An Interaction Network Predicted from Public Data as a Discovery Tool: Application to the Hsp90 Molecular Chaperone Machine

Understanding the functions of proteins requires information about their protein-protein interactions (PPI). The collective effort of the scientific community generates far more data on any given protein than individual experimental approaches. The latter are often too limited to reveal an interactome comprehensively. We developed a workflow for parallel mining of all major PPI databases, containing data from several model organisms, and to integrate data from the literature for a protein of interest. We applied this novel approach to build the PPI network of the human Hsp90 molecular chaperone machine (Hsp90Int) for which previous efforts have yielded limited and poorly overlapping sets of interactors. We demonstrate the power of the Hsp90Int database as a discovery tool by validating the prediction that the Hsp90 co-chaperone Aha1 is involved in nucleocytoplasmic transport. Thus, we both describe how to build a custom database and introduce a powerful new resource for the scientific community

Integrative Bioinformatics Analysis of Proteins Associated with the Cardiorenal Syndrome

The cardiorenal syndrome refers to the coexistence of kidney and cardiovascular disease, where cardiovascular events are the most common cause of death in patients with chronic kidney disease. Both, cardiovascular as well as kidney diseases have been extensively analyzed on a molecular level, resulting in molecular features and associated processes indicating a cross-talk of the two disease etiologies on a pathophysiological level. In order to gain a comprehensive picture of molecular factors contributing to the bidirectional interplay between kidney and cardiovascular system, we mined the scientific literature for molecular features reported as associated with the cardiorenal syndrome, resulting in 280 unique genes/proteins. These features were then analyzed on the level of molecular processes and pathways utilizing various types of protein interaction networks. Next to well established molecular features associated with the renin-angiotensin system numerous proteins involved in signal transduction and cell communication were found, involving specific molecular functions covering receptor binding with natriuretic peptide receptor and ligands as well known example. An integrated analysis of identified features pinpointed a protein interaction network involving mediators of hemodynamic change and an accumulation of features associated with the endothelin and VEGF signaling pathway. Some of these features may function as novel therapeutic targets

Linking the ovarian cancer transcriptome and immunome

<p>Abstract</p> <p>Background</p> <p>Autoantigens have been reported in a variety of tumors, providing insight into the interplay between malignancies and the immune response, and also giving rise to novel diagnostic and therapeutic concepts. Why certain tumor-associated proteins induce an immune response remains largely elusive.</p> <p>Results</p> <p>This paper analyzes the proposed link between increased abundance of a protein in cancerous tissue and the increased potential of the protein for induction of a humoral immune response, using ovarian cancer as an example. Public domain data sources on differential gene expression and on autoantigens associated with this malignancy were extracted and compared, using bioinformatics analysis, on the levels of individual genes and proteins, transcriptional coregulation, joint functional pathways, and shared protein-protein interaction networks. Finally, a selected list of ovarian cancer-associated, differentially regulated proteins was tested experimentally for reactivity with antibodies prevalent in sera of ovarian cancer patients.</p> <p>Genes reported as showing differential expression in ovarian cancer exhibited only minor overlap with the public domain list of ovarian cancer autoantigens. However, experimental screening for antibodies directed against antigenic determinants from ovarian cancer-associated proteins yielded clear reactions with sera.</p> <p>Conclusion</p> <p>A link between tumor protein abundance and the likelihood of induction of a humoral immune response in ovarian cancer appears evident.</p

Reconstruction of functional context from heterogenous data networks

Zsfassung in dt. SpracheIn Kontrast zu rein hypothesengetriebenen Ansätzen verlagerte sich die Forschung in der Molekularbiologie in signifikanter Weise in Richtung explorative Analyse auf Basis reichhaltiger Datensätze. Diese Tatsache gründet auf der Entwicklung von neuen experimentellen Technologien (Stichwort: ’omics’ - Revolution) zur Beobachtung von gesamtzellulären Prozessen, ergänzt durch computergestützte Algorithmen für Datenvorhersage und Systemmodellierung. Verfahren, welche zelluläre Prozesse zu erklären und relevante Schlüsselstellen (Biomarker) zu identifizieren versuchen, bedienen sich der explorativen Analyse und der klassischen Statistik in sequentieller Art und Weise, um letztendlich funktionalen Kontext aus gegebenen Daten ableiten zu können. Aus grundlegenden Ideen der neuen Disziplin Systems Biology entstanden mittlerweile integrativere Ansätze auf Basis dieser gegebenen, jedoch sehr heterogenen Datensätze. Unter diesen Umständen ist anzunehmen, dass eine Entwicklung - weg von einer sequentiellen und hin zu einer parallel getriebenen - integrativeren Analyse stattfinden wird: Die verfügbaren ’large-scale’ Daten umfassen Genexpression, Proteinexpression, -lokalisation und -interaktion. Funktionale Abhängigkeiten werden im Kontext analysiert - im Gegesatz zu dem sequentiellen Ablauf - folgend der Herangehensweise ’from gene to protein to function’. Im Rahmen dieser Diplomarbeit erfolgte die Implementierung einer neuen Methode, welche darauf abzielt, Biomarker im funktionalen Kontext zu entschlüsseln: Unser Konzept folgt dem Ansatz der Dynamischen Hierarchien respektive der Emergenz als zugrundeliegendes organisatorisches Prinzip biologischer Prozesse und erlaubt eine parallele und integrative Analyse der heterogenen ’omics’-Daten.Research in molecular biology has significantly changed towards utilizing explorative analysis on the basis of vast, but heterogeneous data sets - in contrast to purely hypothesis driven approaches. This development has grounded on the availability of novel experimental technologies (the ’omics’ revolution) for monitoring cell-wide events, complemented by computational procedures for data prediction and systems modeling. Present procedures for understanding cellular processes and identification of relevant key players (biomarkers) have utilized explorative analysis and classical statistic approaches in a sequential manner, with the ultimate goal of identifying functional context and associated biomarkers. With upcoming approaches on the basis of Systems Biology a more integrated procedure on the basis of given, but heterogeneous data sets has emerged. Under these premises the sequential linking of data will change towards a parallel, integrated analysis approach: Available large scale data on cellular processes covering gene expression, protein abundance, protein location and their functional interplay are contextually analyzed in contrast to a sequential analysis procedure which focuses on a ’from gene to protein to function’ approach. This thesis outlines a novel computational methodology aimed at deciphering functional context and biomarkers: Our concept is following the framework of dynamical hierarchies and emergence as organizational principle of the underlying biological processes. This framework allows a parallel and integrative analysis of heterogeneous sources from ’omics’.10