OBJECTIVES: To explore the therapeutic mechanism of Arctium lappa extract for treatment of Post-Viral Pneumonia Pulmonary Fibrosis (PPF). METHODS: The chemical constituents of Arctium lappa extracts were identified using UHPLC-Q-TOF-MS/MS. Mouse models of pulmonary fibrosis established by tracheal instillation of bleomycin were treated with Arctium lappa extract, and body weight changes were recorded and lung tissue pathology was examined using HE and Masson staining. Metabolomics analysis was used to identify the differential metabolites and the associated metabolic pathways in the treated mice. The common targets of viral pneumonia and pulmonary fibrosis were acquired from the publicly available databases, and the core targets and active constituents were screened using the protein-protein interaction (PPI) network, GO and KEGG enrichment analyses, and molecular docking, and a "gene-metabolite" regulatory network was constructed. The expressions of the core targets were detected in the lung tissues of the treated mice using Western blotting. RESULTS: Fifty-three chemical constituents were identified from Arctium lappa extract. In the mouse models of pulmonary fibrosis, treatment with Arctium lappa extract significantly improved weight loss and ameliorated lung inflammation and fibrosis. The differential metabolites in the treated mice were enriched in energy metabolism pathways involving citrate cycle, pentose phosphate pathway, glycolysis, tryptophan metabolism, glutamate metabolism and glutathione metabolism, which regulated the production of energy metabolism intermediates. Twenty-three key active compounds (mostly lignans and phenolic acids) and 82 core targets were screened, which were associated with the non-canonical Smad signaling pathways (including PI3K/AKT, HIF-1, MAPK, and Foxo) that participated in the regulation of energy metabolism. Arctium lappa extract also regulated the expressions of epithelial-mesenchymal transition (EMT)‑related proteins (fibronectin, vimentim, and Snail, etc.) and inhibited MAPK signaling pathway activation. CONCLUSIONS Preliminary findings suggest that Arctium lappa treats fibrosis by regulating metabolism to inhibit EMT and involves the modulation of non-canonical Smad signaling pathways, such as MAPK providing theoretical support for its clinical application and further research in treating PPF.
Arctium/chemistry* ; Animals ; Pulmonary Fibrosis/metabolism* ; Mice ; Metabolomics ; Network Pharmacology ; Plant Extracts/pharmacology* ; Signal Transduction ; Drugs, Chinese Herbal/pharmacology* ; Molecular Docking Simulation

OBJECTIVES: To establish a prognostic model for primary liver cancer (PLC) using bioinformatics methods. METHODS: Based on the data from 404 patients in the Cancer Genome Atlas (TCGA) database, we constructed a prognostic model integrating the differentially expressed genes, anoikis, and immune-related genes (DAIs) using univariate Cox regression and the LASSO-Cox approach. The predictive ability of the model was evaluated using Kaplan-Meier method and receiver-operating characteristic curves, and a nomogram was developed to facilitate its clinical applications. Gene set enrichment analysis (GSEA) was performed to explore the associated pathways and relationship between the DAIs and the tumor immune microenvironment, and the half-maximal inhibitory concentration (IC₅₀) of liver cancer drugs was calculated using the "pRRophetic" R package. We also detected the expression of SEMA7A in paired tumor and adjacent tissues from liver cancer patients. RESULTS: We constructed and validated a prognostic model based on 7 DAIs (NR4A3, SEMA7A, IL11, AR, BIRC5, EGF, and SPP1), and obtained consistent results in both the TCGA training cohort and GEO validation cohort (GSE14520), where the patients in the low-risk group were characterized by more favorable clinical outcomes and immune status. By integrating this prognostic signature with clinical information, a composite nomogram was generated. Somatic mutation analysis showed that TTN, TP53, and CTNNB1 mutations accounted for the largest proportion of total mutations, and the patients in the low-risk-low-TMB group had higher survival rate. Drug sensitivity analysis revealed differences in sensitivity to chemotherapeutic agents between high- and low-risk groups and between TP53 mutations and non-mutations. In clinical tissue specimens, SEMA7A expression was significantly higher in liver cancer tissues than in the adjacent tissues. CONCLUSIONS We established a new prognostic model based on DAIs for predicting clinical outcomes and therapeutic response of patients with primary liver cancer.
Humans ; Liver Neoplasms/diagnosis* ; Prognosis ; Anoikis ; Nomograms ; Computational Biology ; Tumor Microenvironment ; Semaphorins/metabolism*

