Sören Wacker

Over the past years, proteomics has moved increasingly towards the analysis of large cohorts of biological specimens. This has been made possible by significant improvements in mass spectrometry technology, chromatographic separation methods, and improved data acquisition strategies. These technological advances now routinely enable experiments that yield vast datasets that substantially outstrip the capacity of existing proteomics data analysis approaches. Processing such large datasets requires purpose-built, quality control tools designed to organize and analyze the data while recording all processing parameters for reproducibility. To address this need, we developed an open-source, Python-based software platform, Large-scale Automated Multi-level Proteomics Evaluation by Python (LAMPrEY), a comprehensive quality-control pipeline for quantitative proteomics analyses of large cohorts of samples. LAMPrEY features GUI-based file submission, automated processing with MaxQuant and RawTools, an interactive analytics dashboard, and an application programming interface (API) for programmatic usage that collectively enable rapid, reproducible analysis and interpretation of proteomics data. We demonstrate the longitudinal monitoring and analytical capabilities of LAMPrEY using TMT11 quantitative proteomics data generated from 910 Enterococcus faecium isolates collected from bloodstream infection patients. LAMPrEY is an open-source software that can be accessed at www.lewisresearchgroup.org/software.

Metabolomics has emerged as a mainstream approach for investigating the complex metabolic underpinnings of living systems, and over recent years, it has increasingly been applied to large cohort studies that tax the limits of existing computational tools. Most existing metabolomics software tools are effective at analyzing small data sets but exhibit a number of shortcomings that limit their utility when applied to large studies: they store entire data sets in memory, they use batch-dependent fitting algorithms, and they do not use concrete metrics for peak fitting, which not only results in inconsistent peak-picking results across samples but also complicates the documentation of data analyses. To address this, we developed the mass-spectrometry metabolomics integrator (MS-MINT), a Python application for processing, analyzing, and visualizing large liquid chromatography-mass spectrometry (LC-MS) data sets. To enable reproducible large-scale data processing, MS-MINT uses a region of interest (ROI)-based approach to extract data. We illustrate the function of this new tool by analyzing metabolites present in the media of a large data set (3334 files) of Staphylococcus aureus cultures. We show that MS-MINT accurately reproduces data generated from other software tools in a fraction of the time. In summary, MS-MINT offers a purpose-built software platform to support large-scale metabolomics data analyses. MS-MINT software is freely available at https://www.lewisresearchgroup.org/software.

Metabolomics is an important approach for studying complex biological systems. Quantitative liquid chromatography-mass spectrometry (LC-MS)-based metabolomics is becoming a mainstream strategy but presents several technical challenges that limit its widespread use. Computing metabolite concentrations using standard curves generated from standard mixtures of known concentrations is a labor-intensive process which is often performed manually. Currently, there are few options for open-source software tools that can automatically calculate metabolite concentrations. Herein, we introduce SCALiR (Standard Curve Application for determining Linear Ranges), a new web-based software tool specifically built for this task, which allows users to automatically transform LC-MS signal data into absolute quantitative data (https://www.lewisresearchgroup.org/software). The algorithm used in SCALiR automatically finds the equation of the line of best fit for each standard curve and uses this equation to calculate compound concentrations from their LC-MS signal. Using a standard mix containing 77 metabolites, we found excellent correlation between the concentrations calculated by SCALiR and the expected concentrations of each compound (R2 = 0.99) and that SCALiR reproducibly calculated concentrations of mid-range standards across ten analytical batches (average coefficient of variation 0.091). SCALiR offers users several advantages, including that it (1) is open-source and vendor agnostic; (2) requires only 10 seconds of analysis time to compute concentrations of >75 compounds; (3) facilitates automation of quantitative workflows; and (4) performs deterministic evaluation of compound quantification limits. SCALiR provides the metabolomics community with a simple and rapid tool that enables rigorous and reproducible quantitative metabolomics studies.

Commensal bacteria are major contributors to mammalian metabolism. We used liquid chromatography mass spectrometry to study the metabolomes of germ-free, gnotobiotic, and specific-pathogen-free mice, while also evaluating the influence of age and sex on metabolite profiles. Microbiota modified the metabolome of all body sites and accounted for the highest proportion of variation within the gastrointestinal tract. Microbiota and age explained similar amounts of variation the metabolome of urine, serum, and peritoneal fluid, while age was the primary driver of variation in the liver and spleen. Although sex explained the least amount of variation at all sites, it had a significant impact on all sites except the ileum. Collectively, these data illustrate the interplay between microbiota, age, and sex in the metabolic phenotypes of diverse body sites. This provides a framework for interpreting complex metabolic phenotypes and will help guide future studies into the role that the microbiome plays in disease.

