Mutations in the POMT1 gene, encoding a protein O-mannosyltransferase essential for α-dystroglycan (α-DG) glycosylation, are frequently observed in a group of rare congenital muscular dystrophies, collectively known as dystroglycanopathies. However, it is hitherto unclear whether the effects seen in affected patients can be fully ascribed to α-DG hypoglycosylation. To study this, here we used comparative mass spectrometry-based proteomics and immunofluorescence microscopy and investigated the changes in the retina of mice in which Pomt1 is specifically knocked out in photoreceptor cells. Our results demonstrate significant proteomic changes and associated structural alteration in photoreceptor cells of Pomt1 cKO mice. In addition to the effects related to impaired α-DG O-mannosylation, we observed morphological alterations in the outer segment that are associated with dysregulation of a relatively understudied POMT1 substrate (KIAA1549), BBSome proteins, and retinal stress markers. In conclusion, our study provides new hypotheses to explain the phenotypic changes that are observed in the retina of patients with dystroglycanopathies.

Glycosylation is one of the most common post-translational modifications (PTM) occurring in a large variety of proteins with important biological functions in human and other higher organisms. Liquid chromatography tandem mass spectrometry (LC-MS/MS) has been routinely used to characterize site-specific protein glycosylation at high throughput in complex glycoproteomic samples. Recently, electron transfer/high-energy collision dissociation (EThcD) was introduced for glycopeptide identification, which offers rich structural information on glycopepides with the fragment ions from the cleavages of both the glycan and the peptide backbone. Herein, we present the software GlycoHybridSeq for automated interpretation of EThcD-MS/MS spectra from glycoproteomic data using a customized scoring function, which enables the functionalities of identifying glycopeptides, characterizing glycosylation sites, and distinguishing some isomeric glycans. We evaluate GlycoHybridSeq on glycoproteomic data collected for cancer biomarker discovery. The results showed that it achieved comparable or better performance than that of Byonic and MSFragger. GlycoHybridSeq is released as an open source software and is ready to be used in large-scale glycoproteomic data analyses.

We present a multivariate metabotyping approach to assess the functional recovery of nonhospitalized COVID-19 patients and the possible biochemical sequelae of "Post-Acute COVID-19 Syndrome", colloquially known as long-COVID. Blood samples were taken from patients ca. 3 months after acute COVID-19 infection with further assessment of symptoms at 6 months. Some 57% of the patients had one or more persistent symptoms including respiratory-related symptoms like cough, dyspnea, and rhinorrhea or other nonrespiratory symptoms including chronic fatigue, anosmia, myalgia, or joint pain. Plasma samples were quantitatively analyzed for lipoproteins, glycoproteins, amino acids, biogenic amines, and tryptophan pathway intermediates using Nuclear Magnetic Resonance (NMR) spectroscopy and mass spectrometry. Metabolic data for the follow-up patients (n = 27) were compared with controls (n = 41) and hospitalized severe acute respiratory syndrome SARS-CoV-2 positive patients (n = 18, with multiple time-points). Univariate and multivariate statistics revealed variable patterns of functional recovery with many patients exhibiting residual COVID-19 biomarker signatures. Several parameters were persistently perturbed, e.g., elevated taurine (p = 3.6 × 10-3 versus controls) and reduced glutamine/glutamate ratio (p = 6.95 × 10-8 versus controls), indicative of possible liver and muscle damage and a high energy demand linked to more generalized tissue repair or immune function. Some parameters showed near-complete normalization, e.g., the plasma apolipoprotein B100/A1 ratio was similar to that of healthy controls but significantly lower (p = 4.2 × 10-3) than post-acute COVID-19 patients, reflecting partial reversion of the metabolic phenotype (phenoreversion) toward the healthy metabolic state. Plasma neopterin was normalized in all follow-up patients, indicative of a reduction in the adaptive immune activity that has been previously detected in active SARS-CoV-2 infection. Other systemic inflammatory biomarkers such as GlycA and the kynurenine/tryptophan ratio remained elevated in some, but not all, patients. Correlation analysis, principal component analysis (PCA), and orthogonal-partial least-squares discriminant analysis (O-PLS-DA) showed that the follow-up patients were, as a group, metabolically distinct from controls and partially comapped with the acute-phase patients. Significant systematic metabolic differences between asymptomatic and symptomatic follow-up patients were also observed for multiple metabolites. The overall metabolic variance of the symptomatic patients was significantly greater than that of nonsymptomatic patients for multiple parameters (χ2p = 0.014). Thus, asymptomatic follow-up patients including those with post-acute COVID-19 Syndrome displayed a spectrum of multiple persistent biochemical pathophysiology, suggesting that the metabolic phenotyping approach may be deployed for multisystem functional assessment of individual post-acute COVID-19 patients.

