Increased CD14⁺HLA-DR^-/low myeloid-derived suppressor cells can be regarded as a biomarker on disease severity and response to therapy in acute coronary syndrome

Human samples

Fresh paired peripheral blood (PB) samples were collected from 68 ACS patients and 35 SAP patients at the First Affiliated Hospital of Anhui Medical University and Feidong People’s Hospital between January 2023 and January 2024. Specifically, the diagnosis of ACS and SAP refers to the clinical diagnostic criteria of ischemic heart disease of the International Society of Cardiology and the World Health Organization (ISFC/WHO) (Braunwald et al., 2000). Additionally, PB samples from 30 healthy donors were included as the healthy control (HC) group. The basic characteristics of the ACS, SAP, and HC participants are summarized in Table 1. The degree of coronary artery stenosis was assessed using the Gensini scoring system (Rampidis et al., 2019), where a total score of ≤24 indicated mild stenosis, 25–49 indicated moderate stenosis, and ≥50 indicated severe stenosis. All patients underwent percutaneous coronary intervention (PCI) and received dual antiplatelet therapy with aspirin and clopidogrel. This study was conducted in accordance with the Declaration of Helsinki, and the study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Anhui Medical University (NO. S20200021). Written informed consent was obtained from all patients and healthy donors, and samples were processed within six hours of collection. Portions of this text were previously published as part of a preprint (https://doi.org/10.21203/rs.3.rs-4461420/v1).

Sample collection

Fresh PB samples were collected from all cohorts using heparin-containing tubes. For the ACS patients, paired pre- and post-therapy PB samples were collected to assess the impact of standard PCI treatment or anticoagulant therapy on the study parameters. All PB samples were collected prior to any treatments, ensuring that the measurements captured the baseline levels for accurate analysis.

Surface staining

PB samples (100 µL) were collected and transferred into test tubes. To facilitate staining, the appropriate concentration of the following fluorescently labeled monoclonal antibodies was added: Human TruStain FcX TM (Clone 422301; BioLegend, San Diego, CA, USA), Brilliant Violet 510 TM anti-human CD45 (Clone 2D1; BioLegend), PE anti-human HLA-DR (Clone LN3; BioLegend), FITC anti-human CD14 (Clone M5E2). The samples were then incubated for 15 min at room temperature to allow for antibody binding and cell surface staining.

Subsequently, red blood cell lysis was performed using red blood cell lysis buffer from BD Biosciences. The lysis process lasted for 15 min, ensuring the removal of red blood cells while preserving the stained cells of interest. After completion, the stained cells were washed twice with phosphate-buffered saline (PBS) to eliminate any remaining debris or unbound antibodies.

Data acquisition and analysis were carried out using a flow cytometer (Cytoflex S; Beckman Coulter, Brea, CA, USA) and the accompanying CytExpert software (Beckman Coulter). M-MDSCs were characterized by the expression of CD14⁺HLA-DR^−/low on CD14⁺monocytes in this study.

Cytokine assay

Simultaneously with PB collection, plasma samples were obtained from all individuals. The PB samples were centrifuged at 3,000 rpm for 10 min at 4 °C to separate the plasma fractions. The obtained plasma fractions were carefully collected and stored at −80 °C until further use to maintain sample integrity. To assess the concentrations of IL-6, IL-10, GM-CSF, and TNF-α in the plasma samples, ELISA Kits (ESM; R&D System) were employed following the manufacturer’s instructions.