Advances in technology and software have radically expanded the scope of metabolomics studies and allow us to monitor a broad transect of central carbon metabolism in routine studies. These increasingly sophisticated tools have shown that many human diseases are modulated by microbial metabolism. Despite this, it remains surprisingly difficult to move beyond these statistical associations and identify the specific molecular mechanisms that link dysbiosis to the progression of human disease. This difficulty stems from both the biological intricacies of host-microbiome dynamics as well as the analytical complexities inherent to microbiome metabolism research. The primary objective of this review is to examine the experimental and computational tools that can provide insights into the molecular mechanisms at work in host-microbiome interactions and to highlight the undeveloped frontiers that are currently holding back microbiome research from fully leveraging the benefits of modern metabolomics.

Psychostimulant drugs, such as cocaine, inhibit dopamine reuptake via blockade of the dopamine transporter (DAT), which is the primary mechanism underpinning their abuse. Atypical DAT inhibitors are dissimilar to cocaine and can block cocaine or methamphetamine induced behaviors, supporting their development as part of a treatment regimen for psychostimulant use disorders. When developing these atypical DAT inhibitors as medications, it is necessary to avoid off-target binding that can produce unwanted side effects or toxicities. In particular, the blockade of a potassium channel, human ether-a-go-go (hERG), can lead to the potentially lethal ventricular tachycardia. In this study, we established a counter screening platform for DAT and against hERG binding by combining machine learning-based quantitative structure-activity relationship (QSAR) modeling, experimental validation, and molecular modeling and simulations. Our results show the available data are adequate to establish robust QSAR models, as validated by chemical synthesis and pharmacological evaluation of a validation set of DAT inhibitors. Further, the QSAR models based on subsets of the data according to experimental approaches used have predictive power as well, which opens the door to target specific functional states of a protein. Complementarily, our molecular modeling and simulations identified the structural elements responsible for a pair of DAT inhibitors having opposite binding affinity trends at DAT and hERG, which can be leveraged for rational optimization of lead atypical DAT inhibitors with desired pharmacological properties.

Drug-induced abnormal heart rhythm known as Torsades de Pointes (TdP) is a potential lethal ventricular tachycardia found in many patients. Even newly released anti-arrhythmic drugs, like ivabradine with HCN channel as a primary target, block the hERG potassium current in overlapping concentration interval. Promiscuous drug block to hERG channel may potentially lead to perturbation of the action potential duration (APD) and TdP, especially when with combined with polypharmacy and/or electrolyte disturbances. The example of novel anti-arrhythmic ivabradine illustrates clinically important and ongoing deficit in drug design and warrants for better screening methods. There is an urgent need to develop new approaches for rapid and accurate assessment of how drugs with complex interactions and multiple subcellular targets can predispose or protect from drug-induced TdP. One of the unexpected outcomes of compulsory hERG screening implemented in USA and European Union resulted in large datasets of IC50 values for various molecules entering the market. The abundant data allows now to construct predictive machine-learning (ML) models. Novel ML algorithms and techniques promise better accuracy in determining IC50 values of hERG blockade that is comparable or surpassing that of the earlier QSAR or molecular modeling technique. To test the performance of modern ML techniques, we have developed a computational platform integrating various workflows for quantitative structure activity relationship (QSAR) models using data from the ChEMBL database. To establish predictive powers of ML-based algorithms we computed IC50 values for large dataset of molecules and compared it to automated patch clamp system for a large dataset of hERG blocking and non-blocking drugs, an industry gold standard in studies of cardiotoxicity. The optimal protocol with high sensitivity and predictive power is based on the novel eXtreme gradient boosting (XGBoost) algorithm. The ML-platform with XGBoost displays excellent performance with a coefficient of determination of up to R2 ~0.8 for pIC50 values in evaluation datasets, surpassing other metrics and approaches available in literature. Ultimately, the ML-based platform developed in our work is a scalable framework with automation potential to interact with other developing technologies in cardiotoxicity field, including high-throughput electrophysiology measurements delivering large datasets of profiled drugs, rapid synthesis and drug development via progress in synthetic biology.

