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Dermatomyositis and polymyositis (DM/PM) are systemic autoimmune diseases characterized by proximal muscle weakness. The underlying pathogenetic mechanism of this disease remains under-researched. Here, using proteomics analysis, a great overlap of differentially expressed plasma exosomal proteins involved in the complement and coagulation cascade pathway, including FGA, FGB, FGG, C1QB, C1QC, and VWF, was identified in DM/PM patients versus healthy controls. Correlation analysis showed that the expression levels of complement-associated proteins (C1QB and C1QC) correlated positively with CRP, ESR, and platelet count. ROC curve analysis demonstrated that complement and coagulation cascade-associated proteins could be strong predictors for DM/PM. In addition, we also identified several other proteins that were differentially expressed in DM and PM. The selected candidate proteins were further validated by parallel reaction monitoring (PRM) and enzyme-linked immunosorbent assay (ELISA). Together, our findings indicate that these exosome-derived proteins might participate in microvascular damage in DM/PM through the activation of the complement and coagulation cascade pathway and function as biomarkers for the clinical diagnosis of DM/PM.

Elevated serum alpha-fetoprotein (AFP) can be observed in liver cirrhosis and hepatocellular carcinoma (HCC). The glycosylation patterns of AFP have been shown to differentiate these conditions, with AFP glycoforms with core fucosylation (AFP-L3) serving as a malignancy risk predictor for HCC. We have developed a method to detect endogenously present AFP proteoforms and to quantify the relative abundance of AFP-L3 glycoforms (AFP-L3%) in serum samples. This method consists of immune enrichment of endogenous AFP, followed by liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) intact protein analysis of AFP. Data are available via ProteomeXchange with identifier PXD038606. Based on the AFP profiles in authentic patient serum samples, we have identified that the frequently observed AFP glycoforms without core fucosylation (AFP-L1) are G2S2 and G2S1, and common AFP-L3 glycoforms are G2FS1 and G2FS2. The intensities of glycoforms in the deconvoluted spectrum are used to quantify AFP-L3% in each sample. The method evaluation included reproducibility, specificity, dilution integrity, and comparison of AFP-L3% with a lectin-binding gel shift electrophoresis (GSE) assay. The AFP-L1 and AFP-L3 proteoforms were reproducibly identified in multiple patient serum samples, resulting in reproducible AFP-L3% quantification. There was considerable agreement between the developed LC-HRMS and commercial GSE methods when quantifying AFP-L3% (Pearson r = 0.63) with a proportional bias.

The development of metastasis severely reduces the life expectancy of patients with colorectal cancer (CRC). Although loss of SMAD4 is a key event in CRC progression, the resulting changes in biological processes in advanced disease and metastasis are not fully understood. Here, we applied a multiomics approach to a CRC organoid model that faithfully reflects the metastasis-supporting effects of SMAD4 inactivation. We show that loss of SMAD4 results in decreased differentiation and activation of pro-migratory and cell proliferation processes, which is accompanied by the disruption of several key oncogenic pathways, including the TGFβ, WNT, and VEGF pathways. In addition, SMAD4 inactivation leads to increased secretion of proteins that are known to be involved in a variety of pro-metastatic processes. Finally, we show that one of the factors that is specifically secreted by SMAD4-mutant organoids─DKK3─reduces the antitumor effects of natural killer cells (NK cells). Altogether, our data provide new insights into the role of SMAD4 perturbation in advanced CRC.

Leveraging biased signaling of G protein-coupled receptors has been proposed as a promising strategy for the development of drugs with higher specificity. However, the consequences of selectively targeting G protein- or β-arrestin-mediated signaling on cellular functions are not comprehensively understood. In this study, we utilized phosphoproteomics to gain a systematic overview of signaling induced by the four biased and balanced dopamine D2 receptor (D2R) ligands MS308, BM138, quinpirole, and sulpiride in an in vitro D2R transfection model. Quantification of 14,160 phosphosites revealed a low impact of the partial G protein agonist MS308 on cellular protein phosphorylation, as well as surprising similarities between the balanced agonist quinpirole and the inverse agonist sulpiride. Analysis of the temporal profiles of ligand-induced phosphorylation events showed a transient impact of the G protein-selective agonist MS308, whereas the β-arrestin-preferring agonist BM138 elicited a delayed, but more pronounced response. Functional enrichment analysis of ligand-impacted phosphoproteins and treatment-linked kinases confirmed multiple known functions of D2R signaling while also revealing novel effects, for example of MS308 on sterol regulatory element-binding protein-related gene expression. All raw data were deposited in MassIVE (MSV000089457).

