TANDEM: matching proteins with tandem mass spectra

Elucidation of extracellular vesicles behavior during capillary isoelectric focusing

In this study, we investigated the behavior of extracellular vesicles (EVs), during capillary isoelectric focusing (cIEF). For that, we used different approaches, imaging cIEF with a whole-column imaging detection (WCID) and conventional cIEF as well as different detection methods (LIF after EV labelling, native fluorescence and UV). Our study reveals that EVs exhibit significant aggregation during their migration toward, and upon reaching, their isoelectric point (pI). By optimizing key parameters such as voltage and the addition of solubilizers, we successfully reduced this issue, particularly with bovine milk EVs. Our findings also showed distinct pI regions observed for EVs isolated from different sources: bovine milk EVs shows acidic pI characteristics (4.0-4.1), while pig and human plasma EVs exhibit more basic pI zones (4.7-4.9 and 5.8-6.7, respectively). The study was extended to cIEF coupled to laser induced fluorescence detection (LIF) using intra-vesicular CFDA-labeled EVs, to better understand their susceptibilities. Prolonged mobilization time due to long capillary lengths adversely affected EV's integrity in conventional cIEF. Our study reveals the necessity to specific cIEF optimization for each EV source due to variations in charge distribution and aggregation behavior across different pI regions. The use of a short capillary length (<10 cm), low electric field and solubilizers such as Tween-20 is recommended to preserve EVs integrity during cIEF-EV studies.

High-Throughput Analysis of Protein Adsorption to a Large Library of Polymers Using Liquid Extraction Surface Analysis-Tandem Mass Spectrometry (LESA-MS/MS)

Biomaterials play an important role in medicine from contact lenses to joint replacements. High-throughput screening coupled with machine learning has identified synthetic polymers that prevent bacterial biofilm formation, prevent fungal cell attachment, control immune cell attachment and phenotype, or direct stem cell fate. In-vitro preadsorption of proteins from culture medium plays a pivotal role in controlling cell response. However, there is a paucity of studies on the screening of protein adsorption into material libraries. Here, we show how quantitative analysis of protein adsorption on a 208-member polymer microarray can be achieved using liquid extraction surface analysis, combined with an adaptation of the droplet microarray (DMA) approach and tandem mass spectrometry (LESA-MS/MS) for protein identification. This study uses a fully defined cell culture medium containing only four proteins (Essential 8) to demonstrate the feasibility of the analysis approach. Our findings show that we can generate quantitative and predictive machine learning models of protein adsorption that elucidate key polymer features that describe the relationship between surface chemistry and protein adsorption. This information is of use for the rational design of new materials with bespoke protein attachment properties for biomaterials, medical devices, or in vitro compound screening.

Importin-alpha transports Norrin to the nucleus to promote proliferation and Notch signaling in glioblastoma stem cells

Norrin, a secreted protein encoded by NDP gene, is recognized for its established role as a paracrine canonical Frizzled-4/Wnt ligand that mediates angiogenesis and barrier function in the brain. However, emerging evidence suggests that Norrin possesses Frizzled-4-independent functions, notably impacting Notch activation and proliferation of cancer stem cells. We conducted a BioID protein-proximity screen to identify Norrin-interacting proteins. Surprisingly, a significant proportion of the proteins we identified were nuclear. Through comprehensive tagging and proximity ligation assays, we demonstrate that Norrin is transported to the nucleus through KPNA2 (member of the Importin-alpha family). Subsequently, we demonstrate that KPNA2 loss of function in patient-derived primary glioblastoma stem cells results in a nuclear to cytoplasmic shift of Norrin distribution, and a complete abrogation of its function in stimulating Notch signaling and cellular proliferation. These results indicate that Norrin is actively transported into the nucleus to regulate vital signaling pathways and cellular functions.

MultiStageSearch: An Iterative Workflow for Unbiased Taxonomic Analysis of Pathogens Using Proteogenomics

The global SARS-CoV-2 pandemic emphasized the need for accurate pathogen diagnostics. While genomics is the gold standard, integrating mass spectrometry-based proteomics offers additional benefits. However, current proteomic and genomic reference databases are often biased toward specific taxa, such as pathogenic strains or model organisms, and proteomic databases are less comprehensive. These biases and gaps can lead to inaccurate identifications. To address these issues, we introduce MultiStageSearch, a multistep database search method that combines proteome and genome databases for taxonomic analysis. Initially, a generalist proteome database is used to infer potential species. Then, MultiStageSearch generates a specialized proteogenomic database for precise identification. This database is preprocessed to filter duplicates and cluster identical open reading frames to reduce genomic database biases. The workflow operates independently of strain-level NCBI taxonomy, enabling the identification of strains not represented in existing taxonomies. We benchmarked the workflow on viral and bacterial samples, demonstrating its superior performance in strain-level taxonomic inference compared to existing methods. MultiStageSearch offers a flexible and accurate approach for pathogen research and diagnostics, overcoming incomplete search spaces and biases inherent in reference databases.

Impact of cardiosphere-derived cells on the maladapted right ventricular muscle in a rat sugen/hypoxia model of pulmonary hypertension with right ventricular dysfunction

BACKGROUND AND AIMS

With pulmonary arterial hypertension (PAH), right ventricular (RV) function is a major determinant of survival. Despite current therapies, maladaptive changes ensue in the RV muscle of PAH patients, culminating in RV dysfunction and failure. The aims of the study were to evaluate the impact of intra-coronary (IC) cardiosphere-derived cells (CDCs) in attenuating the maladaptive pathobiology in the RV muscle and evaluating mechanisms underlying improvements in RV function.

METHODS

Two groups of the Sugen/Hypoxia rat model of PAH, exhibiting significantly reduced RV function, via TAPSE measurements, received either intracoronary infusion of CDCs or PBS placebo. Immunohistochemistry methods were used to assess RV pathobiological changes. Additionally, advanced proteomics were employed to examine protein signaling pathways and upstream regulators.

RESULTS

RV muscle capillarity was significantly reduced in the PAH rats while RV muscle fibrosis was increased. IC CDCs significantly increased RV muscle capillarity back to levels noted in healthy rats and reduced RV free wall fibrosis. Further, a significant reduction in iNOS+ (M1) macrophages was also observed within the RV free wall in CDC-treated animals. Proteomic analysis of RV muscle in CDC- or PBS-treated PAH rats showed alterations in protein pathways related to inflammation, fibrosis, autophagy, cell vitality, and angiogenesis. These changes were consistent with putative coordination by a small number of key upstream regulators (MYC, TP53, HNF4A, TGFB1, and KRAS). TAPSE was significantly reduced in PBS-treated animals but was maintained at or above baseline levels in CDC-treated animals.

CONCLUSIONS

CDC therapy can significantly impact the maladaptive milieu of the RV myocardium in advanced PAH, by altering several pathobiological pathways. Such adjunctive therapy, in addition to those employed to reduce pulmonary vascular resistance, would be a great advance in managing RV failure, for which no effective current approved therapies exist.

High-Throughput Analysis of the Flagella FliK-Dependent Surfaceome and Secretome in Bacillus thuringiensis

Bacterial pathogens employ multiple strategies to invade and damage host tissues while evading immune defenses. Recent studies highlight flagella as crucial contributors to bacterial virulence, not only by facilitating motility, but also by regulating the secretion of virulence factors. However, the role of the flagella-dependent secretome remains largely unexplored. We have recently shown that FliK, a key regulator that defines substrate specificity in the flagellar export apparatus, is essential for the resistance of Bacillus thuringiensis (B. thuringiensis) against antimicrobial peptides (AMPs) and its virulence in a Drosophila infection model. In this study, we used liquid chromatography-tandem mass spectrometry to conduct a large-scale comparative analysis of the proteins secreted in culture supernatant or associated with the cell wall of the ΔfliK mutant and its reference strain. Our results reveal significant differences in the secretome and surfaceome of the ΔfliK mutant compared to the reference strain. These findings emphasize the role of FliK in regulating the production and secretion of several proteins, underscoring the importance of flagella in controlling various biological processes. This work provides valuable insights into the functional characterization of potential candidate proteins involved in B. thuringiensis virulence and AMP resistance mechanisms. Overall, these results open new perspectives for understanding the molecular processes that govern bacterial resistance to AMPs.

Rustims: An Open-Source Framework for Rapid Development and Processing of timsTOF Data-Dependent Acquisition Data

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.

