Browsing by Author "Oliveira, Guiomar"
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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.
- Autism Spectrum Disorder (ASD): genetic, epigenetic and environmental issuesPublication . Marques, Ana Rita; Martiniano, Hugo; Xavier-Santos, João; Asif, M.; Oliveira, Guiomar; Romão, Luísa; Vicente, Astrid M.Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication/interaction and by unusual repetitive and restricted behaviors and interests. ASD often co-occurs in the same families with other neuropsychiatric diseases (NPD), such as intellectual disability, schizophrenia, depression and attention deficit hyperactivity disorder. Genetic factors have an important role in ASD etiology. Multiple copy number variants (CNVs) and single nucleotide variants (SNVs) in candidate genes have been associated with an increased risk to develop ASD [8-10]. Nevertheless, recent heritability estimates and the high genotypic and phenotypic heterogeneity characteristic of ASD indicate a role of environmental and epigenetic factors, such as long noncoding RNA (lncRNA) and microRNA (miRNA), as modulators of genetic expression and clinical presentation. The aim of this project is to understand the role of lncRNA, miRNA and other epigenetic factors in ASD. For this purpose we are, in a first approach, screening for CNVs and SNVs encompassing lncRNA and miRNA loci in two large datasets: the Autism Genome Project (AGP), with CNV data from 2611 autism trios and the ARRA Autism Sequencing Collaboration, with whole exome sequencing data (WES) from 3056 autism trios. These datasets include data from Portuguese ASD probands recruited by our team. Thus far we have explored the variant call format files that contain all WES variants called by GATK. We started by testing different annotation tools and databases to obtain the best subset of variants that will be filtered according to their genomic coordinates and their pathogenic status. We are also selecting the CNVs from the AGP file that contain lncRNA and miRNA loci. The goal is to identify individuals with potential mutations in lncRNA and miRNA loci that may be disrupting their function upon target genes. Experimental validation will be carried out by measuring gene expression in these patients. A second approach will involve exploring available multiplex families in which ASD co-occurs with other NPDs. Segregation analysis will allow us to define patterns of NPD transmission, identify common gene variants and explore the role of modulating epigenetic factors that lead to differential disease expression.
- Autism Spectrum Disorder (ASD): genetic, epigenetic and environmental issuesPublication . Marques, Ana Rita; Martiniano, Hugo; Xavier-Santos, João; Asif, M.; Oliveira, Guiomar; Luísa, Romão; M. Vicente, AstridAutism Spectrum Disorder (ASD) is a neurodevelopmental disorder which affects the brain structure and the proper establishment of the neuronal connectivity.
- 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: 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.
- 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.
- Characterization and expression analysis of a CNV at chromosome 10q22 encompassing 14 genes in an autistic patientPublication . C. Conceição, Inês; Correia, Catarina; Oliveira, Bárbara; Duque, Frederico; Mouga, Susana; Oliveira, Guiomar; M. Vicente, AstridAutism Spectrum Disorders (ASD) have a strong genetic component, with an estimated heritability of over 90%. Recent studies carried out by the Autism Genome Project (AGP) consortium suggest that rare Copy Number Variants (CNV), characterized by submicroscopic chromosomal deletions and duplications, are more frequent in ASD compared to controls, and may play an important role in susceptibility to this disorder. However, to adequately assess pathogenicity, a detailed characterization of patients CNVs is required. We have been characterizing potentially pathogenic rare CNVs identified by the AGP whole genome CNV analysis of 1,275 ASD individuals. CNV validation in patients and parents and characterization were performed by qPCR and Long-range PCR. One autistic patient showed a rare deletion absent in 4964 controls of European ancestry with no psychiatric disease history. This deletion was located at 10q22, and encompassed 14 genes, including ANXA7, ZMYND17, PPP3CB and CAMK2G. We validated this CNV as de novo, and accurate breakpoint determination showed that it is smaller than predicted by CNV identification algorithms, including only part of CAMK2G. We found that a 39 nucleotide addition occurred with the deletion, a mutational mechanism previously observed in other CNVs. Expression analysis of ANXA7, ZMYND17 and PPP3CB in this patient, in comparison with controls, is ongoing. Previous studies identified a genetic association of the ANXA7, PPP3CB and ZMYND17 region with schizophrenia, and significant expression alterations in schizophrenic patients. ANXA7 encodes Annexin7, involved in membrane fusion; interestingly, CNVs in other Annexin genes (ANXA1) have been found in ASD. PPP3CB plays an important role in synaptic plasticity, learning and memory. ZMYND17 has no known function. Our results suggest that alterations in these genes may be risk factors co-observed in autism and schizophrenia. Additional genetic and functional studies may lead to a better understanding of the common pathways between these neuropsychiatric disorders.
