Molecular & Cellular Proteomics最新文献

A central hallmark of neurodegenerative diseases is the irreversible accumulation of misfolded proteins in the brain by aberrant phosphorylation. Understanding the mechanisms underlying protein phosphorylation and its role in pathological protein aggregation within the context of aging is crucial for developing therapeutic strategies aimed at preventing or reversing such diseases. Here, we applied multi-protease digestion and quantitative mass spectrometry to compare and characterize dysregulated proteins and phosphosites in the mouse brain proteome using three different age groups: young-adult (3-4 months), middle-age (10 months), and old mice (19-21 months). Proteins associated with senescence, neurodegeneration, inflammation, cell cycle regulation, the p53 hallmark pathway, and cytokine signaling showed significant age-dependent changes in abundances and level of phosphorylation. Several proteins implicated in Alzheimer's disease (AD) and Parkinson's disease (PD) including tau (Mapt), Nefh, and Dpysl2 (also known as Crmp2) were hyperphosphorylated in old mice brain suggesting their susceptibility to the diseases. Cdk5 and Gsk3b, which are known to phosphorylate Dpysl2 at multiple specific sites, had also increased phosphorylation levels in old mice suggesting a potential crosstalk between them to contribute to AD. Hapln2, which promotes α-synuclein aggregation in patients with PD, was one of the proteins with highest abundance in old mice. CD9, which regulates senescence through the PI3K-AKT-mTOR-p53 signaling was upregulated in old mice and its regulation was correlated with the activation of phosphorylated AKT1. Overall, the findings identify a significant association between aging and the dysregulation of proteins involved in various pathways linked to neurodegenerative diseases with potential therapeutic implications.

Pancreatic ductal adenocarcinoma (PDAC) suffers from a lack of an effective diagnostic method, which hampers improvement in patient survival. Carbohydrate antigen 19-9 (CA19-9) is the only FDA-approved blood biomarker for PDAC, yet its clinical utility is limited due to suboptimal performance. Liquid chromatography-mass spectrometry (LC-MS) has emerged as a burgeoning technology in clinical proteomics for the discovery, verification, and validation of novel biomarkers. A plethora of protein biomarker candidates for PDAC have been identified using LC-MS, yet few has successfully transitioned into clinical practice. This translational standstill is owed partly to insufficient considerations of practical needs and perspectives of clinical implementation during biomarker development pipelines, such as demonstrating the analytical robustness of proposed biomarkers which is critical for transitioning from research-grade to clinical-grade assays. Moreover, the throughput and cost-effectiveness of proposed assays ought to be considered concomitantly from the early phases of the biomarker pipelines for enhancing widespread adoption in clinical settings. Here, we developed a fit-for-purpose multi-marker panel for PDAC diagnosis by consolidating analytically robust biomarkers as well as employing a relatively simple LC-MS protocol. In the discovery phase, we comprehensively surveyed putative PDAC biomarkers from both in-house data and prior studies. In the verification phase, we developed a multiple-reaction monitoring (MRM)-MS-based proteomic assay using surrogate peptides that passed stringent analytical validation tests. We adopted a high-throughput protocol including a short gradient (<10 min) and simple sample preparation (no depletion or enrichment steps). Additionally, we developed our assay using serum samples, which are usually the preferred biospecimen in clinical settings. We developed predictive models based on our final panel of 12 protein biomarkers combined with CA19-9, which showed improved diagnostic performance compared to using CA19-9 alone in discriminating PDAC from non-PDAC controls including healthy individuals and patients with benign pancreatic diseases. A large-scale clinical validation is underway to demonstrate the clinical validity of our novel panel.

Listeria monocytogenes is a foodborne intracellular bacterial model pathogen. Protective immunity against Listeria depends on an effective CD8⁺ T cell response, but very few T cell epitopes are known in mice as a common animal infection model for listeriosis. To identify epitopes, we screened for Listeria immunopeptides presented in the spleen of infected mice by mass spectrometry-based immunopeptidomics. We mapped more than 6000 mouse self-peptides presented on MHC class I molecules, including 12 high confident Listeria peptides from 12 different bacterial proteins. Bacterial immunopeptides with confirmed fragmentation spectra were further tested for their potential to activate CD8⁺ T cells, revealing VTYNYINI from the putative cell wall surface anchor family protein LMON_0576 as a novel bona fide peptide epitope. The epitope showed high biological potency in a prime boost model and can be used as a research tool to probe CD8⁺ T cell responses in the mouse models of Listeria infection. Together, our results demonstrate the power of immunopeptidomics for bacterial antigen identification.

