The microRNAs inhibiting atheroprotective pathways in correlation with atherosclerosis severity in chronic coronary syndrome patients – case control study and network analysis

doi:10.21203/rs.3.rs-5010705/v1

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The microRNAs inhibiting atheroprotective pathways in correlation with atherosclerosis severity in chronic coronary syndrome patients – case control study and network analysis

https://doi.org/10.21203/rs.3.rs-5010705/v1

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Atherosclerotic plaque progression is regulated by microRNAs. In addition to atherogenic pathways, there are also factors that inhibit the plaque development at crucial stages - KLF2, KLF4, Mert-K, IL-10 and TGF-β. These factors are downregulated by the following microRNAs – miR-92a downregulates KLF-2, miR-10b – KLF4, miR-126 – Mert-K, miR-98 – IL10 and miR-29b – TGFβ1 and TGFβ3. A total of 44 patients with chronic coronary syndrome and atherosclerotic lesions confirmed by coronary angiography and 10 healthy volunteers were enrolled in the study. Patients were classified according to atherosclerotic burden (assessed by the Gensini Score) and the presence of advanced atherosclerotic lesion in a coronary branch (i.e. significant stenosis or chronic occlusion). The relative expression levels in plasma of miR-92a, miR-10b, miR-126, miR-98 and miR-29b in plasma were measured by quantitative RT-PCR and relations between these particles were also assessed by network analysis. The study showed that patients with the lowest burden of atherosclerosis had significantly increased levels of miR-126 (57.93 ± 6.87 for Gensini tertile 1 vs. 41.60 ± 4.52 for Gensini tertiles 2 and 3 considered as one group, p = 0.0472), whereas patients with advanced atherosclerosis had significantly increased levels of miR-92a − 51.02 [20.56–72.68] vs 94.93 [67.04-133.78], p = 0.0074). Moreover, the network analysis revealed strong positive correlation between miR-92a and miR-98, miR-10b and miR-126 as well as miR-10b and miR-29 in chronic coronary syndrome patients. The results demonstrated that microRNAs downregulating atheroprotective pathways may differ according to atherosclerotic plaque burden and progression. This finding may suggest a potential role for this miRNA (especially miR-92a) as a diagnostic marker reflecting advanced atherosclerosis with significant lesions, or even as a possible therapeutic target.

Health sciences/Cardiology/Cardiovascular biology/Cardiovascular diseases/Vascular diseases/Atherosclerosis

Health sciences/Molecular medicine

One of the leading causes of mortality worldwide is ischaemic heart disease (IHD). The term also refers to complications of this disease (also known as coronary artery disease – CAD), such as myocardial infarction or ischaemic cardiomyopathy. According to the Global Burden of Disease (GBD) dataset (data between 1990 and 2017), IHD affects 126 million people and causes 9.44 million deaths annually ^1,2. The pathophysiological basis of ischaemic heart disease is atherogenesis, the initiation and progression of atherosclerotic plaque ³. Atherosclerotic plaque precursors (such as fatty streaks) appear early in life, and plaque progression is a long process ⁴. In many people, plaque have a stable phenotype (i.e. a thick fibrous cap that makes such a plaque resistant to rupture), develop progressively and narrow the coronary artery diameter - such a clinical situation has historically been defined as stable coronary artery disease (sCAD) or, according to the latest ESC guidelines – chronic coronary syndrome (CCS)⁵. Patients with CCS may be asymptomatic or may experience symptoms of myocardial ischaemia during periods of increased cardiac workload (e.g. during exercise or emotional stress) due to reduced coronary blood flow that cannot meet the oxygen requirements of the working heart muscle ⁶. Atherosclerotic plaque development is a complex, multistep process involving many components, including local inflammation, oxidized LDL particles, transformation of macrophages into lipid-laden foam cells, change in the phenotype of vascular smooth muscle cells from contractile to synthetic, remodelling and fragmentation of the extracellular matrix, and formation of necrotic cores ^7,8. The aforementioned phenomena are mediated by various cells (such as immune cells), proteins (such as inflammatory cytokines and matrix metalloproteinases), local paracrine factors controlled by expression factors such as nuclear factor kappa B (NFκB)⁹. Transcription factors and their target proteins that promote atherogenesis form a complex network that is controlled by multiple positive and negative feedbacks ^10,11. In addition, this complex network is also regulated by microRNAs ¹². MicroRNAs are small (approximately 21 nucleotides), single-stranded RNA particles that possess target mRNA sequences of 7–8 nucleotides in length, enabling them to bind to target mRNA. This binding causes the target mRNA to be degraded ¹³. As a result, miRNAs act as "molecular switches", making the whole network of atherogenesis even more complex. In addition, a single miRNA particle can target different mRNAs with different affinities, while one mRNA can be regulated by different miRNAs ¹⁴. Owing to bioinformatic techniques and large databases, it is possible to find and select miRNAs that target the gene of interest. Indeed, many miRNAs targeting specific genes involved in the initiation and progression of atherosclerotic plaques have been studied in tissues and animal models of atherogenesis to reveal their role and importance in this process and their potential diagnostic and therapeutic properties ^15,16. Some of them were also assessed in humans with atherosclerosis-related diseases as potential diagnostic biomarkers or even therapeutic targets¹⁷. For instance, miR33, a particle downregulating ABCA1 transporter and enzymes involved in macrophage fatty acid oxidation (HADHB and CROT) is significantly up-regulated in patients with atherosclerosis comparing to non-atherosclerotic individuals^18,19. Another example is miR-181b, which reduces downstream NFκB signalling - its expression is increased in patients with thoracic aortic aneurysms compared to controls. ^20,21. However, in the process of atherogenesis there are also factors that inhibit proatherogenic pathways. In this study, five proteins that inhibit proatherogenic pathways at different stages were selected based on a literature review. These proteins were Kruppel-like Factor 2 (KLF2) and Kruppel-like Factor 4 (KLF4) that maintain endothelial cells integrity and homeostasis, proto-oncogene thyrosin-kinase MER (MertK) – a mediator of efferocytosis, i.e. the phagocytosis (mediated by macrophages) of cellular debris that promotes clearance of extracellular matrix microenvironment and prevents from necrotic cores formation, Interleukin 10 (IL-10) – a suppressor of the immune cells infiltrating the plaque and Transforming Growth Factor β1 and Transforming Growth Factor β3 (TGF-β1 and TGF-β3) – regulators of vascular wall fibroblasts that stabilize the plaque ^22–28. The aim of this study was to evaluate the expression levels of microRNAs (selected from miRNA target prediction databases) that specifically downregulate the above-mentioned atheroprotective proteins in patients with chronic coronary syndrome at different stages of atherosclerosis, also in comparison to healthy volunteers.

