Joana Vilela

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by substantial etiological and clinical heterogeneity. MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression that play critical roles in brain development. As such, a growing body of work suggests a role of miRNA dysregulation in several human pathologies of the brain, including ASD. However, evidence on miRNA involvement in ASD is dispersed across the literature, limiting its utility for biomarker discovery and functional insights. Here, we present miR4ASD, a manually curated and open-access database of human miRNAs implicated in ASD, covering data from case–control studies of both gene expression and genetic variant analysis. This resource integrates structured data from peer-reviewed publications, including study methodology, tissue type, expression alteration (upregulated or downregulated), sample size, number of supporting studies, and reported genetic variants within miRNA genes. To ensure objectivity and precision, 59 studies meeting the strict inclusion criteria were included. miR4ASD also integrates miRNA–target interaction data from external resources and allows users to perform functional enrichment analysis of target genes. By providing a dynamic and accessible platform, miR4ASD enables the systematic exploration of miRNA involvement in ASD and supports research in diagnostics, biomarker discovery, and systems biology. The database is publicly available on GitHub.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by substantial etiological and clinical heterogeneity. MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression that play critical roles in brain development. As such, a growing body of work suggests a role of miRNA dysregulation in several human pathologies of the brain, including ASD. However, existing evidence for ASD is dispersed across multiple tissues, heterogeneous study designs, and cohorts representing different geographic and ancestral backgrounds, limiting its utility for biomarker discovery and functional insights. Here, we present miR4ASD, a manually curated and open-access database of human miRNAs implicated in ASD, covering data from case-control studies of both gene expression and genetic variant analysis. This resource integrates structured data from peer-reviewed publications, including study methodology, tissue type, expression alteration (upregulated or downregulated), sample size, number of supporting studies and reported genetic variants within miRNA genes. To ensure objectivity and precision, only 59 studies meeting strict inclusion criteria were included. By providing a dynamic and accessible platform, miR4ASD enables systematic exploration of miRNA involvement in ASD and supports research in diagnostics, biomarker discovery, and systems biology. The database is publicly available on GitHub [https://github.com/miR4ASD].

Background: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition with unclear physiopathology. Biomarker-based diagnostic tools for early detection and targeted treatments have yet to be developed. MicroRNAs (miRNAs), which are critical regulators of genes involved in brain development and function, are emerging as promising candidates as molecular mechanisms, biomarkers, and therapeutic targets for ASD. This study aimed to identify miRNAs involved in ASD and evaluate their potential functions through in silico analysis. Methods: A comprehensive analysis was performed to find genomic variants with a predicted functional impact on miRNA activity. Using large datasets of Single Nucleotide Variants (SNVs, N = 4300) and Copy Number Variants (CNVs, N = 3570) from ASD cases, we identified miRNAs targeted by CNVs or containing SNVs predicted to disrupt their function in ASD subjects, and compared their frequencies with controls. Selected regulatory miRNA-mRNA networks and their associated biological functions were characterized. Results: Seventy mature miRNAs were identified, including both previously reported and novel ASD candidates, predicted to regulate 2742 brain-expressed genes. Enrichment analysis revealed their involvement in cellular signaling, transcription regulation, protein metabolism, and chromatin organization – biological processes strongly related to ASD. Notably, 63% of these miRNAs are predicted to target 71 known ASD risk genes and the KCNB1, MECP2, NCKAP1 and ZBTB20 genes are each regulated by at least four miRNAs. Conclusions: This gene-based analysis identified miRNAs regulating gene networks and biological pathways implicated in brain function and plasticity, frequently disrupted in ASD. These findings strengthen the evidence for miRNA involvement in ASD, paving the way for novel diagnostic and therapeutic strategies.

