Biology subjects

Powell, J. E.

Publications and source records attributed to Powell, J. E..

29 records · Page 2Linked to original sources

bioRxiv2021

Retinal ganglion cell-specific genetic regulation in primary open angle glaucoma

To assess the transcriptomic profile of disease-specific cell populations, fibroblasts from patients with primary open-angle glaucoma (POAG) were reprogrammed into induced pluripotent stem cells (iPSCs) before being differentiated into retinal organoids and compared to those from healthy individuals. We performed single-cell RNA-sequencing of a total of 330,569 cells and identified cluster-specific molecular signatures. Comparing the gene expression profile between cases and controls, we identified novel genetic associations for this blinding disease. Expression quantitative trait mapping identified a total of 2,235 significant loci across all cell types, 58 of which are specific to the retinal ganglion cell subpopulations, which ultimately degenerate in POAG. Transcriptome-wide association analysis identified genes at loci previously associated with POAG, and analysis, conditional on disease status, implicated 54 statistically significant retinal ganglion cell-specific expression quantitative trait loci. This work highlights the power of large-scale iPSC studies to uncover context-specific profiles for a genetically complex disease.

genetics↗

bioRxiv2021

DevKidCC allows for robust classification and direct comparisons of kidney organoid datasets

Kidney organoids provide a valuable resource to understand kidney development and disease. Clustering algorithms and marker genes fail to accurately and robustly classify cellular identity between human pluripotent stem cell (hPSC)-derived organoid datasets. Here we present a new method able to accurately classify kidney cell subtypes, a hierarchical machine learning model trained using comprehensive reference data from single cell RNA-sequencing of human fetal kidney (HFK). We demonstrate the tools (DevKidCC) performance by application to all published kidney organoid datasets and a novel dataset. DevKidCC is available on Github and can be used on any kidney single cell RNA-sequence data.

bioinformatics↗

bioRxiv2020

DNA methylation is required to maintain DNA replication timing precision and 3D genome integrity

DNA replication timing and three-dimensional (3D) genome organisation occur across large domains associated with distinct epigenome patterns to functionally compartmentalise genome regulation. However, it is still unclear if alternations in the epigenome, in particular cancer-related DNA hypomethylation, can directly result in alterations to cancer higher order genome architecture. Here, we use Hi-C and single cell Repli-Seq, in the colorectal cancer DNMT1 and DNMT3B DNA methyltransferases double knockout model, to determine the impact of DNA hypomethylation on replication timing and 3D genome organisation. First, we find that the hypomethylated cells show a striking loss of replication timing precision with gain of cell-to-cell replication timing heterogeneity and loss of 3D genome compartmentalisation. Second, hypomethylated regions that undergo a large change in replication timing also show loss of allelic replication timing, including at cancer-related genes. Finally, we observe the formation of broad ectopic H3K4me3-H3K9me3 domains across hypomethylated regions where late replication is maintained, that potentially prevent aberrant transcription and loss of genome organisation after DNA demethylation. Together, our results highlight a previously underappreciated role for DNA methylation in maintenance of 3D genome architecture.

molecular biology↗

bioRxiv2020

Single-cell transcriptomics of allo-reactive CD4+ T cells over time reveals divergent fates during gut GVHD

Acute gastrointestinal Graft-versus-Host-Disease (GVHD) is a primary determinant of mortality after allogeneic hematopoietic stem-cell transplantation (alloSCT). It is mediated by alloreactive donor CD4+ T cells that differentiate into pathogenic subsets expressing IFN{gamma}, IL-17A or GM-CSF, and is regulated by subsets expressing IL-10 and/or Foxp3. Developmental relationships between T-helper states during priming in mesenteric lymph nodes (mLN) and effector function in the GI tract remain undefined at genome-scale. We used scRNA-seq and computational modelling to create an atlas of putative differentiation pathways during GVHD. Computational trajectory inference suggested emergence of pathogenic and regulatory states along a single developmental trajectory in mLN. Importantly, we identified an unexpected second trajectory, categorised by little proliferation or cytokine expression, reduced glycolysis, and high TCF1 expression. TCF1hi cells upregulated 4{beta}7 prior to gut migration and failed to express cytokines therein. Nevertheless, they demonstrated recall potential and plasticity following secondary transplantation, including cytokine or Foxp3 expression, but reduced TCF1. Thus, scRNA-seq revealed divergence of allo-reactive CD4+ T cells into quiescent and effector states during gut GVHD, reflecting putative heterogenous priming in vivo. These findings, the first at a single-cell level during GVHD over time, can now be used to interrogate T cell differentiation in patients undergoing alloSCT.

