Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning

DIA-BERT: pre-trained end-to-end transformer models for enhanced DIA proteomics data analysis

Data-independent acquisition mass spectrometry (DIA-MS) has become increasingly pivotal in quantitative proteomics. In this study, we present DIA-BERT, a software tool that harnesses a transformer-based pre-trained artificial intelligence (AI) model for analyzing DIA proteomics data. The identification model was trained using over 276 million high-quality peptide precursors extracted from existing DIA-MS files, while the quantification model was trained on 34 million peptide precursors from synthetic DIA-MS files. When compared to DIA-NN, DIA-BERT demonstrated a 51% increase in protein identifications and 22% more peptide precursors on average across five human cancer sample sets (cervical cancer, pancreatic adenocarcinoma, myosarcoma, gallbladder cancer, and gastric carcinoma), achieving high quantitative accuracy. This study underscores the potential of leveraging pre-trained models and synthetic datasets to enhance the analysis of DIA proteomics.

Evaluation of commercial micro-flow liquid chromatography columns for mass spectrometry-based proteomics

This study aimed to evaluate the separation efficiency and loading capacity of four commercially available micro-flow liquid chromatography (micro-flow LC) columns with 1.0 mm i.d. (PepMap™ C18, HALO® ES-C18, YMC-Triart™ C18 and Acquity UPLC Peptide BEH C18, abbreviated as PepMap, HALO, YMC and BEH) for mass spectrometry-based proteomics analysis. Samples including cytochrome c (cyt-c), human plasma, and HeLa protein digest were used for the tests. The YMC showed much wider peak widths than the other columns, exhibited relatively poor identification results. However, the other three columns showed similar identification performance. Among them, the PepMap was the optimal choice for plasma proteomics as it had a high loading capacity and exhibited the most symmetrical peak shape with a symmetry factor closest to 1.0. In general, our results provided valuable and solid support for the choice of a chromatographic column, which could potentially contributes to the wider application of micro-flow LC-MS/MS in proteomics research.

Maximizing Immunopeptidomics-Based Bacterial Epitope Discovery by Multiple Search Engines and Rescoring

Mass spectrometry-based discovery of bacterial immunopeptides presented by infected cells allows untargeted discovery of bacterial antigens that can serve as vaccine candidates. However, reliable identification of bacterial epitopes is challenged by their extremely low abundance. Here, we describe an optimized bioinformatic framework to enhance the confident identification of bacterial immunopeptides. Immunopeptidomics data of cell cultures infected with Listeria monocytogenes were searched by four different search engines, PEAKS, Comet, Sage and MSFragger, followed by data-driven rescoring with MS2Rescore. Compared with individual search engine results, this integrated workflow boosted immunopeptide identification by an average of 27% and led to the high-confidence detection of 18 additional bacterial peptides (+27%) matching 15 different Listeria proteins (+36%). Despite the strong agreement between the search engines, a small number of spectra (<1%) had ambiguous matches to multiple peptides and were excluded to ensure high-confidence identifications. Finally, we demonstrate our workflow with sensitive timsTOF SCP data acquisition and find that rescoring, now with inclusion of ion mobility features, identifies 76% more peptides compared to Q Exactive HF acquisition. Together, our results demonstrate how integration of multiple search engine results along with data-driven rescoring maximizes immunopeptide identification, boosting the detection of high-confidence bacterial epitopes for vaccine development.

ProSIMSIt: The Best of Both Worlds in Data-Driven Rescoring and Identification Transfer

Multibatch isobaric labeling experiments are frequently applied for clinical and pharmaceutical studies of large sample cohorts. To tackle the critical issue of missing values in such studies, we introduce the ProSIMSIt pipeline. It combines the advantages of tandem mass spectrum clustering via SIMSI-Transfer and data-driven rescoring via Prosit and Oktoberfest. We demonstrate that these two tools are complementary and mutually beneficial. On large-scale cancer cohort data, ProSIMSIt increased the number of peptide spectrum matches (PSMs) by 40% on both global and phosphoproteome data sets. Furthermore, on data from proteome-wide drug-response profiling of post-translational modifications (decryptM), our pipeline substantially increased drug-PTM relations and revealed previously unseen downstream effects of drug target inhibition. ProSIMSIt is available as an open-source Python package with a simple command line interface that allows easy application to MaxQuant result files.

Development and Clinical Evaluation of a Multiplexed Health Surveillance Panel Using Ultra High-Throughput PRM-MS in an Inflammatory Bowel Disease Cohort

Despite advances in clinical proteomics, translating protein biomarker discoveries into clinical use remains challenging due to the technical complexity of the validation process. Targeted MS-based proteomics approaches such as parallel reaction monitoring (PRM) offer sensitive and specific assays for biomarker translation. In this study, we developed a multiplex PRM assay using the Stellar mass spectrometry platform to quantify 57 plasma proteins, including 21 FDA-approved proteins. Loading curves (11-points) were performed at 4 sample throughputs (100, 144, 180, and 300 samples per day) using independent, optimized, and scheduled PRM methods. Following optimization, an inflammatory bowel disease (IBD) cohort of plasma samples (493 IBD, 509 matched controls) was analyzed at a throughput of 180 SPD. To monitor system performance, the study also included 1,000 additional injections for system suitability tests, low-, middle-, and high-quality controls, washes, and blanks. Using this approach, we observed high quantifiability (linearity, sensitivity, reproducibility) in the PRM assay and consistent in data acquisition across a large cohort. We also validated the candidate IBD markers, C-reactive protein and orosomucoid protein, identified in a recent discovery experiment.

SWAPS: A Modular Deep-Learning Empowered Peptide Identity Propagation Framework Beyond Match-Between-Run

Mass spectrometry (MS)-based proteomics relies heavily on MS/MS (MS2) data, which do not fully exploit the available MS1 information. Traditional peptide identity propagation (PIP) methods, such as match-between-runs (MBR), are limited to similar runs, particularly with the same liquid chromatography (LC) gradients, thus potentially underutilizing available proteomics libraries. We introduce SWAPS, a novel and modular MS1-centric framework incorporating advances in peptide property prediction, extensive proteomics libraries, and deep-learning-based postprocessing to enable and explore PIP across more diverse experimental conditions and LC gradients. SWAPS substantially enhances precursor identification, especially in shorter gradients. On the example of 30, 15, and 7.5 min gradients, SWAPS achieves increases of 46.3, 86.2, and 112.1% on precursor level over MaxQuant's MS2-based identifications. Despite the inherent challenges in controlling false discovery rates (FDR) with MS1-based methods, SWAPS demonstrates strong efficacy in deconvoluting MS1 signals, offering powerful discrimination and deeper sequence exploration, while maintaining quantitative accuracy. By building on and applying peptide property predictions in practical contexts, SWAPS reveals that current models, while advanced, are still not fully comparable to experimental measurements, sparking the need for further research. Additionally, its modular design allows seamless integration of future improvements, positioning SWAPS as a forward-looking tool in proteomics.

Identification of mechanistic CKD biomarkers in a rat SNx kidney fibrosis model by transcriptomics and proteomics detectable in biofluids

The rat sub-total nephrectomy (SNx) is a functional model of general chronic kidney disease (CKD) where the main pathological driver is glomerular hypertension representative of several subtypes of CKD. Comprehensive transcriptomics and proteomics analyses on the SNx rats were performed to identify biomarkers in plasma or urine that correlate with kidney disease and functional kidney loss. Kidneys were subjected to collagen I and III staining for fibrosis scoring, SWATH-MS proteomics and bulk RNA-sequencing transcriptomics, with SWATH-MS also performed on plasma and urine. Differential expression analysis demonstrated significant dysregulation of genes and proteins involved in fibrosis, metabolism, and immune response in the SNx rats compared to controls. Gene ontology analysis of the intersecting genes and proteins from both studies demonstrated common biology between animal cohorts that reached the predefined kidney disease thresholds (serum creatinine > two-fold or proteinuria > three-fold increase over sham-operated). Thirteen significantly differential molecules were detected with consistent directional changes in both omics datasets. These molecules were detected independently in kidney (both RNA and protein) and urine (protein only), but not in plasma. Bioinformatics analysis enabled the identification of mechanistic CKD biomarkers including lumican and collagen alpha-1(III) chain, whose co-expression has previously been both implicated in fibrosis and detected in urine in CKD patients.