Autism Spectrum Disorder (ASD) is an early onset neurodevelopmental disorder characterized by impaired social interaction and communication, and repetitive patterns of behavior. Family studies show that ASD is highly heritable, and hundreds of genes have previously been implicated in the disorder; however, the etiology is still not fully clear. Brain imaging and electroencephalography (EEG) are key techniques that study alterations in brain structure and function. Combined with genetic analysis, these techniques have the potential to help in the clarification of the neurobiological mechanisms contributing to ASD and help in defining novel therapeutic targets. To further understand what is known today regarding the impact of genetic variants in the brain alterations observed in individuals with ASD, a systematic review was carried out using Pubmed and EBSCO databases and following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This review shows that specific genetic variants and altered patterns of gene expression in individuals with ASD may have an effect on brain circuits associated with face processing and social cognition, and contribute to excitation–inhibition imbalances and to anomalies in brain volumes.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by communication deficits and repetitive behavioral patterns. Hundreds of candidate genes have been implicated in ASD, including neurotransmission and synaptic (NS) genes; however, the genetic architecture of this disease is far from clear. In this study, we seek to clarify the biological processes affected by NS gene variants identified in individuals with ASD and the global networks that link those processes together. For a curated list of 1216 NS candidate genes, identified in multiple databases and the literature, we searched for ultra-rare (UR) loss-of-function (LoF) variants in the whole-exome sequencing dataset from the Autism Sequencing Consortium (N = 3938 cases). Filtering for population frequency was carried out using gnomAD (N = 60,146 controls). NS genes with UR LoF variants were used to construct a network of protein–protein interactions, and the network’s biological communities were identified by applying the Leiden algorithm. We further explored the expression enrichment of network genes in specific brain regions. We identified 356 variants in 208 genes, with a preponderance of UR LoF variants in the PDE11A and SYTL3 genes. Expression enrichment analysis highlighted several subcortical structures, particularly the basal ganglia. The interaction network defined seven network communities, clustering synaptic and neurotransmitter pathways with several ubiquitous processes that occur in multiple organs and systems. This approach also uncovered biological pathways that are not usually associated with ASD, such as brain cytochromes P450 and brain mitochondrial metabolism. Overall, the community analysis suggests that ASD involves the disruption of synaptic and neurotransmitter pathways but also ubiquitous, but less frequently implicated, biological processes.

Personalized medicine is a concept that has been subject of increasing interest in medical research and practice in the last few years. However, significant challenges stand in the way of practical implementations, namely in regard to extracting clinically valuable insights from the vast amount of biomedical knowledge generated in the last few years. Here, we describe an approach that uses Knowledge Graph Embedding (KGE) methods on a biomedical Knowledge Graph (KG) as a path to reasoning over the wealth of information stored in publicly accessible databases. We built a Knowledge Graph using data from DisGeNET and GO, containing relationships between genes, diseases and other biological entities. The KG contains 93,657 nodes of 5 types and 1,705,585 relationships of 59 types. We applied KGE methods to this KG, obtaining an excellent performance in predicting gene‐disease associations (MR 0.13, MRR 0.96, HITS@1 0.93, HITS@3 0.99, and HITS@10 0.99). The optimal hyperparameter set was used to predict all possible novel gene‐disease associations. An in‐depth analysis of novel gene‐disease predictions for disease terms related to Autism Spectrum Disorder (ASD) shows that this approach produces predictions consistent with known candidate genes and biological pathways and yields relevant insights into the biology of this paradigmatic complex disorder.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with heterogeneous clinical presentation, variable severity, and multiple comorbidities. A complex underlying genetic architecture matches the clinical heterogeneity, and evidence indicates that several co-occurring brain disorders share a genetic component with ASD. In this study, we established a genetic similarity disease network approach to explore the shared genetics between ASD and frequent comorbid brain diseases (and subtypes), namely Intellectual Disability, Attention-Deficit/Hyperactivity Disorder, and Epilepsy, as well as other rarely co-occurring neuropsychiatric conditions in the Schizophrenia and Bipolar Disease spectrum. Using sets of disease-associated genes curated by the DisGeNET database, disease genetic similarity was estimated from the Jaccard coefficient between disease pairs, and the Leiden detection algorithm was used to identify network disease communities and define shared biological pathways. We identified a heterogeneous brain disease community that is genetically more similar to ASD, and that includes Epilepsy, Bipolar Disorder, Attention-Deficit/Hyperactivity Disorder combined type, and some disorders in the Schizophrenia Spectrum. To identify loss-of-function rare de novo variants within shared genes underlying the disease communities, we analyzed a large ASD whole-genome sequencing dataset, showing that ASD shares genes with multiple brain disorders from other, less genetically similar, communities. Some genes (e.g., SHANK3, ASH1L, SCN2A, CHD2, and MECP2) were previously implicated in ASD and these disorders. This approach enabled further clarification of genetic sharing between ASD and brain disorders, with a finer granularity in disease classification and multi-level evidence from DisGeNET. Understanding genetic sharing across disorders has important implications for disease nosology, pathophysiology, and personalized treatment.