MicroRNA selection

The miRNA particles specific for atheroprotective factors KLF2, KLF4, IL-10, MertK and TGF-β3 were selected according to the miRTARBase 7.0 and presented in Table 1 ²⁹.

Table 1

MicroRNA particles selected in the study and their target atheroprotective proteins.
Atheroprotective protein	Specific miRNA targeting the protein in human
KLF2	hsa-miR-92a-3p (abbrev. miR-92a)
KLF4	hsa-miR-10b-5p (abbrev. miR-10b)
MertK	hsa-miR-126-3p (abbrev. miR-126)
IL-10	hsa-miR-98-5p (abbrev. miR-98)
TGF-β3	hsa-miR-29b-3p (abbrev. miR-29b)

Table 1. MicroRNA particles selected in the study and their target atheroprotective proteins.

Study population

A total of 54 consecutive patients admitted for diagnostic evaluation of chronic coronary syndrome to the 1st Department of Cardiology, Central Clinical Hospital, University Clinical Centre, Medical University of Warsaw and a total of 10 healthy volunteers were enrolled in the study. The study was approved by the Bioethics Committee of the Medical University of Warsaw (number KB/88/2022). All experiments were conducted according to relevant guidelines and regulations and informed consent was obtained from all the participants.

Eligibility criteria

All the patients enrolled in the study were eligible for both inclusion and exclusion criteria (Table 2).

Table 2

Exclusion and inclusion criteria of the study. * - according to the ESC Guidelines for the Diagnosis and Management of Chronic Coronary Syndromes (5).
Inclusion criteria
• A clinical indications for coronary angiography in the diagnostic process of chronic coronary syndrome (according to relevant ESC Guidelines)* OR • Results of non-invasive tests (CTCA, treadmill exercise test, SPECT) indicating high risk of event *
Exclusion criteria
• A history of malignant neoplastic disease and active malignant neoplastic disease • Active autoimmune or rheumatic disease • Surgery or invasive intervention within a period of 6 months before sample taking • Chronic kidney disease (G3b, G4, G5 according to KDIGO 2012) and eGFR level (CKD-EPI) < 45 ml/min/1.73 m2 • Diabetes mellitus (according to ADA – American Diabetes Association – Standards of Medical Care in Diabetes – 2018) • Diagnosis of active infection or history of infection within the period of 3 months before sample taking. • A history of previous acute coronary syndrome • A history of cerebrovascular stroke or TIA (transient ischemic attack) • A history of previous percutaneous coronary intervention with stent implantation. • A history of any cardiosurgical procedure • Any artificial element in the body (prosthesis, pacemaker system, vascular draft, embolization coils)

Table 2. Exclusion and inclusion criteria of the study. * - according to the ESC Guidelines for the Diagnosis and Management of Chronic Coronary Syndromes ⁵.

The patients were then subjected to diagnostic procedures for chronic coronary syndrome. Only the patients who underwent coronary angiography with the presence of at least one mild coronary artery stenosis (i.e. stenosis ≥ 30% of the coronary artery branch) were finally enrolled to the study ³⁰. As a result, the number of remaining patients was 44 (Fig. 1).

Figure 1. Study flow chart (patients admitted to hospital for the diagnosis of chronic coronary syndrome).

Healthy volunteers had no history of chronic disease, no history of infection or surgery in the previous 3 months before plasma sample taking, no artificial material in the body, and laboratory biochemical and complete blood count tests without evidence of disease.

Coronary angiography

All enrolled patients underwent coronary angiography during hospitalization. Patients were then classified in two ways – according to the extent atherosclerotic changes as assessed by the Gensini score (Gensini score tertiles) and according to the presence of advanced atherosclerosis ³¹. Advanced atherosclerosis means the presence of significant atherosclerotic change in one of the main branches of coronary arteries (LM, LAD, Cx, RCA, or Dg and Mg branch, if dominant)³². Significant change means:

Stenosis of ≥ 90% of a coronary branch
Stenosis of 70–90% of a coronary branch and significance confirmed with FFR
Stenosis of the left main – either ≥ 50% or confirmed by IVUS
Chronic occlusion of a coronary branch

Patients who didn’t meet the above criteria were classified as patients with non-advanced atherosclerosis.

Blood samples

Blood samples were taken in the morning, before the coronary angiography procedure. Blood samples in EDTA tubes were centrifuged at 1200 × g for 10 minutes to separate plasma from blood cells and then the plasma was aliquoted into microcentrifuge tubes, frozen on dry ice, and stored at − 80°C within 4 hours from the blood draw. Moreover, blood parameters such as complete blood count (with haemoglobin [Hgb] concentration), lipid panel (total cholesterol level, LDL-C, HDL-C, triglycerides), TSH, AspAT, AlAT, glycaemia, and creatinine levels were also measured.

