Extracellular vesicles (EVs) are emitted from cells throughout the body and serve as signaling molecules that mediate disease development. Emerging evidence suggests that per- and polyfluoroalkyl substances (PFAS) impact EV release and content, influencing liver toxicity. Still, the upstream regulators of EV changes affected by PFAS exposure remain unclear. This study evaluated the hypothesis that PFAS exposures, individually and in a mixture, alter the expression of genes involved in EV regulation at concentrations comparable to genes involved in global biological response mechanisms. HepG2 liver cells were treated at multiple concentrations with individual PFOS, PFOA, or PFHxA, in addition to an equimolar PFAS mixture. Gene expression data were analyzed using three pipelines for concentration-response modeling, with results compared against empirically derived datasets. Final benchmark concentration (BMC) modeling was conducted via Laplace model averaging in BMDExpress (v3). BMCs were derived at an individual gene level and across different gene sets, including Gene Ontology (GO) annotations as well as a custom EV regulation gene set. To determine relative PFAS contributions to the evaluated mixture, relative potency factors were calculated across resulting BMCs using PFOS as a standard reference chemical. Results demonstrated that PFAS exposures altered the expression of genes involved in EV regulation, particularly for genes overlapping with endoplasmic reticulum stress. EV regulatory gene changes occurred at similar BMCs as global gene set alterations, supporting concurrent regulation and the role of EVs in PFAS toxicology. This application of transcriptomics-based BMC modeling further validates its utility in capturing both established and novel pathways of toxicity.
Statistical-based feature selection methods have been used for dimension reduction, but only a few studies have explored the impact of selected features on machine learning models. This study aims to investigate the effects of statistical and machine learning-based feature selection methods on spatial prediction models for estimating variations in SO2 concentrations. We collected daily SO2 observations from 1994 to 2018 along with predictor variables such as land-use/land cover allocations, roads, landmarks, meteorological factors, and satellite images, resulting in a total of 428 geographic predictors. Important features were identified using statistical-based feature selection methods including SelectKBest, stepwise feature selection, elastic net, and machine learning-based methods such as random forest. The selected features from the four feature selection methods were fitted to machine learning algorithms including gradient boosting, CatBoost, XGBoost, and stacking ensemble to establish prediction models for estimating SO2 concentrations. SHapley Additive exPlanations (SHAP) was applied to explain the contribution of each selected feature to the model's prediction capability. The results showed that stacking ensemble model outperformed the three single machine learning algorithms. Among the four feature selection methods, the random forest method yielded the highest prediction accuracy (R2=0.80) in the training model, followed by stepwise selection (R2=0.75), SelectKBest (R2=0.75), and elastic net (R2=0.72) in the stacking ensemble model. These results were robust after several validation tests. Our findings suggested that the random forest feature selection method was more suitable for developing machine learning models for air pollution estimation. The identified features also provide important information for urban air pollution management.
As legacy per- and polyfluoroalkyl substances (PFAS) are phased out, numerous substitutes have emerged, raising concerns about their potential health impacts. Using targeted and untargeted approaches, we evaluated plasma PFAS on an -omic scale, examining temporal variability and associated metabolomic disruptions. A total of 400 blood samples from 200 Sister Study participants (collected in 2007-2008 and 2013-2014) were analyzed using liquid chromatography with high-resolution mass spectrometry. Temporal variability was assessed using Spearman correlations and intraclass correlation coefficients. Network analysis, metabolome-wide association studies, and pathway analysis were used to evaluate the impacts of PFAS mixtures on the human metabolome. We detected 24 legacy PFAS via the targeted approach and 1,802 features annotated as potential PFAS via the untargeted approach (21 confirmed by reference standards). While legacy PFAS demonstrated low temporal variability, novel PFAS, especially those that have increased in abundance over time, demonstrated greater temporal variability. The legacy PFAS mixture was associated with lipid and amino acid metabolism, while other PFAS mixtures consisting of novel PFAS affected a wider range of metabolic pathways in addition to amino and lipid metabolism, such as carbohydrate, cofactor and vitamin, and endocrine metabolism. These findings underscore the need for further research on these novel PFAS and their health effects.
PURPOSE:To determine the relationship between germline pathogenic variants (PV) in cancer predisposition genes and the risk of ductal carcinoma in situ (DCIS). EXPERIMENTAL DESIGN:Germline PV frequencies in breast cancer predisposition genes (ATM, BARD1, BRCA1, BRCA2, CDH1, CHEK2, PALB2, RAD51C, and RAD51D) were compared between DCIS cases and unaffected controls and between DCIS and invasive ductal breast cancer (IDC) cases from a clinical testing cohort (n = 9,887), a population-based cohort (n = 3,876), and the UK Biobank (n = 2,421). The risk of contralateral breast cancer (CBC) for DCIS cases with PV was estimated in the population-based cohort. RESULTS:Germline PV were observed in 6.5% and 4.6% of women with DCIS in the clinical testing and population-based cohorts, respectively. BRCA1, BRCA2, and PALB2 PV frequencies were significantly lower among women with DCIS than those with IDC (clinical cohort: 2.8% vs. 5.7%; population-based cohort: 1.7% vs. 3.7%), whereas the PV frequencies for ATM and CHEK2 were similar. ATM, BRCA1, BRCA2, CHEK2, and PALB2 PV were significantly associated with an increased risk of DCIS (OR > 2.0), but only BRCA2 PV were associated with high risk (OR > 4) in both cohorts. The cumulative incidence of CBC among carriers of PV in high-penetrance genes with DCIS was 23% over 15 years. CONCLUSIONS:The enrichment of PV in ATM, BRCA1, BRCA2, CHEK2, and PALB2 among women with DCIS suggests that multigene panel testing may be appropriate for women with DCIS. Elevated risks of CBC in carriers of PV in high-penetrance genes with DCIS confirmed the utility of testing for surgical decision-making.
The presence of redox-active molecules containing catenated sulfur atoms (supersulfides) in living organisms has led to a review of the concepts of redox biology and its translational strategy. Glutathione (GSH) is the body's primary detoxifier and antioxidant, and its oxidized form (GSSG) has been considered as a marker of oxidative status. However, we report that GSSG, but not reduced GSH, prevents ischemic supersulfide catabolism-associated heart failure in male mice by electrophilic modification of dynamin-related protein (Drp1). In healthy exercised hearts, the redox-sensitive Cys644 of Drp1 is highly S-glutathionylated. Nearly 40% of Cys644 is normally polysulfidated, which is a preferential target for GSSG-mediated S-glutathionylation. Cys644 S-glutathionylation is resistant to Drp1 depolysulfidation-dependent mitochondrial hyperfission and myocardial dysfunction caused by hypoxic stress. MD simulation of Drp1 structure and site-directed mutagenetic analysis reveal a functional interaction between Cys644 and a critical phosphorylation site Ser637, through Glu640. Bulky modification at Cys644 via polysulfidation or S-glutathionylation reduces Drp1 activity by disrupting Ser637-Glu640-Cys644 interaction. Disruption of Cys644 S-glutathionylation nullifies the cardioprotective effect of GSSG against heart failure after myocardial infarction. Our findings suggest a therapeutic potential of supersulfide-based Cys bulking on Drp1 for ischemic heart disease.
