ResponseNet: revealing signaling and regulatory networks linking genetic transcriptomic screening data

Cellular response to stimuli is typically complex and involves both regulatory and metabolic processes. Large-scale experimental efforts to identify components of these processes often comprise of genetic screening and transcriptomic profiling assays. We previously established that in yeast genetic screens tend to identify response regulators, while transcriptomic profiling assays tend to identify components of metabolic processes. ResponseNet is a network-optimization approach that integrates the results from these assays with data of known molecular interactions. Specifically, ResponseNet identifies a high-probability sub-network, composed of signaling and regulatory molecular interaction paths, through which putative response regulators may lead to the measured transcriptomic changes. Computationally, this is achieved by formulating a minimum-cost flow optimization problem and solving it efficiently using linear programming tools. The ResponseNet web server offers a simple interface for applying ResponseNet. Users can upload weighted lists of proteins and genes and obtain a sparse, weighted, molecular interaction sub-network connecting their data. The predicted sub-network and its gene ontology enrichment analysis are presented graphically or as text. Consequently, the ResponseNet web server enables researchers that were previously limited to separate analysis of their distinct, large-scale experiments, to meaningfully integrate their data and substantially expand their understanding of the underlying cellular response. ResponseNet is available at http://bioinfo.bgu.ac.il/respnet.Seventh Framework Programme (European Commission) (FP7-PEOPLE-MCA-IRG)United States-Israel Binational Science Foundation (Grant 2009323

Help me Obi-Wan: the influence of facial dominance on perceptions of helpfulness

We all occasionally need the help of others whom we do not know well. In four studies, we studied the influence of the facial appearance of both the potential helper and the help seeker on such a decision. In three studies (1a-1c), across different help domains, participants rated a person with submissive facial appearance as more likely to help. This was mediated via the perception of the submissive person as caring and helpful. The notion that submissive individuals will be perceived as more likely to help when a dominant person asks was only supported in the context of financial help. The preference for a submissive potential helper was also found when participant had to choose a helper for themselves (Study 2). (120 words

Comparative analysis of human tissue interactomes reveals factors leading to tissue-specific manifestation of hereditary diseases.

An open question in human genetics is what underlies the tissue-specific manifestation of hereditary diseases, which are caused by genomic aberrations that are present in cells across the human body. Here we analyzed this phenomenon for over 300 hereditary diseases by using comparative network analysis. We created an extensive resource of protein expression and interactions in 16 main human tissues, by integrating recent data of gene and protein expression across tissues with data of protein-protein interactions (PPIs). The resulting tissue interaction networks (interactomes) shared a large fraction of their proteins and PPIs, and only a small fraction of them were tissue-specific. Applying this resource to hereditary diseases, we first show that most of the disease-causing genes are widely expressed across tissues, yet, enigmatically, cause disease phenotypes in few tissues only. Upon testing for factors that could lead to tissue-specific vulnerability, we find that disease-causing genes tend to have elevated transcript levels and increased number of tissue-specific PPIs in their disease tissues compared to unaffected tissues. We demonstrate through several examples that these tissue-specific PPIs can highlight disease mechanisms, and thus, owing to their small number, provide a powerful filter for interrogating disease etiologies. As two thirds of the hereditary diseases are associated with these factors, comparative tissue analysis offers a meaningful and efficient framework for enhancing the understanding of the molecular basis of hereditary diseases

Tissue-related features of hereditary diseases and their causal genes.

<p>A. The tissue-distribution of hereditary diseases and their causal genes shows that diseases are manifested in few tissues, while most of their germline-aberrant causal genes are expressed in 10 tissues or more. The numbers of expressed causal genes across 1–16 tissues appear in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003632#pcbi.1003632.s021" target="_blank">Table S13</a>. B. Causal genes tend to be more highly expressed in their disease tissues relative to other tissues in which they are expressed. We observed higher median expression levels in disease tissues for 128 out of the 203 germline-aberrant causal genes for which RPKM values were available (p-value<10⁻⁴). The box-plot diagram shows the quartiles (25%, 50% and 75%) of the median RPKM levels of causal genes; for each gene only tissues expressing the gene were considered. C. Causal genes involved in TS-PPI tend to have more TS-PPI in their disease tissues relative to other tissues. Out of 126 genes with TS-PPI, 58 genes had higher median TS-PPI in the disease tissue relative to non-disease tissues in which they are expressed (p-value<10⁻⁴). The box-plot diagram shows the quartiles (25%, 50% and 75%) of the median number of TS-PPI of causal genes, where for each gene only tissues expressing the gene were considered. The first (25%) and second (50%) quartiles of non-disease tissues were zero and therefore overlap with the X axis. D. The majority of the 303 hereditary diseases are associated with elevated expression and/or TS-PPIs of their causal genes in their disease tissues.</p

TS-PPIs illuminate disease-related tissue-specific effects of causal genes.

<p>Orange, blue and grey nodes denote tissue-specific, globally-expressed, and other proteins, respectively; diamond nodes mark hereditary disease genes; edges denote PPIs. A. BRCA1 is a globally-expressed tumor-suppressor hub, and ESR1 is an estrogen receptor protein that activates cellular proliferation. The breast-specific PPI linking BRCA1 and ESR1 provides a potential basis for the breast-specific effects of BRCA1 germline mutations <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003632#pcbi.1003632-Rosen1" target="_blank">[44]</a>. B. A lung-specific PPI connects the widely-expressed epidermal growth factor receptor EGFR and its ligand protein epiregulin (EREG). Germline mutations in EGFR lead to lung cancer <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003632#pcbi.1003632-Centeno1" target="_blank">[30]</a>, and EREG was shown to confer invasive properties in an EGFR-dependent manner <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003632#pcbi.1003632-Zhang1" target="_blank">[31]</a>. C. Muscle-specific PPIs connect the widely expressed trans-membrane cell adhesion receptor dystroglycan 1 (DAG1) to its muscle-specific ligand dystrophin (DMD), and to caveolin 3 (CAV3) which regulates DMD by preventing the DAG1-DMD PPI. Mutations in all three genes give rise to various forms of muscular dystrophies. D. The brain-specific PPIs that link members of the globally-expressed protein complex EIF2B to the netrin-1-receptor DCC may underlie the brain-specific effects of germline mutations in EIF2B complex members <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003632#pcbi.1003632-Tcherkezian1" target="_blank">[35]</a>.</p

Hereditary disease genes and their disease-related TS-PPI.

<p>Hereditary disease genes and their disease-related TS-PPI.</p

The construction of 16 human tissue interactomes by integrating data of tissue expression with data of PPIs.

<p>Data of expression per tissue according to DNA microarray (GNF, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003632#pcbi.1003632-Su1" target="_blank">[12]</a>), protein abundance (HPA, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003632#pcbi.1003632-Berglund1" target="_blank">[14]</a>), and RNA-sequencing (RNA-seq, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003632#pcbi.1003632-Bradley1" target="_blank">[15]</a>) were consolidated into 16 main tissues. In parallel, experimentally detected PPIs were united from BIOGRID <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003632#pcbi.1003632-Stark1" target="_blank">[16]</a>, DIP <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003632#pcbi.1003632-Salwinski1" target="_blank">[17]</a>, IntAct <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003632#pcbi.1003632-Aranda1" target="_blank">[18]</a> and MINT <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003632#pcbi.1003632-Ceol1" target="_blank">[19]</a> to form a global human interactome. Tissue interactomes were then constructed by filtering the global interactome per tissue to contain only PPIs in which both pair-mates were found to be expressed within the tissue.</p