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. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis. RESULTS: A multi biomarker panel (ECadh, IL8, MMP9, EN2, VEGF, past recurrences, BCG therapies and stage at diagnosis) was identified yielding an area under the curve of 0.96. DISCUSSION AND CONCLUSION: This biomarker panel represents a potential diagnostic tool for noninvasive diagnosis of recurrent NMIBC.

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. The performance of individual markers and marker combinations was assessed using ROC analysis. RESULTS: A five-biomarker panel (IL8, MMP9, VEGFA, PTGS2 and EN2) was defined from the candidate set. DISCUSSION AND CONCLUSION: This panel showed a better overall performance than the best individual marker. Further validation studies are needed to evaluate its clinical utility in bladder cancer.

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. We identified alterations in targets of the cholesterol and mevalonate biosynthetic pathways reflective of zoledronate specific effects. We also identified interleukin-19 (IL-19) among other inflammatory signals such as SERPINA3 and DEFB4 utilising microarray analysis. Release of IL-19 protein was highly induced by an additional four nephrotoxic agents, at magnitudes greater than the characterised marker of renal injury, lipocalin-2. We also demonstrate a large increase in levels of IL-19 in urine of patients with chronic kidney disease, which significantly correlated with estimated glomerular filtration rate levels. We suggest IL-19 as a potential new translational marker of renal 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 0.1 or 0.2 µM CPZ, corresponding to C(max). Two PHH cultures were additionally treated with 1 µM CPZ, and the three others with 0.02 µM CPZ. Differences in the total number of gene changes were seen between donors and throughout treatment. Specific transcriptomic hepatotoxicity signatures were created for CPZ and consisted of inflammation/hepatitis, cholestasis, and liver proliferation in all five donors, as well as fibrosis and steatosis, which were observed in four of five donors. Necrosis was present in three of five donors, and an indicative signature of cirrhosis was observed after long-term 14-day repeat treatment, also in three of five donors. The inter-donor variability in the inflammatory response to CPZ treatment was associated with variability in the strength of the response of the transcriptomic hepatotoxicity signatures, suggesting that features of inflammation could be related to the idiosyncratic hepatotoxic effects of CPZ in humans.

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. The molecular characterization of the disease presents as a set of molecular processes together with their interactions, composing a molecular disease model of the cardiorenal syndrome. Integrating omics profiles describing aspects of cardiovascular disease and respective profiles for advanced chronic kidney disease on molecular interaction networks, computation of disease term-specific subgraphs, and complemented by subgraph segmentation allowed delineation of disease term-specific molecular models, at their intersection providing contributors to cardiorenal pathology. Building such molecular disease models allows in a generic way to integrate multi-omics sources for generating comprehensive sets of molecular processes, on such basis providing rationale for biomarker panel selection for further characterizing clinical phenotypes.

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. mRNA concentration and protein abundance profiles, is not straightforward, as a direct overlap of differentially regulated/abundant features, resulting from transcriptomics and proteomics, is for various reasons limited. We present the procedures for integrating omics profiles at the level of protein interaction networks, exemplified by using transcriptomic and proteomic data sets characterizing chronic kidney disease. On the level of direct feature overlap, only a limited number of genes and proteins were found to be significantly affected following a separate transcript and protein profile analysis, including a collagen subtype and uromodulin, both being described in the context of renal failure. On the level of protein pathway and process categories, this minor overlap increases substantially, identifying cell structure, cell adhesion, as well as immunity and defense mechanisms as jointly populated with features individually identified as relevant in transcriptomics and proteomics experiments. Mapping diverse data sources characterizing a given phenotype under the analysis on directed and also undirected protein interaction networks serves in joint functional interpretation of omics data sets.

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. Avian sarcoma-leukosis virus (ASLV) and human T-cell leukemia virus (HTLV), forming the third group, show little preference for any genomic feature. We have previously shown that some human cells sustain mouse mammary tumor virus (MMTV) infection; therefore, we infected a susceptible human breast cell line, Hs578T, and, without introducing a species-specific bias, compared the MMTV integration profile to those of other retroviruses. Additionally, we infected a mouse cell line, NMuMG, and thus we could compare MMTV integration site selection in human and mouse cells. In total, we examined 468 unique MMTV integration sites. Irrespective of whether human or mouse cells were infected, no integration bias favoring transcription start sites was detected, a profile that is reminiscent of that of ASLV and HTLV. However, in contrast to ASLV and HTLV, not even a modest tendency in favor of integration within genes was observed. Similarly, repetitive sequences and genes that are frequently tagged by MMTV in mammary tumors were not preferentially targeted in cell culture either in mouse or in human cells; hence, we conclude that MMTV displays the most random dispersion of integration sites among retroviruses determined so far.

