Coronary Artery Disease risk variant dampens the expression of CALCRL by reducing HSF binding to shear stress responsive enhancer in endothelial cells in vitro

Abstract

Background:

Calcitonin receptor-like (CALCRL) protein is an important mediator of the endothelial fluid shear stress response, which is associated with the genetic risk of coronary artery disease (CAD). In this study we functionally characterized the non-coding regulatory elements carrying CAD risks SNPs and studied their role in the regulation of CALCRL expression in endothelial cells.

Methods:

To functionally characterize the CAD SNPs harbored around the gene CALCRL, we applied an integrative approach encompassing statistical, transcriptional (RNA-Seq), and epigenetic (ATAC-Seq, ChIP-qPCR, EMSA) analyses, alongside luciferase reporter assays, and targeted gene and enhancer perturbations (siRNA, CRISPR-Cas9) in human aortic endothelial cells (HAECs).

Results:

We demonstrate that the regulatory element harboring rs880890 exhibits high enhancer activity and shows significant allelic bias. The A allele was favored over the G allele, particularly under shear stress conditions, mediated through alterations in HSF1 motif and binding. CRISPR deletion of rs880890-enhancer resulted in downregulation of CALCRL expression, whereas Heat shock factor (HSF1) knockdown resulted in a significant decrease in rs880890-enhancer activity and CALCRL expression. A significant decrease in HSF1 binding to the enhancer region in endothelial cells was observed under disturbed flow compared to unidirectional flow. CALCRL knockdown as well as variant perturbation experiments indicated the role of CALCRL in mediating eNOS, apelin, angiopoietin, prostaglandins and endothelin-1 signaling pathways leading to decrease in cell proliferation, tube formation and NO production.

Conclusions:

Overall, our results demonstrate the existence of an endothelial-specific heat shock factor regulated transcriptional enhancer that mediates CALCRL expression. Better understanding of CALCRL gene regulation and the role of SNPs in modulation of CALCRL expression could provide important steps towards understanding genetic regulation of shear stress signaling responses.

Graphical Abstract

Introduction

Coronary artery disease (CAD) is a disease of coronary vessels that develops due to build-up of atherosclerotic plaques in the vessel wall. Genome-wide association studies (GWAS) have identified ~300 risk loci for CAD, that are beginning to shed light into the complexity of its genetic architecture^1–5. Although some associations locate within coding regions, approximately 98% of signals come from non-coding regions, which suggests that single nucleotide polymorphisms (SNPs) within gene regulatory elements play major role in mediating the effects on gene expression^6–8.

One of the identified CAD GWAS loci on chromosome 2q32.1 harbors the calcitonin receptor-like (CALCRL) gene¹, encoding for a seven-transmembrane G-protein-coupled receptor that mediates the pleiotropic effects of calcitonin gene-related peptide (CGRP) and adrenomedullin (ADM)⁹. These two structurally related neuropeptides were originally described as potent vasodilators. Beyond blood pressure regulation, CALCRL is involved in a variety of key biological processes, including cell proliferation, modulation of apoptosis, vascular biology, and inflammation, and is currently emerging as a novel target for the therapy of migraine¹⁰. In solid tumors, antibody-mediated inhibition of CALCRL signaling has been demonstrated to reduce tumor growth via disruption of angiogenesis or via direct antiproliferative effects on cancer cells¹¹. Interestingly, it was further shown that CALCRL is expressed in normal CD34+ hematopoietic progenitors and that CGRP and ADM directly act on CD34+ cells to promote colony formation in vitro, indicating a functional role of CALCRL in physiological myelopoiesis¹²

Recently CALCRL was demonstrated to be a major enhancer of the nitric oxide (NO) pathway in endothelial cells through unidirectional shear stress response¹³. A fundamental function of the endothelial layer of blood vessels is their ability to sense fluid shear stress and to transform this information into intracellular signals¹⁴. These mechano-sensing and -signaling processes are critical for maintaining vascular integrity, thus affecting physiological vascular tone and morphogenesis but also pathological vascular processes including hypertension and atherosclerosis. In the present study, we demonstrate that the non-coding regulatory region in the 3’ end of CALCRL is important for gene expression and show that rs880890, variant associated with both hypertension and CAD risk, exhibits allele specific regulatory activity. We further demonstrate that stress regulates this region through binding of Heat Shock Factor (HSF) transcription factors and silencing of HSF1 significantly reduces CALCRL expression. Overall, our study suggests a causal role for rs880890 carrying enhancer in regulating angiogenesis, proliferation, and nitric oxide production where CALCRL plays a key mediator role.

Materials and methods:

Data availability

The RNA-Seq experiments reported in this study are deposited in the GEO database under the accession number: GSE222118

Cells

TeloHAEC (ATCC, CRL-4052), HepG2 (ATCC, HB-8065), RAW 264.7 (ATCC TIB-71), pre-adipocyte 3T3-L1 (ATCC CL-173) and MOVAS (ATCC CRL-2797) cell lines were used in the luciferase experiment. TeloHAECs were cultured using Vascular Cell Basal Medium (ATCC PCS-100-030) supplemented with Endothelial Cell Growth Kit-VEGF (ATCC PCS-100-041) and 10 % FBS. HepG2 cells were cultured in Dulbecco’s modified Eagle medium (DMEM; 4.5 g/L glucose, 2 mM L-glutamine, Lonza) supplemented with 10% fetal bovine serum (FBS; GIBCO, Thermo Fisher Scientific). RAW 264.7 were cultured using DMEM, and FBS: (heat inactivated in water bath at 56 degree for 30 minutes). 3T3-L1 was cultured using DMEM (Lonza BE12-614F) supplemented with 10% FBS and 2mM L-glutamine. MOVAS was cultured using DMEM supplemented with geneticin. Primary human coronary artery endothelial cells (HCAECs) were cultured in media MV2 (purchased from Promocell) and used between passage 4-6. 100 U/ml penicillin, 100 μg/ml streptomycin was used in all cell-lines used in this study to prevent bacterial contamination.

Gene and eRNA expression quantification

Gene transcript coordinates were identified from GRO-seq data using a custom de novo detection pipeline^15,16. eRNA quantification was performed using Homer¹⁷ analyzeRepeats.pl, with parameters: -noadj -noCondensing -pc 3. Quantification was done from the opposite strand of coding gene transcription for intragenic enhancers and from both strands for intergenic enhancers. Gene end coordinates were expanded by 1500bp to account for transcription at the end of transcripts and annotating intragenic gene enhancers. Counts for eRNA and gene expression were normalized using voom¹⁸ function of limma R package. Only enhancers with counts per million (cpm) > 1 in at least 5 samples were included in the analysis.

