Accurate control of false discovery rates (FDR) and false localization rates (FLR) is central to quantitative proteomics and phosphoproteomics, yet rigorous validation is limited by the absence of high-complexity ground truth data. Here we introduce timsim, a simulation framework using machine-learning and first principle-driven prediction of peptide properties to generate native Bruker-format timsTOF dda-PASEF and dia-PASEF acquisition data with complete ground-truth annotation. Using timsim benchmarks, we show that several dia-PASEF workflows control FDR near the nominal 1% threshold at stripped-sequence level but exhibit inflated true FDR (3–5%) when modified peptidoforms are considered, driven by systematic misassignment of common modifications. In dda-PASEF analyses, match-between-runs produced peak-matching errors of up to 30% under high-density conditions. Simulated phosphoproteomics datasets enabled calibration of site localization scores, identifying a 0.65 site-probability cutoff as an optimal tradeoff between sensitivity and false localization. Timsim provides a scalable resource for rigorous benchmarking and development of proteomics software.

Our study presents and applies a metabolomics-driven multi-omics integration strategy to elucidate dynamic pathway interactions during disease progression. We analyzed longitudinal metabolomics datasets from a Duchenne muscular dystrophy (DMD) mouse model (6-30 weeks) and an acute Bothrops asper envenomation model (1-24 h) to contrast chronic versus acute inflammation. In the DMD model, we predicted phased cross-talk between sphingolipid metabolism and neurotrophin signaling: an early proteomic surge followed by lipid-mediated amplification and a late convergence at the protein level. Arginine and proline metabolism exhibited early metabolite accumulation preceding delayed inferred protein changes, consistent with impaired nitric oxide synthesis and argininemia-like effect. We also predicted late-stage activation of the AGE-RAGE pathway in DMD, likely triggered by ceramide buildup, and an autophagy-related lipid metabolic shift at mid-stage. In the envenomation model, tryptophan-kynurenine and nicotinamide pathways for NAD⁺ biosynthesis were rapidly perturbed at the metabolite level (1-3 h) but induced corresponding predicted enzymes only by 24 h. Thyroid hormone signaling showed an early coupling of substrate availability (tyrosine surge at 1 h) with predicted stress-response proteins and a second, delayed wave of inferred transcriptional regulators at 24 h. Acute envenomation also triggered immediate glycine/serine utilization possibly for antioxidant defense and glycerophospholipid breakdown (via phospholipase A₂), whereas chronic DMD showed sustained glycine/serine engagement and inferred, unresolved phospholipid perturbation without protein-level compensation, which may result from chronic oxidative stress. Overall, our integrative analysis revealed time-specific, multi-layer molecular perturbations distinguishing acute toxin injury from chronic muscle degeneration. Key metabolic control points (ceramide accumulation, arginine flux diversion, autophagy-lipid cross-talk, NAD⁺ salvage timing) were identified, highlighting potential targets for stage-specific therapeutic or nutritional interventions.

Prohibitin 2 (PHB2) is a highly conserved protein with essential roles in cell homeostasis and survival across different cell types. Previous studies have shown that the deletion of PHB2 results in an arrest in proliferation due to impaired mitochondrial function regulated by the dynamin-like GTPase OPA1. The function of PHB2 in immune cells remains unclear; however, some studies suggest that PHB2 plays a role in the cell membranes of B and T cells. In order to elucidate the role of PHB2 in immune cells, we generated PHB2-deficient T cells. Our findings reveal a pivotal role for PHB2 in the proliferation and differentiation of T cells. PHB2 deficiency inhibits T cell proliferation by inducing a cell cycle arrest at the G1 to S phase, thereby preventing the differentiation into effector T cells. Furthermore, in contrast to previous reports, T cells lacking PHB2 are more resistant to apoptosis. Metabolic analysis reveals that PHB2-deficient T cells fail to boost their energy production through glycolysis and oxidative phosphorylation upon activation, hindering their ability to sustain biosynthetic processes and to proliferate in response to activation.

