[Identification of Protein-Coding Gene Markers in Breast Invasive Carcinoma Based on Machine Learning].

Objective To screen out the biomarkers linked to prognosis of breast invasive carcinoma based on the analysis of transcriptome data by random forest (RF),extreme gradient boosting (XGBoost),light gradient boosting machine (LightGBM),and categorical boosting (CatBoost). Methods We obtained the expression data of breast invasive carcinoma from The Cancer Genome Atlas and employed DESeq2,t-test,and Cox univariate analysis to identify the differentially expressed protein-coding genes associated with survival prognosis in human breast invasive carcinoma samples.Furthermore,RF,XGBoost,LightGBM,and CatBoost models were established to mine the protein-coding gene markers related to the prognosis of breast invasive cancer and the model performance was compared.The expression data of breast cancer from the Gene Expression Omnibus was used for validation. Results A total of 151 differentially expressed protein-coding genes related to survival prognosis were screened out.The machine learning model established with C3orf80,UGP2,and SPC25 demonstrated the best performance. Conclusions Three protein-coding genes (UGP2,C3orf80,and SPC25) were screened out to identify breast invasive carcinoma.This study provides a new direction for the treatment and diagnosis of breast invasive carcinoma.

Nuclear translocation of metabolic enzyme PKM2 participates in high glucose-promoted HCC metastasis by strengthening immunosuppressive environment.

Although some cohort studies have indicated a close association between diabetes and HCC, the underlying mechanism about the contribution of diabetes to HCC progression remains largely unknown. In the study, we applied a novel HCC model in SD rat with diabetes and a series of high glucose-stimulated cell experiments to explore the effect of a high glucose environment on HCC metastasis and its relevant mechanism. Our results uncovered a novel regulatory mechanism by which nuclear translocation of metabolic enzyme PKM2 mediated high glucose-promoted HCC metastasis. Specifically, high glucose-increased PKM2 nuclear translocation downregulates chemerin expression through the redox protein TRX1, and then strengthens immunosuppressive environment to promote HCC metastasis. To the best of our knowledge, this is the first report to elucidate the great contribution of a high glucose environment to HCC metastasis from a new perspective of enhancing the immunosuppressive microenvironment. Simultaneously, this work also highlights a previously unidentified non-metabolic role of PKM2 and opens a novel avenue for cross research and intervention for individuals with HCC and comorbid diabetes.

Empagliflozin attenuates liver fibrosis in high-fat diet/streptozotocin-induced mice by modulating gut microbiota.

The effects of SGLT2 inhibitors on hepatic fibrosis in diabetes remain unclear. This study aimed to investigate the effects of empagliflozin on liver fibrosis in high-fat diet/streptozotocin-induced mice and the correlation with gut microbiota. After the application of empagliflozin for 6 weeks, we performed oral glucose tolerance and intraperitoneal insulin tolerance tests to assess glucose tolerance and insulin resistance, and stained liver sections to evaluate histochemical and hepatic pathological markers of liver fibrosis. Moreover, 16S rRNA amplicon sequencing was performed on stool samples to explore changes in the composition of intestinal bacteria. We finally analysed the correlation between gut microbiome and liver fibrosis scores or indicators of glucose metabolism. The results showed that empagliflozin intervention improved glucose metabolism and liver function with reduced liver fibrosis, which might be related to changes in intestinal microbiota. In addition, the abundance of intestinal probiotic Lactobacillus increased, while Ruminococcus and Adlercreutzia decreased after empagliflozin treatment, and correlation analysis showed that the changes in microbiota were positively correlated with liver fibrosis and glucose metabolism. Overall, considering the contribution of the gut microbiota in metabolism, empagliflozin might have improved the beneficial balance of intestinal bacteria composition. The present study provides evidence and indicates the involvement of the gut-liver axis by SGLT2 inhibitors in T2DM with liver fibrosis.

Lactylation prediction models based on protein sequence and structural feature fusion.

