A MicroRNA Signature in Acute Coronary Syndrome Patients and Modulation by Colchicine

Abstract

Background:

Circulating microRNAs (miRNAs) may play a pathogenic role in acute coronary syndromes (ACS). It is not yet known if miRNAs dysregulated in ACS are modulated by colchicine. We profiled miRNAs in plasma samples simultaneously collected from the aorta, coronary sinus, and right atrium in patients with ACS.

Methods:

A total of 396 of 754 miRNAs were detected by TaqMan real-time polymerase chain reaction from EDTA-plasma in a discovery cohort of 15 patients (n = 3 controls, n = 6 ACS standard therapy, n = 6 ACS standard therapy plus colchicine). Fifty-one significantly different miRNAs were then measured in a verification cohort of 92 patients (n = 13 controls, n = 40 ACS standard therapy, n = 39 ACS standard therapy plus colchicine). Samples were simultaneously obtained from the coronary sinus, aortic root, and right atrium.

Results:

Circulating levels of 30 of 51 measured miRNAs were higher in ACS standard therapy patients compared to controls. In patients with ACS, levels of 12 miRNAs (miR-17, -106b-3p, -191, -106a, -146a, -130a, -223, -484, -889, -425-3p, -629, -142-5p) were lower with colchicine treatment. Levels of 7 of these 12 miRNA were higher in ACS standard therapy patients compared to controls and returned to levels seen in control individuals after colchicine treatment. Three miRNAs suppressed by colchicine (miR-146a, miR-17, miR-130a) were identified as regulators of inflammatory pathways. MicroRNAs were comparable across sampling sites with select differences in the transcoronary gradient of 4 miRNA.

Conclusion:

The levels of specific miRNAs elevated in ACS returned to levels similar to control individuals following colchicine. These miRNAs may mediate ACS (via inflammatory pathways) or increase post-ACS risk, and could be potentially used as biomarkers of treatment efficacy.

Keywords

microRNA acute coronary syndrome colchicine inflammation coronary artery disease

Introduction

MicroRNAs (miRNAs) are noncoding RNA molecules that regulate gene expression at posttranscriptional level¹ and are reliably measured in plasma. MicroRNAs act on multiple pathways that drive atherosclerosis, including lipoprotein homeostasis, endothelial inflammation, cytokine response, leukocyte migration, and activation.² Recently, they have been identified as targets for inhibition or mimics in noncardiac conditions with the potential that similar strategies may also attenuate plaque progression and/or stabilize vulnerable plaque.^2,3

Colchicine is a widely available, safe, and inexpensive anti-inflammatory medication, which prevents mitosis by inhibiting microtubule polymerization in neutrophils, but also interferes with activation of the NLRP3 inflammasome, interleukin 1 (IL-1) production, and macrophage activation.⁴ Recent evidence supports a therapeutic role in patients with coronary disease^4
-6 and our group has shown that colchicine has striking anti-inflammatory properties in patients with acute coronary syndrome (ACS), inhibiting transcoronary release of inflammatory cytokines and monocyte activation^7,8 and stabilizing vulnerable plaque on coronary computed tomography.⁹ However, its effect on circulating miRNAs in patients with ACS has not been explored, and in its application in management of patients with ACS we need to understand its mechanism of action and target population.

Thus, we sought firstly to identify differences in circulating miRNA expression, at both local cardiac level and systemically, between patients without angiographically apparent coronary artery disease (CAD) and patients with ACS receiving standard therapy. Secondly, we sought to investigate whether miRNA expression was altered in patients with ACS treated with short-term colchicine. We performed a discovery analysis for 754 known and validated miRNAs on 15 patients to assess differences across groups, and performed a validation analysis on 92 patients, using a custom panel of the most significantly dysregulated miRNA identified in the discovery analysis.

Methods

Study Population

Consecutive consenting patients, who presented with an ACS or chest pain and referred for coronary angiography, were recruited from a tertiary referral hospital in Sydney, Australia. Patients with ACS were determined by the definition by the American College of Cardiology/AHA 2007 Guidelines including elevated troponin, electrocardiographic changes, escalating symptoms or new onset of angina, and documented CAD at angiography. Exclusion criteria included patients with significant liver dysfunction (ALT/AST > 1.5 × ULN), renal dysfunction (estimate glomerular filtration rate <50 mL/min/1.73 m²), significant clinical inflammatory disease, or taking prednisone. Biomarker selection was assessed with 45 samples (15 patients) in the discovery cohort, including n = 6 individuals per ACS groups and 3 controls with 3 sampling sites per individual resulting in 18 samples per ACS groups and 9 controls. The 51 most statistically significant miRNAs were then assessed by custom panel in 276 samples (92 patients with 3 samples simultaneously collected from the aorta, coronary sinus, and right atrium) in the verification cohort. This methodology¹⁰ was specifically chosen in order to identify without bias the presence of circulating miRNAs in a smaller discovery cohort which were then confirmed in a larger appropriately powered verification cohort (see section “Statistical Methods”). Study groups included (i) control patients referred for investigation of chest pain symptoms but with no or very minor angiographic coronary disease (n = 3 discovery, n = 13 validation), (ii) patients with ACS¹¹ (n = 6 discovery, n = 40 validation) on standard therapy, and (iii) patients with ACS treated with standard therapy plus colchicine started 6 to 24 hours prior to the coronary angiography (n = 6 discovery, n = 39 validation; Figure 1).

Figure 1.

Study flow chart. Study flow chart from biobank of patient samples to Discovery cohort and formation of a custom panel of miRNA for a verification cohort. Controls denotes patients with very minor or no coronary artery disease on coronary angiography. ACS indicates acute coronary syndrome; RNA, ribonucleic acid; miRNA, microRNA.