Open-Source and FAIR Research Software for Proteomics

Scientific discovery relies on innovative software as much as experimental methods, especially in proteomics, where computational tools are essential for mass spectrometer setup, data analysis, and interpretation. Since the introduction of SEQUEST, proteomics software has grown into a complex ecosystem of algorithms, predictive models, and workflows, but the field faces challenges, including the increasing complexity of mass spectrometry data, limited reproducibility due to proprietary software, and difficulties integrating with other omics disciplines. Closed-source, platform-specific tools exacerbate these issues by restricting innovation, creating inefficiencies, and imposing hidden costs on the community. Open-source software (OSS), aligned with the FAIR Principles (Findable, Accessible, Interoperable, Reusable), offers a solution by promoting transparency, reproducibility, and community-driven development, which fosters collaboration and continuous improvement. In this manuscript, we explore the role of OSS in computational proteomics, its alignment with FAIR principles, and its potential to address challenges related to licensing, distribution, and standardization. Drawing on lessons from other omics fields, we present a vision for a future where OSS and FAIR principles underpin a transparent, accessible, and innovative proteomics community.

An Approach to Integrate Metagenomics, Metatranscriptomics and Metaproteomics Data in Public Data Resources

The availability of public metaproteomics, metagenomics and metatranscriptomics data in public resources such as MGnify (for metagenomics/metatranscriptomics) and the PRIDE database (for metaproteomics), continues to increase. When these omics techniques are applied to the same samples, their integration offers new opportunities to understand the structure (metagenome) and functional expression (metatranscriptome and metaproteome) of the microbiome. Here, we describe a pilot study aimed at integrating public multi-meta-omics datasets from studies based on human gut and marine hatchery samples. Reference search databases (search DBs) were built using assembled metagenomic (and metatranscriptomic, where available) sequence data followed by de novo gene calling, using both data from the same sampling event and from independent samples. The resulting protein sets were evaluated for their utility in metaproteomics analysis. In agreement with previous studies, the highest number of peptide identifications was generally obtained when using search DBs created from the same samples. Data integration of the multi-omics results was performed in MGnify. For that purpose, the MGnify website was extended to enable the visualisation of the resulting peptide/protein information from three reanalysed metaproteomics datasets. A workflow (https://github.com/PRIDE-reanalysis/MetaPUF) has been developed allowing researchers to perform equivalent data integration, using paired multi-omics datasets. This is the first time that a data integration approach for multi-omics datasets has been implemented from public data available in the world-leading MGnify and PRIDE resources.

MSFragger-DDA+ enhances peptide identification sensitivity with full isolation window search

Liquid chromatography-mass spectrometry based proteomics, particularly in the bottom-up approach, relies on the digestion of proteins into peptides for subsequent separation and analysis. The most prevalent method for identifying peptides from data-dependent acquisition mass spectrometry data is database search. Traditional tools typically focus on identifying a single peptide per tandem mass spectrum, often neglecting the frequent occurrence of peptide co-fragmentations leading to chimeric spectra. Here, we introduce MSFragger-DDA+, a database search algorithm that enhances peptide identification by detecting co-fragmented peptides with high sensitivity and speed. Utilizing MSFragger's fragment ion indexing algorithm, MSFragger-DDA+ performs a comprehensive search within the full isolation window for each tandem mass spectrum, followed by robust feature detection, filtering, and rescoring procedures to refine search results. Evaluation against established tools across diverse datasets demonstrated that, integrated within the FragPipe computational platform, MSFragger-DDA+ significantly increases identification sensitivity while maintaining stringent false discovery rate control. It is also uniquely suited for wide-window acquisition data. MSFragger-DDA+ provides an efficient and accurate solution for peptide identification, enhancing the detection of low-abundance co-fragmented peptides. Coupled with the FragPipe platform, MSFragger-DDA+ enables more comprehensive and accurate analysis of proteomics data.

Identification of Marker Peptides in Gelatins from Sika Deer (Cervus nippon) Using Ultra-High-Performance Liquid Chromatography-Quadrupole-Exactive-Orbitrap Mass Spectrometry

Gelatin from deer has garnered attention as a high-value health-promoting resource given its history of usage as a traditional Chinese medicine and recent studies demonstrating its biological activities. Mass spectrometry-based methods have increasingly been employed for species identification in collagen-based materials, effectively addressing challenges in quality control and authenticity verification. This study aims to identify characteristic marker peptides in gelatins from sika deer (Cervus nippon) to support their effective use as a health-promoting resource. Gelatin samples were enzymatically digested, and the resulting peptide mixtures were analyzed using ultra-high-performance liquid chromatography coupled with quadrupole Q-Exactive-Orbitrap mass spectrometry (UHPLC-Q-Exactive-Orbitrap MS). Marker peptide candidates were selected based on their high detection intensity and a literature review. Among the 28 selected marker peptide candidates, four peptides (P11, R2, R3, and R4) were defined as characteristic of sika deer gelatin. Comparative analyses with gelatins derived from donkey hide, bovine, porcine, and fish samples further confirmed the specificity of these peptides. These findings establish a robust analytical method for verifying the authenticity of sika deer gelatin, contributing to its safe and effective use as a health-promoting resource.

Comparison of the Human Plasma Peptides from the Fit of Fragmentation Spectra versus Accurate Monoisotopic Precursor Mass

In nature, ionized peptides with heavy isotopes and hydrogen rearrangements show a broad mass distribution with signals at discrete delta mass values from -3 to +5 Da by mass spectrometry (MS). For many peptides, the intensity of the +1 or +2 Da isotope exceeds the signal from the monoisotopic mass. Therefore, there is a need for a method that improves peptide identification from heavy isotopes or hydrogen rearrangements based on the fit of tandem mass spectra. Peptides may be identified using an accurate monoisotopic precursor mass with ≤0.1 Da. However, many peptides with heavy isotopes and H-loss can be identified and enumerated based on the fit of their MS/MS spectra alone in the absence of an accurate precursor monoisotopic mass (i.e., ± 3 Da) using the X!TANDEM MS/MS fitting algorithm. In this study, human plasma samples were analyzed with a highly resolving axially harmonic orbital ion trap (OIT) and a sensitive linear quadrupole ion trap (LIT). The MS/MS fragmentation spectra from the OIT can be fit to peptides from the monoisotopic (±0.1 Da) as well as all other precursor masses with a wide mass tolerance (±3 Da). The resulting delta mass distribution can then be plotted and compared to the predicted distribution of heavy isotopes and hydrogen rearrangements to provide a direct biophysical prediction and test the validity of the fit determined by accepting the best-fit MS/MS spectra. The OIT instrument, which has greater resolution, was sampled at 30 nL per minute, while the more sensitive LIT was sampled at 200 nL per minute. The MS/MS spectra generated by each instrument were fit to peptides within a wide window (±3 Da) using the rigorous X!TANDEM algorithm. The OIT and LIT results were compared in an SQL Server database and corrected against analytical and statistical controls. The delta mass distribution of the peptides with hydrogen rearrangements and heavy isotopes was determined from the fit MS/MS spectra using the R statistical program. The OIT sampled MS and MS/MS spectra from the high-intensity precursor ions by focusing on E7 to E9 detector counts. In contrast, the LIT sampled a range of precursor ion intensities focused from E4 to E7 and thus reached lower ion intensity values. As expected, the precursor mass [M + H]+ obtained by the OIT exhibited sharp delta mass peaks at -3, -2, -1, 0, +1, +2, +3, +4, and +5 Da due to naturally occurring heavy isotopes and hydrogen rearrangements. The collection of peptides and proteins identified by OIT and LIT was in qualitative and quantitative agreement with one another, with 99.9% overlap on 2726 protein gene symbols from human plasma and a highly significant relationship by regression analysis. The protein p-values, false discovery rate q-values, and comparisons to the noise MS/MS analytical control and random MS/MS statistical control confirmed the high-confidence MS/MS identifications from both instruments. MS/MS fragmentation spectra from the OIT were fit to peptides. The resulting precursor ion delta mass distribution showed a precise match to the predicted isotope distributions and hydrogen rearrangements of natural peptides. Thus, analysis of delta mass plots provided powerful biophysical evidence for the accuracy of plasma peptide identification from the fit of the MS/MS spectra alone. The high level of agreement on proteins and peptides and the proportional enumeration between proteins identified by the OIT and those identified independently using a LIT confirmed that plasma peptides and proteins may be identified and quantified from MS/MS spectra alone without the need for an accurate measure of the precursor mass. The greater sensitivity and low cost of searching MS/MS spectra in the absence of an accurate mass mean that it is possible to identify and quantify more proteins for the discovery of proteins in clinical populations.