Idiopathic inflammatory myopathies (IIMs) are autoimmune muscle diseases with distinct clinical, histopathological, and molecular features. Among them, inclusion body myositis (IBM) is refractory to immunotherapy and characterized by combined inflammatory and degenerative changes. Polymyositis with mitochondrial pathology (PM-Mito) has been proposed as a prodromal stage of IBM, but molecular profile underlying this spectrum remains poorly defined. Skeletal muscle biopsies from 38 IBM, 14 PM-Mito, 5 anti-synthetase syndrome (ASyS), 3 dermatomyositis (DM), 5 immune-mediated necrotizing myopathy (IMNM), and 7 non-diseased controls (NDC) were analyzed by label-free mass spectrometry and validated by bulk RNA sequencing. Dimensionality reduction was performed using sparse Partial Least Squares Discriminant Analysis (sPLS-DA), followed by differential protein analysis. IBM exhibited a homogeneous and distinct proteomic signature compared with other IIM subtypes, driven by upregulation of MHC class I (e.g. HLA-A) and II (e.g. HLA-DRB1, CD74) molecules, and cytoskeletal proteins (e.g. PDCL3). Comparing IBM to other types of IIM, we also detected increased level of specific histone variants (e.g. HIST2H2AA3, H1FX). Enrichment analysis of the differential proteins underscored increased antigen presentation and T-cell–mediated immunity pathways, with concomitant depletion of mitochondrial respiratory chain, RNA processing, and oxidative phosphorylation components in IBM. PM-Mito shared a proteomic profile with IBM with reduced MT-ND2 levels and increases in lipid storage regulator PLIN1 and extracellular matrix protein COL14A1, among others. In contrast to IBM, PM-Mito preserved type 2 myofiber markers (e.g. MYH2). A specific protein change to PM-Mito was an increase in the cytochrome c oxidase subunit III (MT-CO3), implicating mitochondrial remodelling. Transcriptomic analysis validated the proteomic changes in COL14A1, IGLL4, PLIN1, MT-ND2, SMDT1, and TIMM21, all of which were shared between IBM and PM-Mito. IBM exhibits a unique proteomic landscape distinct from other IIMs. The overlap with PM-Mito suggests that these conditions share molecular features, supporting an interpretation that places PM-Mito in the broader spectrum of IBM. Novel protein markers, including histone variants and cytoskeletal regulators, highlight potential pathways for future research. These findings underscore the need for longitudinal studies exploring therapeutic targets in early disease stages.

Large-scale metabolomics studies are increasingly constrained by the limited throughput of liquid chromatography-mass spectrometry (LC-MS), where comprehensive coverage requires multiple chromatographic separations and ionization modes. While flow injection-mass spectrometry (FI-MS) offers substantially higher throughput, its broader adoption in untargeted workflows has been hindered by limited availability of accessible, user-friendly, and scalable data processing solutions, and reduced annotation confidence. Here, we present MetaboFIMS, an open-source, fully automated, vendor-agnostic computational pipeline for untargeted metabolomics data analysis of FI-MS data, publically available as Shiny application, complemented with an optimized FI-MS acquisition protocol that can be implemented on standard LC-MS instrumentation. The optimized FI-MS method enables analysis times of ≤1 min per sample and ion mode, with MetaboFIMS performing end-to-end spectral processing of mzML files including isotope clustering, run alignment, molecular formula prediction, and annotation against HMDB databases, processing over 500 samples in approximately 2 hours on a standard desktop workstation. We systematic optimized acquisition parameters to maximize sensitivity, reproducibility and metabolome coverage for the analysis of clinical plasma samples, and benchmarked the analytical performance on two clinical cohorts. In a multiple sclerosis study, we show the capacity of FI-MS to capture rapid metabolic perturbations following ublituximab treatment, while in a type 2 diabetes mellitus cohort, FI-MS was able to reproduce empagliflozin-associated reductions in uric acid and 1,5-anhydroglucitol previously identified by LC-MS, demonstrating strong methodological concordance. These results establish FI-MS and MetaboFIMS as a robust, scalable, and high-throughput complement to LC-MS, enabling rapid metabolic phenotyping of large biobanked cohorts.

