Multi-omics profiling reveal cells with novel oncogenic cluster, TRAP1low/CAMSAP3low, emerge more aggressive behavior and poor-prognosis in early-stage endometrial cancer.

The clinical heterogeneity of early-stage endometrial cancer (EC) is worthy of further study to identify high-quality prognostic markers and their potential role in aggressive tumor behavior. Mutation of TP53 was considered as an important primary triage in modified molecular typing for EC, it still cannot precisely predict the prognosis of EC. After proteomic analysis of cancer and para-cancerous tissues from 24 early-stage endometrioid EC patients with different survival outcomes, 13 differentially expressed proteins were screen out while 2 proteins enriched in p53 signaling pathway were further identified by single-cell transcriptome (scRNA-seq). Interestingly, tumor necrosis factor type-1 receptor-associated protein (TRAP1) and calmodulin-regulated spectrin-associated protein family member 3 (CAMSAP3) were found to be significantly downregulated in the specific cell cluster. Expectedly, the signature genes of TRAP1low/CAMSAP3low cluster included classical oncogenes. Moreover, close cellular interactions were observed between myeloid cells and the TRAP1low/CAMSAP3low cluster after systematically elucidating their relationship with tumor microenvironment (TME). The expression of TRAP1 and CAMSAP3 was verified by immunohistochemistry. Thus, a novel prediction model combining TRAP1, CAMSAP3 and TP53 was construct by multi-omics. Compared with the area under the curve, it demonstrated a significantly improvemrnt in the diagnostic efficacy in EC patients from TCGA bank. In conclusion, this work improved the current knowledge regarding the prognosis of early-stage EC through proteomics and scRNA-seq. These findings may lead to improvements in precise risk stratification of early-stage EC patients.

An Integrated Analysis of Anatomical and Sugar Contents Identifies How Night Temperatures Regulate the Healing Process of Oriental Melon Grafted onto Pumpkin.

Graft healing is a complex process affected by environmental factors, with temperature being one of the most important influencing factors. Here, oriental melon grafted onto pumpkin was used to study changes in graft union formation and sugar contents at the graft interface under night temperatures of 18 °C and 28 °C. Histological analysis suggested that callus formation occurred 3 days after grafting with a night temperature of 28 °C, which was one day earlier than with a night temperature of 18 °C. Vascular reconnection with a night temperature of 28 °C was established 2 days earlier than with a night temperature of 18 °C. Additionally, nine sugars were significantly enriched in the graft union, with the contents of sucrose, trehalose, raffinose, D-glucose, D-fructose, D-galactose, and inositol initially increasing but then decreasing. Furthermore, we also found that exogenous glucose and fructose application promotes vascular reconnection. However, exogenous sucrose application did not promote vascular reconnection. Taken together, our results reveal that elevated temperatures improve the process of graft union formation through increasing the contents of sugars. This study provides information to develop strategies for improving grafting efficiency under low temperatures.

PneumoLLM: Harnessing the power of large language model for pneumoconiosis diagnosis.

The conventional pretraining-and-finetuning paradigm, while effective for common diseases with ample data, faces challenges in diagnosing data-scarce occupational diseases like pneumoconiosis. Recently, large language models (LLMs) have exhibits unprecedented ability when conducting multiple tasks in dialogue, bringing opportunities to diagnosis. A common strategy might involve using adapter layers for vision-language alignment and diagnosis in a dialogic manner. Yet, this approach often requires optimization of extensive learnable parameters in the text branch and the dialogue head, potentially diminishing the LLMs' efficacy, especially with limited training data. In our work, we innovate by eliminating the text branch and substituting the dialogue head with a classification head. This approach presents a more effective method for harnessing LLMs in diagnosis with fewer learnable parameters. Furthermore, to balance the retention of detailed image information with progression towards accurate diagnosis, we introduce the contextual multi-token engine. This engine is specialized in adaptively generating diagnostic tokens. Additionally, we propose the information emitter module, which unidirectionally emits information from image tokens to diagnosis tokens. Comprehensive experiments validate the superiority of our methods.

Proteome, Lysine Acetylome, and Succinylome Identify Posttranslational Modification of STAT1 as a Novel Drug Target in Silicosis.

Inhalation of crystalline silica dust induces incurable lung damage, silicosis, and pulmonary fibrosis. However, the mechanisms of the lung injury remain poorly understood, with limited therapeutic options aside from lung transplantation. Posttranslational modifications can regulate the function of proteins and play an important role in studying disease mechanisms. To investigate changes in posttranslational modifications of proteins in silicosis, combined quantitative proteome, acetylome, and succinylome analyses were performed with lung tissues from silica-injured and healthy mice using liquid chromatography-mass spectrometry. Combined analysis was applied to the three omics datasets to construct a protein landscape. The acetylation and succinylation of the key transcription factor STAT1 were found to play important roles in the silica-induced pathophysiological changes. Modulating the acetylation level of STAT1 with geranylgeranylacetone effectively inhibited the progression of silicosis. This report revealed a comprehensive landscape of posttranslational modifications in silica-injured mouse and presented a novel therapeutic strategy targeting the posttranslational level for silica-induced lung diseases.