Elastic net regression

Elastic net multivariable regression model was applied to individual genes, using enhancer RNA as predictive variables. This approach was aimed to identify the enhancers that are most effective in predicting gene expression. For each gene, only enhancers that were in the same topologically associating domain (TAD) and P-value < 0.05 from spearman’s correlation test of significance were used for fitting the model. Caret¹⁹ and glmnet²⁰ R packages and repeatedcv method with 5 repeats and folds set to 5 were used for tuning and cross validating the model. This process involved running the model across a range of alpha values from 0 to 1 in increments of 0.05 and recording the mean cross-validated error at the lambda.min value. Here, alpha and lambda are regularization parameters. Alpha controls the balance between the L1 (Lasso) and L2 (Ridge) regularization terms. A higher alpha emphasizes fewer coefficients, while a lower alpha leans towards Ridge regularization. Enhancers for a given gene were ranked based on their model coefficient to determine their importance for predicting gene expression. Model coefficients refer to the weights assigned to each feature in the predictive model. Therefore, when model was fitted to predict gene expression values using enhancer expression values, best enhancers get higher coefficients and are ranked higher than enhancers with coefficients close to zero.

CRISPR-Cas9–mediated enhancer deletion

The CRISPR reagents were adapted from the Alt-R system from IDT. The guide RNAs were designed using the IDT- tool to minimize off-targeting effects using two guides to create 372-bp deletion (Table S1). The positive control (crRNA targeting HPRT) from the Alt-R CRISPR-Cas9 Human Control Kit (IDT) were used. Delivery of the ribonucleoprotein complex into TeloHAEC was performed using Neon transfection system with a 1350 V pulse of 30 ms width. Forty-eight hours post transfection RNA was purified using a RNeasy Mini Kit (Qiagen) and the cDNA was prepared with a RevertAid First Strand cDNA Synthesis Kit (Thermo Fisher Scientific). The mRNA level of the CALCRL was measured by SYBR green chemistry qPCR using specific primers (Table S2) in a StepOne real-time PCR system (Thermo Fisher Scientific). Three independent experiments with 4 technical replicates were performed. Data (ΔCt values) was checked for normal distribution using Shapiro-Wilk test and F test to compare variance before performing statistical tests and unpaired T-test (two-tailed) was used. p < 0.05 was used to define a significant difference between the groups. To generate clones (ΔEnh) with an enhancer deletion, cells underwent electroporation and were dispensed into a 96-well plate using a single-cell dilution technique. These cells were subsequently cultivated until clones exhibiting the desired enhancer deletion.

Single-cell RNAseq data processing and analysis

scRNA-Seq data previously generated from atherosclerotic human coronary arteries²¹ was obtained from the NCBI GEO repository (accession GSE131778). The data processing and cell type annotation was carried out as described in²² using Seurat package²³. For plotting gene expression in individual cells, the gene counts were depth-normalized to 10,000 total counts per cell and log-transformed.

Dual Luciferase reporter assays

DNA fragments containing the region rs880890-rs840585 with haplotypes (G-T) and (A-C) were amplified by PCR from genomic DNA (gDNA) with Phusion polymerase and specific primers (Table S3). For the dual luciferase reporter assay, amplified DNA (698bp) was subcloned into hSTARR-seq_ORI plasmid (Addgene, #99296) that was used as a backbone for the plasmid constructs. The enhancer inserted plasmid was co-transfected into TeloHAEC, HepG2, RAW 264.7, pre-adipocyte 3T3-L1 and MOVAS cell lines with the control vector pGL4.75 (Promega), which encodes the luciferase gene hRluc (Renilla reniformis). The luciferase constructs prepared with the inserts were verified by sequencing. Luciferase plasmids with minimal promoter containing rs840588(A)_rs840587(C), rs840588(G)_rs840587(A), rs907463(T) and rs907463(G) enhancers were purchased from Vector builder. Transfection was performed in 6-well plate using Lipofectamine Stem Transfection Reagent (ThermoFisher Scientific STEM00008) according to the protocol and Dual-Luciferase® Reporter Assay System (Promega E1980) was used to detect luciferase activity. Three independent experiments with four technical replicates were performed. Data followed normal distribution (Shapiro-Wilk test) and equal variance (F test). Hence, unpaired Student’s T-test (two-tailed) was used for comparing enhancer activity in region carrying rs880890 “A” allele compared to “G”.

STARR-Seq allelic reporter assay processing

TeloHAEC and HepG2 cell lines were used for the experiment. STARR-Seq library and experiment have been described previously in^22,24,25. To assess transcriptional activity of haplotypes of the candidate regulatory regions, STARR-Seq RNA read data was UMI-deduplicated, depth-normalized to library size, and normalized for haplotype abundance in the plasmid DNA (input) library.

Motif analysis of rs880890

To identify the potential transcription factors binding to the region with rs880890, PERFECTOS-APE²⁶ was used with TFBS motif collection, P-value cut-off of 0.0005, fold change cutoff of 2.0 and motifbreakR²⁷ package with default settings.

Endothelial cells ChIP-Seq/ATAC-Seq data analysis related to athero-relevant flows

Since TeloHAECs are homozygous to the region of the SNP rs880890, the following ChIP-Seq data related to endothelial cells (HUVEC) cultured under shear stress (for 6 h at 37°C and with constant CO2 perfusion at ± 3 dyn/cm2) and static was downloaded from GSE116241. Sequencing data was downloaded from NCBI using the SRA Toolkit and processed using the nf-core ChIP-seq pipeline²⁸ to derive bam files. The BaalChIP²⁹ tool was used for allele-specific measurements of transcription factor binding from the ChIP-Seq data. A Bayesian statistical approach was used by the tool to correct the effect of background allele frequency on the observed ChIP-Seq read counts. ATAC-Seq data related to Human aortic endothelial cells (HAEC) that were heterozygous at rs880890 undergoing shear stress were downloaded from GSE112340. Reads were aligned to hg19 with Bowtie2³⁰. SAMtools³¹ was used to convert sam to bam files. BAM files were sorted and indexed using SAMtools sort and index function. IGV viewer³² was used to visualize the SNP position and count the read counts in unidirectional (UF) and disturbed flow (DF).

