Browsing by Author "Vicente, Astrid M."
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- An integrative system biology approach to delineate complex genotype-phenotype associations in Autism Spectrum DisorderPublication . Asif, M.; Martiniano, Hugo F.; Rasga, Célia; Marques, Ana R.; Santos, João X.; Couto, Francisco M.; Vicente, Astrid M.Objectives: The global objective of the study is to improve the current knowledge on ASD prognosis and diagnosis by delineating the complex genotype-phenotype associations using an integrative systems biology approach. For this purpose three specific objectives were pursued: - To identify clinically similar subgroups of individuals with ASD; - To find biological processes disrupted by rare CNVs targeting brain genes in ASD subjects; - To train a machine learning classifier for the clinical prediction of disease progression from genetic information in very young children.
- An integrative system biology approach to delineate complex genotype-phenotype associations in Autism Spectrum DisorderPublication . Asif, M; Martiniano, Hugo F.; Rasga, Celia; Marques, Ana R.; Santos, Joao X.; O., Guiomar; Couto, Francisco M.; Vicente, Astrid M.Introduction: Autism Spectrum Disorder (ASD) is characterized by deficits in social interaction and communication, and by the presence of repetitive behavior and/or restricted interests. ASD manifests with heterogeneous phenotype and has an estimated global prevalence of ~1%;- ASD is difficult to diagnose in very young children. Delayed diagnosis leads to delay in applying behavioral therapies that may help to reduce symptoms, particularly when applied at young age; - Copy Number Variant (CNV) screening has been widely used for primary diagnosis purposes and is associated with phenotypic variability in ASD patients; - Large scale studies have identified hundreds of ASD implicated loci; however, mechanistic and clinical interpretation of these disease-causing variants remains elusive.
- An integrative system biology approach to delineate complex genotype-phenotype associations in Autism Spectrum DisorderPublication . Asif, Muhammad; Couto, Francisco M.; Vicente, Astrid M.Objectives: To identify clinically similar subgroups of individuals with ASD [To reduce ASD heterogeneous phenotype]; To find biological processes disrupted by rare CNVs targeting brain genes in ASD subjects [To maximize the reproducibility with other data sets]; To train a machine learning classifier for the clinical prediction of disease progression from genetic information in very young children [To facilitate clinical doctors in diagnosis]
- Associação entre variantes genéticas e perfil clínico multidimensional de doentes com perturbação do espetro do autismo: uma abordagem integrativaPublication . Asif, Muhammad; Couto, Francisco; Vicente, Astrid M.A complexidade genética e clínica que caracterizam a per turbação do espetro do autismo (PEA) têm limitado o desenvolvimento de biomarcadores que permitam um diagnóstico precoce e um prognóstico fiável, assim como uma abordagem personalizada para a inter venção terapêutica. Neste estudo pretendeu-se desenvolver uma abordagem integrativa para predição da apresentação clínica baseada em informação de variantes genéticas (Copy Number Variants, CNVs), com aplicação clínica no diagnóstico e prognóstico na PEA. Para tal, técnicas de aprendizagem automática (machine learning) foram aplicadas a dados clínicos e genéticos de 2446 doentes com PEA, recrutados no âmbito do consórcio Autism Genome Project. Análise de clustering de dados clínicos multidimensionais definiu, nesta população, dois subgrupos de pacientes com per fis clínicos diferindo significativamente em termos de capacidade verbal, nível cognitivo, gravidade da doença e compor tamento adaptativo. A análise dos CNVs que afetam especificamente genes do cérebro, nos mesmos indivíduos, identificou 15 processos biológicos enriquecidos em genes alterados. A aplicação de um algoritmo de machine learning para classificação dos doentes com apresentação clínica mais disfuncional, com base nos processos biológicos alterados, mostrou que correlações entre fenótipo clínico e biologia subjacente são possíveis na PEA e que, para grupos populacionais com dados informativos, existe um poder preditivo razoável. Para implementação deste conceito na prática clínica serão necessários estudos mais alargados com dados clínicos e genómicos mais completos.
- 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.
