Molecular & Cellular Proteomics最新文献

Tandem mass spectrometry of peptides and proteins generates mass spectra of their gas-phase fragmentation product ions, including N-terminal, C-terminal, and internal fragment ions. Whereas N- and C-terminal ions are routinely assigned and identified using computational methods, internal fragment ions are often difficult to annotate correctly. They become particularly relevant for long peptides and full proteoforms where the peptide backbone is more likely to be fragmented multiple times. Internal fragment ions potentially offer tremendous information regarding amino acid sequences and positions of post-translational modifications of peptides and intact proteins. However, their practical application is challenged by the vast number of theoretical internal fragments that exist for long amino acid sequences, leading to a high risk of false-positive annotations. We analyze the mass spectral contributions of internal fragment ions in spectra from middle-down and top-down experiments and introduce a novel graph-based annotation approach designed to manage the complexity of internal fragments. Our graph-based representation allows us to compare multiple candidate proteoforms in a single graph, and to assess different candidate annotations in a fragment ion spectrum. We demonstrate cases from middle-down and top-down data where internal ions enhance amino acid sequence coverage of polypeptides and proteins and accurate localization of post-translational modifications. We conclude that our graph-based method provides a general approach to process complex tandem mass spectra, enhance annotation of internal fragment ions, and improve proteoform sequencing and characterization by mass spectrometry.

Non-invasive prognostic markers are needed to improve the survival of colorectal cancer (CRC) patients. Towards this goal, we applied untargeted systems glycobiology approaches to snap frozen and formalin-fixed paraffin-embedded tumor tissues and peripheral blood mononuclear cells (PBMCs) from CRC patients spanning different disease stages and matching controls to faithfully uncover molecular changes associated with CRC. Quantitative glycomics and immunohistochemistry (IHC) revealed that non-canonical paucimannosidic N-glycans are elevated in CRC tumors relative to normal adjacent tissues. Cell origin-focused glycoproteomics enabled using the well-curated Human Protein Atlas combined with IHC of CRC tumor tissues recapitulated these findings and indicated that the paucimannosidic proteins were in part from tumor-infiltrating monocytes (e.g. MPO, AZU1) and of CRC cell origin (e.g. LGALS3BP, PSAP). Biosynthetically explaining these observations, N-acetyl-β-D-hexosaminidase (Hex) subunit β (HEXB) was found to be over-expressed in CRC tissues relative to normal adjacent colorectal tissues and colocalization and enzyme inhibition studies confirmed that HEXB facilitates paucimannosidic protein biosynthesis in CRC cells. Employing a sensitive, quick and robust enzyme activity assay, we then showed that Hex activity was elevated in plasma and PBMCs from patients with advanced CRC relative to controls and those with early-stage disease. Surveying a large plasma cohort, the plasma Hex activity was found to be raised in CRC patients relative to normal controls and correlated with the five-year survival of CRC patients indicating that elevated plasma Hex activity is a potential disease risk marker for patient outcome. Our glycoproteomics-driven findings open avenues for better prognostication and disease risk stratification in CRC.

In eukaryotic organisms, protein kinases regulate diverse protein activities and signaling pathways through phosphorylation of specific protein substrates. Isolating and characterizing kinase substrates is vital for defining downstream signaling pathways. The Kinase Client (KiC) assay is an in vitro synthetic peptide LC-MS/MS phosphorylation assay that has enabled identification of protein substrates (i.e., clients) for various protein kinases. For example, previous use of a 2,100-member (2k) peptide library identified substrates for the extracellular ATP receptor-like kinase, P2K1. Many P2K1 clients were confirmed by additional in vitro and in planta studies, including Integrin-Linked Kinase 4 (ILK4), for which we provide the evidence herein. In addition, we developed a new KiC peptide library containing 8,000 (8k) peptides based on phosphorylation sites primarily from Arabidopsis thaliana datasets. The 8k peptides are enriched for sites with conservation in other angiosperm plants, with the paired goals of representing functionally conserved sites and usefulness for screening kinases from diverse plants. Screening the 8k library with the active P2K1 kinase domain identified 177 phosphopeptides, including calcineurin B-like protein (CBL9) and G protein alpha subunit 1 (GPA1), which functions in cellular calcium signaling. We confirmed that P2K1 directly phosphorylates CBL9 and GPA1 through in vitro kinase assays. This expanded 8k KiC assay will be a useful tool for identifying novel substrates across diverse plant protein kinases, ultimately facilitating the exploration of previously undiscovered signaling pathways.

