Journal of Proteome Research最新文献

Biorthogonal noncanonical amino acid tagging is a labeling strategy that covalently adds a biotin-alkyne (BA) to methionine analogs via a click reaction. When methionine analogs are incorporated into a proteome, enrichment of the BA-labeled proteins allows the detection of newly synthesized proteins (NSP) by mass spectrometry. We previously reported that our direct detection of biotin-containing tags strategy increased protein identifications by enriching for BA-peptides instead of BA-proteins. We compared the performance of cleavable BA (DADPS) and uncleavable BA in the identification and TMT quantification of the NSP. DADPS identified and quantified more than double the number of peptides than uncleavable BA. Interrogation of the data revealed that multiple factors are responsible for the superior performance of DADPS.

Malaria is a deadly disease caused by Apicomplexan parasites of the Plasmodium genus. Several species of the Plasmodium genus are known to be infectious to humans, of which P. falciparum is the most virulent. Post-translational modifications (PTMs) of proteins coordinate cell signaling and hence regulate many biological processes in P. falciparum homeostasis and host infection, of which the most highly studied is phosphorylation. Phosphosites on proteins can be identified by tandem mass spectrometry (MS) performed on enriched samples (phosphoproteomics), followed by downstream computational analyses. We have performed a large-scale meta-analysis of 11 publicly available phosphoproteomics data sets to build a comprehensive atlas of phosphosites in the P. falciparum proteome, using robust pipelines aimed at strict control of false identifications. We identified a total of 26,609 phosphorylated sites on P. falciparum proteins, split across three categories of data reliability (gold/silver/bronze). We identified significant sequence motifs, likely indicative of different groups of kinases responsible for different groups of phosphosites. Conservation analysis identified clusters of phosphoproteins that are highly conserved and others that are evolving faster within the Plasmodium genus, and implicated in different pathways. We were also able to identify over 180,000 phosphosites within Plasmodium species beyond falciparum, based on orthologue mapping. We also explored the structural context of phosphosites, identifying a strong enrichment for phosphosites on fast-evolving (low conservation) intrinsically disordered regions (IDRs) of proteins. In other species, IDRs have been shown to have an important role in modulating protein-protein interactions, particularly in signaling, and thus warranting further study for their roles in host-pathogen interactions. All data have been made available via UniProtKB, PRIDE, and PeptideAtlas, with visualization interfaces for exploring phosphosites in the context of other data on Plasmodium proteins.

In modern biomedical research, cultivable cell lines are an indispensable tool, and the selection of cell lines that exhibit specific functional profiles is often critical to success. Cellular functional pathways have evolved through the selection of protein intra- and intermolecular interactions collectively referred to as the interactome. In the present work, quantitative in vivo protein cross-linking and mass spectrometry were used to probe large-scale protein interactome differences among three commonly employed human cell lines, namely, HEK293, MCF-7, and HeLa cells. These data illustrated highly reproducible quantitative interactome levels with R² values larger than 0.8 for all biological replicates. Proteome abundance levels were also measured using data-independent acquisition quantitative proteomics methods. Combining quantitative interactome and proteome information allowed the visualization of cell type-specific interactome changes mediated by proteome level adaptations and independently regulated interactome changes to gain deeper insight into possible drivers of these changes. Among the largest detected alterations in protein interactions and conformations are changes in cytoskeletal proteins, RNA-binding proteins, chromatin remodeling complexes, mitochondrial proteins, and others. Overall, these data demonstrate the utility and reproducibility of quantitative cross-linking to study system-level interactome variations. Moreover, these results illustrate how combined quantitative interactomics and proteomics can provide unique insight into cellular functional landscapes.

Protein glycosylation is a complex post-translational modification that is generally classified as N- or O-linked. Site-specific analysis of glycopeptides is accomplished with a variety of fragmentation methods, depending on the type of glycosylation being investigated and the instrumentation available. For instance, collisional dissociation methods are frequently used for N-glycoproteomic analysis with the assumption that one N-sequon exists per tryptic peptide. Alternatively, electron-based methods are preferable for O-glycosite localization. However, the presence of simultaneously N- and O-glycosylated peptides could suggest the necessity of electron-based fragmentation methods for N-glycoproteomics, which is not commonly performed. Thus, we quantified the prevalence of N- and O-glycopeptides in mucins and other glycoproteins. A much higher frequency of co-occupancy within mucins was detected whereas only a negligible occurrence occurred within nonmucin glycoproteins. This was demonstrated from analyses of recombinant and/or purified proteins, as well as more complex samples. Where co-occupancy occurred, O-glycosites were frequently localized to the Ser/Thr within the N-sequon. Additionally, we found that O-glycans in close proximity to the occupied Asn were predominantly unelaborated core 1 structures, while those further away were more extended. Overall, we demonstrate electron-based methods are required for robust site-specific analysis of mucins, wherein co-occupancy is more prevalent. Conversely, collisional methods are generally sufficient for analyses of other types of glycoproteins.

