Biology subjects

Kovacic, J. C.

Publications and source records attributed to Kovacic, J. C..

7 recordsLinked to original sources

bioRxiv2026

STING-STAT3-SOX18 Axis Drives EndMT and Epigenetic Reprogramming in SAVI Lung Fibrosis

A high prevalence of early-onset interstitial lung disease, including pulmonary fibrosis, in pediatric patients with Stimulator of interferon genes (STING)-Associated Vasculopathy with onset in infancy (SAVI) suggests a critical role for the cGAS-STING pathway in the pathogenesis of pulmonary fibrosis. We identified an endothelial-to-mesenchymal transition (EndMT) signature in lesional lung biopsies from SAVI patients, marked by a loss of endothelial and acquisition of mesenchymal markers. Consistently, induced pluripotent stem cell-derived endothelial cells (iECs) from SAVI patients harboring gain-of-function STING1 mutations spontaneously undergo EndMT, a process rescued in isogenic-correction. In endothelial cells, STING activation induces IRF3-independent STAT3 phosphorylation, initiating a SLUG-dependent mesenchymal transcriptional program while repressing SOX18 and an epigenetically-regulated endothelial maintenance network. Our studies define a non-canonical cGAS-STING-STAT3 signaling axis that couples a mesenchymal transcriptional program with epigenetic silencing of an endothelial maintenance program, promoting TGF{beta}-independent STING-mediated EndMT and endothelial dysfunction, and suggesting STING as a therapeutic target for inflammatory pulmonary fibrosis.

cell biology↗

bioRxiv2022

Integrative single-cell meta-analysis reveals disease-relevant vascular cell states and markers in human atherosclerosis

Coronary artery disease (CAD) and atherosclerosis are characterized by plaque formation in the arteries wall. CAD progression involves complex interactions and phenotypic plasticity within and between distinct vascular and immune cell lineages. Single-cell RNA-seq (scRNA-seq) studies have highlighted lineage-specific transcriptomic signatures, but the reported cell phenotypes in humans remain controversial. Here, we meta-analyzed four scRNA-seq datasets, creating the first map of human cell diversity in atherosclerosis. We generated an atlas of 118,578 high-quality cells, characterized cell-type diversity and provided insights into smooth muscle cell (SMC) phenotypic modulation, transcription factor activity and cell-cell communication. We integrated genome-wide association study (GWAS) data and uncovered a critical role for modulated SMC phenotypes in CAD and coronary calcification. Finally, we identified candidate markers of fibromyocyte and fibrochondrogenic human SMCs (LTBP1 and CRTAC1) that may serve as proxies of atherosclerosis progression. Altogether, we created a unified cellular map of atherosclerosis informing cell state-specific mechanistic and translational studies of cardiovascular diseases.

genomics↗

bioRxiv2022

FHL5 controls vascular disease-associated gene programs in smooth muscle cells

BackgroundGenome-wide association studies (GWAS) have identified hundreds of loci associated with common vascular diseases such as coronary artery disease (CAD), myocardial infarction (MI), and hypertension. However, the lack of mechanistic insights for a majority of these loci limits translation of these findings into the clinic. Among these loci with unknown functions is UFL1-FHL5 (chr6q16.1), a locus that reached genome-wide significance in a recent CAD/MI GWAS meta-analysis. In addition to CAD/MI, UFL1-FHL5 is also implicated to coronary calcium, intracranial aneurysm, and migraine risk, consistent with the widespread pleiotropy observed among other GWAS loci. MethodsWe apply a multimodal approach leveraging statistical fine-mapping, epigenomic profiling, and imaging of human coronary artery tissues to implicate Four-and-a-half LIM domain 5 (FHL5) as the top candidate causal gene. We unravel the molecular mechanisms of the cross-phenotype genetic associations through in vitro functional analyses and epigenomic profiling experiments. ResultsWe prioritized FHL5 as the top candidate causal gene at the UFL1-FHL5 locus through eQTL colocalization methods. FHL5 gene expression was enriched in the SMC and pericyte population in human artery tissues with coexpression network analyses supporting a functional role in regulating SMC contraction. Unexpectedly, under procalcifying conditions, FHL5 overexpression promoted vascular calcification and dysregulated processes related to extracellular matrix organization and calcium handling. Lastly, by mapping FHL5 binding sites and inferring FHL5 target gene function using artery tissue gene regulatory network analyses, we highlight regulatory interactions between FHL5 and downstream CAD/MI loci, such as FOXL1 and FN1 that have roles in vascular remodeling. ConclusionTaken together, these studies provide mechanistic insights into the pleiotropic genetic associations of UFL1-FHL5. We show that FHL5 mediates vascular disease risk through transcriptional regulation of downstream vascular remodeling loci. These trans-acting mechanisms may account for a portion of the heritable risk for complex vascular diseases.

genomics↗

bioRxiv2021

Transcriptome-wide association study of coronary artery disease identifies novel susceptibility genes