OBJECTIVES: To investigate the impact of hepatitis B virus (HBV) infection on oral microbiota and metabolites in patients with metabolic dysfunction-associated fatty liver disease (MAFLD) and the underlying mechanisms. METHODS: This prospective study was conducted in 47 MAFLD patients complicated with chronic hepatitis B (CHB) and 48 MAFLD patients without CHB enrolled from November, 2023 to January, 2024. Fasting tongue coating samples were collected from the patients for analyzing microbial community structures and metabolites using high-throughput 16S rDNA sequencing and non-targeted metabolomics techniques, and their associations with clinical indicators and biological pathways were explored using correlation analysis and functional annotation. RESULTS: The levels of fasting blood glucose, total cholesterol (TC), gamma-glutamyl transferase (GGT), and severity of fatty liver were all significantly lower in MAFLD+CHB group than in MAFLD group. Microbiota analysis showed that the abundances of Patescibacteria (at the phylum level), Hydrogenophaga, and Absconditabacteriales (at the genus level) were significantly increased, while the abundance of Megasphaera was decreased in MAFLD+CHB group. The differential microbiota were significantly correlated with TC, GGT and low-density lipoprotein (r=-0.68‒0.75). Metabolomics analysis revealed that 469 metabolites (including lipids and amino acids) were upregulated and 2306 (including organic oxygen-containing compounds and phenylpropanoids) were downregulated in MAFLD+CHB group, for which KEGG enrichment analysis suggested abnormal activation of the linoleic acid metabolism and glycerophospholipid metabolism pathways. Correlation analysis between microbiota and metabolites indicated that Patescibacteria and Megasphaera, which were positively correlated with lipid metabolites and negatively with fatty acid metabolites, respectively, jointly affected glycolipid metabolism and oxidative stress pathways. CONCLUSIONS Compared to patients with MAFLD alone, MAFLD patients with concurrent chronic HBV infection showed lower levels in some lipid metabolism indicators and the degree of hepatic steatosis, accompanied by alterations in oral microbiota structure and metabolic profiles. The precise mechanisms involved require further investigation to be fully elucidated.
Humans ; Lipid Metabolism ; Prospective Studies ; Microbiota ; Hepatitis B, Chronic/microbiology* ; Male ; Female ; Adult ; Fatty Liver/microbiology* ; Middle Aged ; Mouth/microbiology* ; Metabolomics

OBJECTIVES: To analyze the molecular mechanism of Xixian Pills for treatment of rheumatoid arthritis (RA). METHODS: Forty-eight rats were randomized into 6 groups (n=8), including a normal control group, a collagen-induced arthritis (CIA) model group, 3 Xixian Pills treatment (200, 400 and 800 mg/kg) groups, and a Tripterygium glycosides tablet (TGT) treatment group. In the latter 4 groups, the rats were treated with daily gavage of Xixian Pills or TGT 2 weeks after CIA modeling for 3 consecutive weeks. The differentially expressed proteins in high-dose Xixian Pills group and the model group compared with the normal control group were screened based on the tandem mass spectrometry tag (TMT) technology, and the core targets and signaling pathways were analyzed. The immune cell infiltration and gene expression data were analyzed using ggplot2 and tidyverse packages, and the correlation coefficients between the core targets and the immune cells were calculated. RESULTS: The CIA rats showed significantly increased serum levels of TNF-α and IL-6 and lowered serum IL-10 level. Treatments with high- and medium-dose Xixian Pills and TGT all significantly reduced serum TNF‑α and IL-6 and increased IL-10 levels in CIA rats. Proteomic analysis identified 160 differential proteins between the model group and high-dose Xixian Pills group, and the core targets included CCL5, STAT1, GZMB and IL7R. The areas under the ROC curve of CCL5 and STAT1 were both greater than 0.9. Immunohistochemical and immunofluorescence staining revealed increased levels of CCL5 and STAT1 in the ankle joints of CIA rats, which were significantly decreased after treatment with Xixian Pills. CONCLUSIONS Treatment with Xixian Pills offers protection of the joints in CIA rats possibly by inhibiting joint inflammation via regulating protein expressions of CCL5 and STAT1.
Animals ; Drugs, Chinese Herbal/pharmacology* ; Rats ; Arthritis, Rheumatoid/metabolism* ; Proteomics ; Tripterygium/chemistry* ; Arthritis, Experimental/metabolism* ; Tumor Necrosis Factor-alpha/blood* ; Interleukin-10/blood* ; Interleukin-6/blood* ; Male ; Rats, Sprague-Dawley ; Signal Transduction