Toward Real-Time Proteomics: Blood to Biomarker Quantitation in under One Hour

Multistep multihour tryptic proteolysis has limited the utility of bottom-up proteomics for cases that require immediate quantitative information. The power of proteomics to quantify biomarkers of health status cannot practically assist in clinical care if the dynamics of disease outpaces the turnaround of analysis. The recently available hyperthermoacidic archaeal (HTA) protease "Krakatoa" digests samples in a single 5 to 30 min step at pH 3 and >80 °C in conditions that disrupt most cells and tissues, denature proteins, and block disulfide reformation thereby dramatically expediting and simplifying sample preparation. The combination of quick single-step proteolysis with high-throughput dual-trapping single analytical column (DTSC) liquid chromatography-mass spectrometry (LC-MS) returns actionable data in less than 1 h from collection of unprocessed biofluid. The systematic evaluation of this methodology finds that over 160 proteins are quantified in less than 1 h from 1 μL of whole blood. Furthermore, labile Angiotensin I and II bioactive peptides along with a panel of protein species can be measured at 8 min intervals with a 20 min initial lag using targeted MS. With these methods, we analyzed serum and plasma from 53 individuals and quantified Angiotensin I and II and over 150 proteins including at least 46 that were not detected with trypsin. We discuss some of the implications of real-time proteomics including the immediate potential to advance several clinical and research applications.

Translation dysregulation in cancer as a source for targetable antigens

Aberrant peptides presented by major histocompatibility complex (MHC) molecules are targets for tumor eradication, as these peptides can be recognized as foreign by T cells. Protein synthesis in malignant cells is dysregulated, which may result in the generation and presentation of aberrant peptides that can be exploited for T cell-based therapies. To investigate the role of translational dysregulation in immunological tumor control, we disrupt translation fidelity by deleting tRNA wybutosine (yW)-synthesizing protein 2 (TYW2) in tumor cells and characterize the downstream impact on translation fidelity and immunogenicity using immunopeptidomics, genomics, and functional assays. These analyses reveal that TYW2 knockout (KO) cells generate immunogenic out-of-frame peptides. Furthermore, Tyw2 loss increases tumor immunogenicity and leads to anti-programmed cell death 1 (PD-1) checkpoint blockade sensitivity in vivo. Importantly, reduced TYW2 expression is associated with increased response to checkpoint blockade in patients. Together, we demonstrate that defects in translation fidelity drive tumor immunogenicity and may be leveraged for cancer immunotherapy.

Evaluating the mechanism of action behind controlled hypothermic preservation of donor hearts: A randomized pilot study

BACKGROUND

Controlled hypothermic preservation of donor hearts is associated with decreased post-transplant primary graft dysfunction compared to conventional cold storage. However, mechanisms underlying this benefit in human subjects are unclear.

METHODS

We randomized 20 heart transplant recipients at a single institution to receive donor hearts preserved with either controlled hypothermic preservation or standard cold storage. Right ventricular biopsies were obtained at donor heart recovery, immediately before implantation, and 7 days after transplantation. Protein expression profiles at each time point were evaluated using mass spectrometry, Protein Interaction Network Extractor analysis, and Ingenuity Pathway Analysis.

RESULTS

Immediately before implantation, controlled hypothermic preservation was associated with increased protein expression related to fatty acid metabolism, mitochondrial intermembrane space, and contractile fiber machinery. Pathway analysis indicated increased cell viability, autophagy, and upregulation of AMP-activated protein kinase pathway with controlled hypothermic preservation. By post-transplant day 7, the protein expression profiles of the 2 groups were similar. However, controlled hypothermic preservation was associated with increased expression in the peroxisome proliferator-activated receptor signaling pathway and fatty acid oxidation.

CONCLUSIONS

Controlled hypothermic preservation of donor hearts shows beneficial time-dependent variability in protein expression that may confer improved organ quality at the time of transplantation.

Discovering organic reactions with a machine-learning-powered deciphering of tera-scale mass spectrometry data

The accumulation of large datasets by the scientific community has surpassed the capacity of traditional processing methods, underscoring the critical need for innovative and efficient algorithms capable of navigating through extensive existing experimental data. Addressing this challenge, our study introduces a machine learning (ML)-powered search engine specifically tailored for analyzing tera-scale high-resolution mass spectrometry (HRMS) data. This engine harnesses a novel isotope-distribution-centric search algorithm augmented by two synergistic ML models, assisting with the discovery of hitherto unknown chemical reactions. This methodology enables the rigorous investigation of existing data, thus providing efficient support for chemical hypotheses while reducing the need for conducting additional experiments. Moreover, we extend this approach with baseline methods for automated reaction hypothesis generation. In its practical validation, our approach successfully identified several reactions, unveiling previously undescribed transformations. Among these, the heterocycle-vinyl coupling process within the Mizoroki-Heck reaction stands out, highlighting the capability of the engine to elucidate complex chemical phenomena.

A Scalable, Web-Based Platform for Proteomics Data Processing, Result Storage and Analysis

The exponential increase in proteomics data presents critical challenges for conventional processing workflows. These pipelines often consist of fragmented software packages, glued together using complex in-house scripts or error-prone manual workflows running on local hardware, which are costly to maintain and scale. The MSAID Platform offers a fully automated, managed proteomics data pipeline, consolidating formerly disjointed functions into unified, API-driven services that cover the entire process from raw data to biological insights. Backed by the cloud-native search algorithm CHIMERYS, as well as scalable cloud compute instances and data lakes, the platform facilitates efficient processing of large data sets, automation of processing via the command line, systematic result storage, analysis, and visualization. The data lake supports elastically growing storage and unified query capabilities, facilitating large-scale analyses and efficient reuse of previously processed data, such as aggregating longitudinally acquired studies. Users interact with the platform via a web interface, CLI client, or API, providing flexible, automated access. Readily available tools for accessing result data include browser-based interrogation and one-click visualizations for statistical analysis. The platform streamlines research processes, making advanced and automated proteomic workflows accessible to a broader range of scientists. The MSAID Platform is globally available via https://platform.msaid.io.

Proteomic Plasticity in the Coral Montipora capitata Gamete Bundles after Parent Thermal Bleaching

Coral reefs are vital to marine biodiversity and human livelihoods, but they face significant threats from climate change. Increased ocean temperatures drive massive "bleaching" events, during which corals lose their symbiotic algae and the important metabolic resources those algae provide. Proteomics is a crucial tool for understanding coral function and tolerance to thermal stress, as proteins drive physiological processes and accurately represent cell functional phenotypes. We examined the physiological condition of coral (Montipora capitata) gametes from parents that either experienced thermal bleaching or were nonbleached controls by comparing data dependent (DDA) and data independent (DIA) acquisition methods and peptide quantification (spectral counting and area-under-the-curve, AUC) strategies. For DDA, AUC captured a broader dynamic range than spectral counting. DIA yielded better coverage of low abundance proteins than DDA and a higher number of proteins, making it the more suitable method for detecting subtle, yet biologically significant, shifts in protein abundance in gamete bundles. Gametes from bleached corals showed a broadscale decrease in metabolic proteins involved in carbohydrate metabolism, citric acid cycle, and protein translation. This metabolic plasticity could reveal how organisms and their offspring acclimatize and adapt to future environmental stress, ultimately shaping the resilience and dynamics of coral populations.

