Molecular & Cellular Proteomics最新文献

Peripartum cardiomyopathy (PPCM) is a rare form of acute heart failure that develops in women toward the end of pregnancy or early postpartum. No effective, specific treatment for PPCM is available and heart transplantation or mechanical circulatory support may be required in severe cases where drug treatment for heart failure is insufficient. The mechanisms through which the disease progresses are not well understood, and despite similar clinical characteristics to dilated cardiomyopathy of other etiologies (nonperipartum cardiomyopathy; NPCM) it is not known how the molecular remodeling differs between these groups. We aimed to provide insight into the human PPCM heart using unbiased methodologies, and to use changes occurring within the heart tissue to facilitate biomarker discovery. We obtained heart tissue from female patients with end-stage disease receiving either heart transplantation or left ventricular assist device implantation, or from organ donors without heart disease as a control group. We performed deep proteomics, single nucleus transcriptomics and spatial transcriptomics, providing a comprehensive map of the molecular phenotype in advanced PPCM compared to both control and NPCM hearts. Consistent with similarities in the clinical phenotypes of PPCM and NPCM, we observed regulation of canonical markers of end-stage heart failure in both PPCM and NPCM hearts in comparison to controls. Among the changes specific to PPCM and that were consistently observed across multiple data types and cohorts was an upregulation of chymase and carboxypeptidase A3, consistent with mast cell proliferation/activation. Analysis of the proteome of peripheral blood serum from a larger cohort of patients with PPCM and controls showed that chymase was strongly predictive of cardiomyopathy in peripartum women. PPCM heart tissue is characterized by increased mast cell proteins chymase and carboxypeptidase A3. Chymase may have clinical utility as a biomarker for the diagnosis of cardiomyopathy in peripartum women.

The development of targeted assays that monitor biomedically relevant proteins is an important step in bridging discovery experiments to large scale clinical studies. Targeted assays are currently unable to scale to hundreds or thousands of targets. We demonstrate the generation of large-scale assays using a novel hybrid nominal mass instrument. The scale of these assays is achievable with the Stellar mass spectrometer through the accommodation of shifting retention times by real-time alignment, while being sensitive and fast enough to handle many concurrent targets. Assays were constructed using precursor information from gas-phase fractionation data-independent acquisition (DIA). We demonstrate the ability to schedule methods from orbitrap and linear ion trap acquired gas-phase fractionation DIA library, and compare the quantification of a matrix-matched calibration curve from orbitrap DIA and linear ion trap parallel reaction monitoring (PRM). Two applications of these proposed workflows are shown with a cerebrospinal fluid neurodegenerative disease protein PRM assay and with a Mag-Net enriched plasma extracellular vesicle protein survey PRM assay. In cerebrospinal fluid, our assay targets proteins discovered previously to be associated with Alzheimer's disease in a small independent sample set. For the Mag-Net enriched plasma survey assay, we observe that proteins selected based on their measurement robustness are still able to capture differences in abundance across disease groups in a small sample set. These highlight the application of highly multiplex, targeted protein assays in clinical research.

Post-translational modifications (PTMs) play a central role in cellular regulation and are implicated in numerous diseases. Database searching remains the standard for identifying modified peptides from tandem mass spectra but is hindered by the combinatorial expansion of modification types and sites. De novo peptide sequencing offers an attractive alternative, yet existing methods remain limited to unmodified peptides or a narrow set of PTMs. Here, we curated a large dataset of spectra from endogenous and synthetic peptides from ProteomeTools spanning 19 biologically relevant amino acid-PTM combinations, covering phosphorylation, acetylation, and ubiquitination. We used this dataset to develop Modanovo, an extension of the Casanovo transformer architecture for de novo peptide sequencing. Modanovo achieved robust performance across these amino acid-PTM combinations (median area under the precision-coverage curve 0.92), while maintaining performance on unmodified peptides (0.93), nearly identical to Casanovo (0.94). The model outperformed π-PrimeNovo-PTM and InstaNovo-P and showed increased precision and complementarity to the database search tool MSFragger. Robustness was confirmed across independent datasets, particularly at peptide lengths frequently represented in the curated dataset. Applied to a phosphoproteomics dataset from monkeypox virus-infected cells, Modanovo recovered numerous confident peptides not reported by database search, including new viral phosphosites supported by spectral evidence, thereby demonstrating its complementarity to database-driven identification approaches. These results establish Modanovo as a broadly applicable model for comprehensive de novo sequencing of both modified and unmodified peptides.

Protein denaturation-based assays, such as thermal proximity coaggregation (TPCA) and ion-based proteome-integrated solubility alteration (I-PISA), are powerful tools for characterizing global protein-protein interaction (PPI) networks. These workflows utilize different denaturation methods to probe PPIs, i.e., thermal- or ion-based. How denaturation differences influence PPI network mapping remained to be better understood. Here, we provide an experimental and computational characterization of the effect of the denaturation-based PPI assay on the observed PPI networks. We establish the value of both soluble and insoluble fractions in PPI prediction, determine the ability to minimize sample amount requirement, and assess different relative quantification methods during virus infection. Generating paired TPCA and I-PISA datasets, we define both overlapping sets of proteins and distinct PPI networks specifically captured by these methods. Assessing protein physical properties and subcellar localizations, we show that size, structural complexity, hydrophobicity, and localization influence PPI detection in a workflow-specific manner. We show that the insoluble fractions expand the detectable PPI landscape, underscoring their value in these workflows. Focusing on selected PPI networks (cytoskeletal and DNA repair), we observe the detection of distinct functional populations. Using influenza A infection as a model for cellular perturbation, we demonstrate that the integration of PPI predictions from soluble and insoluble workflows enhances the ability to build biologically informative and interconnected networks. Examining the effects of reducing starting material for TPCA assays, we find that PPI prediction quality remains robust when using a single well of a 96-well plate, a ∼500× reduction in sample input from usual workflows. Introducing simple workflow modifications, we show that label-free data-independent acquisition (DIA) TPCA yields performance comparable to the traditional tandem mass tag (TMT) data-dependent acquisition (DDA) TPCA workflow. This work provides insights into denaturation-based assays, highlights the value of insoluble fractions, and offers practical improvements for enhancing global PPI network mapping.

Although the plasma membrane (PM) is among the most biologically important and therapeutically targeted cellular compartments, it is among the most challenging to faithfully capture using proteomic approaches. The quality of quantitative surfaceomics data depends heavily on the effectiveness of the cell surface enrichment used during sample preparation. Enrichment improves sensitivity for low abundance PM proteins and ensures that the changes detected reflect PM expression changes rather than whole cell changes. Cell surface biotinylation with PM-impermeable, amine-reactive reagents is a facile, accessible, and unbiased approach to enrich PM proteins. However, it results in unexpectedly high contamination with intracellular proteins, reducing its utility. We report that biotinylating human cells with amine-reactive reagents intracellularly labels a small but reproducible population of nonviable cells. Although these dead cells represent only 5 ± 2% of the total, we find that in T cell preparations the dead cells account for 90% of labelled proteins. Depleting Annexin V positive dead T cells postlabelling removes ∼99% of the intracellularly labelled cells, resulting in markedly improved PM identifications, peptide counts, and intensity-based absolute quantification intensities. Correspondingly, we found substantial depletion of intracellular proteins, particularly of nuclear origin. Overall, the cumulative intensity of PM proteins increased from 4% to 55.8% with dead cell depletion. Finally, we demonstrate that immature ER/Golgi glycoforms of CD11a and CD18 are selectively removed by dead-cell depletion. We conclude that high intracellular labelling of nonviable cells is the major source of intracellular protein contaminants in amine-reactive surface enrichment methods and can be reduced by dead-cell depletion postlabelling, improving both the sensitivity and accuracy of PM proteomics.

Post-translational modifications (PTMs) are pivotal in cellular regulations, and their crosstalk is related to various diseases such as cancer. Given the prevalence of PTM crosstalk within close amino acid ranges, identifying peptides with multiple PTMs is essential. However, this task is an NP-hard combinatorial problem with exponential complexity, posing significant challenges for existing analysis methods. Here, we introduce PIPI-C (PTM-Invariant Peptide Identification with a Combinatorial model), a novel search engine that addresses this challenge through a mixed integer linear programming (MILP) model, thereby overcoming the limitations of existing approaches that struggle with high-order PTM combinations. Rigorous validation across diverse datasets confirms PIPI-C's superior performance in detecting PTM combinations. When applied to over 72 million mass spectra of three human cancers-lung squamous cell carcinoma (LSCC), colorectal adenocarcinoma (COAD), and glioblastoma (GBM)-PIPI-C reveals significantly upregulated PTM combinations. In LSCC, 50% of 860 upregulated unique PTM site patterns (UPSPs) (when comparing cancer vs. normal samples) carried at least two PTMs, including literature-supported crosstalks such as di-methylation with trifluoroleucine substitution and amidation with proline-to-valine substitution. Similar findings in COAD and GBM highlight PIPI-C's utility in uncovering cancer-relevant PTM combination landscapes. Overall, PIPI-C provides a robust mathematical framework for decoding complex PTM patterns, advancing our understanding of PTM-driven cellular processes in diseases.

Early embryonic development requires tightly regulated molecular programs to coordinate cell division, fate specification, and spatial patterning. While transcriptomic profiling has been widely performed, proteomic analyses of early vertebrate embryos remain limited owing to technical challenges in embryonic sample preparation. Here, we present an "in-cell proteomics" strategy, which bypasses cell lysis and yolk depletion, processes individual embryos directly in functionalized filter devices, and generates mass spectrometry (MS)-friendly samples in an extremely robust and streamlined manner. This single-vessel approach minimizes sample loss and technical variation, offering a highly sensitive and accurate alternative to low-input and low-cell quantitative proteomics. Coupled with field asymmetric ion mobility spectrometry and single-shot data-independent acquisition MS workflow, this approach enabled us to consistently quantify ∼6200 proteins from a single Xenopus tropicalis embryo, representing the deepest proteomic coverage of early X. tropicalis developmental stages reported to date. Investigation of the temporal proteomes across five cleavage stages (from 1- to 16-cell stages) revealed a drastic proteomic shift between 2- and 4-cell stages, followed by more gradual transitions thereafter. Spatial analysis of dissected 8-cell blastomeres uncovered pronounced molecular asymmetry along the animal-vegetal axis, whereas dorsal-ventral differences were minimal. This study establishes a novel in-cell proteomics technology in conjunction with field asymmetric ion mobility spectrometry and data-independent acquisition MS as a robust platform for high-resolution, low-input developmental proteomics analysis and provides a comprehensive spatiotemporal protein atlas for early X. tropicalis embryos.