RT-PCR analysis

Total RNA was extracted from Peripheral blood mononuclear cells (PBMCs) using TRizol reagent (Invitrogen, Carlsbad, CA, USA). The extracted RNA was then subjected to reverse transcription using the First-Strand Synthesis System (TaKaRa, Dalian, China) to generate first-strand cDNA. Real-time PCR analysis was performed using the Applied Biosystems 7500 Real-time Polymerase Chain Reaction system. SYBR Green PCR Master mix was used in the PCR reactions. The mRNA levels of Arg-1 and inducible nitric oxide synthase (iNOS) were analyzed in each reaction. The following primer sequences were used: Arg-1: forward 5′-GGCTGGTCTGCTTGAGAAAC-3′ and reverse 5′-ATTGCCAAACTGTGGTCTCC-3′. iNOS: forward 5′-CTTTCCAAGACACACTTCACCA-3′ and reverse 5′-TATCTCCTTTGTTACCGCTTCC-3′. To normalize the expression levels, glyceraldehyde phosphate dehydrogenase (GAPDH) was used as a control. The primer sequences for GAPDH were: forward 5′-CAGGAGGCCATTGCTGATGAT-3′ and reverse 5′-GAAGGCTGGGGCTCATTT-3′. The thermal cycling conditions were as follows: an initial denaturation step at 95 °C for 30 s, followed by 40 cycles of amplification with the following profile: 95 °C for 5 s and 60°C for 34 s. The relative expression levels of the target genes were determined using the following formula: 2^{−(Cttarget−Ctcontrol)}, where Ct represents the threshold cycle.

Statistical analysis

The statistical analysis of the data was performed using GraphPad Prism 8 and SPSS 27.0 software. The experimental data were presented as mean ± standard deviation (SD). For comparisons among multiple groups, analysis of variance (ANOVA) was conducted, followed by post hoc pairwise comparisons using the Least Significant Difference (LSD) t-test. When comparing two paired samples, either the non-parametric unpaired Mann–Whitney U-test or the parametric Student’s t-test was employed. To assess correlations between M-MDSCs frequency and each parameter, the Pearson’s coefficient test was utilized. Logistic regression analysis was employed to identify factors influencing the occurrence of middle to serious coronary artery disease. Receiver operating characteristic (ROC) curves were constructed to determine the optimal cut-off value based on the maximum Youden index. In all statistical tests, a p-value less than 0.05 (P < 0.05) was considered statistically significant.

Comparative clinical analysis of ACS, SAP and control group

The clinical data of the three study groups, including initial diagnosis ACS patients, SAP patients, and the HC group, are summarized in Table 1. No significant differences were observed in gender, age, smoking rate, obesity incidence, and total cholesterol (TC) levels among the three groups (P > 0.05).

However, the prevalence of hypertension (60.2%) and diabetes (32.4%) was significantly higher in the ACS group compared to the SAP group (65.7% and 25.7%) and the HC group (26.7% and 10%) (P <  0.05). Additionally, the ACS group had higher TG levels (1.73 ± 0.81 mmol/L) compared to the SAP group (1.58 ± 0.46 mmol/L) and the HC group (1.21 ± 0.42 mmol/L) (P <  0.01), while the HDL-C level (0.96 ±  0.28 mmol/L) was lower than the other two groups (P <  0.01). The LDL-C level showed significant differences among the three groups, with the ACS group having the highest level (2.88 ± 0.83 mmol/L), followed by the SAP group (2.49 ± 0.77 mmol/L), and the HC group having the lowest level (1.32 ±  0.32 mmol/L) (P < 0.01).

Increased frequency of circulating M-MDSCs in ACS patients

In this study, the frequency of circulating M-MDSCs was investigated in ACS patients. Flow cytometry analysis was performed, and M-MDSCs were characterized by the expression of CD14⁺HLA-DR^−/low on CD14⁺ monocytes. Flow cytometry data plots illustrating the gating strategy of human peripheral blood M-MDSCs (Fig. 1A). No significant differences were observed in the frequency of CD14⁺ monocytes between ACS patients, SAP patients, and HC (20.9 ±  5.82% vs 21.09 ± 5.72% vs 19.69 ±  4.69%, P = 0.146, Fig. 1B). However, the levels of M-MDSCs were significantly increased in ACS patients and SAP patients compared to healthy controls (18.92 ± 9.04% vs 7.01 ± 3.6% vs 5.82 ±  3.39%, P = 0.001, Figs. 1A, 1C).

