AdductHunter: identifying protein-metal complex adducts in mass spectra.

Mass spectrometry (MS) is an analytical technique for molecule identification that can be used for investigating protein-metal complex interactions. Once the MS data is collected, the mass spectra are usually interpreted manually to identify the adducts formed as a result of the interactions between proteins and metal-based species. However, with increasing resolution, dataset size, and species complexity, the time required to identify adducts and the error-prone nature of manual assignment have become limiting factors in MS analysis. AdductHunter is a open-source web-based analysis tool that  automates the peak identification process using constraint integer optimization to find feasible combinations of protein and fragments, and dynamic time warping to calculate the dissimilarity between the theoretical isotope pattern of a species and its experimental isotope peak distribution. Empirical evaluation on a collection of 22 unique MS datasetsshows fast and accurate identification of protein-metal complex adducts in deconvoluted mass spectra.

Development of a next-generation endogenous OCT4 inducer and its anti-aging effect in vivo.

The identification of small molecules capable of replacing transcription factors has been a longstanding challenge in the generation of human chemically induced pluripotent stem cells (iPSCs). Recent studies have shown that ectopic expression of OCT4, one of the master pluripotency regulators, compromised the developmental potential of resulting iPSCs, This highlights the importance of finding endogenous OCT4 inducers for the generation of clinical-grade human iPSCs. Through a cell-based high throughput screen, we have discovered several new OCT4-inducing compounds (O4Is). In this work, we prepared metabolically stable analogues, including O4I4, which activate endogenous OCT4 and associated signaling pathways in various cell lines. By combining these with a transcription factor cocktail consisting of SOX2, KLF4, MYC, and LIN28 (referred to as "CSKML") we achieved to reprogram human fibroblasts into a stable and authentic pluripotent state without the need for exogenous OCT4. In Caenorhabditis elegans and Drosophila, O4I4 extends lifespan, suggesting the potential application of OCT4-inducing compounds in regenerative medicine and rejuvenation therapy.

NHC-gold compounds mediate immune suppression through induction of AHR-TGFß1 signalling in vitro and in scurfy mice.

Gold compounds have a long history of use as immunosuppressants, but their precise mechanism of action is not completely understood. Using our recently developed liver-on-a-chip platform we now show that gold compounds containing planar N-heterocyclic carbene (NHC) ligands are potent ligands for the aryl hydrocarbon receptor (AHR). Further studies showed that the lead compound (MC3) activates TGFß1 signaling and suppresses CD4+ T-cell activation in vitro, in human and mouse T cells. Conversely, genetic knockdown or chemical inhibition of AHR activity or of TGFß1-SMAD-mediated signaling offsets the MC3-mediated immunosuppression. In scurfy mice, a mouse model of human immunodysregulation polyendocrinopathy enteropathy X-linked syndrome, MC3 treatment reduced autoimmune phenotypes and extended lifespan from 24 to 58 days. Our findings suggest that the immunosuppressive activity of gold compounds can be improved by introducing planar NHC ligands to activate the AHR-associated immunosuppressive pathway, thus expanding their potential clinical application for autoimmune diseases.

Imidazopyridines as Potent KDM5 Demethylase Inhibitors Promoting Reprogramming Efficiency of Human iPSCs.

Pioneering human induced pluripotent stem cell (iPSC)-based pre-clinical studies have raised safety concerns and pinpointed the need for safer and more efficient approaches to generate and maintain patient-specific iPSCs. One approach is searching for compounds that influence pluripotent stem cell reprogramming using functional screens of known drugs. Our high-throughput screening of drug-like hits showed that imidazopyridines-analogs of zolpidem, a sedative-hypnotic drug-are able to improve reprogramming efficiency and facilitate reprogramming of resistant human primary fibroblasts. The lead compound (O4I3) showed a remarkable OCT4 induction, which at least in part is due to the inhibition of H3K4 demethylase (KDM5, also known as JARID1). Experiments demonstrated that KDM5A, but not its homolog KDM5B, serves as a reprogramming barrier by interfering with the enrichment of H3K4Me3 at the OCT4 promoter. Thus our results introduce a new class of KDM5 chemical inhibitors and provide further insight into the pluripotency-related properties of KDM5 family members.

Literature optimized integration of gene expression for organ-specific evaluation of toxicogenomics datasets.