Micro-RNA

The miRNA particles measured in the plasma samples were: cel-miR-39-3p (a “spike-in” control), hsa miR-93-3p, and hsa-miR-191-5p (control microRNAs) and 5 study miRNAs - hsa-miR-92a-3p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-98-5p and hsa-miR-29b-3b. Serum samples were thawed on ice and centrifuged at 20000 × g for 15 minutes at 4°C. Clear supernatant (300 µl) was transferred to a new DNAse/RNAse free tube and mixed with a denaturing buffer (300 µl) and vortexed for 15 seconds. After 5 minutes of incubation, 30 fmol of 5'-phosphorylated synthetic cel-miR-39-3p (catalog no:10620310) was added to each sample as a “spike-in” control. Procedure of further microRNA isolation was consistent with manufacturer guidelines of mirVana™ miRNA Isolation Kit (AM1560, Thermo Fischer Scientific). In next step, the RNA was reverse transcribed using TaqMan Advanced miRNA cDNA Synthesis Kit (Thermo Fisher Scientific). Libraries of cDNA were stored in -20°C until further steps. The expression levels of microRNA particles were assessed using PowerUp SYBR Green Master Mix (A25777 Thermo Fisher Scientific) according to the manufacturer’s protocol. The primers for each measured miRNA were purchased from Institute of Biochemistry and Biophysics, Polish Academy of Sciences. Primers sequences were obtained from miRbase.org and were as follows: external control („spike-in”) cel-miR-39-3p (TCACCGGGTGTAAATCAGCTTG), internal control miRNAs hsa-mir-191-5p (CAACGGAATCCCAAAAGCAGCTG) and hsa-mir-93-3p (ACTGCTGAGCTAGCACTTCCCG) as well as study miRNA particles - hsa-mir-92a-3p (TATTGCACTTGTCCCGGCCTGT), hsa-miR-10b-5p (TACCCTGTAGAACCGAATTTGTG), hsa-miR-126-3p (TCGTACCGTGAGTAATAATGCG), hsa-miR-98-5p (TGAGGTAGTAAGTTGTATTGTT) and hsa-miR-29b-3p (TAGCACCATTTGAAATCAGTGTT) All qPCRs were conducted in MicroAmp Fast Optical 96 Well Reaction Plates (Thermo Fisher Scientific) in total volume of 20µl. Reactions were run on the Applied Biosystems ViiA7 Real-Time PCR System with ViiA 7 Software (Thermo Fisher Scientific). Samples were assayed in triplicates. The obtained Ct (cycles of threshold) values for study miRNAs - hsa-mir-92a-3p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-98-5p and hsa-miR-29b-3p as well as for internal control miRNAs - hsa-mir-191-5p and hsa-mir-93-3p were normalized by Ct of cel-miR-39-3p and were used to calculate relative expression using the 2-∆∆Ct method.

Statistical analysis

All analyses were performed using STATISTICA 13, StatSoft Inc. Values are presented as mean and standard deviation (SD) or standard error of the mean (SEM) for normally distributed variables and as median and interquartile range for non-normally distributed values. The normality was validated by the Shapiro–Wilk test. Student’s t-test, Cochrane-Cox test (for non-equal variances), and the ANOVA F-test were used for normally distributed continuous variables, whereas the Kruskall–Wallis test and Mann–Whitney U test were used for non-normally distributed continuous variables. Categorical variables were compared with the use of the χ2 test. Network analysis of miRNA was done using R using dplyr, tidyr, ggplot2, qgraph, and svglite packages from CRAN.R-project.org. The p-values < 0.05 were considered statistically significant.

Baseline characteristics of chronic coronary syndrome patients

A population of 44 patients with chronic coronary syndrome was divided according to the extent of atherosclerosis, expressed by the Gensini score (Table 3), or the presence of significant stenosis caused by atherosclerotic plaque in the main coronary branches. Baseline characteristics of the subgroups are shown in Table 4 (according to Gensini tertiles) and Table 5 (according to the presence of significant stenosis, named „advanced plaque”).

Table 3

Gensini Score (GS) ranges according to tertiles.
Gensini Score (GS) Tertiles	Gensini Score range
Lowest GS Tertile (L)	4-15.5
Intermediate GS Tertile (I)	19–31
Highest GS Tertile (H)	32.5–93.5

