Biology subjects

Allen, O.

Publications and source records attributed to Allen, O..

5 recordsLinked to original sources

bioRxiv2025

Psilocybin decreases reward-seeking behavior accompanied by increased activity of parvalbumin neurons with perineuronal nets in the medial prefrontal cortex

Clinical trials suggest that a single dose of psilocybin is an effective treatment for substance use disorders (SUDs). Choice impulsivity is a value-based decision-making bias that predicts drug-intake escalation and is commonly associated with SUDs. The dorsomedial prefrontal cortex (dmPFC) regulates choice impulsivity and is enriched with 5-HT2A receptors that mediate effects of psilocybin. We hypothesized that psilocybin has long-term ([≥]48 hours) effects on choice impulsivity in association with dmPFC inhibitory interneurons with perineuronal nets (PNNs). Male Long Evans rats were trained in a delay discounting task (DDT) where rats chose between delayed large rewards (LR) and immediate small rewards (SR). 48 hours after psilocybin or vehicle injections, DDT was assessed, and rats brains processed for microscopy analysis of extracellular matrix (PNNs) together with inhibitory parvalbumin (PV) interneurons and c-fos as a marker of neuronal activity. Psilocybin acutely increased head-twitch responses. Psilocybin decreased LR choices and increased the latency to LR choices 48 hours after administration. These effects were independent of delay and therefore not consistent with changes in impulsivity. Psilocybin also increased the density of PNN+PV+cFos triple-labeled neurons in the dmPFC. These results suggest that psilocybin decreases reward seeking through the increased activation of dmPFC PV interneurons with PNNs.

neuroscience↗

bioRxiv2025

Cross-Species Evidence for Hippocampal CACNA1C as a Therapeutic Target for Alcohol Use Disorder

Context-induced relapse is a major barrier to recovery from alcohol use disorder (AUD). Identifying molecular targets involved in contextual memories associated with alcohol use may serve as novel pharmacotherapies. Our RNAseq profiling study of the hippocampus from rhesus monkeys with chronic alcohol use identified the voltage-gated calcium channel CACNA1C as a promising therapeutic target. However, data regarding CACNA1C expression in AUD and whether inhibition of CACNA1C can attenuate ethanol contextual memories remains limited. We tested the hypothesis that hippocampal CACNA1C expression is increased in human and nonhuman primates (NHPs) with chronic alcohol use. Further, we used a mouse conditioned place preference (CPP) paradigm to test the hypothesis that Nifedipine, a CACNA1C-selective L-type calcium channel antagonist, can attenuate ethanol-induced CPP. CACNA1C mRNA expression was increased in the hippocampus of subjects with AUD (p<0.03). Increased densities of CACNA1C neurons (p<0.01) and glia (p<0.02) were observed in rhesus monkeys with chronic alcohol use. Ethanol-treated mice spent more time in the ethanol-paired chamber compared to the vehicle animals (p<0.04), demonstrating ethanol-induced CPP. This effect was attenuated by Nifedipine, as time spent in the ethanol-paired chamber in the ethanol + Nifedipine group was not significantly different from the vehicle group. These findings demonstrate that chronic alcohol use increases CACNA1C expression in the hippocampus across species and that a CACNA1C subtype-selective antagonist reduces ethanol-induced CPP. Together, these results support CACNA1C as a promising therapeutic target for context-induced relapse in AUD.

neuroscience↗

bioRxiv2025

Mapping the Transcriptional Landscape of Drug Responses in Primary Human Cells Using High-Throughput DRUG-seq