Transcriptome-wide association studies (TWAS) explore genetic variants affecting gene expression for association with a trait. Here we studied coronary artery disease (CAD) using this approach by first determining genotype-regulated expression levels in nine CAD relevant tissues by EpiXcan in two genetics-of-gene-expression panels, the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) and the Genotype-Tissue Expression (GTEx). Based on these data we next imputed gene expression in respective nine tissues from individual level genotype data on 37,997 CAD cases and 42,854 controls for a subsequent gene-trait association analysis. Transcriptome-wide significant association (P < 3.85e-6) was observed for 114 genes, which by genetic means were differentially expressed predominately in arterial, liver, and fat tissues. Of these, 96 resided within previously identified GWAS risk loci and 18 were novel (CAND1, EGFLAM, EZR, FAM114A1, FOCAD, GAS8, HOMER3, KPTN, MGP, NLRC4, RGS19, SDCCAG3, STX4, TSPAN11, TXNRD3, UFL1, WASF1, and WWP2). Gene set analyses showed that TWAS genes were strongly enriched in CAD-related pathways and risk traits. Associations with CAD or related traits were also observed for damaging mutations in 67 of these TWAS genes (11 novel) in whole-exome sequencing data of UK Biobank. Association studies in human genotype data of UK Biobank and expression-trait association statistics of atherosclerosis mouse models suggested that newly identified genes predominantly affect lipid metabolism, a classic risk factor for CAD. Finally, CRISPR/Cas9-based gene knockdown of RGS19 and KPTN in a human hepatocyte cell line resulted in reduced secretion of APOB100 and lipids in the cell culture medium. Taken together, our TWAS approach was able to i) prioritize genes at known GWAS risk loci and ii) identify novel genes which are associated with CAD.

genetics↗

bioRxiv2021

Cell-specific chromatin landscape of human coronary artery resolves regulatory mechanisms of disease risk

Coronary artery disease (CAD) is a complex inflammatory disease involving genetic influences across several cell types. Genome-wide association studies (GWAS) have identified over 170 loci associated with CAD, where the majority of risk variants reside in noncoding DNA sequences impacting cis-regulatory elements (CREs). Here, we applied single-cell ATAC-seq to profile 28,316 cells across coronary artery segments from 41 patients with varying stages of CAD, which revealed 14 distinct cellular clusters. We mapped ~320,000 accessible sites across all cells, identified cell type-specific elements, transcription factors, and prioritized functional CAD risk variants via quantitative trait locus and sequence-based predictive modeling. We identified a number of candidate mechanisms for smooth muscle cell transition states and identified putative binding sites for risk variants. We further employed CRE to gene linkage to nominate disease-associated key driver transcription factors such as PRDM16 and TBX2. This single cell atlas provides a critical step towards interpreting cis-regulatory mechanisms in the vessel wall across the continuum of CAD risk.

genomics↗

bioRxiv2020

A Library Of Induced Pluripotent Stem Cells From Clinically Well-Characterized, Diverse Healthy Human Individuals

A library of well-characterized human induced pluripotent stem cell (hiPSC) lines from clinically healthy human subjects could serve as a useful resource of normal controls for in vitro human development, disease modeling, genotype-phenotype association studies, and drug response evaluation. We report generation and extensive characterization of a gender-balanced, racially/ethnically diverse library of hiPSC lines from 40 clinically healthy human individuals who range in age from 22-61. The hiPSCs match the karyotype and short tandem repeat identity of their parental fibroblasts, and have a transcription profile characteristic of pluripotent stem cells. We provide whole genome sequencing data for one hiPSC clone from each individual, genomic ancestry determination, and analysis of Mendelian disease genes and risks. We document similar transcriptomic profiles, single-cell RNA-seq derived cell clusters and physiology of cardiomyocytes differentiated from multiple independent hiPSC lines. This extensive characterization makes this hiPSC library a valuable resource for many studies on human biology.

cell biology↗

bioRxiv2018

EnsembleCNV: An ensemble machine learning algorithm to identify and genotype copy number variation using SNP array data

The associations between diseases/traits and copy number variants (CNVs) have not been systematically investigated in genome-wide association studies (GWASs), primarily due to a lack of robust and accurate tools for CNV genotyping. Herein, we propose a novel ensemble learning framework, ensembleCNV, to detect and genotype CNVs using single nucleotide polymorphism (SNP) array data. EnsembleCNV a) identifies and eliminates batch effects at raw data level; b) assembles individual CNV calls into CNV regions (CNVRs) from multiple existing callers with complementary strengths by a heuristic algorithm; c) re-genotypes each CNVR with local likelihood model adjusted by global information across multiple CNVRs; d) refines CNVR boundaries by local correlation structure in copy number intensities; e) provides direct CNV genotyping accompanied with confidence score, directly accessible for downstream quality control and association analysis. Benchmarked on two large datasets, ensembleCNV outperformed competing methods and achieved a high call rate (93.3%) and reproducibility (98.6%), while concurrently achieving high sensitivity by capturing 85% of common CNVs documented in the 1000 Genomes Project. Given this CNV call rate and accuracy, which are comparable to SNP genotyping, we suggest ensembleCNV holds significant promise for performing genome-wide CNV association studies and investigating how CNVs predispose to human diseases.

bioinformatics↗