The primate-specific Nedd4-1(NE) localizes to late endosomes in response to amino acids to suppress autophagy

The ubiquitin ligase Nedd4 (Nedd4-1), comprised of C2-WW(n)-HECT domains, regulates protein trafficking. We recently described a primate-specific Nedd4-1 splice isoform with an extended N-terminus replacing the C2 domain, called Nedd4-1(NE). Here, we show that while canonical Nedd4-1 is primarily localized to the cytosol, Nedd4-1(NE) localizes to late endosomes. This localization is mediated by the NE region, is dependent on amino acid availability, is independent of mTORC1, and is inhibited by the autophagy inducer IKKβ. We further demonstrate that VPS16B, which regulates late endosome to lysosome maturation, is a unique Nedd4-1(NE) substrate that co-localizes with Nedd4-1(NE) in the presence of nutrients. Importantly, a potentially pathogenic homozygous variant identified in the NE region (E70Q) of a patient with lymphangiectasia and protein-losing enteropathy leads to reduced VPS16B ubiquitination by Nedd4-1(NE). Finally, we report that Nedd4-1(NE) inhibits autophagy, likely by disrupting late endosome to autophagosome maturation. This work identified an mTORC1-independent, IKK-driven mechanism to regulate Nedd4-1(NE) localization to late endosomes in primates in response to nutrient availability, and uncovered suppression of autophagy by this ubiquitin ligase.

Integration of proteomics profiling data to facilitate discovery of cancer neoantigens: a survey

Cancer neoantigens are peptides that originate from alterations in the genome, transcriptome, or proteome. These peptides can elicit cancer-specific T-cell recognition, making them potential candidates for cancer vaccines. The rapid advancement of proteomics technology holds tremendous potential for identifying these neoantigens. Here, we provided an up-to-date survey about database-based search methods and de novo peptide sequencing approaches in proteomics, and we also compared these methods to recommend reliable analytical tools for neoantigen identification. Unlike previous surveys on mass spectrometry-based neoantigen discovery, this survey summarizes the key advancements in de novo peptide sequencing approaches that utilize artificial intelligence. From a comparative study on a dataset of the HepG2 cell line and nine mixed hepatocellular carcinoma proteomics samples, we demonstrated the potential of proteomics for the identification of cancer neoantigens and conducted comparisons of the existing methods to illustrate their limits. Understanding these limits, we suggested a novel workflow for neoantigen discovery as perspectives.

Mass Spectrometry-Based Proteomics in Clinical Diagnosis of Amyloidosis and Multiple Myeloma: A Review (2012-2024)

Proteomics is nowadays increasingly becoming part of the routine clinical practice of diagnostic laboratories, especially due to the advent of advanced mass spectrometry techniques. This review focuses on the application of proteomic analysis in the identification of pathological conditions in a hospital setting, with a particular focus on the analysis of protein biomarkers. In particular, the main purpose of the review is to highlight the challenges associated with the identification of specific disease-causing proteins, given their complex nature and the variety of posttranslational modifications (PTMs) they can undergo. PTMs, such as phosphorylation and glycosylation, play critical roles in protein function but can also lead to diseases if dysregulated. Proteomics plays an important role especially in various medical fields ranging from cardiology, internal medicine to hemato-oncology emphasizing the interdisciplinary nature of this field. Traditional methods such as electrophoretic or immunochemical methods have been mainstay in protein detection; however, these techniques are limited in terms of specificity and sensitivity. Examples include the diagnosis of multiple myeloma and the detection of its specific protein or amyloidosis, which relies heavily on these conventional methods, which sometimes lead to false positives or inadequate disease monitoring. Mass spectrometry in this respect emerges as a superior alternative, providing high sensitivity and specificity in the detection and quantification of specific protein sequences. This technique is particularly beneficial for monitoring minimal residual disease (MRD) in the diagnosis of multiple myeloma where traditional methods fall short. Furthermore mass spectrometry can provide precise typing of amyloid proteins, which is crucial for the appropriate treatment of amyloidosis. This review summarizes the opportunities for proteomic determination using mass spectrometry between 2012 and 2024, highlighting the transformative potential of mass spectrometry in clinical proteomics and encouraging its wider use in diagnostic laboratories.

Lipogenic enzyme FASN promotes mutant p53 accumulation and gain-of-function through palmitoylation

The tumor-suppressive function of p53 is frequently disrupted by mutations in cancers. Missense mutant p53 (mutp53) protein often stabilizes and accumulates to high levels in cancers to promote tumorigenesis through the gain-of-function (GOF) mechanism. Currently, the mechanism of mutp53 accumulation and GOF is incompletely understood. Here, we identify the lipogenic enzyme FASN as an important regulator of mutp53 accumulation and GOF. FASN interacts with mutp53 to enhance mutp53 palmitoylation, which inhibits mutp53 ubiquitination to promote mutp53 accumulation and GOF. Blocking FASN genetically or by small-molecule inhibitors suppresses mutp53 palmitoylation to inhibit mutp53 accumulation, which in turn inhibits the growth of mutp53 tumors in orthotopic and subcutaneous xenograft tumor models and transgenic mice, as well as the growth of human tumor organoids carrying mutp53. Our results reveal that mutp53 palmitoylation is an important mechanism underlying mutp53 accumulation and GOF, and targeting FASN is a potential therapeutic strategy for cancers carrying mutp53.

PeptideForest: Semisupervised Machine Learning Integrating Multiple Search Engines for Peptide Identification

The first step in bottom-up proteomics is the assignment of measured fragmentation mass spectra to peptide sequences, also known as peptide spectrum matches. In recent years novel algorithms have pushed the assignment to new heights; unfortunately, different algorithms come with different strengths and weaknesses and choosing the appropriate algorithm poses a challenge for the user. Here we introduce PeptideForest, a semisupervised machine learning approach that integrates the assignments of multiple algorithms to train a random forest classifier to alleviate that issue. Additionally, PeptideForest increases the number of peptide-to-spectrum matches that exhibit a q-value lower than 1% by 25.2 ± 1.6% compared to MS-GF+ data on samples containing mixed HEK and Escherichia coli proteomes. However, an increase in quantity does not necessarily reflect an increase in quality and this is why we devised a novel approach to determine the quality of the assigned spectra through TMT quantification of samples with known ground truths. Thereby, we could show that the increase in PSMs below 1% q-value does not come with a decrease in quantification quality and as such PeptideForest offers a possibility to gain deeper insights into bottom-up proteomics. PeptideForest has been integrated into our pipeline framework Ursgal and can therefore be combined with a wide array of algorithms.

Micro scale chromatography of human plasma proteins for nano LC-ESI-MS/MS

Organic precipitation of proteins with acetonitrile demonstrated complete protein recovery and improved chromatography of human plasma proteins. The separation of 25 μL of human plasma into 22 fractions on a QA SAX resin facilitated more effective protein discovery despite the limited sample size. Micro chromatography of plasma proteins over quaternary amine (QA) strong anion exchange (SAX) resins performed best, followed by diethylaminoethyl (DEAE), heparin (HEP), carboxymethyl cellulose (CMC), and propyl sulfate (PS) resins. Two independent statistical methods, Monte Carlo comparison with random MS/MS spectra and the rigorous X!TANDEM goodness of fit algorithm protein p-values corrected to false discovery rate q-values (q ≤ 0.01) agreed on at least 12,000 plasma proteins, each represented by at least three fully tryptic corrected peptide observations. There was qualitative agreement on 9393 protein/gene symbols between the linear quadrupole versus orbital ion trap but also quantitative agreement with a highly significant linear regression relationship between log observation frequency (F value 4,173, p-value 2.2e-16). The use of a QA resin showed nearly perfect replication of all the proteins that were also found using DEAE-, HEP-, CMC-, and PS-based chromatographic methods combined and together estimated the size of the size of the plasma proteome as ≥12,000 gene symbols.

New insights into uranium stress responses of Arabidopsis roots through membrane- and cell wall-associated proteome analysis

Uranium (U) is a non-essential and toxic metal for plants. In Arabidopsis thaliana plants challenged with uranyl nitrate, we showed that U was mostly (64-71% of the total) associated with the root insoluble fraction containing membrane and cell wall proteins. Therefore, to uncover new molecular mechanisms related to U stress, we used label-free quantitative proteomics to analyze the responses of the root membrane- and cell wall-enriched proteome. Of the 2,802 proteins identified, 458 showed differential accumulation (≥1.5-fold change) in response to U. Biological processes affected by U include response to stress, amino acid metabolism, and previously unexplored functions associated with membranes and the cell wall. Indeed, our analysis supports a dynamic and complex reorganization of the cell wall under U stress, including lignin and suberin synthesis, pectin modification, polysaccharide hydrolysis, and Casparian strips formation. Also, the abundance of proteins involved in vesicular trafficking and water flux was significantly altered by U stress. Measurements of root hydraulic conductivity and leaf transpiration indicated that U significantly decreased the plant's water flux. This disruption in water balance is likely due to a decrease in PIP aquaporin levels, which may serve as a protective mechanism to reduce U toxicity. Finally, the abundance of transporters and metal-binding proteins was altered, suggesting that they may be involved in regulating the fate and toxicity of U in Arabidopsis. Overall, this study highlights how U stress impacts the insoluble root proteome, shedding light on the mechanisms used by plants to mitigate U toxicity.

