Synthetic lethality guiding selection of drug combinations in ovarian cancer.

Background: Synthetic lethality describes a relationship between two genes where single loss of either gene does not trigger significant impact on cell viability, but simultaneous loss of both gene functions results in lethality. Targeting synthetic lethal interactions with drug combinations promises increased efficacy in tumor therapy.

Materials And Methods: We established a set of synthetic lethal interactions using publicly available data from yeast screens which were mapped to their respective human orthologs using information from orthology databases.

Biomarker panels for characterizing microbial community biofilm formation as composite molecular process.

Microbial consortia execute collaborative molecular processes with contributions from individual species, on such basis enabling optimized molecular function. Such collaboration and synergies benefit metabolic flux specifically in extreme environmental conditions as seen in acid mine drainage, with biofilms as relevant microenvironment. However, knowledge about community species composition is not sufficient for deducing presence and efficiency of composite molecular function.

Validation of a protein panel for the noninvasive detection of recurrent non-muscle invasive bladder cancer.

Context: About 50-70% of patients with non-muscle invasive bladder cancer (NMIBC) experience relapse of disease.

Objective: To establish a panel of protein biomarkers incorporated in a multiplexed microarray (BCa chip) and a classifier for diagnosing recurrent NMIBC.

Materials And Methods: Urine samples from 45 patients were tested.

Predictive Biomarkers for Linking Disease Pathology and Drug Effect.

Background: Productivity in drug R&D continues seeing significant attrition in clinical stage testing. Approval of new molecular entities proceeds with slow pace specifically when it comes to chronic, age-related diseases, calling for new conceptual approaches, methodological implementation and organizational adoption in drug development.

Methods: Detailed phenotyping of disease presentation together with comprehensive representation of drug mechanism of action is considered as a path forward, and a big data spectrum has become available covering behavioral, clinical and molecular characteristics, the latter combining reductionist and explorative strategies.

Vascular endothelial growth factor A as predictive marker for mTOR inhibition in relapsing high-grade serous ovarian cancer.

Background: Development of resistance against first line drug therapy including cisplatin and paclitaxel in high-grade serous ovarian cancer (HGSOC) presents a major challenge. Identifying drug candidates breaking resistance, ideally combined with predictive biomarkers allowing precision use are needed for prolonging progression free survival of ovarian cancer patients. Modeling of molecular processes driving drug resistance in tumor tissue further combined with mechanism of action of drugs provides a strategy for identification of candidate drugs and associated predictive biomarkers.

Disease map-based biomarker selection and pre-validation for bladder cancer diagnostic.

Context: Urinary biomarkers are promising as simple alternatives to cystoscopy for the diagnosis of de novo and recurrent bladder cancer.

Objective: To identify a highly sensitive and specific biomarker candidate set with potential clinical utility in bladder cancer.

Materials And Methods: Urinary biomarker concentrations were determined by ELISA.

Interaction networks for identifying coupled molecular processes in microbial communities.

Background: Microbial communities adapt to environmental conditions for optimizing metabolic flux. Such adaption may include cooperative mechanisms eventually resulting in phenotypic observables as emergent properties that cannot be attributed to an individual species alone. Understanding the molecular basis of cross-species cooperation adds to utilization of microbial communities in industrial applications including metal bioleaching and bioremediation processes.

Quantitative in vitro to in vivo extrapolation of tissues toxicity.

Predicting repeated-dosing in vivo drug toxicity from in vitro testing and omics data gathering requires significant support in bioinformatics, mathematical modeling and statistics. We present here the major aspects of the work devoted within the framework of the European integrated Predict-IV to pharmacokinetic modeling of in vitro experiments, physiologically based pharmacokinetic (PBPK) modeling, mechanistic models of toxicity for the kidney and brain, large scale dose-response analyses methods and biomarker discovery tools. All of those methods have been applied to various extent to the drug datasets developed by the project's partners.

Transcriptomics hit the target: Monitoring of ligand-activated and stress response pathways for chemical testing.

High content omic methods provide a deep insight into cellular events occurring upon chemical exposure of a cell population or tissue. However, this improvement in analytic precision is not yet matched by a thorough understanding of molecular mechanisms that would allow an optimal interpretation of these biological changes. For transcriptomics (TCX), one type of molecular effects that can be assessed already is the modulation of the transcriptional activity of a transcription factor (TF).

Development of an in vitro renal epithelial disease state model for xenobiotic toxicity testing.

There is a growing impetus to develop more accurate, predictive and relevant in vitro models of renal xenobiotic exposure. As part of the EU-FP7, Predict-IV project, a major aim was to develop models that recapitulate not only normal tissue physiology but also aspects of disease conditions that exist as predisposing risk factors for xenobiotic toxicity. Hypoxia, as a common micro-environmental alteration associated with pathophysiology in renal disease, was investigated for its effect on the toxicity profile of a panel of 14 nephrotoxins, using the human proximal tubular epithelial RPTECT/TERT1 cell line.

Application of RPTEC/TERT1 cells for investigation of repeat dose nephrotoxicity: A transcriptomic study.

The kidney is a major target organ for toxicity. Incidence of chronic kidney disease (CKD) is increasing at an alarming rate due to factors such as increasing population age and increased prevalence of heart disease and diabetes. There is a major effort ongoing to develop superior predictive models of renal injury and early renal biomarkers that can predict onset of CKD.

From molecular signatures to predictive biomarkers: modeling disease pathophysiology and drug mechanism of action.

