Molecular & Cellular Proteomics最新文献

{"title":"MSstatsTMT improves accuracy of thermal proteome profiling by trading off temperature treatments and biological replicates.","authors":"Amanda M Figueroa-Navedo, Rohan Kapre, Tushita Gupta, Yingrong Xu, Clifford G Phaneuf, Pierre M Jean Beltran, Liang Xue, Alexander R Ivanov, Olga Vitek","doi":"10.1016/j.mcpro.2025.100999","DOIUrl":"https://doi.org/10.1016/j.mcpro.2025.100999","url":null,"abstract":"<p><p>Thermal proteome profiling investigates protein-protein, protein-nucleic acid, or protein-drug interactions, and the impact of metabolite binding and post-translational modifications on these interactions. The experiments quantitatively characterize biological samples treated with small molecules versus controls, and subjected to timed exposures to multiple temperatures. Typically, each enzymatically digested sample is labeled with a tandem mass tag (TMT), where each TMT channel corresponds to a specific temperature treatment, and profiled using liquid chromatography coupled with mass spectrometry in data-dependent data acquisition mode. The resulting mass spectra are processed with computational tools to identify and quantify proteins, and filter out noise. Protein-drug interactions are detected by fitting curves to the protein-level reporter ion abundances across the temperatures. Interacting proteins are identified by shifts in the fitted curves between treated samples and controls. In this manuscript, we focus on data processing and curve fitting in thermal proteome profiling. We review the statistical methods currently used for thermal proteome profiling, and demonstrate that such methods can yield substantially different results. We advocate for the statistical analysis strategy implemented in the open-source R package MSstatsTMT, as it does not require subjective pre-filtering of the data or curve fitting, and appropriately represents all the sources of variation. It supports experimental designs that trade-off temperatures for a larger number of biological replicates, and handles multiple drug concentrations or pools of samples treated with multiple temperatures, thus increasing the sensitivity of the results. We demonstrate these advantages of MSstatsTMT as compared to the currently used alternatives in a series of simulated and experimental datasets, which include conventional thermal proteome profiling and its OnePot counterpart that pools the samples treated at multiple temperatures into one sample, and incorporates multiple doses of a drug. The suggested MSstatsTMT-based workflow is documented in publicly available and fully reproducible R vignettes.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"100999"},"PeriodicalIF":6.1,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144180265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Method for Comparing Proteins Measured in Serum and Plasma by Olink® Proximity Extension Assay.","authors":"Rawan Shraim, Caroline Diorio, Scott W Canna, Erin Macdonald-Dunlop, Hamid Bassiri, Zachary Martinez, Anders Mälarstig, Afrouz Abbaspour, David T Teachey, Robert B Lindell, Edward M Behrens","doi":"10.1016/j.mcpro.2025.101000","DOIUrl":"https://doi.org/10.1016/j.mcpro.2025.101000","url":null,"abstract":"<p><p>Accurate measurement of secreted proteins in serum and plasma is essential for understanding mechanisms and developing reliable biomarkers. Recent technological advancements, such as proximity extension assay (PEA), have enabled high-throughput multiplex protein analyses from small sample volumes in either serum or plasma. Despite the increasing use of PEA-based proteomics and the generation of extensive datasets, integrated data from these two mediums remains challenging due to inherent differences in sample processing. To address this issue, we developed and validated protein-specific transformation factors using linear modeling to normalize protein measurements between serum and plasma proteins quantified using Olink®. Our analysis surveyed 1463 proteins across matched serum and plasma samples, identifying 686 transformation factors. The transformation factors were further validated using independent datasets generated from patients with different disease phenotypes and ages, and 551 of the models and transformation factors were reproducible. These transformation factors provide a valuable resource for normalizing PEA-based proteomic data across serum and plasma, ultimately enhancing the capacity for collaborative analyses and facilitating comprehensive insights across diverse disease phenotypes.