HSF1 knockdown and CALCRL expression under athero-relevant flows

A flow device consisting of a computerized stepper motor UMD-17 (Arcus Technology) and a 1° tapered stainless steel cone was used to generate the physiologically-relevant shear stress pattern³³. The flow device was placed in a 37°C incubator with 5% CO2. The flow patterns used for this experiment can be found in Table S4. Human aortic endothelial cells (HAECs) were seeded in 6-well plates at 100% confluency and cultured in flow media with EGM-2 medium (Lonza) containing 4% dextran. HAECs were subjected to UF or DF for 24 hours³⁴ before being harvested for RNA isolation. HAECs were seeded in 6-well plates in EGM-2 medium at 90% confluency and were transfected with 50 nM siRNA using RNAiMAX (Life Technologies) as described by the manufacturer. siRNA targeting HSF1 (SI03246348) and non-targeting control (1027310) were both purchased from Qiagen. Media was changed the following day to flow media with EGM-2 medium containing 4% dextran. HAECs were subjected to unidirectional flow for another 24 hours³⁴ before being harvested for RNA isolation. RNA was isolated from cells using GenEluteTM Mammalian Total RNA Miniprep Kit (Sigma Aldrich) and reverse transcribed using High-Capacity cDNA Reverse Transcription Kit (ThermoFisher). Quantitative mRNA expression was determined by RT-qPCR using SYBR Green MasterMix (Roche). The mRNA level of the HSF1 knockdown under shear stress was measured by using specific primers (Table S5).

Dual Luciferase reporter assays under athero-relevant flows

For the luciferase reporter assay, the following flow setup was used. Ibidi Pump System Quad (10906, ibidi GmbH), Ibidi μ-Slide VI with ibiTreat surface and channel height of 0.4 mm, length: 17 mm and width: 3.8 mm (80606, ibidi GmbH) were used. Channels were first coated with PureCol collagen solution (5005, Advanced BioMatrix). Next, Telo-HAECs (0.8x10⁵) were seeded into the channel in Vascular Cell Basal Medium. After cell attachment, channels were rinsed once with medium, and perfusion sets (Red, 10962, ibidi GmbH) were installed to the Pump System. Shear stress of 10 dyn/cm² was first established for 30 minutes to allow the cells to adapt for flow, followed by increase of shear stress to 20 dyn/cm² for 45-46 hours. For disturbed flow, oscillatory flow setup for Ibidi Pump System was used with 20 dyn/cm² and switching time of 2 seconds (i.e., 0.25 Hz). The flow rate remains constant except during valve switching simulating oscillatory turbulence.³⁵ Fluid velocity using the μ-Slide VI 0.4 10 dynes= 7.89 mL/min and 20 dynes= 15.77 mL/min. Viscosity (0.0072 dyn*s/cm2) of the fluid is a typical value for cell culture medium supplemented with FBS at 37°C. Formula used to calculate shear stress are as follows: τ = η*176.1*Φ where Φ is flow rate (ml/min), τ is shear stress (dyn/m2) and η is dynamical viscosity dyn.s/cm2. For luciferase assay, channels were first rinsed twice with PBS, dry trypsinized and channels were rinsed with lysis buffer to collect the cells. Data followed normal distribution (Shapiro-Wilk test) and equal variance (F test). Hence, unpaired Student’s T-test (two-tailed) was used for comparing enhancer activity between rs880890_A compared to rs880890_G.

siRNA mediated knockdown of CALCRL

For siRNA treatment, TeloHAECs were reverse transfected with Lipofectamine RNAiMAX transfection reagent (Thermo Fisher) according to the manufacturer’s instructions. The target-specific Silencer Select siRNAs for CALCRL (s19889 and s19890) and scrambled siRNA were obtained from Thermo Fisher. Cells were lysed 24 h after transfection. Three independent experiments with 4 technical replicates were performed. RNA extraction, cDNA synthesis, qPCR and statistical analysis followed the protocol described above for CRISPR-Cas9 experiments. The knockdown efficiency was greater than 95%

Chromatin immunoprecipitation assay (ChIP)-quantitative PCR under athero-relevant flows.

For the ChIP-qPCR, the flow setup was same as used in HSF1 knockdown and CALCRL expression under athero-relevant flows section. The flow device was placed in a 37°C incubator with 5% CO2. Telo-HAECs at 100% confluence, maintained in EGM2 medium containing 4% dextran (Sigma-Aldrich) in 6-well plates, were subjected to athero-protective UF or athero-susceptible DF for 24 hours before cells being processed for ChIP analysis. Telo-HAECs subjected to UF or D for 24 hours were cross-linked, digested, and immunoprecipitated according to manual instruction of Pierce Agarose ChIP Kit (Thermo Scientific). Briefly, cells subjected to flow were washed with warm PBS before cross-linked with 1% formaldehyde. After a 10-minute incubation, glycine was added to 125 mM final concentration and, after 5 minutes, the cells were pelleted down and the supernatant was removed. Cells were then resuspended in 1mL ice-cold PBS and 10 μL protease inhibitors (Halt Cocktail, Pierce) and were pelleted at 3000 x g for 5 minutes before lysed for undergoing MNase digestion at 10 U/μL for 15 min. After recovery of digested chromatin, the solution was immunoprecipitated with anti-HSF1 antibody (Cell Signaling Technology, #4356) at 1.1 μg/ 2 x10⁶ cells, and with normal rabbit IgG as control overnight. After binding to agarose beads, the immunoprecipitate was eluted before DNA clean up and then purified DNA was detected by qPCR performed on LightCycler 480 II (Roche) using SYBR Green I Master (Roche, Indianapolis, IN) with primers (Table S6). Statistical analyses were performed with two tailed unpaired Student’s T-test.

TeloHAEC protein extraction

TeloHAECs were collected (5 x 10⁶) in PBS by scraping from culture flasks and washed twice with cold PBS. Re-suspension of cells was done into 500 μl 1x Hypotonic Buffer (20 mM Tris-HCl, pH 7.4, 10 mM NaCl and 3 mM MgCl2) with protease inhibitor cocktail by pipetting up and down several times, followed by incubation on ice for 15 minutes. 25 μl detergent (10% NP40) was added and vortexed for 10 seconds at highest setting. Centrifugation was carried out of the homogenate for 10 minutes at 3,000 rpm at 4°C. Cytoplasmic fraction in the supernatant was removed. The nuclear pellet was re-suspended in 50 μl complete Cell Extraction Buffer (10 mM Tris, pH 7.4, 2 mM Na3VO4, 100 mM NaCl, 1% Triton X-100, 1 mM EDTA, 10% glycerol, 1 mM EGTA, 0.1% SDS, 1 mM NaF, 0.5% deoxycholate and 20 mM Na4P2O7) for 30 minutes on ice by vortexing at 10 minute intervals followed by sonication, followed by centrifugation for 30 minutes at 14.000 x g at 4°C. Quantitation of protein concentration was performed using the Quant-iT™ Protein Quantitation Kit (Thermo Fisher Scientific).

EMSA

Oligonucleotide probes (Table S7) (15 bp flanking SNP site for reference or alternate allele) with a biotin tag at the 5′ end of the sequence (Integrated DNA Technologies) were incubated with TeloHAEC nuclear extract and the working reagent from the LightShift™ Chemiluminescent EMSA kit (Thermo Fisher Scientific, Catalog number:20148, Rockford, USA). For competitor assays, an unlabeled probe of the same sequence was added to the reaction mixture at 100 × excess. The reaction was incubated for 30 min at room temperature, and then loaded on a 6% retardation gel (Invitrogen, Catalog number: EC6365BOX, California). The contents of the gel were transferred to a nylon membrane and visualized by UV trans illumination Image Lab Software (Bio-rad).