Table 4

Baseline characteristics for chronic coronary syndrome patients classified according to Gensini tertiles. * Severe symptoms of CCS – symptoms classified to III and IV grade of Canadian Cardiovascular Society(33); **p value < 0.05 considered as statistically significant; values with normal distribution and homogenous variation presented as „mean ± SD (standard deviation)”, F-ANOVA statistics test (with Scheffè post-hoc analysis) applied; values with rejected distribution normality or unequal variances presented as „median [interquartile range]”, ANOVA Kruskal-Wallis test applied; quantitative values presented as „number (percentage in group)”, χ² test applied.
	Gensini Score Lowest tertile, L (n = 15)	Gensini Score Intermediate tertile, M (n = 14)	Gensini score Highest tertile, H (n = 15)	P significance**
Age [years]	69 ± 10	71 ± 8	70 ± 13	NS (p = 0.83)
Sex (woman)	8 (53.3%)	4 (28.6%)	9 (60%)	NS (p = 0.2)
Severe symptoms of CCS*	1 (6.7%)	1 (7.1%)	2 (13.3%)	NS (p = 0.78)
COVID-19 in anamnesis	1 (6.7%)	2 (14.3%)	1 (6.7%)	NS (p = 0.72)
Hypertension	12 (80%)	10 (71.4%)	12 (80%)	NS (p = 0.82)
Hyperlipidemia	12 (80%)	8 (57.1%)	12 (80%)	NS (p = 0.28)
Active smoker	1 (6.7%)	4 (28.6%)	4 (26.7%)	NS (p = 0.26)
Previous smoker	4 (26.7%)	6 (42.9%)	5 (33.3%)	NS (p = 0.65)
CAD or PAD	2 (13.3%)	4 (28.6%)	3 (20%)	NS (p = 0.60)
Baseline statin use	10 (66.7%)	6 (42.9%)	6 (40%)	NS (p = 0.28)
Baseline arrythmia	3 (20%)	1 (7.1%)	4 (26.7%)	NS (p = 0.39)
BMI [kg/m2]	26.6 ± 4.7	25.0 ± 3.1	26.3 ± 4.5	NS (p = 0.72)
TC [mg/dl]	161 [142–190]	149.5 [132.5-161.5]	149.5 [130–190]	NS (p = 0.48)
LDL [mg/dl]	77 [66–123]	79.5 [58.0-97.5]	75 [55–98]	NS (p = 0.57)
HDL [mg/dl]	53 [49–67]	45 [41–52]	48 [41–61]	NS (p = 0.14)
TG [mg/dl]	76 [63–100]	116 [103–128]	113.5 [89–189]	Significant between - L and M (p = 0.029) - L and H (p = 0.038)
eGFR [ml/min/1.73m2]	64.3 ± 19.1	65.3 ± 14.3	66.9 ± 20.9	NS (p = 0.93)
Hgb [g/dl]	14.03 ± 1.53	14.19 ± 1.02	13.14 ± 1.73	NS (p = 0.13)
PLT [number]	253 ± 78	196 ± 61	222 ± 55	NS (p = 0.08)
TSH [µIU/ml]	1.27 [0.817–2.27]	1.23 [0.878–2.75]	1.51 [0.611-2.30]	NS (p = 0.94)
AspAT [U/l]	25 [20-30.5]	25 [21–32]	25.5 [23.0–29.0]	NS (p = 0.94)
AlAT [U/l]	24 [17–30]	21 [19–28]	25 [23–38]	NS (p = 0.59)
APTT [s]	29.6 [27.4–32.5]	27.5 [25.5–29.4]	28.9 [27.1–31.0]	NS (p = 0.21)

Table 5

Baseline characteristics for chronic coronary syndrome patients according to the presence of advanced atherosclerosis (i.e. significant atherosclerotic lesions in the main branches). * Severe symptoms of CCS – symptoms classified to III and IV grade of Canadian Cardiovascular Society(33); *p value < 0.05 considered as statistically significant; values with normal distribution presented as „mean ± SD (standard deviation)”, t-test or Cochrane-Cox test applied (according to the presence of homogenous variances); values with rejected distribution normality presented as „median [interquartile range]”, U-Mann-Whitney test applied; quantitative values presented as „number (percentage in the group)”, χ² test applied.
	Patients with non-advanced atherosclerotic plaque (n = 21)	Patients with advanced atherosclerotic plaque (n = 23)	P significance**
Age [years]	71 ± 9	69 ± 11	NS (p = 0.65)
Sex (woman)	9 (42.9%)	12 (52.2%)	NS (p = 0.54)
Severe symptoms of CCS*	1 (4.8%)	3 (13.0%)	NS (p = 0.34)
COVID-19 in anamnesis	2 (9.5%)	2 (8.7%)	NS (p = 0.92)
Hypertension	17 (81.8%)	17 (73.9%)	NS (p = 0.58)
Hyperlipidemia	15 (71.4%)	17 (73.9%)	NS (p = 0.85)
Active smoker	2 (9.5%)	7 (30.4%)	NS (p = 0.085)
Previous smoker	6 (28.6%)	9 (39.1%)	NS (p = 0.46)
CAD or PAD	2 (9.5%)	7 (30.4%)	NS (p = 0.085)
Baseline statin use	12 (57.1%)	10 (43.5%)	NS (p = 0.37)
Baseline arrythmia	4 (19.1%)	4 (17.4%)	NS (p = 0.89)
BMI [kg/m2]	26.4 ± 4.8	25.8 ± 3.8	NS (p = 0.75)
TC [mg/dl]	160.5 [145–174]	148 [129–190]	NS (p = 0.36)
LDL [mg/dl]	81 [67–108]	73 [55–98]	NS (p = 0.23)
HDL [mg/dl]	54 ± 14	51 ± 12	NS (p = 0.38)
TG [mg/dl]	82 [63–106]	116 [97–161]	p = 0.005
eGFR [ml/min/1.73m2]	65.5 ± 18.1	65.5 ± 18.3	NS (p = 0.99)
Hgb [g/dl]	14.02 ± 1.38	13.58 ± 1.60	NS (p = 0.33)
PLT [number]	215 ± 72	232 ± 66	NS (p = 0.42)
TSH [µIU/ml]	1.15 [0.66–1.62]	1.67 [0.95–2.53]	NS (p = 0.22)
AspAT [U/l]	25 [20–31]	27 [24–32]	NS (p = 0.32)
AlAT [U/l]	20 [17–28]	28 [23–35]	NS (p = 0.09)
APTT [s]	28.5 [25.2–30.8]	28.9 [27.1–30.0]	NS (p = 0.62)

Table 3. Gensini Score (GS) ranges according to tertiles.

Table 4. Baseline characteristics for chronic coronary syndrome patients classified according to Gensini tertiles. * Severe symptoms of CCS – symptoms classified to III and IV grade of Canadian Cardiovascular Society³³; **p value < 0.05 considered as statistically significant; values with normal distribution and homogenous variation presented as „mean ± SD (standard deviation)”, F-ANOVA statistics test (with Scheffè post-hoc analysis) applied; values with rejected distribution normality or unequal variances presented as „median [interquartile range]”, ANOVA Kruskal-Wallis test applied; quantitative values presented as „number (percentage in group)”, χ² test applied.

Table 5. Baseline characteristics for chronic coronary syndrome patients according to the presence of advanced atherosclerosis (i.e. significant atherosclerotic lesions in the main branches). * Severe symptoms of CCS – symptoms classified to III and IV grade of Canadian Cardiovascular Society³³; *p value < 0.05 considered as statistically significant; values with normal distribution presented as „mean ± SD (standard deviation)”, t-test or Cochrane-Cox test applied (according to the presence of homogenous variances); values with rejected distribution normality presented as „median [interquartile range]”, U-Mann-Whitney test applied; quantitative values presented as „number (percentage in the group)”, χ² test applied.