The E3 ligase substrate receptor ankyrin and suppressor of cytokine signaling box protein 9 (ASB9) was shown to bind over 10 different proteins including metabolic enzymes such as creatine kinase, filament proteins such as vimentin, and histones. In previous work, we characterized the ASB9-Cullin 5 E3 ligase (ASB9-CRL5) ubiquitylation of creatine kinase and showed that ubiquitylation required the ring-between-ring ligase, ARIH2. Here, we characterized the ASB9-CRL5 ubiquitylation of histones and show that histones histone 3 (H3) and histone 4 (H4) are polyubiquitylated by the ASB9-CRL5 whereas histones Histone 2A and Histone 2B are much poorer substrates. Many, but not all lysines in the histones are ubiquitylated suggesting some substrate specificity. Binding experiments show that the ligase-histone interaction is highly electrostatic and the neddylated ASB9-CRL5 binds with the highest affinity. Histones in nucleosomes or in complex with the chaperone Asf1, are not ubiquitylated. Only K48 and K63 polyubiquitin chains were observed, suggesting that the ubiquitylation probably drives histone degradation. The presence of ASB9 in specific cell types correlates with situations in which free histones H3 and H4 need to be degraded. In this work, we demonstrate that the ASB9-CRL5 is the ligase that facilitates degradation of histones H3 and H4. In addition, this work represents the first example of Cullin-5 mediated ubiquitylation that does not require a ring-between-ring "helper" ligase.

Investigating multiple protein post-translational modifications (PTMs) is critical for unraveling the complexities of protein regulation and the dynamic interplay among PTMs, a growing focus in proteomics. However, simultaneous analysis of diverse PTMs remains a significant technical challenge, as existing workflows struggle to balance throughput, sensitivity, and reproducibility, particularly when sample amounts are limited. To address these limitations, we present MoSAIC, a multi-PTM workflow integrating coenrichment strategies, multiplexing, fractionation, hybrid data acquisition, and unified data analysis, optimized for clinically relevant biological samples. This approach targets phosphorylation, glycosylation, acetylation, and ubiquitination, enabling comprehensive interrogation of these modifications simultaneously. Compared with the traditional Clinical Proteomic Tumor Analysis Consortium workflow, MoSAIC doubles PTM coverage (four versus two PTMs) while maintaining the same instrument time (24 mass spectrometry runs), achieving increased identifications of PTM-modified peptides. By leveraging fractionation and tandem mass tag labeling, we achieved concurrent identification and quantification of PTM-specific peptides from the same sample, enhancing throughput and data consistency. This robust workflow addresses key limitations in multi-PTM proteomics, providing a cost-effective and efficient platform to advance biological and clinical research.

Acute kidney injury (AKI), characterized by a rapid decline in renal function, has high mortality rates and frequently progresses to chronic kidney disease (CKD). A major contributor to AKI is ischemia-reperfusion injury (IRI). However, the global molecular changes underlying the AKI-to-CKD transition post-IRI remain to be fully elucidated. Using 4D label-free proteomic and phosphoproteomic analyses in a murine unilateral IRI model at 1 h, 1 day, 3 days, 7 days, and 28 days post injury, we systematically identified dysregulated proteins, phosphoproteins, and signaling pathways involved in the progression from AKI to CKD. Critically, these analyses consistently revealed the enrichment and sustained activation of NF-κB signaling, a key pathway driving inflammatory and fibrotic responses, across multiple time points. In addition, we identified significant impairment of fatty acid β-oxidation. Notably, our omics analysis specifically identified the dedicator of cytokinesis (Dock) protein family, with Dock2 emerging as a prime candidate due to its known immune regulatory functions. Dock2 expression showed significant upregulation post-IRI and was found predominantly localized to injured tubular epithelial cells. Functional validation demonstrated that Dock2 knockdown attenuated proinflammatory responses in tubular epithelial cells by inhibiting IKKβ-mediated NF-κB activation in vitro. Consistently, pharmacological inhibition of Dock2 by CPYPP ameliorated renal tubular injury, inflammation, and fibrosis in vivo. To our knowledge, this is the first study to reveal the role and mechanism of Dock2 in the AKI-to-CKD progression post-IRI. In conclusion, our findings delineate molecular mechanisms underpinning the transition from AKI to CKD and nominate Dock2 as a promising therapeutic target for mitigating this process.