Allele specific ChIP-qPCR

HAEC donors (n=2; see major resources table) heterozygous for the SNP rs880890 were selected to study allele specific binding of the variant to transcription factor HSF1. For ChIP, the experimental setup was same as used in chromatin immunoprecipitation assay (ChIP)-quantitative PCR under athero-relevant flows section. qPCR was conducted using a custom TaqMan SNP Genotyping Assay for rs880890 (ThermoFisher). This assay uses allele-specific probes coupled to different fluorescent dyes in the same reaction mix for the quantitative detection of the alleles in a single sample. The rs880890 assay amplifies a 130-bp region around this SNP. We calculated haplotype-specific enrichment for both anti-HSF1 and Igg. Fold enrichment was determined by dividing the enrichment of the immunoprecipitated chromatin by the enrichment of the respective input. The experiment was performed with three biological replicates and qPCR was performed in triplicate. Statistical analyses were performed with two tailed unpaired Student’s T-test.

Western blot for si-CALCRL and enhancer deleted samples

Five microgram of total cytoplasmic protein were combined with gel loading buffer, heated to 95 °C for 5 minutes and separated on Any kD™ Mini-PROTEAN® TGX Stain-Free™ Protein Gels (#4568123, Bio-Rad Laboratories). Treated groups were loaded next to its corresponding controls at each time point in each gel. The proteins from enhancer deleted clone (ΔEnh) and si-CALCRL were transferred to 0.2 μm Nitrocellulose membranes (#1704158, Bio-Rad Laboratories) using Trans-Blot Turbo Transfer System (Bio-Rad Laboratories), blocked in 5% non-fat dry milk in TBS-Tween and incubated for 1.5 h at RTand with the primary antibodies Anti-CALCRL (1:500, #PA5-50644, Thermo Fisher Scientific) overnight. After washing, the membranes were incubated with horseradish peroxidase-conjugated anti-rabbit or anti-mouse secondary antibodies (1:2000, #Sc-2357 Santa Cruz Biotechnology or 1:1000 #7076 Cell signaling Technology, respectively). Signal was visualized using Pierce™ ECL Plus Western Blotting Substrate (#32134 Thermo Fisher Scientific) and ChemiDoc (Bio-Rad Laboratories) for imaging. The membranes were stripped after probing with CALCRL primary antibody using RESTORE™ western blot stripping buffer (#PIER21059 Thermo Fisher Scientific) for 45min at 37 degree celsius. The membrane was then reprobed using GAPDH (1: 5000, #39-8600, Thermo Fisher Scientific) primary antibody. Three biological replicates were performed. The ratio of CALCRL to GAPDH was calculated using imageJ³⁶ for each lane and the values of these ratios were normalized to the control group.

RNA‐Sequencing and Data Analysis

For the CALCRL silenced and enhancer deleted samples, total RNA quality was assessed using the Agilent 2100 Bioanalyzer System. RNA library preparation was handled using the QuantSeq 3′ mRNA‐Seq Library Prep Kit FWD (Lexogen according to the manufacturer’s instructions. The library was quantified using Qubit dsDNA HS Assay Kit (Q32854, ThermoFisher Scientific) and its quality was checked with Bioanalyzer using High Sensitivity DNA Kit (5067-4626, Agilent Technologies). Individual libraries were pooled in equimolar ratio (4 nM for each) and sequenced on an Illumina NextSeq 500. The bcl2fastq2 v2.20 (Illumina) was used to demultiplex sequencing data and to convert base call (BCL) files into FASTQ files. Nf-core²⁸ RNA-seq pipeline was used to process fastq files and derive gene counts. Gene counts were normalized for effective library size, and differentially expressed genes (DEGs) were analyzed using DESeq2³⁷. DEGs were defined by a P-value of <0.05 and an absolute fold change of >2.

Expression of downstream target genes under athero-relevant flows.

HCAECs (purchased from Promocell) were seeded on 0.1% gelatin coated slides with a density of 2.5 x 10⁵ and cultured for a minimum of 3 days to ensure complete confluency, production and reorganisation of sub-cellular matrix and maturation of cell-cell junctions. Culture under flow was performed for 72 hours using a parallel plate flow apparatus as described^38–40 under disturbed flow (5 dyn/cm²), or unidirectional flow (1.5 dyn/cm²) shear stress. Total RNA was extracted from HCAECs using the RNeasy Mini Kit (Norgen) according to the manufacturer’s protocol. 100ng RNA were reverse transcribed into cDNA with random primer by reverse transcriptase (RT) (Qiagen). cDNA (relative 1.7ng RNA) was amplified by standard qPCR with Taq DNA polymerase (Sensifast, SybrGreen, LOW-ROX Kit, Bioline). The geometric mean of GAPDH, RPLP0 and PABC4 quantification was used to normalise the expression of each test gene. Paired Student’s T-test was used for data that followed normal distribution using Shapiro-Wilk test. p < 0.05 was used to define a significant difference between the groups (n=7 individual donors).

Network analysis and identification of CALCRL co-expressed genes

The WGCNA⁴¹ (Weighted gene correlation network analysis) was used for identification of co-expressed genes from RNA-Seq samples. WGCNA is a well-established tool for studying biological networks based on pair wise correlation between variables in high-dimensional RNA-Seq data sets. ComBat-seq⁴² was used to remove batch effect from the data. Sequencing data was processed like mentioned in the data analysis section. After rlog transformation, we identified the genes and samples with excessive numbers of missing samples were identified using the function goodSamplesGenesMS and were discarded from the analysis. The samples were then clustered using the function hclust to identify outliers in the dataset. We constructed a weighted gene network using soft thresholding power β. The function pickSoftThreshold was used that aids in choosing a proper soft-thresholding power. We chose a power of 10, which resulted in an approximate scale-free topology network with the scale-free fitting index R2 greater than 0.9. For the remaining genes, the choice of the soft thresholding power β was used to which co-expression similarity is raised to calculate adjacency with function adjacency. The matrix of correlations was converted to an adjacency matrix of connection strengths. To minimize effects of noise and spurious associations, the adjacency was transformed into Topological Overlap Matrix using TOM and the corresponding dissimilarity was calculated. The function hclust was used to hierarchically cluster the genes. Branches of the dendrogram group together densely interconnected are highly co-expressed genes. Modules were then defined as sets of genes with high topological overlap. The module hub gene was defined as the gene in the module with the highest connectivity or based on a high intra-modular connectivity. Cytoscape⁴³ app geneMANIA⁴⁴ was used to identify known and novel co-expressed genes.

