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Campo, P.

Publications and source records attributed to Campo, P..

8 recordsLinked to original sources

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↗

Ivermectin Reduces Withdrawal-Induced Alcohol Intake via CeA GABAergic Enhancement

Although FDA-approved medications for alcohol use disorder (AUD) are available, their efficacy varies across patients, highlighting the need for novel therapeutics that address inter-individual differences in disease etiology and treatment response. Genetic models, particularly heterogeneous stock (HS) rats, recapitulate human-like genetic diversity and behavioral heterogeneity, enabling the dissection of individual differences in vulnerability to AUD and pharmacotherapeutic sensitivity. P2X4 receptors, which are encoded by the gene P2rx4, are ATP-gated ion channels inhibited by ethanol and abundantly expressed in neurons found in reward and stress circuits. P2X4 receptors have emerged as key modulators of ethanol sensitivity and consumption in preclinical models. Here, we genetically predicted P2rx4 expression in whole brain in a cohort of 130 HS rats exposed to chronic intermittent ethanol (CIE) vapor and phenotyped for self-administration during acute abstinence. Rats were dichotomized into high- and low-predicted expression groups. Higher predicted P2rx4 expression was associated with increased post-vapor intake and escalation. In 32 CIE-escalated rats, ivermectin, a positive allosteric modulator of P2X4 receptors, dose-dependently reduced drinking. We stratified rats into three groups: non-responders, mild responders, and high responders. Electrophysiological recordings from CeA slices revealed that ivermectin differentially enhanced GABAergic IPSCs: high-responders exhibited sustained increases in IPSC frequency and selective amplitude reductions, while the two other groups showed transient frequency increases. All groups displayed prolonged rise times, however non-responders showed extended decay times. These findings suggest that P2rx4 upregulation serves as a vulnerability marker for dependence-like behaviors, with ivermectin attenuating withdrawal-driven alcohol consumption by enhancing CeA GABAergic inhibition.

pharmacology and toxicology↗

Perinatal Fentanyl Exposure Drives Enduring Addiction Risk and Central Amygdala Gene Dysregulation

The use of fentanyl and other opioids during pregnancy is a pressing public health issue due to its association with Neonatal Opioid Withdrawal Syndrome (NOWS) and long-term neurobehavioral deficits. Human epidemiologic studies are confounded by both genetic and environmental factors that differ between exposed and unexposed children. We developed a novel rat model of perinatal fentanyl exposure to characterize NOWS symptoms and investigate enduring behavioral and molecular outcomes. Offspring born to fentanyl-exposed dams exhibited reduced survival, lower body weight, spontaneous withdrawal symptoms, and mechanical hypersensitivity. In adolescence, these rats displayed negative affect, while in adulthood, they showed increased fentanyl self-administration, heightened drug-seeking during reinstatement, and elevated corticosterone levels during withdrawal. To explore the molecular underpinnings of these physiological and behavioral outcomes, we conducted RNA-seq in the central amygdala of adult rats, revealing dysregulated pathways related to GPCR signaling, adaptive immune response and neurodevelopmental processes. These transcriptional changes provide insights into the mechanisms driving addiction vulnerability and stress-related behaviors following early fentanyl exposure. Our findings highlight the lasting impact of perinatal opioid exposure in an experimental system that avoids many of the confounds that plague studies in humans, underscoring the need for preclinical models to study NOWS and its long-term consequences. This model offers translational relevance for developing therapeutic strategies to mitigate NOWS and reduce neuropsychiatric risks associated with prenatal opioid exposure.

animal behavior and cognition↗

A Preclinical Alcohol BioBank: Samples from Behaviorally Characterized HS Rats for AUD Research

Alcohol use disorder (AUD) imposes a significant global health burden, yet effective treatments remain limited due to the scarcity of well-characterized biological sample repositories. To address this gap, we established the UCSD Alcohol BioBank, a comprehensive resource containing thousands of samples from over 700 genetically diverse heterogeneous stock (HS) rats. Modeled after successful cocaine and oxycodone biobanks, this repository utilizes the chronic intermittent ethanol vapor exposure (CIE) model, paired with oral self-administration, to characterize AUD-like behaviors, including ethanol consumption, preference, motivation, and withdrawal symptoms such as allodynia and anxiety-like behavior. Longitudinal samples (blood, urine, and feces) are collected before, during, and after ethanol exposure, while terminal samples (brain, heart, liver, kidneys, cecum, reproductive organs, adrenal glands, peripheral blood mononuclear cells) are obtained at intoxication, acute withdrawal, protracted abstinence, or from naive controls. Samples are preserved via snap-freezing or paraformaldehyde fixation to support diverse applications, including genomics, transcriptomics, proteomics, and neuroanatomy. The genetic diversity of HS rats enables genome-wide association studies (GWAS) to identify AUD-related genetic variants. Freely available to non-profit organizations at www.alcoholbiobank.org, with genetic and behavioral data deposited in public repositories, the Alcohol BioBank facilitates collaborative research to uncover biomarkers and develop novel therapies for AUD, addressing a critical need in addiction science.

animal behavior and cognition↗

CircRNA hypomethylation in the human amygdala implicates FKBP5 in alcohol use disorder

