Molecular & Cellular Proteomics最新文献

Obesity remains a worldwide health issue, with visceral adipose tissue as a leading driver of this pathology. As the executors of biological functions in living cells, proteins have their activity regulated by diverse post-translational modifications, including ubiquitination. However, obesity-related changes in ubiquitination of visceral adipose tissue (VAT) proteins are still poorly understood. Here, we obtained the global proteomic and ubiquitylomic data of epididymal VAT from lean and obese male mice by mass spectrometry. Our proteomic analyses revealed significant changes of metabolic pathways involved in fatty acid, acyl-CoA and branched chain amino acids metabolism in obese VAT. Intriguingly, a comparative analysis of proteomic and ubiquitylomic data highlighted discordance in the quantity changes of certain proteins and their ubiquitination levels. Notably, STEAP4 exhibited a markedly reduced protein level coupled with an enhanced K48-linked ubiquitination, suggesting a potential role for ubiquitination-mediated proteasome degradation in VAT dysfunction. Further in vitro experiments revealed that knockdown of STEAP4 in adipocytes impaired mitochondrial function of 3T3-L1 adipocytes. Collectively, this study introduces the first combined proteomic and ubiquitylomic examination of murine VAT, offering novel insights and potential therapeutic targets for obesity.

Melanoma is an aggressive form of skin cancer that often metastasizes through lymph nodes (LNs). Lymphatic small extracellular vesicles (sEVs) derived from melanoma play a crucial role in establishing a premetastatic niche (PMN) within the sentinel lymph node (SLN). Therefore, analyzing the proteomic content of tumor-draining lymphatic sEVs that deliver oncogenic signals to the SLN is vital in understanding the PMN. To investigate this, we performed multiplexing (18 samples) using tandem mass tag labeling to profile the lymphatic sEV proteomes obtained from afferent lymphatic channels leading to the SLN of melanoma patients (n = 6), non-cancer-associated afferent lymphatic channels (n = 3), and postoperative lymphatic fluid after LN dissection (n = 9). We identified 595 new proteomic cargoes compared with those reported in ExoCarta and 1003 new cargo proteins relative to three previously reported lymphatic EV datasets. The analysis revealed 145 differentially expressed proteins of melanoma sEVs that link to increased cellular stress and injury pathways and a decrease in extracellular matrix organization (-log[p value] >7.0). Analysis of the top 50 differentially expressed proteins included expressions of normal, primary, and metastatic samples across multiple omics datasets. Hierarchical clustering with postoperative samples demonstrated nine upregulated and two downregulated proteins specific to melanoma sEVs, which are associated with melanoma progression (p < 0.05). Notably, several common proteins associated with melanoma and postoperative samples were related to the wound healing mechanism. The multiplex immunofluorescence analysis of selected proteins reveals significantly increased expression levels of CD38, galectin-9 (LGALS9), and tenascin-C (TNC) in the lymphatic sinuses of SLN (-) compared with the control LN sinuses. Moreover, higher levels of LGALS9 protein in LN tissue are associated with poor overall survival of melanoma patients (p = 0.0018). In summary, this study reveals an altered landscape of sEV proteome in the afferent lymphatic fluid of melanoma, highlighting distinct sEV proteins that are uniquely present in the SLN during PMN development.

Data-independent acquisition (DIA) mass spectrometry is essential for comprehensive quantification of proteomes, enabling deeper insights into cellular processes and disease mechanisms. On the timsTOF platform, diagonal-PASEF acquisition methods have recently been introduced to directly and continuously cover the observed diagonal shape of the peptide precursor ion distributions. Diagonal-PASEF has already shown great promise, and its adaptation as a routine workflow can be further pushed with improved method development as well as enhanced algorithmic solutions. Here, we conducted a systematic and comprehensive optimization of diagonal-PASEF for 17-min gradients on the timsTOF HT in conjunction with Spectronaut. We demonstrate that Spectronaut fully supports all tested diagonal-PASEF methods independent of the number of slices or overlaps and with minimal user intervention required. We derive an optimized analysis strategy where we coupled diagonal-PASEF acquisitions to retention time down-sampling by summation (RTsum) and thereby exploit the fast-cycling nature of diagonal-PASEF methods. Through the combination of RTsum with diagonal-PASEF, we demonstrate that this strategy yields higher signal-to-noise ratios while retaining the peak shape for analytes of interest ultimately improving overall number of peptide and protein identifications of diagonal-PASEF. Importantly, combining RTsum with diagonal-PASEF improved overall identifications and quantitative precision when compared to dia-PASEF with variable quadrupole isolation widths and across different input amounts for cell line injections. We also tested the performance of diagonal-PASEF in controlled quantitative experiments where diagonal-PASEF outperformed dia-PASEF in the overall number of retained candidates below 1% or 5% error-rate, quantitative precision, and identifications on peptide level and protein level. These data indicate that RTsum demonstrates a positive use case of the high sampling rate of diagonal-PASEF and might therefore be a valuable addition to existing analysis pipelines. Collectively, our findings imply that diagonal-PASEF is developing into a competitive alternative to dia-PASEF and that the data analysis options are making fast progress.

Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy, however, their use is limited by heterogeneous and unpredictable immune-related adverse events (irAEs), which can progress to life-threatening conditions requiring intensive care unit (ICU) admission. Reliable biomarkers for predicting and stratifying ICU-level irAEs are urgently needed to improve immunotherapy safety and critical care management. Here, we performed comprehensive mass spectrometry-based proteomic profiling to identify plasma biomarkers for the prediction and monitoring of irAEs in 65 patients receiving ICI treatment. Our analysis identified 217 differentially abundant proteins and four co-expression modules related to humoral (antibody-mediated) and cellular (T cell-mediated) immunity spanning mild to severe irAEs. Through feature selection and cross-validation with proteomics and ELISA data, we identified two key proteins, IL1RL1 and FABP3, as potential biomarkers for irAE risk. In addition, we developed a plasma proteomic machine learning model (ProIRAE) that demonstrated high and robust predictive performance with area under the receiver-operating characteristic curve (AUROC) values of 0.929 and 0.766 for identifying patients at risk of developing irAEs, and AUROC values of 0.978 and 1.000 for predicting severe irAEs in the discovery and independent validation cohorts, respectively. Collectively, our study provides a valuable plasma proteomic atlas of ICI-related irAEs. The ProIRAE model offers a non-invasive tool for the detection and severity stratification of irAEs, with a great potential to improve precision monitoring and management of immunotherapy complications in critical care settings.

Bottom-up proteomics is a powerful technique for comprehensive analysis of proteins by proteolytic cleavage followed by LC-MS/MS to identify the resulting peptides. Trypsin is the gold-standard protease for bottom-up proteomics, though its cleavage specificity limits peptide identification, depending on the protein sequence. In addition, its optimal pH is weakly alkaline, which can cause modification artifacts such as deamidation. We hypothesized that these limitations might be overcome by using protease type XIII (P13ase) from Aspergillus saitoi, which is active at low pH. P13ase has been used for protein structural analysis by hydrogen-deuterium exchange mass spectrometry, but its cleavage preferences have not been clarified. Here, we demonstrated that P13ase exhibits a preference for cleaving the C-terminal sides of leucine (21%), lysine (19%), and arginine (15%) residues, and that the optimal conditions for P13ase digestion in bottom-up proteomics are pH 3.5 and 37 °C for 60 min. Under these conditions, sequence coverage of more than 90% was achieved for several proteins in HeLa cell extracts, which is unachievable with trypsin. In addition, P13ase digestion reduced artifacts such as deamidation products generated by cyclization reactions and subsequent hydrolysis. These results indicate that P13ase is a promising new tool for precision proteomics.

Mass spectrometry-based proteomics has been applied to many fields and has made major contributions to our understanding of biology and medicine. Translation of this technology and assays to patient testing has been limited despite grand expectations. The amyloid protein identification by LC-MS test is one successful example of the adaptation of this technology by molecular and clinical pathology laboratories. Through the illustration of this assay, we will address some of these challenges and outline a process for validation and implementation of mass spectrometry-based proteomics in the molecular pathology laboratory.