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"101000"},"PeriodicalIF":6.1,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144182517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Employing Expression-Matched Controls Enables High Confidence Proximity-Based Interactome Classification.","authors":"Fulin Jiang, Xuezhen Ge, Eric J Bennett","doi":"10.1016/j.mcpro.2025.101001","DOIUrl":"https://doi.org/10.1016/j.mcpro.2025.101001","url":null,"abstract":"<p><p>Proximity labeling approaches have been widely utilized to define protein interactomes. Due to the inherent promiscuity of proximity labeling using TurboID-based approaches, identification and adoption of appropriate labeling controls is a pivotal step to mitigate background interference and enhance interactome assignment accuracy. Here, we evaluate the effectiveness of both expression controls and data normalization strategies in generating high confidence interactome maps. We demonstrate that the extent of control TurboID protein expression is strongly correlated with overall signal intensity and the number of identified proteins from streptavidin-enrichments. Discordant expression levels between the bait and control samples results in high frequency false-negative and false-positive identifications. Data normalization strategies help correct these expression differences but also introduce data distortion for proteins with high or low endogenous expression. Using the ubiquitin ligases RNF10 and HUWE1 as bait proteins, we demonstrate that matching TurboID expression between control and bait proteins allows for similar sampling of non-specific interactions. Using a matched expression strategy results in significantly reduced background interference and increases the accuracy of interactome assignments. These results document the need to alter proximity-labeling experimental workflows to include the generation of matched expression controls to enhance proximity labeling proteomics interactome mapping robustness and reproducibility.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"101001"},"PeriodicalIF":6.1,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144181299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-Omics Framework for Decoding Disease Mechanisms: Insights from Methylmalonic Aciduria.","authors":"Jianbo Fu, Vito R T Zanotelli, Cedric Howald, Nylsa Chammartin, Ilya Kolpakov, Ioannis Xenarios, D Sean Froese, Bernd Wollscheid, Patrick G A Pedrioli, Sandra Goetze","doi":"10.1016/j.mcpro.2025.100998","DOIUrl":"https://doi.org/10.1016/j.mcpro.2025.100998","url":null,"abstract":"<p><p>The diverse perspectives offered by multi-omics data analysis can aid in identifying the most relevant molecular pathways involved in disease processes, and findings in one layer can substantiate findings in other layers of information. Integrating data from multiple omics sources is becoming increasingly important to improve disease diagnosis and treatment, especially for conditions with complex and poorly understood underlying pathomechanisms. Methylmalonic aciduria (MMA), an inherited metabolic disorder, serves as an illustrative example of such a disease with poorly understood pathogenesis for which published multi-omics data are readily available. Re-using these FAIR data, obtained from the multi-omics digitization of 230 MMA patient samples, we pursued advanced data integration and analysis strategies to integrate different levels of biological information, combining genomic, transcriptomic, proteomic, and metabolomic profiling with biochemical and clinical data, with the aim of elucidating molecular perturbations in individuals affected by MMA. The analysis of protein-quantitative trait loci highlighted the importance of glutathione metabolism in the pathogenesis of methylmalonic acidemia (MMA). This finding was supported by correlation network analyses that integrated proteomics and metabolomics data, alongside gene set enrichment and transcription factor analyses based on disease severity from transcriptomic data. The correlation network analysis also revealed that lysosomal function is compromised in MMA patients, which is critical for maintaining metabolic balance. Our research introduces a comprehensive data analysis framework that effectively addresses the challenge of prioritizing disruptions in molecular pathways by accumulating evidence from multiple omics levels.