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利用NanoString技术对印度新冠肺炎患者队列中的免疫和炎症基因反应进行研究。

Study of immunological and inflammatory gene response in Indian cohort of COVID- 19 patients by NanoString technology.

作者信息

Sundarrajan Sudarson, Sridhar K N, Moorthy Manju, Ramaswamy Gopalakrishna

机构信息

Department of Molecular Biology, Cancyte Technologies Pvt. Ltd., Rangadore Memorial Hospital, Sri Shankara Research Centre, Shankarapuram, Bangalore, 560004, India.

Department of Bioinformatics, TheraCUES Innovations Private Limited, Bangalore, 560092, Karnataka, India.

出版信息

Immunol Res. 2025 Apr 29;73(1):77. doi: 10.1007/s12026-025-09626-5.

Abstract

COVID- 19, which has affected millions of people across the globe as a pandemic, is caused by the SARS-Cov- 2 virus which has a case fatality rate of 2.3%. The clinical outcome of those who had mild and severe infection exhibited different responses for the treatment due to differences in the host immune system. Predicting immune response with reliable biomarkers to monitor the severity and also identifying potential biomarkers that could help the clinician in decision-making would be important and also beneficial for the management of COVID- 19 in the hospital setup. In our study, we have used the NanoString nCounter gene expression assay to investigate the molecular signalling of host to COVID- 19 infection. The nCounter gene expression assay identified 29 genes that were differentially regulated and specific to COVID- 19 infection; out of which, 9 genes (ICAM3, PTAFR, CEACAM6, GBP1, C7, STAT1, CEACAM8, IL16, HLA-DPB1) exhibited strong predictive performance to differentiate COVID- 19 infection from healthy controls (AUC ≥ 0.9). We also observed that three genes (MAP4 K1, CTLA4, and HLA-DQB1) were able to differentiate COVID- 19 from patients with flu-like symptoms. A group of 11 genes (C2, CD14, CDKN1 A, CMKLR1, CYBB, HLA-A, IFNA2, LAG3, MARCO, TLR7, and IL15) showed a dysregulation trend with onset of COVID- 19 infection and settled to normal levels by day 14 as patient recovered. The outcome of our study may help in understanding the host immune response towards COVID- 19 infection.

摘要

新冠疫情已在全球范围内影响了数百万人,它由严重急性呼吸综合征冠状病毒2(SARS-CoV-2)引起,病死率为2.3%。由于宿主免疫系统的差异,轻度和重度感染患者的临床结果在治疗上表现出不同的反应。用可靠的生物标志物预测免疫反应以监测疾病严重程度,并识别有助于临床医生进行决策的潜在生物标志物,对于医院环境中新冠疫情的管理非常重要且有益。在我们的研究中,我们使用了NanoString nCounter基因表达分析来研究宿主对新冠病毒感染的分子信号传导。nCounter基因表达分析确定了29个因新冠病毒感染而受到差异调节的特异性基因;其中,9个基因(ICAM3、PTAFR、CEACAM6、GBP1、C7、STAT1、CEACAM8、IL16、HLA-DPB1)在区分新冠病毒感染与健康对照方面表现出很强的预测性能(曲线下面积≥0.9)。我们还观察到,三个基因(MAP4 K1、CTLA4和HLA-DQB1)能够区分新冠病毒感染与有流感样症状的患者。一组11个基因(C2、CD14、CDKN1 A、CMKLR1、CYBB、HLA-A、IFNA2、LAG3、MARCO、TLR7和IL15)在新冠病毒感染开始时呈现失调趋势,并在患者康复的第14天恢复到正常水平。我们的研究结果可能有助于理解宿主对新冠病毒感染的免疫反应。

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