Two voltage-dependent potassium channels, Kv1.1 (KCNA1) and Kv1.2 (KCNA2), are found to co-localize at the juxtaparanodal region of axons throughout the nervous system and are known to co-assemble in heteromultimeric channels, most likely in the form of the concatemer Kv1.1-1.2(3). Loss of the myelin sheath, as is observed in multiple sclerosis, uncovers the juxtaparanodal region of nodes of Ranvier in myelinated axons leading to potassium conductance, resulting in loss of nerve conduction. The selective blocking of these Kv channels is therefore a promising approach to restore nerve conduction and function. In the present study, we searched for novel inhibitors of Kv1.1-1.2(3) by combining a virtual screening protocol and electrophysiological measurements on a concatemer Kv1.1-1.2(3) stably expressed in Chinese hamster ovary K1 (CHO-K1) cells. The combined use of four popular virtual screening approaches (eHiTS, FlexX, Glide, and Autodock-Vina) led to the identification of several compounds as potential inhibitors of the Kv1.1-1.2(3) channel. From 89 electrophysiologically evaluated compounds, 14 novel compounds were found to inhibit the current carried by Kv1.1-1.2(3) channels by more than 80 % at 10 μM. Accordingly, the IC50 values calculated from concentration-response curve titrations ranged from 0.6 to 6 μM. Two of these compounds exhibited at least 30-fold higher potency in inhibition of Kv1.1-1.2(3) than they showed in inhibition of a set of cardiac ion channels (hERG, Nav1.5, and Cav1.2), resulting in a profile of selectivity and cardiac safety. The results presented herein provide a promising basis for the development of novel selective ion channel inhibitors, with a dramatically lower demand in terms of experimental time, effort, and cost than a sole high-throughput screening approach of large compound libraries.

Background: The involvement of aquaporin (AQP) water and small solute channels in the etiology of several diseases, including cancer, neuromyelitis optica and body fluid imbalance disorders, has been suggested previously. Furthermore, results obtained in a mouse model suggested that AQP9 function contributes to hyperglycemia in type-2 diabetes. In addition, the physiological role of several AQP family members remains poorly understood. Small molecule inhibitors of AQPs are therefore desirable to further study AQP physiological and pathophysiological functions. Methods: The binding of recently established AQP9 inhibitors to a homology model of AQP9 was investigated by molecular dynamics simulations and molecular docking. Putative inhibitor binding sites identified with this procedure were modified by site-directed mutagenesis. Active compounds were measured in a mammalian cell water permeability assay of mutated AQP9 isoforms and tested for changes in inhibitory effects. Controls: Three independent cell lines were established for each mutated AQP9 isoform and functionality of mutant isoforms was established. Principal findings: We have identified putative binding sites of recently established AQP9 inhibitors. This information facilitated successful identification of novel AQP9 inhibitors with low micromolar IC50 values in a cell based assay by in silico screening of a compound library targeting specifically this binding site. Significance: We have established a successful strategy for AQP small molecule inhibitor identification. AQP inhibitors may be relevant as experimental tools, to enhance our understanding of AQP function, and in the treatment of various diseases.

The rapid delayed rectifier current IKr is one of the major K+ currents involved in repolarization of the human cardiac action potential. Various inherited or drug-induced forms of the long QT syndrome (LQTS) in humans are linked to functional and structural modifications in the IKr conducting channels. IKr is carried by the potassium channel Kv11.1 encoded by the gene KCNH2 (commonly referred to as human ether-a-go-go-related gene or hERG). The first necessary step for predicting emergent drug effects on the heart is determining and modeling the binding thermodynamics and kinetics of primary and major off-target drug interactions with subcellular targets. The bulk of drugs that target hERG channels are known to have complex interactions at the atomic scale. Accordingly, one of the goals for this review is to provide comprehensive guide in the universe of computational models aiming to refine our understanding of structure-function relations in Kv11.1 and its isoforms. The special emphasis is placed on the mapping of drug binding sites and tentative mechanisms of channel inhibition and activation by drugs. An overview over recent structural models and mapping of binding sites for blockers and activators of IKr current along with the discussion on agreements and discrepancies among different models is presented. There is an apparent reciprocity or feedback loop between drug binding and action potential of the cardiac myocytes. Thus one has to connect drug binding to a particular receptor so that its functional consequences impact on the action potential duration. The natural pathway is to develop multi-scale models that connect between receptor and cellular scales. The potential for such multi-scale model development is discussed through the lens of common gating models. Accordingly, the second part of this review covers an ongoing development of the kinetic models of gating transitions and cardiac ion currents carried by hERG channels with and without drug bound.

Malfunction of hERG potassium channels, due to inherited mutations or inhibition by drugs, can cause long QT syndrome, which can lead to life-threatening arrhythmias. A three-dimensional structure of hERG is a prerequisite to understand the molecular basis of hERG malfunction. To achieve a consensus model, we carried out an extensive analysis of hERG models based on various alignments of helix S5. We analyzed seven models using a combination of conventional geometry/packing/normality validation methods as well as molecular dynamics simulations and molecular docking. A synthetic test set with the X-ray crystal structure of Kv1.2 with artificially shifted S5 sequences modeled into the structure served as a reference case. We docked the known hERG inhibitors (+)-cisapride, (S)-terfenadine, and MK-499 into the hERG models and simulation snapshots. None of the single analyses unambiguously identified a preferred model, but the combination of all three revealed that there is only one model that fulfils all quality criteria. This model is confirmed by a recent mutation scanning experiment (Ju et al., J. Biol. Chem. 2009, 284, 1000-1008). We expect the modeled structure to be useful as a basis both for computational studies of channel function and kinetics as well as the design of experiments.