Systems pharmacology and genome medicine: a future perspective

Genome medicine uses genomic information in the diagnosis of disease and in prescribing treatment. This transdisciplinary field brings together knowledge on the relationships between genetics, pathophysiology and pharmacology. Systems pharmacology aims to understand the actions and adverse effects of drugs by considering targets in the context of the biological networks in which they exist. Genome medicine forms the base on which systems pharmacology can develop. Experimental and computational approaches enable systems pharmacology to obtain holistic, mechanistic information on disease networks and drug responses, and to identify new drug targets and specific drug combinations. Network analyses of interactions involved in pathophysiology and drug response across various scales of organization, from molecular to organismal, will allow the integration of the systems-level understanding of drug action with genome medicine. The interface of the two fields will enable drug discovery for personalized medicine. Here we provide a perspective on the questions and approaches that drive the development of these new interrelated fields

Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases

<p>Abstract</p> <p>Background</p> <p>In recent years, mammalian protein-protein interaction network databases have been developed. The interactions in these databases are either extracted manually from low-throughput experimental biomedical research literature, extracted automatically from literature using techniques such as natural language processing (NLP), generated experimentally using high-throughput methods such as yeast-2-hybrid screens, or interactions are predicted using an assortment of computational approaches. Genes or proteins identified as significantly changing in proteomic experiments, or identified as susceptibility disease genes in genomic studies, can be placed in the context of protein interaction networks in order to assign these genes and proteins to pathways and protein complexes.</p> <p>Results</p> <p>Genes2Networks is a software system that integrates the content of ten mammalian interaction network datasets. Filtering techniques to prune low-confidence interactions were implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from "seed" lists of human Entrez gene symbols. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list.</p> <p>Conclusion</p> <p>Genes2Networks is powerful web-based software that can help experimental biologists to interpret lists of genes and proteins such as those commonly produced through genomic and proteomic experiments, as well as lists of genes and proteins associated with disease processes. This system can be used to find relationships between genes and proteins from seed lists, and predict additional genes or proteins that may play key roles in common pathways or protein complexes.</p

SNAVI: Desktop application for analysis and visualization of large-scale signaling networks

<p>Abstract</p> <p>Background</p> <p>Studies of cellular signaling indicate that signal transduction pathways combine to form large networks of interactions. Viewing protein-protein and ligand-protein interactions as graphs (networks), where biomolecules are represented as nodes and their interactions are represented as links, is a promising approach for integrating experimental results from different sources to achieve a systematic understanding of the molecular mechanisms driving cell phenotype. The emergence of large-scale signaling networks provides an opportunity for topological statistical analysis while visualization of such networks represents a challenge.</p> <p>Results</p> <p>SNAVI is Windows-based desktop application that implements standard network analysis methods to compute the clustering, connectivity distribution, and detection of network motifs, as well as provides means to visualize networks and network motifs. SNAVI is capable of generating linked web pages from network datasets loaded in text format. SNAVI can also create networks from lists of gene or protein names.</p> <p>Conclusion</p> <p>SNAVI is a useful tool for analyzing, visualizing and sharing cell signaling data. SNAVI is open source free software. The installation may be downloaded from: <url>http://snavi.googlecode.com</url>. The source code can be accessed from: <url>http://snavi.googlecode.com/svn/trunk</url></p

Cohesin complex-associated holoprosencephaly

Marked by incomplete division of the embryonic forebrain, holoprosencephaly is one of the most common human developmental disorders. Despite decades of phenotype-driven research, 80–90% of aneuploidy-negative holoprosencephaly individuals with a probable genetic aetiology do not have a genetic diagnosis. Here we report holoprosencephaly associated with variants in the two X-linked cohesin complex genes, STAG2 and SMC1A, with loss-of-function variants in 10 individuals and a missense variant in one. Additionally, we report four individuals with variants in the cohesin complex genes that are not X-linked, SMC3 and RAD21. Using whole mount in situ hybridization, we show that STAG2 and SMC1A are expressed in the prosencephalic neural folds during primary neurulation in the mouse, consistent with forebrain morphogenesis and holoprosencephaly pathogenesis. Finally, we found that shRNA knockdown of STAG2 and SMC1A causes aberrant expression of HPE-associated genes ZIC2, GLI2, SMAD3 and FGFR1 in human neural stem cells. These findings show the cohesin complex as an important regulator of median forebrain development and X-linked inheritance patterns in holoprosencephaly

Heavy quarkonium: progress, puzzles, and opportunities

A golden age for heavy quarkonium physics dawned a decade ago, initiated by the confluence of exciting advances in quantum chromodynamics (QCD) and an explosion of related experimental activity. The early years of this period were chronicled in the Quarkonium Working Group (QWG) CERN Yellow Report (YR) in 2004, which presented a comprehensive review of the status of the field at that time and provided specific recommendations for further progress. However, the broad spectrum of subsequent breakthroughs, surprises, and continuing puzzles could only be partially anticipated. Since the release of the YR, the BESII program concluded only to give birth to BESIII; the $B$-factories and CLEO-c flourished; quarkonium production and polarization measurements at HERA and the Tevatron matured; and heavy-ion collisions at RHIC have opened a window on the deconfinement regime. All these experiments leave legacies of quality, precision, and unsolved mysteries for quarkonium physics, and therefore beg for continuing investigations. The plethora of newly-found quarkonium-like states unleashed a flood of theoretical investigations into new forms of matter such as quark-gluon hybrids, mesonic molecules, and tetraquarks. Measurements of the spectroscopy, decays, production, and in-medium behavior of c\bar{c}, b\bar{b}, and b\bar{c} bound states have been shown to validate some theoretical approaches to QCD and highlight lack of quantitative success for others. The intriguing details of quarkonium suppression in heavy-ion collisions that have emerged from RHIC have elevated the importance of separating hot- and cold-nuclear-matter effects in quark-gluon plasma studies. This review systematically addresses all these matters and concludes by prioritizing directions for ongoing and future efforts.Comment: 182 pages, 112 figures. Editors: N. Brambilla, S. Eidelman, B. K. Heltsley, R. Vogt. Section Coordinators: G. T. Bodwin, E. Eichten, A. D. Frawley, A. B. Meyer, R. E. Mitchell, V. Papadimitriou, P. Petreczky, A. A. Petrov, P. Robbe, A. Vair

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN