Preventive Medicine and Public Health Service, Hospital Universitario Clínico San Cecilio, Granada, Spain.
GENYO, Center for Genomics and Oncological Research, Granada, Spain.
Front Immunol. 2023 Mar 8;13:1094644. doi: 10.3389/fimmu.2022.1094644. eCollection 2022.
Approximately 13.8% and 6.1% of coronavirus disease 2019 (COVID-19) patients require hospitalization and sometimes intensive care unit (ICU) admission, respectively. There is no biomarker to predict which of these patients will develop an aggressive stage that we could improve their quality of life and healthcare management. Our main goal is to include new markers for the classification of COVID-19 patients.
Two tubes of peripheral blood were collected from a total of 66 (n = 34 mild and n = 32 severe) samples (mean age 52 years). Cytometry analysis was performed using a 15-parameter panel included in the Maxpar Human Monocyte/Macrophage Phenotyping Panel Kit. Cytometry by time-of-flight mass spectrometry (CyTOF) panel was performed in combination with genetic analysis using TaqMan probes for (rs2285666), (rs469390), and (rs2070788) variants. GemStone™ and OMIQ software were used for cytometry analysis.
The frequency of CD163/CD206 population of transitional monocytes (T-Mo) was decreased in the mild group compared to that of the severe one, while T-Mo CD163/CD206 were increased in the mild group compared to that of the severe one. In addition, we also found differences in CD11b expression in CD14 monocytes in the severe group, with decreased levels in the female group (p = 0.0412). When comparing mild and severe disease, we also found that CD45 [p = 0.014; odds ratio (OR) = 0.286, 95% CI 0.104-0.787] and CD14/CD33 (p = 0.014; OR = 0.286, 95% CI 0.104-0.787) monocytes were the best options as biomarkers to discriminate between these patient groups. CD33 was also indicated as a good biomarker for patient stratification by the analysis of GemStone™ software. Among genetic markers, we found that G carriers of (rs2070788) have an increased risk (p = 0.02; OR = 3.37, 95% CI 1.18-9.60) of severe COVID-19 compared to those with A/A genotype. This strength is further increased when combined with CD45, T-Mo CD163/CD206, and C14/CD33.
Here, we report the interesting role of , CD45, CD163/CD206, and CD33 in COVID-19 aggressiveness. This strength is reinforced for aggressiveness biomarkers when and CD45, and CD163/CD206, and and CD14/CD33 are combined.
约有 13.8%和 6.1%的 2019 年冠状病毒病(COVID-19)患者分别需要住院治疗和有时需要入住重症监护病房(ICU)。目前尚无生物标志物可预测这些患者中哪些会发展为侵袭性阶段,我们可以改善其生活质量和医疗管理。我们的主要目标是纳入新的 COVID-19 患者分类标志物。
共采集了 66 份(n=34 例轻症和 n=32 例重症)样本的两管外周血(平均年龄 52 岁)。使用包括在 Maxpar 人单核细胞/巨噬细胞表型分析试剂盒中的 15 个参数面板进行细胞仪分析。使用 TaqMan 探针对 (rs2285666)、 (rs469390)和 (rs2070788)变体进行时间飞行质谱(CyTOF)面板的细胞仪分析和遗传分析。GemStone™和 OMIQ 软件用于细胞仪分析。
轻症组过渡性单核细胞(T-Mo)的 CD163/CD206 群体频率低于重症组,而 T-Mo CD163/CD206 在轻症组中高于重症组。此外,我们还发现重症组 CD14 单核细胞中 CD11b 表达存在差异,女性组水平降低(p=0.0412)。在比较轻症和重症疾病时,我们还发现 CD45[ p=0.014;比值比(OR)=0.286,95%CI 0.104-0.787]和 CD14/CD33(p=0.014;OR=0.286,95%CI 0.104-0.787)单核细胞是区分这些患者群体的最佳标志物选项。GemStone™软件的分析表明 CD33 也是患者分层的良好生物标志物。在遗传标志物中,我们发现与 AA 基因型相比,(rs2070788)的 G 携带者(p=0.02;OR=3.37,95%CI 1.18-9.60)患严重 COVID-19 的风险增加。当与 CD45、T-Mo CD163/CD206 和 C14/CD33 结合时,这种优势进一步增强。
在这里,我们报告了 、CD45、CD163/CD206 和 CD33 在 COVID-19 侵袭性中的有趣作用。当与 CD45、和 CD163/CD206、和 CD14/CD33 结合时,这种优势增强了侵袭性生物标志物的强度。