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Dobson, R. J. B.

Publications and source records attributed to Dobson, R. J. B..

2 recordsLinked to original sources

Loss of Trem2 in microglia leads to widespread disruption of cell co-expression networks in mouse brain

Rare heterozygous coding variants in the Triggering Receptor Expressed in Myeloid cells 2 (TREM2) gene, conferring increased risk of developing late-onset Alzheimer's disease, have been identified. We examined the transcriptional consequences of the loss of Trem2 in mouse brain to better understand its role in disease using differential expression and coexpression network analysis of Trem2 knockout and wild-type mice. We generated RNA-Seq data from cortex and hippocampus sampled at 4 and 8 months. Using brain cell type markers and ontology enrichment, we found subnetworks with cell type and/or functional identity. We primarily discovered changes in an endothelial-gene enriched subnetwork at 4 months, including a shift towards a more central role for the Amyloid Precursor Protein (App) gene, coupled with widespread disruption of other cell-type subnetworks, including a subnetwork with neuronal identity. We reveal an unexpected potential role of Trem2 in the homeostasis of endothelial cells that goes beyond its known functions as a microglial receptor and signalling hub, suggesting an underlying link between immune response and vascular disease in dementia.

systems biology

Does genetic risk help to predict amyloid burden in a non-demented population? A Bayesian approach.

INTRODUCTIONIn this study we investigate the association between A{beta} levels in cerebrospinal fluid (CSF) and genetic risk in a non-demented population. This paper presents the first analysis to use a Bayesian methodology in this area.\n\nMETHODSData from the Alzheimers Disease Neuroimaging Initiative (ADNI) and the EDAR* and DESCRIPA** studies was used in a Bayesian logistic regression analysis. We modeled CSF A{beta} burden using age, diagnosis (healthy control or mild cognitive impairment), APOE and a polygenic risk score (PGRS) associated with Alzheimers Disease (AD). We compared models built using informative priors on age, diagnosis and APOE with non-informative priors on all variables.\n\nRESULTSThe use of informative priors did not improve model performance in the majority of cases. Models using only age, diagnosis and APOE genotype showed the best predictive ability.\n\nDISCUSSIONA previous study indicated that a PGRS of AD case/control status was associated with CSF A{beta} burden in healthy controls. The current study suggests that this association does not lead to models that are more predictive of amyloid positivity than already known factors such as age and APOE.\n\n* Beta amyloid oligomers in the early diagnosis of AD and as marker for treatment response\n\n** Development of screening guidelines and criteria for pre-dementia Alzheimers disease

bioinformatics