To advance our understanding of drug action in physiologically-relevant systems, we developed a high-throughput transcriptomic atlas of compound responses in primary human cell types. Leveraging the scalable and cost-effective Digital RNA with the pertUrbation of Genes (DRUG-seq) assay, we profiled gene expression responses to 89 pharmacologically-active compounds across six concentrations in four distinct primary cell types: aortic smooth muscle cells (AoSMCs), skeletal muscle myoblasts (SkMMs), dermal fibroblasts, and melanocytes. Through rigorous quality control and normalization, we generated reproducible and cell type-resolved transcriptomic signatures, enabling the discovery of both shared and divergent regulatory programs. This dataset revealed core cellular responses, such as brefeldin A-mediated ER stress across all cell types, as well as lineage-specific effects, including dexamethasone-induced hypoxia signaling in AoSMCs, complex inflammatory responses linked to epithelial-to-mesenchymal transition pathways in SkMMs, TGF-{beta}-modulated states in fibroblasts, and dabrafenib-driven transcriptional shifts towards quiescence in melanocytes. By integrating systematic perturbations with primary models, this dataset serves as a resource for building systems-level models of drug response and mechanism. Ultimately, we aim to accelerate predictive pharmacology by enabling high-throughput data generation grounded in human biology and readily usable by artificial intelligence models.

systems biology↗

bioRxiv2025

Disease-associated loci share properties with response eQTLs under common environmental exposures

Many of the genetic loci associated with disease are expected to have context-dependent regulatory effects that are underrepresented in the transcriptomes of healthy, steady-state adult tissues. To understand gene regulation across diverse environmental conditions and cellular contexts, we treated a broad array of human cell types with three environmental exposures in vitro. With single-cell RNA-sequencing data from 1.4 million cells across 51 individuals, we identified hundreds of response expression quantitative loci (eQTLs) that are associated with inter-individual differences in regulatory changes following treatment with nicotine, caffeine, or ethanol in diverse cell types. We also identified dynamic regulatory effects that vary across differentiation trajectories in response to exposure. In contrast to steady-state eQTLs, and similar to disease risk loci, response eQTLs are enriched in distal enhancers and are regulating genes that experienced strong selective constraint, contain complex regulatory landscapes, and display diverse biological functions. We identified response eQTLs that coincide with disease-associated loci not explained by steady-state eQTLs. Our results highlight the complexity of genetic regulatory effects and suggest that our ability to interpret disease-associated loci will benefit from the pursuit of studies of gene-by-environment interactions in diverse biological contexts.

genetics↗

bioRxiv2025

A high-throughput platform for biophysical antibody developability assessment to enable AI/ML model training

Antibodies must bind their targets with high affinity and specificity to achieve useful therapeutic activity. They must also possess suitable developability properties (e.g., thermostability, solubility, viscosity, polyreactivity) to ensure favorable manufacturing, formulation, and in vivo performance. Both binding and developability properties are inherent to a given antibody amino acid sequence. Identification or selection of antibodies possessing suitable binding characteristics is now routine, and de novo computational design models, trained on extensive complementarity-determining region sequence and structural data, are rapidly improving. Developability properties, however, remain difficult to predict largely due to insufficient training data, with empirical testing being heavily used to avoid challenges in late-stage antibody development. To fill this gap, we built a high-throughput antibody developability assay platform designed to generate the large datasets needed to train improved machine learning (ML) models. We optimized and automated known developability assays [Jain et al., 2017], and developed a robust integrated data analytics pipeline. Here we report data on 246 antibodies--representing 106 approved, 135 clinical-stage, and 5 preregistration/withdrawn molecules--across a panel of 10 developability assays, in a "tidy data" format suitable for AI/ML modeling. We used these data to develop an XGBoost [Chen et al., 2016] ML model that better predicts similarity to approved antibodies compared to conventional use of developability warning thresholds. Additionally, we confirm that preliminary predictive models do improve with more training data. Our high-throughput PROPHET-Ab platform enables data generation at the scale needed to develop improved ML models to predict antibody developability. SignificanceSuccessful antibody drugs exhibit important "developability" properties, beyond tight and specific binding to their target, including high expressibility, high stability and solubility, low aggregation propensity, low viscosity, low polyreactivity, and long in vivo half-life. Collectively, developability properties predict favorable manufacturing, storage, administration, and safety, and deficiencies in these properties increase risk for clinical failure. Despite progress in developing machine learning models to predict structure and binding, antibody developability models lag, largely due to a lack of sufficiently large training datasets. We have built a high-throughput platform, PROPHET-Ab, that enables data generation at the scale needed to train improved AI/ML models to predict antibody developability.

biophysics↗