Biology subjects

Vicente, A. M.

Publications and source records attributed to Vicente, A. M..

6 recordsLinked to original sources

bioRxiv2026

The External Globus Pallidus is a Basal Ganglia Output Hub with Action-Specific Circuits

The external globus pallidus (GPe) is traditionally viewed as a homogeneous relay in the indirect basal ganglia pathway that broadly suppresses movement. Using whole-brain anterograde axon mapping, rabies tracing, single-neuron reconstruction, and single-nucleus RNA sequencing, we reveal that the GPe is a major basal ganglia output nucleus, composed of anatomically and molecularly distinct populations. Besides the neurons with canonical projections, and projections to striatum and cortex, the GPe contains specific neuronal populations that target the thalamus and brainstem directly, including the parafascicular thalamus (GPePf) and the pedunculopontine nucleus (GPePPN). These projection-defined subpopulations exhibit distinct behavioral functions. GPe-PPN neurons are selectively suppressed at locomotor onset, and bidirectional manipulation of this pathway is sufficient to promote or suppress locomotion. Notably, D2-MSN stimulation upstream of these neurons evokes locomotion, and co-activation of GPePPN pathway blocks this D2-MSN-evoked locomotion, demonstrating a disinhibitory circuit for action stemming from D2-MSNs. In contrast, GPePf neurons are not engaged during locomotion but are selectively recruited during skilled forelimb actions, and their activation disrupts forelimb movements without affecting locomotion. Together, these findings establish the GPe as a basal ganglia output hub composed of modules that mediate distinct behaviors. This organization revises canonical models of the indirect pathway by demonstrating that D2-MSNs can also facilitate, rather than only suppress, movement depending on the downstream GPe output channels they engage. In briefThe external globus pallidus (GPe), classically considered a relatively homogeneous relay within the indirect basal ganglia pathway, is revealed here as a major basal ganglia output hub. Distinct GPe subpopulations, defined by transcriptomic identity and projection target, form parallel channels to cortical, thalamic and brainstem structures with action-specific functions. These findings uncover a disinhibitory mechanism through which D2-MSNs can facilitate behavior, revising canonical models of basal ganglia function. HighlightsBrain-wide mapping reveals the GPe as a major basal ganglia output node GPe contains novel cell types defined by transcriptomic identity and projection specificity Projection-specific GPe circuits differentially control locomotion and skilled forelimb actions D2-MSNs can disinhibit downstream targets via specific GPe projections and promote action.

neuroscience↗

bioRxiv2019

An amygdala circuit mediates experience-dependent momentary exploratory arrests

Exploration of novel environments ensures survival and evolutionary fitness. This behavior is expressed through exploratory bouts and arrests, which change dynamically based on experience. Neural circuits mediating exploratory behavior should therefore integrate experience and use it to select the proper behavioral output. Using a spatial exploration assay, we uncovered an experience-dependent increase of momentary arrests in visited locations where animals previously arrested. Quantitative analyses of neuronal calcium activity in freely-exploring mice revealed that a large neuronal ensemble in basolateral amygdala is active during self-paced behavioral arrests. This ensemble was recruited in an experience-dependent manner, and closed-loop optogenetic manipulation of these neurons revealed that they are sufficient and necessary to drive experience-dependent arrests. Additionally, we found that neurons in the basolateral amygdala projecting to central amygdala mediate these momentary arrests. These findings uncover an amygdala circuit that mediates momentary exploratory arrests in familiar places, without changing place preference or anxiety/fear-like behaviors.

neuroscience↗

bioRxiv2019

A role for gene-environment interactions in Autism Spectrum Disorder is suggested by variants in genes regulating exposure to environmental factors

