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Mass spectrometry is a powerful technique for analyzing molecules in complex biological samples. However, inter- and intralaboratory variability and bias can affect the data due to various factors, including sample handling and preparation, instrument calibration and performance, and data acquisition and processing. To address this issue, the Quality Control (QC) working group of the Human Proteome Organization's Proteomics Standards Initiative has established the standard mzQC file format for reporting and exchanging information relating to data quality. mzQC is based on the JavaScript Object Notation (JSON) format and provides a lightweight yet versatile file format that can be easily implemented in software. Here, we present open-source software libraries to process mzQC data in three programming languages: Python, using pymzqc; R, using rmzqc; and Java, using jmzqc. The libraries follow a common data model and provide shared functionalities, including the (de)serialization and validation of mzQC files. We demonstrate use of the software libraries in a workflow for extracting, analyzing, and visualizing QC metrics from different sources. Additionally, we show how these libraries can be integrated with each other, with existing software tools, and in automated workflows for the QC of mass spectrometry data. All software libraries are available as open source under the MS-Quality-Hub organization on GitHub (https://github.com/MS-Quality-Hub).

We have recently introduced MAPLE (MAximum Parsimonious Likelihood Estimation), a new pandemic-scale phylogenetic inference method exclusively designed for genomic epidemiology. In response to the need for enhancing MAPLE's performance and scalability, here we present two key components: (i) CMAPLE software, a highly optimized C++ reimplementation of MAPLE with many new features and advancements, and (ii) CMAPLE library, a suite of application programming interfaces to facilitate the integration of the CMAPLE algorithm into existing phylogenetic inference packages. Notably, we have successfully integrated CMAPLE into the widely used IQ-TREE 2 software, enabling its rapid adoption in the scientific community. These advancements serve as a vital step toward better preparedness for future pandemics, offering researchers powerful tools for large-scale pathogen genomic analysis., (© The Author(s) 2024. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.)

The predicted 80 open reading frames (ORFs) of herpes simplex virus 1 (HSV-1) have been intensively studied for decades. Here, we unravel the complete viral transcriptome and translatome during lytic infection with base-pair resolution by computational integration of multi-omics data. We identify a total of 201 transcripts and 284 ORFs including all known and 46 novel large ORFs. This includes a so far unknown ORF in the locus deleted in the FDA-approved oncolytic virus Imlygic. Multiple transcript isoforms expressed from individual gene loci explain translation of the vast majority of ORFs as well as N-terminal extensions (NTEs) and truncations. We show that NTEs with non-canonical start codons govern the subcellular protein localization and packaging of key viral regulators and structural proteins. We extend the current nomenclature to include all viral gene products and provide a genome browser that visualizes all the obtained data from whole genome to single-nucleotide resolution.

Background: Metabolic alterations can serve as targets for diagnosis and cancer therapy. Due to the highly complex regulation of cellular metabolism, definite identification of metabolic pathway alterations remains challenging and requires sophisticated experimentation., Methods: We applied a comprehensive kinetic model of the central carbon metabolism (CCM) to characterise metabolic reprogramming in murine liver cancer., Results: We show that relative differences of protein abundances of metabolic enzymes obtained by mass spectrometry can be used to assess their maximal velocity values. Model simulations predicted tumour-specific alterations of various components of the CCM, a selected number of which were subsequently verified by in vitro and in vivo experiments. Furthermore, we demonstrate the ability of the kinetic model to identify metabolic pathways whose inhibition results in selective tumour cell killing., Conclusions: Our systems biology approach establishes that combining cellular experimentation with computer simulations of physiology-based metabolic models enables a comprehensive understanding of deregulated energetics in cancer. We propose that modelling proteomics data from human HCC with our approach will enable an individualised metabolic profiling of tumours and predictions of the efficacy of drug therapies targeting specific metabolic pathways.

Mass-spectrometry-based lipidomics aims to identify as many lipid species as possible from complex biological samples. Due to the large combinatorial search space, unambiguous identification of lipid species is far from trivial. Mass ambiguities are common in direct-injection shotgun experiments, where an orthogonal separation (e.g., liquid chromatography) is missing. Using the rich information within available lipid databases, we generated a comprehensive rule set describing mass ambiguities, while taking into consideration the resolving power (and its decay) of different mass analyzers. Importantly, common adduct species and isotopic peaks are accounted for and are shown to play a major role, both for perfect mass overlaps due to identical sum formulas and resolvable mass overlaps. We identified known and hitherto unknown mass ambiguities in high- and ultrahigh resolution data, while also ranking lipid classes by their propensity to cause ambiguities. On the basis of this new set of ambiguity rules, guidelines and recommendations for experimentalists and software developers of what constitutes a solid lipid identification in both MS and MS/MS were suggested. For researchers new to the field, our results are a compact source of ambiguities which should be accounted for. These new findings also have implications for the selection of internal standards, peaks used for internal mass calibration, optimal choice of instrument resolution, and sample preparation, for example, in regard to adduct ion formation.