Septic shock, the excessive immune response to pathogen infection, accounts globally for ∼20% of all deaths. Current methods to establish disease severity are unacceptably slow, unspecific, and insensitive, hindering timely and effective treatment. Aiming to establish easy-to-measure glyco-signatures that may identify the most critically unwell patients, we applied comparative glycomics and glycoproteomics to sera longitudinally collected from septic shock survivors (n = 29) and nonsurvivors (n = 8). Glycomics of all 134 serum samples (sampled daily until recovery/death) revealed significant N-glycome dynamics across both patient groups. Unsupervised clustering of the serum N-glycome measured upon intensive care unit (ICU) admission (day 1) indicated survivorship-specific glyco-signatures. We therefore employed machine learning to train a random forest model using the serum N-glycome data. The model accurately classified survivorship outcomes of 35 of 37 patients (accuracy 94.6%) and correctly predicted 29 of 29 survivors (specificity 100%) and six of eight nonsurvivors (sensitivity 75%). Interrogation of the serum N-glycome data revealed that Lewis x (Le^x)-type N-glycans are elevated in nonsurvivors relative to survivors at ICU admission, a finding recapitulated by glycoproteomics. Among the 58 other Le^x-containing serum glycoproteins that were strongly associated with acute phase response and stress pathways, alpha-1-acid-glycoprotein (AGP-1) was identified as a principal carrier of Le^x glycoepitopes with a potential to stratify septic shock survivors from nonsurvivors (AUC 0.90). This study lays a foundation for risk stratification of septic shock patients by uncovering easy-to-assay AGP-1-Le^x glycoforms that identify individuals experiencing poor survival outcomes already upon ICU admission, with the potential to translate to early individualized clinical care at the bedside.

Large macromolecular assemblies are integral to most cellular processes, making their identification and structural characterization an important strategy for advancing our understanding of protein functions. In this pilot study, we investigated large multiprotein assemblies from the cytoplasm of the slime mold Dictyostelium discoideum using shotgun electron microscopy, the combined application of mass spectrometry-based proteomics and cryo-EM to heterogenous mixtures of proteins. With its similarities in cell structure and behavior to mammalian cells, D. discoideum has long served as an invaluable model organism, particularly in the study of immune cell chemotaxis, phagocytosis, bacterial infection, and other processes. We subjected D. discoideum soluble protein complexes to two-step fractionation, performing size-exclusion chromatography followed by mixed-bed ion-exchange chromatography. Isolated fractions containing a subset of high molecular weight-scale protein assemblies were subsequently analyzed using mass spectrometry to identify the proteins and cryo-EM to characterize their structures. Mass spectrometry analysis revealed 179 unique proteins in the isolated fractions, then single-particle cryo-EM analysis generated distinct 2D projections of several visually distinctive protein assemblies, from which we successfully identified and reconstructed three major protein complexes: the 20S proteasome, the dihydrolipoyllysine-residue succinyltransferase (Odo2) of the mitochondrial 2-oxoglutarate dehydrogenase complex, and polyketide synthase 16 (Pks16), thought to be the primary fatty acid synthase of D. discoideum. Based on the Pks16 structure, the first of the 40 D. discoideum PKSs to be experimentally determined, models for the full set of D. discoideum PKSs were constructed with help from AlphaFold 3. Comparative analysis enabled structural characterization of their reaction chambers. Shotgun EM thus provides a view of proteins in their native or near-native biological conformations and scaling up this approach offers an effective route to characterize new structures of multiprotein assemblies directly from complex samples.

Nitric oxide (NO) is a crucial signaling molecule involved in various developmental processes and stress responses through post-translational protein modification and modulation of gene expression. Despite significant advances in understanding the mechanism of NO-mediated protein modifications, how NO regulates gene expression remains largely unclear. Here, we show that the energy sensor KIN10, a catalytic α-subunit of sucrose non-fermenting 1-related kinase 1, plays a vital role in NO-mediated regulation of gene expression in Arabidopsis. NO-mediated S-nitrosylation at Cys-177 of KIN10 inhibits its degradation, leading to protein stabilization. A non-nitrosylatable mutation of Cys-177 to serine results in NO insensitivity and functional deficiencies. Quantitative phosphoproteomic analysis reveals that S-nitrosylation at Cys-177 of KIN10 modulates the phosphorylation of splicing factors within the spliceosome. We propose that NO regulates RNA splicing through the enhancement of KIN10 activity via S-nitrosylation, thereby establishing a molecular link between NO signaling and gene expression.