Wong Wesley, Schaffner Stephen F, Thwing Julie, Seck Mame Cheikh, Gomis Jules, Diedhiou Younouss, Sy Ngayo, Ndiop Medoune, Ba Fatou, Diallo Ibrahima, Sene Doudou, Diallo Mamadou Alpha, Ndiaye Yaye Die, Sy Mouhamad, Sene Aita, Sow Djiby, Dieye Baba, Tine Abdoulaye, Ribado Jessica, Suresh Joshua, Lee Albert, Battle Katherine E, Proctor Joshua L, Bever Caitlin A, MacInnis Bronwyn, Ndiaye Daouda, Hartl Daniel L, Wirth Dyann F, Volkman Sarah K
Harvard T. H. Chan School of Public Health.
The Broad Institute.
Res Sq. 2023 Nov 1:rs.3.rs-3516287. doi: 10.21203/rs.3.rs-3516287/v1.
Genetic surveillance of the parasite shows great promise for helping National Malaria Control Programs (NMCPs) assess parasite transmission. Genetic metrics such as the frequency of polygenomic (multiple strain) infections, genetic clones, and the complexity of infection (COI, number of strains per infection) are correlated with transmission intensity. However, despite these correlations, it is unclear whether genetic metrics alone are sufficient to estimate clinical incidence. Here, we examined parasites from 3,147 clinical infections sampled between the years 2012-2020 through passive case detection (PCD) across 16 clinic sites spread throughout Senegal. Samples were genotyped with a 24 single nucleotide polymorphism (SNP) molecular barcode that detects parasite strains, distinguishes polygenomic (multiple strain) from monogenomic (single strain) infections, and identifies clonal infections. To determine whether genetic signals can predict incidence, we constructed a series of Poisson generalized linear mixed-effects models to predict the incidence level at each clinical site from a set of genetic metrics designed to measure parasite clonality, superinfection, and co-transmission rates. We compared the model-predicted incidence with the reported standard incidence data determined by the NMCP for each clinic and found that parasite genetic metrics generally correlated with reported incidence, with departures from expected values at very low annual incidence (<10/1000/annual [‰]). When transmission is greater than 10 cases per 1000 annual parasite incidence (annual incidence >10 ‰), parasite genetics can be used to accurately infer incidence and is consistent with superinfection-based hypotheses of malaria transmission. When transmission was <10 ‰, we found that many of the correlations between parasite genetics and incidence were reversed, which we hypothesize reflects the disproportionate impact of importation and focal transmission on parasite genetics when local transmission levels are low.
对该寄生虫的基因监测在帮助国家疟疾控制项目(NMCPs)评估寄生虫传播方面显示出巨大潜力。诸如多基因组(多菌株)感染频率、基因克隆以及感染复杂性(感染复杂性指数,每次感染的菌株数量)等基因指标与传播强度相关。然而,尽管存在这些相关性,但仅靠基因指标是否足以估计临床发病率尚不清楚。在此,我们通过被动病例检测(PCD),对2012年至2020年间在塞内加尔各地16个诊所采集的3147例临床感染的寄生虫进行了检测。样本通过一个24个单核苷酸多态性(SNP)分子条形码进行基因分型,该条形码可检测寄生虫菌株,区分多基因组(多菌株)感染与单基因组(单菌株)感染,并识别克隆感染。为了确定基因信号是否能够预测发病率,我们构建了一系列泊松广义线性混合效应模型,以根据一组旨在衡量寄生虫克隆性、重复感染和共同传播率的基因指标来预测每个临床地点的发病率水平。我们将模型预测的发病率与NMCP为每个诊所确定的报告标准发病率数据进行了比较,发现寄生虫基因指标通常与报告的发病率相关,但在年发病率极低(<10/1000/年[‰])时偏离预期值。当传播率高于每年每1000例寄生虫发病率10例(年发病率>10‰)时,寄生虫遗传学可用于准确推断发病率,并且与基于重复感染的疟疾传播假说一致。当传播率<10‰时,我们发现寄生虫遗传学与发病率之间的许多相关性发生了逆转,我们推测这反映了在当地传播水平较低时,输入性病例和局部传播对寄生虫遗传学的不成比例影响。