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. This set of experimental synthetic lethal interactions was complemented by a set of predicted synthetic lethal interactions based on a set of protein meta-data like e.g. molecular pathway assignment. Based on the combined set, we evaluated drug combinations used in late stage clinical development (clinical phase III and IV trials) or already in clinical use for ovarian cancer with respect to their effect on synthetic lethal interactions. We furthermore identified a set of drug combinations currently not being tested in late stage ovarian cancer clinical trials that however have impact on synthetic lethal interactions thus being worth of further investigations regarding their therapeutic potential in ovarian cancer. RESULTS: Twelve of the tested drug combinations addressed a synthetic lethal interaction with the anti-VEGF inhibitor bevacizumab in combination with paclitaxel being the most studied drug combination addressing the synthetic lethal pair between VEGFA and BCL2. The set of 84 predicted drug combinations for example holds the combination of the PARP inhibitor olaparib and paclitaxel, which showed efficacy in phase II clinical studies. CONCLUSION: A set of drug combinations currently not tested in late stage ovarian cancer clinical trials was identified having impact on synthetic lethal interactions thus being worth of further investigations regarding their therapeutic potential in ovarian cancer.

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. For this task molecular resolution of the consortium interactome is to be retrieved, with molecular biomarkers particularly suited for characterizing composite molecular processes involved in biofilm formation and maintenance. A microbial species set identified in 18 copper environmental sites provides a data matrix for deriving a cross-species molecular process model of biofilm formation composed of 191 protein coding genes contributed from 25 microbial species. Computing degree and stress centrality of biofilm molecular process nodes allows selection of network hubs and central connectors, with the top ranking molecular features proposed as biomarker candidates for characterizing biofilm homeostasis. Functional classes represented in the biomarker panel include quorum sensing, chemotaxis, motility and extracellular polysaccharide biosynthesis, complemented by chaperones. Abundance of biomarker candidates identified in experimental data sets monitoring different biofilm conditions provides evidence for the selected biomarkers as sensitive and specific molecular process proxies for capturing biofilm microenvironments. Topological criteria of process networks covering an aggregate function of interest support the selection of biomarker candidates independent of specific community species composition. Such panels promise efficient screening of environmental samples for presence of microbial community composite molecular function.

Characterization of protein-interaction networks in tumors.

BACKGROUND: Analyzing differential-gene-expression data in the context of protein-interaction networks (PINs) yields information on the functional cellular status. PINs can be formally represented as graphs, and approximating PINs as undirected graphs allows the network properties to be characterized using well-established graph measures. This paper outlines features of PINs derived from 29 studies on differential gene expression in cancer. For each study the number of differentially regulated genes was determined and used as a basis for PIN construction utilizing the Online Predicted Human Interaction Database. RESULTS: Graph measures calculated for the largest subgraph of a PIN for a given differential-gene-expression data set comprised properties reflecting the size, distribution, biological relevance, density, modularity, and cycles. The values of a distinct set of graph measures, namely Closeness Centrality, Graph Diameter, Index of Aggregation, Assortative Mixing Coefficient, Connectivity, Sum of the Wiener Number, modified Vertex Distance Number, and Eigenvalues differed clearly between PINs derived on the basis of differential gene expression data sets characterizing malignant tissue and PINs derived on the basis of randomly selected protein lists. CONCLUSION: Cancer PINs representing differentially regulated genes are larger than those of randomly selected protein lists, indicating functional dependencies among protein lists that can be identified on the basis of transcriptomics experiments. However, the prevalence of hub proteins was not increased in the presence of cancer. Interpretation of such graphs in the context of robustness may yield novel therapies based on synthetic lethality that are more effective than focusing on single-action drugs for cancer treatment.

