Biology subjects

Collier, D. A.

Publications and source records attributed to Collier, D. A..

7 recordsLinked to original sources

bioRxiv2022

MAVEN: An R/Shiny app for compound mechanism of action analysis and visualisation using transcriptomics and compound structure data

BackgroundUnderstanding the mechanism of action (MoA) of a compound is an often challenging but equally crucial aspect of drug discovery that can help improve both its efficacy and safety. Computational methods to aid MoA elucidation usually either aim to predict direct drug targets, or attempt to understand modulated downstream pathways or signalling proteins. Such methods usually require extensive coding experience and results are often optimised for further computational processing, making them difficult for wet-lab scientists to perform, interpret and draw hypotheses from. ResultsTo address this issue, we in this work present MAVEN (Mechanism of Action Visualisation and Enrichment), an R/Shiny app which allows for GUI-based prediction of drug targets based on chemical structure, combined with causal reasoning based on causal protein-protein interactions and transcriptomic perturbation signatures. The app computes a systems-level view of the mechanism of action of the input compound. This is visualised as a sub-network linking predicted or known targets to modulated transcription factors via inferred signalling proteins. The tool includes a selection of MsigDB gene set collections to perform pathway enrichment on the resulting network, and also allows for custom gene sets to be uploaded by the researcher. MAVEN is hence a user-friendly, flexible tool for researchers without extensive bioinformatics or cheminformatics knowledge to generate interpretable hypotheses of compound Mechanism of Action. ConclusionsMAVEN is available as a fully open-source tool at https://github.com/laylagerami/MAVEN with options to install in a Docker or Singularity container. Full documentation, including a tutorial on example data, is available at https://laylagerami.github.io/MAVEN.

systems biology↗

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

Whole transcriptome in-silico screening implicates cardiovascular and infectious disease in the mechanism of action underlying atypical antipsychotic side-effects

INTRODUCTIONStroke/thromboembolic events, infections and death are all significantly increased by antipsychotics in dementia but little is known about why they can be harmful. Using a novel application of a drug repurposing paradigm, we aimed to identify potential mechanisms underlying adverse events. METHODWhole transcriptome signatures were generated for SH-SY5Y cells treated with amisulpride, risperidone and volinanserin using RNA-sequencing. Bioinformatic analysis was performed which scored the association between antipsychotic signatures and expression data from 415,252 samples in the NCBI GEO repository. RESULTSAtherosclerosis, venous thromboembolism and influenza NCBI GEO-derived samples scored positively against antipsychotic signatures. Pathways enriched in antipsychotic signatures were linked to the cardiovascular and immune systems (e.g. BDNF, PDGFR-beta, TNF, TGF-beta, selenoamino acid metabolism and influenza infection). CONCLUSIONThese findings for the first time mechanistically link antipsychotics to specific cardiovascular and infectious diseases which are known side effects of their use in dementia, providing new information to explain related adverse events. COMPETING INTERESTSCB has received grants and personal fees from ACADIA Pharmaceuticals and Lundbeck, and personal fees from Heptares, Roche, Lilly, Otsuka, Orion, GlaxoSmithKline and Pfizer. DAC is an employee of Eli Lilly and Company Ltd.

bioinformatics↗

bioRxiv2019

Transcriptional signatures of progressive neuropathology in transgenic tau and amyloid mouse models.