The disruption of gut microbiota caused by antibiotics favors the intestinal colonization of Clostridioides difficile - a Gram-positive, spore-forming anaerobic bacterium that causes potentially fatal gastrointestinal infections. In an endeavor to elucidate the complexities of the gut-brain axis in the context of Clostridium difficile infection (CDI), a murine model has been used to investigate the potential effects of antibiotic administration and subsequent colonization by C. difficile, as well as the impact of three different 10-day treatments (metronidazole, probiotics, and fecal microbiota transplantation), on the cecal metabolome for the first time. This follows our previous research which highlighted the metabolic effect of CDI and these treatments in the brain and employs the same four different metabolomics-based methods (targeted GC-MS/MS, targeted HILIC-MS/MS, untargeted RP-LC-HRMS/MS and untargeted GC-MS). A total of 286 unique metabolites have been identified in the mouse cecal profiles and statistical analysis revealed that CDI, as well as the subsequent treatments, significantly alters cecal metabolites and lipids implicated in various biochemical pathways centered around amino acid metabolism, glycerophospholipid metabolism, and central carbon metabolism. To our knowledge, this study represents the first exploration of the effects of C. difficile-induced colitis and potential treatments on the cecal tissue metabolome.

Owing to the role of the 20S proteasome in a wide spectrum of pathologies, including neurodegenerative disorders, proteasome-associated autoinflammatory syndromes (PRAAS), and cardiovascular diseases, understanding how its structure and composition contribute to dysfunction is crucial. As a 735 kDa protein assembly, the 20S proteasome facilitates normal cellular proteostasis by degrading oxidized and misfolded proteins. Declined proteasomal activity, which can be attributed to perturbations in the structural integrity of the 20S proteasome, is considered one of the main contributors to multiple proteasome-related diseases. Devising methods to characterize the structures of 20S proteasomes provides necessary insight for the development of drugs and inhibitors that restore proper proteasomal function. Here, native mass spectrometry was combined with multiple dissociation techniques, including ultraviolet photodissociation (UVPD), to identify the protein subunits comprising the 20S proteasome. UVPD, demonstrating an ability to uncover structural features of large (>300 kDa) macromolecular complexes, provided complementary information to conventional collision-based methods. Additionally, variable-temperature electrospray ionization was combined with UV photoactivation to study the influence of solution temperature on the stability of the 20S proteasome.

We aimed to investigate the correlation between plasma proteins and metabolites and the occurrence of future strokes using mass spectrometry and bioinformatics as well as to identify other biomarkers that could predict stroke risk in hypertensive patients. In a nested case-control study, baseline plasma samples were collected from 50 hypertensive subjects who developed stroke and 50 gender-, age- and body mass index-matched controls. Plasma untargeted metabolomics and data independent acquisition-based proteomics analysis were performed in hypertensive patients, and 19 metabolites and 111 proteins were found to be differentially expressed. Integrative analyses revealed that molecular changes in plasma indicated dysregulation of protein digestion and absorption, salivary secretion, and regulation of actin cytoskeleton, along with significant metabolic suppression. C4BPA, Caprolactam, Col15A1, and HBB were identified as predictors of stroke occurrence, and the Support Vector Machines (SVM) model was determined to be the optimal predictive model by integrating six machine-learning classification models. The SVM model showed strong performance in both the internal validation set (area under the curve [AUC]: 0.977, 95% confidence interval [CI]: 0.941-1.000) and the external independent validation set (AUC: 0.973, 95% CI: 0.921-0.999).

Throughout the past decade, technological advances in genomics and transcriptomics have revealed pervasive translation throughout mammalian genomes. These putative proteins are usually excluded from proteomics analyses, as they are absent from common protein repositories. A sizable portion of these noncanonical proteins is translated from pseudogenes. Pseudogenes are commonly termed defective copies of coding genes unable to produce proteins. Here, we suggest that proteomics can help in their annotation. First, we define important terms and review specific examples underlining the caveats in pseudogene annotation and their coding potential. Then, we will discuss the challenges inherent to pseudogenes that have thus far rendered complex their confidence in omics data. Finally, we identify recent developments in experimental procedures, instrumentation, and computational methods in proteomics that put the field in a unique position to solve the pseudogene annotation conundrum.