Browsing by Author "Vilela, Joana"
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- Análise de uma rede de similaridade genética entre a perturbação do espetro do autismo e comorbilidades do foro neurológico e neuropsiquiátricoPublication . Vilela, Joana; Martiniano, Hugo; Marques, Ana Rita; Santos, João Xavier; Rasga, Célia; Oliveira, Guiomar; Vicente, Astrid MouraA Perturbação do Espetro do Autismo (PEA) é uma perturbação do neurodesenvolvimento com apresentação clínica heterogénea, nível de gravidade variável e ocorrência de múltiplas comorbilidades. A PEA tem uma arquitetura genética complexa que se reflete na sua heterogeneidade clínica, existindo evidência de uma sobreposição de genes alterados entre esta condição e diversas comorbilidades do foro neurológico e neuropsiquiátrico. Neste estudo, construímos uma rede de interação entre doenças baseada na similaridade genética, para explorar a componente genética compartilhada entre a PEA e comorbilidades neurológicas e neuropsiquiátricas. As doenças analisadas incluem o Défice Intelectual (DI), a Perturbação de Hiperatividade/ Défice de Atenção (PHDA) e a Epilepsia, bem como outras doenças neuropsiquiátricas como a Esquizofrenia (SCZ) e a Perturbação Bipolar (PB). Usando a base de dados de doenças da DisGeNET, a similaridade genética entre as doenças analisadas foi calculada a partir do coeficiente de Jaccard entre pares de doenças, e o algoritmo de Leiden foi usado para identificar comunidades de doenças na rede. Identificámos uma comunidade heterogénea de doenças geneticamente mais semelhantes à PEA, que inclui a Epilepsia, a PB, a PHDA com apresentação combinada, e algumas perturbações no espetro da SCZ. Esta abordagem permitiu obter uma maior clarificação acerca da componente genética compartilhada entre a PEA e comorbilidades neurológicas e neuropsiquiátricas, com implicações importantes para a nosologia, fisiopatologia e o tratamento o personalizado da doença.
- Aplicação de métodos de aprendizagem automática em grafos de conhecimento para medicina personalizadaPublication . Vilela, Joana; Asif, Muhammad; Marques, Ana Rita; Santos, João Xavier; Rasga, Célia; Vicente, Astrid; Martiniano, HugoA Medicina Personalizada é um modelo de prática médica que utiliza o perfil fenotípico e genotípico do indivíduo para melhorar a precisão do diagnóstico, a eficácia terapêutica ou a prevenção de doenças. Neste sentido, a enorme quantidade de dados gerados ao longo dos últimos anos na área biomédica tem contribuído para uma melhor compreensão dos determinantes genéticos de várias patologias e, consequentemente, para a implementação de práticas de Medicina Personalizada em várias áreas, por exemplo na Oncologia e no âmbito das doenças raras. No entanto, ainda subsistem desafios significativos, nomeadamente no que diz respeito à integração de dados biomédicos oriundos de fontes heterogéneas e na obtenção de informação clinicamente relevante. Este trabalho descreve uma abordagem que usa métodos de aprendizagem automática aplicados a um Grafo de Conhecimento (GC) biomédico como um meio para integrar informação armazenada em bases de dados diversas. Este GC contém relações entre genes, doenças e outras entidades biológicas, extraídas de três bases de dados: Ensembl, DisGeNET e Gene Ontology. Neste trabalho exploramos o potencial dos métodos de aprendizagem automática em grafos para produzir informação clinicamente relevante e descrevemos a aplicação desta metodologia à previsão de associações gene-doença. Mostramos ainda que as principais associações gene-doença previstas por esta abordagem podem ser confirmadas em bases de dados externas ou já foram previamente identificadas na literatura.
- Autism Spectrum DisorderPublication . Moura Vicente, Astrid; Vilela, Joana; Marques, Ana Rita1) Identification of neurotransmitter and synaptic gene variants in ASD patients; 2) Are genetic variants targeting noncoding RNAs contributing to ASD risk?; 3) An integrative system biology approach to delineate complex genotype-phenotype associations in ASD; 4) Mining of genes relevant for ASD in large databases.
- Autism Spectrum Disorder: contribution of genetic variants involved in the nonsense-mediated mRNA decayPublication . Marques, Ana Rita; Santos, João Xavier; Vilela, Joana; Rasga, Célia; Martiniano, Hugo; Oliveira, Guiomar; Romão, Luísa; Moura Vicente, AstridIntroduction: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by impairedsocial/communication skills and stereotyped/repetitive behaviors. Genetic factors account for 50-80% of the familialrisk of ASD, but genetic determinants are not fully understood and a role for regulatory processes is plausible. Inthis study, we explored the contribution to ASD etiology of genes involved in an important post-transcriptionalregulatory mechanism implicated in neurodevelopment, the Nonsense-Mediated Decay (NMD). Methods: We first compiled a group of 46 genes encoding NMD factors and regulators. In these genes wesearched for Single Nucleotide Variants (SNVs) and Copy Number Variants (CNVs) in two samples of ASD patients(N=1828 and N=3570, respectively). We observed the frequency of these variants in 60146 controls from gnomADv2.1.1 (for SNVs) and in 10355 controls from the Database of Genomic Variant ( for CNVs). In genes with rarevariants (MAF<1% in controls) predicted to be pathogenic in silico , we further investigated whether these variantsaffect protein domains required for NMD. Results: We identified 270 predicted pathogenic SNVs within 38 genes in 524 ASD patients (28.7% of the total ASDcases) and 38 CNVs located in 18 genes in 38 ASD patients (1% of the ASD cases). Five of these genes, RBM8A , UPF2 , FMR1 , SMG6 and EIF4G1, were previously associated with ASD. We found that 136 variants (122 SNVsand 11 CNVs), in 23 genes, were located within known protein domainsrequired for NMD. These variants, identifiedin 258 ASD patients, may affect proper NMD function and consequently contribute to changes in the expression ofNMD targets. Discussion : In this study we identified genetic variants that may affect NMD function in ASD patients. Since mostNMD targets encode proteins expressed in the brain, we hypothesize that NMD impairment can constitute a riskfactor to ASD pathophysiology. Further studies are needed to better understand the impact of these genetic variantson NMD function and their relevance for ASD.A full understanding of these regulatory mechanisms may constitutean opportunity for the development of therapeutic interventions.
