Psychiatry (Scholarly Publications)Psychiatry (Scholarly Publications)http://hdl.handle.net/2262/1402024-03-28T12:17:59Z2024-03-28T12:17:59ZAutism spectrum disorder genomics: The progress and potential of genomic technologiesLopez, LornaGallagher, Louisehttp://hdl.handle.net/2262/980432022-02-07T18:03:06Z2020-01-01T00:00:00ZAutism spectrum disorder genomics: The progress and potential of genomic technologies
Lopez, Lorna; Gallagher, Louise
Genomic technologies have accelerated research progress in autism spectrum disorder (ASD) genomics and promises to further transform our understanding of the genetic basis of this neurodevelopmental disorder. Here we review the current evidence for the genetic basis of ASD, present the progress of large-scale studies to date and highlight the potential of genomic technologies. In particular, we discuss evidence for the importance of identifying rare genetic variants in family-based studies. Genomics is a key feature of future healthcare and our review illustrates it's potential to improve our biological understanding of neurodevelopmental disorders, and to ultimately aid ASD diagnosis, inform medical decision making and establish genomics as central to personalised medicine.
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2020-01-01T00:00:00ZMethyl-CpG2-binding protein 2 mediates overlapping mechanisms across brain disordersTropea, Danielahttp://hdl.handle.net/2262/962112021-05-05T02:02:12Z2020-01-01T00:00:00ZMethyl-CpG2-binding protein 2 mediates overlapping mechanisms across brain disorders
Tropea, Daniela
MECP2 and its product, Methyl-CpG binding protein 2 (MeCP2), are mostly known for their association to Rett Syndrome (RTT), a rare neurodevelopmental disorder. Additional evidence suggests that MECP2 may underlie other neuropsychiatric and neurological conditions, and perhaps modulate common presentations and pathophysiology across disorders. To clarify the mechanisms of these interactions, we develop a method that uses the binding properties of MeCP2 to identify its targets, and in particular, the genes recognized by MeCP2 and associated to several neurological and neuropsychiatric disorders. Analysing mechanisms and pathways modulated by these genes, we find that they are involved in three main processes: neuronal transmission, immuno-reactivity, and development. Also, while the nervous system is the most relevant in the pathophysiology of the disorders, additional systems may contribute to MeCP2 action through its target genes. We tested our results with transcriptome analysis on Mecp2-null models and cells derived from a patient with RTT, confirming that the genes identified by our procedure are directly modulated by MeCP2. Thus, MeCP2 may modulate similar mechanisms in different pathologies, suggesting that treatments for one condition may be effective for related disorders.
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2020-01-01T00:00:00ZFunctional network mapping reveals state-dependent response to IGF-1 treatment in Rett SyndromeTropea, Danielahttp://hdl.handle.net/2262/962102021-05-05T02:02:03Z2020-01-01T00:00:00ZFunctional network mapping reveals state-dependent response to IGF-1 treatment in Rett Syndrome
Tropea, Daniela
Rett Syndrome (RTT) is a neurodevelopmental disorder associated with mutations in thegeneMeCP2, which is involved in the development and function of cortical networks. The clinical presentation of RTT is generally severe and includes developmental regression and marked neurologic impairment. Insulin-Like growth factor 1 (IGF1) ameliorates RTT-relevant phenotypes in animal models and improves some clinical manifestations in early human trials. However, it remains unclear whether IGF1 treatment has an impact on cortical electrophysiology in line withMeCP2’s role in network formation, and whether these electrophysiological changes are related to clinical response.We performed clinical assessments and resting-state electroencephalogram (EEG) recordings in eighteen patients with classic RTT, nine of whom were treated with IGF1. Among the treated patients,we distinguished those who showed improvements after treatment (responders) from those who did not show any changes (non responders). Clinical assessments were carried out for all individuals with RTT at baseline and 12 months after treatment. Network measures were derived using statistical modelling techniques based on interelectrode coherence measures. We found significant interaction between treatment groups and time points, indicating an effect of IGF1 on network measures. We also found a significant effect of responder status and time point, indicating that these changes in network measures are associated with clinical response to treatment. Further, we found baseline variability in network characteristics, and a machine learning model using these measures applied to pretreatment data predicted treatment response with 100% accuracy (100% sensitivity and 100% specificity) in this small patient group. These results highlight the importance of network pathology in RTT, as well as providing preliminary evidence for the potential of network measures as tools for the characterisation of disease subtypes and as biomarkers for clinical trials.
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2020-01-01T00:00:00ZDerivation of iPSC lines from three young healthy donors of Caucasian origin (NUIGi035-A; NUIGi036-A; NUIGi037-A)Gallagher, Louisehttp://hdl.handle.net/2262/956032021-03-09T03:03:05Z2020-01-01T00:00:00ZDerivation of iPSC lines from three young healthy donors of Caucasian origin (NUIGi035-A; NUIGi036-A; NUIGi037-A)
Gallagher, Louise
The induced pluripotent stem cell (iPSC) technology has offered an unprecedented opportunity for disease modelling and drug discovery. Here we used non-integrating Sendai viral method and derived iPSCs from three young healthy Caucasian donors. All iPSCs expressed pluripotency markers highly and could be differentiated into three germ lineages. They possess normal karyotype which was confirmed by whole genome SNP array. The availability of the healthy control iPSCs offers an opportunity for phenotypic comparison and genome editing for a variety of diseases.
2020-01-01T00:00:00Z