Plasma Collection and Storage

Patients underwent coronary angiography and simultaneous blood sampling from 3 locations (right atrium, aorta, and coronary sinus) prior to coronary angiography. Whole blood was collected in 9 mL EDTA blood tubes and transported on ice for processing within an hour of collection. Whole blood was centrifuged to separate plasma from red blood cells at 524G force (1500 rpm) at room temperature for 10 minutes. Plasma was then separated into aliquots for immediate freezing at −80°C. Plasma aliquots were thawed on ice at the time of processing for assay measurements. All patients were heparinized with 2500 IU of intravenous heparin presampling as part of the catheterization procedure. The subset of patients with ACS given colchicine received 1 mg followed by 500 µg 1 hour later, orally, between 6 and 24 hours prior to the procedure. We have previously shown that this dosing regimen results in a significant reduction in monocyte activation and transcoronary inflammatory cytokine levels.^7,8 Acute coronary syndrome individuals eligible for the study were assigned consecutively, 1:1 to standard therapy plus colchicine or standard therapy alone but were not blinded; however, blood processing and storage were blinded to disease state and treatment. All samples were deidentified and randomised for processing from RNA isolation to quantitative real-time polymerase chain reaction (qPCR).

RNA Isolation

Ribonucleic acid (RNA) was isolated from 100 µL of plasma for assessment of miRNA expression using QIAcube HT (Qiagen, Hilden, Germany), an automated platform, where 75 µL of nuclease-free water is added at the elution step. Briefly 10 µg glycogen and 0.5 mL of Trizol (Thermo Fisher Scientific, Waltham, Massachusetts) were added to 100 µL of plasma and the mixture was thoroughly vortexed. This was followed by the addition of a spike-in control ath-miR-172a (AGAAUCUUGAUGCUGCAU; 250 fmol) and 0.1 mL chloroform, and the resulting mixture was then vigorously shaken. Aqueous and organic phases were separated by centrifugation at 3100 × g at 4°C, for 25 minutes. RNA extraction via precipitation and elution was carried out on the QIAcube-HT automated robotics system using RNeasy 96 QIAcube HT kit and RNA was stored at −80°C. Concentration of RNA was determined using Nanodrop 2000 on the day of miRNA profiling. Samples with RNA concentrations of lesser than 35 ng/µL (for discovery samples) or 4 ng/µL (for validation samples) were not included.

Discovery Open Array PCR, Custom Open Array PCR, and Quality Controls

Discovery panels of 754 known and validated human miRNAs were assessed by qPCR on Thermo Fisher’s OpenArray platform following a slight variation of the manufacturer’s protocol, as detailed earlier.^12,13 Custom OpenArray plates were designed for validation using 51 most dysregulated miRNAs selected from the discovery analysis. Synthesis of complementary DNA, preamplification, and qPCR were performed and a low sample input protocol, developed with the manufacturer, was followed. Five quality control miRNA were included in the Custom Open Array chip. See Online Supplement for detailed methodology outlining Discovery and Custom Open Array panels.

Validation of Discovery and Custom Open Array Platforms

Thirty-eight patient samples were profiled for the 51 selected custom miRNAs on both panels. A total of 1938 matched pairs compared between platforms demonstrated a good correlation, R of 0.721, P < .001. A Bland-Altman plot (Supplemental Figure 1) of miRNA profiling in all samples showed good agreement between platforms at lower (<30) cycle threshold (Ct) values, but Ct values, mostly above 35, were dissimilar and not included in our verification cohort analysis. We employed additional sample detection tool to confirm that the OpenArray feature is not empty (using the post-PCR OpenArray slide image QC). No imputation was performed. We ensured that there are no missing values using the QC image of PCR Open Array slide. This affirmed the absence of miRNA in question and therefore a value of 39 is assigned to those undetectable miRNA.^14,15 Further, we used an Amp score of > 1.24 and Cq confidence > 0.6 to filter the miRNAs with true amplification.

Statistical Methods

Based on the results from our discovery cohort, sample size calculations indicated a required N = 22 per group for a 2-fold difference in miRNA expression (alpha = .05 and power = 0.90, with a wide standard deviation). Our verification cohort includes N = 119 and N = 112 samples (from 40 and 39 patients per group), which exceeds the desired power for this study.¹⁶

Statistical analysis was performed in SPSS version 24, Graphpad Prism Version 7, and Statistica for Windows (TIBCO Software Inc., version 13, Palo Alto, California). Nonnormally distributed variables were adjusted by logarithmic transformation prior to statistical analysis. Gensini score, troponin level, and neutrophil counts were log transformed prior to analysis. Descriptive data were expressed as mean and standard deviation or standard error of the mean (SEM) and categorical data as number and percentage. All miRNA data were expressed as normalized/adjusted Ct-value and SEM Ct-value or fold change (calculated as 2^ΔCt-value) as described elsewhere.¹⁵ Cycle threshold value normalization was by global normalization (Discovery cohort) or normalization to the averages of Reverse transciption (RT) and RNA isolation-stage-specific Arabidopsis thaliana spike-in miRNAs (Verification cohort). Comparisons between groups for clinical variables were made using χ², Fischer exact, and Independent Student t test as appropriate. Multivariable linear regression was used to assess associations of Gensini score and troponin with individual miRNA. Analysis of variance was used for comparisons of 3 or more groups in the discovery cohort. Bland-Altman plot and correlation analysis were used to assess comparable expression between OpenArray discovery and custom platforms, and repeats. Student t test was used to assess differences between groups in control and ACS and ACS standard therapy and ACS standard therapy plus colchicine in the verification cohort. A 2-sided P value of .05 was set as the boundary for declaring statistical significance. Pathway analysis was carried out using the Ingenuity Pathway Analysis (IPA) software (Qiagen).