diaPASEF Analysis for HLA-I Peptides Enables Quantification of Common Cancer Neoantigens

Human leukocyte antigen class I (HLA-I) molecules present short peptide sequences from endogenous or foreign proteins to cytotoxic T cells. The low abundance of HLA-I peptides poses significant technical challenges for their identification and accurate quantification. While mass spectrometry is currently a method of choice for direct system-wide identification of cellular immunopeptidomes, there is still a need for enhanced sensitivity in detecting and quantifying tumor-specific epitopes. As gas phase separation in data-dependent MS data acquisition increased HLA-I peptide detection by up to 50%, here, we aimed to evaluate the performance of data-independent acquisition (DIA) in combination with parallel accumulation serial fragmentation ion mobility (diaPASEF) for high-sensitivity identification of HLA presented peptides. Our streamlined diaPASEF workflow enabled identification of 11,412 unique peptides from 12.5 million A375 cells and 3426 8-11mers from as low as 500,000 cells with high reproducibility. By taking advantage of HLA binder-specific in silico predicted spectral libraries, we were able to further increase the number of identified HLA-I peptides. We applied SILAC-DIA to a mixture of labeled HLA-I peptides, calculated heavy-to-light ratios for 7742 peptides across five conditions and demonstrated that diaPASEF achieves high quantitative accuracy up to 5-fold dilution. Finally, we identified and quantified shared neoantigens in a monoallelic C1R cell line model. By spiking in heavy synthetic peptides, we verified the identification of the peptide sequences and calculated relative abundances for 13 neoantigens. Taken together, diaPASEF analysis workflows for HLA-I peptides can increase the peptidome coverage for lower sample amounts. The sensitivity and quantitative precision provided by DIA can enable the detection and quantification of less abundant peptide species such as neoantigens across samples from the same background.

Decrypting the molecular basis of cellular drug phenotypes by dose-resolved expression proteomics

Proteomics is making important contributions to drug discovery, from target deconvolution to mechanism of action (MoA) elucidation and the identification of biomarkers of drug response. Here we introduce decryptE, a proteome-wide approach that measures the full dose-response characteristics of drug-induced protein expression changes that informs cellular drug MoA. Assaying 144 clinical drugs and research compounds against 8,000 proteins resulted in more than 1 million dose-response curves that can be interactively explored online in ProteomicsDB and a custom-built Shiny App. Analysis of the collective data provided molecular explanations for known phenotypic drug effects and uncovered new aspects of the MoA of human medicines. We found that histone deacetylase inhibitors potently and strongly down-regulated the T cell receptor complex resulting in impaired human T cell activation in vitro and ex vivo. This offers a rational explanation for the efficacy of histone deacetylase inhibitors in certain lymphomas and autoimmune diseases and explains their poor performance in treating solid tumors.

Deep Learning Enhances Precision of Citrullination Identification in Human and Plant Tissue Proteomes

Citrullination is a critical yet understudied post-translational modification (PTM) implicated in various biological processes. Exploring its role in health and disease requires a comprehensive understanding of the prevalence of this PTM at a proteome-wide scale. Although mass spectrometry has enabled the identification of citrullination sites in complex biological samples, it faces significant challenges, including limited enrichment tools and a high rate of false positives due to the identical mass with deamidation (+0.9840 Da) and errors in monoisotopic ion selection. These issues often necessitate manual spectrum inspection, reducing throughput in large-scale studies. In this work, we present a novel data analysis pipeline that incorporates the deep learning model Prosit-Cit into the MS database search workflow to improve both the sensitivity and the precision of citrullination site identification. Prosit-Cit, an extension of the existing Prosit model, has been trained on ∼53,000 spectra from ∼2500 synthetic citrullinated peptides and provides precise predictions for chromatographic retention time and fragment ion intensities of both citrullinated and deamidated peptides. This enhances the accuracy of identification and reduces false positives. Our pipeline demonstrated high precision on the evaluation dataset, recovering the majority of known citrullination sites in human tissue proteomes and improving sensitivity by identifying up to 14 times more citrullinated sites. Sequence motif analysis revealed consistency with previously reported findings, validating the reliability of our approach. Furthermore, extending the pipeline to a tissue proteome dataset of the model plant Arabidopsis thaliana enabled the identification of ∼200 citrullination sites across 169 proteins from 30 tissues, representing the first large-scale citrullination mapping in plants. This pipeline can be seamlessly applied to existing proteomics datasets, offering a robust tool for advancing biological discoveries and deepening our understanding of protein citrullination across species.

Deep plasma and tissue proteome profiling of knockout mice reveals pathways associated with Svep1 deficiency

Despite strong causal associations with cardiovascular and metabolic disorders including coronary artery disease, hypertension, and type 2 diabetes, as well as a range of other diseases, the exact function of the protein SVEP1 remains largely unknown. Animal models have been employed to investigate how SVEP1 contributes to disease, with a focus on murine models exploring its role in development, cardiometabolic disease and platelet biology. In this study, we aimed to comprehensively phenotype the proteome of Svep1 +/- mice compared to wild-type (WT) littermates using liquid chromatography-tandem mass spectrometry (LC-MS/MS) bottom-up proteomics in plasma, heart, aorta, lung, and kidney to identify dysregulated pathways and biological functions associated with Svep1 deficiency. Our findings reveal that Svep1 deficiency leads to significant proteomic alterations across the mouse, with the highest number of dysregulated proteins observed in plasma and kidney. Key dysregulated proteins in plasma include upregulation of ADGRV1, CDH1, and MYH6, and downregulation of MTIF2 and AKAP13 which, alongside other proteins dysregulated across tissues, indicate disruption in cell adhesion, extracellular matrix organisation, platelet degranulation, and Rho GTPase pathways. Novel findings include significant enrichment of complement cascades in plasma, suggesting dysregulation of innate immune responses and hemostasis due to Svep1 deficiency. Pathways related to chylomicron assembly and lipid metabolism were also enriched. Additionally, we developed a high-throughput quantitative targeted LC-MS/MS assay to measure endogenous levels of murine SVEP1. SVEP1 was detectable in lung homogenate and showed a significant reduction in SVEP1 levels in Svep1 +/- vs. WT, but was not identified in plasma, heart, aorta, or kidney, likely due to expression levels below the assay's detection limit. Overall, this deep phenotyping study provides insight into the systemic impact of Svep1 deficiency.

An In-Depth Coho Salmon (Oncorhynchus kisutch) Ovarian Follicle Proteome Reveals Coordinated Changes Across Diverse Cellular Processes during the Transition From Primary to Secondary Growth

Teleost fishes are a highly diverse, ecologically essential group of aquatic vertebrates that include coho salmon (Oncorhynchus kisutch). Coho are semelparous and all ovarian follicles develop synchronously. Owing to their ubiquitous distribution, teleosts provide critical sources of food worldwide through subsistence, commercial fisheries, and aquaculture. Enhancement of hatchery practices requires detailed knowledge of teleost reproductive physiology. Despite decades of research on teleost reproductive processes, an in-depth proteome of teleost ovarian development has yet to be generated. We have described a coho salmon ovarian proteome of over 5700 proteins, generated with data independent acquisition, revealing the proteins that change through the transition from primary to secondary ovarian follicle development. This transition is critical during the onset of puberty and for determining egg quality and embryonic development. Primary follicle development was marked by differential abundances of proteins in carbohydrate metabolism, protein turnover, and the complement pathway, suggesting elevated metabolism as the follicles develop through stages of oogenesis. The greatest proteomic shift occurred during the transition from primary to secondary follicle growth, with increased abundance of proteins underlying cortical alveoli formation, extracellular matrix reorganization, iron binding, and cell-cell signaling. This work provides a foundation for identifying biomarkers of salmon oocyte stage and quality.

