Biology subjects

Preikschat, A.

Publications and source records attributed to Preikschat, A..

3 recordsLinked to original sources

bioRxiv2025

Benchmarking Software for DDA-PASEF Immunopeptidomics

Mass spectrometry (MS) is the method of choice for high-throughput identification of immunopeptides, which are generated by intracellular proteases, unlike proteomics peptides that are typically derived from trypsin-digested proteins. Therefore, the searching space for immunopeptides is not limited by proteolytic specificity, requiring more sophisticated software algorithms to handle the increased complexity. Despite the widespread use of MS in immunopeptidomics, there is a lack of systematic evaluation of data processing software, making it challenging to identify the optimal solution. In this study, we provide a comprehensive benchmarking of the most widespread/used data-dependent acquisition (DDA)-based software platforms for immunopeptidomics: MaxQuant, FragPipe, PEAKS and MHCquant. The evaluation was conducted using data obtained from the JY cell line using the Thunder-DDA-PASEF method. We assessed each softwares ability to identify immunopeptides and compared their identification confidence. Additionally, we examined potential biases in the results and tested the impact of database size on immunopeptide identification efficiency. Our findings demonstrate that all software platforms successfully identify the most prominent subset of immunopeptides with 1% false discovery rate (FDR) control, achieving medium to high identification confidence correlations. The largest number of immunopeptides were identified using the commercial PEAKS software, which is closely followed by FragPipe, making it a viable non-commercial alternative. However, we observed that larger database sizes negatively impacted the performance of some software platforms more than others. These results provide valuable insights into the strengths and limitations of current MS data processing tools for immunopeptidomics, supporting the immunopeptidomics/MS community in determining the right choice of software.

bioinformatics↗

bioRxiv2024

TIMS2Rescore: A DDA-PASEF optimized data-driven rescoring pipeline based on MS2Rescore

The high throughput analysis of proteins with mass spectrometry (MS) is highly valuable for understanding human biology, discovering disease biomarkers, identifying therapeutic targets, and exploring pathogen interactions. To achieve these goals, specialized proteomics subfields - such as plasma proteomics, immunopeptidomics, and metaproteomics - must tackle specific analytical challenges, such as an increased identification ambiguity compared to routine proteomics experiments. Technical advancements in MS instrumentation can counter these issues by acquiring more discerning information at higher sensitivity levels, as is exemplified by the incorporation of ion mobility and parallel accumulation - serial fragmentation (PASEF) technologies in timsTOF instruments. In addition, AI-based bioinformatics solutions can help overcome ambiguity issues by integrating more data into the identification workflow. Here, we introduce TIMS2Rescore, a data-driven rescoring workflow optimized for DDA-PASEF data from timsTOF instruments. This platform includes new timsTOF MS2PIP spectrum prediction models and IM2Deep, a new deep learning-based peptide ion mobility predictor. Furthermore, to fully streamline data throughput, TIMS2Rescore directly accepts Bruker raw mass spectrometry data, and search results from ProteoScape and many other search engines, including MS Amanda and PEAKS. We showcase TIMS2Rescore performance on plasma proteomics, immunopeptidomics (HLA class I and II), and metaproteomics data sets. TIMS2Rescore is open-source and freely available at https://github.com/compomics/tims2rescore.

bioinformatics↗

bioRxiv2022

Macrophages inhibit Coxiella burnetii by the ACOD1-itaconate pathway for containment of Q fever

Infection with the intracellular bacterium Coxiella (C.) burnetii can cause chronic Q fever with severe complications and limited treatment options. Here, we identify the enzyme cis- aconitate decarboxylase 1 (ACOD1 or IRG1) and its product itaconate as protective host immune pathway in Q fever. Infection of mice with C. burnetii induced expression of several anti-microbial candidate genes, including Acod1. In macrophages, Acod1 was essential for restricting C. burnetii replication, while other antimicrobial pathways were dispensable. Intratracheal or intraperitoneal infection of Acod1-/- mice caused increased C. burnetii burden, significant weight loss and stronger inflammatory gene expression. Exogenously added itaconate restored pathogen control in Acod1-/- mouse macrophages and blocked replication in human macrophages. In axenic cultures, itaconate directly inhibited growth of C. burnetii. Finally, treatment of infected Acod1-/-mice with itaconate efficiently reduced the tissue pathogen load. Thus, ACOD1-derived itaconate is a key factor in the macrophage-mediated defense against C. burnetii and may be exploited for novel therapeutic approaches in chronic Q fever.

immunology↗