- The current stage of Italy in the implementation of genomics into the National Healthcare System: an application of the B1MG maturity level modelPublication . Baccolini, Valentina; Pitini, Erica; Galeone, Daniela; Marzuillo, Carolina; Cicchetti, Americo; Arca, Marcello; Vicente, Astrid M.; Boccia, Stefania; Villari, PaoloIntroduction: Genomics holds significant promise for prevention and clinical care yet integrating it into the national healthcare system (NHS) requires considerable system-wide changes. This study assessed the current stage of Italy in the use of genomics, to map critical areas for improvement and contribute to a strategic plan. Methods: A total of 18 experts rated individually the level of maturity of the Italian NHS on a scale from 1 (lowest) to 5 (highest) using the B1MG Maturity Level Model tool. This instrument is an European matrix of 49 indicators grouped into eight domains: governance, economic aspects, ethics and legislation, public awareness, workforce skills, clinical organization, clinical guidelines, and data infrastructure. Consensus procedures were performed within each domain to finally agree on one maturity level per indicator. Results: Despite a few national initiatives, Italy shows a local level of implementation in most indicators. Genomic medicine is considered a priority, but still lacks an updated strategy and investment plans. A higher maturity is reached for ethical and legal aspects, but there is a strong need to invest in workforce training, citizen engagement and literacy, and large-scale adoption of tools and novel technologies. Infrastructures and guidelines to improve data storage, management, analysis, interpretation, and sharing are not yet widespread available. Discussion: Italy is at the beginning of its journey towards a sustainable implementation of genomics. An updated national strategy with coordinated actions and investment plans is needed to make progress in key areas, including personnel education, public engagement, technical infrastructure, and clinical organization.
- Estudo Longitudinal de Exposição Ambiental a ToxinasPublication . Rasga, Célia; Lopes, Ana Leonie; Vicente, Astrid M.Quando exposto a factores ambientais de risco, um indivíduo com uma ou mais destas variantes genéticas vai ser mais sensível, e vai reagir de forma diferente comparado com um indivíduo sem essas mesmas variantes. O estudo longitudinal de exposição ambiental a toxinas é um dos primeiros a explorar interações gene-ambiente em Perturbação do Espectro do Autismo (PEA). O ELEAT permite-nos avaliar, indirectamnete, a exposição aos factores ambientais relevantes para a PEA.
- FunVar: A systematic pipeline to unravel the convergence patterns of genetic variants in ASD, a paradigmatic complex diseasePublication . Asif, Muhammad; Vicente, Astrid M.; Couto, Francisco M.In recent years, the technological advances for capturing genetic variation in large populations led to the identification of large numbers of putative or disease-causing variants. However, their mechanistic understanding is lagging far behind and has posed new challenges regarding their relevance for disease phenotypes, particularly for common complex disorders. In this study, we propose a systematic pipeline to infer biological meaning from genetic variants, namely rare Copy Number Variants (CNVs). The pipeline consists of three modules that seek to (1) improve genetic data quality by excluding low confidence CNVs, (2) identify disrupted biological processes, and (3) aggregate similar enriched biological processes terms using semantic similarity. The proposed pipeline was applied to CNVs from individuals diagnosed with Autism Spectrum Disorder (ASD). We found that rare CNVs disrupting brain expressed genes dysregulated a wide range of biological processes, such as nervous system development and protein polyubiquitination. The disrupted biological processes identified in ASD patients were in accordance with previous findings. This coherence with literature indicates the feasibility of the proposed pipeline in interpreting the biological role of genetic variants in complex disease development. The suggested pipeline is easily adjustable at each step and its independence from any specific dataset and software makes it an effective tool in analyzing existing genetic resources. The FunVar pipeline is available at https://github.com/lasigeBioTM/FunVar and includes pre and post processing steps to effectively interpret biological mechanisms of putative disease causing genetic variants.
- How personalised medicine will transform healthcare by 2030: the ICPerMed visionPublication . Vicente, Astrid M.; Ballensiefen, Wolfgang; Jönsson, Jan-IngvarThis commentary presents the vision of the International Consortium for Personalised Medicine (ICPerMed) on how personalised medicine (PM) will lead to the next generation of healthcare by 2030. This vision focuses on five perspectives: individual and public engagement, involvement of health professionals, implementation within healthcare systems, health-related data, and the development of sustainable economic models that allow improved therapy, diagnostic and preventive approaches as new healthcare concepts for the benefit of the public. We further identify four pillars representing transversal issues that are crucial for the successful implementation of PM in all perspectives. The implementation of PM will result in more efficient and equitable healthcare, access to modern healthcare methods, and improved control by individuals of their own health data, as well as economic development in the health sector.
- Identification of biological mechanisms underlying a multidimensional ASD phenotype using machine learningPublication . Asif, Muhammad; Martiniano, Hugo F.M.C.; Marques, Ana Rita; Santos, João Xavier; Vilela, Joana; Rasga, Celia; Oliveira, Guiomar; Couto, Francisco M.; Vicente, Astrid M.The complex genetic architecture of Autism Spectrum Disorder (ASD) and its heterogeneous phenotype makes 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 the 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 behavior 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 by using multidimensional clinical measures, and establishes genotype-phenotype correlations in ASD. However, predictions are strongly dependent on patient's information content. Findings are therefore a first step toward the translation of genetic information into clinically useful applications, and emphasize the need for larger datasets with very complete clinical and biological information.
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