Fast, Quantitative and Variant Enabled Mapping of Peptides to Genomes.

Current tools for visualization and integration of proteomics with other omics datasets are inadequate for large-scale studies and capture only basic sequence identity information. Furthermore, the frequent reformatting of annotations for reference genomes required by these tools is known to be highly error prone. We developed PoGo for mapping peptides identified through mass spectrometry to overcome these limitations. PoGo reduced runtime and memory usage by 85% and 20%, respectively, and exhibited overall superior performance over other tools on benchmarking with large-scale human tissue and cancer phosphoproteome datasets comprising ∼3 million peptides. In addition, extended functionality enables representation of single-nucleotide variants, post-translational modifications, and quantitative features. PoGo has been integrated in established frameworks such as the PRIDE tool suite and OpenMS, as well as a standalone tool with user-friendly graphical interface. With the rapid increase of quantitative high-resolution datasets capturing proteomes and global modifications to complement orthogonal genomics platforms, PoGo provides a central utility enabling large-scale visualization and interpretation of transomics datasets

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes.

Cross-talk between genes, transcripts, and proteins is the key to cellular responses; hence, analysis of molecular levels as distinct entities is slowly being extended to integrative studies to enhance the understanding of molecular dynamics within cells. Current tools for the visualization and integration of proteomics with other omics datasets are inadequate for large-scale studies. Furthermore, they only capture basic sequence identify, discarding post-translational modifications and quantitation. To address these issues, we developed PoGo to map peptides with associated post-translational modifications and quantification to reference genome annotation. In addition, the tool was developed to enable the mapping of peptides identified from customized sequence databases incorporating single amino acid variants. While PoGo is a command line tool, the graphical interface PoGoGUI enables non-bioinformatics researchers to easily map peptides to 25 species supported by Ensembl genome annotation. The generated output borrows file formats from the genomics field and, therefore, visualization is supported in most genome browsers. For large-scale studies, PoGo is supported by TrackHubGenerator to create web-accessible repositories of data mapped to genomes that also enable an easy sharing of proteogenomics data. With little effort, this tool can map millions of peptides to reference genomes within only a few minutes, outperforming other available sequence-identity based tools. This protocol demonstrates the best approaches for proteogenomics mapping through PoGo with publicly available datasets of quantitative and phosphoproteomics, as well as large-scale studies

Common mouse models of tauopathy reflect early but not late human disease

BACKGROUND: Mouse models that overexpress human mutant Tau (P301S and P301L) are commonly used in preclinical studies of Alzheimer’s Disease (AD) and while several drugs showed therapeutic effects in these mice, they were ineffective in humans. This leads to the question to which extent the murine models reflect human Tau pathology on the molecular level. METHODS: We isolated insoluble, aggregated Tau species from two common AD mouse models during different stages of disease and characterized the modification landscape of the aggregated Tau using targeted and untargeted mass spectrometry-based proteomics. The results were compared to human AD and to human patients that suffered from early onset dementia and that carry the P301L Tau mutation. RESULTS: Both mouse models accumulate insoluble Tau species during disease. The Tau aggregation is driven by progressive phosphorylation within the proline rich domain and the C-terminus of the protein. This is reflective of early disease stages of human AD and of the pathology of dementia patients carrying the P301L Tau mutation. However, Tau ubiquitination and acetylation, which are important to late-stage human AD are not represented in the mouse models. CONCLUSION: AD mouse models that overexpress human Tau using risk mutations are a suitable tool for testing drug candidates that aim to intervene in the early formation of insoluble Tau species promoted by increased phosphorylation of Tau. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13024-023-00601-y

Population-scale proteome variation in human induced pluripotent stem cells

Human disease phenotypes are driven primarily by alterations in protein expression and/or function. To date, relatively little is known about the variability of the human proteome in populations and how this relates to variability in mRNA expression and to disease loci. Here, we present the first comprehensive proteomic analysis of human induced pluripotent stem cells (iPSC), a key cell type for disease modelling, analysing 202 iPSC lines derived from 151 donors, with integrated transcriptome and genomic sequence data from the same lines. We characterised the major genetic and non-genetic determinants of proteome variation across iPSC lines and assessed key regulatory mechanisms affecting variation in protein abundance. We identified 654 protein quantitative trait loci (pQTLs) in iPSCs, including disease-linked variants in protein-coding sequences and variants with trans regulatory effects. These include pQTL linked to GWAS variants that cannot be detected at the mRNA level, highlighting the utility of dissecting pQTL at peptide level resolution.Peer reviewe