Major histocompatibility complex (MHC, or human leukocyte antigen, HLA) peptide ligands can be exploited to develop immunotherapies targeting immunogenic disease-specific immunopeptides, such as virus- or cancer mutation-derived peptides. Liquid chromatography coupled with mass spectrometry (LC-MS)-based immunopeptidomics is the gold standard for identifying MHC ligands. We previously optimized a workflow enabling the identification of more than 10,000 MHC class I ligands per cell line. This process comprises three major steps: (I) a high-recovery immunopeptidome enrichment, (II) an optimized MS acquisition in the timsTOF Pro called Thunder-Data-Dependent Acquisition with Parallel Accumulation-SErial Fragmentation (Thunder-DDA-PASEF), and (III) peptide identification using PEAKS XPro boosted by MS2Rescore data-driven rescoring. Here, we describe our workflow for deep-coverage immunopeptidomics step-by-step, from sample preparation to data analysis and validation.

3'-nucleotidases/nucleases, distinct class I nucleases of protozoan parasites, play a pivotal role in extracellular purine salvage. As Leishmania are purine auxotrophs and lack de novo synthesis, ectoenzymes facilitating nucleotide and nucleic acid cleavage are indispensable for subsequent uptake. Employing quantitative proteomics, we characterized a class I nuclease p1/s1 cluster in L. major that comprises enzymes exhibiting dual 3'-nucleotidase and endonuclease activity. Expression of these enzymes is induced upon miltefosine or staurosporine treatment and was specifically detected in stationary-phase, but not in logarithmic-phase promastigotes. After confirming secretion of p1/s1, ecto-enzymatic activity was detected on parasites and in the culture supernatant. Viable null mutants deficient for the p1/s1 cluster were only obtained when a diCre-based inducible knockout system was applied, whereas direct deletion approaches were lethal. The viable knockout strains exhibited significantly reduced 3'-nucleotidase/nuclease activity. Notably, these parasites adapted by compensatory enrichment of various alternative purine salvage proteins at the proteomic level. Furthermore, both enzymatic functions implied mechanisms of host-pathogen interactions to facilitate infection establishment: Utilizing 3'-nucleotidase activity, Leishmania generate extracellular adenosine to suppress inflammatory cytokine secretion from macrophages and reduce lymphocyte proliferation in a human primary cell model. The presence of ecto-nucleases also allowed these parasites to degrade and survive neutrophil extracellular traps, a potent first-line innate immune mechanism in pathogen defense. In summary, our integrative approach combining proteomics, immunological and genome editing methods expands current knowledge about Leishmania major 3'-nucleotidases/nucleases. By offering new insights into the diverse involvements in host-pathogen interactions, we highlight p1/s1 as pivotal factor during infection and potential drug target.

The EmDia trial, designed to study the effects of the sodium glucose cotransporter-2 (SGLT2) inhibitor empagliflozin on cardiovascular comorbidities in type 2 diabetes mellitus (T2DM) patients, has been investigated for short-term metabolic alterations by a limited set of clinical assays. To expand on this data, we report on the development of a liquid chromatography-mass spectrometry (LC-MS)-based metabolomics approach employing an optimized metabolite separation by pentafluorophenyl chromatography. High-confidence metabolite annotation based on reference standards allows for fast and robust metabolic characterization of large plasma cohorts due to scalability. Applied to EmDia, we show the high predictive power of our methodology for several clinical parameters, including a near-perfect prediction of fasting blood glucose (R2 = 0.97), and demonstrate how empagliflozin leads to reduced plasma levels of deoxyhexoses, such as 1,5-anhydroglucitol, a short-term biomarker for glycemic control. SUMMARY: Clinical metabolomics studies continue to gain interest due to their comprehensive metabolite coverage, offering insights into metabolic alterations in health and disease. In this study, we present a robust data-independent acquisition liquid chromatography-mass spectrometry-based metabolomics workflow employing an optimized metabolite separation by pentafluorophenyl chromatography that showcases a comprehensive coverage of plasma metabolites. Applied to characterize plasma metabolite profiles in samples of EmDia, a placebo controlled study investigating the effect of the SGLT2 inhibitor empagliflozin, we assess the predictive power of metabolite signals for clinical parameters describing organ physiologies and pathophysiologies. Descriptive statistics are applied to the metabolite profiles to identify empagliflozin intake-associated metabolite markers.

