Biology subjects

Figtree, G. A.

Publications and source records attributed to Figtree, G. A..

4 recordsLinked to original sources

bioRxiv2025

Endothelial Colony-Forming Cell Transcriptomic Profiling in CT-defined Coronary Artery Disease from the BioHEART-CT Study Implicate CCBE1 in Mitochondrial Dysfunction-associated Atherosclerosis

BackgroundEndothelial dysfunction is an early contributor to atherosclerosis. This study combined CT imaging of coronary artery disease (CAD) and patient-dervied endothelial colony-forming cells (ECFCs) transcriptional profiling to investigate potential mechanisms underlying endothelial dysfunction in atherosclerosis. MethodsTwenty-six individuals with CT-defined CAD and eighteen non-CAD controls were included in the Discovery Cohort for bulk RNA sequencing and transcriptomic analysis of ECFCs. Differential gene expression analysis was performed, and candidate genes were selected based on logFC and p-value. Candidate genes were carried forward for gene expression validation using quantitative real-time PCR (qRT-PCR) in a Validation Cohort. Mitochondrial reactive oxygen species (mROS) production and mitochondrial mass were assessed using multi-colour flow cytometry. Functional validation of the top candidate was conducted in using human umbilical vein endothelial cells (HUVECs) using loss-of-function genetic approach. Expression Quantitative Trait Loci (eQTL)-association analysis was conducted using genotype data from the BioHEART-CT cohort. ResultsPairwise analysis identified six differentially expressed protein-coding genes in CAD ECFCs: CCBE1 (Collagen and Calcium Binding EGF Domain-Containing 1), SPINT2, CRISPLD1, PIEZO2, EPB41L3, and AC005943.1. qRT-PCR in the Validation Cohort confirmed significantly higher CCBE1 expression in CAD patients. Individuals with relative CCBE1 fold change expression>10 had a 2.8-fold increase in the log-odds ratio of CT-defined CAD. CAD ECFCs displayed elevated mROS and mitochondrial mass. CCBE1 knockdown in HUVECs reduced mROS and mitochondrial mass without affecting proliferation or permeability, but shifted cells into a metabolically elevated state, marked by increased ATP production, respiration and glycolysis. CCBE1 cis-eQTLs were associated with increased odds of CAD in the BioHEART-CT cohort. ConclusionsCCBE1 expression in ECFCs was higher in patients with CT-defined CAD versus non-CAD. Quantitative assessment of transcript levels supported a causal relationship between greater CCBE1 expression and CAD burden and risk, and functional experiments on CCBE1 knockdown demonstrated improved mitochondrial function in human endothelial cells. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=138 SRC="FIGDIR/small/670989v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@4e609aorg.highwire.dtl.DTLVardef@1a85810org.highwire.dtl.DTLVardef@12d97cdorg.highwire.dtl.DTLVardef@494ab3_HPS_FORMAT_FIGEXP M_FIG C_FIG Novelty and SignificanceO_ST_ABSWhat is Known?C_ST_ABSO_LIThe endothelium plays a critical role in vascular health and susceptibility to atherosclerosis. C_LIO_LIMitochondrial dysfunction has been implicated in atherosclerosis, but its role and mechanism in individual susceptibility to CAD in humans is not known. C_LI What New Information Does This Article Contribute?O_LINovel approach integrating CT imaging with ECFC functional data to link vascular structure with endothelial biology ex vivo. C_LIO_LIPatients with CT-defined CAD had 3.6-fold higher CCBE1 expression compared to non-CAD within the Validation Cohort. C_LIO_LICis-eQTL-association analysis revealed increased odds of CAD. C_LIO_LICCBE1 knockdown improved mitochondrial function in human endothelial cells. C_LIO_LITogether, these 4 lines of evidence point to a novel and causal role for CCBE1 in human CAD. C_LI

cell biology↗

bioRxiv2024

Polymorphic tandem repeats shape single-cell gene expression across the immune landscape

