Genetic insights into the gut microbiota and risk of facial skin aging: A Mendelian randomization study.

BACKGROUND: A growing number of experimental studies have shown an association between the gut microbiota (GM) and facial skin aging. However, the causal relationship between GM and facial skin aging remains unclear to date. METHODS: We conducted a two-sample Mendelian randomization (MR) analysis to investigate the potential causal relationship between GM and facial skin aging. MR analysis was mainly performed using the inverse-variance weighting (IVW) method, complemented by the weighted median (MW) method, MR-Egger regression, and weighted mode, and sensitivity analysis was used to test the reliability of MR analysis results. RESULTS: Eleven GM taxa associated with facial skin aging were identified by IVW method analysis, Family Victivallaceae (p = 0.010), Genus Eubacterium coprostanoligenes group (p = 0.038), and Genus Parasutterella (p = 0.011) were negatively associated with facial skin aging, while Phylum Verrucomicrobia (p = 0.034), Family Lactobacillaceae (p = 0.017) and its subgroups Genus Lactobacillus (p = 0.038), Genus Parabacteroides (p = 0.040), Genus Eggerthella (p = 0.049), Genus Family XIII UCG001 (p = 0.036), Genus Phascolarctobacterium (p = 0.027), and Genus Ruminococcaceae UCG005 (p = 0.012) were positively associated with facial skin aging. At Class and Order levels, we did not find a causal relationship between GM and facial skin aging. Results of sensitivity analyses did not show evidence of pleiotropy and heterogeneity. CONCLUSION: Our findings confirm the causal relationship between GM and facial skin aging, providing a new perspective on delaying facial aging.

Identification of platelet-related subtypes and diagnostic markers in pediatric Crohn's disease based on WGCNA and machine learning.

Background: The incidence of pediatric Crohn's disease (PCD) is increasing worldwide every year. The challenges in early diagnosis and treatment of PCD persist due to its inherent heterogeneity. This study's objective was to discover novel diagnostic markers and molecular subtypes aimed at enhancing the prognosis for patients suffering from PCD. Methods: Candidate genes were obtained from the GSE117993 dataset and the GSE93624 dataset by weighted gene co-expression network analysis (WGCNA) and differential analysis, followed by intersection with platelet-related genes. Based on this, diagnostic markers were screened by five machine learning algorithms. We constructed predictive models and molecular subtypes based on key markers. The models were evaluated using the GSE101794 dataset as the validation set, combined with receiver operating characteristic curves, decision curve analysis, clinical impact curves, and calibration curves. In addition, we performed pathway enrichment analysis and immune infiltration analysis for different molecular subtypes to assess their differences. Results: Through WGCNA and differential analysis, we successfully identified 44 candidate genes. Following this, employing five machine learning algorithms, we ultimately narrowed it down to five pivotal markers: GNA15, PIK3R3, PLEK, SERPINE1, and STAT1. Using these five key markers as a foundation, we developed a nomogram exhibiting exceptional performance. Furthermore, we distinguished two platelet-related subtypes of PCD through consensus clustering analysis. Subsequent analyses involving pathway enrichment and immune infiltration unveiled notable disparities in gene expression patterns, enrichment pathways, and immune infiltration landscapes between these subtypes. Conclusion: In this study, we have successfully identified five promising diagnostic markers and developed a robust nomogram with high predictive efficacy. Furthermore, the recognition of distinct PCD subtypes enhances our comprehension of potential pathogenic mechanisms and paves the way for future prospects in early diagnosis and personalized treatment.

Dietary factors and the risk of atopic dermatitis: a Mendelian randomisation study.