Oral squamous cell carcinoma (OSCC) is the most common manifestation of oral cancer. It has been proposed that periodontal pathogens contribute to OSCC progression, mainly by their virulence factors. However, the main periodontal pathogen and its mechanism to modulate OSCC cells remains not fully understood. In this study we investigate the main host-pathogen pathways in OSCC by computational proteomics and the mechanism behind cancer progression by the oral microbiome. The main host-pathogen pathways were analyzed in the secretome of biopsies from patients with OSCC and healthy controls by mass spectrometry. Then, functional assays were performed to evaluate the host-pathogen pathways highlighted in oral cancer. Host proteins associated with LPS response, cell migration/adhesion, and metabolism of amino acids were significantly upregulated in the human cancer proteome, whereas the complement cascade was downregulated in malignant samples. Then, the microbiome analysis revealed large number and variety of peptides from Fusobacterium nucleatum (F. nucleatum) in OSCC samples, from which several enzymes from the L-glutamate degradation pathway were found, indicating that L-glutamate from cancer cells is used as an energy source, and catabolized into butyrate by the bacteria. In fact, we observed that F. nucleatum modulates the cystine/glutamate antiporter in an OSCC cell line by increasing SLC7A11 expression, promoting L-glutamate efflux and favoring bacterial infection. Finally, our results showed that F. nucleatum and its metabolic derivates promote tumor spheroids growth, spheroids-derived cell detachment, epithelial-mesenchymal transition and Galectin-9 upregulation. Altogether, F. nucleatum promotes pro-tumoral mechanism in oral cancer.
Humans ; Fusobacterium nucleatum/metabolism* ; Mouth Neoplasms/metabolism* ; Disease Progression ; Proteomics ; Carcinoma, Squamous Cell/metabolism* ; Host-Pathogen Interactions ; Metabolic Networks and Pathways ; Case-Control Studies ; Mass Spectrometry

Stem cells play a crucial role in maintaining tissue regenerative capacity and homeostasis. However, mechanisms associated with stem cell senescence require further investigation. In this study, we conducted a proteomic analysis of human dental pulp stem cells (HDPSCs) obtained from individuals of various ages. Our findings showed that the expression of NUP62 was decreased in aged HDPSCs. We discovered that NUP62 alleviated senescence-associated phenotypes and enhanced differentiation potential both in vitro and in vivo. Conversely, the knocking down of NUP62 expression aggravated the senescence-associated phenotypes and impaired the proliferation and migration capacity of HDPSCs. Through RNA-sequence and decoding the epigenomic landscapes remodeled induced by NUP62 overexpression, we found that NUP62 helps alleviate senescence in HDPSCs by enhancing the nuclear transport of the transcription factor E2F1. This, in turn, stimulates the transcription of the epigenetic enzyme NSD2. Finally, the overexpression of NUP62 influences the H3K36me2 and H3K36me3 modifications of anti-aging genes (HMGA1, HMGA2, and SIRT6). Our results demonstrated that NUP62 regulates the fate of HDPSCs via NSD2-dependent epigenetic reprogramming.
Humans ; Dental Pulp/cytology* ; Nuclear Pore Complex Proteins/genetics* ; Cellular Senescence/genetics* ; Stem Cells/metabolism* ; Epigenesis, Genetic ; Cell Proliferation ; Cell Differentiation ; Histone-Lysine N-Methyltransferase/metabolism* ; Cells, Cultured ; Cellular Reprogramming ; Cell Movement ; Proteomics

The pursuit of healthy aging has long rendered aging and senescence captivating. Age-related ailments, such as cardiovascular diseases, diabetes, and neurodegenerative disorders, pose significant threats to individuals. Recent studies have shed light on the intricate mechanisms encompassing genetics, epigenetics, transcriptomics, and metabolomics in the processes of senescence and aging, as well as the establishment of age-related pathologies. Amidst these underlying mechanisms governing aging and related pathology metabolism assumes a pivotal role that holds promise for intervention and therapeutics. The advancements in metabolomics techniques and analysis methods have significantly propelled the study of senescence and aging, particularly with the aid of multiscale metabolomics which has facilitated the discovery of metabolic markers and therapeutic potentials. This review provides an overview of senescence and aging, emphasizing the crucial role metabolism plays in the aging process as well as age-related diseases.
Humans ; Aging/metabolism* ; Metabolomics/methods* ; Neurodegenerative Diseases/metabolism* ; Cardiovascular Diseases/metabolism*