To identify how much of the co-expressed genes are enriched in the differential expressed dataset of CALCRL depletion, a gene enrichment analysis was performed using the Hypergeometric t-test⁴⁵. The parameters for the hypergeometric t-test are as follows. Number of successes (k=49) was the co-expressed genes significantly differentially expressed under CALCRL depletion (P <0.05), sample size (s=195) was the total co-expressed genes, number of successes in the population (M= 1460) represented all the genes that were significantly differently expressed under CALCRL depletion (P-value<0.05; in both siRNA and enhancer deletion) while population size (N= 11820) was the total number of genes included in the analysis.

Griess and LDH cytotoxicity assays

Nitrate colorimetric assay kit (Cayman chemical 780001) was used to measure the total nitrate production in enhancer deleted clone (ΔEnh). Cells from ΔEnh and control cells (from similar passage as the enhancer deleted clones) were plated in Ibidi μ-Slide VI and underwent unidirectional, disturbed, and static conditions as mentioned in Dual Luciferase reporter assays under athero-relevant flows methods section. Culture media was collected after 48 hours. Total nitrate/nitrite concentration of ΔEnh clone and control samples under UF and DF were normalized to static condition. UF The assay was performed according to manufacturer’s protocol. LDH cytotoxicity assay kit (Cayman chemicals 601170) was used to measure cell death upon treatment of oxidized LDL (Thermofisher L34357) and LDL (Thermofisher L3486). ΔEnh and control cells were plated in 96 well plate and subjected to 200ug/ml oxLDL and LDL treatment for 24h. The assay was performed according to manufacturer’s protocol. Statistical analyses were performed with unpaired Student T-test (two tailed).

Cell proliferation assay

Transfected cells were seeded at 5x10³cells/ well in 200 μl of complete media in E-plates (ACEA Biosciences, San Diego) and grown for 48 hours while monitored with an xCELLigence DP system (ACEA Biosciences) which measures electrical impedance across interdigitated gold micro-electrodes integrated on the bottom of tissue culture plates. The xCELLigence recorded cell index readings every 1 hour for 3 days. Three biological replicates were used to determine cell proliferation and represented the relative numbers of cells compared to control cells. A two-way repeated measure (RM) ANOVA test with Šídák's multiple comparisons test was used to compare CALCRL siRNA knockout treatment to scrambled (negative) control and ΔEnh clone to control; with Padj ≤ 0.05 deemed significant.

Tube formation assay

Forty-eight–well plate was coated with Matrigel basement membrane matrix 150 μL/well (Corning, growth factor reduced). For CALCRL knockdown and ΔEnh clones, TeloHAECs (40000 cells) were seeded on to the Matrigel 48 hours after siRNA transfection. Knockdown efficiencies of more than 95% were consistently attained detected by qPCR. Images were taken from 9 spots per replicate every 4 hours until 24 hours using Incucyte (S3 System). Each siRNA knockdown had 3 technical replicates in each experiment, and experiments were repeated 3 times. Analysis was done with ImageJ³⁶ and associated macro tool Angiogenesis Analyzer⁴⁶ where total tube length was selected as the parameter of evaluation. For time-course analysis, each time point represents the average tube length from all spots of all replicates with same treatments at the same time point. Data were checked for normal distribution before performing statistical tests. Unpaired Student’s T-test (two-tailed) was used for data since values followed normal distribution and equal variance.

Results

Identification of CALCRL regulatory enhancer by co-expression analysis

CALCRL is a G protein-coupled receptor and a recently identified CAD locus¹¹. Further analysis of the SNPs within the CALCRL locus using Open Target Genetics-portal⁴⁷ demonstrated that the SNPs are also associated with hypertension (Figure 1A). Since majority of the variants were located within non-coding regions and overlapped enhancer elements marked by DNase hypersensitivity and H3K27ac, we next sought to identify the relative importance of individual enhancers in the regulation of CALCRL expression. Starting from the premise that enhancer (e)RNAs are co-expressed with their target genes^48,49, we constructed co-expression networks based on regularized logistic regression. This was achieved by comparing co-activity patterns of eRNAs and coding gene expression across cell types based on public GRO-Seq data on 336 samples representing 45 cell types¹⁵. Our model reports a linear combination of coefficients derived from eRNA expression to describe how important each enhancer is as predictor for gene expression. Complex heatmap⁵⁰ of twelve regulatory regions (Figure 1B, C) were included in our analysis and enhancer-8 demonstrated the highest regression coefficient of 0.31 and was thus predicted to be central for the regulation of CALCRL expression (Table S8). The plot of GRO-Seq enhancer-8 eRNA and CALCRL mRNA expression showed a positive correlation with a P-value of 1.38E–11 (Figure S1A). Hi-C data from TeloHAECs⁵¹ further confirmed the interaction between enhancer-8 and the promoter of CALCRL (Figure S1B). Finally, to confirm the predicted regulatory role of enhancer-8 in controlling endothelial CALCRL expression, the Alt-R CRISPR-Cas9 system was used to selectively delete a 372-bp genomic region in TeloHAECs using electroporation. PCR assays and western blot detected around 50% genetic deletion in regulatory region enclosing rs880890 and reduction in CALCRL expression in TeloHAECs (Figure S2). The enhancer deleted cells (ΔEnh) exhibited significantly lower gene expression (P=0.003) compared to control cells confirming the importance of the enhancer elements in regulating CALCRL expression (Figure 1D).

Figure 1:

A) Association of rs880890 with CVD related traits from Open Targets Genetics-portal⁴⁷. rs880890 was significantly associated with hypertension and CAD. B) WashU browser shot of CAD loci with CALCRL region showing the regulatory regions (1-12) included in the study. A close-up look of the enhancer-8 harboring CAD associated SNPs. DNAse hypersensitivity shows that region with rs880890/rs840585 is accessible compared to nearby regions C) Complex heatmap⁵⁰ showing the eRNA expression of each enhancer included in the study, CALCRL mRNA expression, R2 value of enhancers and the CAD SNPs annotations. D) Results of CRISPR-Cas9–mediated regulatory region deletion in TeloHAECs (ΔEnh clone). qPCR was performed from three independent experiments. The statistical significance was evaluated using a two-tailed Studenťs t-test (unpaired). For the bar plot, significance is denoted with an asterisk. *** p=0.003.

