Molecular & Cellular Proteomics最新文献

Wheldone, a fungal metabolite, was identified as a cytotoxic compound in high-grade serous ovarian cancer (HGSOC). Wheldone induced caspase 3/7-dependent apoptosis and reduced migration, invasion, and spheroid growth. Wheldone stimulated apoptosis in chemoresistant HGSOC models. Wheldone treatment caused significant downregulation of HNRNPD, a DNA repair protein, and increased DNA damage that could be blocked by N-acetyl-L-cysteine. In vivo, wheldone displayed minimal toxicity but was rapidly cleared from circulation, despite in vitro metabolic stability. Wheldone treatment in vivo did not demonstrate significant reduction in tumor burden. Therefore, in order to overcome these liabilities, it was necessary to find the protein target of wheldone so that modifications can be made to improve the drug-like characteristics of the compound. Using the drug-target interaction proteomics method, DiffPOP (differential precipitation of proteins), wheldone was found to act as an inhibitor of KIF11, a motor protein essential for mitotic spindle formation. An ATPase biochemical cell-free assay confirmed direct binding and functional inhibition of KIF11. Wheldone resulted in G2/M arrest and downstream regulation of mitotic proteins such as TPX2, AURKA, and phospho-histone H3. Proteomics after treatment of wheldone in four different HGSOC cancer cell lines all supported changes consistent with mitotic spindle assembly disruption. Further, KIF11 was one of only 13 proteins upregulated in all four cell lines treated. Overall, wheldone was found to be a fungal metabolite that inhibits KIF11 in chemoresistant ovarian cancer, with future studies needed to improve its pharmacokinetics and delivery.

Protein secretion is essential for the growth and virulence of Mycobacterium tuberculosis (Mtb), yet the organization and function of its secretion pathways remain poorly understood. We reviewed the existing literature, combined it with systematic queries, and finalized annotations based on experimental data and computational predictions to compile a curated list of 92 secretory components and 198 reactions involved in Sec, Tat, and ESX pathways. Using CRISPRi, targeted depletion of SecA1 or TatAC impaired both in vitro growth and ex vivo survival. Label-free quantitative secretome analysis revealed decreased export of substrates dependent on SecA1 and TatAC, with enrichment of cytosolic proteins in culture filtrates, indicating increased membrane dysbiosis. Membrane proteomics showed elevated levels of proteins engaged in intermediary and lipid metabolism, while proteins associated with the cell wall and cell processes decreased, suggesting weakened membrane integrity. Loss of SecA1 or TatAC increased membrane permeability, with the effect being more pronounced in the case of TatAC, and caused structural abnormalities seen under electron microscopy. Overall, our integrated multi-omics and functional genetics studies demonstrate that the SecA1 and Tat pathways are essential for maintaining membrane homeostasis in Mtb. These results suggest that essential secretory proteins may be promising targets for therapeutic intervention.

Saturated fatty acids such as palmitic acid (PA) can induce lipotoxic stress, whereas monounsaturated fatty acids like oleic acid (OA) often promote adaptive responses through lipid droplets (LDs) formation. Here, we reveal that epithelial-mesenchymal transition (EMT) profoundly influences the lipotoxic response of colorectal cancer cells. Using the epithelial-like HCT15 and mesenchymal-like HCT116 cell lines, we combined proteomic, metabolic, and imaging analyses to elucidate how EMT status determines lipid storage capacity and resistance to PA-induced toxicity. A Basal proteomic profiling highlighted a striking divergence in metabolic changes: HCT15 cells displayed enhanced glycolysis and reduced expression of LDs biogenesis proteins, while HCT116 cells exhibited oxidative metabolism and a "lipid-rich" proteomic signature enriched in PLIN2, GPAT3, and DGAT1. Functionally, PA triggered massive cytotoxicity and failed to induce LDs in HCT15 cells, correlating with DGAT1/2 downregulation and suppressed triacylglycerol synthesis. In contrast, HCT116 cells showed modest LDs accumulation, preserved mitochondrial function, and strong resistance to lipotoxic stress. OA treatment restored LDs formation and cell viability in both models, underscoring the protective role of unsaturated fatty acids. Notably, forced EMT induction in HCT15 cells by PMA markedly enhanced LDs accumulation and reduced PA-induced death, confirming that EMT confers metabolic plasticity and lipid-buffering capacity. These findings demonstrate that EMT status modulates differential lipid handling and stress adaptation in colon cancer cells, linking mesenchymal transition to enhanced LDs biogenesis and survival under lipotoxic conditions. Data are available via ProteomeXchange with identifier PXD071641.