Association between characteristics and disease status of ACS patients with M-MDSCs

To determine the clinical significance of increased M-MDSCs in ACS patients, the patients were categorized based on different clinical characteristics. Among the total 68 ACS patients, there were 19 patients with acute ST-segment elevation myocardial infarction (STEMI), 23 patients with acute non-ST-segment elevation myocardial infarction (NSTEMI), and 26 patients with unstable angina pectoris (UA). Initially, STEMI patients exhibited a significantly higher frequency of M-MDSCs compared to NSTEMI patients and UA patients (25.28 ± 8.97% vs 18.36 ± 9.56% vs 14.76 ± 5.6%, P = 0.001, Fig. 2A). However, there was no significant difference in the frequency of M-MDSCs between NSTEMI patients and UA patients (P > 0.05). Based on the severity of coronary artery stenosis, the patients were further classified as mild (25 patients), moderate (28 patients), and severe (15 patients). Significantly elevated M-MDSCs frequency were observed in severe patients and moderate patients compared to mild patients. (25.22 ± 8.17% vs 20.45 ± 9.61% vs 13.42 ± 5.11%, P = 0.001, Fig. 2B). However, no significant difference was found in the frequency of M-MDSCs between severe and moderate patients (P > 0.05). Considering the number of coronary artery lesions, the patients were grouped as follows: single vessel lesions (23 cases), two vessel lesions (33 cases), and three vessel lesions (12 cases). Patients with three vessel lesions had a significantly higher frequency of M-MDSCs compared to those with single vessel lesions (25.46 ±  8.3% vs 19.38 ± 9.05% vs 14.85 ±  7.37%, P = 0.003, Fig. 2C). However, there were no significant differences in the frequency of M-MDSCs between patients with single and two vessel lesions, as well as between patients with two and three vessel lesions (P > 0.05). According to the Killip classification of cardiac function, the patients were categorized into three groups: level II (24 patients), level III (31 patients), and level IV (13 patients). Patients with level IV cardiac function had a significantly higher frequency of M-MDSCs compared to those with level II and III (29.03 ± 6.88% vs 17.67 ±  8.17% vs 15.05 ± 7.11%, P = 0.001, Fig. 2D). However, there was no significant difference in the frequency of M-MDSCs between patients with level I and II (P > 0.05). Interestingly, obese patients (22 patients) had significantly higher levels of M-MDSCs compared to non-obese patients (46 patients) (23.38 ±  9.39% vs 16.78 ± 8.13%, P = 0.004, Fig. 2E). To evaluate the relationship between the frequency of M-MDSCs and treatment outcomes, all ACS patients received emergency PCI surgery and anticoagulant therapy such as aspirin and clopidogrel for more than 1 month. The frequency of M-MDSCs significantly decreased after therapy (18.92 ± 9.04% vs 7.79 ± 4.34%, P = 0.001, Fig. 2F). No significant differences were observed in the frequency of M-MDSCs among different age groups, genders, hypertension, smoking, or diabetes (Fig. S1).

M-MDSCs associated cytokine profile in ACS patients

This study investigated the cytokine profile associated with M-MDSCs in ACS patients. The levels of IL-6, IL-10, GM-CSF, and TNF-α were measured in the peripheral blood of ACS and SAP patients. The results showed significantly higher plasma concentrations of IL-6, IL-10, GM-CSF, and TNF-α in ACS patients compared to SAP patients and HC. The plasma concentrations of IL-6 were significantly increased in ACS patients compared to SAP patients and HC (28.41 ± 9.46 pg/ml vs 14.5 ± 3.24 pg/ml vs 11.07 ± 4.1 pg/ml, P = 0.001, Fig. 3A). Similar findings were observed for IL-10 (56.12 ± 14.31 ng/ml vs 36.08 ± 13.61 ng/ml vs 10.56 ± 3.08 ng/ml, P = 0.001, Fig. 3B), GM-CSF (123 ± 34.06 pg/ml vs 68.3 ± 23.96 pg/ml vs 28.6 ± 12.89 pg/ml, P = 0.001, Fig. 3C), and TNF-α (52.6 ± 15.19 pg/ml vs 28.2 ± 5.97 pg/ml vs 11.12 ± 4.06 pg/ml, P = 0.001, Fig. 3D).

