Neal Allison T
Department of Biology, Norwich University, Northfield, VT, United States of America.
PeerJ. 2021 Nov 2;9:e12448. doi: 10.7717/peerj.12448. eCollection 2021.
Malaria parasites reproduce asexually, leading to the production of large numbers of genetically identical parasites, here termed a clonal line or clone. Infected hosts may harbor one or more clones, and the number of clones in a host is termed multiplicity of infection (MOI). Understanding the distribution of parasite clones among hosts can shed light on the processes shaping this distribution and is important for modeling MOI. Here, I determine whether the distribution of clones of the lizard malaria parasite differ significantly from statistical distributions commonly used to model MOI and logical extensions of these models.
The number of clones per infection was assessed using four microsatellite loci with the maximum number of alleles at any one locus used as a simple estimate of MOI for each infection. I fit statistical models (Poisson, negative binomial, zero-inflated models) to data from four individual sites to determine a best fit model. I also simulated the number of alleles per locus using an unbiased estimate of MOI to determine whether the simple (but potentially biased) method I used to estimate MOI influenced model fit.
The distribution of clones among hosts at individual sites differed significantly from traditional Poisson and negative binomial distributions, but not from zero-inflated modifications of these distributions. A consistent excess of two-clone infections and shortage of one-clone infections relative to all fit distributions was also observed. Any bias introduced by the simple method for estimating of MOI did not appear to qualitatively alter the results.
The statistical distributions used to model MOI are typically zero-truncated; truncating the Poisson or zero-inflated Poisson yield the same distribution, so the reasonable fit of the zero-inflated Poisson to the data suggests that the use of the zero-truncated Poisson in modeling is adequate. The improved fit of zero-inflated distributions relative to standard distributions may suggest that only a portion of the host population is located in areas suitable for transmission even at small sites (<1 ha). Collective transmission of clones and premunition may also contribute to deviations from standard distributions.
疟原虫进行无性繁殖,从而产生大量基因相同的寄生虫,在此称为克隆系或克隆。受感染的宿主可能携带一个或多个克隆,宿主中的克隆数量称为感染复数(MOI)。了解寄生虫克隆在宿主之间的分布情况有助于揭示形成这种分布的过程,并且对于MOI建模很重要。在此,我确定蜥蜴疟原虫克隆的分布是否与常用于MOI建模的统计分布以及这些模型的逻辑扩展有显著差异。
使用四个微卫星位点评估每次感染的克隆数量,将任一基因座的最大等位基因数用作每次感染MOI的简单估计值。我将统计模型(泊松分布、负二项分布、零膨胀模型)拟合到来自四个独立地点的数据,以确定最佳拟合模型。我还使用MOI的无偏估计值模拟每个基因座的等位基因数量,以确定我用于估计MOI的简单(但可能有偏差)方法是否影响模型拟合。
各个地点宿主之间的克隆分布与传统的泊松分布和负二项分布有显著差异,但与这些分布的零膨胀修正无显著差异。相对于所有拟合分布,还观察到两克隆感染持续过量,一克隆感染短缺。估计MOI的简单方法所引入的任何偏差似乎并未在定性上改变结果。
用于MOI建模的统计分布通常是零截断的;截断泊松分布或零膨胀泊松分布会产生相同的分布,因此零膨胀泊松分布对数据的合理拟合表明在建模中使用零截断泊松分布是合适的。相对于标准分布,零膨胀分布拟合度的提高可能表明即使在小地点(<1公顷),也只有一部分宿主种群位于适合传播的区域。克隆的集体传播和带虫免疫也可能导致与标准分布的偏差。