Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2020.04.12.038141

Environment-dependent behavioral traits and experiential factors shape addiction vulnerability

Fouyssac, M.·Puaud, M.·Ducret, E.·Marti-Prats, L.·Vanhille, N.·Ansquer, S.·Zhang, X.·Belin-Rauscent, A.·Giuliano, C.·Houeto, J.-L.·Everitt, B.·Belin, D.
View original source ↗Export metadata ↓

Abstract

The transition from controlled drug use to drug addiction depends on an interaction between a vulnerable individual, their environment and a drug. Here we tested the hypothesis that conditions under which individuals live influence behavioral vulnerability traits and experiential factors operating in the drug taking environment to determine the vulnerability to addiction. The role of behavioural vulnerability traits in mediating the influence of housing conditions on the tendency to acquire cocaine self-administration was characterised in 48 rats housed in either an enriched (EE) or a standard (SE) environment. Then, the influence of these housing conditions on the individual vulnerability to develop addiction-like behaviour for cocaine or alcohol was measured in 72 EE or SE rats after several months of cocaine self-administration or intermittent alcohol drinking, respectively. The determining role of negative experiential factors in the drug taking context was further investigated in 48 SE rats that acquired alcohol drinking as a self-medication strategy. The environment influenced the acquisition of drug intake through its effect on behavioral markers of resilience to addiction. In contrast, the initiation of drug taking as a coping strategy or in a negative state occasioned by the contrast between enriched housing conditions and a relatively impoverished drug taking setting, facilitated the development of compulsive cocaine and alcohol intake. These data demonstrate that addiction vulnerability depends on environmentally determined experiential factors, suggesting that initiating drug use through negative reinforcement-based self-medication facilitates the development of addiction in vulnerable individuals. Significance StatementThe factors that underlie an individuals vulnerability to switch from controlled, recreational drug use to addiction are not well understood. We showed that in individuals housed in enriched conditions, the experience of drugs in the relative social and sensory impoverishment of the drug taking context, and the associated change in behavioural traits of resilience to addiction, exacerbate the vulnerability to develop compulsive drug intake. We further demonstrated that the acquisition of alcohol drinking as a mechanism to cope with distress increases the vulnerability to develop compulsive alcohol intake. Together these results demonstrate that experiential factors in the drug taking context shape the vulnerability to addiction.

Source connections

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Fouyssac, M., Puaud, M., Ducret, E., Marti-Prats, L., Vanhille, N., Ansquer, S., Zhang, X., Belin-Rauscent, A., Giuliano, C., Houeto, J.-L., Everitt, B., Belin, D.. 2020-04-13. Environment-dependent behavioral traits and experiential factors shape addiction vulnerability. https://doi.org/10.1101/2020.04.12.038141

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗
bioRxiv2026

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗
bioRxiv2026

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