The study of drug toxicity in human organs is complicated by their complex inter-relations and by the obvious difficulty to testing drug effects on biologically relevant material. Animal models and human cell cultures offer alternatives for systematic and large-scale profiling of drug effects on gene expression level, as typically found in the so-called toxicogenomics datasets. However, the complexity of these data, which includes variable drug doses, time points, and experimental setups, makes it difficult to choose and integrate the data, and to evaluate the appropriateness of one or another model system to study drug toxicity (of particular drugs) of particular human organs. Here, we define a protocol to integrate drug-wise rankings of gene expression changes in toxicogenomics data, which we apply to the TG-GATEs dataset, to prioritize genes for association to drug toxicity in liver or kidney. Contrast of the results with sets of known human genes associated to drug toxicity in the literature allows to compare different rank aggregation approaches for the task at hand. Collectively, ranks from multiple models point to genes not previously associated to toxicity, notably, the PCNA clamp associated factor (PCLAF), and genes regulated by the master regulator of the antioxidant response NFE2L2, such as NQO1 and SRXN1. In addition, comparing gene ranks from different models allowed us to evaluate striking differences in terms of toxicity-associated genes between human and rat hepatocytes or between rat liver and rat hepatocytes. We interpret these results to point to the different molecular functions associated to organ toxicity that are best described by each model. We conclude that the expected production of toxicogenomics panels with larger numbers of drugs and models, in combination with the ongoing increase of the experimental literature in organ toxicity, will lead to increasingly better associations of genes for organism toxicity.

Liver-Kidney-on-Chip To Study Toxicity of Drug Metabolites.

Advances in organ-on-chip technologies for the application in in vitro drug development provide an attractive alternative approach to replace ethically controversial animal testing and to establish a basis for accelerated drug development. In recent years, various chip-based tissue culture systems have been developed, which are mostly optimized for cultivation of one single cell type or organoid structure and lack the representation of multi organ interactions. Here we present an optimized microfluidic chip design consisting of interconnected compartments, which provides the possibility to mimic the exchange between different organ specific cell types and enables to study interdependent cellular responses between organs and demonstrate that such tandem system can greatly improve the reproducibility and efficiency of toxicity studies. In a simplified liver-kidney-on-chip model, we showed that hepatic cells that grow in microfluidic conditions abundantly and stably expressed metabolism-related biomarkers. Moreover, we applied this system for investigating the biotransformation and toxicity of Aflatoxin B1 (AFB1) and Benzoalphapyrene (BαP), as well as the interaction with other chemicals. The results clearly demonstrate that the toxicity and metabolic response to drugs can be evaluated in a flow-dependent manner within our system, supporting the importance of advanced interconnected multiorgans in microfluidic devices for application in in vitro toxicity testing and as optimized tissue culture systems for in vitro drug screening.

Evaluation of in vivo and in vitro models of toxicity by comparison of toxicogenomics data with the literature.

Toxicity affecting humans is studied by observing the effects of chemical substances in animal organisms (in vivo) or in animal and human cultivated cell lines (in vitro). Toxicogenomics studies collect gene expression profiles and histopathology assessment data for hundreds of drugs and pollutants in standardized experimental designs using different model systems. These data are an invaluable source for analyzing genome-wide drug response in biological systems. However, a problem remains that is how to evaluate the suitability of heterogeneous in vitro and in vivo systems to model the many different aspects of human toxicity. We propose here that a given model system (cell type or animal organ) is supported to appropriately describe a particular aspect of human toxicity if the set of compounds associated in the literature with that aspect of toxicity causes a change in expression of genes with a particular function in the tested model system. This approach provides candidate genes to explain the toxicity effect (the differentially expressed genes) and the compounds whose effect could be modeled (the ones producing both the change of expression in the model system and that are associated with the human phenotype in the literature). Here we present an application of this approach using a computational pipeline that integrates compound-induced gene expression profiles (from the Open TG-GATEs database) and biomedical literature annotations (from the PubMed database) to evaluate the suitability of (human and rat) in vitro systems as well as rat in vivo systems to model human toxicity.

Large oligomeric complex structures can be computationally assembled by efficiently combining docked interfaces.

Macromolecular oligomeric assemblies are involved in many biochemical processes of living organisms. The benefits of such assemblies in crowded cellular environments include increased reaction rates, efficient feedback regulation, cooperativity and protective functions. However, an atom-level structural determination of large assemblies is challenging due to the size of the complex and the difference in binding affinities of the involved proteins. In this study, we propose a novel combinatorial greedy algorithm for assembling large oligomeric complexes from information on the approximate position of interaction interfaces of pairs of monomers in the complex. Prior information on complex symmetry is not required but rather the symmetry is inferred during assembly. We implement an efficient geometric score, the transformation match score, that bypasses the model ranking problems of state-of-the-art scoring functions by scoring the similarity between the inferred dimers of the same monomer simultaneously with different binding partners in a (sub)complex with a set of pregenerated docking poses. We compiled a diverse benchmark set of 308 homo and heteromeric complexes containing 6 to 60 monomers. To explore the applicability of the method, we considered 48 sets of parameters and selected those three sets of parameters, for which the algorithm can correctly reconstruct the maximum number, namely 252 complexes (81.8%) in, at least one of the respective three runs. The crossvalidation coverage, that is, the mean fraction of correctly reconstructed benchmark complexes during crossvalidation, was 78.1%, which demonstrates the ability of the presented method to correctly reconstruct topology of a large variety of biological complexes.