Molecular & Cellular Proteomics最新文献

A plethora of studies suggest that a high-fat diet in combination with a high amyloid load causes synaptic insulin resistance and is a risk factor for Alzheimer's disease. Our understanding of the underlying mechanisms is still fragmented. To gain new insights, we conducted integrated proteomic and phosphoproteomic profiling of hippocampal synaptosomes from WT and a transgenic mouse line with a high amyloid load (heterozygous TBA2.1 mice) that show no overt signs of neurodegeneration and dementia. Mice were fed with a regular or high-fat diet. Data-independent acquisition quantified over 5400 proteins, revealing a stable synaptic proteome across conditions. However, the combination of high amyloid load and high-fat diet triggered coordinated remodeling of lipid metabolism pathways, particularly mitochondrial and peroxisomal fatty acid catabolism. Phosphoproteomic analysis showed pronounced activation of lipid- and stress-responsive kinases, including protein kinase C-α, along with increased inhibitory phosphorylation of insulin receptor substrates (IRS1/2). In vitro experiments indicate that blocking protein kinase C-α indeed prevents synaptic insulin resistance in primary neurons. The findings suggest that this proteomic workflow, combined with kinase pathway analysis, can reveal nodal points for interventions in a complex disease state with a trajectory to Alzheimer's disease.

Loss of the protein scaffold Coiled-coil and C2 domain containing 1A (CC2D1A) leads to intellectual disability, autism spectrum disorder, and other neurodevelopmental presentations in humans. CC2D1A interactions have been studied in different cell lines proposing diverse roles in endolysosomal maturation and intracellular signaling, but the composition and function of the CC2D1A interactome remain poorly understood, especially in the brain. We performed comprehensive proteomic analyses to characterize CC2D1A binding partners, first comparing immunoprecipitations with three different anti-CC2D1A antibodies in HEK293 cells and then probing the mouse hippocampus. In HEK cells, gene ontology analysis revealed broad interaction networks in the nucleus, mitochondrion, and cytosol with a variety of functions unified by the best characterized CC2D1A interactor, the Endosomal sorting complex required for transport III (ESCRT-III) component Charged multivesicular body protein 4B (CHMP4B), and reflecting the pleiotropic role of CC2D1A in membrane trafficking and protein signaling. In the hippocampus, using stringent criteria, we identified 41 high-confidence interactors in addition to CHMP4B revealing roles for protein translation, cytoskeletal organization, and synaptic function. The HEK studies had also pointed to Coiled-coil and C2 domain containing 1B (CC2D1B), the only paralog of CC2D1A, as an interactor. We confirmed that not only the two proteins can bind in the brain, but also localize in different synaptic compartments, showing that CC2D1A is uniquely enriched in the post-synapse. This supports a unique function of CC2D1A in regulation of synaptic transmission that could explain the more severe cognitive deficits in humans and mice upon its loss. To our knowledge these findings provide the most comprehensive characterization of the CC2D1A interactome to date, elucidating novel, multifaceted, and dynamic cellular functions, providing potential implications for its role in neurodevelopmental disorders.

Post-COVID-19 sequelae have become an emerging global health issue, but the mechanisms for the sustained susceptibility of convalescents to the sequelae remain poorly understood. Here we report the use of a restricted open-search approach to explore the molecular imprints of SARS-CoV-2 infection left on the proteome of 412 COVID-19 patients and convalescences. A total of 827 non-standard amino acid variations, chemically modified residues as well as post-translational modifications, termed non-coded amino acids (ncAAs), are found spreading over 29,814 sites in patient's serum proteins. Markedly, widespread ncAAs are induced and sustainedly imprinted on the serum proteome predominately perturbing the immunoglobulin-mediated immune response, complement activation and coagulation regulation even 12 months after recovery. Sustained amino acid variations and chemical modifications are found in the complementary‑determining regions (CDRs) of the variable region of immunoglobulin contributing to the interactions between the emerging antibody and antigens; durable chemical amino acid modifications found in the hyper ncAA-modified regions of the constant region of immunoglobulin important for the interaction with the complement and regulatory receptors. In the complement system, inducible ncAAs are memorized in the components essential for the complement activation, amplification cascades and membrane attack processes. Thus, the workflow described in this study can be used to identify the molecular imprints of viral infection at the proteomic scale, particularly the specific antibodies and the immune targets left in COVID-19 patients and convalescents.

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.

Mass spectrometry (MS) is the method of choice for high-throughput identification of immunopeptides, which are generated by intracellular proteases, unlike proteomics peptides that are typically derived from trypsin-digested proteins. Therefore, the searching space for immunopeptides is not limited by proteolytic specificity, requiring more sophisticated software algorithms to handle the increased complexity. Despite the widespread use of MS in immunopeptidomics, there is a lack of systematic evaluation of data processing software, making it challenging to identify the optimal solution. In this study, we provide a comprehensive benchmarking of the most widespread/used data-dependent acquisition-based software platforms for immunopeptidomics: MaxQuant (https://maxquant.org/), FragPipe (https://fragpipe.nesvilab.org/), PEAKS (https://www.bioinfor.com/peaks-software/) and major histocompatibility complexquant. The evaluation was conducted using data obtained from the JY cell line using the Thunder-data-dependent acquisition-parallel accumulation and serial fragmentation method. We assessed each software's ability to identify immunopeptides and compared their identification confidence. Additionally, we examined potential biases in the results and tested the impact of database size on immunopeptide identification efficiency. Our findings demonstrate that all software platforms successfully identify the most prominent subset of immunopeptides with 1% false discovery rate control, achieving medium to high identification confidence correlations. The largest number of immunopeptides was identified using the commercial PEAKS software, which is closely followed by FragPipe, making it a viable non-commercial alternative. However, we observed that larger database sizes negatively impacted the performance of some software platforms more than others. These results provide valuable insights into the strengths and limitations of current MS data processing tools for immunopeptidomics, supporting the immunopeptidomics/MS community in determining the right choice of software.