Lubricin/proteoglycan-4 (PRG-4) is a mucinous glycoprotein that lubricates cartilage and maintains normal tissue function and cell homeostasis. Altered O-glycoproteforms of lubricin have been found in osteoarthritis (OA) synovial fluid (SF), which could ostensibly be used to diagnose early onset OA. However, SF is invasive to obtain and generally would not be surveyed from otherwise healthy individuals. Thus, a plasma-based OA screening tool focused on lubricin glycosylation could be a less invasive method to aid in early-stage OA diagnosis. In this report, we used glycomics and glycoproteomics to characterize glycoproteoforms of OA lubricin in SF and plasma. We obtained near-complete sequence coverage of lubricin's mucin domain and its glycosylation using matched SF and plasma from OA patients (N=5). From SF lubricin we observed a spectrum of O-glycans ranging from a single GalNAcα1-Ser/Thr monosaccharide up to branched pentasaccharides. In contrast, plasma based lubricin was predominantly decorated with sialylated Galβ1-3GalNAcα1-Ser/Thr (Sialyl T). To explain the glycosylation differences observed between SF and plasma lubricin, we present splice variant-specific peptides found within the non-glycosylated region, revealing that that the longest spliceoform of lubricin was present exclusively in SF, while additional shorter splice variants could only be detected in plasma. Based on our glycoproteomic data, we developed and validated a lectin assay for lubricin, and applied this on a larger cohort of matched SF/plasma (N=19) to confirm the glycosylation differences between SF and plasma proteoforms. Next, we leveraged our assay to screen over 100 OA patient samples (OA patients N=108/controls N=38) to probe plasma lubricin as an OA biomarker. Here, we detected a decrease in α2,6 linked sialic acid in OA patients and further show that the extent of α2,6 and α2,3 sialylation on plasma-associated lubricin correlated with patient characteristics, especially Body Mass Index (BMI).

Citrullination is a critical yet understudied post-translational modification (PTM) implicated in various biological processes. Exploring its role in health and disease requires a comprehensive understanding of the prevalence of this PTM at a proteome-wide scale. Although mass spectrometry has enabled the identification of citrullination sites in complex biological samples, it faces significant challenges, including limited enrichment tools and a high rate of false positives due to the identical mass with deamidation (+0.9840 Da) and errors in monoisotopic ion selection. These issues often necessitate manual spectrum inspection, reducing throughput in large-scale studies. In this work, we present a novel data analysis pipeline that incorporates the deep learning model Prosit-Cit into the MS database search workflow to improve both the sensitivity and precision of citrullination site identification. Prosit-Cit, an extension of the existing Prosit model, has been trained on ∼53,000 spectra from ∼2,500 synthetic citrullinated peptides and provides precise predictions for chromatographic retention time and fragment ion intensities of both citrullinated and deamidated peptides. This enhances the accuracy of identification and reduces false positives. Our pipeline demonstrated high precision on the evaluation dataset, recovering the majority of known citrullination sites in human tissue proteomes and improving sensitivity by identifying up to 14 times more citrullinated sites. Sequence motif analysis revealed consistency with previously reported findings, validating the reliability of our approach. Furthermore, extending the pipeline to a tissue proteome dataset of the model plant Arabidopsis thaliana enabled the identification of ∼200 citrullination sites across 169 proteins from 30 tissues, representing the first large-scale citrullination mapping in plants. This pipeline can be seamlessly applied to existing proteomics datasets, offering a robust tool for advancing biological discoveries and deepening our understanding of protein citrullination across species.