The study of the cellular secretome using proteomic techniques continues to capture the attention of the research community across a broad range of topics in biomedical research. Due to their untargeted nature, independence from the model system used, historically superior depth of analysis, as well as comparative affordability, mass spectrometry-based approaches traditionally dominate such analyses. More recently, however, affinity-based proteomic assays have massively gained in analytical depth, which together with their high sensitivity, dynamic range coverage as well as high throughput capabilities render them exquisitely suited to secretome analysis. In this review, we revisit the analytical challenges implied by secretomics and provide an overview of affinity-based proteomic platforms currently available for such analyses, using the study of the tumor secretome as an example for basic and translational research.

This work presents a detailed determination of site-specific N-glycan distributions of the recombinant influenza glycoproteins hemagglutinin (HA) and neuraminidase. Variation in glycosylation among recombinant glycoproteins is not predictable and can depend on details of the biomanufacturing process as well as details of protein structure. In this study, recombinant influenza proteins were analyzed from eight strains of four different suppliers. These include five HA and three neuraminidase proteins, each produced from a HEK293 cell line. Digestion was conducted using a series of complex multienzymatic methods designed to isolate glycopeptides containing single N-glycosylated sites. Site-specific glycosylation profiles of intact glycopeptides were produced using a recently developed method and comparisons were made using spectral similarity scores. Variation in glycan abundances and distribution was most pronounced between different strains of virus (similarity score = 383 out of 999), whereas digestion replicates and injection replicates showed relatively little variation (similarity score = 957). Notably, glycan distributions for homologous regions of influenza glycoprotein variants showed low variability. Due to the multiple possible sources of variation and inherent analytical difficulties in site-specific glycan determinations, variations were individually examined for multiple factors, including differences in supplier, production batch, protease digestion, and replicate measurement. After comparing all glycosylation distributions, four distinguishable classes could be identified for the majority of sites. Finally, attempts to identify glycosylation distributions on adjacent potential N-glycosylated sites of one HA variant were made. Only the second site (NnST) was found to be occupied using two rarely used proteases in proteomics, subtilisin and esperase, both of which did selectively cleave these adjacent sites.

We have developed a one-step isolation method for protein N-terminal peptides from LysargiNase digests by pipette tip-based strong cation exchange (SCX) chromatography. This CHAMP-N (CHromatographic AMplification of Protein N-terminal peptides) method using disposable and parallel-processable SCX tips instead of conventional HPLC SCX columns facilitates simple, sensitive, reproducible, and high-throughput N-terminomic profiling without sacrificing the high identification numbers and selectivity achieved by the HPLC-based method. By applying the CHAMP-N method to HEK293T cells, we identified novel cleavage sites for signal and transit peptides and non-canonical translation initiation sites. Finally, for proteome-wide terminomics, we present a simple and comprehensive N- and C-terminomics platform employing three different tip-based approaches, including CHAMP-N, in which protease digestion and one-step isolation by tip LC are commonly used to achieve complementary terminome coverages.

High-throughput intact glycopeptide analysis is crucial for elucidating the physiological and pathological status of the glycans attached to each glycoprotein. Mass spectrometry-based glycoproteomic methods are challenging because of the diversity and heterogeneity of glycan structures. Therefore, we developed an MS1-based site-specific glycoform analysis method named "Glycan heterogeneity-based Relational IDentification of Glycopeptide signals on Elution profile (Glyco-RIDGE)" for a more comprehensive analysis. This method detects glycopeptide signals as a cluster based on the mass and chromatographic properties of glycopeptides and then searches for each combination of core peptides and glycan compositions by matching their mass and retention time differences. Here, we developed a novel browser-based software named GRable for semi-automated Glyco-RIDGE analysis with significant improvements in glycopeptide detection algorithms, including "parallel clustering." This unique function improved the comprehensiveness of glycopeptide detection and allowed the analysis to focus on specific glycan structures, such as pauci-mannose. The other notable improvement is evaluating the "confidence level" of the GRable results, especially using MS2 information. This function facilitated reduced misassignment of the core peptide and glycan composition and improved the interpretation of the results. Additional improved points of the algorithms are "correction function" for accurate monoisotopic peak picking; one-to-one correspondence of clusters and core peptides even for multiply sialylated glycopeptides; and "inter-cluster analysis" function for understanding the reason for detected but unmatched clusters. The significance of these improvements was demonstrated using purified and crude glycoprotein samples, showing that GRable allowed site-specific glycoform analysis of intact sialylated glycoproteins on a large-scale and in-depth. Therefore, this software will help us analyze the status and changes in glycans to obtain biological and clinical insights into protein glycosylation by complementing the comprehensiveness of MS2-based glycoproteomics. GRable can be freely run online using a web browser via the GlyCosmos Portal (https://glycosmos.org/grable).