Lysine lactylation (Kla) is a newly discovered posttranslational modification that is involved in important life activities, such as glycolysis-related cell function, macrophage polarization and nervous system regulation, and has received widespread attention due to the Warburg effect in tumor cells. In this work, we first design a natural language processing method to automatically extract the 3D structural features of Kla sites, avoiding potential biases caused by manually designed structural features. Then, we establish two Kla prediction frameworks, Attention-based feature fusion Kla model (ABFF-Kla) and EBFF-Kla, to integrate the sequence features and the structure features based on the attention layer and embedding layer, respectively. The results indicate that ABFF-Kla and Embedding-based feature fusion Kla model (EBFF-Kla), which fuse features from protein sequences and spatial structures, have better predictive performance than that of models that use only sequence features. Our work provides an approach for the automatic extraction of protein structural features, as well as a flexible framework for Kla prediction. The source code and the training data of the ABFF-Kla and the EBFF-Kla are publicly deposited at: https://github.com/ispotato/Lactylation_model.

Global profiling of protein lactylation in Caenorhabditis elegans.

Lactylation, as a novel posttranslational modification, is essential for studying the functions and regulation of proteins in physiological and pathological processes, as well as for gaining in-depth knowledge on the occurrence and development of many diseases, including tumors. However, few studies have examined the protein lactylation of one whole organism. Thus, we studied the lactylation of global proteins in Caenorhabditis elegans to obtain an in vivo lactylome. Using an MS-based platform, we identified 1836 Class I (localization probabilities > 0.75) lactylated sites in 487 proteins. Bioinformatics analysis showed that lactylated proteins were mainly located in the cytoplasm and involved in the tricarboxylic acid cycle (TCA cycle) and other metabolic pathways. Then, we evaluated the conservation of lactylation in different organisms. In total, 41 C. elegans proteins were lactylated and homologous to lactylated proteins in humans and rats. Moreover, lactylation on H4K80 was conserved in three species. An additional 238 lactylated proteins were identified in C. elegans for the first time. This study establishes the first lactylome database in C. elegans and provides a basis for studying the role of lactylation.

Profiling of SARS-CoV-2 neutralizing antibody-associated antigenic peptides signature using proteome microarray.

The profile of antibodies against antigenic epitopes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during neutralizing antibody (NAb) decay has not been clarified. Using a SARS-CoV-2 proteome microarray that contained viral antigenic peptides, we analyzed the characteristics of the humoral response in patients with coronavirus disease 19 (COVID-19) in a longitudinal study. A total of 89 patients were recruited, and 226 plasma samples were serially collected in 2020. In the antigenic peptide microarray, the level of immunoglobulin G (IgG) antibodies against peptides within the S2 subunit (S-82) and a conserved gene region in variants of interest, open reading frame protein 10 (ORF10-3), were closely associated with the presence of SARS-CoV-2 NAbs. In an independent evaluation cohort of 232 plasma samples collected from 116 COVID-19 cases in 2020, S82-IgG titers were higher in NAbs-positive samples (p = 0.002) than in NAbs-negative samples using enzyme-linked immunosorbent assay. We further collected 66 plasma samples from another cohort infected by Omicron BA.1 virus in 2022. Compared with the samples with lower S82-IgG titers, NAb titers were significantly higher in the samples with higher S82-IgG titers (p = 0.04). Our findings provide insights into the understanding of the decay-associated signatures of SARS-CoV-2 NAbs.

Analysis of differential membrane proteins related to matrix stiffness-mediated metformin resistance in hepatocellular carcinoma cells.

BACKGROUND: Our previous work shows that increased matrix stiffness not only alters malignant characteristics of hepatocellular carcinoma (HCC) cells, but also attenuates metformin efficacy in treating HCC cells. Here, we identified differential membrane proteins related to matrix stiffness-mediated metformin resistance for better understand therapeutic resistance of metformin in HCC. METHODS: Differential membrane proteins in HCC cells grown on different stiffness substrates before and after metformin intervention were screened and identified using isobaric tags for relative and absolute quantification (iTRAQ) labeling coupled with the liquid chromatography-tandem mass spectrometry (LC-MS/MS), then bioinformatic analysis were applied to determine candidate membrane protein and their possible signaling pathway. RESULTS: A total of 5159 proteins were identified and 354 differential membrane proteins and membrane associated proteins, which might be associated with matrix stiffness-mediated metformin resistance were discovered. Then 94 candidate membrane proteins including 21 up-regulated protein molecules and 73 down-regulated protein molecules were further obtained. Some of them such as Annexin A2 (ANXA2), Filamin-A (FLNA), Moesin (MSN), Myosin-9 (MYH9), Elongation factor 2 (eEF2), and Tax1 binding Protein 3 (TAX1BP3) were selected for further validation. Their expressions were all downregulated in HCC cells grown on different stiffness substrates after metformin intervention. More importantly, the degree of decrease was obviously weakened on the higher stiffness substrate compared with that on the lower stiffness substrate, indicating that these candidate membrane proteins might contribute to matrix stiffness-mediated metformin resistance in HCC. CONCLUSIONS: There was an obvious change in membrane proteins in matrix stiffness-mediated metformin resistance in HCC cells. Six candidate membrane proteins may reflect the response of HCC cells under high stiffness stimulation to metformin intervention, which deserve to be investigated in the future.