immunology↗

bioRxiv2019

Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank

Genotype-by-environment interaction (GEI) is a fundamental component in understanding complex trait variation. However, it remains challenging to identify genetic variants with GEI effects in humans largely because of the small effect sizes and the difficulty of monitoring environmental fluctuations. Here, we demonstrate that GEI can be inferred from genetic variants associated with phenotypic variability in a large sample without the need of measuring environmental factors. We performed a genome-wide variance quantitative trait locus (vQTL) analysis of ~5.6 million variants on 348,501 unrelated individuals of European ancestry for 13 quantitative traits in the UK Biobank, and identified 75 significant vQTLs with P<2.0x10-9 for 9 traits, especially for those related to obesity. Direct GEI analysis with five environmental factors showed that the vQTLs were strongly enriched with GEI effects. Our results indicate pervasive GEI effects for obesity-related traits and demonstrate the detection of GEI without environmental data.

genetics↗

bioRxiv2018

The Medical Genome Reference Bank: Whole genomes and phenotype of 2,570 healthy elderly

Population health research is increasingly focused on the genetic determinants of healthy ageing, but there is no public resource of whole genome sequences and phenotype data from healthy elderly individuals. Here we describe the Medical Genome Reference Bank (MGRB), comprising whole genome sequence and phenotype of 2,570 elderly Australians depleted for cancer, cardiovascular disease, and dementia. We analysed the MGRB for single-nucleotide, indel and structural variation in the nuclear and mitochondrial genomes. Individuals in the MGRB had fewer disease-associated common and rare germline variants, relative to both cancer cases and the gnomAD and UK BioBank cohorts, consistent with risk depletion. Pervasive age-related somatic changes were correlated with grip strength in men, suggesting blood-derived whole genomes may also provide a biologic measure of age-related functional deterioration. The MGRB provides a broadly applicable reference cohort for clinical genetics and genomic association studies, and for understanding the genetics of healthy ageing. This research has been conducted using the UK Biobank Resource under Application Number 17984.

genomics↗

bioRxiv2018

scPred: Single cell prediction using singular value decomposition and machine learning classification

Single-cell RNA sequencing has enabled the characterization of highly specific cell types in many human tissues, as well as both primary and stem cell-derived cell lines. An important facet of these studies is the ability to identify the transcriptional signatures that define a cell type or state. In theory, this information can be used to classify an unknown cell based on its transcriptional profile; and clearly, the ability to accurately predict a cell type and any pathologic-related state will play a critical role in the early diagnosis of disease and decisions around the personalized treatment for patients. Here we present a new generalizable method (scPred) for prediction of cell type(s), using a combination of unbiased feature selection from a reduced-dimension space, and machine-learning classification. scPred solves several problems associated with the identification of individual gene feature selection, and is able to capture subtle effects of many genes, increasing the overall variance explained by the model, and correspondingly improving the prediction accuracy. We validate the performance of scPred by performing experiments to classify tumor versus non-tumor epithelial cells in gastric cancer, then using independent molecular techniques (cyclic immunohistochemistry) to confirm our prediction, achieving an accuracy of classifying the disease state of individual cells of 99%. Moreover, we apply scPred to scRNA-seq data from pancreatic tissue, colorectal tumor biopsies, and circulating dendritic cells, and show that scPred is able to classify cell subtypes with an accuracy of 96.1-99.2%. Collectively, our results demonstrate the utility of scPred as a single cell prediction method that can be used for a wide variety of applications. The generalized method is implemented in software available here: https://github.com/IMB-Computational-Genomics-Lab/scPred/

genomics↗

bioRxiv2018

Longitudinal expression profiling of CD4+ and CD8+ cells in patients with active to quiescent Giant Cell Arteritis

BackgroundGiant cell arteritis (GCA) is the most common form of vasculitis affecting elderly people. It is one of the few true ophthalmic emergencies. GCA is a heterogenous disease, symptoms and signs are variable thereby making it challenging to diagnose and often delaying diagnosis. A temporal artery biopsy is the gold standard to test for GCA, and there are currently no specific biochemical markers to categorize or aid diagnosis of the disease. We aimed to identify a less invasive method to confirm the diagnosis of GCA, as well as to ascertain clinically relevant predictive biomarkers by studying the transcriptome of purified peripheral CD4+ and CD8+ T lymphocytes in patients with GCA.\n\nMethods and FindingsWe recruited 16 patients with histological evidence of GCA at the Royal Victorian Eye and Ear Hospital (RVEEH), Melbourne, Australia, and aimed to collect blood samples at six time points: acute phase, 2-3 weeks, 6-8 weeks, 3 months, 6 months and 12 months after clinical diagnosis. CD4+ and CD8+ T-cells were positively selected at each time point through magnetic-assisted cell sorting (MACS). RNA was extracted from all 195 collected samples for subsequent RNA sequencing. The expression profiles of patients were compared to those of 16 age-matched controls. Over the 12-month study period, polynomial modelling analyses identified 179 and 4 statistically significant transcripts with altered expression profiles (FDR < 0.05) between cases and controls in CD4+ and CD8+ populations, respectively. In CD8+ cells, we identified two transcripts that remained differentially expressed after 12 months, namely SGTB, associated with neuronal apoptosis, and FCGR3A, which has been found in association with Takayasu arteritis (TA), another large vessel vasculitis. We detected genes that correlate with both symptoms and biochemical markers used in the acute setting for predicting long-term prognosis. 15 genes were shared across 3 phenotypes in CD4 and 16 across CD8 cells. In CD8, IL32 was common to 5 phenotypes: a history of Polymyalgia Rheumatica, both visual disturbance and raised neutrophils at the time of presentation, bilateral blindness and death within 12 months. Altered IL32 gene expression could provide risk evaluation of GCA diagnosis at the time of presentation and give an indication of prognosis, which may influence management.\n\nConclusionsThis is the first longitudinal gene expression study undertaken to identify robust transcriptomic biomarkers of GCA. Our results show cell type-specific transcript expression profiles, novel gene-phenotype associations, and uncover important biological pathways for this disease. These data significantly enhance the current knowledge of relevant biomarkers, their association with clinical prognostic markers, as well as potential candidates for detecting disease activity in whole blood samples. In the acute phase, the gene-phenotype relationships we have identified could provide insight to potential disease severity and as such guide us in initiating appropriate patient management.