Results

Discovery Analysis

The discovery cohort of 15 patients underwent sampling from 3 sites: (i) the coronary sinus, (ii) aorta, and (iii) right atrium. Twelve of 15 patients presented with an ACS, 6 of whom were treated with colchicine 6 to 24 hours prior to blood sampling. Three patients with no or minimal angiographic disease served as controls. Groups were comparable across clinical and biochemical parameters and medication use (Supplemental Table 1).

MicroRNA Expression

A discovery panel of 754 known and validated miRNAs expressed in human cell systems was used to profile plasma miRNAs. Of these 754 miRNAs, only a proportion get released into plasma, through stressed and dying cells and exosome packaging as shown previously. Thus, as anticipated only 396 miRNAs were detected in plasma samples and between 72 and 263 miRNAs were detected in each individual patient sample. Of these, a total of 164 miRNAs (41%) were found to be significantly different (P < .05) across groups (Supplemental Table 2). A set of 51 of the most significantly dysregulated miRNAs (ranked based on the P value, all < .0035) were selected from these discovery analyses for a custom panel of miRNA in a verification cohort. All miRNA demonstrated an increased expression in ACS standard therapy patients versus control. Notably, colchicine treatment significantly reduced the expression of these miRNAs similar to levels observed in control patients (Figure 2; Supplemental Table 3 and Supplemental Figure 3). We analyzed differences in miRNA levels across the sampling sites and found that all 51 miRNA remained significantly different across treatment groups irrespective of blood sampling site (Supplemental Table 4). A customized TaqMan real-time PCR array using these 51 miRNAs and 5 control miRNAs (2 spike-in controls, endogenous control U6, negative control with a duplicate assay) was developed to analyze this signature of 51 miRNAs on 92 other patients at 3 sampling sites (N = 276 samples) for our verification cohort.

Figure 2.

Discovery cohort microRNA (miRNA) differentially expressed across treatment group. Panel A represents high expression Ct < 30 in control group. Panel B represents low expression Ct > 30 in control group, and Panel C represents undetectable miRNA in control group. Panel D (The Violin plot) represents the expression of all microRNAs shown in panels A-C. White circles show the medians for all 51 microRNAs measured in all discovery samples. The black box limits indicate the 25th and 75th percentiles as determined by R software and whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles. The polygons (violins) represent density estimates of each sample/replicate and site data and extends to extreme values.

Figure 3.

Gene Pathway Identification of mRNA targets of miRNA increased in ACS and reduced with colchicine treatment in ACS. Gene targets of 3 miRNA—miR-146a-5p, miR-130a-3p, and miR-17-5p identified by ingenuity pathway analysis in colchicine-modulated miRNA in patients with ACS. Highlighted gene targets include those we believe may be involved in colchicine modulation through miRNA in ACS. ACS indicates acute coronary syndrome; miRNA, microRNA; mRNA, messenger RNA.

Verification Analysis

Ninety-two patients (n = 13 control, n = 40 ACS standard therapy, n = 39 ACS standard therapy plus colchicine) recruited consecutively were assessed in the verification cohort. Clinical and demographic characteristics were similar across groups. The cohort was predominantly male with a mean age of 63.9 years. Of the patients with ACS, 75% had a Non-ST elevation acute myocardial infarct (NSTEMI) and 41% of the cohort were diabetic. Use of antiplatelet agents was higher in both ACS groups compared to control but were comparable between ACS groups with and without colchicine treatment (Table 1). Statin therapy was comparable between all 3 groups. Troponin levels pre-percutaneous coronary intervention (if clinically indicated) were elevated in both ACS groups compared to control, P = .004, but comparable between ACS groups with and without colchicine treatment, P = .78. There were no differences in white cell count or neutrophil count across the 3 groups at the time of angiography. Gensini scores, a measure of coronary disease burden, were higher in both ACS groups compared to control, P = .001, but there was no difference between the ACS standard therapy and ACS standard therapy plus colchicine groups, P = .966 (Table 1).

Table 1.

Clinical Variables Across Treatment Groups.^a

Variable	N	Control, n = 13	ACS (standard therapy), n = 40	ACS (colchicine), n = 39	P Value	P Value^b
Gender, male	92	10 (77%)	37 (93%)	34 (87%)	.28
Age, years	92	63 (9.4)	64 (12.7)	64 (8.8)	.937
BMI	88	26.92 (4.825)	27.57 (6.718)	28.88 (4.618)	.462
Diabetes	91
Yes		4 (33%)	14 (35%)	19 (49%)	.41
No		8 (66%)	26 (65%)	20 (51%)
Hypertension	91				.16
Yes		5 (41.7%)	29 (72.5%)	26 (66.7%)
No		7 (58.3%)	11 (27.5%)	13 (33.3%)
FHx CVS Disease	89				.171
Yes		0 (0%)	9 (22.5%)	10 (26.3%)
No		11 (100%)	31 (77.5%)	28 (73.7%)
Dyslipidemia	91				.264
Yes		6 (50%)	30 (75%)	27 (69.2%)
No		6 (50%)	10 (25%)	12 (30.8%)
Current smoker	90				.432
Yes		5 (45.5%)	13 (32.5%)	10 (25.6%)
No		6 (54.5%)	27 (67.5%)	29 (74.4%)
Smoker ever	90				.67
Yes		6 (54.5%)	27 (67.5%)	27 (69.2%)
No		5 (45.5%)	13 (32.5%)	12 (30.8%)
Previous AMI	91				1
Yes		2 (16.7%)	9 (22.5%)	9 (23.1%)
No		10 (83.3%)	31 (77.5%)	30 (76.9%)
ACS subtype	79				.65
AMI			29 (72.5%)	30 (76.9%)
UAP			11 (27.5%)	9 (23.1%)
Previous CABG	91				.604
Yes		0 (0%)	4 (10%)	2 (5.1%)
No		12 (100%)	36 (90%)	37 (94.9%)
Previous PCI	91				.514
Yes		1 (8.3%)	10 (25%)	10 (25.6%)
No		11 (91.7%)	30 (75%)	29 (74.4%)
Medications
Aspirin (yes)	91	10 (83.3%)	40 (100%)	38 (97.4%)	.023	.494
Thienopyridines (yes)	91	6 (50%)	38 (95%)	36 (92.3%)	.001	.675
Statin (yes)	91	9 (75%)	34 (85%)	32 (82.1%)	.645	.728
Bloods
WCC	92	7.02 (1.757)	8.29 (2.228)	8.34 (2.290)	.148	.916
Neutrophil count	91	3.92 (1.172)	5 (1.761)	5.01 (2.252)	.177	.987
Troponin pre-PCI	77	6.25 (2.63)	598 (1076)	684 (1201)	.004	.776
Gensini	91	0.85 (1.197)	40.09 (37.267)	39.74 (34.842)	.001	.966