Proteomic Diversity in Bacteria: Insights and Implications for Bacterial Identification

Mass spectrometry-based proteomics has revolutionized bacterial identification and elucidated many molecular mechanisms underlying bacterial growth, community formation, and drug resistance. However, most research has been focused on a few model bacteria, overlooking bacterial diversity. In this study, we present the most extensive bacterial proteomic resource to date, covering 303 species, 119 genera, and five phyla with over 636,000 unique expressed proteins, confirming the existence of over 38,700 hypothetical proteins. Accessible via the public resource ProteomicsDB, this dataset enables quantitative exploration of proteins within and across species. Additionally, we developed MS2Bac, a bacterial identification algorithm that queries NCBI's bacterial proteome space in two iterations. MS2Bac achieved over 99% species-level and 89% strain-level accuracy, surpassing methods like MALDI-TOF and FTIR, as demonstrated with food-derived bacterial isolates. MS2Bac also effectively identified bacteria in clinical samples, highlighting the potential of MS-based proteomics as a routine diagnostic tool.

Massively parallel assessment of designed protein solution properties using mass spectrometry and peptide barcoding

Library screening and selection methods can determine the binding activities of individual members of large protein libraries given a physical link between protein and nucleotide sequence, which enables identification of functional molecules by DNA sequencing. However, the solution properties of individual protein molecules cannot be probed using such approaches because they are completely altered by DNA attachment. Mass spectrometry enables parallel evaluation of protein properties amenable to physical fractionation such as solubility and oligomeric state, but current approaches are limited to libraries of 1,000 or fewer proteins. Here, we improved mass spectrometry barcoding by co-synthesizing proteins with barcodes optimized to be highly multiplexable and minimally perturbative, scaling to libraries of >5,000 proteins. We use these barcodes together with mass spectrometry to assay the solution behavior of libraries of de novo-designed monomeric scaffolds, oligomers, binding proteins and nanocages, rapidly identifying design failure modes and successes.

Detection of human unannotated microproteins by mass spectrometry-based proteomics: a community assessment

Thousands of short open reading frames (sORFs) are translated outside of annotated coding sequences. Recent studies have pioneered searching for sORF-encoded microproteins in mass spectrometry (MS)-based proteomics and peptidomics datasets. Here, we assessed literature-reported MS-based identifications of unannotated human proteins. We find that studies vary by three orders of magnitude in the number of unannotated proteins they report. Of nearly 10,000 reported sORF-encoded peptides, 96% were unique to a single study, and 12% mapped to annotated proteins or proteoforms. Manual curation of a benchmark dataset of 406 manually evaluated spectra from 204 sORF-encoded proteins revealed large variation in peptide-spectrum match (PSM) quality between studies, with immunopeptidomics studies generally reporting higher quality PSMs than conventional enzymatic digests of whole cell lysates. We estimate that 65% of predicted sORF-encoded protein detections in immunopeptidomics studies were supported by high-quality PSMs versus 7.8% in non-immunopeptidomics datasets. Our work stresses the need for standardized protocols and analysis workflows to guide future advancements in microprotein detection by MS towards uncovering how many human microproteins exist.

Determination of Trends Underlying Aspartic Acid Isomerization in Intact Proteins Reveals Unusually Rapid Isomerization of Tau

Spontaneous chemical modifications in long-lived proteins can potentially change protein structure in ways that impact proteostasis and cellular health. For example, isomerization of aspartic acid interferes with protein turnover and is anticorrelated with cognitive acuity in Alzheimer's disease. However, few isomerization rates have been determined for Asp residues in intact proteins. To remedy this deficiency, we used protein extracts from SH-SY5Y neuroblastoma cells as a source of a complex, brain-relevant proteome with no baseline isomerization. Cell lysates were aged in vitro to generate isomers, and extracted proteins were analyzed by data-independent acquisition (DIA) liquid chromatography-mass spectrometry (LC-MS). Although no Asp isomers were detected at day 0, isomerization increased over time and was quantifiable for 105 proteins by day 50. Data analysis revealed that the isomerization rate is influenced by both primary sequence and secondary structure, suggesting that steric hindrance and backbone rigidity modulate isomerization. Additionally, we examined lysates extracted under gentle conditions to preserve protein complexes and found that protein-protein interactions often slow isomerization. Base catalysis was explored as a means to accelerate Asp isomerization due to findings of accelerated asparagine deamidation. However, no substantial rate enhancement was found for isomerization, suggesting fundamental differences in acid-base chemistry. With an enhanced understanding of Asp isomerization in proteins in general, we next sought to better understand Asp isomerization in tau. In vitro aging of monomeric and aggregated recombinant tau revealed that tau isomerizes significantly faster than any similar protein within our data set, which is likely related to its correlation with cognition in Alzheimer's disease.

Detecting Human Contaminant Genetically Variant Peptides in Nonhuman Samples

During proteomics data analysis, experimental spectra are searched against a user-defined protein database consisting of proteins that are reasonably expected to be present in the sample. Typically, this database contains the proteome of the organism under study concatenated with expected contaminants, such as trypsin and human keratins. However, there are additional contaminants that are not commonly added to the database. In this study, we describe a new set of protein contaminants and provide evidence that they can be detected in mass spectrometry-based proteomics data. Specifically, we provide evidence that human genetically variant peptides (GVPs) can be detected in nonhuman samples. GVPs are peptides that contain single amino acid polymorphisms that result from nonsynonymous single nucleotide polymorphisms in protein-coding regions of DNA. We reanalyzed previously collected nonhuman data-dependent acquisition (DDA) and data-independent acquisition (DIA) data sets and detected between 0 and 135 GVPs per data set. In addition, we show that GVPs are unlikely to originate from nonhuman sources and that a subset of eight GVPs are commonly detected across data sets.

The Enzyme Effect: Broadening the Horizon of MS Optimization to Nontryptic Digestion in Proteomics

In recent years, alternative enzymes with varied specificities have gained importance in MS-based bottom-up proteomics, offering orthogonal information about biological samples and advantages in certain applications. However, most mass spectrometric workflows are optimized for tryptic digests. This raises the questions of whether enzyme specificity impacts mass spectrometry and if current methods for nontryptic digests are suboptimal. The success of peptide and protein identifications relies on the information content of MS/MS spectra, influenced by collision energy in collision-induced dissociation. We investigated this by conducting LC-MS/MS measurements with different enzymes, including trypsin, Arg-C, Glu-C, Asp-N, and chymotrypsin, at varying collision energies. We analyzed peptide scores for thousands of peptides and determined optimal collision energy (CE) values. Our results showed a linear m/z dependence for all enzymes, with Glu-C, Asp-N, and chymotrypsin requiring significantly lower energies than trypsin and Arg-C. We proposed a tailored CE selection method for these alternative enzymes, applying ca. 20% lower energy compared to tryptic peptides. This would result in a 10-15 eV decrease on a Bruker QTof instrument and a 5-6 NCE% (normalized collision energy) difference on an Orbitrap. The optimized method improved bottom-up proteomics performance by 8-32%, as measured by peptide identification and sequence coverage. The different trends in fragmentation behavior were linked to the effects of C-terminal basic amino acids for Arg-C and trypsin, stabilizing y fragment ions. This optimized method boosts the performance and provides insight into the impact of enzyme specificity. Data sets are available in the MassIVE repository (MSV000095066).