Advancing MS-based proteomics toward clinical applications evolves around developing standardized start-to-finish and fit-for-purpose workflows for clinical specimens. Steps along the method design involve the determination and optimization of several bioanalytical parameters such as selectivity, sensitivity, accuracy, and precision. In a joint effort, eight proteomics laboratories belonging to the MSCoreSys initiative including the CLINSPECT-M, MSTARS, DIASyM, and SMART-CARE consortia performed a longitudinal round-robin study to assess the analysis performance of plasma and serum as clinically relevant samples. A variety of LC-MS/MS setups including mass spectrometer models from ThermoFisher and Bruker as well as LC systems from ThermoFisher, Evosep, and Waters Corporation were used in this study. As key performance indicators, sensitivity, precision, and reproducibility were monitored over time. Protein identifications range between 300 and 400 IDs across different state-of-the-art MS instruments, with timsTOF Pro, Orbitrap Exploris 480, and Q Exactive HF-X being among the top performers. Overall, 71 proteins are reproducibly detectable in all setups in both serum and plasma samples, and 22 of these proteins are FDA-approved biomarkers, which are reproducibly quantified (CV < 20% with label-free quantification). In total, the round-robin study highlights a promising baseline for bringing MS-based measurements of serum and plasma samples closer to clinical utility.

Sepsis affects ∼50 million people annually, with mortality rates of 20–50%. Host-pathogen interactions are implicated in its etiology but are understudied. Bacterial polyphosphates are metabolites with pleiotropic functions in stress response, virulence and host evasion. Here, we reveal polyphosphate-mediated changes during sepsis. In cecal ligation and puncture (CLP)-induced sepsis, gnotobiotic mice colonized with wild type E. coli showed reduced survival versus mice with polyphosphate kinase-deficient E. coli (38% vs 75%), implying polyphosphates mediate lethality. Moreover, polyphosphates impaired local innate immune responses, altered basal and maximal metabolism (OXPHOS, glycolysis), and dysregulated transcriptional programs of metabolic genes (Ldha, Hk2, Slc2a1, Atp5h) as studied by snRNA-Seq in macrophages. Polyphosphates bound to lactate dehydrogenase and affected its activity. In the serum from patients with sepsis (n = 140) polyphosphate accumulation was highest compared to systemic inflammatory response syndrome (n = 114) and healthy controls (n = 143), and higher in non-survivors of sepsis than survivors. Polyphosphate amounts positively correlated with lactate, an established prognostic marker for sepsis. Notably, in murine CLP sepsis, recombinant exopolyphosphate improved survival and lactate by neutralizing polyphosphates. Our findings elucidate how polyphosphates disrupt macrophage functioning, reprogramming innate immune responses and influencing sepsis outcomes. This work was funded by the National Institutes of Health (R01AI153613 to M.B.) and Deutsche Forschungsgemeinschaft (BO3482/3-3, BO3482/4-1 to M.B.). Microbial, Parasitic, and Fungal Immunology (MPF)

Mass spectrometry is essential for analyzing and quantifying biological samples. The timsTOF platform is a prominent commercial tool for this purpose, particularly in bottom-up acquisition scenarios. The additional ion mobility dimension requires more complex data processing, yet most current software solutions for timsTOF raw data are proprietary or closed-source, limiting integration into custom workflows. We introduce rustims, a framework implementing a flexible toolbox designed for processing timsTOF raw data, currently focusing on data-dependent acquisition (DDA-PASEF). The framework employs a dual-language approach, combining efficient, multithreaded Rust code with an easy-to-use Python interface. This allows for implementations that are fast, intuitive, and easy to integrate. With imspy as its main Python scripting interface and sagepy for Sage search engine bindings, rustims enables fast, integrable, and intuitive processing. We demonstrate its capabilities with a pipeline for DDA-PASEF data including rescoring and integration of third-party tools like the Prosit intensity predictor and an extended ion mobility model. This pipeline supports tryptic proteomics and nontryptic immunopeptidomics data, with benchmark comparisons to FragPipe and PEAKS. Rustims is available on GitHub under the MIT license, with installation packages for multiple platforms on PyPi and all analysis scripts accessible via Zenodo.