Optimization of protein identifications through the use of different chromatographic approaches and bioinformatic pipelines

RATIONALE

Selection of proteomic workflows for a given project can be a daunting task. This research provides a guide outlining the impact on protein identification of different steps such as chromatographic separation, data acquisition strategies, and bioinformatic pipelines. The data presented here will help experts and nonexpert proteomic users to increase proteome coverage and peptide identification.

METHODS

HeLa protein digests were analyzed through different C18 chromatographic columns (15 and 50 cm in length), using top 12 data-dependent acquisition (DDA), top 20 DDA, and data-independent acquisition (DIA) with a nanospray source in positive mode in a Thermo Q Exactive instrument. The raw data were analyzed using different search engines, rescoring approaches, and multi-engine searches. The results were analyzed in the context of peptide and protein identifications, precursor properties, and computation requirements to understand the differences between methods.

RESULTS

Our results showed that higher column lengths and top N DDA approaches were able to significantly increase protein identifications. The use of multiple search engines yielded limited gains, whereas the use of rescoring methods clearly outperformed other strategies. Finally, DIA approaches, although successful at generating new identifications, had a limited performance influenced by the previous collection of DDA data, which could prohibitively increase instrument time. Nonetheless, the use of library-free methods showed promising results.

CONCLUSIONS

Our results highlight the impact of different experimental approaches on proteome coverage. Changes in chromatographic columns, data acquisition, or bioinformatic analysis can significantly increase the number of protein identifications (>400%). Thus, this research provides a reference upon which to build a successful proteomic workflow with different considerations at every step.

Selected Ion Extraction of Peptides with Heavy Isotopes and Hydrogen Loss Reduces the Type II Error in Plasma Proteomics

Naturally occurring peptides display a wide mass distribution after ionization due to the presence of heavy isotopes of C, H, N, O, and S and hydrogen loss. There is a crucial need for sensitive methods that collect as much information as possible about all plasma peptide forms. Statistical analysis of the delta mass distribution of peptide precursors from MS/MS spectra that were matched to 63,077 peptide sequences by X!TANDEM revealed Gaussian peaks representing heavy isotopes and hydrogen loss at integer delta mass values of -3, -2, -1, 0, +1, +2, +3, +4, and +5 Da. Human plasma samples were precipitated in acetonitrile, and the resulting proteins were collected over a quaternary amine resin, eluted with NaCl, digested with trypsin, and analyzed by nano liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) with an orbital ion trap (OIT). Fragment spectra (MS/MS) generated from the OIT data were fit to human fully tryptic peptides by X!TANDEM, which led to the identification of 3,888 protein gene symbols represented by three or more peptides (n ≥ 3). The peptide counts to plasma proteins from experimental MS/MS spectra were corrected against 29 blank LC-ESI-MS/MS spectra and 30 million random MS/MS control spectra to yield 2,784 true positive proteins (n ≥ 3; q ≤ 0.01). Peptides identified by fragmenting ions with Gaussian heavy isotopes and hydrogen loss that were matched to known plasma proteins, such as albumin (ALB), were shown to be true positives and agreed with the peptide sequences identified in the monoisotopic peak. Accepting the ions from the monoisotopic peak alone (±0.1 Da) yielded only 382 plasma proteins (n ≥ 3; type I error q ≤ 0.01; type II error ∼86%). In contrast, accepting all ions within ±0.1 Da around the hydrogen loss, monoisotopic, and heavy isotopic peaks led to the identification of 963 proteins (n ≥ 3; q ≤ 0.01; type II error ∼60%). Using the power of the OIT to resolve the Gaussian peaks from heavy isotopes and hydrogen loss resulted in the identification of three times more proteins with high confidence and a much lower type II error than analyzing peptides from the monoisotopic peak alone. The resolving power of the OIT may be exploited to increase observation frequencies and provide greater proteomic coverage and statistical power in comparative proteomics studies.

The phage protein paratox is a multifunctional metabolic regulator of Streptococcus

Streptococcus pyogenes, or Group A Streptococcus (GAS), is a commensal bacteria and human pathogen. Central to GAS pathogenesis is the presence of prophage encoded virulence genes. The conserved phage gene for the protein paratox (Prx) is genetically linked to virulence genes, but the reason for this linkage is unknown. Prx inhibits GAS quorum sensing and natural competence by binding the transcription factor ComR. However, inhibiting ComR does not explain the virulence gene linkage. To address this, we took a mass spectrometry approach to search for other Prx interaction partners. The data demonstrates that Prx binds numerous DNA-binding proteins and transcriptional regulators. We show binding of Prx in vitro with the GAS protein Esub1 (SpyM3_0890) and the phage protein JM3 (SpyM3_1246). An Esub1:Prx complex X-ray crystal structure reveals that Esub1 and ComR possess a conserved Prx-binding helix. Computational modelling predicts that the Prx-binding helix is present in several, but not all, binding partners. Namely, JM3 lacks the Prx-binding helix. As Prx is conformationally dynamic, this suggests partner-dependent binding modes. Overall, Prx acts as a metabolic regulator of GAS to maintain the phage genome. As such, Prx maybe a direct contributor to the pathogenic conversion of GAS.

Comprehensive Peptide Mapping Is Crucial for Proteogenomics and Proteomics

Proteogenomics enables the confirmation and refinement of gene models, the detection of new ones, and the proposition of alternative transcripts using support at the protein level. Such evidence is usually generated using mass spectrometry and subsequent result mapping to various sequence databases. This workflow entails several problems: (1) To speed up the analysis, only a small set of expected proteins is searched; (2) database search tools generally do not provide mapping to the genome; and (3) upon new releases of the sequence databases, expensive rerunning of all results would need to be performed. Therefore, fast and accurate peptide mapping is needed as part of proteogenomic pipelines. Unfortunately, some available tools have technical shortcomings. Thus, a set of test cases was developed to allow tool developers to test their implementations comprehensively. The need for comprehensive testing is exemplified by PGx and PGM, two published tools that could only solve a subset of test cases. Lelantos passed all test cases. A set of comprehensive test cases has been developed to overcome these issues. Many unpublished peptide mapping tools are part of proteogenomic workflows, and such tools would also benefit from comprehensive testing. Finally, peptide mapping may also be crucial for proteomics because sequence databases change over time. In response, peptide remapping should be performed to ensure that peptides identifying a protein are proteotypic in a larger sequence context.

The co-chaperone DNAJA2 buffers proteasomal degradation of cytosolic proteins with missense mutations

Mutations can disrupt the native function of protein by causing misfolding, which is generally handled by an intricate protein quality control network. To better understand the triaging mechanisms for misfolded cytosolic proteins, we screened a human mutation library to identify a panel of unstable mutations. The degradation of these mutated cytosolic proteins is largely dependent on the ubiquitin proteasome system. Using BioID proximity labelling, we found that the co-chaperones DNAJA1 and DNAJA2 are key interactors with one of the mutated proteins. Notably, the absence of DNAJA2 increases the turnover of the mutant but not the wild-type protein. Our work indicates that specific missense mutations in cytosolic proteins can promote enhanced interactions with molecular chaperones. Assessment of the broader panel of cytosolic mutant proteins shows that the co-chaperone DNAJA2 exhibits two distinct behaviours - acting to stabilize a wide array of cytosolic proteins, including wild-type variants, and to specifically 'buffer' some mutant proteins to reduce their turnover. Our work illustrates how distinct elements of the protein homeostasis network are utilized in the presence of a cytosolic misfolded protein.

A Review of Protein Inference

Protein inference is an often neglected though crucial step in most proteomic experiments. In the bottom-up proteomic approach, the actual molecules of interest, the proteins, are digested into peptides before measurement on a mass spectrometer. This approach introduces a loss of information: The actual proteins must be inferred based on the identified peptides. While this might seem trivial, there are certain problems, one of the biggest being the presence of peptides that are shared among proteins. These amino acid sequences can, based on the database used for identification, belong to more than one protein. If such peptides are identified in a sample, it cannot be said which proteins actually were in the sample, but only an estimate on the most probable proteins or protein groups can be given based on a predefined inference strategy.Here we describe the effect of the chosen database for peptide identification on the number of shared peptides. Afterward, the mainly used protein inference methods will be sketched, and the necessity of stringent false discovery rate on peptide and protein level is discussed. Finally, we explain how the tool "PIA or protein inference algorithms" can be used together with the workflow environment KNIME and OpenMS to perform protein inference in a common proteomic experiment.