Abbreviations: ACS, acute coronary syndrome; AMI, acute myocardial infarction; ANOVA, analysis of variance; BMI, body mass index; CABG, coronary artery bypass grafting; FHx CVS, family history of cardiovascular disease; PCI, percutaneous coronary intervention; UAP, unstable angina pectoris; WCC, white cell count.

^a Verification cohort N = 92. Control represents those patients with very minor or no coronary disease at angiography. ACS (standard therapy) represents the patients with acute coronary syndrome (ACS). ACS (colchicine) represents patients with ACS treated with standard therapy plus colchicine. Data presented as mean (standard deviation) for continuous variables and number (percentage) for categorical variables. Variables were log adjusted for normal distribution as required, including neutrophils, Gensini score, and troponin. P value is from χ², Fisher exact, ANOVA.

^b P is from Student t test between ACS (standard therapy) and ACS (colchicine) groups only. All P significant at .05 level.

MicroRNA Expression in Control and ACS Standard Therapy Patients

We first assessed differences in miRNA expression between samples from control individuals and ACS standard therapy patients and found that the expression of 30 miRNAs were significantly higher (P < .05) in patients with ACS. The fold difference in the levels of microRNAs between these groups ranged from 1.75- to 10.85-fold (Table 2).

Table 2.

MicroRNA Expression Difference Between Control and Patients With ACS.^a

Variable	N	Control, n = 38		ACS Standard Therapy, n = 119		Fold Difference ACS Standard Therapy Over Control	P
Variable	N	Mean Ct Value	SD	Mean Ct Value	SD	Fold Difference ACS Standard Therapy Over Control	P
hsa-miR-181c	157	35.63	5.399	32.62	5.831	8.056	.005
hsa-miR-106b	157	21.88	2.402	20.88	2.533	2.000	.031
hsa-miR-130a	157	21.40	2.686	19.98	2.614	2.676	.006
hsa-let-7e	157	22.43	2.986	21.23	3.339	2.297	.040
hsa-miR-30c	157	20.64	2.824	19.35	2.854	2.445	.017
hsa-miR-142-3p	157	20.65	3.057	19.18	2.910	2.770	.012
hsa-miR-17	157	16.27	2.208	15.31	2.529	1.945	.028
hsa-miR-191	157	18.29	3.508	16.79	3.436	2.828	.025
hsa-miR-720	157	20.85	1.949	19.99	1.804	1.815	.019
hsa-miR-484	157	17.22	2.264	16.13	2.161	2.129	.011
hsa-miR-374	157	23.75	6.188	20.75	3.444	8.000	.007
hsa-miR-128a	157	26.66	5.920	24.31	5.214	5.098	.033
hsa-miR-628-3p	157	28.61	6.279	26.19	5.418	5.352	.038
hsa-miR-126	157	18.85	3.355	17.36	3.207	2.809	.019
hsa-miR-20a	157	17.41	2.021	16.60	2.401	1.753	.045
hsa-miR-26a-1-3p	157	31.39	7.129	28.38	6.572	8.056	.024
hsa-miR-320	157	18.64	1.881	17.73	1.865	1.879	.012
hsa-miR-26a	157	20.12	3.474	18.60	3.437	3.182	.022
hsa-miR-15a	157	29.19	6.689	26.53	5.673	6.320	.031
rno-miR-7a-1-3p	157	23.11	4.384	21.49	3.442	3.074	.042
hsa-miR-146a	157	17.53	3.023	16.13	3.102	2.639	.016
hsa-miR-20a-3p	157	34.83	5.908	32.08	6.243	6.727	.016
hsa-let-7f	157	29.12	7.180	26.13	6.230	7.945	.025
hsa-miR-106b-3p	157	29.23	6.553	26.83	5.937	5.278	.050
hsa-miR-21	157	19.67	2.030	18.49	2.212	2.266	.003
mmu-miR-134	157	25.89	4.377	24.11	4.432	3.434	.033
hsa-miR-889	157	33.36	6.443	30.21	6.457	8.877	.011
hsa-miR-411	157	34.20	6.203	30.76	6.401	10.853	.004
hsa-miR-362	157	32.80	6.443	30.40	6.053	5.278	.046
hsa-miR-139-5p	157	24.82	3.701	23.24	3.387	2.990	.023

Abbreviations: ACS indicates acute coronary syndrome; Ct, cycle threshold; SD, standard deviation.

^a Verification Cohort N = 56. ACS standard therapy patients represents the patients with acute coronary syndrome (ACS). Control represents those patients with very minor or no coronary disease at angiography. Data presented as mean Ct value and standard deviation. Fold difference represents 2^ΔCT. P value is from Student t test significant at .05 level.