Metabolic perturbations and inflammatory mediators play a fundamental role in both early and late adverse post-acute ischemic stroke outcomes. Using data from the observational MAGIC (MArker bioloGici nell'Ictus Cerebrale) study, we evaluated the effect of 130 serum metabolic features, using a nuclear magnetic spectroscopy approach, on the following outcomes: hemorrhagic transformation at 24 h after stroke, non-response to intravenous thrombolytic treatment with the recombinant tissue plasminogen activator (rt-PA), and the 3 month functional outcome. Blood circulating metabolites, lipoproteins, and inflammatory markers were assessed at the baseline and 24 h after rt-PA treatment. Adjusting for the major determinants for unfavorable outcomes (i.e., age, sex, time onset-to-treatment, etc.), we found that acetone and 3-hydroxybutyrate were associated with symptomatic hemorrhagic transformation and with non-response to rt-PA; while 24 h after rt-PA, levels of triglycerides high-density lipoprotein (HDL) and triglycerides low-density lipoprotein (LDL) were associated with 3 month mortality. Cholesterol and phospholipids levels, mainly related to smaller and denser very low-density lipoprotein (VLDL) and LDL subfractions were associated with 3 month poor functional outcomes. We also reported associations between baseline 24 h relative variation (Δ) in VLDL subfractions and ΔC-reactive protein, Δinterleukin-10 levels with hemorrhagic transformation. All observed metabolic changes reflect a general condition of energy failure, oxidative stress, and systemic inflammation that characterize the development of adverse outcomes.

The structure of a protein defines its function and integrity and correlates with the protein folding stability (PFS). Quantifying PFS allows researchers to assess differential stability of proteins in different disease or ligand binding states, providing insight into protein efficacy and potentially serving as a metric of protein quality. There are a number of mass spectrometry (MS)-based methods to assess PFS, such as Thermal Protein Profiling (TPP), Stability of Proteins from Rates of Oxidation (SPROX), and Iodination Protein Stability Assay (IPSA). Despite the critical value that PFS studies add to the understanding of mechanisms of disease and treatment development, proteomics research is still primarily dominated by concentration-based studies. We found that a major reason for the lack of PFS studies is the lack of a user-friendly data processing tool. Here we present the first user-friendly software, CHalf, with a graphical user interface for calculating PFS. Besides calculating site-specific PFS of a given protein from chemical denature folding stability assays, CHalf is also compatible with thermal denature folding stability assays. CHalf also includes a set of data visualization tools to help identify changes in PFS across protein sequences and in between different treatment conditions. We expect the introduction of CHalf to lower the barrier of entry for researchers to investigate PFS, promoting the usage of PFS in studies. In the long run, we expect this increase in PFS research to accelerate our understanding of the pathogenesis and pathophysiology of disease.

We have developed the underrepresented post-translational modification (PTM) database (urPTMdb), a PTM gene set database to accelerate the discovery of enriched protein modifications in experimental data. urPTMdb provides curated lists of proteins reported to be substrates of underrepresented modifications. Their enrichment in proteomics datasets can reveal unexpected PTM regulations. urPTMdb can be implemented in existing workflows, or used in TeaProt, an online Shiny tool that integrates upstream transcription factor enrichment analysis with downstream pathway analysis through an easy-to-use interactive interface. TeaProt annotates user-uploaded data with drug-gene interactions, subcellular localizations, phenotypic functions, gene-disease associations, and enzyme-gene interactions. TeaProt enables gene set enrichment analysis (GSEA) to discover enrichments in gene sets from various resources, including MSigDB, CHEA, and urPTMdb. We demonstrate the utility of urPTMdb and TeaProt through the analysis of a previously published Western diet-induced remodeling of the tongue proteome, which revealed altered cellular processes associated with energy metabolism, interferon alpha/gamma response, adipogenesis, HMGylation substrate enrichment, and transcription regulation through PPARG and CEBPA. Additionally, we analyzed the interactome of ADP-ribose glycohydrolase TARG1, a key enzyme that removes mono-ADP-ribosylation. This analysis identified an enrichment of ADP-ribosylation, ribosomal proteins, and proteins localized in the nucleoli and endoplasmic reticulum. TeaProt and urPTMdb are accessible at https://tea.coffeeprot.com/.