IntroductionAutism Spectrum Disorder (ASD) is a clinically heterogeneous neurodevelopmental disorder defined by deficits in social communication and interaction and repetitive and stereotyped interests and behaviors. ASD heritability estimates of 50-83% support a strong role of genetics in its onset, with large sequencing studies reporting a high burden of rare potentially pathogenic copy number variants (CNVs) and single nucleotide variants (SNVs) in affected subjects. Recent data strongly suggests that prenatal to postnatal exposure to ubiquitous environmental factors (e.g. environmental toxins, medications and nutritional factors) contribute to ASD risk. Detoxification processes and physiological permeability barriers (i.e. blood-brain barrier, placenta and respiratory cilia) are crucial in regulating exposure and response to external agents during early development. Thus, the objectives of this study were: 1) to find genes involved in detoxification and regulation of barriers permeability with a high load of relevant CNVs and SNVs in ASD subjects; 2) to explore interactions between the identified genes and environmental factors relevant for the disorder.\n\nMaterial and MethodsThrough literature and databases review we searched for genes involved in detoxification and regulation of barriers permeability processes. Genetic data collected from large datasets of subjects with ASD (Autism Genome Project (AGP), Simmons Simplex Collection (SSC), and Autism Sequencing Consortium (ASC)) was used to identify potentially pathogenic variants targeting detoxification and barrier genes. Data from control subjects without neuropsychiatric disorder history was used for comparison purposes. The Comparative Toxicogenomics Database (CTD) was interrogated to identify putatively relevant gene-environment interactions reported in humans throughout the literature.\n\nResultsWe compiled a list of 519 genes involved in detoxification and regulation of permeability barriers. The analysis of AGP and SSC data resulted in the identification of 7 genes more-frequently targeted by CNVs in ASD-subjects from both datasets, after Bonferroni correction for multiple testing (AGP: P<3.5211x10-4; SSC: P< 4.587x10-4). Moreover, 8 genes were exclusively targeted by CNVs from ASD subjects. Regarding SNVs analyses using the ASC dataset, we found 40 genes targeted by potentially pathogenic loss-of-function and/or missense SNVs exclusive to 6 or more cases. The CTD was interrogated for interactions between 55 identified genes and 54 terms for unique chemicals associated with the disorder. A total of 212 gene-environment interaction pairs, between 51/55 (92.7%) genes and 38/54 (70.4%) chemicals, putatively relevant for ASD, were discovered. ABCB1, ABCG2, CYP2C19, GSTM1, CYP2D6, and SLC3A2 were the genes that interacted with more chemicals, while valproic acid, benzo(a)pyrene (b(a)p), bisphenol A, particulate matter and perfluorooctane sulfonic acid (PFOS) were the top chemicals.\n\nDiscussionThe identified genes code for functionally diverse proteins, ranging from enzymes that increase the degradability of xenobiotics (CYP450s, UGTs and GSTs), to transporters (ABCs and SLCs), proteins that regulate the correct function of barriers (claudins and dyneins) and placental hormones. The identified gene-environment interactions may reflect the fact that some genes and chemicals are understudied and that the potential neurotoxicity of many substances is unreported. We suggest that environmental factors can have pathogenic effects when individuals carry variants targeting these genes and discuss the potential mechanisms by which these genes can influence ASD risk.\n\nConclusionWe reinforce the hypothesis that gene-environment interactions are relevant, at least, for a subset of ASD cases. Given that no treatment exists for the pathology, the identification of relevant modifiable exposures can contribute to the development of preventive strategies for health management policies in ASD.

genetics↗

bioRxiv2019

Adapting the ELEAT: Early Life Exposure Assessment Tool) to Portugal - a pilot study to tackle gene-environment interactions in Autism Spectrum Disorder

BackgroundAutism Spectrum Disorder (ASD) is a pervasive and clinically heterogeneous neurodevelopmental disorder characterized by deficits in social communication and interaction skills, and repetitive and stereotyped behaviours. It is known that ASD has a strong genetics component, but heritability estimates of 50-80% suggest that modifiable non-genetic factors may play an important role in the onset of the disorder. Recently, pre-, peri and post-natal exposure to a variety of environmental factors has been implicated in ASD. Yet, the comprehensive assessment of environmental exposures in this pathology, using large population datasets, is still lacking. The objective of this study was to pilot an environmental exposure assessment tool in Portugal.\n\nMethodsTo examine environmental exposures in a population of Portuguese children with ASD, we translated, adapted and piloted the Early Life Exposure Assessment Tool (ELEAT). The ELEAT was originally developed to assess environmental factors in studies of neurodevelopmental disorders. It is a questionnaire filled by mothers of children with ASD, enquiring about Demographic Information, Maternal Conditions/Medical Interventions, Breastfeeding and Child Diet, Maternal Diet, Supplements, Lifestyle, Home and Environment, Environment, Occupation and Exposures. The ELEAT gathers information about environmental exposure along key phases for early neurodevelopment, from 3 months prior to conception, pregnancy, labor and delivery to the first year of life of the child. Two focus groups were realized, one with mothers of typically-developing children and another with mothers of children with ASD, in order to discuss the mothers opinion regarding the tool comprehensiveness and relevance.\n\nResultsThe large majority of mothers were sure about their answers for all modules, with a small fraction of the group reporting difficulties for the Occupations/Exposures module. Most mothers considered the ELEAT to be a little too long, but generally found that the instructions were clear and, most importantly, agreed that the questions were important.\n\nConclusionsIntegration of the pilot feedback will allow us to enhance the tool and optimize its usage in Portuguese-speaking communities, improving its capacity to assemble accurate environmental data from diverse cultural settings, and to be extended to larger population datasets. Combined with genetic and clinical data, the ELEAT will contribute to the identification of modifiable lifestyle and environmental risk factors for ASD. Such evidence may eventually provide the opportunity for disease prevention or reduced severity by mitigating exposure when genetic susceptibility is identified early in life.