Proteomic characterization of a foraminiferal test's organic matrix

Foraminifera are unicellular protists capable of precipitating calcite tests, which fossilize and preserve geochemical signatures of past environmental conditions dating back to the Cambrian period. The biomineralization mechanisms responsible for the mineral structures, which are key to interpreting palaeoceanographic signals, are poorly understood. Here, we present an extensive analysis of the test-bound proteins. Using liquid chromatography-tandem mass spectrometry, we identify 373 test-bound proteins in the large benthic foraminifer Amphistegina lobifera, the majority of which are highly acidic and rich in negatively charged residues. We detect proteins involved in vesicle formation and active Ca2+ trafficking, but in contrast, do not find similar proteins involved in Mg2+ transport. Considering findings from this study and previous ones, we propose a dual ion transport model involving seawater vacuolization, followed by the active release of Ca2+ from the initial vacuoles and subsequent uptake into newly formed Ca-rich vesicles that consequently enrich the calcification fluid. We further speculate that Mg2+ passively leaks through the membrane from the remaining Mg-rich vesicles, into the calcifying fluid, at much lower concentrations than in seawater. This hypothesis could not only explain the low Mg/Ca ratio in foraminiferal tests compared to inorganic calcite, but could possibly also account for its elevated sensitivity to temperature compared with inorganically precipitated CaCO3.

Warming and polymetallic stress induce proteomic and physiological shifts in the neurotoxic Alexandrium pacificum as possible response to global changes

Harmful Algal Blooms involving the dinoflagellate Alexandrium pacificum continue to increase in ecosystems suffering the climate warming and anthropogenic pressure. Changes in the total proteome and physiological traits of the Mediterranean A. pacificum SG C10-3 strain were measured in response to increasing temperature (24 °C, 27 °C, 30 °C) and trace metal contamination (Cu2+, Pb2+, Zn2+, Cd2+). Warming reduced the cell densities and maximal growth rate (μmax), but the strain persisted at 30 °C with more large cells. The polymetallic stress increased cell sizes, reduced cell growth at 24 °C-27 °C and it increased this at 30 °C. Toxin profiles showed a predominance of GTX4 (32-38 %), then C2 (11-34 %) or GTX6 (18-24 %) among the total Paralytic Shellfish Toxins, however these were modified under warming, showing increased contents in GTX1 (among the most toxic), GTX5, C1 and NeoSTX, while dc-NeoSTX and STX (among the most toxic) only appeared at 30 °C. Under polymetallic contamination, warming also increased contents in GTX5 and NeoSTX. In contrast, polymetallic stress, or warming had harmful effects on C2 contents. Proteins were more quantitatively produced by A. pacificum SG C10-3 under warming in accordance with the high levels of up-regulated proteins found in the total proteome in this condition. Polymetallic stress, only or combined with warming, led to low proteomic modifications (1 % or 4 %), whereas warming induced strong 52 % modified proteomic response, mainly based on up-regulated proteins involved in photosynthesis (light harvesting complex protein), carbohydrate metabolism (arylsulfatase) and translation (ribosomal proteins), and with the lesser down-regulated proteins principally associated with the lipid metabolism (type I polyketide synthase). Our results show that warming triggers a strong up-regulated A. pacificum SG C10-3 proteomic response, which, coupled to modified cell sizes and toxin profiles, could help it to withstand stress conditions. This could presage the success of A. pacificum in anthropized ecosystems submitted to global warming in which this dinoflagellate also might be more toxic.

PgxSAVy: A tool for comprehensive evaluation of variant peptide quality in proteogenomics - catching the (un)usual suspects

Variant peptides resulting from single nucleotide polymorphisms (SNPs) can lead to aberrant protein functions and have translational potential for disease diagnosis and personalized therapy. Variant peptides detected by proteogenomics are fraught with high number of false positives, but there is no uniform and comprehensive approach to assess variant quality across analysis pipelines. Despite class-specific FDR along with ad-hoc filters, the problem is far from solved. These protocols are typically manual and tedious, and thus not uniform across labs. We demonstrate that variant peptide rescoring, integrated with intensity, variant event information and search result features, allows better discrimination of correct variant peptides. Implemented into PgxSAVy - a tool for quality control of variant peptides, this method can tackle the high rate of false positives. PgxSAVy provides a rigorous framework for quality control and annotations of variant peptides on the basis of (i) variant quality, (ii) isobaric masses, and (iii) disease annotation. PgxSAVy demonstrated high accuracy by identifying true variants with 98.43% accuracy on simulated data. Large-scale proteogenomic reanalysis of ∼2.8 million spectra (PXD004010 and PXD001468) resulted in 12,705 variant peptide spectrum matches (PSMs), of which PgxSAVy evaluated 3028 (23.8%), 1409 (11.1%) and 8268 (65.1%) as confident, semi-confident and doubtful respectively. PgxSAVy also annotates the variants based on their pathogenicity and provides support for assisted manual validation. The analysis of proteins carrying variants can provide fine granularity in discovering important pathways. PgxSAVy will advance personalized medicine by providing a comprehensive framework for quality control and prioritization of proteogenomics variants. PgxSAVy is freely available at https://pgxsavy.igib.res.in/ as a webserver and https://github.com/anuragraj/PgxSAVy as a stand-alone tool.

Recruitment of FBXO22 for targeted degradation of NSD2

Targeted protein degradation (TPD) is an emerging therapeutic strategy that would benefit from new chemical entities with which to recruit a wider variety of ubiquitin E3 ligases to target proteins for proteasomal degradation. Here we describe a TPD strategy involving the recruitment of FBXO22 to induce degradation of the histone methyltransferase and oncogene NSD2. UNC8732 facilitates FBXO22-mediated degradation of NSD2 in acute lymphoblastic leukemia cells harboring the NSD2 gain-of-function mutation p.E1099K, resulting in growth suppression, apoptosis and reversal of drug resistance. The primary amine of UNC8732 is metabolized to an aldehyde species, which engages C326 of FBXO22 to recruit the SCFFBXO22 Cullin complex. We further demonstrate that a previously reported alkyl amine-containing degrader targeting XIAP is similarly dependent on SCFFBXO22. Overall, we present a potent NSD2 degrader for the exploration of NSD2 disease phenotypes and a new FBXO22-recruitment strategy for TPD.

The lysosomal lipid transporter LIMP-2 is part of lysosome-ER STARD3-VAPB-dependent contact sites

LIMP-2 (also known as SCARB2) is an abundant lysosomal membrane protein. Previous studies have shown that LIMP-2 functions as a virus receptor, a chaperone for lysosomal enzyme targeting and a lipid transporter. The large luminal domain of LIMP-2 contains a hydrophobic tunnel that enables transport of phospholipids, sphingosine and cholesterol from the lysosomal lumen to the membrane. The question about the fate of the lipids after LIMP-2-mediated transport is largely unexplored. To elucidate whether LIMP-2 is present at contact sites between lysosomes and the endoplasmic reticulum (ER), we performed a proximity-based interaction screen. This revealed that LIMP-2 interacts with the endosomal protein STARD3 and the ER-resident protein VAPB. Using imaging and co-immunoprecipitation, we demonstrated colocalization and physical interaction between LIMP-2 and these proteins. Moreover, we found that interaction of LIMP-2 with VAPB required the presence of STARD3. Our findings suggest that LIMP-2 is present at ER-lysosome contact sites, possibly facilitating cholesterol transport from the lysosomal to the ER membrane. This suggests a novel mechanism for inter-organelle communication and lipid trafficking mediated by LIMP-2.

Nucleolar Pol II interactome reveals TBPL1, PAF1, and Pol I at intergenic rDNA drive rRNA biogenesis

Ribosomal DNA (rDNA) repeats harbor ribosomal RNA (rRNA) genes and intergenic spacers (IGS). RNA polymerase (Pol) I transcribes rRNA genes yielding rRNA components of ribosomes. IGS-associated Pol II prevents Pol I from excessively synthesizing IGS non-coding RNAs (ncRNAs) that can disrupt nucleoli and rRNA production. Here, compartment-enriched proximity-dependent biotin identification (compBioID) revealed the TATA-less-promoter-binding TBPL1 and transcription-regulatory PAF1 with nucleolar Pol II. TBPL1 localizes to TCT motifs, driving Pol II and Pol I and maintaining its baseline ncRNA levels. PAF1 promotes Pol II elongation, preventing unscheduled R-loops that hyper-restrain IGS Pol I-associated ncRNAs. PAF1 or TBPL1 deficiency disrupts nucleolar organization and rRNA biogenesis. In PAF1-deficient cells, repressing unscheduled IGS R-loops rescues nucleolar organization and rRNA production. Depleting IGS Pol I-dependent ncRNAs is sufficient to compromise nucleoli. We present the nucleolar interactome of Pol II and show that its regulation by TBPL1 and PAF1 ensures IGS Pol I ncRNAs maintaining nucleolar structure and function.