Comparison of baseline characteristics revealed one significant difference - patients in the lowest tertile had significantly lower plasma triglycerides than those in the middle and higher tertiles. Similarly, patients with non-advanced plaque presented significantly lower plasma triglycerides than patients with advanced plaque. Moreover, there were also more patients with peripheral atherosclerotic disease or carotid atherosclerotic disease and active smokers among those with advanced atherosclerotic plaque comparing to those with non-advanced plaque, but the difference didn’t reach statistical significance.

The miR-92a, miR-10b, miR-126, miR-98 and miR-29b expression levels according to Gensini tertiles

The relative expression levels of hsa-miR-92a-3p, hsa-miR-10b-5p, hsa-miR-126-3p, hsa-miR-98-5p and hsa-miR-29b-3p in patients with chronic coronary syndrome classified according to Gensini tertiles are shown in Table 6 and Fig. 2.

Table 6

The study microRNA expression level values according to GS Gensini Score Tertiles. *- values expressed by median [IQR, i.e. interquartile range], ** p-significance, < 0.05 considered as statistically significant, *** - significant difference between hsa-miR-126p-3p median expression levels according to GS Tertiles.
microRNAs	Gensini Tertile 1*	Gensini Tertile 2*	Gensini Tertile 3*	P**
Hsa-miR-92a-3p	72.12 [24.78-115.59]	68.80 [20.56–91.92]	73.60 [53.09-108.63]	NS(0.456)
Hsa-miR-10b-5p	0.072 [0.041–0.161]	0.056 [0.036–0.157]	0.090 [0.036–0.169]	NS(0.9)
Hsa-miR-126-3p	57.62 [37.97-77.00]	32.97 [21.33–50.01]	45.15 [33.18–60.48]	0.0356***
Hsa-miR-98-5p	0.018 [0.007–0.045]	0.037 [0.017–0.090]	0.024 [0.012–0.053]	NS(0.384)
Hsa-miR-29b-3p	0.795 [0.584–1.245]	0.756 [0.444–1.446]	1.018 [0.653–1.326]	NS(0.862)

Table 6. The study microRNA expression level values according to GS Gensini Score Tertiles. *- values expressed by median [IQR, i.e. interquartile range], ** p-significance, < 0.05 considered as statistically significant, *** - significant difference between hsa-miR-126p-3p median expression levels according to GS Tertiles.

Figure 2. The study miRNA expression levels (A - hsa-miR-92a-3p, B - hsa-miR-10b-5p, C - hsa-miR-126-3p, D - hsa-miR-98-5p, E - hsa-miR-29b-3p) according to Gensini Score Tertiles (Gensini Tertile 1, 2 and 3, characterized in Table 1). * - p < 0.05 considered as statistically significant.

The expression levels of hsa-miR-92a-3p, hsa-miR-10b-5p, hsa-miR-98-5p, hsa-miR-29b-3p didn't differ according to the atherosclerotic plaque burden, presented as Gensini tertiles. Unlike the level of hsa-miR-126-3p. The hsa-miR-126-3p expression level was significantly higher in patients with the lowest Gensini score ("Gensini Tertile 1" group) compared to patients with an intermediate Gensini score ("Gensini Tertile 2" group) (57.62 [37.97-77.00] vs. 32.97 [21.33–57.01], p = 0.012, results presented as median [IQR]). When the Gensini Tertiles 2 and 3 were considered as one group, the comparison between the Gensini Tertile 1 group and the Gensini Tertile 2 and 3 group also showed a statistically significant difference - higher expression level in Gensini Tertile 1 compared to Gensini Tertile 2 and 3 (as one group) − 57.93 ± 6.87 vs. 41.60 ± 4.52, p = 0.0472, results presented as mean ± SEM (i.e. standard error of the mean) (Fig. 3).

Figure 3. The differences of hsa-miR-126-3p expression levels classified according to A – three Gensini Tertiles (Mann-Whitney U Tests applied between all three Gensini Tertile groups comparing each other, results expressed as median [IQR]), B – Gensini Tertile 1 comparing to Gensini Tertile 2 and 3 taken together (normal distribution, homogenous variances, Student t-test applied, results presented as mean ± SEM). * - result statistically significant, p < 0.05 considered as statistically significant.

The miR-92a, miR-10b, miR-126, miR-98 and miR-29b expression levels according to the presence of advanced atherosclerotic plaque.

Patients were classified according to the presence of advanced atherosclerotic plaque in the main coronary artery (as described in Materials and Methods), and the expression levels of the study microRNAs in both groups (i.e. advanced vs. non-advanced atherosclerotic plaque) are shown in Table 7 and Fig. 4.

Table 7

The study microRNA expression level values according to the presence of advanced atherosclerotic plaque. *- values expressed by median [IQR, i.e. interquartile range], ^# values expressed by mean [SEM, i.e. standard error of the mean], **p-significance, < 0.05 considered as statistically significant, *** - significant difference between hsa-miR-92a-3p median expression level according to the presence of advanced atherosclerotic plaque.
microRNAs	Patients with non-advanced atherosclerotic plaque (n = 21)	Patients with advanced atherosclerotic plaque (n = 23)	P**
Hsa-miR-92a-3p*	51.02 [20.56–72.68]	94.93 [67.04-133.78]	0.0074***
Hsa-miR-10b-5p*	0.056 [0.024–0.161]	0.090 [0.036–0.169]	0.3369
Hsa-miR-126-3p^#	47.46 ± 5.94	46.91 ± 5.32	0.9455
Hsa-miR-98-5p*	0.027 [0.014–0.045]	0.024 [0.014–0.053]	0.8041
Hsa-miR-29b-3p*	0.790 [0.584–1.159]	1.018 [0.444–1.42]	0.6014

Table 7. The study microRNA expression level values according to the presence of advanced atherosclerotic plaque. *- values expressed by median [IQR, i.e. interquartile range], ^# values expressed by mean [SEM, i.e. standard error of the mean], **p-significance, < 0.05 considered as statistically significant, *** - significant difference between hsa-miR-92a-3p median expression level according to the presence of advanced atherosclerotic plaque.