A Disintegrin And Metalloproteinase 9 (ADAM9) is a cell-surface protease that can shed the ectodomain of membrane protein substrates. Dysregulated ADAM9 activity has been implicated in several diseases, such as solid tumors, autoimmunity, inflammatory diseases, and coronavirus disease 2019. Despite its importance, the substrates and targets of ADAM9 in normal and pathological processes are poorly understood. Here, we developed an integrative proteotranscriptomics approach to systematically identify the transcriptional and post-transcriptional targets of ADAM9 in HCT116 cells, which have a stable diploid karyotype suitable for omics analyses. Using this approach, we uncovered major signaling pathways downstream of ADAM9, including the oncogenic mechanistic target of rapamycin pathway and the tumor suppressor Forkhead Box O pathway. We also identified several direct and indirect substrates for ADAM9, which may mediate the pathophysiological roles of this protease. This study provides new mechanistic insights into the function of ADAM9 as well as a method that can be applied to other membrane proteases.

The fecal immunochemical test (FIT) for detecting fecal occult blood, used alone or in combination with other stool biomarkers, has been demonstrated to be effective in the context of colorectal cancer (CRC) screening programs. However, FIT yields a significant proportion of false positives leading to unnecessary colonoscopies. In this study, we have investigated whether leftover FIT stool samples could be repurposed for proteomics analysis as a triage step for patients before recommending colonoscopy. High-throughput mass spectrometry analyses on a set of 141 FIT-positive samples (50 controls with no lesion, 45 with advanced adenomas and 46 with CRC) in combination with machine learning tools were used. Results showed that with a specificity ≥90%, a large proportion of the false FIT positives could be identified thus providing an efficient strategy for reducing unnecessary colonoscopies. Furthermore, CRC cases were also precisely predicted to be true positives, thus providing an approach for prioritizing patients for colonoscopy. In conclusion, this study demonstrates the feasibility of using proteomics for analysis of leftover FIT stool samples as an intermediate step to triage patients selected for colonoscopy in CRC screening programs.

Heparan sulfates (HSs) are a group of heterogenous linear, sulfated polysaccharides that play a role 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 (nondepolymerized) structures, leaving a significant gap in HS technologies for clinical application. Mass spectrometry-based profiling with bioinformatics offers an approach that can retain variety in large datasets. Using healthy human plasmas, we developed a mass spectrometry 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 four groups of kidney disease patients revealed a new subset cluster (21%, 4/19) of membranous glomerulopathy patients with distinct HS profiles, highlighting the potential of HS glycoprofiling as a powerful new approach to clinical practice, which warrants future development into quantitative oligosaccharide glycosaminoglycanomics and clinical diagnostics of kidney and other diseases.

Esophageal squamous cell carcinoma (ESCC) exhibits high prevalence in China and poor prognosis despite neoadjuvant chemotherapy (NACT), with significant chemoresistance development. Tumor-associated metabolic reprogramming and NACT-induced cellular stress promote lactate accumulation, which serves as a precursor for lysine lactylation (Kla), a post-translational modification (PTM) potentially regulating cancer progression. We hypothesized that systematic characterization of the lactylome in response to NACT could reveal critical molecular mechanisms underlying treatment and identify new therapeutic vulnerabilities in ESCC. Herein, through comprehensive proteomic and lactylome profiling of tumor and adjacent normal tissues from 31 ESCC patients (with or without NACT treatment), we identified 8281 proteins and 1836 Kla sites across 62 samples. NACT induced substantial lactylome alterations with 307 differentially expressed Kla sites predominantly in non-histone proteins involved in DNA damage response and metabolic pathways. Our data revealed that while NACT-induced suppression of energy metabolism, coupled with upregulated HRD1 complex expression, may exert potential pro-apoptotic effects, the activation of ribosome biogenesis and increased nucleoprotein lactylation triggered tumor-protective mechanisms. Mechanistically, we demonstrated that DNA damage and elevated lactate levels induced PARP1 K654 lactylation, enhancing its enzymatic activity and augmenting poly(ADP-ribosyl)ation of downstream targets, potentially playing a pivotal role in chemotherapy resistance-associated pathways. This comprehensive tissue-level landscape of Kla dynamics in ESCC response to chemotherapy establishes Kla as a critical regulatory mechanism in treatment response, potentially offering novel therapeutic targets and predictive biomarkers for personalized treatment strategies.

The isolation of major histocompatibility complex (MHC) ligands and subsequent analysis by mass spectrometry is considered the gold standard for defining targets for T cell-based immunotherapies. However, as many targets of high tumor specificity are only presented at low abundance on the cell surface of tumor cells, the efficient isolation of these peptides is crucial for their successful detection. Here, we demonstrate how optimizing the MHC ligand isolation strategy, based on both the presenting MHC alleles and the individual peptide level, enhances the identification of specific MHC ligands. This ideally acknowledges not only the hydrophobicity but also the post-translational modifications of the respective MHC ligands. To further improve the identification and characterization of MHC ligands, we developed an MHC class I ligand prediction algorithm (ARDisplay-I) that outperforms current state-of-the-art tools when benchmarked against competitors such as netMHCpan 4.1, MixMHCpred, or MHCflurry. Implementing these strategies can augment the development of T cell receptor-based therapies by improving the identification of novel immunotherapy targets and enriching the resources available in the computational immunology field through a superior MHC presentation prediction algorithm.