TCF4 trinucleotide repeat expansions and UV irradiation increase susceptibility to ferroptosis in Fuchs endothelial corneal dystrophy

Fuchs endothelial corneal dystrophy (FECD), the leading indication for corneal transplantation in the U.S., causes loss of corneal endothelial cells (CECs) and corneal edema leading to vision loss. FECD pathogenesis is linked to impaired response to oxidative stress and environmental ultraviolet A (UVA) exposure. Although UVA is known to cause nonapoptotic oxidative cell death resulting from iron-mediated lipid peroxidation, ferroptosis has not been characterized in FECD. We investigated the roles of genetic background and UVA exposure in causing CEC degeneration in FECD. Using ungenotyped FECD patient surgical samples, we found increased levels of cytosolic ferrous iron (Fe2+) and lipid peroxidation in end-stage diseased tissues compared with healthy controls. Using primary and immortalized cell cultures modeling the TCF4 intronic trinucleotide repeat expansion genotype, we found altered gene and protein expression involved in ferroptosis compared to controls including elevated levels of Fe2+, basal lipid peroxidation, and the ferroptosis-specific marker transferrin receptor 1. Increased cytosolic Fe2+ levels were detected after physiologically relevant doses of UVA exposure, indicating a role for ferroptosis in FECD disease progression. Cultured cells were more prone to ferroptosis induced by RSL3 and UVA than controls, indicating ferroptosis susceptibility is increased by both FECD genetic background and UVA. Finally, cell death was preventable after RSL3 induced ferroptosis using solubilized ubiquinol, indicating a role for anti-ferroptosis therapies in FECD. This investigation demonstrates that genetic background and UVA exposure contribute to iron-mediated lipid peroxidation and cell death in FECD, and provides the basis for future investigations of ferroptosis-mediated disease progression in FECD.

MSFragger-DDA+ Enhances Peptide Identification Sensitivity with Full Isolation Window Search

Liquid chromatography-mass spectrometry (LC-MS) based proteomics, particularly in the bottom-up approach, relies on the digestion of proteins into peptides for subsequent separation and analysis. The most prevalent method for identifying peptides from data-dependent acquisition (DDA) mass spectrometry data is database search. Traditional tools typically focus on identifying a single peptide per tandem mass spectrum (MS2), often neglecting the frequent occurrence of peptide co-fragmentations leading to chimeric spectra. Here, we introduce MSFragger-DDA+, a novel database search algorithm that enhances peptide identification by detecting co-fragmented peptides with high sensitivity and speed. Utilizing MSFragger's fragment ion indexing algorithm, MSFragger-DDA+ performs a comprehensive search within the full isolation window for each MS2, followed by robust feature detection, filtering, and rescoring procedures to refine search results. Evaluation against established tools across diverse datasets demonstrated that, integrated within the FragPipe computational platform, MSFragger-DDA+ significantly increases identification sensitivity while maintaining stringent false discovery rate (FDR) control. It is also uniquely suited for wide-window acquisition (WWA) data. MSFragger-DDA+ provides an efficient and accurate solution for peptide identification, enhancing the detection of low-abundance co-fragmented peptides. Coupled with the FragPipe platform, MSFragger-DDA+ enables more comprehensive and accurate analysis of proteomics data.

Trypsin Digestion Conditions of Human Plasma for Observation of Peptides and Proteins from Tandem Mass Spectrometry

Previous meta-analysis indicated that plasma or serum proteome groups using various experimental conditions detected different peptides from the same plasma proteins, which is strong evidence for the veracity of blood fluid LC-ESI-MS/MS but also evidences that the trypsin digestion step is a key source of variation in plasma proteomics. Agreement between different digestion conditions and MS/MS algorithms may serve as an independent confirmation of the validity of the LC-ESI-MS/MS analysis of plasma peptides. Plasma contains a high percentage of albumin held together by multiple disulfide bonds; hence, reduction and/or alkylation may greatly enhance the digestion efficiency of albumin. Plasma proteins were precipitated in 90% acetonitrile, collected over quaternary amine resin, and eluted in NaCl prior to digestion treatments. To determine the effect of trypsin digestion methods, the plasma proteins were digested in 600 mM urea and 5% acetonitrile with trypsin alone, or reduced with 2 mM DTT followed by trypsin, or DTT followed by 15 mM iodoacetamide and then trypsin. The resulting peptides were analyzed by LC-ESI-MS/MS with a linear quadrupole ion trap (LIT). The MS/MS spectra were directly fit to peptides by the X!TANDEM and SEQUEST algorithms. Blank noise injections served as the analytical control, and 30 million random MS/MS served as the statistical control. Digesting human plasma with DTT reduction, or reduction and alkylation, resulted in a dramatic increase in the number and observation frequency of albumin peptides. In contrast, digestion with trypsin alone suppressed the observation of albumin, and instead, many low abundance plasma and cellular proteins showed higher observation frequency. Digestion with trypsin alone increased the observation frequency of APOC1, ACAN, ATRN, CPB2, GP2, GPX3, HBA1, PAPD5, PKD1, and many cellular proteins. After correction against noise and random controls, SEQUEST showed good agreement with the true positive plasma proteins identified by X!TANDEM and resulted in an R-squared of 0.5238 with an F-statistic of 10,930 on 9,935 protein gene symbols with a p-value < 2.2e-16. Digestion of plasma with trypsin alone avoids the complete digestion of albumin and permits the enhanced detection of some other cellular proteins from plasma. Different digestion approaches were complimentary and together resulted in a more comprehensive plasma proteome. The protein FDR q-values, the modest effect of background and Monte Carlo correction, and the significant STRING analysis were all consistent with the high fidelity of the rigorous X!TANDEM algorithm. In contrast, SEQUEST required significant correction against noise and statistical controls and selection of high cross correlation (XCorr) scores to show good agreement with X!TANDEM. There was qualitative and quantitative agreement between plasma proteins digested without alkylation from the orbital ion trap (OIT) versus the LIT instrument that showed highly significant regression against the X!TANDEM OIT monoisotopic results, those from heavy isotopes and other masses from X!TANDEM, and with those from MaxQuant. There was significant qualitative and quantitative agreement between the complementary digestion conditions consistent with the good fidelity of plasma analysis by LC-ESI-MS/MS with a sensitive linear ion trap.

metaExpertPro: A Computational Workflow for Metaproteomics Spectral Library Construction and Data-Independent Acquisition Mass Spectrometry Data Analysis

Analysis of large-scale data-independent acquisition mass spectrometry metaproteomics data remains a computational challenge. Here, we present a computational pipeline called metaExpertPro for metaproteomics data analysis. This pipeline encompasses spectral library generation using data-dependent acquisition MS, protein identification and quantification using data-independent acquisition mass spectrometry, functional and taxonomic annotation, as well as quantitative matrix generation for both microbiota and hosts. By integrating FragPipe and DIA-NN, metaExpertPro offers compatibility with both Orbitrap and timsTOF MS instruments. To evaluate the depth and accuracy of identification and quantification, we conducted extensive assessments using human fecal samples and benchmark tests. Performance tests conducted on human fecal samples indicated that metaExpertPro quantified an average of 45,000 peptides in a 60-min diaPASEF injection. Notably, metaExpertPro outperformed three existing software tools by characterizing a higher number of peptides and proteins. Importantly, metaExpertPro maintained a low factual false discovery rate of approximately 5% for protein groups across four benchmark tests. Applying a filter of five peptides per genus, metaExpertPro achieved relatively high accuracy (F-score = 0.67-0.90) in genus diversity and showed a high correlation (rSpearman = 0.73-0.82) between the measured and true genus relative abundance in benchmark tests. Additionally, the quantitative results at the protein, taxonomy, and function levels exhibited high reproducibility and consistency across the commonly adopted public human gut microbial protein databases IGC and UHGP. In a metaproteomic analysis of dyslipidemia patients, metaExpertPro revealed characteristic alterations in microbial functions and potential interactions between the microbiota and the host.