Deep Learning Predicts Non-Normal Transmission Distributions in High-Field Asymmetric Waveform Ion Mobility (FAIMS) Directly from Peptide Sequence

Peptide ion mobility adds an extra dimension of separation to mass spectrometry-based proteomics. The ability to accurately predict peptide ion mobility would be useful to expedite assay development and to discriminate true answers in a database search. There are methods to accurately predict peptide ion mobility through drift tube devices, but methods to predict mobility through high-field asymmetric waveform ion mobility (FAIMS) are underexplored. Here, we successfully model peptide ions' FAIMS mobility using a multi-label classification scheme to account for non-normal transmission distributions. We trained two models from over 100,000 human peptide precursors: a random forest and a long-term short-term memory (LSTM) neural network. Both models had different strengths, and the ensemble average of model predictions produced a higher F2 score than either model alone. Finally, we explored cases where the models make mistakes and demonstrate the predictive performance of F2 = 0.66 (AUROC = 0.928) on a new test data set of nearly 40,000 E. coli peptide ions. The deep learning model is easily accessible via https://faims.xods.org.

Filter-Aided Extracellular Vesicle Enrichment (FAEVEr) for Proteomics

Extracellular vesicles (EVs), membrane-delimited nanovesicles that are secreted by cells into the extracellular environment, are gaining substantial interest due to their involvement in cellular homeostasis and their contribution to disease pathology. The latter in particular has led to an exponential increase in interest in EVs as they are considered to be circulating packages containing potential biomarkers and are also a possible biological means to deliver drugs in a cell-specific manner. However, several challenges hamper straightforward proteome analysis of EVs as they are generally low abundant and reside in complex biological matrices. These matrices typically contain abundant proteins at concentrations that vastly exceed the concentrations of proteins found in the EV proteome. Therefore, extensive EV isolation and purification protocols are imperative and many have been developed, including (density) ultracentrifugation, size-exclusion, and precipitation methods. Here, we describe filter-aided extracellular vesicle enrichment (FAEVEr) as an approach based on 300 kDa molecular weight cutoff filtration that allows the processing of multiple samples in parallel within a reasonable time frame and at moderate cost. We demonstrate that FAEVEr is capable of quantitatively retaining EV particles on filters, while allowing extensive washing with the mild detergent Tween-20 to remove interfering non-EV proteins. The retained particles are directly lysed on the filter for a complete recovery of the EV protein cargo toward proteome analysis. Here, we validate and optimize FAEVEr on recombinant EV material and apply it on conditioned medium as well as on complex bovine serum, human plasma, and urine. Our results indicate that EVs isolated from MCF7 cells cultured with or without serum have a drastic different proteome because of nutrient deprivation.

Enhanced Discovery of Alternative Proteins (AltProts) in Mouse Cardiac Development Using Data-Independent Acquisition (DIA) Proteomics

Alternative proteins (AltProts) are a class of proteins encoded by DNA sequences previously classified as noncoding. Despite their historically being overlooked, recent studies have highlighted their widespread presence and distinctive biological roles. So far, direct detection of AltProt has been relying on data-dependent acquisition (DDA) mass spectrometry (MS). However, data-independent acquisition (DIA) MS, a method that is rapidly gaining popularity for the analysis of canonical proteins, has seen limited application in AltProt research, largely due to the complexities involved in constructing DIA libraries. In this study, we present a novel DIA workflow that leverages a fragmentation spectra predictor for the efficient construction of DIA libraries, significantly enhancing the detection of AltProts. Our method achieved a 2-fold increase in the identification of AltProts and a 50% reduction in missing values compared to DDA. We conducted a comprehensive comparison of four AltProt databases, four DIA-library construction strategies, and three analytical software tools to establish an optimal workflow for AltProt analysis. Utilizing this workflow, we investigated the mouse heart development process and identified over 50 AltProts with differential expression between embryonic and adult heart tissues. Over 30 unannotated mouse AltProts were validated, including ASDURF, which played a crucial role in cardiac development. Our findings not only provide a practical workflow for MS-based AltProt analysis but also reveal novel AltProts with potential significance in biological functions.

Chronic IL-1-Exposed LNCaP Cells Evolve High Basal p62-KEAP1 Complex Accumulation and NRF2/KEAP1-Dependent and -Independent Hypersensitive Nutrient Deprivation Response

Chronic inflammation is a cancer hallmark and chronic exposure to interleukin-1 (IL-1) transforms castration-sensitive prostate cancer (PCa) cells into more fit castration-insensitive PCa cells. p62 is a scaffold protein that protects cells from nutrient deprivation via autophagy and from cytotoxic reactive oxygen via NFκB and NRF2 antioxidant signaling. Herein, we report that the LNCaP PCa cell line acquires high basal accumulation of the p62-KEAP1 complex when chronically exposed to IL-1. p62 promotes non-canonical NRF2 antioxidant signaling by binding and sequestering KEAP1 to the autophagosome for degradation. But despite high basal p62-KEAP1 accumulation, only two of several NRF2-induced genes analyzed, GCLC and HMOX1, showed high basal mRNA levels, suggesting that the high basal p62-KEAP1 accumulation does not result in overall high basal NRF2 activity. Nutrient starvation induces NRF2-dependent GCLC upregulation and HMOX1 repression, and we found that chronic IL-1-exposed LNCaP cells show hypersensitivity to serum starvation-induced GCLC and HMOX1 regulation. Thus, chronic IL-1 exposure affects cell response to nutrient stress. While HMOX1 expression remains NRF2/KEAP1-dependent in chronic IL-1-exposed LNCaP cells, GCLC expression is NRF2/KEAP1-independent. Furthermore, the high basal p62-KEAP1 complex accumulation is not required to regulate GCLC or HMOX1 expression, suggesting cells chronically exposed to IL-1 evolve a novel NRF2-independent role for the p62/KEAP1 axis.

ARID1A mutations protect follicular lymphoma from FAS-dependent immune surveillance by reducing RUNX3/ETS1-driven FAS-expression

The cell death receptor FAS and its ligand (FASLG) play crucial roles in the selection of B cells during the germinal center (GC) reaction. Failure to eliminate potentially harmful B cells via FAS can lead to lymphoproliferation and the development of B cell malignancies. The classic form of follicular lymphoma (FL) is a prototypic GC-derived B cell malignancy, characterized by the t(14;18)(q32;q21)IGH::BCL2 translocation and overexpression of antiapoptotic BCL2. Additional alterations were shown to be clinically relevant, including mutations in ARID1A. ARID1A is part of the SWI/SNF nucleosome remodeling complex that regulates DNA accessibility ("openness"). However, the mechanism how ARID1A mutations contribute to FL pathogenesis remains unclear. We analyzed 151 FL biopsies of patients with advanced-stage disease at initial diagnosis and found that ARID1A mutations were recurrent and mainly disruptive, with an overall frequency of 18%. Additionally, we observed that ARID1A mutant FL showed significantly lower FAS protein expression in the FL tumor cell population. Functional experiments in BCL2-translocated lymphoma cells demonstrated that ARID1A is directly involved in the regulation of FAS, and ARID1A loss leads to decreased FAS protein and gene expression. However, ARID1A loss did not affect FAS promotor openness. Instead, we identified and experimentally validated a previously unknown co-transcriptional complex consisting of RUNX3 and ETS1 that regulates FAS expression, and ARID1A loss leads to reduced RUNX3 promotor openness and gene expression. The reduced FAS levels induced by ARID1A loss rendered lymphoma cells resistant to both soluble and T cell membrane-anchored FASLG-induced apoptosis, and significantly diminished CAR T cell killing in functional experiments. In summary, we have identified a functionally and clinically relevant mechanism how FL cells can escape FAS-dependent immune surveillance, which may also impact the efficacy of T cell-based therapies, including CAR T cells.

Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment

A pressing statistical challenge in the field of mass spectrometry proteomics is how to assess whether a given software tool provides accurate error control. Each software tool for searching such data uses its own internally implemented methodology for reporting and controlling the error. Many of these software tools are closed source, with incompletely documented methodology, and the strategies for validating the error are inconsistent across tools. In this work, we identify three different methods for validating false discovery rate (FDR) control in use in the field, one of which is invalid, one of which can only provide a lower bound rather than an upper bound, and one of which is valid but under-powered. The result is that the field has a very poor understanding of how well we are doing with respect to FDR control, particularly for the analysis of data-independent acquisition (DIA) data. We therefore propose a theoretical formulation of entrapment experiments that allows us to rigorously characterize the behavior of the various entrapment methods. We also propose a more powerful method for evaluating FDR control, and we employ that method, along with other existing techniques, to characterize a variety of popular search tools. We empirically validate our entrapment analysis in the fairly well-understood DDA setup before applying it in the DIA setup. We find that none of the DIA search tools consistently controls the FDR at the peptide level, and the tools struggle particularly with analysis of single cell datasets.

Immunopeptidomics for autoimmunity: unlocking the chamber of immune secrets

T cells mediate pathogenesis of several autoimmune disorders by recognizing self-epitopes presented on Major Histocompatibility Complex (MHC) or Human Leukocyte Antigen (HLA) complex. The majority of autoantigens presented to T cells in various autoimmune disorders are not known, which has impeded autoantigen identification. Recent advances in immunopeptidomics have started to unravel the repertoire of antigenic epitopes presented on MHC. In several autoimmune diseases, immunopeptidomics has led to the identification of novel autoantigens and has enhanced our understanding of the mechanisms behind autoimmunity. Especially, immunopeptidomics has provided key evidence to explain the genetic risk posed by HLA alleles. In this review, we shed light on how immunopeptidomics can be leveraged to discover potential autoantigens. We highlight the application of immunopeptidomics in Type 1 Diabetes (T1D), Systemic Lupus Erythematosus (SLE), and Rheumatoid Arthritis (RA). Finally, we highlight the practical considerations of implementing immunopeptidomics successfully and the technical challenges that need to be addressed. Overall, this review will provide an important context for using immunopeptidomics for understanding autoimmunity.

Integrated, high-throughput metabolomics approach for metabolite analysis of four sprout types

In this study, we combined two distinct extraction and separation techniques with the aim of comprehensively collecting metabolite features in sprouts, particularly hydrophilic compounds. By synergistically analyzing the data using MS-DIAL and MetaboAnalystR, we obtained a greater number of annotated metabolites and explored differences in annotation across analytical tools. We found that this approach significantly increased the number of detected metabolite features and the final identification counts. Furthermore, we explored the functional component characteristics of four sprout types. This study provides data supporting the potential of sprouts as nutritious vegetables and functional food ingredients, emphasizing their value in the development of functional foods.

Challenges and Insights in Absolute Quantification of Recombinant Therapeutic Antibodies by Mass Spectrometry: An Introductory Review

This review describes mass spectrometry (MS)-based approaches for the absolute quantification of therapeutic monoclonal antibodies (mAbs), focusing on technical challenges in sample treatment and calibration. Therapeutic mAbs are crucial for treating cancer and inflammatory, infectious, and autoimmune diseases. We trace their development from hybridoma technology and the first murine mAbs in 1975 to today's chimeric and fully human mAbs. With increasing commercial relevance, the absolute quantification of mAbs, traceable to an international standard system of units (SI units), has attracted attention from science, industry, and national metrology institutes (NMIs). Quantification of proteotypic peptides after enzymatic digestion using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) has emerged as the most viable strategy, though methods targeting intact mAbs are still being explored. We review peptide-based quantification, focusing on critical experimental steps like denaturation, reduction, alkylation, choice of digestion enzyme, and selection of signature peptides. Challenges in amino acid analysis (AAA) for quantifying pure mAbs and peptide calibrators, along with software tools for targeted MS data analysis, are also discussed. Short explanations within each chapter provide newcomers with an overview of the field's challenges. We conclude that, despite recent progress, further efforts are needed to overcome the many technical hurdles along the quantification workflow and discuss the prospects of developing standardized protocols and certified reference materials (CRMs) for this goal. We also suggest future applications of newer technologies for absolute mAb quantification.

TopDIA: A Software Tool for Top-Down Data-Independent Acquisition Proteomics

Top-down mass spectrometry is widely used for proteoform identification, characterization, and quantification owing to its ability to analyze intact proteoforms. In the past decade, top-down proteomics has been dominated by top-down data-dependent acquisition mass spectrometry (TD-DDA-MS), and top-down data-independent acquisition mass spectrometry (TD-DIA-MS) has not been well studied. While TD-DIA-MS produces complex multiplexed tandem mass spectrometry (MS/MS) spectra, which are challenging to confidently identify, it selects more precursor ions for MS/MS analysis and has the potential to increase proteoform identifications compared with TD-DDA-MS. Here we present TopDIA, the first software tool for proteoform identification by TD-DIA-MS. It generates demultiplexed pseudo MS/MS spectra from TD-DIA-MS data and then searches the pseudo MS/MS spectra against a protein sequence database for proteoform identification. We compared the performance of TD-DDA-MS and TD-DIA-MS using Escherichia coli K-12 MG1655 cells and demonstrated that TD-DIA-MS with TopDIA increased proteoform and protein identifications compared with TD-DDA-MS.

diaTracer enables spectrum-centric analysis of diaPASEF proteomics data

Data-independent acquisition has become a widely used strategy for peptide and protein quantification in liquid chromatography-tandem mass spectrometry-based proteomics studies. The integration of ion mobility separation into data-independent acquisition analysis, such as the diaPASEF technology available on Bruker's timsTOF platform, further improves the quantification accuracy and protein depth achievable using data-independent acquisition. We introduce diaTracer, a spectrum-centric computational tool optimized for diaPASEF data. diaTracer performs three-dimensional (mass to charge ratio, retention time, ion mobility) peak tracing and feature detection to generate precursor-resolved "pseudo-tandem mass spectra", facilitating direct ("spectral-library free") peptide identification and quantification from diaPASEF data. diaTracer is available as a stand-alone tool and is fully integrated into the widely used FragPipe computational platform. We demonstrate the performance of diaTracer and FragPipe using diaPASEF data from triple-negative breast cancer, cerebrospinal fluid, and plasma samples, data from phosphoproteomics and human leukocyte antigens immunopeptidomics experiments, and low-input data from a spatial proteomics study. We also show that diaTracer enables unrestricted identification of post-translational modifications from diaPASEF data using open/mass-offset searches.

Towards routine proteome profiling of FFPE tissue: insights from a 1,220-case pan-cancer study

Proteome profiling of formalin-fixed paraffin-embedded (FFPE) specimens has gained traction for the analysis of cancer tissue for the discovery of molecular biomarkers. However, reports so far focused on single cancer entities, comprised relatively few cases and did not assess the long-term performance of experimental workflows. In this study, we analyze 1220 tumors from six cancer entities processed over the course of three years. Key findings include the need for a new normalization method ensuring equal and reproducible sample loading for LC-MS/MS analysis across cohorts, showing that tumors can, on average, be profiled to a depth of >4000 proteins and discovering that current software fails to process such large ion mobility-based online fractionated datasets. We report the first comprehensive pan-cancer proteome expression resource for FFPE material comprising 11,000 proteins which is of immediate utility to the scientific community, and can be explored via a web resource. It enables a range of analyses including quantitative comparisons of proteins between patients and cohorts, the discovery of protein fingerprints representing the tissue of origin or proteins enriched in certain cancer entities.