Genome- and phenome-wide association studies implicate the RNA demethylase FTO in alcohol use disorder (AUD), yet the RNA methylation landscape in AUD remains poorly characterized. Analyzing postmortem human basolateral amygdala (BLA) tissue, a key brain region in AUD-related behaviors, we found extensive m6A hypomethylation uniquely affecting circular RNAs (circRNAs). Notably, FKBP5-hosted circRNAs (circFKBP5s) exhibited pronounced hypomethylation correlating with elevated expression of FKBP5 mRNA isoforms. These findings were replicated in an animal model of alcohol dependence. Predictive analyses suggest that circFKBP5s influence genes involved in neurodevelopmental processes and neuronal identity. These findings uncover a novel aspect of AUD neurobiology linked to circRNA methylation.

molecular biology↗

Perception of near-threshold visual stimuli is influenced by pre-stimulus alpha-band amplitude but not by alpha phase

Ongoing brain activity preceding visual stimulation has been suggested to shape conscious perception. The underlying mechanisms are still under debate, although alpha oscillations have been pointed out as the main explanatory candidate. According to the pulsed-inhibition framework, bouts of functional inhibition arise in each alpha cycle, allowing information to be processed in a pulsatile manner. Consequently, it has been hypothesized that perceptual outcome can be influenced by the specific phase of alpha oscillations prior to the stimulus onset, although empirical findings are controversial. In this study, we aimed to shed light on the role of pre-stimulus alpha oscillations in visual perception. To this end, we recorded electroencephalographic (EEG) activity while participants performed three near-threshold visual detection tasks with different attentional involvement: a no-cue task, a non-informative cue task (50% cue validity), and an informative cue task (100% cue validity). Cluster-based permutation statistics were complemented with Bayesian analyses to test the effect of pre-stimulus oscillatory amplitude and phase on visual awareness. We additionally examined whether these effects differed on trials with low and high oscillatory amplitude, as expected from the pulsed-inhibition theory. Our results show a clear effect of pre-stimulus alpha amplitude on conscious perception, but only when alpha fluctuated spontaneously and was not modulated by attention, supporting the notion that alpha-band power indexes neural excitability. In contrast, we did not find any evidence that pre-stimulus alpha phase influences the perceptual outcome, not even when differentiating between low and high amplitude trials. Furthermore, Bayesian analysis provided moderate evidence in favor of the absence of phase effects. Taken together, our results challenge the central theoretical predictions of the pulsed-inhibition framework, at least for the particular experimental conditions used here.

neuroscience↗

Predicting Working Memory performance based on specific individual EEG spatiotemporal features

Working Memory (WM) is a limited capacity system for storing and processing information, which varies from subject to subject. Several works show the ability to predict the performance of WM with machine learning (ML) methods, and although good prediction results are obtained in these works, ignoring the intersubject variability and the temporal and spatial characterization in a WM task to improve the prediction in each subject. In this paper, we take advantage of the spectral properties of WM to characterize the individual differences in visual WM capacity and predict the subjects performance. Feature selection was implemented through the selection of electrodes making use of methods to treat unbalanced classes. The results show a correlation between the accuracy achieved with an Regularized Linear Discriminant Analysis (RLDA) classifier using the power spectrum of the EEG signal and the accuracy achieved by each subject in the behavioral experiment response of a WM task with retro-cue. The proposed methodology allows identifying spatial and temporal characteristics in the WM performance in each subject. Our methodology shows that it is possible to predict the WM performance in each subject. Finally, our results showed that by knowing the spatiotemporal characteristics that predict WM performance, it is possible to customize a WM task and optimize the use of electrodes for agile processing adapted to a specific subject. Thus, we pave the way for implementing neurofeedback through a Brain-Computer Interface.

neuroscience↗

The natural frequencies of the resting human brain: an MEG-based atlas

Brain oscillations are considered to play a pivotal role in neural communication. However, detailed information regarding the typical oscillatory patterns of individual brain regions is surprisingly scarce. In this study we applied a multivariate data-driven approach to create an atlas of the natural frequencies of the resting human brain on a voxel-by-voxel basis. We analysed resting-state magnetoencephalography (MEG) data from 128 healthy adult volunteers obtained from the Open MEG Archive (OMEGA). Spectral power was computed in source space in 500 ms steps for 82 frequency bins logarithmically spaced from 1.7 to 99.5 Hz. We then applied k-means clustering to detect characteristic spectral profiles and to eventually identify the natural frequency of each voxel. Our results revealed a region-specific organisation of intrinsic oscillatory activity, following both a medial-to-lateral and a posterior-to-anterior gradient of increasing frequency. In particular, medial fronto-temporal regions were characterised by slow rhythms (delta/theta). Posterior regions presented natural frequencies in the alpha band, although with differentiated generators in the precuneus and in sensory-specific cortices (i.e., visual and auditory). Somatomotor regions were distinguished by the mu rhythm, while the lateral prefrontal cortex was characterised by oscillations in the high beta range (>20 Hz). Importantly, the brain map of natural frequencies was highly replicable in two independent subsamples of individuals. To the best of our knowledge, this is the most comprehensive atlas of ongoing oscillatory activity performed to date. Furthermore, the identification of natural frequencies is a fundamental step towards a better understanding of the functional architecture of the human brain.

neuroscience↗