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"100998"},"PeriodicalIF":6.1,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144172821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LncRNA MALAT1 facilitates HIV-1 replication by upregulation of CHCHD2 and downregulation of IFN-I expression.","authors":"Mei-Rong Wang, Cheng-Si Bai, Jian-Wei Dai, Lan Yang, Fang-Yi Quan, Jian-Chun Ma, Xing-Yuan Chen, Shao-Wei Zhu, Ying-Qi Xu, Zhou-Fu Xiang, Ya-le Jiang, Qi Cheng, Wei-Hao Zhang, Ke-Han Chen, Jian-Hua Wang, Yong Feng, Xiao-Ping Chen, Yong Xiong, Shu-Liang Chen, Wei Hou, Hai-Rong Xiong","doi":"10.1016/j.mcpro.2025.100997","DOIUrl":"https://doi.org/10.1016/j.mcpro.2025.100997","url":null,"abstract":"<p><p>Long noncoding RNAs (lncRNAs) are effective regulators of both RNA and protein functions throughout cell biology, including viral replication. Emerging studies have shown that lncRNAs activate or inhibit the replication and latency of HIV-1 by regulating different cellular mechanisms. Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is an oncogenic lncRNA required for paraspeckle integrity and has been proven to be linked to viral infection. However, the mechanisms by which it influences HIV-1 infection in macrophages remain unclear. In this study, we performed RNA-deep sequencing to compare the profiles of lncRNAs in macrophages with or without HIV-1 and found that MALAT1 was dramatically upregulated in HIV-1-infected macrophages. MALAT1 knockdown inhibited HIV-1 infection, whereas MALAT1 overexpression enhanced viral replication, indicating that MALAT1 promotes HIV-1 replication. We further performed proteomics analysis and found that coiled-coil-helix-coiled-coil-helix domain-containing 2 (CHCHD2) was the most downregulated protein affected by RNAi-mediated knockdown of MALAT1. We next demonstrated that MALAT1 favored HIV-1 replication in a CHCHD2-dependent manner and functioned as a competing endogenous RNA to regulate CHCHD2 expression by sponging miR-145-5p, which could mutually bind the MALAT1 and 3'UTR of chchd2 mRNA. Furthermore, knockdown of endogenous MALAT1 or CHCHD2 with specific small interfering RNAs (siRNAs) promoted the expression of IRF7, and enhanced the promoter activities of interferons-α and -β, increasing their production as well as that of a critical interferon-stimulated gene (ISG), myxovirus resistance protein B (MxB). Moreover, MALAT1 or CHCHD2 knockdown promoted the expression of STAT2 to enhance the production of downstream MxB, which expanded the role of CHCHD2 as a negative regulator of the innate immune response. These findings improve our understanding of MALAT1/miR-145-5p/CHCHD2 pathway regulation of HIV-1 replication in macrophages, providing new insights into potential targeted therapeutic interventions.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"100997"},"PeriodicalIF":6.1,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144143009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-independent Acquisition Proteomics Identifies Plasma Prostaglandin-H2D-isomerase as an Early Diagnostic Biomarker for STEMI and NSTEMI.","authors":"Hong-Mei Xue, Hai-Tao Hou, Yu Song, Huan-Xin Chen, Yun-Qiang Zhang, Wen-Tao Sun, Jie Zhou, Xiao-Lin Zhou, Na Sun, Qin Yang, Guo-Wei He","doi":"10.1016/j.mcpro.2025.100996","DOIUrl":"https://doi.org/10.1016/j.mcpro.2025.100996","url":null,"abstract":"<p><p>Myocardial infarction (MI) including ST-elevated MI (STEMI) and non-ST-elevated MI (NSTEMI), remains a leading cause of death worldwide. This study aimed to identify the early diagnostic biomarkers for STEMI and NSTEMI. Plasma samples from 386 patients was classified into four groups: control (CON) (n=62), unstable angina (UA) (n=62), STEMI (n=182), and NSTEMI (n=80). The protein profiles were analyzed using data-independent acquisition (DIA)-based proteomics to identify differentially abundant proteins (DAPs) followed by bioinformatics analysis and ELISA validation. In STEMI, 93 DAPs were detected. Among the selected DAPs that were further validated in a new cohort of patients, prostaglandin-H2 D-isomerase (PTGDS) was elevated at the earliest onset time of STEMI (T1, 1.45h (95%CI: 1.16-1.73)) or NSTEMI (T1, 1.48h (95%CI: 0.97-1.98)) while the current biomarkers (hs-TnI, Myo, CKMB, and BNP) remained within normal ranges. The analysis of diagnostic indices for plasma PTGDS demonstrated a sensitivity of 63.95% and specificity of 65.38% in STEMI, 70% and 71.15% in NSTEMI. Moreover, AUC was 0.61 (95%CI: 0.53-0.69) in STEMI and 0.78 (95%CI: 0.70-0.86) in NSTEMI. The present study demonstrates that in MI patients, plasma PTGDS increases at an earlier stage of onset time than the current biomarkers with similar sensitivity and specificity. Therefore, PTGDS has high potential to be developed as an early diagnostic biomarker. In particular, PTGDS might be of greater clinical significance for patients suspected for NSTEMI, for which biomarker could be more effective in identifying high-risk patients suffering from MI at early stage.