| Literature DB >> 28484274 |
Nazanin Hakimzadeh1,2, Joëlle Elias2, Gilbert W M Wijntjens2, Ruud Theunissen3,4, Angela van Weert1, Martijn W Smulders4,5, Nynke van den Akker3,4, Perry D Moerland6, Hein J Verberne7, Loes P Hoebers2, Jose P S Henriques2, Anja M van der Laan2, Mustafa Ilhan4, Mark Post3,4, Sebastiaan C A M Bekkers4,5, Jan J Piek8.
Abstract
An expansive collateral artery network is correlated with improved survival in case of adverse cardiac episodes. We aimed to identify cellular microRNAs (miRNA; miR) important for collateral artery growth. Chronic total occlusion (CTO) patients (n = 26) were dichotomized using pressure-derived collateral flow index (CFIp) measurements; high collateral capacity (CFIp > 0.39; n = 14) and low collateral (CFIp < 0.39; n = 12) capacity. MiRNA profiling via next generation sequencing from various monocyte phenotypes (freshly isolated monocytes, monocytes cultured without stimulant, or stimulation with lipopolysaccharide, interleukin 4, transforming growth factor beta-1, or interferon gamma) revealed significantly different miRNA expression patterns between high versus low collateral capacity patients. Validation by real-time polymerase chain reaction demonstrated significantly decreased expression of miR339-5p in all stimulated monocyte phenotypes of low collateral capacity patients. MiR339-5p showed significant correlation with CFIp values in stimulated monocytes. Ingenuity pathway analysis of predicted gene targets of miR339-5p and differential gene expression data from high versus low CFIp patients (n = 20), revealed significant association with STAT3 pathway, and also suggested a possible regulatory role for this signaling pathway. These results identify a novel association between miR339-5p and coronary collateral function. Future work examining modulation of miR339-5p and downstream effects on the STAT3 pathway and subsequent collateral vessel growth are warranted.Entities:
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Year: 2017 PMID: 28484274 PMCID: PMC5431477 DOI: 10.1038/s41598-017-01695-3
Source DB: PubMed Journal: Sci Rep ISSN: 2045-2322 Impact factor: 4.379
Patient Characteristics.
| Characteristic | CFIp < 0.39 (n = 12) | CFIp > 0.39 (n = 14) | P-value |
|---|---|---|---|
| Collateral flow index, mean ± SD | 0.29 ± 0.05 | 0.55 ± 0.12 | <0.0001 |
| Age (years), mean ± SD | 62 ± 11 | 64 ± 7 | 0.58 |
| Male gender, n (%) | 11 (92) | 13 (93) | 1.00 |
| BMI (kg/m2), mean ± SD | 28.1 ± 2.94 | 28.2 ± 5.14 | 0.95 |
| Coronary Risk Factors | |||
| Hypertension, n (%) | 7 (58) | 11(79) | 0.65 |
| Family history of CAD, n (%) | 5 (42) | 6 (43) | 1.00 |
| Hypercholesterolaemia, n (%) | 5 (42) | 1 (7.1) | 0.06 |
| Current smoker, n (%) | 2 (17) | 4 (29) | 0.65 |
| Past smoker, n (%) | 6 (50) | 4 (29) | 0.42 |
| Target vessel | |||
| LAD (%) | 6 (50) | 2 (14) | 0.12 |
| RCA (%) | 3 (25) | 8 (57) | 0.12 |
| RCX (%) | 3 (25) | 3 (21) | 0.12 |
| History of Angina | 8 (67) | 9 (64) | 1.00 |
| Medication | |||
| Salicylates, n (%) | 9 (75) | 14 (100) | 0.08 |
| ACE-inhibitors/ARBs, n (%) | 1 (8.3) | 5 (36) | 0.17 |
| β-blockers, n (%) | 12 (100) | 13 (93) | 1.00 |
| Statins, n (%) | 11 (92) | 1 (7.1) | <0.0001 |
| Clopidogrel, n (%) | 3 (25) | 3 (21) | 1.00 |
| Calcium Antagonists, n (%) | 4 (33) | 5 (36) | 1.00 |
| Nitrates, n (%) | 5 (42) | 3 (21) | 0.40 |
| Laboratory values | |||
| Hb (mmol/L) | 9.09 ± 0.72 | 8.57 ± 1.07 | 0.18 |
| RBC (1012/L) | 4.92 ± 0.30 | 4.70 ± 0.44 | 0.17 |
| WBC (109/L) | 7.67 ± 1.88 | 14.6 ± 23.9 | 0.35 |
| Thrombocytes (109/L) | 249 ± 48.6 | 233 ± 45.2 | 0.42 |
| Neutrophils (109/L) | 4.83 ± 1.46 | 5.11 ± 1.02 | 0.62 |
| Eosinophils (109/L) | 0.189 ± 0.090 | 0.181 ± 0.092 | 0.84 |
| Lymphocytes (109/L) | 2.01 ± 0.79 | 1.98 ± 0.62 | 0.93 |
| Monocytes (109/L) | 0.66 ± 0.15 | 0.67 ± 0.19 | 0.90 |
ACE, Angiotensin converting enzyme; ARBs, angiotensin receptor blockers; BMI, body mass index; CAD, coronary artery disease; CFIp, collateral flow index; Hb, hemoglobin; LAD, left anterior descending; RBC, red blood cells; RCA, right coronary artery; RCX, right circumflex; WBC, white blood cell.