Ankylosing spondylitis (AS) is linked to an increased prevalence of myocardial infarction (MI). However, research dedicated to elucidating the pathogenesis of AS-MI is lacking. In this study, we explored the biomarkers for enhancing the diagnostic and therapeutic efficiency of AS-MI. Datasets were obtained from the Gene Expression Omnibus database. We employed weighted gene co-expression network analysis and machine learning models to screen hub genes. A receiver operating characteristic curve and a nomogram were designed to assess diagnostic accuracy. Gene set enrichment analysis was conducted to reveal the potential function of hub genes. Immune infiltration analysis indicated the correlation between hub genes and the immune landscape. Subsequently, we performed single-cell analysis to identify the expression and subcellular localization of hub genes. We further constructed a transcription factor (TF)-microRNA (miRNA) regulatory network. Finally, drug prediction and molecular docking were performed. S100A12 and MCEMP1 were identified as hub genes, which were correlated with immune-related biological processes. They exhibited high diagnostic value and were predominantly expressed in myeloid cells. Furthermore, 24 TFs and 9 miRNA were associated with these hub genes. Enzastaurin, meglitinide, and nifedipine were predicted as potential therapeutic agents. Our study indicates that S100A12 and MCEMP1 exhibit significant potential as biomarkers and therapeutic targets for AS-MI, offering novel insights into the underlying etiology of this condition.
Humans ; Spondylitis, Ankylosing/complications* ; Systems Biology/methods* ; Myocardial Infarction/diagnosis* ; Biomarkers/metabolism* ; MicroRNAs/genetics* ; Gene Regulatory Networks ; Gene Expression Profiling ; Machine Learning

Acute respiratory distress syndrome (ARDS) is a clinical syndrome characterized by diffuse alveolar and interstitial edema caused by damage to alveolar-capillary and epithelial cells, often induced by infection, sepsis, trauma, and other factors. It is marked by progressive hypoxemia and respiratory distress. Due to the diverse causes of ARDS, the unclear pathogenesis, and the absence of effective predictive markers or biomarkers, there are no effective treatment measures available, resulting in a high mortality rate. ARDS is increasingly recognized for its heterogeneity, biomarkers, and the emergence of new opportunities for the development of diagnostic tools and personalized treatment strategies provided by omics technologies. A single omics analysis cannot fully reveal the heterogeneity and complexity of ARDS, while multi-omics analysis can provide a more systematic and comprehensive understanding of ARDS. Using clinical samples is closer to the actual disease situation compared to animal models. Multi-omics studies based on clinical samples have achieved significant progress in elucidating the pathophysiology of ARDS, identifying ARDS subtypes, and identifying biomarkers related to ARDS. This review focuses on the current applications of genomics, transcriptomics, metabolomics, and proteomics analyses based on clinical samples in the ARDS field, with a focus on the application of these omics methods in ARDS heterogeneity, potential biomarkers, and pathogenesis. It also introduces the differences in the application of different clinical samples in ARDS omics research, in order to gain a deeper and more comprehensive understanding of the pathogenesis of ARDS and explore new strategies for its prevention and treatment.
Respiratory Distress Syndrome/diagnosis* ; Humans ; Metabolomics ; Proteomics ; Genomics ; Biomarkers ; Multiomics

Sepsis is a life-threatening organ dysfunction syndrome caused by a dysregulated host response to infection. Due to different infection sources, pathogens and basic conditions of patients, there is significant heterogeneity in clinical manifestations, response to treatment and prognosis of patients with sepsis. Accurate classification and individualized treatment of sepsis will help to further improve the prognosis of patients with sepsis. In recent years, the integration of artificial intelligence and bioinformatics has brought new opportunities for the research of sepsis classification. This review systematically introduces a variety of sepsis classification methods and their clinical application value. The clinical data in the electronic medical record, such as the dynamic changes of vital signs such as body temperature, can be used as the basis for sepsis classification. Different subtypes of body temperature trajectories have differences in physiological characteristics and prognosis, which contributes to predict the prognosis of patients and guide fluid management strategies. Biomarker classification can more comprehensively reflect the pathophysiological state of patients. Immune index classification is helpful to identify immunocompromised patients so as to carry out targeted immunotherapy. Transcriptome data and genotyping reveal the heterogeneity of sepsis at the molecular level and provide a new perspective for precision medicine. In addition, a detailed systematic review of sepsis-related organ function damage, such as acute respiratory distress syndrome (ARDS), acute kidney injury (AKI), and acute liver injury, has also been conducted, which is helpful to develop targeted organ protection and treatment strategies. These typing methods have shown good application prospects in clinical practice. However, there are still limitations in the current research, such as typing stability and biomarker selection, which need to be further explored. Future research should focus on the development of stable and efficient typing tools to achieve precise treatment of sepsis and improve the prognosis of patients.
Humans ; Sepsis/classification* ; Multiple Organ Failure/classification* ; Prognosis ; Artificial Intelligence ; Biomarkers ; Computational Biology ; Respiratory Distress Syndrome