Protein quantitation via mass spectrometry relies on peptide proxies for the parent protein from which abundances are estimated. Owing to the variability in signal from individual peptides, accurate absolute quantitation usually relies on the addition of an external standard. Typically, this involves stable isotope-labeled peptides, delivered singly or as a concatenated recombinant protein. Consequently, the selection of the most appropriate surrogate peptides and the attendant design in recombinant proteins termed QconCATs are challenges for proteome science. QconCATs can now be built in a "a-la-carte" assembly method using synthetic biology: ALACATs. To assist their design, we present "AlacatDesigner", a tool that supports the peptide selection for recombinant protein standards based on the user's target protein. The user-customizable tool considers existing databases, occurrence in the literature, potential post-translational modifications, predicted miscleavage, predicted divergence of the peptide and protein quantifications, and ionization potential within the mass spectrometer. We show that peptide selections are enriched for good proteotypic and quantotypic candidates compared to empirical data. The software is freely available to use either via a web interface AlacatDesigner, downloaded as a Desktop application or imported as a Python package for the command line interface or in scripts.

It has long been known that biological species can be identified from mass spectrometry data alone. Ten years ago, we described a method and software tool, compareMS2, for calculating a distance between sets of tandem mass spectra, as routinely collected in proteomics. This method has seen use in species identification and mixture characterization in food and feed products, as well as other applications. Here, we present the first major update of this software, including a new metric, a graphical user interface and additional functionality. The data have been deposited to ProteomeXchange with dataset identifier PXD034932.

Spectral library search can enable more sensitive peptide identification in tandem mass spectrometry experiments. However, its drawbacks are the limited availability of high-quality libraries and the added difficulty of creating decoy spectra for result validation. We describe MS Ana, a new spectral library search engine that enables high sensitivity peptide identification using either curated or predicted spectral libraries as well as robust false discovery control through its own decoy library generation algorithm. MS Ana identifies on average 36% more spectrum matches and 4% more proteins than database search in a benchmark test on single-shot human cell-line data. Further, we demonstrate the quality of the result validation with tests on synthetic peptide pools and show the importance of library selection through a comparison of library search performance with different configurations of publicly available human spectral libraries.

Stochastic, intensity-based precursor isolation can result in isotopically enriched fragment ions. This problem is exacerbated for large peptides and stable isotope labeling experiments using deuterium or 15N. For stable isotope labeling experiments, incomplete and ubiquitous labeling strategies result in the isolation of peptide ions composed of many distinct structural isomers. Unfortunately, existing proteomics search algorithms do not account for this variability in isotopic incorporation, and thus often yield poor peptide and protein identification rates. We sought to resolve this shortcoming by deriving the expected isotopic distributions of each fragment ion and incorporating them into the theoretical mass spectra used for peptide-spectrum-matching. We adapted the Comet search platform to integrate a modified spectral prediction algorithm we term Conditional fragment Ion Distribution Search (CIDS). Comet-CIDS uses a traditional database searching strategy, but for each candidate peptide we compute the isotopic distribution of each fragment to better match the observed m/z distributions. Evaluating previously generated D2O and 15N labeled data sets, we found that Comet-CIDS identified more confident peptide spectral matches and higher protein sequence coverage compared to traditional theoretical spectra generation, with the magnitude of improvement largely determined by the amount of labeling in the sample.

Biological networks are often used to represent complex biological systems, which can contain several types of entities. Analysis and visualization of such networks is supported by the Cytoscape software tool and its many apps. While earlier versions of stringApp focused on providing intraspecies protein-protein interactions from the STRING database, the new stringApp 2.0 greatly improves the support for heterogeneous networks. Here, we highlight new functionality that makes it possible to create networks that contain proteins and interactions from STRING as well as other biological entities and associations from other sources. We exemplify this by complementing a published SARS-CoV-2 interactome with interactions from STRING. We have also extended stringApp with new data and query functionality for protein-protein interactions between eukaryotic parasites and their hosts. We show how this can be used to retrieve and visualize a cross-species network for a malaria parasite, its host, and its vector. Finally, the latest stringApp version has an improved user interface, allows retrieval of both functional associations and physical interactions, and supports group-wise enrichment analysis of different parts of a network to aid biological interpretation. stringApp is freely available at https://apps.cytoscape.org/apps/stringapp.