The heart is a vital muscular organ in vertebrate animals, responsible for maintaining blood circulation through rhythmic contraction. Although previous studies have investigated the heart proteome, the full hierarchical molecular network at cell-type and region resolved level, illustrating the specialized roles and crosstalk among different cell types and regions, remains unclear. Here, we presented an atlas of cell-type resolved proteome for mouse heart and region resolved proteome for both mouse and human hearts. In-depth proteomic analysis identified 11,794 proteins across four cell types and 11,995 proteins across six regions of the mouse heart. To further illustrate protein expression patterns in both physiological and pathological conditions, we conducted proteomic analysis on human heart samples from four regions with dilated cardiomyopathy (DCM). We quantified 8,201 proteins in DCM tissue and 8,316 proteins in adjacent unaffected myocardium (AUM) tissue across the four human heart regions. Notably, we found that the retinoic acid synthesis pathway was significantly enriched in the DCM-affected left ventricle, and functional experiments demonstrated that all-trans retinoic acid (atRA) efficiently rescued Ang II-induced myocardial hypertrophy and transverse aorta constriction (TAC)- induced heart failure. In conclusion, our datasets uncovered the functional features of different cell types and their synergistic cooperation centered by cell-type specific transcription factors (ctsTF) in different regions, while these TF-TG (target gene) axes were significantly altered in DCM. Additionally, atRA was demonstrated to be an efficient treatment for heart failure. This work presented a panoramic heart proteome map, offering a valuable resource for future cardiovascular research.

Mesothelioma is an incurable, asbestos-exposure related cancer that typically affects the lining or pleura of the lungs. Symptoms typically develop many decades after initial exposure to asbestos, leaving an enduring legacy of disease. Current disease burden is peaking worldwide and thus there is a massive unmet clinical need for curative therapies. Recently, immune checkpoint blockade-based therapy has been adopted as a first-line of treatment for mesothelioma. Vaccine-induced augmentation of immune responses unleashed during checkpoint blockade may provide further clinical benefit in mesothelioma. In this study we explore the human leukocyte antigen class I landscape (or immunopeptidome) of mesothelioma in patient derived cell line and clinical material (pleural effusion samples). We identify a range of peptide antigens derived from targets including cancer testis antigens, endogenous retroviruses as well as novel post-translational modification of peptides. This information will facilitate the characterization of the immune response to these antigens to determine which class of antigen is most immunogenic and has the potential to be tested in future vaccine studies.

Noncanonical micropeptides or called novel microproteins, i.e., polypeptides mostly under 10 kDa, are encoded by genomic sequences that have been previously annotated as noncoding but now known as small open reading frames (sORFs). The recent identification of microproteins encoded by sORFs has provided evidence that many sORFs encode functional microproteins that play crucial roles in various biological processes. T cell activation is a critical biological process for adaptive immune response. Understanding key players in this process will allow us to decipher the complex mechanisms as well as develop immunotherapy for treating a wide range of diseases. Although there have been extensive studies on canonical proteins in T cell activation, the novel microproteins in T cells and their roles have been uncharted water to date. Nascent proteins are defined as newly synthesized polypeptides emerged during the translation of mRNA. In this study, we combined nascent proteomics and quantitative proteomics to identify 411 novel microproteins in primary human T cells, including 83 nascent microproteins. We activated the T cell function with either PMA/Ionomycin (distal activation) or CD3/CD28 activating antibodies (proximal activation), and obtained a comprehensive canonical protein and microprotein profiles to pinpoint common and distinct differentially expressed proteins under these two activation conditions. After experimental testing, three microproteins numbered T1, T2 and T3 were found to be functional in regulating T cell activation. Bioinformatic and proteomic analyses suggested that T1 was functional related to immune as negative feedback to T cell activation. Our study not only established an integrated approach to uncover and elucidate novel microproteins but also highlight the significant role of microproteins in regulating T cell activation.