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. On this basis integrative analytics in the realm of Systems Biology has emerged, essentially aiming at traversing associations into causal relationships for bridging molecular disease specifics and clinical phenotype surrogates and finally explaining drug response and outcome. RESULTS: From a conceptual perspective bottom-up modeling approaches are available, with dynamical hierarchies as formalism capable of describing clinical findings as emergent properties of an underlying molecular process network comprehensively resembling disease pathology. In such representation biomarker candidates serve as proxy of a molecular process set, at the interface of a corresponding representation of drug mechanism of action allowing patient stratification and prediction of drug response. In practical implementation network analytics on a protein coding gene level has provided a number of example cases for matching disease presentation and drug molecular effect, and workflows combining computational hypothesis generation and experimental evaluation have become available for systematically optimizing biomarker candidate selection. CONCLUSION: With biomarker-based enrichment strategies in adaptive clinical trials, implementation routes for tackling development attrition are provided. Predictive biomarkers add precision in drug development and as companion diagnostics in clinical practice.

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. RESULTS: Consolidation of transcriptomics profiles and biomedical literature mining results provides 1242 proteins linked with ovarian cancer drug resistance. Integrating this set on a protein interaction network followed by graph segmentation results in a molecular process model representation of drug resistant HGSOC embedding 409 proteins in 24 molecular processes. Utilizing independent transcriptomics profiles with follow-up data on progression free survival allows deriving molecular biomarker-based classifiers for predicting recurrence under first line therapy. Biomarkers of specific relevance are identified in a molecular process encapsulating TGF-beta, mTOR, Jak-STAT and Neurotrophin signaling. Mechanism of action molecular model representations of cisplatin and paclitaxel embed the very same signaling components, and specifically proteins afflicted with the activation status of the mTOR pathway become evident, including VEGFA. Analyzing mechanism of action interference of the mTOR inhibitor sirolimus shows specific impact on the drug resistance signature imposed by cisplatin and paclitaxel, further holding evidence for a synthetic lethal interaction to paclitaxel mechanism of action involving cyclin D1. CONCLUSIONS: Stratifying drug resistant high grade serous ovarian cancer via VEGFA, and specifically treating with mTOR inhibitors in case of activation of the pathway may allow adding precision for overcoming resistance to first line therapy.

Detection of coregulation in differential gene expression profiles.

Genomics and proteomics approaches generate distinct gene expression and protein profiles, listing individual genes embedded in broad functional terms as gene ontologies. However, interpretation of gene profiles in a regulatory and functional context remains a major issue. Elucidation of regulatory mechanisms at the gene expression level via analysis of promoter regions is a prominent procedure to decipher such gene regulatory networks. We propose a novel genetic algorithm (GA) to extract joint promoter modules in a set of coexpressed genes as resulting from differential gene expression experiments. Algorithm design has focused on the following constraints: (I) identification of the major promoter modules, which are (II) characterized by a maximum number of joint motifs and (III) are found in a maximum number of coexpressed genes. The capability of the GA in detecting multiple modules was evaluated on various test data sets, analyzing the impact of the number of motifs per promoter module, the number of genes associated with a module, as well as the total number of distinct promoter modules encoded in a sequence set. In addition to the test data sets, the GA was evaluated on two biological examples, namely a muscle-specific data set and the upstream sequences of the beta-actin gene (ACTB) derived from different species, complemented by a comparison to alternative promoter module identification routines.

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. Our approach is rather generic and could be adapted to other drugs or drug candidates. It marks a successful integration of the work of the different teams toward a common goal of predictive quantitative in vitro to in vivo extrapolation.

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. In the EU FP7 funded project, Predict-IV, we investigated the human renal proximal tubule cells line, RPTEC/TERT1 for their applicability to long term nephrotoxic mechanistic studies. To this end, we used a tiered strategy to optimise dosing regimes for 9 nephrotoxins. Our final testing protocol utilised differentiated RPTEC/TERT1 cells cultured on filter inserts treated with compounds at both the apical and basolateral side, at concentrations not exceeding IC10, for 14 days in a 24 h repeat application. Transepithelial electrical resistance and supernatant lactate were measured over the duration of the experiments and genome wide transcriptomic profiles were assayed at day 1, 3 and 14. The effect of hypoxia was investigated for a subset of compounds. The transcriptomic data were analysed to investigate compound-specific effects, global responses and mechanistically informative signatures. In addition, several potential clinically useful renal injury biomarkers were identified.