Figure 4. The study miRNA expression levels (A - hsa-miR-92a-3p, B - hsa-miR-10b-5p, C - hsa-miR-126-3p, D - hsa-miR-98-5p, E - hsa-miR-29b-3p) according to the presence of advanced atherosclerotic plaque, * - p < 0.05 considered as statistically significant. ADV – patients with advanced atherosclerotic plaque, NON-ADV – patients without the presence of advanced atherosclerotic plaque

The results showed that patients with advanced atherosclerotic plaque had a significantly higher expression level of hsa-miR-92a-3p than patients without advanced atherosclerotic plaque (51.02 [20.56–72.68] vs 94.93 [67.04-133.78], p = 0.0074, values expressed as median [IQR]). There were no such correlations for other microRNAs in the study.

Comparison between atherosclerotic patients with healthy volunteers

The patients with chronic coronary syndrome were also compared with healthy volunteers. The healthy volunteers were significantly younger, had higher HDL and lower TG levels, and there were no differences in sex, LDL, haemoglobin, platelet count, and TSH (Table 8).

Table 8

Baseline characteristics for the study population (patients with diagnosed chronic coronary syndrome) and healthy volunteers. *p value < 0.05 considered as statistically significant; values with normal distribution presented as „mean ± SD (standard deviation)”, t-test or Cochrane-Cox test applied (according to the presence of homogenous variances); values with rejected distribution normality presented as „median [interquartile range]”, Mann-Whitney U test applied; quantitative values presented as „number (percentage in group)”, χ² test applied.
	Healthy wolunteers (n = 10)	Patients with chronic coronary syndrome (n = 44)	P significance*
Age [years]	39 ± 11	69 ± 10	p = 0.000003
Sex (woman)	6 (60%)	21 (47.4%)	NS (p = 0.72)
BMI [kg/m2]	24 ± 3.6	26.1 ± 4.1	NS (p = 0.17)
TC [mg/dl]	184 [173–203]	151 [130–175]	NS (p = 0.36)
LDL [mg/dl]	97 [89–117]	77 [61–108]	NS (p = 0.054)
HDL [mg/dl]	64 [57–69]	50 [42–63]	p = 0.006
TG [mg/dl]	76 [67–110]	102 [78–135]	p = 0.037
Hgb [g/dl]	13.91 ± 1.48	13.80 ± 1.50	NS (p = 0.83)
PLT [number]	241 ± 61	224 ± 69	NS (p = 0.45)
TSH [µIU/ml]	1.34 [0.64–2.06]	1.36 [0.82–2.30]	NS (p = 0.79)

Table 8. Baseline characteristics for the study population (patients with diagnosed chronic coronary syndrome) and healthy volunteers. *p value < 0.05 considered as statistically significant; values with normal distribution presented as „mean ± SD (standard deviation)”, t-test or Cochrane-Cox test applied (according to the presence of homogenous variances); values with rejected distribution normality presented as „median [interquartile range]”, Mann-Whitney U test applied; quantitative values presented as „number (percentage in group)”, χ² test applied.

The analysis between healthy volunteers and patients with CCS (regardless of the degree of atherosclerosis) showed no significant difference between these groups, but the expression levels of hsa-miR92a-3p and hsa-miR-98-5p were slightly lower in healthy volunteers (Table 9, Fig. 5).

Table 9

The study microRNA expression level values according to the presence of advanced atherosclerotic plaque. *- values expressed by median [IQR, i.e. interquartile range], ^# values expressed by mean [SEM, i.e. standard error of the mean], **p-significance, < 0.05 considered as statistically significant,
microRNAs	Healthy volunteers (n = 10)	Patients with CCS (n = 44)	p significance**
Hsa-miR-92a-3p*	66.82 [31.77–85.81]	71.86 [42.23-106.72]	NS (p = 0.38)
Hsa-miR-10b-5p*	0.094 [0.057–0.125]	0.067 [0.036–0.168]	NS (p = 0.64)
Hsa-miR-126-3p^#	50.48 ± 8.12	47.16 ± 3.92	NS (p = 0.72)
Hsa-miR-98-5p*	0.013 [0.006–0.02]	0.026 [0.014–0.047]	NS (p = 0.18)
Hsa-miR-29b-3p*	0.925 [0.734–1.799]	0.841 [0.496–1.33]	NS (p = 0.29)

Table 9. The study microRNA expression level values according to the presence of advanced atherosclerotic plaque. *- values expressed by median [IQR, i.e. interquartile range], ^# values expressed by mean [SEM, i.e. standard error of the mean], **p-significance, < 0.05 considered as statistically significant,

Figure 5. The study miRNA expression levels (A - hsa-miR-92a-3p, B - hsa-miR-10b-5p, C - hsa-miR-126-3p, D - hsa-miR-98-5p, E - hsa-miR-29b-3p) in chronic coronary syndrome patients compared to healthy volunteers.

Analysis between healthy volunteers and patients at different stages of atherosclerosis (assessed by both Gensini score and the presence of advanced atherosclerotic plaque) showed only a significant difference in hsa-miR-92a-3p expression levels between healthy volunteers and patients with advanced atherosclerotic plaque (Supplementary Tables 1 and 2). Other analyses between all study microRNAs in healthy volunteers and all groups of atherosclerotic patients showed no significant differences (including miR-126-3p expression levels in healthy volunteers and atherosclerotic patients classified by Gensini scores) (Figs. 6 and 7).

