Biology subjects

Ubaldi, M.

Publications and source records attributed to Ubaldi, M..

4 recordsLinked to original sources

bioRxiv2026

Genomic SEM reveals shared genetic architecture and expands locus discovery for locomotor activity in heterogeneous stock rats

Locomotor response to a novel environment is a heritable, addiction-relevant behavioral phenotype in rodents. We characterized its genetic basis in 9,341 male and female heterogeneous stock (HS) rats from eight studies. Locomotor activity was heritable across studies, while pairwise genetic correlations were positive but variable, indicating shared yet nonidentical genetic influences. Given nonuniform genetic architecture, we compared three complementary approaches to locus discovery that analyzed SNP effects 1) on a pooled locomotor phenotype (MEGA), 2) across studies under a fixed-effect model (METAL), or 3) on a shared genetic factor (Genomic SEM). To ensure comparability, all three locus-discovery approaches were applied to a common subset of seven studies. Together, these approaches identified 38 loci, with both convergence and method-specific discovery across analyses. Genomic SEM identified 31 loci, compared to 8 for MEGA and 13 for METAL. To interpret the loci, we integrated brain molecular QTLs, predicted coding and splice consequences, and open-field locomotor phenotypes from knockouts of orthologous mouse genes. Our results highlighted established neurobehavioral candidates spanning locomotor and reward-related biology (Negr1 and Lmo4), serotonergic signaling (Htr1b), and GABAergic signaling (Gabrg2), as well as relatively novel genes including Tcerg1l, Pcdh10, and Tmem132e. Our results illustrate how Genomic SEM can integrate genetically correlated yet heterogeneous behavioral measures and enable discovery of loci underlying their shared genetic architecture, highlighting its utility for complex-trait studies in rodents.

genetics↗

bioRxiv2025

Machine Learning-Based Model for Behavioral Analysis in Rodents: Application to the Forced Swim Test

The Forced Swim Test (FST) is a widely used preclinical model for assessing antidepressant efficacy, studying stress response, and evaluating depressive-like behaviours in rodents. Over the last 10 years, more than 5,500 scientific articles reporting the use of the FST have been published. Despite its widespread use, the FST behaviours are still manually scored, resulting in a labor-intensive and time-consuming process that is prone to human bias and variability. Despite eliminating some biases, existing automated systems are costly and typically only able to distinguish between immobility and active behaviours. Therefore, they are often unable to accurately differentiate the major subtypes of movement patterns, such as swimming and climbing. To address these limitations, we propose a novel approach based on machine learning (ML) using a three-dimensional residual convolutional neural network (3D RCNN) that processes video pixels directly, capturing the spatiotemporal dynamics of rodent behaviour. Our ML model was validated against manual scoring in rats treated with fluoxetine and desipramine, two antidepressants known to induce distinct behavioural patterns. The ML model successfully differentiated among swimming, climbing, and immobility behaviours, demonstrating its potential as a standardized and unbiased tool for automatized behavioural analysis in the FST. Subsequently, we successfully validated our model by testing its ability to distinguish between drugs that predominantly evoke climbing (i.e., amitriptyline), those that preferentially facilitate swimming (i.e., paroxetine), and those that evoke both in a more balanced manner (i.e., venlafaxine). This approach represents a significant advancement in preclinical research, providing a more accurate and efficient method to analyze forced swimming data in rodents. We anticipate that due to its adaptability, in addition to the FST, the model could be applied to various behavioural tests in laboratory animals.

neuroscience↗

bioRxiv2024

Long-access heroin self-administration induces region specific reduction of grey matter volume and microglia reactivity in the rat.

In opioid use disorder (OUD) patients, a decrease in brain grey matter volume (GMV) has been reported. It is unclear whether this is the consequence of prolonged exposure to opioids or is a predisposing causal factor in OUD development. To investigate this, we conducted a structural MRI longitudinal study in NIH Heterogeneous Stock rats exposed to heroin self-administration and age-matched naive controls housed in the same controlled environment. Structural MRI scans were acquired before (MRI1) and after (MRI2) a prolonged period of long access heroin self-administration resulting in escalation of drug intake. Heroin intake resulted in reduced GMV in various cortical and sub-cortical brain regions. In drug-naive controls no difference was found between MRI1 and MRI2. Notably, the degree of GMV reduction in the medial prefrontal cortex (mPFC) and the insula positively correlated with the amount of heroin consumed and the escalation of heroin use. In a preliminary gene expression analysis, we identified a number of transcripts linked to immune response and neuroinflammation. This prompted us to hypothesize a link between changes in microglia homeostasis and loss of GMV. For this reason, we analyzed the number and morphology of microglial cells in the mPFC and insula. The number of neurons and their morphology was also evaluated. The primary motor cortex, where no GMV change was observed, was used as negative control. We found no differences in the number of neurons and microglia cells following heroin. However, in the same regions where reduced GMV was detected, we observed a shift towards a rounder shape and size reduction in microglia, suggestive of their homeostatic change towards a reactive state. Altogether these findings suggest that escalation of heroin intake correlates with loss of GMV in specific brain regions and that this phenomenon is linked to changes in microglial morphology.

neuroscience↗

bioRxiv2021

Network-Based Discovery of Opioid Use Vulnerability in Rats Using the Bayesian Stochastic Block Model

Opioid use disorder is a psychological condition that affects over 200,000 people per year in the U.S., causing the Centers for Disease Control and Prevention to label the crisis as a rapidly spreading public health epidemic. It has been found that the behavioral relationship between opioid exposure and development of opioid use disorder varies greatly between individuals, implying existence of sup-populations with varying degrees of opioid vulnerability. In this study, we assessed several behavioral variables across heroin taking, refraining and seeking to establish how these factors interact with one another resulting in a heroin dependent, resilient, or vulnerable behavioral phenotype. Over 400 (male and female) heterogeneous stock rats were used in these two studies, and data were collected from two geographically distinct locations. Rats underwent heroin self-administration training, followed by a progressive ratio and heroin-primed reinstatement test. Next, rats underwent extinction training and a cue-induced reinstatement test. To assess how these variables contribute to heroin addiction vulnerability, we developed a network-based data analysis workflow. Specifically, we integrated different cohorts of rats, remove possible batch effects, and constructed a rat-rat similarity network based on their behavioral patterns. We then implemented community detection on this similarity network using a Bayesian degree-corrected stochastic block model to uncover sub-populations of rats with differing levels of opioid vulnerability. We discovered three distinct behavioral sub-populations, each with significantly different behavioral outcomes that allowed for unique characterization of each cluster in terms of vulnerability to opioid use and seeking. We implement this analysis workflow as an open source R package, named mlsbm.

animal behavior and cognition↗