MicroRNA Expression in ACS Standard Therapy Patients and ACS standard Therapy Plus Colchicine Patients

The expression of 12 miRNAs out of 51 was significantly higher in ACS standard therapy patients versus ACS standard therapy plus colchicine (Table 3, Supplemental Figure 5). Of those miRNAs, 7 were also significantly higher in patients with ACS compared to controls, including miR-130a, -17, -484, -146a, -106b-3p, -191, -889. Fold difference in miRNA expression ranged from 1.69 to 4.26 between the 2 ACS groups. We carried out gene pathway analysis on these miRNAs (Ingenuity Pathway Analysis, Qiagen) for the identification of possible pathways that these miRNAs target. This analysis identified key miRNAs (miR-146a, miR-17, and miR-130a) known to participate in the IL-1β, transforming growth factor β (TGF-β), and tumor necrosis factor α (TNF-α) inflammatory pathways (Figure 3).

Table 3.

MicroRNA Expression Different Between ACS and ACS Treated with Colchicine.^a

Verification cohort N = 79	N	ACS (standard therapy), n = 119		ACS (colchicine), n = 112		Fold Difference, ACS (standard therapy) over ACS (colchicine)	P
miRNA		Mean Ct value	SD	Mean Ct value	SD
hsa-miR-130a	231	19.98	2.614	20.91	3.564	1.905	.026
hsa-miR-425-3p	231	26.70	6.630	28.68	7.701	3.945	.038
hsa-miR-17	231	15.31	2.529	16.38	3.965	2.099	.016
hsa-miR-191	231	16.79	3.436	18.06	4.566	2.412	.018
hsa-miR-106a	231	15.57	2.653	16.61	4.007	2.056	.023
hsa-miR-484	231	16.13	2.161	16.89	3.196	1.693	.036
hsa-miR-142-5p	231	25.47	5.127	26.98	6.242	2.848	.046
hsa-miR-629	231	30.34	5.188	31.82	5.859	2.789	.044
hsa-miR-223	231	13.65	4.194	15.07	5.363	2.676	.026
hsa-miR-146a	231	16.13	3.102	17.25	4.321	2.173	.025
hsa-miR-106b-3p	231	26.83	5.937	28.92	7.111	4.257	.017
hsa-miR-889	231	30.21	6.457	32.06	6.909	3.605	.037

Abbreviations: ACS indicates acute coronary syndrome; Ct, cycle threshold; SD, standard deviation.

^a ACS (standard therapy) represents the patients with acute coronary syndrome (ACS) treated with standard therapy. ACS (colchicine) represents patients with ACS treated with standard therapy plus colchicine. Data presented as mean Ct value and standard deviation. Fold difference represents 2^ΔCT. P value is from Student t test significant at .05 level.

MicroRNA Expression According to Sampling Site and Transcoronary Gradients

There were no differences between aortic, coronary sinus, and venous sampling sites in any miRNA expression for the 51 miRNAs profiled using the custom panel (data not shown). There were 4 miRNA with significantly different transcoronary gradients (defined as coronary sinus—aortic values, a measure of transcoronary passage). The transcoronary gradient for miR-362 was 22.37-fold higher (ΔCt 4.48, P = .016) in control individuals versus ACS standard therapy patients. In patients with ACS, colchicine treatment significantly changed transcoronary gradients of 3 miRNA: miR-324-5p and miR-223 were 4- and 5-fold higher (ΔCt 2.04 and 2.22 respectively, P = .046 for both), versus ACS standard therapy, while miR-181c was 6-fold (ΔCt 2.52, P = .04) lower in colchicine-treated patients with ACS versus ACS standard therapy patients (Supplemental Figure 6).

Association of miRNA With Extent of Coronary Disease and Troponin Release

There was no association between extent of coronary disease as measured by Gensini score and any miRNA in the verification cohort. This remained the same when assessing Gensini score by tertiles and only in the ACS groups (data not shown). There was no significant association between any miRNA in the verification cohort and troponin assay preangiography in the ACS groups.

Discussion

To our knowledge this is the first study to use an unbiased approach to assess miRNA expression in patients with ACS compared to control individuals and the first study to assess miRNA levels in colchicine-treated patients with ACS. There are 3 major findings. (i) A set of 30 miRNAs were found to be expressed at higher levels in plasma from patients with ACS compared to patients without coronary disease. Many of these miRNAs have not been previously shown to be altered in patients with ACS. (ii) In patients with ACS, colchicine treatment significantly reduced the expression of 12 miRNAs, of which 3 are key inflammatory-related miRNA. (iii) In all 51 candidate miRNAs, expression was comparable across aortic coronary sinus and venous sampling sites, although select miRNA showed transcoronary differences, indicating that our selected panel of miRNAs can assess local cardiac changes during ACS and its treatment response via systemic venous sampling, underscoring their potential as a biomarker of ACS (and its treatment).

Novelty of miRNA Quantification on OpenArray Panel

Our study is the first to take an unbiased discovery approach to identify significantly different miRNAs using a highly sensitive TaqMan qPCR OpenArray platform in a well characterized ACS population. Previous studies examining circulating miRNAs in cardiovascular disease states^17

-21 have often preselected miRNA(s) known to be expressed in cells involved in atherosclerosis²⁰ and proceeded to assess these candidate microRNAs. Other studies have used different techniques for miRNA profiling including microarray.^22,23 We chose a discovery approach to investigate a large number of known/validated miRNAs by quantitative PCR on an OpenArray Platform across disease states, treatment groups, and sampling sites. This approach allowed for the generation of an unbiased, custom OpenArray panel to be used for validation studies in a larger cohort, including several miRNA candidates not yet reported in ACS disease pathways.

