Biology subjects

Dobson, R. J. B.

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

8 recordsLinked to original sources

bioRxiv2021

TREM2 impacts brain microglia, oligodendrocytes and endothelial co-expression modules revealing genes and pathways important in Alzheimer s disease

A microglia response to pathogenic signals in diseases such as Alzheimers disease (AD) has long been recognised, but recent genetic findings have cemented their direct causal contribution to AD and thus the potential to target them or their effector pathways as a possible treatment strategy. TREM2 is a highly penetrant microglia risk gene for AD, which appears central to the coordination of the damage response by microglia in AD. Its absence has a negative impact on Tau and amyloid symptoms and pathologies. Full knowledge of its pathway and relationships with other brain cells in AD has not been fully characterised, but will be essential to fully evaluate the impact of manipulating this pathway for treatment development and to establish the best targets for achieving this. We used whole genome RNA sequencing of hippocampus and cortical brain samples from control, AD, and AD TREM2 risk carriers to identify TREM2-dependent genes driving changes in pathways, processes and cell types in AD. Through highly influential intra and intermodular hub genes and overall changes in the levels of gene expression, TREM2-DAP12 was found to strongly influence a number of other microglia, oligodendrocyte and endothelial genes, notably those involved in complement and Fc{gamma} receptor function, microglia-associated ribosomal genes and oligodendrocyte genes, particularly proteosomal subunits. These strong TREM2 centred co-expression relationships were significantly disrupted in AD cases with a TREM2 risk variant, revealing for the first time genes and pathways directly impacted by TREM2 in the brains of AD patients. Consistent with its function as a lipid sensor, our data supports a role for TREM2 in mediating oligodendrocyte and/or myelin clearance in AD which may be essential not only for preserving healthy tissue homeostasis but may also serve to minimise the persistence of antigenic peptides and lipids which may lead to detrimental pro-inflammatory sequelae. Further work should expand our knowledge of TREM2 on complement and Fc{gamma} receptor function and its impact on oligodendcrotye and myelin integrity and further evaluate the genes and pathways we have identified as possible treatment targets for AD.

neuroscience↗

bioRxiv2020

TMEM106B and CPOX are genetic determinants of cerebrospinal fluid Alzheimer's disease biomarker levels

BackgroundNeurofilament light (NF-L), chitinase-3-like protein 1 (YKL-40), and neurogranin (Ng) are utilized as biomarkers for Alzheimers disease (AD), to monitor axonal damage, astroglial activation, and synaptic degeneration, respectively. Here we performed genome-wide association study (GWAS) analyses using all three biomarkers as outcome. MethodsDNA and cerebrospinal fluid (CSF) samples originated from the European Medical Information Framework AD Multimodal Biomarker Discovery (EMIF-AD MBD) study. Overlapping genotype/phenotype data were available for n=671 (NF-L), 677 (YKL-40), and 672 (Ng) individuals. GWAS analyses applied linear regression models adjusting for relevant covariates. FindingsWe identify novel genome-wide significant associations with markers in TMEM106B and CSF levels of NF-L. Additional novel signals were observed with DNA variants in CPOX and CSF levels of YKL-40. Lastly, we confirmed previous work suggesting that YKL-40 levels are regulated by cis protein quantitative trait loci (pQTL) in CHI3L1. InterpretationOur study provides important new insights into the genetic architecture underlying inter-individual variation in all three tested AD-related CSF biomarkers. In particular, our data shed light on the sequence of events regarding the initiation and progression of neuropathological processes relevant in AD.

genomics↗

bioRxiv2019

Genome-wide association study of Alzheimer’s disease CSF biomarkers in the EMIF-AD Multimodal Biomarker Discovery dataset

Alzheimers disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Susceptibility to AD is considerably determined by genetic factors which hitherto were primarily identified using case-control designs. Elucidating the genetic architecture of additional AD-related phenotypic traits, ideally those linked to the underlying disease process, holds great promise in gaining deeper insights into the genetic basis of AD and in developing better clinical prediction models. To this end, we generated genome-wide single-nucleotide polymorphism (SNP) genotyping data in 931 participants of the European Medical Information Framework Alzheimers Disease Multimodal Biomarker Discovery (EMIF-AD MBD) sample to search for novel genetic determinants of AD biomarker variability. Specifically, we performed genome-wide association study (GWAS) analyses on 16 traits, including 14 measures of amyloid-beta (A{beta}) and tau-protein species in the cerebrospinal fluid (CSF). In addition to confirming the well-established effects of apolipoprotein E (APOE) on diagnostic outcome and phenotypes related to A{beta}42, we detected novel potential signals in the zinc finger homeobox 3 (ZFHX3) for CSF-A{beta}38 and CSF-A{beta}40 levels, and confirmed the previously described sex-specific association between SNPs in geminin coiled-coil domain containing (GMNC) and CSF-tau. Utilizing the results from independent case-control AD GWAS to construct polygenic risk scores (PRS) revealed that AD risk variants only explain a small fraction of CSF biomarker variability. In conclusion, our study represents a detailed first account of GWAS analyses on CSF-A{beta} and -tau related traits in the EMIF-AD MBD dataset. In subsequent work, we will utilize the genomics data generated here in GWAS of other AD-relevant clinical outcomes ascertained in this unique dataset.