Post-translational modifications of proteins in cardiovascular diseases examined by proteomic approaches

Over 400 different types of post-translational modifications (PTMs) have been reported and over 200 various types of PTMs have been discovered using mass spectrometry (MS)-based proteomics. MS-based proteomics has proven to be a powerful method capable of global PTM mapping with the identification of modified proteins/peptides, the localization of PTM sites and PTM quantitation. PTMs play regulatory roles in protein functions, activities and interactions in various heart related diseases, such as ischemia/reperfusion injury, cardiomyopathy and heart failure. The recognition of PTMs that are specific to cardiovascular pathology and the clarification of the mechanisms underlying these PTMs at molecular levels are crucial for discovery of novel biomarkers and application in a clinical setting. With sensitive MS instrumentation and novel biostatistical methods for precise processing of the data, low-abundance PTMs can be successfully detected and the beneficial or unfavorable effects of specific PTMs on cardiac function can be determined. Moreover, computational proteomic strategies that can predict PTM sites based on MS data have gained an increasing interest and can contribute to characterization of PTM profiles in cardiovascular disorders. More recently, machine learning- and deep learning-based methods have been employed to predict the locations of PTMs and explore PTM crosstalk. In this review article, the types of PTMs are briefly overviewed, approaches for PTM identification/quantitation in MS-based proteomics are discussed and recently published proteomic studies on PTMs associated with cardiovascular diseases are included.

Widespread release of translational repression across Plasmodium's host-to-vector transmission event

Malaria parasites must respond quickly to environmental changes, including during their transmission between mammalian and mosquito hosts. Therefore, female gametocytes proactively produce and translationally repress mRNAs that encode essential proteins that the zygote requires to establish a new infection. While the release of translational repression of individual mRNAs has been documented, the details of the global release of translational repression have not. Moreover, changes in the spatial arrangement and composition of the DOZI/CITH/ALBA complex that contribute to translational control are also not known. Therefore, we have conducted the first quantitative, comparative transcriptomics and DIA-MS proteomics of Plasmodium parasites across the host-to-vector transmission event to document the global release of translational repression. Using female gametocytes and zygotes of P. yoelii, we found that ~200 transcripts are released for translation soon after fertilization, including those encoding essential functions. Moreover, we identified that many transcripts remain repressed beyond this point. TurboID-based proximity proteomics of the DOZI/CITH/ALBA regulatory complex revealed substantial spatial and/or compositional changes across this transmission event, which are consistent with recent, paradigm-shifting models of translational control. Together, these data provide a model for the essential translational control mechanisms that promote Plasmodium's efficient transmission from mammalian host to mosquito vector.

Relevance of mutation-derived neoantigens and non-classical antigens for anticancer therapies

Efficacy of cancer immunotherapies relies on correct recognition of tumor antigens by lymphocytes, eliciting thus functional responses capable of eliminating tumor cells. Therefore, important efforts have been carried out in antigen identification, with the aim of understanding mechanisms of response to immunotherapy and to design safer and more efficient strategies. In addition to classical tumor-associated antigens identified during the last decades, implementation of next-generation sequencing methodologies is enabling the identification of neoantigens (neoAgs) arising from mutations, leading to the development of new neoAg-directed therapies. Moreover, there are numerous non-classical tumor antigens originated from other sources and identified by new methodologies. Here, we review the relevance of neoAgs in different immunotherapies and the results obtained by applying neoAg-based strategies. In addition, the different types of non-classical tumor antigens and the best approaches for their identification are described. This will help to increase the spectrum of targetable molecules useful in cancer immunotherapies.

Embracing native diversity to enhance the maximum quantum efficiency of photosystem II in maize

The sustainability of maize cultivation would benefit tremendously from early sowing, but is hampered by low temperatures during early development in temperate climates. We show that allelic variation within the gene encoding subunit M of the NADH-dehydrogenase-like (NDH) complex (ndhm1) in a European maize landrace affects several quantitative traits that are relevant during early development in cold climates through NDH-mediated cyclic electron transport around photosystem I, a process crucial for photosynthesis and photoprotection. Beginning with a genome-wide association study for maximum potential quantum yield of photosystem II in dark-adapted leaves (Fv/Fm), we capitalized on the large phenotypic effects of a hAT transposon insertion in ndhm1 on multiple quantitative traits (early plant height [EPH], Fv/Fm, chlorophyll content, and cold tolerance) caused by the reduced protein levels of NDHM and associated NDH components. Analysis of the ndhm1 native allelic series revealed a rare allele of ndhm1 that is associated with small albeit significant improvements of Fv/Fm, photosystem II efficiency in light-adapted leaves (ΦPSII), and EPH compared with common alleles. Our work showcases the extraction of favorable alleles from locally adapted landraces, offering an efficient strategy for broadening the genetic variation of elite germplasm by breeding or genome editing.

Role of ZFHX4 in orofacial clefting based on human genetic data and zebrafish models

Orofacial clefting (OFC) is a frequent congenital anomaly and can occur either in the context of underlying syndromes or in isolation (nonsyndromic). The two common OFC phenotypes are cleft lip with/without cleft palate (CL/P) and cleft palate only (CPO). In this study, we searched for penetrant CL/P genes, by evaluating de novo copy number variants (CNV) from an exome sequencing dataset of 50 nonsyndromic patient-parent trios. We detected a heterozygous 86 kb de novo deletion affecting exons 4-11 of ZFHX4, a gene previously associated with OFC. Genetic and phenotypic data from our in-house and the AGORA cohort (710 and 229 individuals with nonsyndromic CL/P) together with literature and database reviews demonstrate that ZFHX4 variants can lead to both nonsyndromic and syndromic forms not only of CL/P but also CPO. Expression analysis in published single-cell RNA-sequencing data (mouse embryo, zebrafish larva) at relevant time-points support an important role of Zfhx4/zfhx4 in craniofacial development. To characterize the role of zfhx4 in zebrafish craniofacial development, we knocked out/down the zebrafish orthologue. Cartilage staining of the zfhx4 CRISPR F0 knockout and morpholino knockdown at 4 days post-fertilization showed an underdeveloped and abnormally shaped ethmoid plate and cartilaginous jaw (resembling micrognathia). While there is evidence for the dominant inheritance of ZFHX4 variants in OFC, we here present a patient with a possible recessive inheritance. In conclusion, ZFHX4 has a highly heterogeneous phenotypic spectrum and variable mode of inheritance. Our data highlight that ZFHX4 should be considered in genetic testing in patients with nonsyndromic clefting.

Hybrid Quadrupole Mass Filter-Radial Ejection Linear Ion Trap and Intelligent Data Acquisition Enable Highly Multiplex Targeted Proteomics

Targeted mass spectrometry (MS) methods are powerful tools for the selective and sensitive analysis of peptides identified in global discovery experiments. Selected reaction monitoring (SRM) is the most widely accepted clinical MS method due to its reliability and performance. However, SRM and parallel reaction monitoring (PRM) are limited in throughput and are typically used for assays with around 100 targets or fewer. Here we introduce a new MS platform featuring a quadrupole mass filter, collision cell, and linear ion trap architecture, capable of targeting 5000-8000 peptides per hour. This high multiplexing capability is facilitated by acquisition rates of 70-100 Hz and real-time chromatogram alignment. We present a Skyline external software tool for building targeted methods based on data-independent acquisition chromatogram libraries or unscheduled analysis of heavy labeled standards. Our platform demonstrates ∼10× lower limits of quantitation (LOQs) than traditional SRM on a triple quadrupole instrument for highly multiplexed assays, due to parallel product ion accumulation. Finally, we explore how analytical figures of merit vary with method duration and the number of analytes, providing insights into optimizing assay performance.

