Biology subjects

Cohen, S. A.

Publications and source records attributed to Cohen, S. A..

4 recordsLinked to original sources

bioRxiv2025

Selectivity of the time-dependent M. tuberculosis LeuRS inhibitor ganfeborole is driven by target vulnerability

Ganfeborole (GSK3036656) inhibits the Mycobacterium tuberculosis leucyl-tRNA-synthetase (mtLeuRS) and is in phase 2a clinical trials for the treatment of tuberculosis. Here we show that ganfeborole is a time-dependent inhibitor of mtLeuRS (IC50 1 nM) and generates a post-antibiotic effect of 77 h at 50xMIC (MIC 0.058 M) with M. tuberculosis H37Rv (Mtb), indicating that mtLeuRS is a highly vulnerable drug target and supporting the excellent in vivo efficacy of the drug. Ganfeborole is also a potent time-dependent inhibitor of Escherichia coli LeuRS (ecLeuRS, IC50 2 nM), however no antibacterial activity is observed towards E. coli up to 1 mM ganfeborole despite the observation that less potent ganfeborole analogs have antibacterial activity. To rationalize this observation, we propose that ganfeborole forms a complex with AMP that binds to the ecLeuRS editing site but does not impact aminoacylation. In support, addition of 12.5 M norvaline generates a ganfeborole MIC of 0.4 M since ecLeuRS is unable to hydrolyze norvaline-tRNALeu. Additionally, mutations that reduce the affinity and residence time of ganfeborole-AMP on ecLeuRS result in antibacterial activity. We propose that the activity of ganfeborole towards Mtb is because mtLeuRS is a highly vulnerable target so that only low levels of enzyme need to be inhibited by the ganfeborole-tRNALeu complex in contrast to ecLeuRS, which we previously demonstrated is a low vulnerability target.

biochemistry↗

bioRxiv2025

Modeling Withdrawal States in Opioid-Dependent Mice with Machine Learning

Understanding opioid withdrawal behaviors in preclinical models is critical to improving therapeutic approaches for opioid use disorder (OUD). However, quantifying these withdrawal behaviors remains a difficult process for researchers, given the subtlety of behaviors and variation across individuals. To overcome these difficulties, we developed a scalable behavioral analysis pipeline using LUPE (Light aUtomated Pain Evaluator), an open-source framework integrating video acquisition, pose estimation, supervised and unsupervised classification, and expert-guided behavior discovery. Mice undergoing naloxone-precipitated opioid withdrawal were recorded and analyzed using DeepLabCut for markerless pose estimation. We hand-annotated withdrawal-specific behaviors, including jumping, genital licking, grooming, and paw tremors, and normal behaviors, including walking, rearing, and being still, using Behavioral Observation Research Interactive Software (BORIS) to generate frame-by-frame ethograms. The annotations and pose data were then imported into Active learning Segmentation of Open field in DeepLabCut (A-SOiD), an active learning platform for behavior classification. A-SOiD successfully detected some behaviors (e.g., grooming and rearing) which were of a longer duration, though other rapid behaviors (e.g., jumping and paw tremors) were inconsistently captured. While no novel behavioral motifs have been discovered yet, ongoing work aims to refine model performance. This LUPE-based pipeline sets the groundwork for standardized, high-resolution behavior quantification and is being applied to additional datasets to investigate whether new components of the withdrawal phenotype emerge across experimental conditions.

neuroscience↗

bioRxiv2025

Identifying Modulators of the Post-Antibiotic Effect

The post-antibiotic effect (PAE) is the delay in bacterial regrowth following antibiotic removal. It has important implications for dosing regimens since drugs that have extended activity following their elimination can be dosed less frequently, widening the therapeutic window. While the PAE has been associated with target vulnerability and the rate of target turnover, little is known about the genetic components that modulate the PAE. Here, we developed a high-throughput assay to screen the Escherichia coli Keio collection of [~]4000 deletion strains, identifying genes that enhance the PAE for CHIR-090, an inhibitor of UDP-3-O-(R-3-hydroxymyristoyl)-N-acetylglucosamine deacetylase (LpxC). This screen revealed approximately 400 gene knockouts that enhanced the PAE of CHIR-090. The list of PAE enhancers was enriched for genes involved in transmembrane transport and outer membrane synthesis. Notably, deletion of the rfaE gene, which is involved in lipopolysaccharide (LPS) biosynthesis, increased the PAE of the LpxC inhibitors CHIR-090 and LPC-058 by 2 h and 3 h, respectively. Consistent with this phenotype, co-treatment of wild-type E. coli with an RfaE inhibitor increased the PAE of CHIR-090 or LPC-058 by 1 h. To probe the mechanism of this interaction, we measured the rate of LpxC turnover and found that knocking out rfaE reduced its half-life by 2-fold, suggesting that disrupting RfaE increases the stability of LpxC, increasing target vulnerability and enhancing the PAE of LpxC inhibitors. SIGNIFICANCEAntibiotics are generally dosed at very high levels leading to unwanted side effects and non-compliance, which in turn results in the emergence of drug-resistant bacterial infections. The goal of this work was to develop strategies that will enable antibiotics to be dosed at lower levels, thereby improving safety and compliance. In the present work, we have screened 4,000 strains of Escherichia coli to identify compounds that result in an increase in the post-antibiotic effect (PAE), which is the delay in bacterial regrowth following antibiotic exposure and removal. Drugs that cause a PAE are dosed less frequently, and the method we describe will provide a new approach to developing safer drugs.

microbiology↗

bioRxiv2025

Central amygdalar PKCδ neurons mediate fentanyl withdrawal

Aversion to opioid withdrawal is a significant barrier to achieving lasting opioid abstinence. The central amygdala (CeA), a key brain region for pain, threat-detection, autonomic engagement, and valence assignment, is active during opioid withdrawal. However, the role of molecularly distinct CeA neural populations in withdrawal remains underexplored. Here, we investigated the activity dynamics, brain-wide connectivity, and functional contribution of Protein Kinase C-delta (PKC{delta})-expressing neurons in the CeA lateral capsule (CeLCPKC{delta}) during fentanyl withdrawal in mice. Mapping activity-dependent gene expression in CeLCPKC{delta} neurons revealed a highly withdrawal-active subregion in the anterior half of the CeA. Fiber photometry calcium imaging showed that opioid-naive CeLCPKC{delta} neurons respond to salient noxious and startling stimuli. In fentanyl-dependent mice, naloxone-precipitated withdrawal increased spontaneous neural activity and enhanced responses to noxious stimuli. Chronic inhibition of CeLCPKC{delta} neurons throughout fentanyl exposure, via viral overexpression of the potassium channel Kir2.1, attenuated withdrawal signs in fentanyl-dependent mice. Lastly, we identified putative opioid-sensitive inputs to CeLCPKC{delta} neurons using rabies-mediated monosynaptic circuit tracing and color-switching tracers to map mu-opioid receptor-expressing inputs to the CeLC. Collectively, these findings suggest that the hyperactivity of CeLCPKC{delta} neurons underlies the somatic signs of fentanyl withdrawal, offering new insights into the amygdala cell-types and circuits involved in opioid dependence.

neuroscience↗