Unraveling networks of co-regulated genes on the sole basis of genome sequences

With the growing number of available microbial genome sequences, regulatory signals can now be revealed as conserved motifs in promoters of orthologous genes (phylogenetic footprints). A next challenge is to unravel genome-scale regulatory networks. Using as sole input genome sequences, we predicted cis-regulatory elements for each gene of the yeast Saccharomyces cerevisiae by discovering over-represented motifs in the promoters of their orthologs in 19 Saccharomycetes species. We then linked all genes displaying similar motifs in their promoter regions and inferred a co-regulation network including 56 919 links between 3171 genes. Comparison with annotated regulons highlights the high predictive value of the method: a majority of the top-scoring predictions correspond to already known co-regulations. We also show that this inferred network is as accurate as a co-expression network built from hundreds of transcriptome microarray experiments. Furthermore, we experimentally validated 14 among 16 new functional links between orphan genes and known regulons. This approach can be readily applied to unravel gene regulatory networks from hundreds of microbial genomes for which no other information is available except the sequence. Long-term benefits can easily be perceived when considering the exponential increase of new genome sequences

Collaboratively charting the gene-to-phenotype network of human congenital heart defects

Background How to efficiently integrate the daily practice of molecular biologists, geneticists, and clinicians with the emerging computational strategies from systems biology is still much of an open question. Description We built on the recent advances in Wiki-based technologies to develop a collaborative knowledge base and gene prioritization portal aimed at mapping genes and genomic regions, and untangling their relations with corresponding human phenotypes, congenital heart defects (CHDs). This portal is not only an evolving community repository of current knowledge on the genetic basis of CHDs, but also a collaborative environment for the study of candidate genes potentially implicated in CHDs - in particular by integrating recent strategies for the statistical prioritization of candidate genes. It thus serves and connects the broad community that is facing CHDs, ranging from the pediatric cardiologist and clinical geneticist to the basic investigator of cardiogenesis. Conclusions This study describes the first specialized portal to collaboratively annotate and analyze gene-phenotype networks. Of broad interest to the biological community, we argue that such portals will play a significant role in systems biology studies of numerous complex biological processes. CHDWiki is accessible at http://www.esat.kuleuven.be/~bioiuser/chdwikistatus: publishe

Evaluation of clustering algorithms for protein-protein interaction networks

BACKGROUND: Protein interactions are crucial components of all cellular processes. Recently, high-throughput methods have been developed to obtain a global description of the interactome (the whole network of protein interactions for a given organism). In 2002, the yeast interactome was estimated to contain up to 80,000 potential interactions. This estimate is based on the integration of data sets obtained by various methods (mass spectrometry, two-hybrid methods, genetic studies). High-throughput methods are known, however, to yield a non-negligible rate of false positives, and to miss a fraction of existing interactions. The interactome can be represented as a graph where nodes correspond with proteins and edges with pairwise interactions. In recent years clustering methods have been developed and applied in order to extract relevant modules from such graphs. These algorithms require the specification of parameters that may drastically affect the results. In this paper we present a comparative assessment of four algorithms: Markov Clustering (MCL), Restricted Neighborhood Search Clustering (RNSC), Super Paramagnetic Clustering (SPC), and Molecular Complex Detection (MCODE). RESULTS: A test graph was built on the basis of 220 complexes annotated in the MIPS database. To evaluate the robustness to false positives and false negatives, we derived 41 altered graphs by randomly removing edges from or adding edges to the test graph in various proportions. Each clustering algorithm was applied to these graphs with various parameter settings, and the clusters were compared with the annotated complexes. We analyzed the sensitivity of the algorithms to the parameters and determined their optimal parameter values. We also evaluated their robustness to alterations of the test graph. We then applied the four algorithms to six graphs obtained from high-throughput experiments and compared the resulting clusters with the annotated complexes. CONCLUSION: This analysis shows that MCL is remarkably robust to graph alterations. In the tests of robustness, RNSC is more sensitive to edge deletion but less sensitive to the use of suboptimal parameter values. The other two algorithms are clearly weaker under most conditions. The analysis of high-throughput data supports the superiority of MCL for the extraction of complexes from interaction networks

Etude bioinformatique du réseau d'interactions entre protéines de transport ches les Fungi

Les protéines associées aux membranes sont d'une importance cruciale pour la cellule. Cependant, en raison d'une plus grande difficulté de manipulation, les données biochimiques les concernant sont très lacunaires, notamment au point de vue de la formation de complexes entre ces protéines.L'objectif global de notre travail consiste à combler ces lacunes et à préciser les interactions entre protéines membranaires chez la levure Saccharomyces cerevisiae et plus précisément, entre les transporteurs. Nous avons commencé notre travail par l'étude d'un jeu de données d'interactions à grande échelle entre toutes les perméases détectées par une méthode de double hybride spécialement adaptée aux protéines insolubles (split ubiquitin). Premièrement, la qualité des données a été estimée en étudiant le comportement global des données et des témoins négatifs et positifs. Les données ont ensuite été standardisées et filtrées de façon à ne conserver que les plus significatives. Ces interactions ont ensuite été étudiées en les modélisant dans un réseau d'interactions que nous avons étudié par des techniques issues de la théorie des graphes. Après une évaluation systématique de différentes méthodes de clustering, nous avons notamment recherché au sein du réseau des groupes de protéines densément interconnectées et de fonctions similaires qui correspondraient éventuellement à des complexes protéiques. Les résultats révélés par l'étude du réseau expérimental se sont révélés assez décevants. En effet, même si nous avons pu retrouver certaines interactions déjà décrites, un bon nombre des interactions filtrées semblait n'avoir aucune réalité biologique et nous n'avons pu retrouver que très peu de modules de protéines de fonction semblable hautement inter-connectées. Parmi ceux-ci, il est apparu que les transporteurs d'acides aminés semblaient interagir entre eux.L'approche expérimentale n'ayant eu que peu de succès, nous l'avons contournée en utilisant des méthodes de génomique comparative d'inférence d'interactions fonctionnelles. Dans un premier temps, malgré une évaluation rigoureuse, l'étude des profils phylogénétiques (la prédiction d'interactions fonctionnelles en étudiant la corrééélation des profils de présence - absence des gènes dans un ensemble de génomes), n'a produit que des résultats mitigés car les perméases semblent très peu conservées dès lors que l'on considère d'autres organismes que les \Doctorat en Sciencesinfo:eu-repo/semantics/nonPublishe