Differences Between Control and ACS miRNA Expression

We found that 30 microRNAs, from our panel, were expressed at significantly higher levels in ACS standard therapy patients compared to control individuals. Of these, many have previously been shown to be circulating in patients with ACS (miRNA-106b, -21, -17, -20a, -126 -146a, -26a, -30c,-142-3p, -320a, -720),^19,20,24
-26 circulating in stable CAD (miR-126, -146a)^2,3,22,23,27 or within human atheroma (miR-130a, -21, -let7f).^28,29 Furthermore, miRNA with higher expression in ACS standard therapy patients versus control, in our panel, have been shown to be altered in regulation of atherosclerosis (miR-15a, 128a),^2,30 as a consequence of myocardial ischemia (miR-139-5p)³¹ and in response to antiplatelet treatment (miR-191).³² In contrast to our study, which found higher abundance of miR-181c, -484, -let 7e, -139-5p, -362 in patients with ACS, previous studies have shown these are less abundant in stable CAD compared to controls.²² These differences may be due to the different pathobiology of stable CAD compared to ACS. The lack of correlation with troponin with any miRNA in this panel suggests these differences in miRNA are not associated with extent of myocardial injury. We can only speculate these miRNAs have other pathobiological roles in coronary disease as opposed to being markers of myocardial injury or myocyte cell death or repair in the setting of ACS. Some of these miRNAs have been previously identified to be associated with atheroma and with inflammatory signaling pathways and may be alternate biological markers of unstable plaque.

Several miRNAs from our panel have been shown to act on inflammatory signaling pathways known to promote plaque instability, but until now have not been identified in patients with ACS. These include miR-181c, -362, and -130a acting on TNF-α, toll-like receptors (TLR), and NFkβ.^33,34 The remaining miRNAs in our panel (miR-889, -411, -106b-3p, -374, -20a-3p, -628-3p, -26a-1-3p, mmu-miR-134, and rno-miR-7a-1-3p) with increased expression in ACS standard therapy versus control patients have not been identified in either atherosclerosis or inflammatory pathways, and further mechanistic studies are required to define their role in these settings.

Differences in miRNA Expression Between Colchicine Treated and ACS (Standard Therapy) Patients

In this study, we investigated the influence of a dedicated anti-inflammatory agent, colchicine, on miRNA expression in patients with ACS. In regard to what we know already, colchicine has multiple described mechanisms of action including modulation of the NLRP3 Inflammasome,⁸ through microtubule assembly,³⁵ E–selectin and L-selectin activity on endothelial cells (EC), inhibition of IL-1 production by activated neutrophils and downregulation of TNF receptors.³⁶ It has also been shown to reduce the proliferative and migratory activity of vascular smooth muscle cells (VSMC’s).³⁵ We have previously shown that acute colchicine therapy results in suppression of inflammatory cytokine production and inhibition of inflammasome assembly in patients with ACS.^7,8 Clinical studies in colchicine more recently have shown it is useful in cardiac inflammation including acute and recurrent pericarditis³⁷ and in post cardiotomy syndrome.³⁸ Colchicine has been used in one small study in patients with ACS without a difference in outcome and successfully in patients with stable coronary disease with an outcome of reduced ACS over 3 years of follow-up.^4,5 Furthermore, the recently published COLCOT study assessing low-dose colchicine post myocardial infarction has shown reduced risk of coronary ischemia and stroke.⁶ Thus, recent evidence indeed supports pursuing its role in patients with unstable coronary disease. However, to date, no study has specifically investigated the effects of colchicine on circulating miRNA in patients with ACS.

We found that the expression of 12 miRNAs was significantly reduced in ACS standard therapy plus colchicine patients versus ACS standard therapy patients. On gene pathway analysis we identified 3 key miRNAs (miR-146a, miR-17, and miR-130a) known to participate in inflammatory pathways.

MicroRNA-146a has multiple gene targets including TLR, IL-1β, TNF-α, TGF-β, and chemokines. MicroRNA-146a has been previously shown to be overexpressed in THP-1 cells in intercritical gout, resulting in reduced pro-inflammatory gene expression (including reduced IL-1β gene expression).³⁹ MicroRNA-146a has been shown to be upregulated in plaque and TNF-α and IL-1β induce expression of miR-146a in EC.² It has also been shown to suppress NFkB signaling pathways.⁴⁰ In our study, decreased circulating levels of miR-146a in ACS standard therapy plus colchicine patients may represent increased uptake of miR-146a into peripheral blood mononuclear cells and EC to inhibit intracellular gene expression of IL-1β, NFkB activation, and TNF-α signaling.

MicroRNA-17 was shown to act on TGFBR2, a cell surface receptor in the TGF-β pathway. The MiR-17/92a cluster has been previously shown to inactivate the TGF-β pathway,⁴¹ which in turn results in plaque instability.⁴² In our study, increased expression of miR-17 in ACS standard therapy patients may act as a pro-inflammatory signal to reduce TGF-β, with colchicine therapy decreasing miR-17 and therefore promoting plaque stability.

MicroRNA-130a has been shown to downregulate SMAD4 resulting in reduced sensitivity to TGFBR1 and unregulated proliferation of immature granulocytes in bone marrow⁴³ and promotion of VSMC proliferation.⁴⁴ Reduced expression of miR-130a by colchicine in patients with ACS may act to reduce cell proliferation via the TGF-β pathway. In murine models, expression of miR-130a is upregulated in response to TNF-α driven inflammatory signaling.⁴⁵ In our study, miR-130a may be increased in response to TNF-α activation in ACS standard therapy patients and reduced in ACS standard therapy plus colchicine patients through direct suppression of miR-130a or via TNF-α inhibition.