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"100996"},"PeriodicalIF":6.1,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144143004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven extraction of human kinase-substrate relationships from omics datasets.","authors":"Benjamin Dominik Maier, Borgthor Petursson, Alessandro Lussana, Evangelia Petsalaki","doi":"10.1016/j.mcpro.2025.100994","DOIUrl":"https://doi.org/10.1016/j.mcpro.2025.100994","url":null,"abstract":"<p><p>Phosphorylation forms an important part of the signalling system that cells use for decision making and regulation of processes such as cell division and differentiation. In human, >90% of identified phosphosites don't have annotations regarding the relevant upstream kinase. At the same time around 30% of kinases (as annotated in Uniprot) have no known target. This knowledge gap stresses the need to make large scale, data-driven computational predictions. In this study, we have created a machine learning-based model to derive a probabilistic kinase-substrate network from omics datasets. Our methodology displays improved performance compared to other state-of-the-art kinase-substrate prediction methods and provides predictions for more kinases. Importantly, it better captures new experimentally-identified kinase-substrate relationships. It can therefore allow the improved prioritisation of kinase-substrate pairs for illuminating the dark human cell signalling space. Our model is integrated into a web server, SELPHI_2.0, to allow unbiased analysis of phosphoproteomics data, facilitating the design of downstream experiments to uncover mechanisms of signal transduction across conditions and cellular contexts.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"100994"},"PeriodicalIF":6.1,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144093630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Native Top-down analysis of membrane protein complexes directly from in vitro and native membranes.","authors":"Wonhyeuk Jung, Aniruddha Panda, Jaywon Lee, Snehasish Ghosh, Jared B Shaw, Kallol Gupta","doi":"10.1016/j.mcpro.2025.100993","DOIUrl":"https://doi.org/10.1016/j.mcpro.2025.100993","url":null,"abstract":"<p><p>Macromolecular organization of proteins and lipids in cellular membranes is fundamental to cell functionality. Recent advances in native mass spectrometry (nMS) have established it as a key analytical tool for capturing these associations. This typically necessitates the extraction of target membrane proteins from their physiological environments into detergent-like surroundings. In our recent studies using in vitro synthetic liposomes, we discovered that gas phase supercharging can selectively destabilize lipid bilayers and enable MS1 detection of embedded and associated protein-lipid complexes. Here, we further extend and apply this methodology to native cell-derived membrane vesicles. We demonstrate our ability to detect and ID protein complexes and their proteoforms directly from native membranes using supercharger-assisted pre-quadrupole activation followed by downstream native top-down MS/MS that combines both collision-based and electron capture-based fragmentations approaches. We first demonstrated this approach through native top-down identification of several integral membrane proteins from in vitro membranes. Subsequently, we developed a protocol to produce nMS-ready native membrane vesicles. Applying to E. coli total membranes, we generated nMS-ready vesicles and identified both integral and membrane-associated protein complexes of homomeric and heteromeric nature using our supercharging-enabled nTD-platform. For the hetero-pentameric BAM-complex, which includes the integral membrane protein BAM-A, we detected several lipidated proteoforms. For peripheral homo-dimeric DLDH, we identified bound endogenous metabolite co-factors. Furthermore, using BAM-complex, a crucial antibiotic target, we show how this platform could be utilized to study drug binding to membrane proteins directly from their native membranes.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"100993"},"PeriodicalIF":6.