Aging Gut Microbiome in Healthy and Unhealthy Aging.

The characteristics of human aging manifest in tissue and organ function decline, heightening susceptibility to age-related ailments, thereby presenting novel challenges to fostering and sustaining healthy longevity. In recent years, an abundance of research on human aging has surfaced. Intriguingly, evidence suggests a pervasive correlation among gut microbiota, bodily functions, and chronic diseases. From infancy to later stages of adulthood, healthy individuals witness dynamic shifts in gut microbiota composition. This microbial community is associated with tissue and organ function deterioration (e.g., brain, bones, muscles, immune system, vascular system) and heightened risk of age-related diseases. Thus, we present a narrative review of the aging gut microbiome in both healthy and unhealthy aging contexts. Additionally, we explore the potential for adjustments to physical health based on gut microbiome analysis and how targeting the gut microbiome can potentially slow down the aging process.

Developing a Diagnostic Model to Predict the Risk of Asthma Based on Ten Macrophage-Related Gene Signatures.

Objective: Asthma (AS) is a chronic inflammatory disease of the airway, and macrophages contribute to AS remodeling. Our study aims at screening macrophage-related gene signatures to build a risk prediction model and explore its predictive abilities in AS diagnosis. Methods: Three microarray datasets were downloaded from the GEO database. The Limma package was used to screen differentially expressed genes (DEGs) between AS and controls. The ssGSEA algorithm was used to determine immune cell proportions. The Pearson correlation coefficient was computed to select the macrophage-related DEGs. The LASSO and RFE algorithms were implemented to filter the macrophage-related DEG signatures to establish a risk prediction model. Receiver operating characteristic (ROC) curves were used to assess the diagnostic ability of the prediction model. Finally, the qPCR was used to detect the expression of selected differential genes in sputum from healthy people and asthmatic patients. Results: We obtained 1,189 DEGs between AS and controls from the combined datasets. By evaluating immune cell proportions, macrophages showed a significant difference between the two groups, and 439 DEGs were found to be associated with macrophages. These genes were mainly enriched in the gene ontology-biological process of immune and inflammatory responses, as well as in the KEGG pathways of cytokine-cytokine receptor interaction and biosynthesis of antibiotics. Finally, 10 macrophage-related DEG signatures (EARS2, ATP2A2, COLGALT1, GART, WNT5A, AK5, ZBTB16, CCL17, ADORA3, and CXCR4) were screened as an optimized gene set to predict AS diagnosis, and they showed diagnostic abilities with AUCs of 0.968 and 0.875 in ROC curves of combined and validation datasets, respectively. The mRNA expressions of EARS2, ATP2A2, COLGALT1, and GART in the control group were higher than in AS group, while the expressions of WNT5A, AK5, ZBTB16, CCL17, ADORA3, and CXCR4 in the control group were lower than that in the AS group. Conclusion: We proposed a diagnostic model based on 10 macrophage-related genes to predict AS risk.\.

Metabonomic Analysis of Metabolites Produced by Escherichia coli in Patients With and Without Sepsis.

Aim: To analyze the metabolites of the most common sepsis-related pathogen and their correlation with clinical indicators. Methods: Information of bacterial-infection patients in Huzhou Central hospital was retrospectively investigated and analyzed. The most common pathogen inducing sepsis was selected. Then, the metabolic profiles of pathogens from blood were detected by liquid chromatography/mass spectrometry. Cluster and classification analysis, KEGG pathway enrichment analysis, multidimensional OPLS-DA, Z scores, correlation analysis were used to analyze the metabolites. Results: Escherichia coli (E. coli) was the pathogen that caused the most infection (about 21%) and sepsis. Amino acids, peptides, terpene glycosides, carbohydrates were the main metabolites of E.coli and they were mainly digestive and endocrine-related compounds. Most of them were related to amino acids metabolism, cofactors and vitamins metabolism, biosynthesis of secondary metabolites, et al. Moreover, metabolites were involved in purine metabolism, neuroactive ligand-receptor interaction, ABC transporters, etc. Then, over 70 differential metabolites such as tyramine, tryptophan, 3- hydroxymalondialdehyde were screened in E.coli from nonseptic and septic patients. They were mainly involved in phenylalanine metabolism, tryptophan metabolism, protein digestion and absorption. Distribution of metabolites of E. coli from nonseptic and septic patients was obviously different. What is more, differential metabolites had evidently correlation with SOFA score, APPACHE II score, C-reactive protein, erythrocyte, platelet, aspartate aminotransferase, coagulation function, lactic acid (p < 0.01). Conclusion: The different metabolic profile of E. coli from nonseptic and septic patients indicated that differential metabolites might be associated with sepsis.

Key Markers Involved in the Anticolon Cancer Response of CD8+ T Cells through the Regulation of Cholesterol Metabolism.

BACKGROUND: T cell-mediated antitumor immune response is the basis of colorectal cancer (CRC) immunotherapy. Cholesterol plays an important role in T cell signal transduction and function. Apolipoprotein E (APOE) plays a major role in cholesterol metabolism. OBJECTIVE: To screen and analyze key markers involved in the anticolon cancer response of CD8+ T cells through the regulation of cholesterol metabolism. METHODS: Based on the median cutoff of the expression value of APOE according to the data downloaded from The Cancer Genome Atlas and Gene Expression Omnibus database, patients were grouped into low and high expression groups. Differences in clinical factors were assessed, and survival analysis was performed. Differentially expressed genes (DEGs) in the high and low expression groups were screened, followed by the analysis of differences in tumor-infiltrating immune cells and weighted gene coexpression network analysis results. The closely related genes to APOE were identified, followed by enrichment analysis, protein-protein interaction (PPI) network analysis, and differential expression analysis. Immunohistochemical staining (IHC) was used to detect the expression of CD8 in CRC tissues. RESULTS: There were significant differences in prognosis and pathologic_N between the APOE low and high expression groups. A total of 2,349 DEGs between the high and low expression groups were selected. A total of 967 genes were obtained from the blue and brown modules. The probability of distribution of CD8+ T cells differed significantly between the two groups, and 320 closely related DEGs of APOE were screened. Genes including the HLA gene family, B2M, IRF4, and STAT5A had a higher degree in the PPI network. GEO datasets verified the prognosis and the related DEGs of APOE. IHC staining verified the relationship between the distribution of CD8+ T cells and APOE expression. CONCLUSION: Genes including the HLA gene family, B2M, IRF4, and STAT5A might be the key genes involved in the anticolon cancer response of CD8+ T cells through the regulation of cholesterol metabolism.