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  • 1
    Online-Ressource
    Online-Ressource
    Identification of biological mechanisms underlying a multidimensional ASD phenotype using machine learning
    Springer Science and Business Media LLC ; 2020
    In:  Translational Psychiatry Vol. 10, No. 1 ( 2020-01-28)
    Details
    In: Translational Psychiatry, Springer Science and Business Media LLC, Vol. 10, No. 1 ( 2020-01-28)
    Kurzfassung: 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.
    Materialart: Online-Ressource
    ISSN: 2158-3188
    URL: Article
    DOI: 10.1038/s41398-020-0721-1
    Sprache: Englisch
    Verlag: Springer Science and Business Media LLC
    Publikationsdatum: 2020
    ZDB Id: 2609311-X
    Standort Signatur Einschränkungen Verfügbarkeit
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    Online-Ressource
  • 2
    Online-Ressource
    Online-Ressource
    Gene Variants Involved in Nonsense-Mediated mRNA Decay Suggest a Role in Autism Spectrum Disorder
    MDPI AG ; 2022
    In:  Biomedicines Vol. 10, No. 3 ( 2022-03-13), p. 665-
    Details
    In: Biomedicines, MDPI AG, Vol. 10, No. 3 ( 2022-03-13), p. 665-
    Kurzfassung: 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.
    Materialart: Online-Ressource
    ISSN: 2227-9059
    URL: Article
    DOI: 10.3390/biomedicines10030665
    Sprache: Englisch
    Verlag: MDPI AG
    Publikationsdatum: 2022
    ZDB Id: 2720867-9
    Standort Signatur Einschränkungen Verfügbarkeit
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    Online-Ressource
  • 3
    Online-Ressource
    Online-Ressource
    Biomedical knowledge graph embeddings for personalized medicine: Predicting disease‐gene associations
    Wiley ; 2023
    In:  Expert Systems Vol. 40, No. 5 ( 2023-06)
    Details
    In: Expert Systems, Wiley, Vol. 40, No. 5 ( 2023-06)
    Kurzfassung: 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.
    Materialart: Online-Ressource
    ISSN: 0266-4720 , 1468-0394
    URL: Issue
    URL: Article
    DOI: 10.1111/exsy.v40.5
    DOI: 10.1111/exsy.13181
    Sprache: Englisch
    Verlag: Wiley
    Publikationsdatum: 2023
    ZDB Id: 284011-X
    ZDB Id: 283676-2
    ZDB Id: 2016958-9
    Standort Signatur Einschränkungen Verfügbarkeit
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    Online-Ressource
  • 4
    Online-Ressource
    Online-Ressource
    Table_8.XLSX
    Frontiers Media SA ; 2022
    In:  Frontiers in Molecular Neuroscience Vol. 15 ( 2022-8-18)
    Details
    In: Frontiers in Molecular Neuroscience, Frontiers Media SA, Vol. 15 ( 2022-8-18)
    Kurzfassung: 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.
    Materialart: Online-Ressource
    ISSN: 1662-5099
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fnmol.2022.932305
    DOI: 10.3389/fnmol.2022.932305.s001
    DOI: 10.3389/fnmol.2022.932305.s002
    DOI: 10.3389/fnmol.2022.932305.s003
    DOI: 10.3389/fnmol.2022.932305.s004
    DOI: 10.3389/fnmol.2022.932305.s005
    DOI: 10.3389/fnmol.2022.932305.s006
    DOI: 10.3389/fnmol.2022.932305.s007
    DOI: 10.3389/fnmol.2022.932305.s008
    Sprache: Unbekannt
    Verlag: Frontiers Media SA
    Publikationsdatum: 2022
    ZDB Id: 2452967-9
    Standort Signatur Einschränkungen Verfügbarkeit
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    Online-Ressource
  • 5
    Online-Ressource
    Online-Ressource
    Table_2.xlsx
    Frontiers Media SA ; 2022
    In:  Frontiers in Neuroscience Vol. 16 ( 2022-5-19)
    Details
    In: Frontiers in Neuroscience, Frontiers Media SA, Vol. 16 ( 2022-5-19)
    Kurzfassung: 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.
    Materialart: Online-Ressource
    ISSN: 1662-453X
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fnins.2022.862315
    DOI: 10.3389/fnins.2022.862315.s001
    DOI: 10.3389/fnins.2022.862315.s002
    Sprache: Unbekannt
    Verlag: Frontiers Media SA
    Publikationsdatum: 2022
    ZDB Id: 2411902-7
    Standort Signatur Einschränkungen Verfügbarkeit
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    Online-Ressource
  • 6
    Online-Ressource
    Online-Ressource
    Bridging Genetic Insights with Neuroimaging in Autism Spectrum Disorder—A Systematic Review
    MDPI AG ; 2024
    In:  International Journal of Molecular Sciences Vol. 25, No. 9 ( 2024-04-30), p. 4938-
    Details
    In: International Journal of Molecular Sciences, MDPI AG, Vol. 25, No. 9 ( 2024-04-30), p. 4938-
    Kurzfassung: 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.
    Materialart: Online-Ressource
    ISSN: 1422-0067
    URL: Article
    DOI: 10.3390/ijms25094938
    Sprache: Englisch
    Verlag: MDPI AG
    Publikationsdatum: 2024
    ZDB Id: 2019364-6
    SSG: 12
    Standort Signatur Einschränkungen Verfügbarkeit
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    Online-Ressource
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