Quantification and Simulation of Liquid Chromatography-Mass Spectrometry Data

Computational mass spectrometry is a fast evolving field that has attracted increased attention over the last couple of years. The performance of software solutions determines the success of analysis to a great extent. New algorithms are required to reflect new experimental procedures and deal with new instrument generations. One essential component of algorithm development is the validation (as well as comparison) of software on a broad range of data sets. This requires a gold standard (or so-called ground truth), which is usually obtained by manual annotation of a real data set. Comprehensive manually annotated public data sets for mass spectrometry data are labor-intensive to produce and their quality strongly depends on the skill of the human expert. Some parts of the data may even be impossible to annotate due to high levels of noise or other ambiguities. Furthermore, manually annotated data is usually not available for all steps in a typical computational analysis pipeline. We thus developed the most comprehensive simulation software to date, which allows to generate multiple levels of ground truth and features a plethora of settings to reflect experimental conditions and instrument settings. The simulator is used to generate several distinct types of data. The data are subsequently employed to evaluate existing algorithms. Additionally, we employ simulation to determine the influence of instrument attributes and sample complexity on the ability of algorithms to recover information. The results give valuable hints on how to optimize experimental setups. Furthermore, this thesis introduces two quantitative approaches, namely a decharging algorithm based on integer linear programming and a new workflow for identification of differentially expressed proteins for a large in vitro study on toxic compounds. Decharging infers the uncharged mass of a peptide (or protein) by clustering all its charge variants. The latter occur frequently under certain experimental conditions. We employ simulation to show that decharging is robust against missing values even for high complexity data and that the algorithm outperforms other solutions in terms of mass accuracy and run time on real data. The last part of this thesis deals with a new state-of-the-art workflow for protein quantification based on isobaric tags for relative and absolute quantitation (iTRAQ). We devise a new approach to isotope correction, propose an experimental design, introduce new metrics of iTRAQ data quality, and confirm putative properties of iTRAQ data using a novel approach. All tools developed as part of this thesis are implemented in OpenMS, a C++ library for computational mass spectrometry

Proteomics quality control : a 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 quality control pipeline called Proteomics Quality Control (PTXQC) for bottom-up LC-MS data generated by the MaxQuant software pipeline. PTXQC creates a quality control report containing a comprehensive and powerful set of quality control 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 non-specialists. 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 RT 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

Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer

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

The deep-subsurface sulfate reducer Desulfotomaculum kuznetsovii employs two methanol-degrading pathways

Methanol is generally metabolized through a pathway initiated by a cobalamine-containing methanol methyltransferase by anaerobic methylotrophs (such as methanogens and acetogens), or through oxidation to formaldehyde using a methanol dehydrogenase by aerobes. Methanol is an important substrate in deep-subsurface environments, where thermophilic sulfate-reducing bacteria of the genus Desulfotomaculum have key roles. Here, we study the methanol metabolism of Desulfotomaculum kuznetsovii strain 17T, isolated from a 3000-m deep geothermal water reservoir. We use proteomics to analyze cells grown with methanol and sulfate in the presence and absence of cobalt and vitamin B12. The results indicate the presence of two methanol-degrading pathways in D. kuznetsovii, a cobalt-dependent methanol methyltransferase and a cobalt-independent methanol dehydrogenase, which is further confirmed by stable isotope fractionation. This is the first report of a microorganism utilizing two distinct methanol conversion pathways. We hypothesize that this gives D. kuznetsovii a competitive advantage in its natural environment.Research was funded by grants of the Division of Chemical Sciences (CW-TOP 700.55.343) and Earth and Life Sciences (ALW 819.02.014) of The Netherlands Organisation for Scientific Research (NWO), the European Research Council (ERC grant 323009), and the Gravitation grant (024.002.002) of the Netherlands Ministry of Education, Culture and Scienceinfo:eu-repo/semantics/publishedVersio

The reductive glycine pathway allows autotrophic growth of Desulfovibrio desulfuricans

Supplementary informationis available for this paper athttps://doi.org/10.1038/s41467-020-18906-7Six CO2 fixation pathways are known to operate in photoautotrophic and chemoautotrophic microorganisms. Here, we describe chemolithoautotrophic growth of the sulphate-reducing bacterium Desulfovibrio desulfuricans (strain G11) with hydrogen and sulphate as energy substrates. Genomic, transcriptomic, proteomic and metabolomic analyses reveal that D. desulfuricans assimilates CO2 via the reductive glycine pathway, a seventh CO2 fixation pathway. In this pathway, CO2 is first reduced to formate, which is reduced and condensed with a second CO2 to generate glycine. Glycine is further reduced in D. desulfuricans by glycine reductase to acetyl-P, and then to acetyl-CoA, which is condensed with another CO2 to form pyruvate. Ammonia is involved in the operation of the pathway, which is reflected in the dependence of the autotrophic growth rate on the ammonia concentration. Our study demonstrates microbial autotrophic growth fully supported by this highly ATP-efficient CO2 fixation pathway.We acknowledge Änne-Michaelis and William Newell for assistance with the LC-MS forthe metabolomics experiments and Daniel Amador-Noguez for access to the LC-MS usedfor13C intracellular metabolomic analysis. We thank Ines Cardoso Pereira and John vander Oost for critically reading the manuscript. This research was funded by the Neth-erlands Organisation for Scientific Research (NWO) through SIAM Gravitation Grant024.002.002 and the Innovation Program Microbiology (WUR), NJC acknowledgesfunding from NWO through a Rubicon Grant (019.163LW.035) and a Veni Grant(VI.Veni.192.156).info:eu-repo/semantics/publishedVersio

Integrative functional genomics decodes herpes simplex virus 1

Funder: Alexander von Humboldt-Stiftung (Alexander von Humboldt Foundation); doi: https://doi.org/10.13039/100005156Abstract: 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