Binukumar B K | PubMed Health

PubMed2025

Antiepileptic drugs carbamazepine and valproic acid mediate transcriptional activation of CYP1A1 via aryl hydrocarbon receptor and regulation of estrogen metabolism.

Cytochrome P450 1A1 (CYP1A1) actively catalyzes estrogen hydroxylation reactions and maintains the levels of neuroactive steroid estradiol. The widely prescribed first-line anti-epileptic drugs (AEDs) are considered to be a potent inducer of CYP1A1 and have also been observed to affect serum estradiol and calcium levels in patients with epilepsy. However, the ability of AEDs to interfere with CYP enzyme function and estrogen disposition is a relatively unexplored area. Here we investigate the effect of widely prescribed AEDs (carbamazepine and valproic acid) on CYP1A1 regulation and the levels of estradiol and calcium in cell supernatants of hepatocellular, HepG2, and neuronal, SH-SY5Y cells. We observed that both the AEDs significantly increased CYP1A1 expression and enzyme activity, which was accompanied by a decrease in estradiol and calcium levels in HepG2 cells. This induction of CYP1A1 mRNA and protein was fully prevented by aryl hydrocarbon receptor (AHR) knockdown and StemRegenin 1 (SR1) antagonism. Notably, the AEDs did not affect the AHR expression but regulated its nuclear translocation, potentially driving the transcriptional upregulation of CYP1A1. Furthermore, the knockdown of CYP1A1 in HepG2 cells elucidated a marked increase in estradiol and calcium levels. Later, this increase subsided upon AED exposure. Lastly, we observed a similar trend in estradiol and calcium alterations in SH-SY5Y cells on AED exposure, speculating the involvement of CYP1A1 induction via AEDs at neuronal sites. This work demonstrates that AEDs mediate the upregulation of CYP1A1 via an AHR-dependent mechanism and influence estrogen and calcium homeostasis.

Humans↗

PubMed2024

WilsonGenAI a deep learning approach to classify pathogenic variants in Wilson Disease.

BACKGROUND: Advances in Next Generation Sequencing have made rapid variant discovery and detection widely accessible. To facilitate a better understanding of the nature of these variants, American College of Medical Genetics and Genomics and the Association of Molecular Pathologists (ACMG-AMP) have issued a set of guidelines for variant classification. However, given the vast number of variants associated with any disorder, it is impossible to manually apply these guidelines to all known variants. Machine learning methodologies offer a rapid way to classify large numbers of variants, as well as variants of uncertain significance as either pathogenic or benign. Here we classify ATP7B genetic variants by employing ML and AI algorithms trained on our well-annotated WilsonGen dataset. METHODS: We have trained and validated two algorithms: TabNet and XGBoost on a high-confidence dataset of manually annotated, ACMG & AMP classified variants of the ATP7B gene associated with Wilson's Disease. RESULTS: Using an independent validation dataset of ACMG & AMP classified variants, as well as a patient set of functionally validated variants, we showed how both algorithms perform and can be used to classify large numbers of variants in clinical as well as research settings. CONCLUSION: We have created a ready to deploy tool, that can classify variants linked with Wilson's disease as pathogenic or benign, which can be utilized by both clinicians and researchers to better understand the disease through the nature of genetic variants associated with it.

Hepatolenticular Degeneration↗