Allele specific activity of rs880890-harboured enhancer in Telo-HAECs

The enhancer-8 harbored five CAD associated SNPs, and we next focused on characterizing rs880890 and rs840585 in the DNAse hypersensitivity region in more detail. (Figure 1B zoom out). GTEX⁵² database suggested that CALCRL expression is ubiquitous throughout tissues (Figure S3) with the highest expression seen in lung, artery, and adipose tissue. To identify the cell type contributing to the expression of CALCRL in the arterial tissue, we took advantage of single cell data from mouse single cell atlas (Tabula muris⁵³). tSNE visualization of all tissues from tabula muris identified CALCRL expression predominantly in endothelial cells (Figure 2A). In addition, analysis of public scRNA-Seq data from human atherosclerotic lesions²¹ demonstrated high expression of CALCRL in endothelial cells and to lesser degree in smooth muscle cells (Figure 2B; Figure S4). In line with the cell type specific gene expression, enhancer-8 was only found accessible in endothelial cells of the human atherosclerotic aorta based on our previously published single nucleus ATAC-Seq data²² (Figure 2C).

Figure 2:

A) Single cell RNA-Seq data from Tabula muris⁵³ representing data accross 20 organs and tissues from mice shows that CALCRL is highly expressed in endothelial cells, especially in the lung. B) Single cell RNA-Seq data from human coronary arteries²¹ demonstrating that CALCRL is predominantly expressed in endothelial cells followed by smooth muscle cells and fibroblasts. C) Pseudobulk coverage track visualization of single nucleus ATAC-Seq signal²² at the CALCRL loci showing endothelial cell type-specific activity of the rs880890 containing regulatory element. D) The activity of the rs880890-containing enhancer was investigated in MOVAS, HEPG2, RAW 264.7, 3T3-L1 and TeloHAECs. Luciferase assay showing a significant increase in enhancer activity in region carrying rs880890 “A” allele compared to “G” allele in TeloHAEC followed by RAW 264.7 while other cell types did not show enhancer activity. E) STARR-Seq results from TeloHAEC and HepG2 showing the enhancer activity in the presence of rs880890 or rs840585. Results show that difference in enhancer activity was observed only in the presence of rs880890 in TeloHAEC and no difference in enhancer activity in HepG2 was seen (n=3). The statistical significance was evaluated using a two-tailed Studenťs t-test (unpaired). For the bar plot, significance is denoted with an asterisk. ** p<0.01.

To study if the cell type specificity could be driven by the enhancer-8, we measured the enhancer activity of 698bp region in endothelial cells (TeloHAEC), pre-adipocytes (3T3L1), smooth muscle cells (MOVAS), hepatocytes (HepG2) and macrophages (RAW264.7). Enhancer-8 harbored five CAD SNPs of which two were in the DNase hypersensitive region. The SNPs were assessed through a luciferase assay. While rs840588, rs840587 and rs907463 didn’t show any difference in enhancer activity (Figure S5A), haplotype (Ref: rs880890(A)_rs840585(C) and Alt: rs880890(G)_rs840585(T)) showed significant difference compared to the empty plasmid. Low enhancer activity was detected in all cell types except TeloHAECs which demonstrated 100-fold higher activity. Importantly, when we compared the luciferase signal of “A” allele compared to the “G” allele, there was a 40% increase in the activity of “A” compared to “G” (Figure 2D). This cis-eQTL was confirmed in GTEx database where rs880890 “A” allele was significantly associated with increased expression of CALCRL compared to “G” allele (Figure S5B). Also supporting our results, a trend of allele specific deposition of histone mark H3K27ac and the binding of endothelial specific transcription factor ETS-related gene (ERG) was also detected in a cohort of 21-44 HAEC donors⁵⁴ (Figure S6A). This is in line with the predicted ERG motifs in enhancer-8 situated 238bp upstream and 291bp downstream around HSF1 motif at rs880890 SNP.

To separate between the two SNPs in mediating the allele specific effect, we cloned the naturally occurring three haplotype combinations into a reporter vector with haplotype 1 containing the reference alleles rs880890(A) and rs840585(C) while haplotype 2 carrying rs880890(G) allele and haplotype 3 the rs840585(T) allele. Our analysis suggests that compared to haplotype 1, only haplotype 2 showed allele specific enhancer activity while hap3 showed no effect (Figure 2E) pinpointing rs880890 as the most probable causal SNP. Importantly, rs880890 also demonstrated significant evolutionary conservation (PhyloP -log p-value = 0.57) compared to rs840585 that was predicted to be fast evolving (PhyloP -log p-value = -0.99).⁵⁵ To understand the allele specific binding of transcription factors to the rs880890, we performed electromobility shift assay (EMSA) using biotinylated 31-bp probes targeting either the reference or alternative allele in TeloHAECs. Competitor assays were performed by incubating the reaction with ×100 excess of unlabeled (no biotin) oligonucleotide complexes with identical sequence (Figure S7). The results demonstrated an increased protein binding to the “A” allele when compared to the "G” allele of rs880890 (Figure 3A). Sequence based motif analysis revealed that heat shock factors such as HSF1, HSF2 and HSF4 are likely to bind to the CALCRL regulating enhancer at the rs880890 SNP region with a preference towards the “A” allele in binding affinity (Figure S8; Table S9). HSFs are known to activate heat shock proteins during stress⁵⁶ and in endothelial cells, HSF1 increase corresponds to elevated levels of eNOS⁵⁷. To further explore this, allele-specific ChIP-qPCR was conducted to assess HSF1 binding in heterozygous HAEC donors, employing a custom TaqMan Genotyping Assay for rs880890. Confirming motif predictions, HSF1 showed a 1.63-fold higher enrichment at the A allele compared to the G allele (Donor 1: p = 0.004 and Donor 2: p=0.007, Figure 3B).

Figure 3.

A) EMSA for the rs880890 SNP showing (arrow pointing) that the “A” allele significantly gains binding affinity the “G” allele. B) Allele specific ChIP-qPCR showing 1.63-fold higher enrichment of HSF1 binding at A allele compared to G allele (Donor 1: p = 0.004 and Donor 2: p=0.007). C) Luciferase assay showing a significant decrease in enhancer-8 activity during HSF1 knockdown D) Luciferase assay showing a further decrease in enhancer-8 activity under disturbed flow (DF) compared to unidirectional flow (UF) in the presence of “G” allele. E) ATAC-seq demonstrating higher accessibility of the rs880890 cis-regulatory element (enhancer-8) in HAECs under UF compared to DF. F) Increased ATAC-seq and ChIP-Seq reads in rs880890-containing region in HAECs under UF compared to DF. Bar plot shows increased ATAC/ChIP-Seq reads in cells under UF and lower reads from the G allele-containing chromosome under DF compared to A allele. G) A significant decrease (P<0.01) in the expression of CALCRL was detected under DF compared to UF. H) Knockdown of HSF1 under shear stress resulted in significant downregulation of CALCRL expression (P=0.02). I) HSF1 ChIP-PCR showed significant changes in binding activity in HAEC under DF when compared to UF. The condition was true for both SNP centric (binding detection with rs880890 at the center) and peak centric (enhancer-8 peak’s center). The statistical significance for the experiments in the figure was evaluated using a two-tailed Studenťs t-test (unpaired). For the bar plot, significance is denoted with an asterisk. ** p<0.01 and * p<0.05