Human plasma is routinely collected during clinical care and constitutes a rich source of biomarkers for diagnostics and patient stratification. Liquid chromatography-mass spectrometry (LC-MS)-based proteomics is a key method for plasma biomarker discovery, but the high dynamic range of plasma proteins poses significant challenges for MS analysis and data processing. To benchmark the quantitative performance of neat plasma analysis, we introduce a multispecies sample set based on a human tryptic plasma digest containing varying low level spike-ins of yeast and E. coli tryptic proteome digests, termed PYE. By analysing the sample set on state-of-the-art LC-MS platforms across twelve different sites in data-dependent (DDA) and data-independent acquisition (DIA) modes, we provide a data resource comprising a total of 1116 individual LC-MS runs. Centralized data analysis shows that DIA methods outperform DDA-based approaches regarding identifications, data completeness, accuracy, and precision. DIA achieves excellent technical reproducibility, as demonstrated by coefficients of variation (CVs) between 3.3% and 9.8% at protein level. Comparative analysis of different setups clearly shows a high overlap in identified proteins and proves that accurate and precise quantitative measurements are feasible across multiple sites, even in a complex matrix such as plasma, using state-of-the-art instrumentation. The collected dataset, including the PYE sample set and strategy presented, serves as a valuable resource for optimizing the accuracy and reproducibility of LC-MS and bioinformatic workflows for clinical plasma proteome analysis.

BACKGROUND AND AIMS:Inhibiting γ-secretase-mediated Notch signaling has been explored as a potential treatment for Alzheimer's disease and cancer. However, clinical trials have revealed that this approach can lead to side effects, such as gut inflammation. Notch signaling has been shown to be a key mediator of intestinal epithelial homeostasis. We aimed to investigate the molecular mechanisms of γ-secretase inhibition-associated colitis. METHODS:Mice and small intestinal organoids were treated with γ-secretase inhibitors and analyzed for intestinal epithelial cell (IEC) differentiation and inflammation-associated markers using different molecular and histological approaches, along with transcriptomic and proteomic analyses. To evaluate the role of the microbiome in colitis development, mice undergoing pharmacological γ-secretase inhibition were treated with antibiotics. Additionally, inflammatory bowel disease (IBD) patient samples and control samples were analyzed to assess the expression of Notch signaling pathway components in IECs. RESULTS:This study shows that pharmacological γ-secretase inhibition induces inflammation in both the small and large intestine of mice, a phenotype that could be rescued upon microbiota depletion. Inhibiting the γ-secretase induced structural disruption of the epithelium and inflammatory cytokine release. On a molecular level, epithelial organoids exhibited disrupted IEC differentiation and impaired proliferation, associated with defective Notch signaling. Finally, analysis of IBD patients revealed deregulation of Notch pathway components within IECs. CONCLUSIONS:In conclusion, systemic use of γ-secretase inhibitors disrupts epithelial cell function by impairing IEC differentiation and triggering gut inflammation in mice. These findings should be considered when designing future therapeutic interventions involving γ-secretase inhibitors.

The integration of multi-omics data offers transformative potential for elucidating complex molecular mechanisms underlying biological processes and diseases. In this study, we developed a lipid-metabolite-protein network that combines a protein-protein interaction network and enzymatic and genetic interactions of proteins with metabolites and lipids to provide a unified framework for multi-omics integration. Using hyperbolic embedding, the network visualizes connections across omics layers, accessible through a user-friendly Shiny R (version 1.10.0) software package. This framework ranks molecules across omics layers based on functional proximity, enabling intuitive exploration. Application in a cardiovascular disease (CVD) case study identified lipids and metabolites associated with CVD-related proteins. The analysis confirmed known associations, like cholesterol esters and sphingomyelin, and highlighted potential novel biomarkers, such as 4-imidazoleacetate and indoleacetaldehyde. Furthermore, we used the network to analyze empagliflozin's temporal effects on lipid metabolism. Functional enrichment analysis of proteins associated with lipid signatures revealed dynamic shifts in biological processes, with early effects impacting phospholipid metabolism and long-term effects affecting sphingolipid biosynthesis. Our framework offers a versatile tool for hypothesis generation, functional analysis, and biomarker discovery. By bridging molecular layers, this approach advances our understanding of disease mechanisms and therapeutic effects, with broad applications in computational biology and precision medicine.