Pflegerische Handlungsfelder in der Gesundheitsförderung und Prävention im Bereich der Sexarbeit

Kein Abstract vorhanden

Proteogenomics for Personalised Molecular Profiling

Technological advancements in mass spectrometry allowing quantification of almost complete proteomes make proteomics a key platform for generating unique functional molecular data. Furthermore, the integrative analysis of genomic and proteomic data, termed proteogenomics, has emerged as a new field revealing insights into gene expression regulation, cell signalling, and disease processes. However, the lack of software tools for high-throughput integration and unbiased modification and variant detection hinder efforts for large-scale proteogenomics studies. The main objectives of this work are to address these issues by developing and applying new software tools and data analysis methods. Firstly, I address mapping of peptide sequences to reference genomes. I introduce a novel tool for high-throughput mapping and highlight its unique features facilitating quantitative and post-translational modification mapping alongside accounting for amino acid substitutions. The performance is benchmarked. Furthermore, I offer an additional tool that permits generation of web accessible hubs of genome wide mappings. To enable unbiased identification of post-translational modifications and amino acid substitutions for high resolution mass spectrometry data, I present algorithmic updates the mass tolerant blind spectrum comparison tool ’MS SMiV’. I demonstrate the applicability of the changes by benchmarking against a published mass tolerant database search of a high resolution tandem mass spectrometry dataset. I then present the application of ‘MS SMiV’ on a panel of 50 colorectal cancer cell lines. I show that the adaption of ‘MS SMiV’ outperforms traditional sequence database based identification of single amino acid variants. Furthermore, I highlight the utility of mass tolerant spectrum matching in combination with isobaric labelled quantitative proteomics in distinguishing between post-translational modifications and amino acid variants of similar mass. In the last part of this work I integrate both tools with a high-throughput proteogenomic identification pipeline and apply it to a pilot study of chondrocytes derived from 12 osteoarthritic individuals. I show the value of this approach in identifying variation between individuals and molecular levels and highlight them with individual examples. I show that multi-plexed proteogenomics can be used to infer genotypes of individuals.This work was supported by NIH grant ( U41HG007234 ) to the GENCODE project and Wellcome Trust grant ( WT098051 ) to the Sanger Institute

FLEXIQuant-LF to quantify protein modification extent in label-free proteomics data

Improvements in LC-MS/MS methods and technology have enabled the identification of thousands of modified peptides in a single experiment. However, protein regulation by post-translational modifications (PTMs) is not binary, making methods to quantify the modification extent crucial to understanding the role of PTMs. Here, we introduce FLEXIQuant-LF, a software tool for large-scale identification of differentially modified peptides and quantification of their modification extent without knowledge of the types of modifications involved. We developed FLEXIQuant-LF using label-free quantification of unmodified peptides and robust linear regression to quantify the modification extent of peptides. As proof of concept, we applied FLEXIQuant-LF to data-independent-acquisition (DIA) data of the anaphase promoting complex/cyclosome (APC/C) during mitosis. The unbiased FLEXIQuant-LF approach to assess the modification extent in quantitative proteomics data provides a better understanding of the function and regulation of PTMs. The software is available at https://github.com/SteenOmicsLab/FLEXIQuantLF.Peer Reviewe

A Parallelization Strategy for the Time Efficient Analysis of Thousands of LC/MS Runs in High-Performance Computing Environment

Combining robust proteomics instrumentation with high-throughput enabling liquid chromatography (LC) systems (e.g., timsTOF Pro and the Evosep One system, respectively) enabled mapping the proteomes of 1000s of samples. Fragpipe is one of the few computational protein identification and quantification frameworks that allows for the time-efficient analysis of such large data sets. However, it requires large amounts of computational power and data storage space that leave even state-of-the-art workstations underpowered when it comes to the analysis of proteomics data sets with 1000s of LC mass spectrometry runs. To address this issue, we developed and optimized a Fragpipe-based analysis strategy for a high-performance computing environment and analyzed 3348 plasma samples (6.4 TB) that were longitudinally collected from hospitalized COVID-19 patients under the auspice of the Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) study. Our parallelization strategy reduced the total runtime by ∼90% from 116 (theoretical) days to just 9 days in the high-performance computing environment. All code is open-source and can be deployed in any Simple Linux Utility for Resource Management (SLURM) high-performance computing environment, enabling the analysis of large-scale high-throughput proteomics studies