Previous studies have revealed an association between dietary factors and atopic dermatitis (AD). To explore whether there was a causal relationship between diet and AD, we performed Mendelian randomisation (MR) analysis. The dataset of twenty-one dietary factors was obtained from UK Biobank. The dataset for AD was obtained from the publicly available FinnGen consortium. The main research method was the inverse-variance weighting method, which was supplemented by MR‒Egger, weighted median and weighted mode. In addition, sensitivity analysis was performed to ensure the accuracy of the results. The study revealed that beef intake (OR = 0·351; 95 % CI 0·145, 0·847; P = 0·020) and white bread intake (OR = 0·141; 95 % CI 0·030, 0·656; P = 0·012) may be protective factors against AD. There were no causal relationships between AD and any other dietary intake factors. Sensitivity analysis showed that our results were reliable, and no heterogeneity or pleiotropy was found. Therefore, we believe that beef intake may be associated with a reduced risk of AD. Although white bread was significant in the IVW analysis, there was large uncertainty in the results given the wide 95 % CI. Other factors were not associated with AD in this study.

Pan-genome analysis of the Burkholderia gladioli PV. Cocovenenans reveal the extent of variation in the toxigenic gene cluster.

Burkholderia gladioli has been reported as the pathogen responsible for cases of foodborne illness in many countries. The poisonous bongkrekic acid (BA) produced by B. gladioli was linked to a gene cluster absent in non-pathogenic strains. The whole genome sequence of eight bacteria strains, which were screened from the collected 175 raw food and environmental samples, were assembled and analyzed to detect a significant association of 19 protein-coding genes with the pathogenic status. Except for the common BA synthesis-related gene, several other genes, including the toxin-antitoxin genes, were also absent in the non-pathogenic strains. The bacteria strains with the BA gene cluster were found to form a single cluster in the analysis of all B. gladioli genome assemblies for the variants in the gene cluster. Divergence of this cluster was detected in the analysis for both the flanking sequences and those of the whole genome level, which indicates its complex origin. Genome recombination was found to cause a precise sequence deletion in the gene cluster region, which was found to be predominant in the non-pathogenic strains indicating the possible effect of horizontal gene transfer. Our study provided new information and resources for understanding the evolution and divergence of the B. gladioli species.

Gradient differences of immunotherapy efficacy in metastatic melanoma related to sunlight exposure pattern: A population-based study.

Background: Immune checkpoint inhibitors (ICIs) have revolutionized metastatic melanoma (MM) treatment in just a few years. Ultraviolet (UV) in sunlight is the most significant environmental cause of melanoma, which is considered to be the main reason for tumor mutation burden (TMB) increase in melanoma. High TMB usually predicts that PD-1 inhibitors are effective. The sunlight exposure pattern of MM might be a clinical feature that matches TMB. The relationship between sunlight exposure patterns and immunotherapy response in MM is unclear. This study aims to investigate the correlation between sunlight exposure patterns and immunotherapy response in MM and establish nomograms that predict 3- and 5-year overall survival (OS) rate. Methods: We searched the Surveillance, Epidemiology, and End Results (SEER) database and enrolled MM cases from 2005-2016. According to the advent of ICIs in 2011, the era was divided into the non-ICIs era (2005-2010) and the ICIs era (2011-2016). Patients were divided into three cohorts according to the primary site sunlight exposure patterns: head and neck in the first cohort, trunk arms and legs in the second cohort, and acral sites in the third cohort. We compared survival differences for each cohort between the two eras, performed stratified analysis, established nomograms for predicting 3- and 5-year OS rate, and performed internal validation. Results: Comparing the survival difference between the ICIs and non-ICIs era, head and neck melanoma showed the greatest improvement in survival, with 3- and 5-year OS rate increasing by 10.2% and 9.1%, respectively (P=0.00011). In trunk arms and legs melanoma, the 3- and 5-year OS rate increased by 4.6% and 3.9%, respectively (P<0.0001). There is no improvement in survival in acral melanoma (AM) between the two eras (P=0.78). The receiver operating characteristic (ROC) curve, area under the ROC curve (AUC) and calibration graphs show good discrimination and accuracy of nomograms. Decision curve analysis (DCA) suggests good clinical utility of nomograms. Conclusions: Based on the classification of sunlight exposure patterns, there is a gradient difference in immunotherapy efficacy for MM. The degree of sunlight exposure is positively correlated with immunotherapy response. The nomograms are sufficiently accurate to predict 3- and 5-year OS rate for MM, allowing for individualized clinical decisions for future clinical work.