genomics↗

bioRxiv2017

Single Cell RNA Sequencing of stem cell-derived retinal ganglion cells.

We used human embryonic stem cell-derived retinal ganglion cells (RGCs) to characterize the transcriptome of 1,174 cells at the single cell level. The human embryonic stem cell line BRN3B-mCherry A81-H7 was differentiated to RGCs using a guided differentiation approach. Cells were harvested at day 36 and subsequently prepared for single cell RNA sequencing. Our data indicates the presence of three distinct subpopulations of cells, with various degrees of maturity. One cluster of 288 cells upregulated genes involved in axon guidance together with semaphorin interactions, cell-extracellular matrix interactions and ECM proteoglycans, suggestive of a more mature phenotype.

genomics↗

bioRxiv2017

Identification of 55,000 Replicated DNA Methylation QTL

DNA methylation plays an important role in the regulation of transcription. Genetic control of DNA methylation is a potential candidate for explaining the many identified SNP associations with disease that are not found in coding regions. We replicated 52,916 cis and 2,025 trans DNA methylation quantitative trait loci (mQTL) using methylation measured on Illumina HumanMethylation450 arrays in the Brisbane Systems Genetics Study (n=614 from 177 families) and the Lothian Birth Cohorts of 1921 and 1936 (combined n = 1366). The trans mQTL SNPs were found to be over-represented in 1Mbp subtelomeric regions, and on chromosomes 16 and 19. There was a significant increase in trans mQTL DNA methylation sites in upstream and 5 UTR regions. No association was observed between either the SNPs or DNA methylation sites of trans mQTL and telomere length. The genetic heritability of a number of complex traits and diseases was partitioned into components due to mQTL and the remainder of the genome. Significant enrichment was observed for height (p = 2.1x10-10), ulcerative colitis (p = 2x10-5), Crohns disease (p = 6x10-8) and coronary artery disease (p = 5.5x10-6) when compared to a random sample of SNPs with matched minor allele frequency, although this enrichment is explained by the genomic location of the mQTL SNPs.

genomics↗

bioRxiv2016

Constraints on eQTL fine mapping in the presence of multi-site local regulation of gene expression

Expression QTL (eQTL) detection has emerged as an important tool for unravelling of the relationship between genetic risk factors and disease or clinical phenotypes. Most studies use single marker linear regression to discover primary signals, followed by sequential conditional modeling to detect secondary genetic variants affecting gene expression. However, this approach assumes that functional variants are sparsely distributed and that close linkage between them has little impact on estimation of their precise location and magnitude of effects. In this study, we address the prevalence of secondary signals and bias in estimation of their effects by performing multi-site linear regression on two large human cohort peripheral blood gene expression datasets (each greater than 2,500 samples) with accompanying whole genome genotypes, namely the CAGE compendium of Illumina microarray studies, and the Framingham Heart Study Affymetrix data. Stepwise conditional modeling demonstrates that multiple eQTL signals are present for ~40% of over 3500 eGenes in both datasets, and the number of loci with additional signals reduces by approximately two-thirds with each conditioning step. However, the concordance of specific signals between the two studies is only ~30%, indicating that expression profiling platform is a large source of variance in effect estimation. Furthermore, a series of simulation studies imply that in the presence of multi-site regulation, up to 10% of the secondary signals could be artefacts of incomplete tagging, and at least 5% but up to one quarter of credible intervals may not even include the causal site, which is thus mis-localized. Joint multi-site effect estimation recalibrates effect size estimates by just a small amount on average. Presumably similar conclusions apply to most types of quantitative trait. Given the strong empirical evidence that gene expression is commonly regulated by more than one variant, we conclude that the fine-mapping of causal variants needs to be adjusted for multi-site influences, as conditional estimates can be highly biased by interference among linked sites.

genetics↗