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.

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 MaxQuant's 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 .

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., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: There 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., Results: Here, 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., Conclusions: Taken 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.

Levothyroxine as active pharmaceutical ingredient of formulations used for the treatment of hypothyroidism is distributed worldwide and taken by millions of people. An important issue in terms of compound stability is its capability to react with ambient oxygen, especially in case of long term compound storage at elevated temperature. In this study we demonstrate that ultrahigh-performance liquid chromatography coupled to UV spectrometry and high-resolution mass spectrometry (UHPLC-UV-HRMS) represent very useful approaches to investigate the influence of ambient oxygen on the degradation kinetics of levothyroxine in the solid state at enhanced degradation conditions. Moreover, the impurity pattern of oxidative degradation of levothyroxine is elucidated and classified with respect to degradation kinetics at different oxygen levels. Kinetic analysis of thyroxine bulk material at 100 °C reveals bi-phasic degradation kinetics with a distinct change in degradation phases dependent on the availability of oxygen. The results clearly show that contact of the bulk material to ambient oxygen is a key factor for fast compound degradation. Furthermore, the combination of time-resolved HRMS data and automated data processing is shown to allow insights into the kinetics and mechanism of impurity formation on individual compound basis. By comparing degradation profiles, four main classes of profiles linked to reaction pathways of thyroxine degradation were identifiable. Finally, we show the capability of automated data processing for the matching of different stressing conditions, in order to extract information about mechanistic similarities. As a result, degradation kinetics is influenced by factors like availability of oxygen, stressing time, or stressing temperature, while the degradation mechanisms appear to be conserved., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Rapid and efficient quality control according to the public authority regulations is mandatory to guarantee safety of the pharmaceuticals and to save resources in the pharmaceutical industry. In the case of so-called "grandfather products" like the synthetic thyroid hormone thyroxine, strict regulations enforce a detailed chemical analysis in order to characterize potentially toxic or pharmacologically relevant impurities. We report a straightforward workflow for the comprehensive impurity profiling of synthetic thyroid hormones and impurities employing ultrahigh-performance liquid chromatography (UHPLC) hyphenated to high-resolution mass spectrometry (HRMS). Five different batches of synthetic thyroxin were analyzed resulting in the detection of 71 impurities within 3 min total analysis time. Structural elucidation of the compounds was accomplished via a combination of accurate mass measurements, computer based calculations of molecular formulas, multistage high-resolution mass spectrometry (HRMS(n)), and nuclear magnetic resonance spectroscopy, which enabled the identification of 71 impurities, of which 47 have been unknown so far. Thirty of the latter were structurally elucidated, including products of deiodination, aliphatic chain oxidation, as well as dimeric compounds as new class of thyroid hormone derivatives. Limits of detection for the thyroid compounds were in the 6 ng/mL range for negative electrospray ionization mass spectrometric detection in full scan mode. Within day and day-to-day repeatabilities of retention times and peak areas were below 0.5% and 3.5% R.SD. The performance characteristics of the method in terms of robustness and information content clearly show that UHPLC-HRMS is adequate for the rapid and reliable detection, identification, and semiquantitative determination of trace levels of impurities in synthetic pharmaceuticals.

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.

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., (Copyright © 2012 Elsevier B.V. All rights reserved.)

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.

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 .

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.

Mass spectrometry is today a key analytical technique to elucidate the amount and content of proteins expressed in a certain cellular context. The degree of automation in proteomics has yet to reach that of genomic techniques, but even current technologies make a manual inspection of the data infeasible. This article addresses the key algorithmic problems bioinformaticians face when handling modern proteomic samples and shows common solutions to them. We provide examples on how algorithms can be combined to build relatively complex analysis pipelines, point out certain pitfalls and aspects worth considering and give a list of current state-of-the-art tools.

In electrospray ionization mass spectrometry (ESI-MS), peptide and protein ions are usually observed in multiple charge states. Moreover, adduction of the multiply charged species with other ions frequently results in quite complex signal patterns for a single analyte, which significantly complicates the derivation of quantitative information from the mass spectra. Labeling strategies targeting the MS1 level further aggravate this situation, as multiple biological states such as healthy or diseased must be represented simultaneously. We developed an integer linear programming (ILP) approach, which can cluster signals belonging to the same peptide or protein. The algorithm is general in that it models all possible shifts of signals along the m/z axis. These shifts can be induced by different charge states of the compound, the presence of adducts (e.g., potassium or sodium), and/or a fixed mass label (e.g., from ICAT or nicotinic acid labeling), or any combination of the above. We show that our approach can be used to infer more features in labeled data sets, correct wrong charge assignments even in high-resolution MS, improve mass precision, and cluster charged species in different charge states and several adduct types.