Tandem repeats (TRs) - highly polymorphic, repetitive sequences across the human genome - are important regulators of gene expression but remain underexplored due to challenges in accurate genotyping and analysis1. Here, we generate new whole genome and single-cell RNA sequencing from >5.4 million blood-derived cells across 1,925 individuals in two cohorts [Cuomo et al., accompanying manuscript], and perform meta-analysis to characterize the impact of variation in >1.7 million TR loci on immune cell type-specific gene expression. We identify >69,000 single-cell expression TR loci (sc-eTRs), 30.7% of which are specific to one of 28 immune cell types, and reveal dynamic regulatory effects using cell-state inference. Matched single-cell ATAC sequencing profiles from >3.4 million nuclei in 922 individuals [Xue et al., accompanying manuscript]. uncover chromatin accessibility QTLs for nearly one-third of expression-associated TRs, supporting coordinated effects on cis-regulatory architecture. Fine-mapping implicates 1,490 TRs as candidate causal drivers of gene expression in 6.1% of tested genes, and colocalization analyses highlight >200 genes in which TRs likely mediate genetic associations with immune and hematological traits. Together, these results provide a genome-wide, multiomic view of TR-mediated regulation in the human immune system, establishing TRs as key contributors to cell type-specific regulatory variation and complex trait architecture.

genomics↗

bioRxiv2024

Towards Principled Modeling Of Coronary Artery Calcium Scores With Zero-Inflated Regression

AO_SCPLOWBSTRACTC_SCPLOWThe Coronary Artery Calcium Score (CACS) is a widely-used measure of Coronary Artery Disease (CAD). The primary use of CACS is to identify subclinical CAD and estimate risk of future cardiovascular events such as acute myocardial infarction. The development of coronary atherosclerosis is well known to be accelerated in response to risk factors such as age, hypertension, hypercholeterolaemia and smoking. However, there is substantial variability in an individuals susceptibility or resistance to CAD against these risk factors. Quantifiying the deviation from "expected" CACS provides an novel opportunity to inform multi-omic and similar unbiased discovery of new markers and mechanisms of CAD trained against CT imaging. Standard linear regression struggles to model CACS due to the high prevalence of zeros (which reflect the absence of measurable coronary plaque). Prior works have variously handled this by discarding measurements with CACS of zero or by binning the CACS into broad categorical groups. Such approaches discard meaningful data, motivating the need for a more principled approach to handling the data distribution. In this work, we explored zero-inflated regression as a possible approach to modeling CACS using a cohort of patients from the BioHEART-CT study, and devised metrics to validate performance. We identified zero-inflated negative binomial regression, zero-inflated gamma regression and zero-inflated lognormal regression as promising approaches for handling the distributional properties of CACS, where the best method to use can vary depending on the dataset considered. A key contribution of our work is to demonstrate how these models can also estimate the percentile of the observed CACS relative to the distribution that would be expected after controlling for the inputs, thereby avoiding the need for data-hungry binning-based approaches.

bioinformatics↗

bioRxiv2020

hRUV: Hierarchical approach to removal of unwanted variation for large-scale metabolomics data

Liquid chromatography-mass spectrometry based metabolomics studies are increasingly applied to large population cohorts, running for several weeks to months, even extending to years of data acquisition. This inevitably introduces unwanted intra- and inter-batch variations over time that can overshadow true biological signals and thus hinder potential biological discoveries. To date, normalization approaches have struggled to mitigate the variability introduced by technical factors whilst preserving biological variance, especially for protracted acquisitions. Here, we designed an experiment with an arrangement to embed biological sample replicates to measure the variance within and between batches for over 1,000 human plasma samples run over 44 days. We integrate these replicates in a novel workflow to remove unwanted variation in a hierarchical structure (hRUV) by progressively merging the adjustments in neighbouring batches. We demonstrate significant improvement of hRUV over existing methods in maintaining biological signals whilst removing unwanted variation for large scale metabolomics studies.

bioinformatics↗