Figure 1Differential microRNA expression in patients with high and low collateral capacity. Heat map and un-supervised hierarchical clustering of the top 50 microRNAs with the highest coefficient of variation (based on log2-transformed tag per million normalized data) in monocytes/macrophages of patients with high (n = 5) or low (n = 5) capacity of the collateral circulation. Each row represents one microRNA and each column represents one sample. The color scale shows the relative expression level of microRNA across samples, where red color depicts an expression level above mean and green color represents down regulated expression. H: high collateral capacity; L: Low collateral capacity.
Figure 2Relative cellular miR339-5p expression is lower in patients with low collateral capacity. Quantitative polymerase chain reaction measurements of miR339-5p in monocyte/macrophages of the total patient cohort (n = 26) with high versus low collateral capacity. (a) Freshly isolated CD14 monocyte, (b) monocyte cultured without stimulant or monocytes cultured with stimulant (c: LPS, d: IFNɣ, e: IL4, f: TGFβ1). Data are presented as mean ± SD. LPS: Lipopolysaccharide; IFNɣ: Interferon gamma; IL4: Interleukin 4; TGFβ1: Transforming growth factor beta 1.
Figure 3Correlation between CFIp and relative miR339-5p expression in each monocyte/macrophage group. Relative miR339-5p is significantly correlated with CFIp in certain monocyte/macrophage phenotypes (IFNɣ, IL4, TGFβ1).
Figure 4Relative cellular miR155-5p expression is higher in M1 stimulation phenotypes. To examine the effects of monocyte/macrophage stimulation, we compared relative cellular miR155-5p expression levels in the various monocyte/macrophage groups to monocytes cultured without stimulant (A: CD14 vs. No stimulant; B: LPS vs. No stimulant; C: IFNɣ vs. No stimulant; D: IL4 vs. No stimulant; E: TGFβ1 vs. No stimulant). Data presented are based on quantitative polymerase chain reaction measurements of miR155-5p in monocyte/macrophages of the total patient cohort (n = 26) with high (CFIp > 0.39) or low (CFIp < 0.39) collateral capacity. Data are presented as mean ± SD. *p < 0.05, **p < 0.01, ***p < 0.001. CFIp: Collateral flow index; LPS: Lipopolysaccharide; IFNɣ: Interferon gamma; IL4: Interleukin 4; TGFβ1: Transforming growth factor beta 1.
Top enriched canonical pathways, molecular as well as cellular functions of miR339-5p based on Ingenuity Pathway Analysis.
| Canonical Pathways | p-value | Overlap |
|---|---|---|
| Tumoricidal Function of Hepatic Natural Killer Cells | 2.21E-02 | 8.3% (2/24) |
| STAT3 Pathway | 3.33E-02 | 4.1% (3/73) |
| Hippo signaling | 5.01E-02 | 3.5% (3/86) |
| VEGF Family Ligand-Receptor Interactions | 5.30E-02 | 3.4% (3/88) |
| Inhibition of Matrix Metalloproteases | 5.40E-02 | 5/1% (2/39) |
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| Cellular Development | 1.91E-02-3.35E-05 | 77 |
| Cellular Growth and Proliferation | 1.91E-02-3.35E-05 | 88 |
| Cell Morphology | 1.91E-02-1.28E-04 | 45 |
| Cellular Assembly and Organization | 1.91E-02-1.28E-04 | 55 |
| Cellular Function and Maintenance | 1.91E-02-1.28E-04 | 59 |
STAT3: Signal transducer and activator of transcription 3; VEGF: Vascular endothelial growth factor. P–value range for molecular and cellular functions represents the range of p-values generated for each subcategory and the respective molecules within each subcategory.
Association between the STAT3 pathway and respective gene sets (miR339-5p predicted gene targets, or genes differentially expressed between high and low collateral capacity patients in monocytes cultured with no stimulant, or stimulation with LPS or IL4).
| Gene set | p-value | Target molecules |
|---|---|---|
| miR339-5p predicted gene targets | p < 0.05 | MAP3K10, FLT1, CDC25A |
| Monocytes cultured for 20 h without stimulant | p < 0.0001 | IGF2R |
| LPS | p < 0.0001 | TGFBR2 |
| IL4 | p < 0.001 | PTPN2 |
The listed target molecules are molecules that are linked to each respective gene-set and are associated with the STAT3 pathway. High-lighted molecules share a common parent node in the STAT3 pathway; the common parent node being growth factor receptor (GF receptor).CDC25A, Cell division cycle 25 homolog A; FLT1, FMS related tyrosine kinase 1; IGF2R, Insulin-like growth factor 2 receptor; TGFBR2, Transforming growth factor beta receptor 2.