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144086652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proteomics and phosphoproteomics revealed dysregulated kinases and potential therapy for liver fibrosis.","authors":"Xinyu Cheng, Li Kang, Jinfang Liu, Qingye Wang, Zhenpeng Zhang, Li Zhang, Yuping Xie, Lei Chang, Daobing Zeng, Lantian Tian, Lingqiang Zhang, Ping Xu, Yanchang Li","doi":"10.1016/j.mcpro.2025.100991","DOIUrl":"https://doi.org/10.1016/j.mcpro.2025.100991","url":null,"abstract":"<p><p>Liver fibrosis is the initial stage of most liver diseases, and it is also a pathological process involving the liver in the late stages of many metabolic diseases. Therefore, it is important to systematically understand the pathological mechanism of liver fibrosis and seek therapeutic approaches for intervention and treatment of liver fibrosis. Disordered proteins and their post-translational modifications, such as phosphorylation, play vital roles in the occurrence and development of liver fibrosis. However, the regulatory mechanisms that govern this process remain poorly understood. In this study, we analyzed and quantified the liver proteome and phosphoproteome of CCl₄-induced early liver fibrosis model in mice. Proteomic analysis revealed that the pathways involved in extracellular matrix (ECM) recombination, collagen formation, metabolism and other related disorders, and protein phosphorylation modification pathways were also significantly enriched. In addition, western blotting and phosphoproteomics demonstrated that phosphorylation levels were elevated in the context of liver fibrosis. A total of 13,152 phosphosites were identified, with 952 sites increased while only 156 ones decreased. Furthermore, the upregulated phosphorylation sites, which exhibited no change at the proteome level mainly shared a common [xxxSPxxx] motif. Consequently, the kinases-substrates analysis ascertained the overactive kinases of these up-regulated substrates, which ultimately led to the identification of 13 significantly altered kinases within this dataset. These kinases were mainly catalogued into the STE, CMGC, and CAMK kinase families. Among them, STK4, GSK3α and CDK11B were subsequently validated though cellular and animal experiments, and the results demonstrated that their inhibitors could effectively reduce the activation of hepatic stellate cells and ECM production. These kinases may represent potential therapeutic targets for liver fibrosis, and their inhibitors may serve as promising anti- hepatic fibrosis drugs.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"100991"},"PeriodicalIF":6.1,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144078703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chronic alcohol consumption reprograms hepatic metabolism through organelle-specific acetylation in mice.","authors":"Mirjavid Aghayev, Megan R McMullen, Serguei Ilchenko, Andrea Arias-Alvarado, Victor Lufi, Jack Mathis, Hannah Marchuk, Tsung-Heng Tsai, Guo-Fang Zhang, Laura E Nagy, Takhar Kasumov","doi":"10.1016/j.mcpro.2025.100990","DOIUrl":"https://doi.org/10.1016/j.mcpro.2025.100990","url":null,"abstract":"<p><p>Post-translational acetylation of proteins by acetyl-CoA is a crucial regulator of proteostasis and substrate metabolism. Ethanol metabolism in the liver induces protein accumulation, acetylation and metabolic disruption. While acetylation impacts enzyme activity and stability, its role in ethanol-related protein accumulation and metabolic dysfunction remains unclear. Using stable isotope-based proteomics, acetylomics, and metabolic profiling in a mouse model of chronic ethanol-induced liver injury, we demonstrate that ethanol induces hepatic steatosis, inflammation, oxidative stress, and proteinopathy linked to altered protein turnover. Ethanol increased the cytosolic protein turnover related to oxidative stress and detoxification, while reducing turnover of mitochondrial metabolic enzymes. It also elevated the acetylation of mitochondrial enzymes and nuclear histones with minimal cytosolic changes, impairing mitochondrial protein degradation. These changes were associated with altered levels of acyl-CoAs and acyl-carnitines, amino acids, and tricarboxylic acid (TCA) cycle intermediates, reflecting impaired fatty acid oxidation, nitrogen disposal and TCA cycle activities. These results suggest that ethanol-induced acetylation contributes to liver injury and that targeting acetylation may offer treatment for alcohol-induced liver diseases.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"100990"},"PeriodicalIF":6.1,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144078222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}