Mali, K. | bioRxiv Science

bioRxiv2026

Wildfire emitted particulate matter induces ovarian hyperandrogenism through aryl hydrocarbon receptor activation

Wildfires have become more frequent and intense worldwide. Wildfire emitted particulate matter (WFPM) can be more toxic than urban background PM due to its greater content of nanoscale size (WFPM0.1) and presence of more polar organic compounds, including polycyclic aromatic hydrocarbons (PAHs). While exposure to WFPM has been linked to cardiovascular and respiratory diseases, its impact on female reproduction remains elusive. Here, we used an in vivo mouse intratracheal exposure model and a 3D ovarian follicle culture system, together with molecular, transcriptomic, and computational approaches, to examine the female reproductive effects of lab-synthesized (LS-WFPM0.1) and real-world Canadian WFPM0.1 (C-WFPM0.1), collected from the New York City and New Jersey metropolitan area during the June 2023 wildfire events. Intratracheal exposure to environmentally relevant dose of LS-WFPM0.1 disrupted mouse estrous cycles and elevated serum concentrations of estradiol and testosterone. RT-qPCR and single-follicle RNA-sequencing (RNA-seq) analysis revealed altered steroidogenic genes, transcriptomic changes, and activation of aryl hydrocarbon receptor (AhR) in antral follicles from mice treated with LS-WFPM0.1. LS-WFPM0.1 consistently increased testosterone secretion and stimulated genes related to androgen synthesis and AhR in vitro. Single-follicle and single-oocyte RNA-seq analysis identified differentially expressed genes related to inflammation in somatic cells and mitochondrial respiratory chain in oocytes. Both C-WFPM0.1 and benzo[a]pyrene, a high-molecular-weight PAH, reproduced these ovarian defects. Mechanistically, AhR inhibition reversed hyperandrogenism induced by WFPM0.1. Together, our findings suggest that WFPM0.1, an increasingly pervasive environmental exposure, adversely impacts female reproductive functions by disrupting ovarian steroidogenesis and inducing hyperandrogenism through AhR activation, highlighting an urgent unmet need for further mechanistic studies and epidemiological investigations to define the reproductive risks of wildfire smoke exposure in human populations.

pharmacology and toxicology↗

bioRxiv2025

Identification of the Best Filter-based Feature Selection Techniques for Microarray Datasets

This article basically explores the impact of univariate & multivariate filter-based feature selection methodologies on enhancing the classification performance for the real-life classification problems. Our study considers two univariate filter-based feature selection techniques, namely, Chi-square and Fisher score, as well as two multivariate filter-based feature selection techniques, viz., Symmetrical Uncertainty and Minimum Redundancy-Maximum Relevance (mRMR). These methods are applied to feature selection from Five diverse collections of datasets, including datasets related to Mixed-lineage Leukaemia (MLL), Lung Cancer, Ovarian Cancer, Central Nervous System (CNS), and Colon Cancer. For each feature, fitness values are calculated using the four aforementioned feature selection methods. After that, a stratified 10-fold cross-validation procedure is conducted using Support Vector Machines (SVM) and Multilayer Perceptrons (MLP) to determine the classification accuracy for each feature. A set of five microarray datasets was used in this evaluation in order to assess the effectiveness of the filter methods. The results of this study represent the first comprehensive analysis and comparison of gene expression datasets filtered using a variety of ranking strategies. Among these approaches, entropy-based methods (e.g., mRMR) emerge as the most effective. The mRMR method demonstrates Outstanding performance outcomes of accuracy, F1-score, and Root Mean Square Error (RMSE). When comparing classifier performance, the F1-score, which combines precision and recall, is particularly useful, while the RMSE measures prediction accuracy. Chi-square, Fisher Score, and Symmetrical Uncertainty (SU) follow as the second, third, and fourth best approaches, respectively. Although the SVM classifier demonstrates superior performance, the difference in accuracy between SVM and the MLP classifier is marginal. Key PointsO_LIOur study considers two univariate filter-based feature selection techniques, namely, Chi-square and Fisher score, as well as two multivariate filter-based feature selection techniques, viz., Symmetrical Uncertainty and Minimum Redundancy-Maximum Relevance (mRMR). C_LIO_LIThese methods are applied to feature selection from Five diverse collections of datasets, including datasets related to Mixed-lineage Leukaemia (MLL), Lung Cancer, Ovarian Cancer, Central Nervous System (CNS), and Colon Cancer. C_LIO_LIAfter that, a stratified 10-fold cross-validation procedure is conducted using Support Vector Machines (SVM) and Multilayer Perceptrons (MLP) to determine the classification accuracy for each feature. C_LIO_LIAmong these approaches, entropy-based methods (e.g., mRMR) emerge as the most effective. The mRMR method demonstrates Outstanding performance outcomes of accuracy, F1-score, and Root Mean Square Error (RMSE). C_LI

bioinformatics↗