Browsing by Author "Couto, Francisco 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]
- 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.
- 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.
- Increased frequency of the autism broader phenotype in mothers transmitting etiological CNVs to sons affected by Autism Spectrum Disorder (ASD)Publication . Asif, M.; Vicente, A.M.; Couto, Francisco M.Autism Spectrum Disorder (ASD) is a frequent and complex neurodevelopmental disorder, characterized by impairments in social communication and repetitive behaviors and with a high male to female ratio: ~4:1. Genetic factors, including rare Copy Number Variants (CNVs), have a substantial impact in ASD risk1, and are associated with specific phenotypic manifestations2. Recent studies reported that rare inherited CNVs are enriched in mothers of ASD children compared with mothers of controls and are preferentially transmitted from mothers to ASD children suggesting a sex bias in CNV transmission; further, the imbalanced transmission of small pathogenic CNVs from unaffected mothers to their sons with ASD has been described3,4. An increased prevalence of autism-like personality traits is found in unaffected relatives of ASD children, suggesting a genetic liability of a broader autism phenotype (BAP)5. The BAP in parents of autistic children can be assessed by the Social Responsiveness Scale (SRS)6 and Broad Autism Phenotype Questionnaire (BAPQ)7 reports. The SRS is 65-item questionnaire to identify sub-clinical social impairments and interpersonal behaviour in individuals. The BAPQ is a 36-item questionnaire measures social aloofness, rigid personality, and pragmatic language deficits in both parents and children.
- Serum proteomics signature of Cystic Fibrosis patients: A complementary 2-DE and LC–MS/MS approachPublication . Charro, Nuno; Hood, Brian L.; Faria, Daniel; Pacheco, Paula; Azevedo, Pilar; Lopes, Carlos; Bugalho de Almeida, António; Couto, Francisco M.; Conrads, Thomas P.; Penque, DeborahComplementary 2D-PAGE and ‘shotgun’ LC–MS/MS approaches were combined to identify medium and low-abundant proteins in sera of Cystic Fibrosis (CF) patients (mild or severe pulmonary disease) in comparison with healthy CF-carrier and non-CF carrier individuals aiming to gain deeper insights into the pathogenesis of this multifactorial genetic disease. 78 differentially expressed spots were identified from 2D-PAGE proteome profiling yielding 28 identifications and postulating the existence of post-translation modifications (PTM). The ‘shotgun’ approach highlighted altered levels of proteins actively involved in CF: abnormal tissue/airway remodeling, protease/antiprotease imbalance, innate immune dysfunction, chronic inflammation, nutritional imbalance and Pseudomonas aeruginosa colonization. Members of the apolipoproteins family (VDBP, ApoA-I, and ApoB) presented gradually lower expression from non-CF to CF-carrier individuals and from those to CF patients, results validated by an independent assay. The multifunctional enzyme NDKB was identified only in the CF group and independently validated by WB. Its functions account for ion sensor in epithelial cells, pancreatic secretion, neutrophil-mediated inflammation and energy production, highlighting its physiological significance in the context of CF. Complementary proteomics-based approaches are reliable tools to reveal pathways and circulating proteins actively involved in a heterogeneous disease such as CF.
- System medicine approach to improve diagnosis and prognosis in Autism Spectrum Disorder (ASD), based on extensive genomic, biochemical and clinical dataPublication . Asif, Muhammad; Vicente, A.M.; Couto, Francisco M.Background: Autism Spectrum Disorder (ASD) is characterized by social communication impairments and repetitive behaviors, a clinical presentation spectrum and a high male to female ratio. Twin and family studies indicated a strong genetic basis for ASD, with approximately 20% of ASD etiologies residing with identified genetic abnormalities. Behavioral traits in the ASD spectrum are prevalent in unaffected family members, highlighting a trait heritability likely mediated by genetic factors that impact ASD risk. However, the impact of genetic factors on the phenotype and its variability is still not well understood. Due to the absence of underlying biological markers, ASD is still diagnosed by assessing the individual’s behavior. The understanding of the biological basis of ASD can contribute to an earlier diagnosis and then to early intervention, which can have a substantial positive effect on child developmen.
- System medicine approach to improve diagnosis and prognosis in Autism Spectrum Disorders (ASD), based on extensive genomic, biochemical and clinical dataPublication . Asif, M.; Couto, Francisco M.; Vicente, Astrid M.Objectives: The purpose of this project is two fold: 1. To identify associations among autism phenotypic manifestations and disrupted molecular pathways by using genetic, clinical and functionally annotated data;2. To identify clinical subgroups in ASD patients associated with specific disrupted biological pathways.
- Translating the complex ASD genetic architecture into clinical phenotype using an integrative system biology approachPublication . Asif, M.; Martiniano, Hugo F.; Rasga, Celia; Marques, Ana R.; Santos, João X.; Couto, Francisco M.; Vicente, A.M.Objective: The global objective of this study is to improve ASD diagnosis and prognosis by dissecting the complex genotype-phenotype associations using an integrative systems biology approach.