Global analysis of lysine acetylation in the brain cortex of K18-hACE2 mice infected with SARS-CoV-2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected hundreds of millions of people all over the world and thus threatens human life. Clinical evidence shows that SARS-CoV-2 infection can cause several neurological consequences, but the existing antiviral drugs and vaccines have failed to stop its spread. Therefore, an understanding of the response to SARS-CoV-2 infection of hosts is vital to find a resultful therapy. Here, we employed a K18-hACE2 mouse infection model and LC-MS/MS to systematically evaluate the acetylomes of brain cortexes in the presence and absence of SARS-CoV-2 infection. Using a label-free strategy, 3829 lysine acetylation (Kac) sites in 1735 histone and nonhistone proteins were identified. Bioinformatics analyses indicated that SARS-CoV-2 infection might lead to neurological consequences via acetylation or deacetylation of important proteins. According to a previous study, we found 26 SARS-CoV-2 proteins interacted with 61 differentially expressed acetylated proteins with high confidence and identified one acetylated SARS-CoV-2 protein nucleocapsid phosphoprotein. We greatly expanded the known set of acetylated proteins and provide the first report of the brain cortex acetylome in this model and thus a theoretical basis for future research on the pathological mechanisms and therapies of neurological consequences after SARS-CoV-2 infection.

Global profiling of lysine lactylation in human lungs.

Lactate is closely related to various cellular processes, such as angiogenesis, responses to hypoxia, and macrophage polarization, while regulating natural immune signaling pathways and promoting neurogenesis and cognitive function. Lysine lactylation (Kla) is a novel posttranslational modification, the examination of which may lead to new understanding of the nonmetabolic functions of lactate and the various physiological and pathological processes in which lactate is involved, such as infection, tumorigenesis and tumor development. Using liquid chromatography-tandem mass spectrometry (LC-MS/MS), researchers have identified lactylation in human gastric cancer cells and some other species, but no research on lactylation in human lungs has been reported. In this study, we performed global profiling of lactylation in human lungs under normal physiological conditions, and 724 Kla sites in 451 proteins were identified. After comparing the identified proteins with those reported in human lactylation datasets, 141 proteins that undergo lactylation were identified for the first time in this study. Our work expands the database on human lactylation and helps advance the study on lactylation function and regulation under physiological and pathological conditions.

Early-stage predictors of deterioration among 3145 nonsevere SARS-CoV-2-infected people community-isolated in Wuhan, China: A combination of machine learning algorithms and competing risk survival analyses.

OBJECTIVE: To determine which early-stage variables best predicted the deterioration of coronavirus disease 2019 (COVID-19) among community-isolated people infected with severe acute respiratory syndrome coronavirus 2 and to test the performance of prediction using only inexpensive-to-measure variables. METHODS: Medical records of 3145 people isolated in two Fangcang shelter hospitals (large-scale community isolation centers) from February to March 2020 were accessed. Two complementary methods-machine learning algorithms and competing risk survival analyses-were used to test potential predictors, including age, gender, severity upon admission, symptoms (general symptoms, respiratory symptoms, and gastrointestinal symptoms), computed tomography (CT) signs, and comorbid chronic diseases. All variables were measured upon (or shortly after) admission. The outcome was deterioration versus recovery of COVID-19. RESULTS: More than a quarter of the 3145 people did not present any symptoms, while one-third ended isolation due to deterioration. Machine learning models identified moderate severity upon admission, old age, and CT ground-glass opacity as the most important predictors of deterioration. Removing CT signs did not degrade the performance of models. Competing risk models identified age ≥ 35 years, male gender, moderate severity upon admission, cough, expectoration, CT patchy opacity, CT consolidation, comorbid diabetes, and comorbid cardiovascular or cerebrovascular diseases as significant predictors of deterioration, while a stuffy or runny nose as a predictor of recovery. CONCLUSIONS: Early-stage prediction of COVID-19 deterioration can be made with inexpensive-to-measure variables, such as demographic characteristics, severity upon admission, observable symptoms, and self-reported comorbid diseases, among asymptomatic people and mildly to moderately symptomatic patients.