M-MDSCs are known to secrete various cytokines and enzymes to exert immunosuppressive effects, including Arg-1 and iNOS. This study also measured the levels of MDSC-associated enzymes in ACS patients. It was found that the level of Arg-1 mRNA was significantly increased in ACS patients compared to SAP patients and HC (5.21 ± 1.03 vs 3.47 ± 1.01 vs 3.25 ± 1.24, P = 0.001, Fig. 3E). The level of iNOS mRNA was also higher in ACS patients, although not significantly (Fig. 3F).

Furthermore, the study analyzed the correlation between M-MDSCs levels and associated cytokines in ACS patients. Positive correlations were observed between M-MDSCs levels and the expression of IL-6 (r =0.64, P = 0.01, Fig. 4A), GM-CSF (r =0.61, P = 0.01, Fig. 4B), and Arg-1 (r =0.56, P = 0.01, Fig. 4C) in ACS patients. However, no significant correlations were found between M-MDSCs levels and the expression levels of IL-10, TNF-α, and iNOS (P > 0.05).

Analysis of multiple risk factors and the degree coronary artery stenosis in ACS patients

In the logistic regression analysis, this study investigated multiple risk factors and their association with the degree of coronary artery stenosis in ACS patients. The dependent variable was the degree of stenosis, while the independent variables included sex, gender, age, hypertension, smoking, obesity, diabetes, TG, TC, HDL-C, LDL-C, and M-MDSCs. The results of the analysis, as shown in Table 2, indicated that several factors were statistically significant predictors of the severity of coronary artery stenosis in ACS patients. Specifically, diabetes, TG, HDL-C, and M-MDSCs were identified as significant risk factors. Among these factors, TG emerged as the most significant risk factor, with the highest odds ratio (OR), indicating a strong association with the degree of stenosis. Diabetes and M-MDSCs also showed significant associations with the severity of stenosis. Interestingly, HDL-C exhibited a significant inverse association with the degree of coronary artery stenosis.

Multiple risk factors in predicting the severity of coronary artery stenosis

The researchers performed ROC curve analysis to assess the diagnostic performance of TG, HDL-C, and M-MDSCs levels in predicting the severity of coronary artery stenosis. The ROC curve analysis provides information on the sensitivity and specificity of these factors at different threshold values. For TG, the area under the curve (AUC) value was found to be 0.76. The critical threshold value for TG was determined to be 1.87 mmol/L. At this threshold, the sensitivity was 50% and the specificity was 96.43%. These values indicate that TG has moderate diagnostic performance in predicting the severity of coronary artery stenosis (Fig. 5A). Regarding HDL-C, the AUC value was higher at 0.86. The critical threshold value for HDL-C was determined to be 1.18 mmol/L. At this threshold, the sensitivity was found to be 97.5%, indicating a high ability to correctly identify patients with severe stenosis. However, the specificity was 71.43%, suggesting a relatively lower ability to accurately identify patients without severe stenosis (Fig. 5B). For M-MDSCs levels, the AUC value was 0.88, indicating a good diagnostic performance. The critical threshold value for M-MDSCs was determined to be 17.55%. At this threshold, the sensitivity was 72.5%, indicating a moderate ability to correctly identify patients with severe stenosis. The specificity was higher at 85.71%, suggesting a good ability to accurately identify patients without severe stenosis (Fig. 5C).