cis-regulatory element, rs880890 and CALCRL are all flow-responsive

As CALCRL is a key enhancer of eNOS pathway implicated in fluid shear stress response, and HSF1⁵⁸ has been shown to be activated by shear stress, we wanted to investigate the effect of rs880890 SNP on enhancer activity under such conditions. As expected, ECs exhibited morphological changes under unidirectional flow, disturbed flow and static flow conditions Figure S9. Luciferase assay confirmed significantly lower enhancer activity after HSF1 knockdown, while an even further decrease in the luciferase activity of enhancer was observed under disturbed flow compared to unidirectional flow (Figure 3C-D). ATAC-seq analysis of HAECs subjected to unidirectional flow mimicking hemodynamics in human distal carotid artery or the disturbed flow waveform mimicking the hemodynamics in human carotid demonstrated that the rs880890-harboring enhancer is more accessible in HAEC under UF compared to DF (Figure 3E). Moreover, analysis of the publicly available H3K27ac ChIP-Seq data allowed us to interrogate the allelic bias of rs880890 under shear stress in endothelial cells. The results confirmed that “A” allele is indeed biased towards higher enhancer activity binding compared to “G” allele and this effect is more prominent under shear stress in HAECs and HUVECs (Figure 3F; Table S10). These effects were concordant with the regulation of CALCRL expression which was higher in HAECs subjected to UF and lower upon DF (P<0.001) (Figure 3G). HSF1 knockdown under unidirectional flow (Figure 3H) resulted in a significant decrease in CALCRL expression. This is in line with the directionality of HSF1 expression under shear stress with lower HSF1 levels detected under DF compared to UF⁵⁹. In contrast, ERG knockout resulted in no significant change in CALCRL expression under unidirectional flow, whereas we did note a significant decrease under disturbed flow. This suggests a potential co-regulatory relationship between ERG and HSF1 (Figure S6B). To further validate the anticipated binding of HSF1 to enhancer-8, we conducted a targeted HSF1 ChIP-PCR. Our findings revealed a marked reduction in HSF1 binding to the enhancer region in HAEC under DF conditions compared to UF (Figure 3I), thus confirming that shear stress alters the binding of HSF1 to the enhancer.

CALCRL knockdown and enhancer deleted cells affects vasodilation and endothelial specific pathways

To understand the downstream effects of CALCRL and CALCRL regulating enhancer, we performed RNA-Seq on upon siRNA-mediated CALCRL knock down and CRISPR-mediated rs880890-enhancer deletion. We then used the rlog transformed matrix of our RNA-Seq data to construct co-expression network using 11820 genes across 22 samples. This allowed identification of 31 distinct gene co-expression modules in our RNA-Seq data depicted in distinct colors (Figure S10). The CALCRL gene was found in the module blue along with 143 co-expressed genes. Dendrogram of consensus module eigengenes obtained on the consensus correlation was used to merge the similar modules blue and cyan with a threshold of 0.1 resulting in identification of 195 genes co-expressed with CALCRL. Selected genes are illustrated on a geneMANIA network (Figure 4A) that demonstrated the relationships between CALCRL and co-expressed genes. Interestingly, we observed potentially new CALCRL co-expressed genes such as APLN, ADAMTS18 and AKAP12.

Figure 4:

A) Network generated by Cytoscape app geneMANIA. Black nodes with blue outline represent known CALCRL co-expressed genes while black nodes with green border are novel co-expressed genes. Purple connections denotes co-expression, cyan denotes pathway, pink denotes physical interaction and purple denotes co-localization. B) Functional enrichment plot of CALCRL siRNA and rs880890-enhancer deleted samples using IPA. z-score indicates a predicted activation or inhibition of a pathway (i.e. Erythropoietin, Angiopoietin, Relaxin, Nitric Oxide, Renin-Angiotensin, Apelin Endothelial, Endothelin-1, Adrenomedullin, Thrombopoietin and eNOS signaling pathways are predicted to be repressed). C-D) Volcano plot highlighting selected candidates differentially expressed in RNA-Seq data from CALCRL siRNA and rs880890-enhancer deleted samples.

Further analysis of the differentially expressed genes (DEGs) identified 390 and 990 genes significantly regulated (padj<0.05) upon gene silencing or enhancer deletion, respectively (Table S11 and S12). Importantly, this included 25% of the 195 CALCRL co-expressed genes, demonstrating significant enrichment (hypergeometric t-test P-value=6.5E-07). Functional enrichment analysis demonstrated that similar pathways were regulated by both the gene and enhancer repression (Figure 4B). The most important pathways affected included the repression of apelin, endothelin, adrenomedullin, eNOS and angiopoietin signaling pathways. Apelin (APLN), Cyclooxygenase (PTGS2) genes themselves were significantly downregulated in both RNA-Seq experiments indicating they could be direct downstream targets of CALCRL (Figure 4C-D). Among DEGs genes, PTGS1 and PTGS2 were also downregulated in disturbed flow compared to unidirectional flow further linking these genes to shear stress (Figure S11A–B). Interestingly, we also observed a downregulation trend of EDN1. The RNA-Seq results also indicated that depletion of CALCRL expression leads to repression of TGF-β signaling (THBS1, PTGS2, and TGFBR2) and angiogenesis regulatory genes (PDPK1, ANGPTL4). In line with this, knockdown of CALCRL and enhancer deletion led to a significant decrease in cell proliferation (Figure 5A-B) and tube formation (Figure 5C through E).

Figure 5.

A-B) Effect of CALCRL downregulation on proliferation of TeloHAECs. Normalized cell index (xCELLigence system) upon siRNA knockdown of CALCRL (si-CALCRL) and enhancer deleted clone (ΔEnh clone) in TeloHAECs shows significant decrease in cell proliferation. A two-way repeated measure ANOVA test with Šídák's multiple comparisons test for CALCRL siRNA treatment vs scrambled negative controls and ΔEnh clone vs. control at each time point: *P < 0.05; **P < 0.01; ***P < 0.001; Data points are average values of three biological replicates. C) Effect of si-CALCRL and ΔEnh clone on tube formation. Heat map of the averaged total tube lengths 4–16 h after plating TeloHAECs on Matrigel (n=3). D) Fold change in tube length formation. Each bar represents the average + SEM of total tube lengths obtained from each well image. Tube length values comparing siCALCRL to siNegative (P=0.026) and ΔEnh clone (P=0.023) to control was used analyzed by 2-tailed Student t test. *P<0.05. E) Representative of 9 spot well output images from Incucyte after 24 h siCALCRL treatment (n=3) and ΔEnh clones (n=3). F) OxLDL (200ug/ml) and LDL (200ug/ml)) induced cell death in enhancer deleted clone (ΔEnh clone) and control cells (TeloHAEC with similar passage as ΔEnh clone). oxLDL induced cytotoxicity showed significantly higher cytotoxicity in ΔEnh clones (P=0.026). G) NO production in ΔEnh clone and control cells (TeloHAEC with similar passage as ΔEnh clone). NO production was detected by Griess assay, n = 3, Total nitrate/nitrite concentration was significantly downregulated in DF compared to UF (P = 0.035) in clones. For the bar plots, the statistical significance was evaluated using a two-tailed Studenťs t-test (unpaired). Significance is denoted with an asterisk. * p<0.05