Figure 6. The study hsa-miR-92a-3p expression levels between healthy volunteers and patients with non-advanced atherosclerosis (NON-ADV) and the presence of advanced atherosclerotic plaque (ADV); p < 0.05 considered as statistically significant.

Figure 7. The study hsa-miR-126-3p expression levels between healthy volunteers and patients with atherosclerosis classified by atherosclerotic burden according to Gensini Tertiles.

Network analysis of microRNAs for chronic coronary syndrome patients and healthy volunteers

The network analysis was undertaken to find out correlations between microRNAs within both CCS patients (panel A) and healthy volunteers (panel B). Figure 8 presents results of this network analysis.. Substantial between-group differences could be noticed in networks architectures. In CCS patients a strong positive relationship (i.e. higher the expression of one particle, higher the expression of the other one) between miR-10b and miR-29b, miR-10b and miR-126 as well as miR-92a and miR-98 was found. In HV patients miR-98 correlated positively with both miR-10b and miR-29b, however, there was a strong negative correlation between miR-10b and miR-92a. The absolute differences in correlation matrices for the relationship between examined mi-RNAs in the patient group vs. HV were presented on Figure S1.

Figure 8. Network analysis shows differences in the relationship between the expression of examined mi-RNA patients with CCS (panel A) and HV (panel B). The width of the edge and saturation are related to the strength of the connection (correlation) between nodes (denoted as circles, i.e. particular mi-RNAs). Positive relationships are illustrated with red color, while negative with blue. Names of micro RNA have been shortened to increase the clarity of the graph.

First, the study analysis revealed a significant difference in miR92a expression levels in patients with advanced atherosclerotic plaque compared to both atherosclerotic patients without advanced plaque and healthy volunteers. Second, miR-126 expression was increased in patients with a low atherosclerotic burden, as assessed by the Gensini score, compared to patients with a higher burden. Although it is impossible to determine whether a particular microRNA is a causative factor in the progression of atherosclerosis from an observational study (up-regulated microRNA may be a bystander or an element of a negative feedback loop), the fact that expression levels differ at different stages of atherogenesis may reflect its potentially important role in the overall process. Regarding miR-92a, one of its important target genes is KLF2. The role of KLF2 in atheroprotection, as well as the role of miR-92a in the downregulation of KLF2, has been emphasized in several publications ^34,35. KLF2 protects endothelial cells by several mechanisms, such as reducing the expression of pro-inflammatory cytokines (TNFα, IL-6, IL-1β and VCAM-1) after LPS stimulation and reducing the expression of LOX1 (receptor for lipoproteins) under oscillatory shear stress. In addition, KLF2 promotes autophagy in macrophages and prevents the transformation of macrophages into foam cells ^36,37. Other miR-92a target genes include ABCA1, SIRT6, and KLF4, which also have atheroprotective properties ^38–41. A study of patients with chronic coronary syndrome compared with healthy volunteers showed no statistically significant difference between atherosclerotic patients and healthy controls ⁴². However, a study of microRNA particles in circulating lipoproteins by Niculescu et al. showed increased miR-92a expression levels in patients with atherosclerosis compared to controls, particularly in patients with unstable angina in the HDL3 lipoprotein fraction ⁴³. The above data suggest that atherosclerotic plaque progression may be correlated with miR-92a upregulation, suggesting a potential diagnostic or even therapeutic potential of this particle (e.g. determination of the "progression potential" of atherosclerotic plaques at different stages). The mouse study by Loyer et al. showed that in vivo blockade of miR-92a reduced endothelial inflammation and plaque size ⁴⁴.

As mentioned above, the other microRNA that differs in expression levels according to atherosclerotic plaque burden in our study is miR-126. The miR-126 downregulates Mert-K, an atheroprotective factor that promotes the clearance of apoptotic bodies within the plaque. The experiment in the mouse model showed that a 50% decrease in Mert-K mRNA transcription (due to different genetic traits) resulted in increased atherosclerotic plaque burden and decreased phagocytic activity demonstrated by peritoneal macrophages, suggesting the importance of factors regulating Mert-K ⁴⁵. However, the atheroprotective role of Mert-K is particularly important in advanced atherosclerotic lesions, where effective clearance of apoptotic bodies is paramount ⁴⁶. This would suggest that miR-126 is more likely to be upregulated in patients with advanced atherosclerotic plaque rather than in the early stages, as shown in our study. The studies in humans give different results. On the one hand, miR-126 expression was found to be increased in patients with early-onset chronic coronary syndrome compared to healthy controls ⁴⁷. On the other hand, miR-126 expressed in blood mononuclear cells was significantly upregulated in patients with chronic coronary syndrome (n = 119) compared to controls (n = 96), while in patients with chronic coronary syndrome, miR-126 expression was inversely correlated with atherosclerotic burden as assessed by the Gensini score (similar to the results of our study) ⁴⁸. It is important to note that Mert-K is not the only target of miR-126. The most recent study on a mouse model of middle cerebral artery occlusion demonstrated the protective role of miR-126 in mice subjected to cerebral infarction due to its ability to downregulate the pro-inflammatory adhesion molecules VCAM-1 and E-selectins ⁴⁹. VCAM-1 has previously been shown to be involved in the early stages of atherosclerotic plaque development ⁵⁰. In addition, in our study, miR-126 expression was slightly lower in healthy volunteers than in patients with early atherosclerosis (1st Gensini Tertile), although without statistical significance.

Our study didn't show any statistically significant difference for the other microRNAs studied - miR-10b, miR-98, and miR-29b - according to plaque burden or progression. However, miR-10b and miR-98 were expressed at very low relative expression levels (relative to the constitutively expressed microRNA), so statistical significance for their differential expression between study groups was more difficult to achieve.

