. Importantly, our experimental expression data of the genes within the LOH/AI hot/cold spots in stroma accurately clustered normal stroma from cancer stroma. We also identified 15 LOH/AI markers that were associated with Gleason score, which were validated functionally in each compartment by transcriptome data. Independent immunohistochemical validation of STIM2 within a stromal significant LOH marker (identified as associated with Gleason grade) confirmed its downregulation in the transition from moderate to high Gleason grade. Conclusions: Compartment-specific genomic and transcriptomic alterations accurately distinguish clinical and pathologic outcomes, suggesting new biomarkers for prognosis and targeted therapeutics. Clin Cancer Res; 18(6); 1578–87. ©2012 AACR.

Chromosome 3 status in uveal melanoma: a comparison of fluorescence in situ hybridization and single-nucleotide polymorphism array.

PURPOSE To compare fluorescence in situ hybridization (FISH) using a centromeric probe for chromosome 3 (CEP3) and 3p26 locus-specific probe with single-nucleotide polymorphism array (SNP-A) analysis in the detection of high-risk uveal melanoma. METHODS Fifty cases of uveal melanoma (28 males, 22 females) treated by enucleation between 2004 and 2010 were analyzed. Fresh tissue was used for FISH and SNP-A analysis. FISH was performed using a CEP3 and a 3p26 locus-specific probe. Tumor size, location, and clinical outcome were recorded during the 7-year study period (median follow-up: 35.5 months; mean: 38.5 months). The sensitivity, specificity, positive predictive value, and negative predictive value were calculated. RESULTS Monosomy 3 was detected by FISH-CEP3 in 27 tumors (54%), FISH-3p26 deletion was found in 30 (60%), and SNP-A analysis identified 31 (62%) of the tumors with monosomy 3. Due to technical failures, FISH and SNP-A were noninterpretable in one case (2%) and two cases (4%), respectively. In both cases of SNP-A failure, tumors were positive for FISH 3p26 deletion and in a single case of FISH failure, monosomy 3 was found using SNP-A. No statistically significant differences were observed in any of the sensitivity or specificity measures. CONCLUSIONS For prediction of survival at 36 months, FISH CEP3, FISH 3p26, and SNP-A were comparable. A combination of prognostication techniques should be used in an unlikely event of technical failure (2%-4%).