- CNV Characterization, Inheritance and Phenotypic Correlations in Families With AutismPublication . C. Conceição, Inês; Correia, Catarina; Oliveira, Bárbara; M. Rama, Maria; Café, Cátia; Almeida, Joana; Mouga, Susana; Duque, Frederico; Oliveira, Guiomar; M. Vicente, AstridAutism Spectrum Disorders (ASD) have a strong genetic component, with an estimated heritability of over 90%1. Recent studies carried out by the Autism Genome Project (AGP) consortium suggest that rare Copy Number Variants (CNVs), characterized by submicroscopic chromosomal deletions and duplications, are more frequent in ASD compared to controls, and may play an important role in susceptibility to this disorder2. However, to adequately assess pathogenicity, a detailed characterization of patients CNVs and phenotype is required. The goal of this study was to establish the clinical and etiological relevance for ASD of potentially pathogenic CNVs identified in a Portuguese population sample by whole genome CNV analysis, through the detailed characterization of CNVs and correlation with clinical phenotypes. Analysis of the AGP genome-wide CNV results using 1M SNP microarray2 identified a total of 14218 CNVs in 342 Portuguese probands. We selected 291 CNVs, present in 191 individuals (19 females and 172 males), using the following criteria: 1) CNVs that contained implicated/candidate genes for ASD; 2) CNVs in genomic regions known to be implicated/candidate for ASD; 3) CNVs in regions associated with syndromes with ASD symptoms; and 4) high confidence CNVs that did not overlap more than 20% with controls in available databases. We explored recurrence rates, genic content, regulatory elements, inheritance patterns and phenotypic correlations.
- 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.
- Disease similarity network analysis of Autism Spectrum Disorder and comorbid brain disordersPublication . Vilela, Joana; Martiniano, Hugo; Marques, Ana Rita; Santos, João; Rasga, Célia; Oliveira, Guiomar; Vicente, Astrid MouraBackground/Objectives: Autism Spectrum Disorder (ASD) is a clinically heterogeneous neurodevelopmental disorder , with variable severity and multiple comorbidities. Given the previous evidence of genetic overlap between ASD and several comorbid brain disorders, we sought to explore the genetic similarity with ASD across a range of brain disorders, using a genetic similarity disease network approach. Methods: We developed a genetic similarity disease network between ASD and Intellectual Disability, Attention-Deficit/Hyperactivity Disorder, Epilepsy, Schizophrenia and Bipolar Disease spectrum. Using gene-disease associations from the DisGeNET database, genetic similarities were estimated from the Jaccard coefficient between disease pairs. The Leiden algorithm identified network disease communities and shared biological pathways. Loss-of-function (LoF) rare de novo variants within shared genes underlying the disease communities were identified using the MSSNG whole-genome sequencing dataset. Results: We identified three disease communities. ASD is included in a heterogeneous community with Epilepsy, Bipolar Disorder, Attention-Deficit/Hyperactivity Disorder combined type, and some disorders in the Schizophrenia Spectrum. ASD and Intellectual Disability are in separate communities. The genes SHANK3, ASH1L, SCN2A, and CHD2, which are candidate genes for diseases in all communities, have a higher number of de novo rare LoF SNVs in ASD subjects. Conclusion: This approach enabled further clarification of genetic sharing between ASD and comorbid brain disorders, as we took advantage from a finer granularity in disease classification and multi-level evidence from DisGeNET, with important implications for disease nosology, pathophysiology, and personalized treatment.