Importantly, colchicine therapy reduced levels of all 3 miRNAs identified to levels found in control patients. This suggests colchicine has the ability to return miRNA profiles to those of a nondiseased state.

MicroRNA-106a and MiR-223, 2 other miRNAs with reduced expression in colchicine-treated patients were not identified by IPA but are known to participate in inflammatory pathways. MicroRNA-106a activates macrophages through suppression of SIRPα in murine models⁴⁶ and is elevated in circulating plasma in patients with inflammatory bowel disease but until now has not been identified in patients with ACS. Here, we demonstrate its inhibition by colchicine, suggesting another anti-inflammatory mechanism via inhibition of macrophage activation and subsequent release of inflammatory cytokines.

MicroRNA-223 is potentially atheroprotective in patients with ACS.⁴⁷ It prevents pro-inflammatory polarization of macrophages⁴⁸ and inhibits NLRP3 assembly in monocytes⁴⁹ as well as reducing endothelial expression of ICAM-1.⁵⁰ In our study, we found reduced circulating expression of miR-223 in ACS standard therapy plus colchicine patients compared to patients with ACS, suggesting uptake into inflammatory cells to reduce NLRP3 assembly and subsequent cytokine release consistent with previous work by our group.⁸

Several microRNA, miR-425-3p, -484, -142-5p, -629, -889, -106b-3p (Supplemental Table 5) modulated by colchicine, have not previously been recognized to have increased expression in inflammatory disease or ACS and further exploration of their role in this setting is required. Several miRNA upregulated in ACS standard therapy patients, previously shown to be associated with inflammatory disease were not modulated by colchicine in patients with ACS including miR-21, let-7e, miR-126, -20a, -181c.^51

-54 Taken together, these data suggest that colchicine has a broad mechanism of action, acting on multiple miRNA-regulated inflammatory pathways that drive plaque vulnerability. However, whether colchicine directly inhibits these miRNAs or acts on upstream regulators is a matter for future investigation.

MiRNA Expression Across Sampling Sites and the Coronary Bed

Few studies have looked at miRNAs in multiple simultaneously acquired sampling sites. Previously work has shown that coronary sinus sampling may more accurately reflect local coronary processes than peripheral venous blood sampling.^7,55 De Rosa et al showed differential expression across the coronary bed of muscle-enriched miRNAs and inflammatory-related miR-126.²¹ Furthermore, a study examining a range of vascular miRNAs demonstrated that most miRNAs were well correlated, with no significant difference between coronary sinus and peripheral venous blood.⁵⁶

In our study, expression of all 51 miRNAs were comparable across sampling sites, suggesting that they are altered systemically rather than locally, according to disease state and treatment effect, suggesting that venous sampling alone is sufficient to track disease activity or treatment response. However, there were 4 of the 51 miRNAs that exhibited significantly altered transcoronary (CS-A) concentration gradients.

In patients with ACS, there was reduced expression of miR-362 across the coronary bed compared with control individuals, suggesting vascular endothelial cell or monocyte uptake of miR-362. In gastric cancer cells, miR-362 has been shown to activate NFkβ,³⁴ which in turn is known to drive multiple atheroinflammatory processes.⁵⁷ Thus, reduced expression of miR-362 across the coronary bed in patients with ACS may reflect increased cellular uptake of miR-362 and activation of NFkβ within coronary plaque.

We found that colchicine significantly increased the CS-A gradient of miR-223 and 324-5p compared with untreated patients with ACS suggesting that it promotes their release at a local cardiac level. As discussed above, miR-223 has been shown to be atheroprotective while miR-324-5p is not as yet known to participate in atherosclerotic or inflammatory processes. We also found that colchicine decreased the CS-A gradient of miR-181c compared to ACS standard therapy patients, reflecting local tissue uptake. MiR-181c has been shown to suppress CD4 T-cell activation, directly suppress toll-like receptor 4 activity, and downregulate TNF-α.^33,54 Taken together, these data indicate that colchicine may also modulate inflammation at a local coronary level, through direct or indirect effects on the transcoronary passage of miRNAs. However, no differences between venous and arterial sampling sites of any of these candidate miRNAs suggest they are also systemically altered and thus can be reliably measured from a venous sample.

Strengths and Limitations

The study strengths, as discussed above, are the unbiased identification and validation of a set of miRNAs with the ability to reliably detect differences between patients with ACS and patients without CAD and treatment response to colchicine in ACS irrespective of sampling site, using a sensitive, reproducible platform. A limitation of this study is the lack of small RNA-sequencing data for discovery analysis, which is a technological constraint requiring large amounts of high-quality RNA that is difficult to obtain from small volumes of plasma obtained from patients with ACS/controls. The OpenArray platform provides a next best discovery platform for such analyses. Another limitation is the lack of data on circulating inflammatory cytokines/mediators and lack of intravascular imaging to confirm markers of plaque stability. Although patients were not randomized to treatment groups, this limitation was handled by blinding, deidentification, and randomization of samples during RNA isolation and miRNA profiling workflows. Lastly, recruiting patients undergoing coronary angiography for investigation of chest pain as controls may have reflected some activation of inflammatory pathways despite an absence of significant coronary disease in these patients but, if anything, this would have reduced the differences seen with ACS. The present study was not designed to identify a miRNA signature that would diagnose ACS. The identified miRNA signature provides predictive power for understanding treatment efficacies, such as the return to baseline levels postcolchicine in ACS. This signature could potentially aid in drug discovery. Further clarification of gene target pathways and target validation using Luciferase reporter assays are necessary to elucidate exact biologic mechanisms of differentially expressed miRNA are beyond the scope of this article.