Nonalcoholic fatty liver disease (NAFLD) is a hepatic condition characterized excessive fat accumulation in the liver with advanced stage nonalcoholic steatohepatitis (NASH), potentially leading to liver fibrosis, cirrhosis, and cancer. Currently, the identification and classification of NASH require invasive liver biopsy, which has certain limitations. Mass spectrometry-based proteomics can detect crucial proteins and pathways implicated in NASH development and progression. We collected liver and serum samples from choline-deficient, L-amino acid-defined high-fat diet fed NASH C57BL/6J mice and human serum samples to examine proteomic alterations and identify early biomarkers for NASH diagnosis. In-depth targeted multiple reaction monitoring (MRM) scanning and immunoblotting assays were used to verify the biomarker candidates from mouse liver and serum samples, and enzyme-linked immunosorbent assay (ELISA) was employed to analyze human serum samples. The MRM analysis of NASH liver revealed 50 proteins with altered expression (18 up- and 32 downregulated) that are involved in biological processes such as detoxification, fibrosis, inflammation, and fatty acid metabolism. Ingenuity pathway analysis identified impaired protein synthesis, cellular stress and defense, cellular processes and communication, and metabolism in NASH mouse liver. Immunoblotting analysis confirmed that the expression of proteins associated with fatty acid metabolism (Aldo B and Fasn) and urea cycle (Arg1, Cps1, and Otc) was altered in mouse liver and serum. Further analysis on human serum samples using ELISA confirmed the increased expression of multiple proteins, including Aldo B, Asl, and Lgals3, demonstrating values of 0.917, 0.979, and 0.965 of area under the curve in NASH diagnosis. These findings offer valuable insights into the molecular mechanisms of NASH and possible diagnostic biomarkers for early detection.

Understanding dysregulated genes and pathways in cancer is critical for precision oncology. Integrating mass spectrometry-based proteomic data with transcriptomic data presents unique opportunities for systematic analyses of dysregulated genes and pathways in pan-cancer. Here, we compiled a comprehensive set of datasets, encompassing proteomic data from 2,404 samples and transcriptomic data from 7,752 samples across 13 cancer types. Comparisons between normal or adjacent normal tissues (ANTs) and tumor tissues identified several dysregulated pathways including mRNA splicing, interferon pathway, fatty acid metabolism, and complement coagulation cascade in pan-cancer. Additionally, pan-cancer up- and down-regulated genes (PCUGs and PCDGs) were also identified. Notably, RRM2 and ADH1B, two genes belong to PCUGs and PCDGs, respectively, were identified as robust pan-cancer diagnostic biomarkers. TNM stage-based comparisons revealed dysregulated genes and biological pathways involved in cancer progression, among which the dysregulation of complement coagulation cascade and epithelial-mesenchymal transition are frequent in multiple types of cancers. A group of pan-cancer continuously up- and down-regulated proteins in different tumor stages (PCCUPs and PCCDPs) were identified. We further constructed prognostic risk stratification models for corresponding cancer types based on dysregulated genes, which effectively predict the prognosis for patients with these cancers. Drug prediction based on PCUPs and PCDPs as well as PCCUPs and PCCDPs revealed that small molecule inhibitors targeting CDK, HDAC, MEK, JAK, PI3K, and others might be effective treatments for pan-cancer, thereby supporting drug repurposing. We also developed web tools for cancer diagnosis, pathologic stage assessment, and risk evaluation. Overall, this study highlights the power of combining proteomic and transcriptomic data to identify valuable diagnostic and prognostic markers as well as drug targets and treatments for cancer.