Ndiaye Mame Diarra Bousso, Rasoloharimanana Lova Tsikiniaina, Razafimahatratra Solohery Lalaina, Ratovoson Rila, Rasolofo Voahangy, Ranaivomanana Paulo, Raskine Laurent, Hoffmann Jonathan, Randremanana Rindra, Rakotosamimanana Niaina, Schoenhals Matthieu
Institut Pasteur de Madagascar, Antananarivo, Madagascar.
Medical and Scientific Department, Fondation Mérieux, Lyon, France.
Heliyon. 2023 Jun;9(6):e17264. doi: 10.1016/j.heliyon.2023.e17264. Epub 2023 Jun 13.
The world is facing a 2019 coronavirus (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this context, efficient serological assays are needed to accurately describe the humoral responses against the virus. These tools could potentially provide temporal and clinical characteristics and are thus paramount in developing-countries lacking sufficient ongoing COVID-19 epidemic descriptions.
We developed and validated a Luminex xMAP® multiplex serological assay targeting specific IgM and IgG antibodies against the SARS-CoV-2 Spike subunit 1 (S1), Spike subunit 2 (S2), Spike Receptor Binding Domain (RBD) and the Nucleocapsid protein (N). Blood samples collected periodically for 12 months from 43 patients diagnosed with COVID-19 in Madagascar were tested for these antibodies. A random forest algorithm was used to build a predictive model of time since infection and symptom presentation.
The performance of the multiplex serological assay was evaluated for the detection of SARS-CoV-2 -IgG and -IgM antibodies. Both sensitivity and specificity were equal to 100% (89.85-100) for S1, RBD and N (S2 had a lower specificity = 95%) for IgG at day 14 after enrolment. This multiplex assay compared with two commercialized ELISA kits, showed a higher sensitivity. Principal Component Analysis was performed on serologic data to group patients according to time of sample collection and clinical presentations. The random forest algorithm built by this approach predicted symptom presentation and time since infection with an accuracy of 87.1% (95% CI = 70.17-96.37, = 0.0016), and 80% (95% CI = 61.43-92.29, = 0.0001) respectively.
This study demonstrates that the statistical model predicts time since infection and previous symptom presentation using IgM and IgG response to SARS-CoV2. This tool may be useful for global surveillance, discriminating recent- and past- SARS-CoV-2 infection, and assessing disease severity.
This study was funded by the French Ministry for Europe and Foreign Affairs through the REPAIR COVID-19-Africa project coordinated by the Pasteur International Network association. WANTAI reagents were provided by WHO AFRO as part of a Sero-epidemiological "Unity" Study Grant/Award Number: 2020/1,019,828-0 P·O 202546047 and Initiative 5% grant n°AP-5PC-2018-03-RO.
世界正面临由严重急性呼吸综合征冠状病毒2（SARS-CoV-2）引起的2019冠状病毒病（COVID-19）大流行。在此背景下，需要高效的血清学检测方法来准确描述针对该病毒的体液免疫反应。这些工具可能会提供时间和临床特征，因此对于缺乏足够COVID-19疫情描述的发展中国家至关重要。
我们开发并验证了一种Luminex xMAP®多重血清学检测方法，该方法针对针对SARS-CoV-2刺突蛋白亚基1（S1）、刺突蛋白亚基2（S2）、刺突受体结合域（RBD）和核衣壳蛋白（N）的特异性IgM和IgG抗体。从马达加斯加43例确诊为COVID-19的患者中定期采集12个月的血样，检测这些抗体。使用随机森林算法建立感染时间和症状出现时间的预测模型。
评估了多重血清学检测方法检测SARS-CoV-2 -IgG和-IgM抗体的性能。在入组后第14天，S1、RBD和N的IgG敏感性和特异性均等于100%（89.85 - 100）（S2的特异性较低 = 95%）。与两种商业化ELISA试剂盒相比，这种多重检测方法显示出更高的敏感性。对血清学数据进行主成分分析，以根据样本采集时间和临床表现对患者进行分组。通过这种方法建立的随机森林算法预测症状出现和感染后的时间，准确率分别为87.1%（95% CI = 70.17 - 96.37，P = 0.0016）和80%（95% CI = 61.43 - 92.29，P = 0.0001）。
本研究表明，统计模型使用针对SARS-CoV2的IgM和IgG反应预测感染时间和既往症状出现情况。该工具可能有助于全球监测、区分近期和既往SARS-CoV-2感染以及评估疾病严重程度。
本研究由法国欧洲和外交部通过由巴斯德国际网络协会协调的REPAIR COVID-19 - 非洲项目资助。万泰试剂由世卫组织非洲区域办事处作为血清流行病学“团结”研究资助/奖励编号：2020/1,019,828 - 0 P·O 202546047和5%倡议资助编号AP - 5PC - 2018 - 03 - RO提供。
