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Ansari, F.

Publications and source records attributed to Ansari, F..

2 recordsLinked to original sources

Rescuing Ischemic Brain Injury by Rewiring Mitochondrial Electron Flow

Mitochondrial metabolic flux alterations are critical drivers of acute ischemia-reperfusion (IR) brain injury. Reverse electron transfer (RET), defined as the upstream flow of electrons from the quinone pool to complex I, is a major source of pathological reactive oxygen species (ROS) under stress conditions. In an in vivo brain IR model, oxygen deprivation induces the buildup of RET-supporting substrates, with glycerol 3-phosphate identified as the dominant contributor in addition to succinate. Rapid oxidation of these substrates by brain mitochondria upon reoxygenation drives massive ROS production, while also leading to over-reduction and dissociation of the complex I flavin mononucleotide (FMN) cofactor. The resulting FMN-deficient complex I becomes catalytically impaired, unable to oxidize NADH or to produce ROS. To mitigate RET and preserve complex I function, we used transgenic mice xenotopically expressing alternative oxidase (AOX). This enzyme bypasses complexes III and IV by directly oxidizing the reduced quinone pool and passing electrons onto molecular oxygen. AOX expression did not alter complex I abundance, supercomplexes assembly, or basal respiration rates, but effectively diverted electrons from the quinone pool, decreasing RET flux via complex I and limiting ROS generation during IR. For the first time we showed that AOX expression and attenuation of RET preserved complex I FMN binding, suppressed oxidative stress, and conferred neuroprotection in vivo. Our findings reveal a novel strategy for rewiring mitochondrial electron flux to mitigate initial IR brain injury, highlighting modulation of the quinone pool by AOX as a potential therapeutic strategy for IR.

biochemistry↗

DANCE: An open-source analysis pipeline and low-cost hardware to quantify aggression and courtship in Drosophila

Quantifying animal behavior is pivotal for identifying the underlying neuronal and genetic mechanisms involved. Computational approaches have enabled automated analysis of complex behaviors such as aggression and courtship in Drosophila. However, existing approaches rely on rigid, rule-based algorithms and expensive hardware, limiting sensitivity to behavioral variations and accessibility. Here, we describe the Drosophila Aggression and Courtship Evaluator (DANCE), a low-cost, open-source platform that combines machine learning-based classifiers and inexpensive hardware to quantify aggression and courtship. DANCE consists of six novel behavioral classifiers trained using a supervised machine learning algorithm. DANCE classifiers address key limitations of rule-based algorithms, capturing dynamic behavioral variations more effectively. DANCE hardware is constructed using repurposed medicine blister packs and acrylic sheets, with recordings performed using smartphones, making it affordable and accessible. Benchmarking demonstrated that DANCE hardware performs comparably to sophisticated, high-cost setups. We validated DANCE in diverse contexts, including social isolation versus enrichment, which modulates aggression and courtship; RNAi-mediated downregulation of the neuropeptide Dsk; and optogenetic silencing of dopaminergic neurons, which promotes aggression. DANCE provides a cost-effective and portable solution for studying Drosophila behaviors in resource-limited settings or near natural habitats. Its accessibility and robust performance democratize behavioral neuroscience, enabling rapid screening of genes and neuronal circuits underlying complex social behaviors.

neuroscience↗