Discovery of variant peptides such as a single amino acid variant (SAAV) in shotgun proteomics data is essential for personalized proteomics. Both the resolution of shotgun proteomics methods and the search engines have improved dramatically, allowing for confident identification of SAAV peptides. However, it is not yet known if these methods are truly successful in accurately identifying SAAV peptides without prior genomic information in the search database. We studied this in unprecedented detail by exploiting publicly available long-read RNA sequences and shotgun proteomics data from the gold standard reference cell line NA12878. Searching spectra from this cell line with the state-of-the-art open modification search engine ionbot against carefully curated search databases resulted in 96.7% false-positive SAAVs and an 85% lower true positive rate than searching with peptide search databases that incorporate prior genetic information. While adding genetic variants to the search database remains indispensable for correct peptide identification, inclusion of long-read RNA sequences in the search database contributes only 0.3% new peptide identifications. These findings reveal the differences in SAAV detection that result from various approaches, providing guidance to researchers studying SAAV peptides and developers of peptide spectrum identification tools.

Phosphoinositides are minor components of cell membranes, but play crucial roles in numerous signal transduction pathways. To obtain quantitative measures of phosphoinositides, sensitive, accurate, and comprehensive methods are needed. Here, we present a quantitative targeted ion chromatography-mass spectrometry-based workflow that separates phosphoinositide isomers and increases the quantitative accuracy of measured phosphoinositides. Besides testing different analytical characteristics such as extraction and separation efficiency, the reproducibility of the developed workflow was also investigated. The workflow was verified in resting and stimulated human platelets, fat cells, and rat hippocampal brain tissue, where the LOD and LOQ for phosphoinositides were at 312.5 and 625 fmol, respectively. The robustness of the workflow is shown with different applications that confirms its suitability to analyze multiple less-abundant phosphoinositides.

Changes in N-glycosylation on specific peptide sites of serum proteins have been investigated as potential markers for diagnosis of nonalcoholic steatohepatitis (NASH)-related HCC. To accomplish this work, a novel workflow involving broad-scale marker discovery in serum followed by targeted marker evaluation of these glycopeptides were combined. The workflow involved an LC-Stepped HCD-DDA-MS/MS method coupled with offline peptide fractionation for large-scale identification of N-glycopeptides directly from pooled serum samples (each n = 10) as well as differential determination of N-glycosylation changes between disease states. We then evaluated several potentially diagnostic N-glycopeptides among 78 individual patient samples (40 cirrhosis, 28 early stage NASH HCC, and 10 late-stage NASH HCC) by LC-Stepped HCD-PRM-MS/MS to quantitatively analyze 65 targeted glycopeptides from 7 glycoproteins. Of these targets, we found site-specific N-glycopeptides n169GSLFAFR_HexNAc(4)Hex(5)NeuAc(2) and n242ISDGFDGIPDNVDAALALPAHSYSGR_HexNAc(5)Hex(6)Fuc(1)NeuAc(3) from VTNC were significantly increased comparing samples from patients with NASH cirrhosis and NASH HCC (p < 0.05). When combining results of these 2 glycopeptides with AFP, the ROC curve analysis demonstrated the AUC value increased to 0.834 (95% CI, 0.748-0.921) and 0.847 (95% CI, 0.766-0.932), respectively, as compared to that of AFP alone (AUC = 0.791, 95% CI, 0.690-0.892). These 2 glycopeptides may serve as potential biomarkers for early HCC diagnosis in patients with NASH related cirrhosis.

While mass spectrometry still dominates proteomics research, alternative and potentially disruptive, next-generation technologies are receiving increased investment and attention. Most of these technologies aim at the sequencing of single peptide or protein molecules, typically labeling or otherwise distinguishing a subset of the proteinogenic amino acids. This note considers some theoretical aspects of these future technologies from a bottom-up proteomics viewpoint, including the ability to uniquely identify human proteins as a function of which and how many amino acids can be read, enzymatic efficiency, and the maximum read length. This is done through simulations under ideal and non-ideal conditions to set benchmarks for what may be achievable with future single-molecule sequencing technology. The simulations reveal, among other observations, that the best choice of reading N amino acids performs similarly to the average choice of N+1 amino acids, and that the discrimination power of the amino acids scales with their frequency in the proteome. The simulations are agnostic with respect to the next-generation proteomics platform, and the results and conclusions should therefore be applicable to any single-molecule partial peptide sequencing technology.

The fields of application of functional proteomics are not limited to the study of protein-protein interactions; they also extend to those involving protein complexes that bind DNA or RNA. These interactions affect fundamental processes such as replication, transcription, and repair in the case of DNA, as well as transport, translation, splicing, and silencing in the case of RNA. Analytical or preparative experimental approaches, both in vivo and in vitro, have been developed to isolate and identify DNA/RNA binding proteins by exploiting the advantage of the affinity shown by these proteins toward a specific oligonucleotide sequence. The present review proposes an overview of the approaches most commonly employed in proteomics applications for the identification of nucleic acid-binding proteins, such as affinity purification (AP) protocols, EMSA, chromatin purification methods, and CRISPR-based chromatin affinity purification, which are generally associated with mass spectrometry methodologies for the unbiased protein identification.