Using ATCLSTM-Kcr to predict and generate the human lysine crotonylation database.

Lysine crotonylation (Kcr) is an evolutionarily conserved protein post-translational modifications, which plays an important role in cellular physiology and pathology, such as chromatin remodeling, gene transcription regulation, telomere maintenance, inflammation, and cancer. Tandem mass spectrometry (LC-MS/MS) has been used to identify the global Kcr profiling of human, at the same time, many computing methods have been developed to predict Kcr sites without high experiment cost. Deep learning network solves the problem of manual feature design and selection in traditional machine learning (NLP), especially the algorithms in natural language processing which treated peptides as sentences, thus can extract more in-depth information and obtain higher accuracy. In this work, we establish a Kcr prediction model named ATCLSTM-Kcr which use self-attention mechanism combined with NLP method to highlight the important features and further capture the internal correlation of the features, to realize the feature enhancement and noise reduction modules of the model. Independent tests have proved that ATCLSTM-Kcr has better accuracy and robustness than similar prediction tools. Then, we design pipeline to generate MS-based benchmark dataset to avoid the false negatives caused by MS-detectability and improve the sensitivity of Kcr prediction. Finally, we develop a Human Lysine Crotonylation Database (HLCD) which using ATCLSTM-Kcr and the two representative deep learning models to score all lysine sites of human proteome, and annotate all Kcr sites identified by MS of current published literatures. HLCD provides an integrated platform for human Kcr sites prediction and screening through multiple prediction scores and conditions, and can be accessed on the website:www.urimarker.com/HLCD/. SIGNIFICANCE: Lysine crotonylation (Kcr) plays an important role in cellular physiology and pathology, such as chromatin remodeling, gene transcription regulation and cancer. To better elucidate the molecular mechanisms of crotonylation and reduce the high experimental cost, we establish a deep learning Kcr prediction model and solve the problem of false negatives caused by the detectability of mass spectrometry (MS). Finally, we develop a Human Lysine Crotonylation Database to score all lysine sites of human proteome, and annotate all Kcr sites identified by MS of current published literatures. Our work provides a convenient platform for human Kcr sites prediction and screening through multiple prediction scores and conditions.

Proteomic and phosphoproteomic characteristics of the cortex, hippocampus, thalamus, lung, and kidney in COVID-19-infected female K18-hACE2 mice.

BACKGROUND: Neurological damage caused by coronavirus disease 2019 (COVID-19) has attracted increasing attention. Recently, through autopsies of patients with COVID-19, the direct identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in their central nervous system (CNS) has been reported, indicating that SARS-CoV-2 might directly attack the CNS. The need to prevent COVID-19-induced severe injuries and potential sequelae is urgent, requiring the elucidation of large-scale molecular mechanisms in vivo. METHODS: In this study, we performed liquid chromatography-mass spectrometry-based proteomic and phosphoproteomic analyses of the cortex, hippocampus, thalamus, lungs, and kidneys of SARS-CoV-2-infected K18-hACE2 female mice. We then performed comprehensive bioinformatic analyses, including differential analyses, functional enrichment, and kinase prediction, to identify key molecules involved in COVID-19. FINDINGS: We found that the cortex had higher viral loads than did the lungs, and the kidneys did not have SARS-COV-2. After SARS-CoV-2 infection, RIG-I-associated virus recognition, antigen processing and presentation, and complement and coagulation cascades were activated to different degrees in all five organs, especially the lungs. The infected cortex exhibited disorders of multiple organelles and biological processes, including dysregulated spliceosome, ribosome, peroxisome, proteasome, endosome, and mitochondrial oxidative respiratory chain. The hippocampus and thalamus had fewer disorders than did the cortex; however, hyperphosphorylation of Mapt/Tau, which may contribute to neurodegenerative diseases, such as Alzheimer's disease, was found in all three brain regions. Moreover, SARS-CoV-2-induced elevation of human angiotensin-converting enzyme 2 (hACE2) was observed in the lungs and kidneys, but not in the three brain regions. Although the virus was not detected, the kidneys expressed high levels of hACE2 and exhibited obvious functional dysregulation after infection. This indicates that SARS-CoV-2 can cause tissue infections or damage via complicated routes. Thus, the treatment of COVID-19 requires a multipronged approach. INTERPRETATION: This study provides observations and in vivo datasets for COVID-19-associated proteomic and phosphoproteomic alterations in multiple organs, especially cerebral tissues, of K18-hACE2 mice. In mature drug databases, the differentially expressed proteins and predicted kinases in this study can be used as baits to identify candidate therapeutic drugs for COVID-19. This study can serve as a solid resource for the scientific community. The data in this manuscript will serve as a starting point for future research on COVID-19-associated encephalopathy. FUNDING: This study was supported by grants from the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences, the National Natural Science Foundation of China, and the Natural Science Foundation of Beijing.