Conclusion

We show here, for the first time, that colchicine therapy in patients with ACS acutely and significantly modulates a number of miRNAs in patients with ACS, some of which are known to participate in a range of inflammatory pathways and others that have not been previously identified in human studies. This lends further weight to the notion that colchicine has broad anti-inflammatory properties and supports its role in suppressing acute inflammation in patients with ACS. Rapid detection of miRNAs in a clinical setting is on the horizon⁵⁸ and as such, this study demonstrates the potential utility of our miRNA panel to diagnose disease state, and assess cardiac (local) response to treatment via peripheral venous blood sampling. Future directions include investigating mechanisms by which colchicine alters miRNA expression in patients with ACS and determining the predictive and prognostic potential of this miRNA panel in ACS diagnosis beyond conventional testing and treatment response in this population.

Supplemental Material

Supplemental Material, Data_supplement_trackedaccepted050420 - A MicroRNA Signature in Acute Coronary Syndrome Patients and Modulation by Colchicine

Supplemental Material, Data_supplement_trackedaccepted050420 for A MicroRNA Signature in Acute Coronary Syndrome Patients and Modulation by Colchicine by Jennifer Y. Barraclough, Mugdha V. Joglekar, Andrzej S. Januszewski, Gonzalo Martínez, David S. Celermajer, Anthony C. Keech, Anandwardhan A. Hardikar and Sanjay Patel in Journal of Cardiovascular Pharmacology and Therapeutics

Supplemental Material

Supplemental Material, FigureS1 - A MicroRNA Signature in Acute Coronary Syndrome Patients and Modulation by Colchicine

Supplemental Material, FigureS1 for A MicroRNA Signature in Acute Coronary Syndrome Patients and Modulation by Colchicine by Jennifer Y. Barraclough, Mugdha V. Joglekar, Andrzej S. Januszewski, Gonzalo Martínez, David S. Celermajer, Anthony C. Keech, Anandwardhan A. Hardikar and Sanjay Patel in Journal of Cardiovascular Pharmacology and Therapeutics

Supplemental Material

Supplemental Material, FigureS2 - A MicroRNA Signature in Acute Coronary Syndrome Patients and Modulation by Colchicine

Supplemental Material, FigureS2 for A MicroRNA Signature in Acute Coronary Syndrome Patients and Modulation by Colchicine by Jennifer Y. Barraclough, Mugdha V. Joglekar, Andrzej S. Januszewski, Gonzalo Martínez, David S. Celermajer, Anthony C. Keech, Anandwardhan A. Hardikar and Sanjay Patel in Journal of Cardiovascular Pharmacology and Therapeutics

Supplemental Material

Supplemental Material, FigureS4 - A MicroRNA Signature in Acute Coronary Syndrome Patients and Modulation by Colchicine

Supplemental Material, FigureS4 for A MicroRNA Signature in Acute Coronary Syndrome Patients and Modulation by Colchicine by Jennifer Y. Barraclough, Mugdha V. Joglekar, Andrzej S. Januszewski, Gonzalo Martínez, David S. Celermajer, Anthony C. Keech, Anandwardhan A. Hardikar and Sanjay Patel in Journal of Cardiovascular Pharmacology and Therapeutics

Supplemental Material

Supplemental Material, FigureS6 - A MicroRNA Signature in Acute Coronary Syndrome Patients and Modulation by Colchicine

Supplemental Material, FigureS6 for A MicroRNA Signature in Acute Coronary Syndrome Patients and Modulation by Colchicine by Jennifer Y. Barraclough, Mugdha V. Joglekar, Andrzej S. Januszewski, Gonzalo Martínez, David S. Celermajer, Anthony C. Keech, Anandwardhan A. Hardikar and Sanjay Patel in Journal of Cardiovascular Pharmacology and Therapeutics

Supplemental Material

Supplemental Material, Figure_S5 - A MicroRNA Signature in Acute Coronary Syndrome Patients and Modulation by Colchicine

Supplemental Material, Figure_S5 for A MicroRNA Signature in Acute Coronary Syndrome Patients and Modulation by Colchicine by Jennifer Y. Barraclough, Mugdha V. Joglekar, Andrzej S. Januszewski, Gonzalo Martínez, David S. Celermajer, Anthony C. Keech, Anandwardhan A. Hardikar and Sanjay Patel in Journal of Cardiovascular Pharmacology and Therapeutics

Supplemental Material

Supplemental Material, Supplemental_Figure3 - A MicroRNA Signature in Acute Coronary Syndrome Patients and Modulation by Colchicine

Supplemental Material, Supplemental_Figure3 for A MicroRNA Signature in Acute Coronary Syndrome Patients and Modulation by Colchicine by Jennifer Y. Barraclough, Mugdha V. Joglekar, Andrzej S. Januszewski, Gonzalo Martínez, David S. Celermajer, Anthony C. Keech, Anandwardhan A. Hardikar and Sanjay Patel in Journal of Cardiovascular Pharmacology and Therapeutics

Footnotes

Acknowledgments

The authors acknowledge Wilson Wong, Cody-Lee Maynard, and Mrs Fathima Shihana for their contribution in processing and analysis. The authors also acknowledge Rodney Henriquez for his contribution to plasma processing and storage. The authors would like to acknowledge Sydney Informatics Hub for IPA software support.

Author Contributions

All authors take responsibility for all aspects of the reliability, freedom from bias of the data presented, and their discussed interpretation.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: S.P. received funding from the Ramaciotti Institute Health Investment Grant. G.M. receives funding from a CONICYT research grant (FONDECYT Iniciación 11170205). A.A.H. is supported through a CDA from the JDRF Australia, and M.V.J. is supported through an advanced postdoctoral fellowship from the JDRF International. A.C.K. is supported by an NHMRC SPR Fellowship.

ORCID iD

Jennifer Y. Barraclough

Supplemental Material

Supplemental material for this article is available online.

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