Unusually Rapid Isomerization of Aspartic Acid in Tau

Spontaneous chemical modifications in long-lived proteins can potentially change protein structure in ways that impact proteostasis and cellular health. For example, isomerization of aspartic acid interferes with protein turnover and is anticorrelated with cognitive acuity in Alzheimer's disease. However, few isomerization rates have been determined for Asp residues in intact proteins. To remedy this deficiency, we used protein extracts from SH-SY5Y neuroblastoma cells as a source of a complex, brain-relevant proteome with no baseline isomerization. Cell lysates were aged in vitro to generate isomers, and extracted proteins were analyzed by data-independent acquisition (DIA) liquid chromatography-mass spectrometry (LC-MS). Although no Asp isomers were detected at Day 0, isomerization increased across time and was quantifiable for 105 proteins by Day 50. Data analysis revealed that isomerization rate is influenced by both primary sequence and secondary structure, suggesting that steric hindrance and backbone rigidity modulate isomerization. Additionally, we examined lysates extracted under gentle conditions to preserve protein complexes and found that protein-protein interactions often slow isomerization. Base catalysis was explored as a means to accelerate Asp isomerization due to findings of accelerated asparagine deamidation. However, no substantial rate enhancement was found for isomerization, suggesting fundamental differences in acid-base chemistry. With an enhanced understanding of Asp isomerization in proteins in general, we next sought to better understand Asp isomerization in tau. In vitro aging of monomeric and aggregated recombinant tau revealed that tau isomerizes significantly faster than any similar protein within our dataset, which is likely related to its correlation with cognition in Alzheimer's disease.

Mass Spectral Feature Analysis of Ubiquitylated Peptides Provides Insights into Probing the Dark Ubiquitylome

Ubiquitylation is a structurally and functionally diverse post-translational modification that involves the covalent attachment of the small protein ubiquitin to other protein substrates. Trypsin-based proteomics is the most common approach for globally identifying ubiquitylation sites. However, we estimate that such methods are unable to detect ∼40% of ubiquitylation sites in the human proteome, i.e., "the dark ubiquitylome", including many important for human health and disease. In this meta-analysis of three large ubiquitylomic data sets, we performed a series of bioinformatic analyses to assess experimental features that could aid in uniquely identifying site-specific ubiquitylation events. Spectral predictions from Prosit were compared to experimental spectra of tryptic ubiquitylated peptides, revealing previously uncharacterized fragmentation of the diGly scar. Analysis of the LysC-derived ubiquitylated peptides reveals systematic, multidimensional peptide fragmentation, including diagnostic b-ions from fragmentation of the LysC ubiquitin scar. Comprehensively, these findings provide diagnostic spectral signatures of modification events that could be applied to new analysis methods for nontryptic ubiquitylomics.

Proteomic analysis of lung responses to SARS-CoV-2 infection in aged non-human primates: clinical and research relevance

With devastating health and socioeconomic impact worldwide, much work is left to understand the Coronavirus Disease 2019 (COVID-19), with emphasis in the severely affected elderly population. Here, we present a proteomics study of lung tissue obtained from aged vs. young rhesus macaques (Macaca mulatta) and olive baboons (Papio Anubis) infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Using age as a variable, we identified common proteomic profiles in the lungs of aged infected non-human primates (NHPs), including key regulators of immune function, as well as cell and tissue remodeling, and discuss the potential clinical relevance of such parameters. Further, we identified key differences in proteomic profiles between both NHP species, and compared those to what is known about SARS-CoV-2 in humans. Finally, we explored the translatability of these animal models in the context of aging and the human presentation of the COVID-19.

Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines

Next-generation T-cell-directed vaccines for COVID-19 focus on establishing lasting T-cell immunity against current and emerging SARS-CoV-2 variants. Precise identification of conserved T-cell epitopes is critical for designing effective vaccines. Here we introduce a comprehensive computational framework incorporating a machine learning algorithm-MHCvalidator-to enhance mass spectrometry-based immunopeptidomics sensitivity. MHCvalidator identifies unique T-cell epitopes presented by the B7 supertype, including an epitope from a + 1-frameshift in a truncated Spike antigen, supported by ribosome profiling. Analysis of 100,512 COVID-19 patient proteomes shows Spike antigen truncation in 0.85% of cases, revealing frameshifted viral antigens at the population level. Our EpiTrack pipeline tracks global mutations of MHCvalidator-identified CD8 + T-cell epitopes from the BNT162b4 vaccine. While most vaccine epitopes remain globally conserved, an immunodominant A*01-associated epitope mutates in Delta and Omicron variants. This work highlights SARS-CoV-2 antigenic features and emphasizes the importance of continuous adaptation in T-cell vaccine development.

ProtGraph: a tool for the quick and comprehensive exploration and exploitation of the peptide search space derived from protein sequence databases using graphs

Due to computational resource limitations, in mass spectrometry based proteomics only a limited set of peptide sequences is used for the matching against measured spectra. We present an approach to represent proteins by graphs and allow not only the canonical sequences but also known isoforms and annotated amino acid variations, e.g. originating from genomic mutations, and further common protein sequence features contained in Uniprot KB or other protein databases. Our C++ and Python implementation enables a groundbreaking comprehensive characterization of the peptide search space, encompassing for the first time all available annotations in a protein database (in combination more than $10^{200}$ possibilities). Additionally, it can be used to quickly extract the relevant subset of the search space for peptide to spectrum matching, e.g. filtering by the peptide mass. We demonstrate the advantages and innovative findings of our implementation compared to previous workflows by re-analysing publicly available datasets.

Hepatic SerpinA1 improves energy and glucose metabolism through regulation of preadipocyte proliferation and UCP1 expression

Lipodystrophy and obesity are associated with insulin resistance and metabolic syndrome accompanied by fat tissue dysregulation. Here, we show that serine protease inhibitor A1 (SerpinA1) expression in the liver is increased during recovery from lipodystrophy caused by the adipocyte-specific loss of insulin signaling in mice. SerpinA1 induces the proliferation of white and brown preadipocytes and increases the expression of uncoupling protein 1 (UCP1) to promote mitochondrial activation in mature white and brown adipocytes. Liver-specific SerpinA1 transgenic mice exhibit increased browning of adipose tissues, leading to increased energy expenditure, reduced adiposity and improved glucose tolerance. Conversely, SerpinA1 knockout mice exhibit decreased adipocyte mitochondrial function, impaired thermogenesis, obesity, and systemic insulin resistance. SerpinA1 forms a complex with the Eph receptor B2 and regulates its downstream signaling in adipocytes. These results demonstrate that SerpinA1 is an important hepatokine that improves obesity, energy expenditure and glucose metabolism by promoting preadipocyte proliferation and activating mitochondrial UCP1 expression in adipocytes.

IS-PRM-Based Peptide Targeting Informed by Long-Read Sequencing for Alternative Proteome Detection

Alternative splicing is a major contributor of transcriptomic complexity, but the extent to which transcript isoforms are translated into stable, functional protein isoforms is unclear. Furthermore, detection of relatively scarce isoform-specific peptides is challenging, with many protein isoforms remaining uncharted due to technical limitations. Recently, a family of advanced targeted MS strategies, termed internal standard parallel reaction monitoring (IS-PRM), have demonstrated multiplexed, sensitive detection of predefined peptides of interest. Such approaches have not yet been used to confirm existence of novel peptides. Here, we present a targeted proteogenomic approach that leverages sample-matched long-read RNA sequencing (lrRNA-seq) data to predict potential protein isoforms with prior transcript evidence. Predicted tryptic isoform-specific peptides, which are specific to individual gene product isoforms, serve as "triggers" and "targets" in the IS-PRM method, Tomahto. Using the model human stem cell line WTC11, LR RNaseq data were generated and used to inform the generation of synthetic standards for 192 isoform-specific peptides (114 isoforms from 55 genes). These synthetic "trigger" peptides were labeled with super heavy tandem mass tags (TMT) and spiked into TMT-labeled WTC11 tryptic digest, predicted to contain corresponding endogenous "target" peptides. Compared to DDA mode, Tomahto increased detectability of isoforms by 3.6-fold, resulting in the identification of five previously unannotated isoforms. Our method detected protein isoform expression for 43 out of 55 genes corresponding to 54 resolved isoforms. This lrRNA-seq-informed Tomahto targeted approach is a new modality for generating protein-level evidence of alternative isoforms─a critical first step in designing functional studies and eventually clinical assays.