Detection of the freshness of rice by chemiluminescence.

Reactive oxygen species (ROS) are usually produced in rice under aerobic environmental conditions, resulting in peroxidative changes in polyunsaturated fatty acids, and affecting the deterioration of rice during storage. In addition, as an important enzyme that participates in removing ROS, peroxidase is also present in rice, and takes part in protecting rice from attack by ROS. Moreover, loss of peroxidase activity may give rise to rice deterioration during storage. Therefore, measuring peroxidase activity makes it possible to ascertain the freshness of rice. In addition, peroxidase can also catalyze the luminol-hydrogen peroxide system. Based on this, in this work we established a new chemiluminescence (CL) method that was used to detect the freshness of stored rice. Under optimal experimental conditions, we showed that the freshness of rice can be measured using this CL method. This study is the first to detect the freshness of rice using a CL method, opening up a novel direction for the application of CL.

[Role of Eukaryotic Translation Elongation Factor 1 Family Members in the Tumorigenesis and Progression of Lung Adenocarcinoma].

Objective To investigate the role and mechanism of eukaryotic translation elongation factor 1(EEF1) family members (EEF1D,EEF1A1,and EEF1A2) in lung adenocarcinoma (LUAD) based on public databases.Methods We examined EEF1 member expression levels in human LUAD samples via The Cancer Genome Atlas in the UCSC Xena browser and the Clinical Proteomic Tumor Analysis Consortium.We analyzed the mRNA and protein levels of EEF1D,EEF1A1,and EEF1A2 and their correlations with pathological variables via the Mann-Whitney U test.The Kaplan-Meier curves were established to assess the prognostic values of EEF1D,EEF1A1,and EEF1A2.The single-sample gene set enrichment analysis algorithm was employed to explore the relationship between the expression levels of EEF1 members and tumor immune cell infiltration.Spearman and Pearson correlation analyses were performed to examine the relationship between the expression levels of EEF1 members and those of the genes in the phosphatidylinositol 3-kinase/protein kinase B signaling pathway.The immunohistochemical assay was employed to determine the expression levels of EEF1D,EEF1A1,and EEF1A2 in the LUAD tissue (n=75) and paracancer tissue (n=75) samples.Results The mRNA and protein levels of EEF1D,EEF1A1,and EEF1A2 showed significant differences between tumor and paracancer tissues (all P<0.001).The patients with high protein levels of EEF1A1 showed bad prognosis in terms of overall survival (P=0.039),and those with high protein levels of EEF1A2 showed good prognosis in terms of overall survival (P=0.012).The influence of the mRNA level of EEF1D on prognosis was associated with pathological characteristics.The expression levels of EEF1 members were significantly associated with the infiltration of various immune cells and the expression of key molecules in the phosphatidylinositol 3-kinase/protein kinase B signaling pathway.Conclusion EEF1D,EEF1A1,and EEF1A2 are associated with the progression of LUAD,serving as the candidate prognostic markers for LUAD.

Phosphoproteome profiling of mouse liver during normal aging.