Finally, we performed LDH cytotoxicity assay on ΔEnh clones, to assess the effect of LDL (200ug/ml) and oxLDL (200 μg/mL) on cell viability. The results demonstrated that the oxLDL treatment induce significant increase (P=0.026) in cell growth in ΔEnh clone (Figure 5F) compared to control cells. To explore the difference in the production of NO under flow, ΔEnh clones and control cells were exposed to unidirectional and disturbed flow followed by detection of NO production by using Griess assay. In our results, show in Figure 5G and Figure S11C, we observe that ΔEnh clones under DF showed significant reduction in NO production compared with UF (P=0.035) further confirming the vital role of enhancer-8 in regulation of CALCRL for NO production.

Discussion

Blood flows through the arteries with tangential frictional force that acts on the surface of endothelial cells⁶⁰. The frictional force, or shear stress, spans a range of spatiotemporal scales and contributes to regional and focal heterogeneity of endothelial gene expression, which is important in vascular pathology⁶¹. To this end, exposure to multi-directional ‘disturbed’ blood flow at arterial bifurcations and curves primes endothelial activation, promoting pathological processes that contribute to atherosclerosis, the major cause of coronary artery disease (CAD)⁶². Unidirectional flow/shear stress promotes eNOS expression and activity, lack of which contributes to development of atherosclerosis at disturbed flow sites. Krause et al.⁶³ recently reported that the noncoding common variant at rs17114036, associated with CAD/IS in GWAS, regulates PLPP3 expression in endothelium through increased enhancer activity that is dynamically regulated by unidirectional flow and transcription factor KLF2. Here, we extend the discovery and characterization of genetic variants associated with CAD acting through shear stress regulation, by identifying a mechanosensitive endothelial enhancer that regulates CALCRL expression. Specifically, we identified a non-coding, common genetic variant rs880890 that modified levels of CALCRL expression in endothelial cells. We demonstrate that rs880890 confers increased enhancer activity that is dynamically regulated by unidirectional flow and HSF transcription factor(s).

Our data suggests that decreased endothelial production of CALCRL could be associated with atherosclerosis. Endothelial-specific deficiency of CALCRL has previously shown to lead to increased formation of atherosclerotic lesions in mice⁶⁰. In addition to CAD, 2q32.1 locus is also associated with hypertension. In line with this, in our study, the G allele at rs880890 is associated with increased risk for hypertension⁶⁴. Since G allele of rs880890 reduces CALCRL production, that is normally needed for increase of cAMP levels, activate protein kinase A (PKA) and eNOS¹³, reduced NO production could explain its association with hypertension. Interestingly, the minor allele frequency (G) of rs880890 is highly variable between the ethnicities ranging from 0.2 in the African population to 0.9 in Asian populations such as Japanese⁶⁵ suggesting population specific differences in the risk susceptibility mediated by this locus.

Vasoreactivity is an important characteristic of blood vessels to maintain adequate supply to perfused tissues dependent on required metabolic need. eNOS derived NO is an endogenous vasodilatory gas that continually regulates the diameter of blood vessels and maintains an anti-proliferative and anti-apoptotic environment in the vessel wall⁶⁶. Our data from perturbation experiments demonstrate that CALCRL could play a major role in regulation of several vasodilatory factors. To this end, we identify apelin (APLN)⁶⁷, endothelin (EDN1)^68,69 as potential downstream target of CALCRL. Although we didn’t observe a difference in NO production in DF compared to UF flow and in non-flow static conditions, the difference in NO production was observed in the ΔEnh clone under DF compared to UF. In addition, PTGS1 (COX-1) and PTGS2 (COX-2), key enzymes which convert arachidonic acid to prostaglandins to mediate vasodilation and inhibition of platelet aggregation,⁷⁰ were identified. Further investigations are warranted to shed light on the involvement of CALCRL in regulating the expression of APLN, PTGS1, PTGS2 and EDN1.

CALCRL/adrenomedullin/G-protein-alpha activation in endothelial cells have been shown as a promising approach to inhibit progression of atherosclerosis by reducing endothelial inflammation⁶⁰. In this study, we provide in-vitro evidence that CALCRL expression is regulated by the concerted action of genetic variation and shear stress to promote pathogenic mechanisms leading to plaque formation. We present a model where the ‘G’ allele of the rs880890 associated with increased risk of CAD confers decreased endothelial enhancer activity thereby reducing CALCRL expression. Under disturbed flow, changes in histone acetylation limits DNA accessibility, further reducing HSF1/2/4 binding and CALCRL expression. Changes in CALCRL expression may influence vasoconstriction and atherosclerotic plaque formation through the regulation of eNOS, apelin, adrenomedullin, renin-angiotensin, angiopoietin and endothelin-1 signaling pathway with reduced expression promoting pathology. In summary, this study has identified a previously unreported mechanosensitive pathway, and HSFs as important transcription factors that exert an anti-atherosclerotic effect in endothelial cells. Our data highlights the utility of using GWAS data to illuminate the molecular mechanisms that drive pathology and provides a plausible mechanism for how human SNPs in CALCRL regulate cardiovascular disease.

Highlights:

• Identified a CAD risk variant (rs880890) that influences CALCRL expression through differential enhancer activity, showing a preference for the A allele.

• Demonstrated an important role of HSF1 in regulating CALCRL expression, where its knockdown or the deletion of the rs880890 enhancer significantly reduces CALCRL levels, especially under disturbed flow conditions.

• Highlighted CALCRĽs essential function in endothelial cells, mediating key signaling pathways (eNOS, apelin, angiopoietin, prostaglandins, endothelin-1) crucial for cell proliferation, tube formation, and nitric oxide production.

• Our findings uncover a novel mechanism by which genetic variants and shear stress collaboratively dictate CALCRL expression, offering new insights into the genetic regulation of coronary artery disease risk.

Nonstandard Abbreviations and Acronyms:

CAD

Coronary Artery Disease

DF

Disturbed flow

GWAS

genome-wide association study

STARR-Seq

self-transcribing active regulatory region sequencing

UF

Unidirectional flow