Division of Malaria Research, Proteo-Science Center, Ehime University, Matsuyama, Japan.
Centre for Research in Infectious Diseases, Directorate of Research and Innovation, Mount Kenya University, Thika, Kenya.
Front Immunol. 2022 Jun 9;13:887219. doi: 10.3389/fimmu.2022.887219. eCollection 2022.
Concerted efforts to fight malaria have caused significant reductions in global malaria cases and mortality. Sustaining this will be critical to avoid rebound and outbreaks of seasonal malaria. Identifying predictive attributes that define clinical malaria will be key to guide development of second-generation tools to fight malaria. Broadly reactive antibodies against variable surface antigens that are expressed on the surface of infected erythrocytes and merozoites stage antigens are targets of naturally acquired immunity and prime candidates for anti-malaria therapeutics and vaccines. However, predicting the relationship between the antigen-specific antibodies and protection from clinical malaria remains unresolved. Here, we used new datasets and multiple approaches combined with re-analysis of our previous data to assess the multi-dimensional and complex relationship between antibody responses and clinical malaria outcomes. We observed 22 antigens (17 PfEMP1 domains, 3 RIFIN family members, merozoite surface protein 3 (PF3D7_1035400), and merozoites-associated armadillo repeats protein (PF3D7_1035900) that were selected across three different clinical malaria definitions (1,000/2,500/5,000 parasites/µl plus fever). In addition, Principal Components Analysis (PCA) indicated that the first three components (Dim1, Dim2 and Dim3 with eigenvalues of 306, 48, and 29, respectively) accounted for 66.1% of the total variations seen. Specifically, the Dim1, Dim2 and Dim3 explained 52.8%, 8.2% and 5% of variability, respectively. We further observed a significant relationship between the first component scores and age with antibodies to PfEMP1 domains being the key contributing variables. This is consistent with a recent proposal suggesting that there is an ordered acquisition of antibodies targeting PfEMP1 proteins. Thus, although limited, and further work on the significance of the selected antigens will be required, these approaches may provide insights for identification of drivers of naturally acquired protective immunity as well as guide development of additional tools for malaria elimination and eradication.
齐心协力抗击疟疾已显著降低了全球疟疾发病和死亡人数。为避免疟疾反弹和季节性暴发，维持这一成果至关重要。确定定义临床疟疾的预测属性将是指导开发第二代抗疟工具的关键。广泛针对感染红细胞表面和裂殖体阶段抗原表达的可变表面抗原的反应性抗体是天然获得性免疫的靶标，也是抗疟治疗和疫苗的主要候选物。然而，预测抗原特异性抗体与临床疟疾保护之间的关系仍未解决。在这里，我们使用了新的数据集和多种方法，并结合对我们以前数据的重新分析，评估了抗体反应与临床疟疾结果之间的多维复杂关系。我们观察到 22 种抗原（17 个 PfEMP1 结构域、3 个 RIFIN 家族成员、裂殖体表面蛋白 3（PF3D7_1035400）和裂殖体相关的类角蛋白重复蛋白（PF3D7_1035900），这些抗原是通过三种不同的临床疟疾定义（1000/2500/5000 个寄生虫/µl 加发热）选择的。此外，主成分分析（PCA）表明，前三个成分（特征值分别为 306、48 和 29 的 Dim1、Dim2 和 Dim3）占总变异的 66.1%。具体来说，Dim1、Dim2 和 Dim3 分别解释了 52.8%、8.2%和 5%的可变性。我们还观察到第一成分得分与年龄之间存在显著关系，PfEMP1 结构域抗体是关键的贡献变量。这与最近的一项建议一致，即针对 PfEMP1 蛋白的抗体呈有序获得。因此，尽管有限，并且需要进一步研究选定抗原的意义，但这些方法可能为确定天然获得性保护性免疫的驱动因素提供见解，并指导开发用于消除和根除疟疾的额外工具。
