Arno Lukas

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.

Characterization of protein-interaction networks in tumors

BackgroundAnalyzing 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.ResultsGraph 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.ConclusionCancer 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.

Protein biomarkers associated with acute renal failure and chronic kidney disease

Acute renal failure (ARF) as well as chronic kidney disease (CKD) are currently categorized according to serum creatinine concentrations. Serum creatinine, however, has shortcomings because of its low predictive values. The need for novel markers for the early diagnosis and prognosis of renal diseases is imminent, particularly for markers reflecting intrinsic organ injury in stages when glomerular filtration is not impaired. This review summarizes protein markers discussed in the context of ARF as well as CKD, and provides an overview on currently available discovery results following ‘omics’ techniques. The identified set of candidate marker proteins is discussed in their cellular and functional context. The systematic review of proteomics and genomics studies revealed 56 genes to be associated with acute or chronic kidney disease. Context analysis, i.e. correlation of biological processes and molecular functions of reported kidney markers, revealed that 15 genes on the candidate list were assigned to the most significant ontology groups: immunity and defence. Other significantly enriched groups were cell communication (14 genes), signal transduction (22 genes) and apoptosis (seven genes). Among 24 candidate protein markers, nine proteins were also identified by gene expression studies. Next generation candidate marker proteins with improved diagnostic and prognostic values for kidney diseases will be derived from whole genome scans and protemics approaches. Prospective validation still remains elusive for all proposed candidates.

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

BackgroundThe 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. Specifically if many alternative antigens are available the reduction of required synthesized peptides until one successful candidate is found is of central technical interest. We have investigated the integration of B-cell epitope prediction with the variability of antigen and the conservation of patterns for post-translational modification (PTM) prediction to improve over state of the art in the field. In particular the application of machine-learning methods shows promising results.ResultsWe find that protein regions leading to the production of functionally altering antibodies are often characterized by a distinct increase in the cumulative sum of three presented parameters. Furthermore the concept to maximize antigenicity, minimize variability and minimize the likelihood of post-translational modification for the identification of relevant sites leads to biologically interesting observations. Primarily, for about 50% of antigen the approach works well with individual area under the ROC curve (AROC) values of at least 0.65. On the other hand a significant portion reveals equivalently low AROC values of < = 0.35 indicating an overall non-Gaussian distribution. While about a third of 57 antigens are seemingly intangible by our approach our results suggest the existence of at least two distinct classes of bioinformatically detectable epitopes which should be predicted separately. As a side effect of our study we present a hand curated dataset for the validation of protectivity classification. Based on this dataset machine-learning methods further improve predictive power to a class separation in an equilibrated dataset of up to 83%.ConclusionWe present a computational method to automatically select and rank peptides for the stimulation of potentially protective or otherwise functionally altering antibodies. It can be shown that integration of variability, post-translational modification pattern conservation and B-cell antigenicity improve rational selection over random guessing. Probably more important, we find that for about 50% of antigen the approach works substantially better than for the overall dataset of 57 proteins. Essentially as a side effect our method optimizes for presumably best applicable peptides as they tend to be likely unmodified and as invariable as possible which is answering needs in diagnosis and treatment of pathogen infection. In addition we show the potential for further improvement by the application of machine-learning methods, in particular Random Forests.

Gene expression and biomarkers in renal transplant ischemia reperfusion injury

The incidence of postischemic acute renal allograft failure (ARF) occurs in roughly 25% of cadaveric donor kidney recipients. This high rate remained virtually unchanged over the last decades despite modification in recipient management and modern immunosuppressive strategies. It has recently been shown that among other reasons, the systemic inflammation in the brain death cadaveric organ donor contributes to subsequent ARF in the recipient. This review focuses on the consequences of ischemia and reperfusion on the cellular level and offers potential solutions for the reduction of ARF. Genome‐wide gene expression analysis together with sophisticated biostatistical analysis made it possible to identify several candidate gene products and proteins that may act as specific and sensitive biomarker for renal inflammation and ischemia. These markers may be very helpful in the clinical management of patients with a high a priori risk of subsequent ARF such as recipients of marginal donor kidneys. Ongoing clinical trials will evaluate whether immunosuppression of the cadaveric organ donor before organ harvest will have the potential to reduce inflammation in the transplant kidney and subsequently lead to a reduction in the rate of ARF.

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.

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. The core of our dependency graph comprises data objects encoding the functional categorization of a particular gene, its tissue-specific reference gene expression, as well as known interactions and subcellular location of assigned proteins. On the basis of these genome, transcriptome, and proteome data we compute pair-wise object (gene) dependencies and interpret them as weighted edges in a dependency graph. Mapping of omics profiles on this graph can be used to identify connectors linking features of the omics list, in turn providing the basis for identification of subgraphs and motifs characterizing the cellular state under analysis. We exemplify this approach by analyzing differential gene expression data characterizing B-cell lymphoma and demonstrate the identification of B-cell lymphoma associated subgraphs.

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.

Discrete simulation of regulatory homo- and heterodimerization in the apoptosis effector phase

MOTIVATION Quantitative simulation of molecular reaction networks is among the most promising approaches towards an understanding of complex biochemical pathways. Numerous qualitative as well as quantitative data from diverse experimental settings, in particular from genomics and proteomics, have to be contextually linked to convert static data into dynamic functionality. RESULTS This paper presents the Lattice Molecular Automaton, a Cellular Automaton-based simulation tool, capable of representing complex molecular dynamics at different levels of granularity. A data structure concept represents molecular units, whose dynamics, embedded on a 2D grid, is defined via detailed intermolecular interaction profiles. The data structures hold diverse information as molecular type, potential, as well as kinetic energy states, which allows a precise representation of intracellular reaction networks. The molecular dynamics is performed via local computation of individual molecular states on the lattice, which, in conjunction with discretized space and time, enables excellent scalability of this simulation concept. This paper finally gives Lattice Molecular Automaton simulation results on key elements of apoptosis, the cell death cascade, in particular focusing on the regulatory function of homo- and heterodimerization of members of the Bcl-2 protein family in the apoptosis effector phase. The regulatory proteins Bcl2, Bax, and Bak constitute a diffusion-driven molecular switch with inherent damping of apoptosis induction, thereby controlling the apoptosis reaction cascade under noisy, external apoptosis inducing conditions.

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 Cmax. 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.