D-peaks: a visual tool to display ChIP-seq peaks along the genome.

ChIP-sequencing is a method of choice to localize the positions of protein binding sites on DNA on a whole genomic scale. The deciphering of the sequencing data produced by this novel technique is challenging and it is achieved by their rigorous interpretation using dedicated tools and adapted visualization programs. Here, we present a bioinformatics tool (D-peaks) that adds several possibilities (including, user-friendliness, high-quality, relative position with respect to the genomic features) to the well-known visualization browsers or databases already existing. D-peaks is directly available through its web interface http://rsat.ulb.ac.be/dpeaks/ as well as a command line tool.Journal ArticleResearch Support, Non-U.S. Gov'tSCOPUS: ar.jinfo:eu-repo/semantics/publishe

YTPdb: a wiki database of yeast membrane transporters.

Membrane transporters constitute one of the largest functional categories of proteins in all organisms. In the yeast Saccharomyces cerevisiae, this represents about 300 proteins ( approximately 5% of the proteome). We here present the Yeast Transport Protein database (YTPdb), a user-friendly collaborative resource dedicated to the precise classification and annotation of yeast transporters. YTPdb exploits an evolution of the MediaWiki web engine used for popular collaborative databases like Wikipedia, allowing every registered user to edit the data in a user-friendly manner. Proteins in YTPdb are classified on the basis of functional criteria such as subcellular location or their substrate compounds. These classifications are hierarchical, allowing queries to be performed at various levels, from highly specific (e.g. ammonium as a substrate or the vacuole as a location) to broader (e.g. cation as a substrate or inner membranes as location). Other resources accessible for each transporter via YTPdb include post-translational modifications, K(m) values, a permanently updated bibliography, and a hierarchical classification into families. The YTPdb concept can be extrapolated to other organisms and could even be applied for other functional categories of proteins. YTPdb is accessible at http://homes.esat.kuleuven.be/ytpdb/.Journal ArticleResearch Support, Non-U.S. Gov'tSCOPUS: ar.jinfo:eu-repo/semantics/publishe

pAKT pathway activation is associated with PIK3CA mutations and good prognosis in luminal breast cancer in contrast to p-mTOR pathway activation

Molecular signaling: Two components of same pathway have divergent effects One of the key signaling pathways involved in regulating cancer cell growth and survival is not linearly connected in luminal breast cancers, as researchers had long assumed. A team led by Amir Sonnenblick from Tel Aviv University, Israel, and Christos Sotiriou from Institut Jules Bordet, Belgium, studied tumors with elevated expression of two different proteins in the PI3K/AKT/mTOR pathway. They showed that tumors with activated AKT had distinct gene expression profiles from tumors with activated mTOR. What’s more, patients with AKT activation had better outcomes on average and were more likely to carry mutations in the oncogene PIK3CA, whereas patients with mTOR activation tended to relapse sooner and were more likely to carry mutations in the tumor suppressor p53. The findings suggest that doctors should look closely at PI3K/AKT/mTOR signaling activation when personalizing treatment decisions

Low Dose Radiation Causes Skin Cancer in Mice and Has a Differential Effect on Distinct Epidermal Stem Cells.

The carcinogenic effect of ionizing radiation has been evaluated based on limited populations accidently exposed to high dose radiation. In contrast, insufficient data are available on the effect of low dose radiation (LDR), such as radiation deriving from medical investigations and interventions, as well as occupational exposure that concern a large fraction of western populations. Using mouse skin epidermis as a model, we showed that LDR results in DNA damage in sebaceous gland (SG) and bulge epidermal stem cells (SCs). While the first commit apoptosis upon low dose irradiation, the latter survive. Bulge SC survival coincides with higher HIF-1α expression and a metabolic switch upon LDR. Knocking down HIF-1α sensitizes bulge SCs to LDR-induced apoptosis, while upregulation of HIF-1α in the epidermis, including SG SCs, rescues cell death. Most importantly, we show that LDR results in cancer formation with full penetrance in the radiation-sensitive Patched1 heterozygous mice. Overall, our results demonstrate for the first time that LDR can be a potent carcinogen in individuals predisposed to cancer. Stem Cells 2017.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Genomic aberrations in young and elderly breast cancer patients.

Age at breast cancer diagnosis is a known prognostic factor. Previously, several groups including ours have shown that young age at diagnosis is associated with higher prevalence of basal-like tumors and aggressive tumor phenotypes. Yet the impact of age at diagnosis on the genomic landscape of breast cancer remains unclear. In this study, we examined the pattern of somatic mutations, chromosomal copy number variations (CNVs) and transcriptomic profiles in young and elderly breast cancer patients.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

The pattern of somatic mutations and chromosomal copy number variations (CNV) in young breast cancer (BC) patients (pts).

Poster Session, Breast Cancer—HER2/ER, J Clin Oncol 33, 2015 (suppl; abstr 579)info:eu-repo/semantics/publishe