[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.

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.

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.

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.

[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.

[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.

[Untargeted Metabolomic Profiling of Liver and Serum in Mouse during Normal Aging].

Objective To obtain the metabolome profiles in liver and serum of mice during normal aging. Methods The liver and serum samples of ten 2-month-old mice and ten 18-month-old C57BL/6J mice under physiological conditions were collected.Metabolites were identified and quantified by liquid chromatography-tandem mass spectrometry.The overall assessment,differential screening,and functional analysis were performed with the filtered high-quality data. Results In the negative-ion mode and positive-ion mode,242 and 399 metabolites were identified in the liver and 265 and 230 in serum,respectively.The difference of metabolome between young and old mice was moderate.The upregulated metabolites identified in aging liver were related to the metabolism of riboflavin,glucose,and arachidonic acid,while the downregulated ones were associated with the metabolism of pyrimidine,purine,glycerophospholipid,glutathione,and nicotinamide.Altered metabolites in serum during aging were involved in a variety of nucleic acid metabolism-related pathways,such as pyrimidine metabolism,purine metabolism,one carbon pool by folate,and amino sugar and nucleotide sugar metabolism. Conclusions The metabolome profiles of mouse liver and serum both revealed dysregulated nucleic acid metabolism pathways during normal aging.This study provides metabolome data for further research on aging-associated mechanism and may support the discovery of intervention methods for aging.

Dual synchronization of fractional-order chaotic systems via a linear controller.

The problem of the dual synchronization of two different fractional-order chaotic systems is studied. By a linear controller, we realize the dual synchronization of fractional-order chaotic systems. Finally, the proposed method is applied for dual synchronization of Van der Pol-Willis systems and Van der Pol-Duffing systems. The numerical simulation shows the accuracy of the theory.

[An epidemiological study on diabetic retinopathy among type 2 diabetic patients in Shanghai].

OBJECTIVE: To investigate the prevalence and risk factors of diabetic retinopathy (DR) among type 2 diabetic patients aged over 30 in Shanghai central area. METHODS: 1039 patients diagnosed with type 2 diabetes mellitus (DM) aged over 30 were investigated by randomized cluster sampling in Shanghai central area and data from 767 of those patients were analyzed. RESULTS: (1) Among all of the 1534 digital ocular fundus images from 767 patients, 87.6% of the images from 672 patients were gradable. (2) Among all of the 672 patients with gradable ocular fundus images, the prevalence of non-proliferative diabetic retinopathy (NPDR) was 21.6%, while proliferative diabetic retinopathy (PDR) was 1.3%. The rates of mild, moderate and severe NPDR were 8.8%, 11.2% and 1.6% respectively. (3) DR patients were characterized with elder age, higher HbA1c, urea nitrogen and serum creatinine. DM duration and the level of fasting plasma glucose were risk factors for DR. CONCLUSION: The overall prevalence of DR in type 2 diabetic patients aged over 30 in Shanghai central area was 22.9% and the DR risk factors were found to include duration of diabetes and fasting plasma glucose level.