Dendritic cells (DCs) rely on Toll-like receptor 9 (TLR9) to detect unmethylated CpG motifs in microbial DNA, triggering essential immune responses. While the downstream signaling pathways of TLR9 activation are well characterized, their impact on S-palmitoylation is unknown. S-palmitoylation, involving the reversible attachment of palmitic acid to cysteine residues, plays a crucial role in regulating protein function and is catalyzed by the ZDHHC family of palmitoyl-acyltransferases (PATs). In this study, we investigated the S-palmitoylated proteome of bone marrow-derived GM-CSF DCs (GM-DCs) at resting and following TLR9 activation with CpGB. Using the click-chemistry-compatible analog 17-octadecynoic acid (17-ODYA) and mass spectrometry (MS)-based proteomics, we characterized dynamic remodeling of S-palmitoylation in response to TLR9 activation. This included enrichment of targets involved in immune and metabolic pathways. Transcriptomic analysis of mice and human DCs revealed TLR9-driven modulation of PAT-encoding genes. Subsequently, we explored the contribution of Zdhhc9 expression to the regulation of S-palmitoylation in DCs. Using gene knockout approaches, we identified candidate protein targets potentially linked to ZDHHC9 activity. Interestingly, modulation of Zdhhc9 expression alone did not influence DC maturation, suggesting that other PATs might compensate for its activity. Together, our findings reveal a novel layer of regulation in TLR9 signaling mediated by S-palmitoylation.

Regulatory T (Treg) cells, a subset of CD4+ T cells, play a crucial role in immunoregulation. Notably, CCR8-expressing Treg cells in tissues also contribute to organ homeostasis and repair. To determine whether these tissue-regenerative programs are active in the tumor microenvironment, we employed single-cell chromatin accessibility and genome-wide DNA methylation analyses to investigate CCR8+ tissue Treg cells isolated from human tumor and adjacent tumor-free tissues. Our findings indicate that CCR8+ tissue Treg cells from tumor and corresponding tumor-free tissues exhibit a high degree of similarity, suggesting that the tumor microenvironment may harbor highly activated tissue Treg cells. This observation was consistent across various tumor types and origins, including primary tumors and metastases. Using quantitative proteomics, we identified several candidate factors associated with the regenerative and suppressive programs of Treg cells, which may serve as potential reservoir of druggable targets for future therapeutic interventions. ### Competing Interest Statement M.D. received personal fees from Odyssey Therapeutics outside the submitted work.

K2P2.1 (gene: Kcnk2), a two-pore-domain potassium channel, regulates leukocyte transmigration across the blood-brain barrier by a yet unknown mechanism. We demonstrate that Kcnk2-/- mouse brain microvascular endothelial cells (MBMECs) exhibit an altered cytoskeletal structure and surface morphology with increased formation of membrane protrusions. Cell adhesion molecules cluster on those protrusions and facilitate leukocyte adhesion and migration in vitro and in vivo. We observe downregulation of K2P2.1 and activation of actin modulating proteins (cofilin 1, Arp2/3) in inflamed wildtype MBMECs. In the mechanosensitive conformation, K2P2.1 shields the phospholipid PI(4,5)P2 from interaction with other actin regulatory proteins, especially cofilin 1. Consequently, after stimulus-related K2P2.1 downregulation and dislocation from PI(4,5)P2, actin rearrangements are induced. Thus, K2P2.1-mediated regulatory processes are essential for actin dynamics, fast, reversible, and pharmacologically targetable.