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. With significant advancements in metagenomics the composition of microbial communities became amenable for integrative analysis on the level of entangled molecular processes involving more than one species, in turn offering a data matrix for analyzing the molecular basis of cooperative phenomena. METHODS: We present an analysis framework aligned with a dynamical hierarchies concept for unraveling emergent properties in microbial communities, and exemplify this approach for a co-culture setting of At. ferrooxidans and At. thiooxidans. This minimum microbial community demonstrates a significant increase in bioleaching efficiency compared to the activity of individual species, involving mechanisms of the thiosulfate, the polysulfide and the iron oxidation pathway. RESULTS: Populating gene-centric data structures holding rich functional annotation and interaction information allows deriving network models at the functional level coupling energy production and transport processes of both microbial species. Applying a network segmentation approach on the interaction network of ortholog genes covering energy production and transport proposes a set of specific molecular processes of relevance in bioleaching. The resulting molecular process model essentially involves functionalities such as iron oxidation, nitrogen metabolism and proton transport, complemented by sulfur oxidation and nitrogen metabolism, as well as a set of ion transporter functionalities. At. ferrooxidans-specific genes embedded in the molecular model representation hold gene functions supportive for ammonia utilization as well as for biofilm formation, resembling key elements for effective chalcopyrite bioleaching as emergent property in the co-culture situation. CONCLUSIONS: Analyzing the entangled molecular processes of a microbial community on the level of segmented, gene-centric interaction networks allows identification of core molecular processes and functionalities adding to our mechanistic understanding of emergent properties of microbial consortia.

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. Changes in ATP, glutathione and resazurin reduction, after 14 days of daily repeat exposure, revealed a number of compounds, including adefovir dipivoxil with enhanced toxicity in hypoxia. We observed intracellular accumulation of adefovir in hypoxia and suggest decreases in the efflux transport proteins MRP4, MRP5, NHERF1 and NHERF3 as a possible explanation. MRP5 and NHERF3 were also down-regulated upon treatment with the HIF-1 activator, dimethyloxalylglycine. Interestingly, adefovir dependent gene expression shifted from alterations in cell cycle gene expression to an inflammatory response in hypoxia. The ability to investigate aspects of disease states and their influence on renal toxin handling is a key advantage of in vitro systems developed here. They also allow for detailed investigations into mechanisms of compound toxicity of potential importance for compromised tissue exposure.

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). As more ChIP-seq datasets reporting genes specifically bound by a TF become publicly available for mining, the generation of target gene lists of TFs of toxicological relevance becomes possible, based on actual protein-DNA interaction and modulation of gene expression. In this study, we generated target gene signatures for Nrf2, ATF4, XBP1, p53, HIF1a, AhR and PPAR gamma and tracked TF modulation in a large collection of in vitro TCX datasets from renal and hepatic cell models exposed to clinical nephro- and hepato-toxins. The result is a global monitoring of TF modulation with great promise as a mechanistically based tool for chemical hazard identification.

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. We exemplify a computational workflow for expanding from statistics-based association analysis toward deriving molecular pathway and process models for characterizing phenotypes and drug mechanism of action. Interference analysis on the molecular model level allows identification of predictive biomarker candidates for testing drug response. We discuss this strategy on diabetic nephropathy (DN), a complex clinical phenotype triggered by diabetes and presenting with renal as well as cardiovascular endpoints. A molecular pathway map indicates involvement of multiple molecular mechanisms, and selected biomarker candidates reported as associated with disease progression are identified for specific molecular processes. Selective interference of drug mechanism of action and disease-associated processes is identified for drug classes in clinical use, in turn providing precision medicine hypotheses utilizing predictive biomarkers.

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. CsA was quantified in supernatants and cellular lysates by LC-MS/MS for kinetic modeling. There was a rapid cellular uptake and accumulation of CsA, with a non-linear relationship between intracellular and applied concentrations. CsA at 15µM induced mitochondrial disturbances and activation of the Nrf2-oxidative-damage and the unfolded protein-response pathways. All three omic streams provided complementary information, especially pertaining to Nrf2 and ATF4 activation. No stress induction was detected with 5µM CsA; however, both concentrations resulted in a maximal secretion of cyclophilin B. The study demonstrates for the first time that CsA-induced stress is not directly linked to its primary pharmacology. In addition we demonstrate the power of integrated omics for the elucidation of signaling cascades brought about by compound induced cell stress.