Heparan sulfates (HS) are a group of heterogenous linear, sulfated polysaccharides that play a role in in health and many diseases including cancer, cardiovascular, and kidney diseases. The structural variety of HS has greatly challenged the development and utility of HS analytics, particularly for native (non-depolymerized) structures, leaving a significant gap in HS technologies for clinical application. Mass spectrometry (MS)-based profiling with bioinformatics offers a top-down approach that can retain variety in large data sets. Using healthy human plasmas, we developed an MS glycoprofiling approach for native HS oligosaccharides, which retains the structural complexity of each individual HS chain and generates an HS 'index' (or Heparan-ome) for each patient. As a proof of concept, analysis of 53 plasma samples ranging from 4 groups of kidney disease patients revealed a new subset cluster (21%, 4/19) of membranous glomerulopathy (MG) patients with distinct HS profiles, highlighting the potential of HS glycoprofiling as a powerful new approach into clinical practice, which warrants future development into quantitative oliGAGomics and clinical diagnostics of kidney and other diseases.

Introduction: Kidney transplantation (KTx) is the preferred treatment for kidney failure, offering improved survival, quality of life and cost-effectiveness compared to dialysis. However, post-transplant management is challenging due to the limited lifespan of transplanted organs, often requiring repeat transplants. Current methods for monitoring post-transplant complications are invasive and have limitations. Therefore, there is urgent need for novel non-invasive biomarkers. This study investigates the proteomic composition of urine to understand renal biology during the process of transplantation and to identify potential markers for outcome prediction.

Materials and methods: Urine samples were collected from donors before transplantation and from recipients 4 weeks and 1 year after transplantation. Proteomic analysis was performed using mass spectrometry and label-free quantification. Statistical analyses included principal component analysis (PCA) and enrichment analysis. The resulting key findings were confirmed in an independent validation cohort. In addition, correlative regression models to evaluate the relationship between protein abundance and clinical outcomes in the further course after transplantation were performed.

Results: 106 urine samples in the setting of 70 kidney transplantations were analyzed. PCA revealed distinct clustering of donor and recipient samples, indicating significant proteomic changes after transplantation. Hierarchical clustering and gene ontology analysis identified molecular changes as a response to transplantation and showed an over-representation of relevant pathways related to inflammation, cell immune response and coagulation in both original and validation cohort. Multivariate regression analysis, including linear and logistic regression, identified 11 potential protein biomarkers including ORM2, IL1RAP, APP, and FABP4 as predictors of eGFR 12 months after and 1 HP as predictor of infections within the first year after transplantation, respectively.

Discussion: This study underscores the potential of non-invasive urine proteomics for identifying biological processes involved in kidney transplantation and for enhancing post-transplant monitoring and outcome prediction. We identified 12 potential biomarkers with added value to standard clinical parameters linked to transplant outcomes, which will be promising candidates for future outcome monitoring after KTx.