A Moderate Water Deficit Induces Profound Changes in the Proteome of Developing Maize Ovaries

Water deficit is a major cause of yield loss for maize (Zea mays), leading to ovary abortion when applied at flowering time. To help understand the mechanisms involved in this phenomenon, the proteome response to water deficit has been analysed in developing ovaries at the silk emergence stage and five days later. Differential analysis, abundance pattern clustering and co-expression networks were performed in order to draw a general picture of the proteome changes all along ovary development and under the effect of water deficit. The results show that even mild water deficit has a major impact on ovary proteome, but this impact is very different from a response to stress. A part of the changes can be related to a slowdown of ovary development, while another part cannot. In particular, ovaries submitted to water deficit show an increase in proteins involved in protein biosynthesis and in vesicle transport together with a decrease in proteins involved in amino acid metabolism and proteolysis. According to the functions of increased proteins, the changes may be linked to auxin, brassinosteroids and jasmonate signalling but not abscisic acid.

Streptomyces small laccase expressed in Aspergillus Niger as a new addition for the lignocellulose bioconversion toolbox

Laccases are multi-copper oxidases that are usually composed of three Cu-oxidase domains. Domains one and three house the copper binding sites, and the second domain is involved in forming a substrate-binding cleft. However, Streptomyces species are found to have small laccases (SLAC) that lack one of the three Cu-oxidase domains. This type of SLAC with interesting lignocellulose bioconversion activities has not been reported in Aspergillus niger. In our research, we explored the expression and engineering of the SLAC from Streptomyces leeuwenhoekii C34 in A. niger. Genes encoding two versions of the SLAC were expressed. One encoding the SLAC in its native form and a second encoding the SLAC fused to two N-terminal CBM1 domains. The latter is a configuration also known for specific yeast laccases. Both SLAC variants were functionally expressed in A. niger as shown by in vitro activity assays and proteome analysis. Laccase activity was also analyzed toward bioconversion of lignocellulosic rice straw. From this analysis it was clear that the SLAC activity improved the efficiency of saccharification of lignocellulosic biomass by cellulase enzyme cocktails.

Dear-PSM: A deep learning-based peptide search engine enables full database search for proteomics

Peptide spectrum matching is the process of linking mass spectrometry data with peptide sequences. An experimental spectrum can match thousands of candidate peptides with variable modifications leading to an exponential increase in candidates. Completing the search within a limited time is a key challenge. Traditional searches expedite the process by restricting peptide mass errors and variable modifications, but this limits interpretive capability. To address this challenge, we propose Dear-PSM, a peptide search engine that supports full database searching. Dear-PSM does not restrict peptide mass errors, matching each spectrum to all peptides in the database and increasing the number of variable modifications per peptide from the conventional 3-20. Leveraging inverted index technology, Dear-PSM creates a high-performance index table of experimental spectra and utilizes deep learning algorithms for peptide validation. Through these techniques, Dear-PSM achieves a speed breakthrough 7 times faster than mainstream search engines on a regular desktop computer, with a remarkable 240-fold reduction in memory consumption. Benchmark test results demonstrate that Dear-PSM, in full database search mode, can reproduce over 90% of the results obtained by mainstream search engines when handling complex mass spectrometry data collected from different species using various instruments. Furthermore, it uncovers a substantial number of new peptides and proteins. Dear-PSM has been publicly released on the GitHub repository https://github.com/jianweishuai/Dear-PSM.

A new generation of nanobody research tools using improved mass spectrometry-based discovery methods

Single-domain antibodies ("nanobodies") derived from the variable region of camelid heavy-chain only antibody variants have proven to be widely useful tools for research, therapeutic, and diagnostic applications. In addition to traditional display techniques, methods to generate nanobodies using direct detection by mass spectrometry and DNA sequencing have been highly effective. However, certain technical challenges have limited widespread application. We have optimized a new pipeline for this approach that greatly improves screening sensitivity, depth of antibody coverage, antigen compatibility, and overall hit rate and affinity. We have applied this improved methodology to generate significantly higher affinity nanobody repertoires against widely used targets in biological research-i.e., GFP, tdTomato, GST, and mouse, rabbit, and goat immunoglobulin G. We have characterized these reagents in affinity isolations and tissue immunofluorescence microscopy, identifying those that are optimal for these particularly demanding applications, and engineering dimeric constructs for ultra-high affinity. This study thus provides new nanobody tools directly applicable to a wide variety of research problems, and improved techniques enabling future nanobody development against diverse targets.

SeFilter-DIA: Squeeze-and-Excitation Network for Filtering High-Confidence Peptides of Data-Independent Acquisition Proteomics

Mass spectrometry is crucial in proteomics analysis, particularly using Data Independent Acquisition (DIA) for reliable and reproducible mass spectrometry data acquisition, enabling broad mass-to-charge ratio coverage and high throughput. DIA-NN, a prominent deep learning software in DIA proteome analysis, generates peptide results but may include low-confidence peptides. Conventionally, biologists have to manually screen peptide fragment ion chromatogram peaks (XIC) for identifying high-confidence peptides, a time-consuming and subjective process prone to variability. In this study, we introduce SeFilter-DIA, a deep learning algorithm, aiming at automating the identification of high-confidence peptides. Leveraging compressed excitation neural network and residual network models, SeFilter-DIA extracts XIC features and effectively discerns between high and low-confidence peptides. Evaluation of the benchmark datasets demonstrates SeFilter-DIA achieving 99.6% AUC on the test set and 97% for other performance indicators. Furthermore, SeFilter-DIA is applicable for screening peptides with phosphorylation modifications. These results demonstrate the potential of SeFilter-DIA to replace manual screening, providing an efficient and objective approach for high-confidence peptide identification while mitigating associated limitations.

Predicting peptide properties from mass spectrometry data using deep attention-based multitask network and uncertainty quantification

Database search algorithms reduce the number of potential candidate peptides against which scoring needs to be performed using a single (i.e. mass) property for filtering. While useful, filtering based on one property may lead to exclusion of non-abundant spectra and uncharacterized peptides - potentially exacerbating the streetlight effect. Here we present ProteoRift, a novel attention and multitask deep-network, which can predict multiple peptide properties (length, missed cleavages, and modification status) directly from spectra. We demonstrate that ProteoRift can predict these properties with up to 97% accuracy resulting in search-space reduction by more than 90%. As a result, our end-to-end pipeline is shown to exhibit 8x to 12x speedups with peptide deduction accuracy comparable to algorithmic techniques. We also formulate two uncertainty estimation metrics, which can distinguish between in-distribution and out-of-distribution data (ROC-AUC 0.99) and predict high-scoring mass spectra against correct peptide (ROC-AUC 0.94). These models and metrics are integrated in an end-to-end ML pipeline available at https://github.com/pcdslab/ProteoRift.

Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.

Giant polyketide synthase enzymes in the biosynthesis of giant marine polyether toxins

Prymnesium parvum are harmful haptophyte algae that cause massive environmental fish kills. Their polyketide polyether toxins, the prymnesins, are among the largest nonpolymeric compounds in nature and have biosynthetic origins that have remained enigmatic for more than 40 years. In this work, we report the "PKZILLAs," massive P. parvum polyketide synthase (PKS) genes that have evaded previous detection. PKZILLA-1 and -2 encode giant protein products of 4.7 and 3.2 megadaltons that have 140 and 99 enzyme domains. Their predicted polyene product matches the proposed pre-prymnesin precursor of the 90-carbon-backbone A-type prymnesins. We further characterize the variant PKZILLA-B1, which is responsible for the shorter B-type analog prymnesin-B1, from P. parvum RCC3426 and thus establish a general model of haptophyte polyether biosynthetic logic. This work expands expectations of genetic and enzymatic size limits in biology.

Quantitative proteomic analysis of plasma membranes from the fish pathogen Saprolegnia parasitica reveals promising targets for disease control

The phylum Oomycota contains economically important pathogens of animals and plants, including Saprolegnia parasitica, the causal agent of the fish disease saprolegniasis. Due to intense fish farming and banning of the most effective control measures, saprolegniasis has re-emerged as a major challenge for the aquaculture industry. Oomycete cells are surrounded by a polysaccharide-rich cell wall matrix that, in addition to being essential for cell growth, also functions as a protective "armor." Consequently, the enzymes responsible for cell wall synthesis provide potential targets for disease control. Oomycete cell wall biosynthetic enzymes are predicted to be plasma membrane proteins. To identify these proteins, we applied a quantitative (iTRAQ) mass spectrometry-based proteomics approach to the plasma membrane of the hyphal cells of S. parasitica, providing the first complete plasma membrane proteome of an oomycete species. Of significance is the identification of 65 proteins enriched in detergent-resistant microdomains (DRMs). In silico analysis showed that DRM-enriched proteins are mainly involved in molecular transport and β-1,3-glucan synthesis, potentially contributing to pathogenesis. Moreover, biochemical characterization of the glycosyltransferase activity in these microdomains further supported their role in β-1,3-glucan synthesis. Altogether, the knowledge gained in this study provides a basis for developing disease control measures targeting specific plasma membrane proteins in S. parasitica.IMPORTANCEThe significance of this research lies in its potential to combat saprolegniasis, a detrimental fish disease, which has resurged due to intensive fish farming and regulatory restrictions. By targeting enzymes responsible for cell wall synthesis in Saprolegnia parasitica, this study uncovers potential avenues for disease control. Particularly noteworthy is the identification of several proteins enriched in membrane microdomains, offering insights into molecular mechanisms potentially involved in pathogenesis. Understanding the role of these proteins provides a foundation for developing targeted disease control measures. Overall, this research holds promise for safeguarding the aquaculture industry against the challenges posed by saprolegniasis.