developmental biology↗

bioRxiv2018

Identifying disease genes using machine learning and gene functional similarities, assessed through Gene Ontology

Identifying disease genes from a vast amount of genetic data is one of the most challenging tasks in the post-genomic era. Also, complex diseases present highly heterogeneous genotype, which difficult biological marker identification. Machine learning methods are widely used to identify these markers, but their performance is highly dependent upon the size and quality of available data.\n\nIn this study, we demonstrated that machine learning classifiers trained on gene functional similarities, using Gene Ontology (GO), can improve the identification of genes involved in complex diseases. For this purpose, we developed a supervised machine learning methodology to predict complex disease genes. The proposed pipeline was assessed using Autism Spectrum Disorder (ASD) candidate genes. A quantitative measure of gene functional similarities was obtained by employing different semantic similarity measures. To infer the hidden functional similarities between ASD genes, various types of machine learning classifiers were built on quantitative semantic similarity matrices of ASD and non-ASD genes. The classifiers trained and tested on ASD and non-ASD gene functional similarities outperformed previously reported ASD classifiers. For example, a Random Forest (RF) classifier achieved an AUC of 0. 80 for predicting new ASD genes, which was higher than the reported classifier (0.73). Additionally, this classifier was able to predict 73 novel ASD candidate genes that were were enriched for core ASD phenotypes, such as autism and obsessive-compulsive behavior. In addition, predicted genes were also enriched for ASD co-occurring conditions, including Attention Deficit Hyperactivity Disorder (ADHD).\n\nWe also developed a KNIME workflow with the proposed methodology which allows users to configure and execute it without requiring machine learning and programming skills. Machine learning is an effective and reliable technique to decipher ASD mechanism by identifying novel disease genes, but this study further demonstrated that their performance can be improved by incorporating a quantitative measure of gene functional similarities. Source code and the workflow of the proposed methodology are available at https://github.com/Muh-Asif/ASD-genes-prediction.

bioinformatics↗

bioRxiv2018

An integrative systems medicine approach to delineate complex genotype-phenotype associations in Autism Spectrum Disorder

The complex genetic architecture of Autism Spectrum Disorder (ASD) and its heterogeneous phenotype make molecular diagnosis and patient prognosis challenging tasks. To establish more precise genotype-phenotype correlations in ASD, we developed a novel machine learning integrative approach, which seeks to delineate associations between patients clinical profiles and disrupted biological processes inferred from their Copy Number Variants (CNVs) that span brain genes. Clustering analysis of relevant clinical measures from 2446 ASD cases in the Autism Genome Project identified two distinct phenotypic subgroups. Patients in these clusters differed significantly in ADOS-defined severity, adaptive behaviour profiles, intellectual ability and verbal status, the latter contributing the most for cluster stability and cohesion. Functional enrichment analysis of brain genes disrupted by CNVs in these ASD cases identified 15 statistically significant biological processes, including cell adhesion, neural development, cognition and polyubiquitination, in line with previous ASD findings. A Naive Bayes classifier, generated to predict the ASD phenotypic clusters from disrupted biological processes, achieved predictions with a high Precision (0.82) but low recall (0.39), for a subset of patients with higher biological Information Content scores. This study shows that milder and more severe clinical presentations can have distinct underlying biological mechanisms. It further highlights how machine learning approaches can reduce clinical heterogeneity using multidimensional clinical measures, and establish genotype-phenotype correlations in ASD. However, predictions are strongly dependent on patients information content. Findings are therefore a first step towards the translation of genetic information into clinically useful applications, but emphasize the need for larger datasets with very complete clinical and biological information.

systems biology↗