Biology subjects

Cooper, A. A.

Publications and source records attributed to Cooper, A. A..

3 recordsLinked to original sources

bioRxiv2024

Deep sequencing of proteotoxicity modifier genes uncovers a Presenilin-2/beta-amyloid-actin genetic risk module shared among alpha-synucleinopathies

Whether neurodegenerative diseases linked to misfolding of the same protein share genetic risk drivers or whether different protein-aggregation pathologies in neurodegeneration are mechanistically related remains uncertain. Conventional genetic analyses are underpowered to address these questions. Through careful selection of patients based on protein aggregation phenotype (rather than clinical diagnosis) we can increase statistical power to detect associated variants in a targeted set of genes that modify proteotoxicities. Genetic modifiers of alpha-synuclein ([a]S) and beta-amyloid (A{beta}) cytotoxicity in yeast are enriched in risk factors for Parkinsons disease (PD) and Alzheimers disease (AD), respectively. Here, along with known AD/PD risk genes, we deeply sequenced exomes of 430 [a]S/A{beta} modifier genes in patients across alpha-synucleinopathies (PD, Lewy body dementia and multiple system atrophy). Beyond known PD genes GBA1 and LRRK2, rare variants AD genes (CD33, CR1 and PSEN2) and A{beta} toxicity modifiers involved in RhoA/actin cytoskeleton regulation (ARGHEF1, ARHGEF28, MICAL3, PASK, PKN2, PSEN2) were shared risk factors across synucleinopathies. Actin pathology occurred in iPSC synucleinopathy models and RhoA downregulation exacerbated [a]S pathology. Even in sporadic PD, the expression of these genes was altered across CNS cell types. Genome-wide CRISPR screens revealed the essentiality of PSEN2 in both human cortical and dopaminergic neurons, and PSEN2 mutation carriers exhibited diffuse brainstem and cortical synucleinopathy independent of AD pathology. PSEN2 contributes to a common-risk signal in PD GWAS and regulates [a]S expression in neurons. Our results identify convergent mechanisms across synucleinopathies, some shared with AD.

genomics↗

bioRxiv2021

Establishing gene regulatory networks from Parkinson's disease risk loci

The latest meta-analysis of genome wide association studies (GWAS) identified 90 independent single nucleotide polymorphisms (SNPs) across 78 genomic regions associated with Parkinsons disease (PD), yet the mechanisms by which these variants influence the development of the disease remains largely elusive. To establish the functional gene regulatory networks associated with PD-SNPs, we utilised an approach combining spatial (chromosomal conformation capture) and functional (expression quantitative trait loci; eQTL) data. We identified 518 genes subject to regulation by 76 PD-SNPs across 49 tissues, that encompass 36 peripheral and 13 CNS tissues. Notably, one third of these genes were regulated via trans-acting mechanisms (distal; risk locus-gene separated by > 1Mb, or on different chromosomes). Of particular interest is the identification of a novel trans-eQTL-gene connection between rs10847864 and SYNJ1 in the adult brain cortex, highlighting a convergence between familial studies and PD GWAS loci for SYNJ1 (PARK20) for the first time. Furthermore, we identified 16 neuro-development specific eQTL-gene regulatory connections within the foetal cortex, consistent with hypotheses suggesting a neurodevelopmental involvement in the pathogenesis of PD. Through utilising Louvain clustering we extracted nine significant and highly intra-connected clusters within the entire gene regulatory network. The nine clusters are enriched for specific biological processes and pathways, some of which have not previously been associated with PD. Together, our results not only contribute to an overall understanding of the mechanisms and impact of specific combinations of PD-SNPs, but also highlight the potential impact gene regulatory networks may have when elucidating aetiological subtypes of PD.

genetics↗

bioRxiv2017

blkbox: Integration Of Multiple Machine Learning Approaches To Identify Disease Biomarkers

MotivationMachine learning (ML) is a powerful tool to create supervised models that can distinguish between classes and facilitate biomarker selection in high-dimensional datasets, including RNA Sequencing (RNA-Seq). However, it is variable as to which is the best performing ML algorithm(s) for a specific dataset, and identifying the optimal match is time consuming. blkbox is a software package including a shiny frontend, that integrates nine ML algorithms to select the best performing classifier for a specific dataset. blkbox accepts a simple abundance matrix as input, includes extensive visualization, and also provides an easy to use feature selection step to enable convenient and rapid potential biomarker selection, all without requiring parameter optimization.\n\nResultsFeature selection makes blkbox computationally inexpensive while multi-functionality, including nested cross-fold validation (NCV), ensures robust results. blkbox identified algorithms that outperformed prior published ML results. Applying NCV identifies features, which are utilized to gain high accuracy.\n\nAvailabilityThe software is available as a CRAN R package and as a developer version with extended functionality on github (https://github.com/gboris/blkbox).\n\nContactb.guennewig@garvan.org.au

bioinformatics↗