Chromatin configuration serves as a principal indicator of GV (germinal vesicle)-stage oocyte quality. However, the underlying mechanisms governing the chromatin configuration transition from NSN (non-surrounded nucleolus) to SN (surrounded nucleolus) remain unclear. In this study, by conducting a quantitative proteomic analysis, we identified an increased expression of the MIB2 (MIB E3 ubiquitin protein ligase 2) protein in SN oocytes. Specific depletion of MIB2 in SN oocytes not only leads to severe disruption of the meiotic apparatus and a higher incidence of aneuploidy but also adversely affects meiotic maturation and early embryo development. Notably, overexpression of MIB2 in NSN oocytes facilitates the chromatin configuration transition. Meantime, we observed that forced expression of MIB2 in NSN oocytes significantly mitigates spindle/chromosome disorganization and aneuploidy. In summary, our results suggest that chromatin configuration transition regulated by MIB2 is crucial for oocytes to acquire developmental competence.

Data-independent acquisition (DIA) has revolutionized the field of mass spectrometry (MS)-based proteomics over the past few years. DIA stands out for its ability to systematically sample all peptides in a given m/z range, allowing an unbiased acquisition of proteomics data. This greatly mitigates the issue of missing values and significantly enhances quantitative accuracy, precision, and reproducibility compared to many traditional methods. This review focuses on the critical role of DIA analysis software tools, primarily focusing on their capabilities and the challenges they address in proteomic research. Advances in MS technology, such as trapped ion mobility spectrometry, or high field asymmetric waveform ion mobility spectrometry require sophisticated analysis software capable of handling the increased data complexity and exploiting the full potential of DIA. We identify and critically evaluate leading software tools in the DIA landscape, discussing their unique features, and the reliability of their quantitative and qualitative outputs. We present the biological and clinical relevance of DIA-MS and discuss crucial publications that paved the way for in-depth proteomic characterization in patient-derived specimens. Furthermore, we provide a perspective on emerging trends in clinical applications and present upcoming challenges including standardization and certification of MS-based acquisition strategies in molecular diagnostics. While we emphasize the need for continuous development of software tools to keep pace with evolving technologies, we advise researchers against uncritically accepting the results from DIA software tools. Each tool may have its own biases, and some may not be as sensitive or reliable as others. Our overarching recommendation for both researchers and clinicians is to employ multiple DIA analysis tools, utilizing orthogonal analysis approaches to enhance the robustness and reliability of their findings.

The ATR kinase protects cells against DNA damage and replication stress and represents a promising anti-cancer drug target. The ATR inhibitors (ATRi) berzosertib and gartisertib are both in clinical trials for the treatment of advanced solid tumors as monotherapy or in combination with genotoxic agents. We carried out quantitative phospho-proteomic screening for ATR biomarkers that are highly sensitive to berzosertib and gartisertib, using an optimized mass spectrometry pipeline. Screening identified a range of novel ATR-dependent phosphorylation events, which were grouped into three broad classes: (i) targets whose phosphorylation is highly sensitive to ATRi and which could be the next generation of ATR biomarkers; (ii) proteins with known genome maintenance roles not previously known to be regulated by ATR; (iii) novel targets whose cellular roles are unclear. Class iii targets represent candidate DNA damage response proteins and, with this in mind, proteins in this class were subjected to secondary screening for recruitment to DNA damage sites. We show that one of the proteins recruited, SCAF1, interacts with RNAPII in a phospho-dependent manner and recruitment requires PARP activity and interaction with RNAPII. We also show that SCAF1 deficiency partly rescues RAD51 loading in cells lacking the BRCA1 tumor suppressor. Taken together these data reveal potential new ATR biomarkers and new genome maintenance factors.