Heritability estimates support the contribution of genetics and the environment to the etiology of Autism Spectrum Disorder (ASD), but a role for gene-environment interactions is insufficiently explored. Genes involved in detoxification pathways and physiological permeability barriers (e.g., blood-brain barrier, placenta and respiratory airways), which regulate the effects of exposure to xenobiotics during early stages of neurodevelopment when the immature brain is extremely vulnerable, may be particularly relevant in this context. Our objective was to identify genes involved in the regulation of xenobiotic detoxification or the function of physiological barriers (the XenoReg genes) presenting predicted damaging variants in subjects with ASD, and to understand their interaction patterns with ubiquitous xenobiotics previously implicated in this disorder. We defined a panel of 519 XenoReg genes through literature review and database queries. Large ASD datasets were inspected for in silico predicted damaging Single Nucleotide Variants (SNVs) (N = 2,674 subjects) or Copy Number Variants (CNVs) (N = 3,570 subjects) in XenoReg genes. We queried the Comparative Toxicogenomics Database (CTD) to identify interaction pairs between XenoReg genes and xenobiotics. The interrogation of ASD datasets for variants in the XenoReg gene panel identified 77 genes with high evidence for a role in ASD, according to pre-specified prioritization criteria. These include 47 genes encoding detoxification enzymes and 30 genes encoding proteins involved in physiological barrier function, among which 15 are previous reported candidates for ASD. The CTD query revealed 397 gene-environment interaction pairs between these XenoReg genes and 80% (48/60) of the analyzed xenobiotics. The top interacting genes and xenobiotics were, respectively, CYP1A2, ABCB1, ABCG2, GSTM1, and CYP2D6 and benzo-(a)-pyrene, valproic acid, bisphenol A, particulate matter, methylmercury, and perfluorinated compounds. Individuals carrying predicted damaging variants in high evidence XenoReg genes are likely to have less efficient detoxification systems or impaired physiological barriers. They can therefore be particularly susceptible to early life exposure to ubiquitous xenobiotics, which elicit neuropathological mechanisms in the immature brain, such as epigenetic changes, oxidative stress, neuroinflammation, hypoxic damage, and endocrine disruption. As exposure to environmental factors may be mitigated for individuals with risk variants, this work provides new perspectives to personalized prevention and health management policies for ASD.

Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental condition with unclear etiology. Many genes have been associated with ASD risk, but the underlying mechanisms are still poorly understood. An important post-transcriptional regulatory mechanism that plays an essential role during neurodevelopment, the Nonsense-Mediated mRNA Decay (NMD) pathway, may contribute to ASD risk. In this study, we gathered a list of 46 NMD factors and regulators and investigated the role of genetic variants in these genes in ASD. By conducting a comprehensive search for Single Nucleotide Variants (SNVs) in NMD genes using Whole Exome Sequencing data from 1828 ASD patients, we identified 270 SNVs predicted to be damaging in 28.7% of the population. We also analyzed Copy Number Variants (CNVs) from two cohorts of ASD patients (N = 3570) and discovered 38 CNVs in 1% of cases. Importantly, we discovered 136 genetic variants (125 SNVs and 11 CNVs) in 258 ASD patients that were located within protein domains required for NMD. These gene variants are classified as damaging using in silico prediction tools, and therefore may interfere with proper NMD function in ASD. The discovery of NMD genes as candidates for ASD in large patient genomic datasets provides evidence supporting the involvement of the NMD pathway in ASD pathophysiology.