- Autism Spectrum Disorder: gene variants involved in the nonsense-mediated mRNA decay pathwayPublication . Marques, Ana Rita; Martiniano, Hugo; Santos, J.X.; Vilela, Joana; Asif, M.; Rasga, C.; Oliveira, G.; Romão, Luísa; Vicente, AstridGenetic factors account for 50-80% of the familial risk of Autism Spectrum Disorder (ASD), but most of the genetic determinants are still unknown and a role for other regulatory mechanisms is likely. The nonsense-mediated decay (NMD) pathway is essential to control mRNA quality and has an important role in the regulation of the transcriptome. Mutations in genes involved in the NMD pathway, such as the UPF3B gene, a core component of this pathway, were previously linked to ASD. In this study we explored the potential role of NMD factors in ASD. We generated a list of 153 genes involved in the NMD pathway using AmiGO, Reactome and a systematic literature review. To identify potentially pathogenic variants in the NMD genes, we analyzed whole exome sequencing data (WES) data from 1338 ASD subjects. We also searched for Copy Number Variants (CNVs) targeting NMD genes in ASD patients (n=3570) and checked their frequency in controls (n=9649). We identified 43 high impact variants in 28 NMD genes, including the UPF3B and ACE, two genes previously implicated in ASD. Importantly, 11 were novel candidate genes that carry loss-of-function and missense (deleterious and damaging) variants with a frequency of 1 to 5% in this ASD dataset. Additionally, 5 NMD genes were found to be targeted by CNVs in 12 ASD subjects but none of the controls. The discovery of 33 NMD genes that are intriguing candidates for ASD in large patient genomic datasets provides evidence supporting the involvement of the NMD pathway in ASD pathophysiology.
- Autism Spectrum Disorder: modulation of genomic variant effects on brain structure and functionPublication . Vilela, Joana; Martiniano, Hugo; Marques, Ana Rita; Xavier Santos, João; Asif, Muhammad; Rasga, Célia; Oliveira, Guiomar; Vicente, Astrid M.The main objective of this work is to identify Single Nucleotide Variants (SNVs) that play a role in ASD etiology in neurotransmission and synaptic genes since there is strong genomic and functional evidence that these biological processes are altered in ASD.
- Biomedical Knowledge Graph Embeddings for Personalized MedicinePublication . Vilela, Joana; Asif, Muhammad; Marques, Ana Rita; Xavier Santos, João; Rasga, Célia; Vicente, Astrid; Martiniano, HugoPersonalized medicine promises to revolutionize healthcare in the coming years. However significant challenges remain, namely in regard to integrating 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 as a path to reasoning over the wealth of information stored in publicly accessible databases. We use curated databases such as Ensembl, DisGeNET and Gene Ontology as data sources to build a Knowledge Graph containing relationships between genes, diseases and other biological entities and explore the potential of KGE methods to derive medically relevant insights from this KG. To showcase the method’s usefulness we describe two use cases: a) prediction of gene-disease associations and b) clustering of disease embeddings. We show that the top gene-disease associations predicted by this approach can be confirmed in external databases or have already been identified in the literature. An analysis of clusters of diseases, with a focus on Autism Spectrum Disorder (ASD), affords novel insights into the biology of this paradigmatic complex disorder and the overlap of its genetic background with other diseases
- Biomedical knowledge graph embeddings for personalized medicine: Predicting disease‐gene associationsPublication . Vilela, Joana; Asif, Muhammad; Marques, Ana Rita; Santos, João Xavier; Rasga, Célia; Vicente, Astrid; Martiniano, HugoPersonalized 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.
- Bridging Genetic Insights with Neuroimaging in Autism Spectrum Disorder - A Systematic ReviewPublication . Vilela, Joana; Rasga, Célia; Santos, João Xavier; Martiniano, Hugo; Marques, Ana Rita; Oliveira, Guiomar; Vicente, Astrid Moura; MDPIAutism 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.
- Disease similarity network analysis of Autism Spectrum Disorder and comorbid brain disordersPublication . Vilela, Joana; Martiniano, Hugo; Marques, Ana Rita; Santos, João Xavier; Rasga, Célia; Oliveira, Guiomar; Vicente, Astrid MouraAutism 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.