Autism spectrum disorder (ASD) depends on a clinical interview with no biomarkers to aid
diagnosis. The current investigation interrogated single-nucleotide polymorphisms (SNPs)
of individuals with ASD from the Autism Genetic Resource Exchange (AGRE) database. SNPs
were mapped to Kyoto Encyclopedia of Genes and Genomes (KEGG)-derived pathways to identify
affected cellular processes and develop a diagnostic test. This test was then applied to
two independent samples from the Simons Foundation Autism Research Initiative (SFARI) and
Wellcome Trust 1958 normal birth cohort (WTBC) for validation. Using AGRE SNP data from a
Central European (CEU) cohort, we created a genetic diagnostic classifier consisting of
237 SNPs in 146 genes that correctly predicted ASD diagnosis in 85.6% of CEU cases.
This classifier also predicted 84.3% of cases in an ethnically related Tuscan
cohort; however, prediction was less accurate (56.4%) in a genetically dissimilar
Han Chinese cohort (HAN). Eight SNPs in three genes (KCNMB4, GNAO1,
GRM5) had the largest effect in the classifier with some acting as
vulnerability SNPs, whereas others were protective. Prediction accuracy diminished as the
number of SNPs analyzed in the model was decreased. Our diagnostic classifier correctly
predicted ASD diagnosis with an accuracy of 71.7% in CEU individuals from the SFARI
(ASD) and WTBC (controls) validation data sets. In conclusion, we have developed an
accurate diagnostic test for a genetically homogeneous group to aid in early detection of
ASD. While SNPs differ across ethnic groups, our pathway approach identified cellular
processes common to ASD across ethnicities. Our results have wide implications for
detection, intervention and prevention of ASD.
Schizophrenia is a debilitating disorder that typically begins in adolescence and is characterized by perceptual abnormalities, delusions, cognitive and behavioural disturbances and functional impairments. While current treatments can be effective, they are often insufficient to alleviate the full range of symptoms. Schizophrenia is associated with structural brain abnormalities including grey and white matter volume loss and impaired connectivity. Recent findings suggest these abnormalities follow a neuroprogressive course in the earliest stages of the illness, which may be associated with episodes of acute relapse. Neuroinflammation has been proposed as a potential mechanism underlying these brain changes, with evidence of increased density and activation of microglia, immune cells resident in the brain, at various stages of the illness. We review evidence for microglial dysfunction in schizophrenia from both neuroimaging and neuropathological data, with a specific focus on studies examining microglial activation in relation to the pathology of grey and white matter. The studies available indicate that the link between microglial dysfunction and brain change in schizophrenia remains an intriguing hypothesis worthy of further examination. Future studies in schizophrenia should: (i) use multimodal imaging to clarify this association by mapping brain changes longitudinally across illness stages in relation to microglial activation; (ii) clarify the nature of microglial dysfunction with markers specific to activation states and phenotypes; (iii) examine the role of microglia and neurons with reference to their overlapping roles in neuroinflammatory pathways; and (iv) examine the impact of novel immunomodulatory treatments on brain structure in schizophrenia.
Linked Articles
This article is part of a themed section on Inflammation: maladies, models, mechanisms and molecules. To view the other articles in this section visit 10.1111/bph.2016.173.issue-4
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