BACKGROUND: Aging is a complex biological process accompanied by a time-dependent functional decline that affects most living organisms. Omics studies help to comprehensively understand the mechanism of aging and discover potential intervention methods. Old mice are frequently obese with a fatty liver. METHODS: We applied mass spectrometry-based phosphoproteomics to obtain a global phosphorylation profile of the liver in mice aged 2 or 18 months. MaxQuant was used for quantitative analysis and PCA was used for unsupervised clustering. RESULTS: Through phosphoproteome analysis, a total of 5,685 phosphosites in 2,335 proteins were filtered for quantitative analysis. PCA analysis of both the phosphoproteome and transcriptome data could distinguish young and old mice. However, from kinase prediction, kinase-substrate interaction analysis, and KEGG functional enrichment analysis done with phosphoproteome data, we observed high phosphorylation of fatty acid biosynthesis, ß-oxidation, and potential secretory processes, together with low phosphorylation of the Egfr-Sos1-Araf/Braf-Map2k1-Mapk1 pathway and Ctnnb1 during aging. Proteins with differentially expressed phosphosites seemed more directly related to the aging-associated fatty liver phenotype than the differentially expressed transcripts. The phosphoproteome may reveal distinctive biological functions that are lost in the transcriptome. CONCLUSIONS: In summary, we constructed a phosphorylation-associated network in the mouse liver during normal aging, which may help to discover novel antiaging strategies.

Global profiling of lysine succinylation in human lungs.

The lysine succinylation (Ksucc) is involved in many core energy metabolism pathways and affects the metabolic process in mitochondria, making this modification highly valuable for studying diseases related to mitochondrial disorders. In this paper, we used liquid chromatography with tandem mass spectrometry (LC-MS/MS) to perform the first global profiling of succinylation in human lungs under normal physiological conditions. Using an MS-based platform, we identified 1485 Ksucc sites in 568 proteins. We then compared these sites with those previously identified in human succinylome studies to investigate specific succinylated proteins and identify their possible functions in the lung and to explore the substrate preferences of succinylation modifiers in different cell lines and at different subcellular localizations. Our work expands the succinylation database and supplementary materials on the human succinylome and will thus help in further study of the function of Ksucc and regulation under related physiological and pathological conditions.

Type IIB PKA is highly expressed in ß cells and controls cell proliferation via regulating Cyclin D1 expression.

ß cell number is maintained mainly by cell proliferation and cell apoptosis. Protein kinase A (PKA) pathway is an important intracellular signalling-mediating ß cell proliferation. However, the precise roles of PKA isoforms are not well-defined. We found that the RIIB subunit of PKA is expressed specifically by ß cells of mouse and human islets. Sixty percent pancreatectomy caused increased ß cell proliferation. Deletion of type IIB PKA by disruption of RIIB expression further promoted ß cell proliferation, leading to enhanced ß cell mass expansion. RIIB KO mice also showed increased insulin levels and improved glucose tolerance. Mechanistically, activation of type IIB PKA decreased Cyclin D1 levels and inhibition of RIIB expression increased Cyclin D1 levels. Consistently, activation of type IIB PKA inhibited cell cycle entry. These results suggest that type IIB PKA plays a pivotal role in ß cell proliferation via regulating Cyclin D1 expression.

[Proteome and Acetylome Profiling of Livers in C57BL/6J Male Mice during Normal Aging].

Objective To obtain the proteome and acetylome profiles of livers in mice during normal aging.Methods We applied tandem mass tag labeling and liquid chromatography tandem mass spectrometry and achieved proteome and acetylome data in C57BL/6J male mice aged 2 and 18 months under physiological conditions.Results A total of 4712 proteins were quantified by proteome profiling,and 4818 acetylated sites in 1367 proteins by acetylome profiling.The proteome and acetylome revealed moderate differences in the livers of young and old mice.There were 195 differentially expressed proteins in the proteome and 113 differentially expressed acetylated sites corresponding to 76 proteins in the acetylome.Functional enrichment analysis for the proteome showed that aging-associated upregulated proteins were mainly involved in fatty acid metabolism,epoxygenase P450 pathway,drug catabolic process,organic hydroxy compound metabolic process,and arachidonic acid metabolic process,while the downregulated proteins were related to regulation of gene silencing,nucleosome assembly,protein heterotetramerization,response to interferon,protein-DNA complex assembly and other processes.For the acetylome,the proteins with aging-associated upregulated acetylated sites mainly participated in cofactor metabolism,small molecule catabolic process,ribose phosphate metabolic process,ribonucleotide metabolic process,and purine-containing compound metabolic process,while the proteins with downregulated acetylated sites were associated with sulfur compound metabolic process,response to unfolded protein,and amino acid metabolic process.Conclusion We profiled the proteome and acetylome of livers in mice during normal aging and generated datasets for further research on aging.