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Chaudry, O.

Publications and source records attributed to Chaudry, O..

2 recordsLinked to original sources

Females outperform males in spatial learning despite increased amyloid plaques and microgliosis in a TgF344-AD rat model of Alzheimer's disease

Alzheimers disease (AD) is a progressive neurodegenerative disease and is the sixth leading cause of death in the US. AD is more prevalent in females than males. While estrogen provides neuroprotection in females, sex mediated differences in the development of AD pathology are not fully elucidated. Therefore, a comparison of the events that develop between sexes in the early-stage of AD pathology may reveal new potential targets for more effective therapeutic intervention. To address sex differences, we analyzed early stage 9-month male and female TgF344-AD (Tg-AD) rats, an AD model carrying the APPswe and Presenilin 1 (PS1{Delta}E9) mutations that develops progressive age-dependent AD pathology similar to humans. Using active place avoidance (aPAT) tests that assess hippocampal-dependent spatial learning and memory, we found significant deficits in Tg-AD females compared to wild type females, but no significant difference between the two male genotypes. Moreover, significant sex differences were observed in that Tg-AD females outperformed Tg-AD males in several measures of the aPAT test. Unexpectedly, Tg-AD females displayed higher levels of hippocampal amyloid plaques and amoeboid microglia than their Tg-AD male littermates. Furthermore, Tg-AD females experienced less hippocampal neuronal loss and had higher GluA2 subunit levels than Tg-AD males. Based on our findings, we propose that estrogen may protect females against cognitive impairment at early stages of AD by regulating GluA2 levels independently of amyloid plaque deposition and gliosis. Elucidating this potential protective mechanism of action of estrogen in AD could lead to new targets for early intervention.

neuroscience↗

Multi-scale predictive modeling discovers Ibudilast as a polypharmacological agent to improve hippocampal dependent spatial learning and memory and mitigate plaque and tangle pathology in a transgenic rat model of Alzheimer's disease

Alzheimers disease (AD) is a multifactorial disease that exhibits cognitive deficits, neuronal loss, amyloid plaques, neurofibrillary tangles and neuroinflammation in the brain. We developed a multi-scale predictive modeling strategy that integrates machine learning with biophysics and systems pharmacology to model drug actions from molecular interactions to phenotypic responses. We predicted that ibudilast (IBU), a phosphodiesterase inhibitor and toll-like receptor 4 (TLR4) antagonist, inhibited multiple kinases (e.g., IRAK1 and GSG2) as off-targets, modulated multiple AD-associated pathways, and reversed AD molecular phenotypes. We address for the first time the efficacy of ibudilast (IBU) in a transgenic rat model of AD. IBU-treated transgenic rats showed improved cognition and reduced hallmarks of AD pathology. RNA sequencing analyses in the hippocampus showed that IBU affected the expression of pro-inflammatory genes in the TLR signaling pathway. Our results identify IBU as a potential therapeutic to be repurposed for reducing neuroinflammation in AD by targeting TLR signaling.

pharmacology and toxicology↗