genomics↗

bioRxiv2019

Multi-omic analyses reveal antibody-dependent natural killer cell-mediated cytotoxicity in autoimmune thyroid diseases

The pathogenesis of autoimmune thyroid diseases (AITD) is poorly understood. We previously observed systemic depletion of IgG core fucosylation and antennary 1,2 fucosylation of peripheral blood mononuclear cells in AITD, correlated with thyroid peroxidase antibody (TPOAb) levels. We hypothesized that deficiency in IgG core fucose enhances antibody-dependent cell-mediated cytotoxicity of thyrocytes by TPOAb, contributing to thyroid autoimmunity. Multi-omic evaluations in 622 individuals (172 with AITD) from the TwinsUK cohort showed decreased IgG core fucosylation levels associated with a subpopulation of natural killer (NK) cells featuring CD335, CD314, and CD158b immunoreceptors, and increased levels of apoptosis-associated Caspase-2 and Interleukin-1, positively associated with AITD. AITD-associated genetic variants rs1521 and rs3094228 alter expression of thyrocyte ligands of the CD314 and CD158b immunoreceptors on NK cells. The combination of low-core fucose IgG associated with an NK cell subpopulation and genetic variant-promoted ligand activation in thyrocytes may promote antibody-dependent NK cell-mediated cytotoxicity of thyrocytes in AITD.

immunology↗

bioRxiv2019

Working Towards a Blood-Derived Gene Expression Biomarker Specific for Alzheimer’s Disease

BackgroundA significant number of studies have investigated the use of blood-derived gene expression profiling as a biomarker for Alzheimers Disease (AD). However, the typical approach of developing classification models trained on subjects with AD and complimentary cognitive healthy controls may result in markers of general illness rather than being AD-specific. Incorporating additional related neurological and age-related disorders during the classification model development process may lead to the discovery of an AD-specific expression signature.\n\nMethodsTwo XGBoost classification models were developed and optimised. The first used the typical approach, training on 160 AD and 160 cognitively normal controls, while the second was trained in 6318 AD and 6318 mixed controls. Up-sampling was performed in each training set to the minority classes to avoid sampling bias, and both classification models were evaluated in an independent dataset consisting of 127 AD and 687 mixed controls. The mixed control group represents a heterogeneous ageing population consisting of Parkinsons Disease, Multiple Sclerosis, Amyotrophic Lateral Sclerosis, Bipolar Disorder, Schizophrenia, Coronary Artery Disease, Rheumatoid Arthritis, Chronic Obstructive Pulmonary Disease, and cognitively healthy subjects.\n\nResultsThe typical approach resulted in a 74 gene classification model with a validation performance of 58.3% sensitivity, 30.3% specificity, 13.4% PPV and 79.7% NPV. In contrast, the second approach resulted in a 28 gene classification model with an overall improved validation performance of 46.5% sensitivity, 95.6% specificity, 66.3% PPV and 90.6% NPV.\n\nConclusionsThe addition of related neurological and age-related disorders into the AD classification model developmental process identified a more AD-specific expression signature, with improved ability to distinguish AD from other related diseases and cognitively healthy controls. However, this was at the cost of sensitivity. Further improvement is still required to identify a robust blood transcriptomic signature specific to AD.

genetics↗

bioRxiv2018

A Meta-Analysis of Alzheimer’s Disease Brain Transcriptomic Data

BackgroundMicroarray technologies have identified imbalances in the expression of specific genes and biological pathways in Alzheimers disease (AD) brains. However, there is a lack of reproducibility across individual AD studies, and many related neurodegenerative and mental health disorders exhibit similar perturbations. We are yet to identify robust transcriptomic changes specific to AD brains. Methods and ResultsTwenty-two AD, eight Schizophrenia, five Bipolar Disorder, four Huntington's disease, two Major Depressive Disorder and one Parkinsons disease dataset totalling 2667 samples and mapping to four different brain regions (Temporal lobe, Frontal lobe, Parietal lobe and Cerebellum) were analysed. Differential expression analysis was performed independently in each dataset, followed by meta-analysis using a combining p-value method known as Adaptively Weighted with One-sided Correction. This identified 323, 435, 1023 and 828 differentially expressed genes specific to the AD temporal lobe, frontal lobe, parietal lobe and cerebellum brain regions respectively. Seven of these genes were consistently perturbed across all AD brain regions with SPCS1 gene expression pattern replicating in RNA-seq data. A further nineteen genes were perturbed specifically in AD brain regions affected by both plaques and tangles, suggesting possible involvement in AD neuropathology. Biological pathways involved in the "metabolism of proteins" and viral components were significantly enriched across AD brains. ConclusionThis study solely relied on publicly available microarray data, which too often lacks appropriate phenotypic information for robust data analysis and needs to be addressed by future studies. Nevertheless, with the information available, we were able to identify specific transcriptomic changes in AD brains which could make a significant contribution towards the understanding of AD disease mechanisms and may also provide new therapeutic targets.

genetics↗

bioRxiv2018

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↗

bioRxiv2017

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↗