[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.

Research on the structure function recognition of PLOS.

Purpose: The present study explores and investigates the efficiency of deep learning models in identifying discourse structure and functional features and explores the potential application of natural language processing (NLP) techniques in text mining, information measurement, and scientific communication. Method: The PLOS literature series has been utilized to obtain full-text data, and four deep learning models, including BERT, RoBERTa, SciBERT, and SsciBERT, have been employed for structure-function recognition. Result: The experimental findings reveal that the SciBERT model performs outstandingly, surpassing the other models, with an F1 score. Additionally, the performance of different paragraph structures has been analyzed, and it has been found that the model performs well in paragraphs such as method and result. Conclusion: The study's outcomes suggest that deep learning models can recognize the structure and functional elements at the discourse level, particularly for scientific literature, where the SciBERT model performs remarkably. Moreover, the NLP techniques have extensive prospects in various fields, including text mining, information measurement, and scientific communication. By automatically parsing and identifying structural and functional information in text, the efficiency of literature management and retrieval can be improved, thereby expediting scientific research progress. Therefore, deep learning and NLP technologies hold significant value in scientific research.

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.

Enhanced Thermal Shock Resistance of High-Temperature Organic Adhesive by CF-SiCNWs Binary Phase Structure.

The development of high-temperature organic adhesive for bonding ultra-high-temperature ceramics with excellent thermal shock resistance has important significance to thermal protection systems for high-temperature environment application. In this study, high-temperature organic adhesive (HTOA) with carbon-fiber-SiC nanowires (CF-SiCNWs) binary phase enhancement structure was prepared. The method is that the SiCNWs grow on the chopped carbon-fiber surface and in the matrix of modified HTOA during high-temperature heat treatment with the help of a catalyst by a tip-growth way and with a vapor-liquid-solid (V-L-S) growth pattern. The results showed that the CF-SiCNWs binary phase enhancement structure plays a significant role in improving thermal shock resistance of high-temperature organic adhesive. The retention rate of the joint bond strength for the bonding samples after 20 cycles of thermal shock testing reaches 39.19%, which is higher than for the ones without CF, whose retain rate is only 6.78%. The shear strength of the samples with the CF-SiCNWs binary phase enhancement structure was about 10% higher than for those without the enhancement structure after 20 cycles of thermal shock.

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.

Altered Respiratory Microbiomes, Plasma Metabolites, and Immune Responses in Influenza A Virus and Methicillin-Resistant Staphylococcus aureus Coinfection.

Influenza A virus (IAV)-methicillin-resistant Staphylococcus aureus (MRSA) coinfection causes severe respiratory infections. The host microbiome plays an important role in respiratory tract infections. However, the relationships among the immune responses, metabolic characteristics, and respiratory microbial characteristics of IAV-MRSA coinfection have not been fully studied. We used specific-pathogen-free (SPF) C57BL/6N mice infected with IAV and MRSA to build a nonlethal model of IAV-MRSA coinfection and characterized the upper respiratory tract (URT) and lower respiratory tract (LRT) microbiomes at 4 and 13 days postinfection by full-length 16S rRNA gene sequencing. Immune response and plasma metabolism profile analyses were performed at 4 days postinfection by flow cytometry and liquid chromatography-tandem mass spectrometry (LC-MS/MS). The relationships among the LRT microbiota, the immune response, and the plasma metabolism profile were analyzed by Spearman's correlation analysis. IAV-MRSA coinfection showed significant weight loss and lung injury and significantly increased loads of IAV and MRSA in bronchoalveolar lavage fluid (BALF). Microbiome data showed that coinfection significantly increased the relative abundances of Enterococcus faecalis, Enterobacter hormaechei, Citrobacter freundii, and Klebsiella pneumoniae and decreased the relative abundances of Lactobacillus reuteri and Lactobacillus murinus. The percentages of CD4+/CD8+ T cells and B cells in the spleen; the levels of interleukin-9 (IL-9), interferon gamma (IFN-γ), tumor necrosis factor alpha (TNF-α), IL-6, and IL-8 in the lung; and the level of mevalonolactone in plasma were increased in IAV-MRSA-coinfected mice. L. murinus was positively correlated with lung macrophages and natural killer (NK) cells, negatively correlated with spleen B cells and CD4+/CD8+ T cells, and correlated with multiple plasma metabolites. Future research is needed to clarify whether L. murinus mediates or alters the severity of IAV-MRSA coinfection. IMPORTANCE The respiratory microbiome plays an important role in respiratory tract infections. In this study, we characterized the URT and LRT microbiota, the host immune response, and plasma metabolic profiles during IAV-MRSA coinfection and evaluated their correlations. We observed that IAV-MRSA coinfection induced severe lung injury and dysregulated host immunity and plasma metabolic profiles, as evidenced by the aggravation of lung pathological damage, the reduction of innate immune cells, the strong adaptation of the immune response, and the upregulation of mevalonolactone in plasma. L. murinus was strongly correlated with immune cells and plasma metabolites. Our findings contribute to a better understanding of the role of the host microbiome in respiratory tract infections and identified a key bacterial species, L. murinus, that may provide important references for the development of probiotic therapies.

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.