Protein structural dynamics drive changes in protein function, making their capture essential for interrogating biological systems. Here we review limited proteolysis coupled to mass spectrometry (LiP-MS), a structural and chemical proteomics method that uses changes in susceptibility to protease cleavage to profile proteome-wide protein structural changes within complex biological samples. In the decade since its development, LiP-MS has become a broadly used structural proteomics method with peptide-level resolution. It has identified drug targets, delineated altered cellular pathways in response to complex perturbations, revealed structural information on otherwise challenging protein targets, and enabled demonstration of the new concept of structural biomarkers of disease. Because LiP-MS simultaneously probes numerous types of molecular events, such as molecular binding, changes in enzyme activity, chemical modifications, allosteric conformational changes, aggregation, and unfolding, it supports a new proteomics workflow which we term 3D proteomics. This workflow enables the detection of specific functional sites within proteins that are altered upon perturbation, thereby guiding the generation of molecular hypotheses. Further, by globally profiling structural in addition to protein abundance changes, LiP-MS has proven able to greatly increase the information content of functional proteomics screens. In sum, LiP-MS has supported the development of a novel conceptual framework for generating, visualizing and interpreting structural proteomics data with peptide level resolution, thereby comprehensively probing biological systems. Here we survey the applications of LiP-MS, discuss methodological variants developed by us and others, and describe the use of this new type of omics readout for structural, functional, chemical and biomarker discovery proteomics.

Group A Streptococcus (Streptococcus pyogenes, GAS) is a bacterial pathogen that commonly causes local infections in humans and can lead to invasive diseases. GAS infections trigger complex host immune and tissue responses, yet how these processes are coordinated over time and across different tissues remains poorly understood. To explore the spectrum of GAS infection, we examined responses in a skin infection model at multiple proteome levels, characterizing local and distant tissues with variable infection responses. We map changes in canonical innate and adaptive immune signaling while uncovering new mechanisms in the context of skin infection. We uncover the robust and time-dependent expression of one family of proteins, chitinase-like proteins, that coincides with immune cell infiltration of local tissues. Because immuno-modulatory networks are tightly regulated through post-translational modifications, we integrated global proteomic data with cytokine signaling and key phosphoproteome changes. This analysis revealed correlations between mTOR and kinase signaling pathways that diverge at local and systemic tissues. Our systems-based approach provides a rigorous evaluation of a GAS skin infection, characterizing host proteome remodeling across experimental groups and individual mice.

This report describes single-cell proteomic analyses of cells dissociated from a complex mammalian tissue using direct label-free mass spectrometry (SCP-MS). The nanoDTSC approach was applied to profile individual cells from aorta of male and female wild-type and Fbn1^C1041G/+ Marfan mice. Leiden clustering identified all major aortic cell types including 7 distinct smooth muscle cell subtypes, with informative differences in cell proportions and differentially expressed proteins within cell types observed for both genotype and sex. Comparisons between single-cell RNA and single-cell proteomic profiles showed similarities in detection of major subtypes but not differentiation between smooth muscle cell subtypes. Integrated multi-omics analysis further identified genotype-dependent enrichment of unique SMC subtypes, relative to either protein or RNA datasets. Multiplexed-fluorescence based spatial proteomics validated several of these key genotype markers. Overall, these studies demonstrate the power of SCP-MS to detect novel aneurysm biology and serve as a guide for future development of SCP-MS methodology as it is applied to complex tissue cell mixtures and its integration with other omic modalities.

Accurate determination of protein sequences is central to biology. Protein-based therapeutics, such as antibodies and nanobodies, are not encoded in reference genomes, challenging their accurate characterization via standard proteomics. Current methods rely on indirect inference, fragmented outputs, and labor-intensive workflows, which hinder functional insights and routine application. Here, we present a generalizable, end-to-end workflow for direct protein sequencing, combining streamlined sample preparation, AI-driven de novo peptide sequencing, and tailored assembly to reconstruct contiguous protein sequences. A novel composite scoring framework prioritizes longer assemblies and coverage, enhancing accuracy and reducing ambiguity. Validation across diverse protein modalities demonstrates its utility and ability to robustly sequence functionally critical regions of selected proteins. This workflow represents an advance in precision proteomics with promising applications in therapeutic discovery, immune profiling, and protein science.