Omics profiling significantly expanded the molecular landscape describing clinical phenotypes. Association analysis resulted in first diagnostic and prognostic biomarker signatures entering clinical utility. However, utilizing Omics for deepening our understanding of disease pathophysiology, and further including specific interference with drug mechanism of action on a molecular process level still sees limited added value in the clinical setting.

Interleukin-19 as a translational indicator of renal injury.

Accurate detection and prediction of renal injury are central not only to improving renal disease management but also for the development of new strategies to assess drug safety in pre-clinical and clinical testing. In this study, we utilised the well-characterised and differentiated human renal proximal tubule cell line, RPTEC/TERT1 in an attempt to identify markers of renal injury, independent of the mechanism of toxicity. We chose zoledronate as a representative nephrotoxic agent to examine global transcriptomic alterations using a daily repeat bolus protocol over 14 days, reflective of sub-acute or chronic injury.

Transcriptomic hepatotoxicity signature of chlorpromazine after short- and long-term exposure in primary human sandwich cultures.

Drug-induced liver injury is the most frequent reason for market withdrawal of approved drugs, and is difficult to predict in animal models. Here, we analyzed transcriptomic data derived from short- and long-term cultured primary human hepatocytes (PHH) exposed to the well known human hepatotoxin chlorpromazine (CPZ). Samples were collected from five PHH cultures after short-term (1 and 3 days) and long-term (14 days) repeat daily treatment with 0.

Molecular models of the cardiorenal syndrome.

Molecular profiling techniques have provided extensive sets of molecular features characterizing clinical phenotypes, but further extrapolation to mechanistic molecular models of disease pathophysiology faces major challenges. Here, we describe a computational procedure for delineating molecular disease models utilizing omics profiles, and exemplify the methodology on aspects of the cardiorenal syndrome describing the clinical association of declining kidney function and increased cardiovascular event rates. Individual molecular features as well as selected molecular processes were identified as linking cardiovascular and renal pathology as a combination of cross-organ mediators and common pathophysiology.

Application of integrated transcriptomic, proteomic and metabolomic profiling for the delineation of mechanisms of drug induced cell stress.

High content omic techniques in combination with stable human in vitro cell culture systems have the potential to improve on current pre-clinical safety regimes by providing detailed mechanistic information of altered cellular processes. Here we investigated the added benefit of integrating transcriptomics, proteomics and metabolomics together with pharmacokinetics for drug testing regimes. Cultured human renal epithelial cells (RPTEC/TERT1) were exposed to the nephrotoxin Cyclosporine A (CsA) at therapeutic and supratherapeutic concentrations for 14days.

Comparative analysis of perturbed molecular pathways identified in in vitro and in vivo toxicology studies.

The development of in vitro toxicological testing strategies are hampered by the difficulty in extrapolation to the intact organism. Academic toxicological literature contains a wealth of mechanistically rich information, especially arising from omic studies, which could potentially be utilized to uncover commonalities between in vitro and in vivo observations on the cellular level. Using a literature mining strategy, we identified 1221 unique human genes as being associated to nephrotoxicity, hepatotoxicity, or CNS toxicity, either linked to in vitro, in vivo, or both experimental conditions.

Computational analysis workflows for Omics data interpretation.

Progress in experimental procedures has led to rapid availability of Omics profiles. Various open-access as well as commercial tools have been developed for storage, analysis, and interpretation of transcriptomics, proteomics, and metabolomics data. Generally, major analysis steps include data storage, retrieval, preprocessing, and normalization, followed by identification of differentially expressed features, functional annotation on the level of biological processes and molecular pathways, as well as interpretation of gene lists in the context of protein-protein interaction networks.

Data and knowledge management in cross-Omics research projects.

Cross-Omics studies aimed at characterizing a specific phenotype on multiple levels are entering the -scientific literature, and merging e.g. transcriptomics and proteomics data clearly promises to improve Omics data interpretation.

Synthetic lethal hubs associated with vincristine resistant neuroblastoma.

Chemotherapy of cancer experiences a number of shortcomings including development of drug resistance. This fact also holds true for neuroblastoma utilizing chemotherapeutics as vincristine. We performed a comparative analysis of molecular and cellular mechanisms associated with vincristine resistance utilizing cell line as well as human tissue data.

Linking transcriptomic and proteomic data on the level of protein interaction networks.

Integration and joint analysis of omics profiles derived on the genome, transcriptome, proteome and metabolome levels is a natural next step in realizing a Systems Biology view of cellular processes. However, merging, e.g.

A dependency graph approach for the analysis of differential gene expression profiles.

A central aim of differential gene expression profile analysis is to provide an interpretation of given data at the level of biological processes and pathways. However, traversing descriptive data into context is not straightforward. We present a gene-centric dependency graph approach supporting an interpretation of omics profiles at the level of affected networks.

Analysis and prediction of protective continuous B-cell epitopes on pathogen proteins.

Background: The application of peptide based diagnostics and therapeutics mimicking part of protein antigen is experiencing renewed interest. So far selection and design rationale for such peptides is usually driven by T-cell epitope prediction, available experimental and modelled 3D structure, B-cell epitope predictions such as hydrophilicity plots or experience. If no structure is available the rational selection of peptides for the production of functionally altering or neutralizing antibodies is practically impossible.

Linking the ovarian cancer transcriptome and immunome.

Background: 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.

Results: 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.

Mouse mammary tumor virus integration site selection in human and mouse genomes.

Based on integration site preferences, retroviruses can be placed into three groups. Viruses that comprise the first group, murine leukemia virus and foamy virus, integrate preferentially near transcription start sites. The second group, notably human immunodeficiency virus and simian immunodeficiency virus, preferentially targets transcription units.