Chris Bielow

Tools for Label-free Peptide Quantification

The increasing scale and complexity of quantitative proteomics studies complicate subsequent analysis of the acquired data. Untargeted label-free quantification, based either on feature intensities or on spectral counting, is a method that scales particularly well with respect to the number of samples. It is thus an excellent alternative to labeling techniques. In order to profit from this scalability, however, data analysis has to cope with large amounts of data, process them automatically, and do a thorough statistical analysis in order to achieve reliable results. We review the state of the art with respect to computational tools for label-free quantification in untargeted proteomics. The two fundamental approaches are feature-based quantification, relying on the summed-up mass spectrometric intensity of peptides, and spectral counting, which relies on the number of MS/MS spectra acquired for a certain protein. We review the current algorithmic approaches underlying some widely used software packages and briefly discuss the statistical strategies for analyzing the data.

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.

OpenMS: a flexible open-source software platform for mass spectrometry data analysis

High-resolution mass spectrometry (MS) has become an important tool in the life sciences, contributing to the diagnosis and understanding of human diseases, elucidating biomolecular structural information and characterizing cellular signaling networks. However, the rapid growth in the volume and complexity of MS data makes transparent, accurate and reproducible analysis difficult. We present OpenMS 2.0 (http://www.openms.de), a robust, open-source, cross-platform software specifically designed for the flexible and reproducible analysis of high-throughput MS data. The extensible OpenMS software implements common mass spectrometric data processing tasks through a well-defined application programming interface in C++ and Python and through standardized open data formats. OpenMS additionally provides a set of 185 tools and ready-made workflows for common mass spectrometric data processing tasks, which enable users to perform complex quantitative mass spectrometric analyses with ease.

Extensive identification and analysis of conserved small ORFs in animals.

BackgroundThere is increasing evidence that transcripts or transcript regions annotated as non-coding can harbor functional short open reading frames (sORFs). Loss-of-function experiments have identified essential developmental or physiological roles for a few of the encoded peptides (micropeptides), but genome-wide experimental or computational identification of functional sORFs remains challenging.ResultsHere, we expand our previously developed method and present results of an integrated computational pipeline for the identification of conserved sORFs in human, mouse, zebrafish, fruit fly, and the nematode C. elegans. Isolating specific conservation signatures indicative of purifying selection on amino acid (rather than nucleotide) sequence, we identify about 2,000 novel small ORFs located in the untranslated regions of canonical mRNAs or on transcripts annotated as non-coding. Predicted sORFs show stronger conservation signatures than those identified in previous studies and are sometimes conserved over large evolutionary distances. The encoded peptides have little homology to known proteins and are enriched in disordered regions and short linear interaction motifs. Published ribosome profiling data indicate translation of more than 100 novel sORFs, and mass spectrometry data provide evidence for more than 70 novel candidates.ConclusionsTaken together, we identify hundreds of previously unknown conserved sORFs in major model organisms. Our computational analyses and integration with experimental data show that these sORFs are expressed, often translated, and sometimes widely conserved, in some cases even between vertebrates and invertebrates. We thus provide an integrated resource of putatively functional micropeptides for functional validation in vivo.

Mechanism of cisplatin proximal tubule toxicity revealed by integrating transcriptomics, proteomics, metabolomics and biokinetics.

Cisplatin is one of the most widely used chemotherapeutic agents for the treatment of solid tumours. The major dose-limiting factor is nephrotoxicity, in particular in the proximal tubule. Here, we use an integrated omics approach, including transcriptomics, proteomics and metabolomics coupled to biokinetics to identify cell stress response pathways induced by cisplatin. The human renal proximal tubular cell line RPTEC/TERT1 was treated with sub-cytotoxic concentrations of cisplatin (0.5 and 2 μM) in a daily repeat dose treating regime for up to 14 days. Biokinetic analysis showed that cisplatin was taken up from the basolateral compartment, transported to the apical compartment, and accumulated in cells over time. This is in line with basolateral uptake of cisplatin via organic cation transporter 2 and bioactivation via gamma-glutamyl transpeptidase located on the apical side of proximal tubular cells. Cisplatin affected several pathways including, p53 signalling, Nrf2 mediated oxidative stress response, mitochondrial processes, mTOR and AMPK signalling. In addition, we identified novel pathways changed by cisplatin, including eIF2 signalling, actin nucleation via the ARP/WASP complex and regulation of cell polarization. In conclusion, using an integrated omic approach together with biokinetics we have identified both novel and established mechanisms of cisplatin toxicity.

TOPPAS: a graphical workflow editor for the analysis of high-throughput proteomics data.

Mass spectrometry coupled to high-performance liquid chromatography (HPLC-MS) is evolving more quickly than ever. A wide range of different instrument types and experimental setups are commonly used. Modern instruments acquire huge amounts of data, thus requiring tools for an efficient and automated data analysis. Most existing software for analyzing HPLC-MS data is monolithic and tailored toward a specific application. A more flexible alternative consists of pipeline-based tool kits allowing the construction of custom analysis workflows from small building blocks, e.g., the Trans Proteomics Pipeline (TPP) or The OpenMS Proteomics Pipeline (TOPP). One drawback, however, is the hurdle of setting up complex workflows using command line tools. We present TOPPAS, The OpenMS Proteomics Pipeline ASsistant, a graphical user interface (GUI) for rapid composition of HPLC-MS analysis workflows. Workflow construction reduces to simple drag-and-drop of analysis tools and adding connections in between. Integration of external tools into these workflows is possible as well. Once workflows have been developed, they can be deployed in other workflow management systems or batch processing systems in a fully automated fashion. The implementation is portable and has been tested under Windows, Mac OS X, and Linux. TOPPAS is open-source software and available free of charge at http://www.OpenMS.de/TOPPAS .