EndoGenius: Optimized Neuropeptide Identification from Mass Spectrometry Datasets

Neuropeptides represent a unique class of signaling molecules that have garnered much attention but require special consideration when identifications are gleaned from mass spectra. With highly variable sequence lengths, neuropeptides must be analyzed in their endogenous state. Further, neuropeptides share great homology within families, differing by as little as a single amino acid residue, complicating even routine analyses and necessitating optimized computational strategies for confident and accurate identifications. We present EndoGenius, a database searching strategy designed specifically for elucidating neuropeptide identifications from mass spectra by leveraging optimized peptide-spectrum matching approaches, an expansive motif database, and a novel scoring algorithm to achieve broader representation of the neuropeptidome and minimize reidentification. This work describes an algorithm capable of reporting more neuropeptide identifications at 1% false-discovery rate than alternative software in five Callinectes sapidus neuronal tissue types.

Full Native timsTOF PASEF-Enabled Quantitative Proteomics with the i2MassChroQ Software Package

Ion mobility mass spectrometry has become popular in proteomics lately, in particular because the Bruker timsTOF instruments have found significant adoption in proteomics facilities. The Bruker's implementation of the ion mobility dimension generates massive amounts of mass spectrometric data that require carefully designed software both to extract meaningful information and to perform processing tasks at reasonable speed. In a historical move, the Bruker company decided to harness the skills of the scientific software development community by releasing to the public the timsTOF data file format specification. As a proteomics facility that has been developing Free Open Source Software (FOSS) solutions since decades, we took advantage of this opportunity to implement the very first FOSS proteomics complete solution to natively read the timsTOF data, low-level process them, and explore them in an integrated quantitative proteomics software environment. We dubbed our software i2MassChroQ because it implements a (peptide)identification-(protein)inference-mass-chromatogram-quantification processing workflow. The software benchmarking results reported in this paper show that i2MassChroQ performed better than competing software on two critical characteristics: (1) feature extraction capability and (2) protein quantitative dynamic range. Altogether, i2MassChroQ yielded better quantified protein numbers, both in a technical replicate MS runs setting and in a differential protein abundance analysis setting.

Autophagic signaling promotes systems-wide remodeling in skeletal muscle upon oncometabolic stress by D2-HG

OBJECTIVES

Cachexia is a metabolic disorder and comorbidity with cancer and heart failure. The syndrome impacts more than thirty million people worldwide, accounting for 20% of all cancer deaths. In acute myeloid leukemia, somatic mutations of the metabolic enzyme isocitrate dehydrogenase 1 and 2 cause the production of the oncometabolite D2-hydroxyglutarate (D2-HG). Increased production of D2-HG is associated with heart and skeletal muscle atrophy, but the mechanistic links between metabolic and proteomic remodeling remain poorly understood. Therefore, we assessed how oncometabolic stress by D2-HG activates autophagy and drives skeletal muscle loss.

METHODS

We quantified genomic, metabolomic, and proteomic changes in cultured skeletal muscle cells and mouse models of IDH-mutant leukemia using RNA sequencing, mass spectrometry, and computational modeling.

RESULTS

D2-HG impairs NADH redox homeostasis in myotubes. Increased NAD+ levels drive activation of nuclear deacetylase Sirt1, which causes deacetylation and activation of LC3, a key regulator of autophagy. Using LC3 mutants, we confirm that deacetylation of LC3 by Sirt1 shifts its distribution from the nucleus into the cytosol, where it can undergo lipidation at pre-autophagic membranes. Sirt1 silencing or p300 overexpression attenuated autophagy activation in myotubes. In vivo, we identified increased muscle atrophy and reduced grip strength in response to D2-HG in male vs. female mice. In male mice, glycolytic intermediates accumulated, and protein expression of oxidative phosphorylation machinery was reduced. In contrast, female animals upregulated the same proteins, attenuating the phenotype in vivo. Network modeling and machine learning algorithms allowed us to identify candidate proteins essential for regulating oncometabolic adaptation in mouse skeletal muscle.

CONCLUSIONS

Our multi-omics approach exposes new metabolic vulnerabilities in response to D2-HG in skeletal muscle and provides a conceptual framework for identifying therapeutic targets in cachexia.

Phenotypic, genotypic and proteomic variations between poor and robust colonizing Campylobacter jejuni strains

Campylobacter jejuni is one of the major causes of bacterial gastrointestinal disease in humans worldwide. This foodborne pathogen colonizes the intestinal tracts of chickens, and consumption of chicken and poultry products is identified as a common route of transmission. We analyzed two C. jejuni strains after oral challenge with 105 CFU/ml of C. jejuni per chick; one strain was a robust colonizer (A74/C) and the other a poor colonizer (A74/O). We also found extensive phenotypic differences in growth rate, biofilm production, and in vitro adherence, invasion, intracellular survival, and transcytosis. Strains A74/C and A74/O were genotypically similar with respect to their whole genome alignment, core genome, and ribosomal MLST, MLST, flaA, porA, and PFGE typing. The global proteomes of the two congenic strains were quantitatively analyzed by ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) and 618 and 453 proteins were identified from A74/C and A74/O isolates, respectively. Cluster of Orthologous Groups (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed that carbon metabolism and motility proteins were distinctively overexpressed in strain A74/C. The robust colonizer also exhibited a unique proteome profile characterized by significantly increased expression of proteins linked to adhesion, invasion, chemotaxis, energy, protein synthesis, heat shock proteins, iron regulation, two-component regulatory systems, and multidrug efflux pump. Our study underlines phenotypic, genotypic, and proteomic variations of the poor and robust colonizing C. jejuni strains, suggesting that several factors may contribute to mediating the different colonization potentials of the isogenic isolates.

Alternative proteoforms and proteoform-dependent assemblies in humans and plants

The variability of proteins at the sequence level creates an enormous potential for proteome complexity. Exploring the depths and limits of this complexity is an ongoing goal in biology. Here, we systematically survey human and plant high-throughput bottom-up native proteomics data for protein truncation variants, where substantial regions of the full-length protein are missing from an observed protein product. In humans, Arabidopsis, and the green alga Chlamydomonas, approximately one percent of observed proteins show a short form, which we can assign by comparison to RNA isoforms as either likely deriving from transcript-directed processes or limited proteolysis. While some detected protein fragments align with known splice forms and protein cleavage events, multiple examples are previously undescribed, such as our observation of fibrocystin proteolysis and nuclear translocation in a green alga. We find that truncations occur almost entirely between structured protein domains, even when short forms are derived from transcript variants. Intriguingly, multiple endogenous protein truncations of phase-separating translational proteins resemble cleaved proteoforms produced by enteroviruses during infection. Some truncated proteins are also observed in both humans and plants, suggesting that they date to the last eukaryotic common ancestor. Finally, we describe novel proteoform-specific protein complexes, where the loss of a domain may accompany complex formation.

Mitochondrial inorganic polyphosphate is required to maintain proteostasis within the organelle

The existing literature points towards the presence of robust mitochondrial mechanisms aimed at mitigating protein dyshomeostasis within the organelle. However, the precise molecular composition of these mechanisms remains unclear. Our data show that inorganic polyphosphate (polyP), a polymer well-conserved throughout evolution, is a component of these mechanisms. In mammals, mitochondria exhibit a significant abundance of polyP, and both our research and that of others have already highlighted its potent regulatory effect on bioenergetics. Given the intimate connection between energy metabolism and protein homeostasis, the involvement of polyP in proteostasis has also been demonstrated in several organisms. For example, polyP is a bacterial primordial chaperone, and its role in amyloidogenesis has already been established. Here, using mammalian models, our study reveals that the depletion of mitochondrial polyP leads to increased protein aggregation within the organelle, following stress exposure. Furthermore, mitochondrial polyP is able to bind to proteins, and these proteins differ under control and stress conditions. The depletion of mitochondrial polyP significantly affects the proteome under both control and stress conditions, while also exerting regulatory control over gene expression. Our findings suggest that mitochondrial polyP is a previously unrecognized, and potent component of mitochondrial proteostasis.