The onset and progression of Alzheimers disease (AD) is characterized by increasing intracellular aggregation of hyperphosphorylated tau protein and accumulation of {beta}-amyloid (A{beta}) in the neocortex. Despite recent success in identifying genetic risk factors for AD the transcriptional mechanisms involved in disease progression are not fully understood. We used transgenic mice harbouring human tau (rTg4510) and amyloid precursor protein (J20) mutations to investigate transcriptional changes associated with the development of both tau and amyloid pathology. Using highly-parallel RNA sequencing we profiled transcriptional variation in the entorhinal cortex at four time points identifying robust genotype-associated differences in entorhinal cortex gene expression in both models. We quantified neuropathological burden across multiple brain regions in the same individual mice, identifying widespread changes in gene expression paralleling the development of tau pathology in rTg4510 mice. Differentially expressed transcripts included genes associated with familial AD from genetic studies of human patients, and genes annotated to both common and rare variants identified in GWAS and exome-sequencing studies of late-onset sporadic AD. Systems-level analyses identified discrete co-expression networks associated with the progressive accumulation of tau, with these enriched for genes and pathways previously implicated in the neuro-immunological and neurodegenerative processes driving AD pathology. Finally, we report considerable overlap between tau-associated networks and AD-associated co-expression modules identified in the human cortex. Our data provide further support for an immune-response component in the accumulation of tau, and reveal novel molecular pathways associated with the progression of AD neuropathology.

genomics↗

bioRxiv2019

Meta-analysis of the human brain transcriptome identifies heterogeneity across human AD coexpression modules robust to sample collection and methodological approach

Alzheimers disease (AD) is a complex and heterogenous brain disease that affects multiple inter-related biological processes. This complexity contributes, in part, to existing difficulties in the identification of successful disease-modifying therapeutic strategies. To address this, systems approaches are being used to characterize AD-related disruption in molecular state. To evaluate the consistency across these molecular models, a consensus atlas of the human brain transcriptome was developed through coexpression meta-analysis across the AMP-AD consortium. Consensus analysis was performed across five coexpression methods used to analyze RNA-seq data collected from 2114 samples across 7 brain regions and 3 research studies. From this analysis, five consensus clusters were identified that described the major sources of AD-related alterations in transcriptional state that were consistent across studies, methods, and samples. AD genetic associations, previously studied AD-related biological processes, and AD targets under active investigation were enriched in only three of these five clusters. The remaining two clusters demonstrated strong heterogeneity between males and females in AD-related expression that was consistently observed across studies. AD transcriptional modules identified by systems analysis of individual AMP-AD teams were all represented in one of these five consensus clusters except ROS/MAP-identified Module 109, which was specific for genes that showed the strongest association with changes in AD-related gene expression across consensus clusters. The other two AMP-AD transcriptional analyses reported modules that were enriched in one of the two sex-specific Consensus Clusters. The fifth cluster has not been previously identified and was enriched for genes related to proteostasis. This study provides an atlas to map across biological inquiries of AD with the goal of supporting an expansion in AD target discovery efforts.

systems biology↗

bioRxiv2018

Assessing 42 inflammatory markers in 321 control subjects and 887 major depressive disorder cases: BMI and other confounders and overall predictive ability for current depression

Inflammatory markers such as cytokines represent potential biomarkers for major depressive disorder (MDD). Many, generally small studies have examined the role of single markers and found significant associations. We assessed 42 inflammatory markers, namely cytokines, in the blood of 321 control subjects and 887 MDD cases. We tested whether individual inflammatory marker levels were significantly affected by MDD case/control status, current episode, or current depression severity, co-varying for age, sex, body mass index (BMI), smoking, current antidepressant use, ethnicity, assay batch and study effects. We further used machine learning algorithms to investigate if we could use our data to blindly discriminate MDD patients, or those in a current episode. We found broad and powerful influences of confounding factors on log-protein levels. Notably, IL-6 levels were very strongly influenced by BMI (p = 1.37 x 10-43, variance explained = 18%), while Interleukin-16 was the most significant predictor of current depressive episode (p = 0.003, variance explained = 0.9%, q < 0.1). No single inflammatory marker predicted MDD case/control status when a subject was not in a depressed episode, nor did any predict depression severity. Machine learning results revealed that using inflammatory marker data with clinical confounder information significantly increased precision for differentiating MDD patients who were in an episode. To conclude, a wide panel of inflammatory markers alongside clinical information may aid in predicting the onset of symptoms, but no single inflammatory protein is likely to represent a clinically useful biomarker for MDD diagnosis or prognosis. We note that the potential influence of physical health related and population stratification related confounders on inflammatory biomarker studies in psychiatry is considerable.

neuroscience↗

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↗