Evaluation of drug-induced neurotoxicity based on metabolomics, proteomics and electrical activity measurements in complementary CNS in vitro models

The present study was performed in an attempt to develop an in vitro integrated testing strategy (ITS) to evaluate drug-induced neurotoxicity. A number of endpoints were analyzed using two complementary brain cell culture models and an in vitro blood-brain barrier (BBB) model after single and repeated exposure treatments with selected drugs that covered the major biological, pharmacological and neuro-toxicological responses. Furthermore, four drugs (diazepam, cyclosporine A, chlorpromazine and amiodarone) were tested more in depth as representatives of different classes of neurotoxicants, inducing toxicity through different pathways of toxicity. The developed in vitro BBB model allowed detection of toxic effects at the level of BBB and evaluation of drug transport through the barrier for predicting free brain concentrations of the studied drugs. The measurement of neuronal electrical activity was found to be a sensitive tool to predict the neuroactivity and neurotoxicity of drugs after acute exposure. The histotypic 3D re-aggregating brain cell cultures, containing all brain cell types, were found to be well suited for OMICs analyses after both acute and long term treatment. The obtained data suggest that an in vitro ITS based on the information obtained from BBB studies and combined with metabolomics, proteomics and neuronal electrical activity measurements performed in stable in vitro neuronal cell culture systems, has high potential to improve current in vitro drug-induced neurotoxicity evaluation.

MSSimulator: Simulation of mass spectrometry data.

Mass spectrometry coupled to liquid chromatography (LC-MS and LC-MS/MS) is commonly used to analyze the protein content of biological samples in large scale studies, enabling quantitation and identification of proteins and peptides using a wide range of experimental protocols, algorithms, and statistical models to analyze the data. Currently it is difficult to compare the plethora of algorithms for these tasks. So far, curated benchmark data exists for peptide identification algorithms but data that represents a ground truth for the evaluation of LC-MS data is limited. Hence there have been attempts to simulate such data in a controlled fashion to evaluate and compare algorithms. We present MSSimulator, a simulation software for LC-MS and LC-MS/MS experiments. Starting from a list of proteins from a FASTA file, the simulation will perform in-silico digestion, retention time prediction, ionization filtering, and raw signal simulation (including MS/MS), while providing many options to change the properties of the resulting data like elution profile shape, resolution and sampling rate. Several protocols for SILAC, iTRAQ or MS(E) are available, in addition to the usual label-free approach, making MSSimulator the most comprehensive simulator for LC-MS and LC-MS/MS data.

Proteomics Quality Control: Quality Control Software for MaxQuant Results

Mass spectrometry-based proteomics coupled to liquid chromatography has matured into an automatized, high-throughput technology, producing data on the scale of multiple gigabytes per instrument per day. Consequently, an automated quality control (QC) and quality analysis (QA) capable of detecting measurement bias, verifying consistency, and avoiding propagation of error is paramount for instrument operators and scientists in charge of downstream analysis. We have developed an R-based QC pipeline called Proteomics Quality Control (PTXQC) for bottom-up LC-MS data generated by the MaxQuant software pipeline. PTXQC creates a QC report containing a comprehensive and powerful set of QC metrics, augmented with automated scoring functions. The automated scores are collated to create an overview heatmap at the beginning of the report, giving valuable guidance also to nonspecialists. Our software supports a wide range of experimental designs, including stable isotope labeling by amino acids in cell culture (SILAC), tandem mass tags (TMT), and label-free data. Furthermore, we introduce new metrics to score MaxQuants Match-between-runs (MBR) functionality by which peptide identifications can be transferred across Raw files based on accurate retention time and m/z. Last but not least, PTXQC is easy to install and use and represents the first QC software capable of processing MaxQuant result tables. PTXQC is freely available at https://github.com/cbielow/PTXQC .

Investigation of reaction mechanisms of drug degradation in the solid state: a kinetic study implementing ultrahigh-performance liquid chromatography and high-resolution mass spectrometry for thermally stressed thyroxine.

A reaction scheme was derived for the thermal degradation of thyroxine in the solid state, using data obtained from ultrahigh-performance liquid chromatography and high-resolution mass spectrometry (UHPLC-HRMS). To study the reaction mechanism and kinetics of the thermal degradation of the pharmaceutical in the solid state, a workflow was developed by generating compound-specific, time-dependent degradation or formation curves of at least 13 different degradation products. Such curves allowed one to distinguish between first- and second-generation degradation products, as well as impurities resulting from chemical synthesis. The structures of the degradation products were derived from accurate molecular masses and multistage mass spectrometry. Deiodination and oxidative side chain degradation were found to be the major degradation reactions, resulting in the formation of deiodinated thyroxines, as well as acetic acid, benzoic acid, formaldehyde, acetamide, hydroxyacetic acid, oxoacetic acid, hydroxyacetamide, or oxoacetamide derivatives of thyroxine or deiodinated thyroxine. Upon additional structural verification of mass spectrometric data using nuclear magnetic resonance spectroscopy, this comprehensive body of data sheds light on an elaborate, radical-driven reaction scheme, explaining the presence or formation of impurities in thermally stressed thyroxine.