Nevertheless, network analysis of microRNAs in CCS patients and healthy volunteers brought interesting results – first, a positive relation between miR-98 and miR-92a, miR-10b and miR-29b, as well as miR-10b and miR-126 in CCS patients. The investigations about concomitant expression of aforementioned microRNAs are scarce and there are very few network studies in this area, especially upon patients with coronary artery disease. There was a study upon rheumatoid arthritis patients comparing to healthy volunteers in which it was revealed that male patients with rheumatoid arthritis present increased expression levels of both miR-92a and miR-98 comparing to female rheumatoid arthritis patients⁵¹. There were more studies upon miR-10b, miR-29b and miR-126 concomitant expression. For instance, a study upon patients with CTO (chronic total occlusion) revealed increased expression level of both miR-10b and miR-126 in CTO patients with insufficient collateral network development ⁵². The concomitant expression of miR-10b and miR-29b was investigated in cancer samples – endometrial adenocarcinoma (a positive relation) and glioblastoma (a negative relation) ^53,54. Second, in HV patients miR-98 correlated positively with both miR-10b and miR-29b, however, there was a strong negative correlation between miR-10b and miR-92a. The study upon saliva samples from patients with esophageal cancer revealed that miR98 and miR10b construct a regular network (together with miR-144, miR-451, miR-98, miR-486-5p and miR-363) ⁵⁵. Concomitant expression of miR98 and miR29b and their negative relation was confirmed in one study upon cultured hippocampal neurons in their reaction to oxygen-glucose deprivation⁵⁶. A negative relation of miR-92a and miR-10b is particularly surprising, because these particles regulate the same target – KLF pathway. The study upon samples of minimally invasive thyroid follicular carcinoma obtained by laser microdissection showed miR-92a and miR-10b up-regulation in samples from metastatic patients (compared to non-metastatic patients). We find no studies demonstrating their negative correlation. The network analysis performed in our study might suggest interaction between pathways regulated by aforementioned microRNAs in progression of atherosclerosis, but response on question about the role of such potential interactions is not possible to be done.

Finally, the Gensini score is not an ideal tool for assessing the stage of atherosclerotic plaque progression; the presence of significant plaque seems to be more indicative of atherosclerosis staging. Molecular processes in advanced plaques are different from those in earlier stages (50). Therefore, we decided to classify the study population according to the presence of advanced atherosclerotic plaque (assessed by coronary angiography and additional methods such as FFR), in addition to the assessment of the Gensini score.

STUDY LIMITATIONS

The main limitation of the study is the apparent small study population, although strict exclusion criteria were used to create as homogeneous a population as possible. In addition, regulatory proteins were selected from the literature, which introduces a risk of bias. The target microRNAs were selected from microRNA target prediction databases, without a high-throughput pilot study (such as microarray). Another limitation of the study was that the definition of advanced atherosclerotic plaque was based solely on coronary angiography, without intravascular imaging by IVUS or OCT, making it impossible to assess the characteristics of different atherosclerotic plaques (thin cap, lipid abundance). Moreover, in reference to the network analysis it is necessary to point out a low number of subjects (especially healthy volunteers) and low expression levels of especially miR98 which makes the obtained relations less evident.

The study showed that some microRNAs that downregulate atheroprotective pathways differ according to plaque burden or progression in patients with stable coronary artery disease (chronic coronary syndrome without a history of stroke, myocardial infarction, stent implantation, CABG, or diabetes). This observation, albeit in a small study sample, may indicate an association between miR-92a expression levels and the stage of atherosclerotic plaque progression. From a long-term future perspective, the information on microRNA expression profile at a certain level of coronary artery disease progression might reflect the level of plasma atherogenicity and suggest different therapeutic approaches to either stabilize or even regress atherosclerotic plaques in coronary arteries.

Ethics approval and consent to participate

The study obtained approval by the Bioethical Commitee, Medical University of Warsaw KB/88/2022

Each participant signed informed consent for participation in the study in the form accepted by the Bioethical Commitee.

Consent for publication

NON APPLICABLE

Availability of data and materials

Data is provided within the manuscript or supplementary information files.

Competing interests

None conflict of interest declared.

Funding

The study was funded by the research grant from National Center of Science (NCN) 2019/33/N/NZ5/02764

Authors contribution

Michał Kowara – study design, patients enrollment, samples collection, database creation, data analysis, manuscript preparation, figures preparation

Michał Kopka – molecular analysis, manuscript preparation

Karolina Kopka – molecular analysis, manuscript preparation

Renata Główczyńska – patients enrollment

Sławomir Kujawski – data analysis, manuscript preparation

Piotr Baruś – coronary angiography analysis

Agnieszka Cudnoch-Jędrzejewska – study design, study supervison, manuscript reviewal

Acknowledgements

The Authors would like to acknowledge Professor Paweł Zalewski, MD, PhD, from Ludwik Rydygier Collegium Medicum in Bydgoszcz Nicolaus Copernicus University in Toruń for precious advice in study design.

The Authors would like to acknowledge Professor Paweł Włodarski, MD, PhD, from Medical University of Warsaw for precious advice regarding methodology of the study.

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Competing interest reported. One of the Authors, Sławomir Kujawski (SK) is serving as handle editor in the Scientific Reports

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The microRNAs inhibiting atheroprotective pathways in correlation with atherosclerosis severity in chronic coronary syndrome patients – case control study and network analysis

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Abstract

Figures

BACKGROUND

Materials and Methods

MicroRNA selection

Study population

Eligibility criteria

Coronary angiography

Blood samples

Micro-RNA

Statistical analysis

RESULTS

Baseline characteristics of chronic coronary syndrome patients

The miR-92a, miR-10b, miR-126, miR-98 and miR-29b expression levels according to Gensini tertiles

Comparison between atherosclerotic patients with healthy volunteers

Network analysis of microRNAs for chronic coronary syndrome patients and healthy volunteers

DISCUSSION

STUDY LIMITATIONS

CONCLUSION

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1