Ma Zhanshan Sam
Computational Biology and Medical Ecology Lab, State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.
Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China.
Front Microbiol. 2021 Aug 20;12:699939. doi: 10.3389/fmicb.2021.699939. eCollection 2021.
Using 2,733 longitudinal vaginal microbiome samples (representing local microbial communities) from 79 individuals (representing meta-communities) in the states of healthy, BV (bacterial vaginosis) and pregnancy, we assess and interpret the relative importance of stochastic forces (e.g., stochastic drifts in bacteria demography, and stochastic dispersal) vs. deterministic selection (e.g., host genome, and host physiology) in shaping the dynamics of human vaginal microbiome (HVM) diversity by an integrated analysis with multi-site neutral (MSN) and niche-neutral hybrid (NNH) modeling. It was found that, when the traditional "default" -value = 0.05 was specified, the neutral drifts were predominant (≥50% metacommunities indistinguishable from the MSN prediction), while the niche differentiations were moderate (<20% from the NNH prediction). The study also analyzed two challenging uncertainties in testing the neutral and/or niche-neutral hybrid models, i.e., lack of full model specificity - non-unique fittings of same datasets to multiple models with potentially different mechanistic assumptions - and lack of definite rules for setting the -value thresholds (also noted as -value when referring to the threshold of -value in this article) in testing null hypothesis (model). Indeed, the two uncertainties can be interdependent, which further complicates the statistical inferences. To deal with the uncertainties, the MSN/NNH test results under a series of -values ranged from 0.05 to 0.95 were presented. Furthermore, the influence of -value threshold-setting on the model specificity, and the effects of woman's health status on the neutrality level of HVM were examined. It was found that with the increase of -value threshold from 0.05 to 0.95, the overlap (non-unique) fitting of MSN and NNH decreased from 29.1 to 1.3%, whereas the specificity (uniquely fitted to data) of MSN model was kept between 55.7 and 82.3%. Also with the rising -value threshold, the difference between healthy and BV groups become significant. These findings suggested that traditional single -value threshold (such as the standard value = 0.05) might be insufficient for testing the neutral and/or niche neutral hybrid models.
我们使用来自79名个体（代表元群落）的2733份纵向阴道微生物组样本（代表局部微生物群落），这些个体处于健康、细菌性阴道病（BV）和怀孕状态，通过多位点中性（MSN）和生态位 - 中性混合（NNH）建模的综合分析，评估并解释随机力量（例如细菌种群统计学中的随机漂移和随机扩散）与确定性选择（例如宿主基因组和宿主生理学）在塑造人类阴道微生物组（HVM）多样性动态中的相对重要性。研究发现，当指定传统的“默认”值 = 0.05时，中性漂移占主导（≥50%的元群落与MSN预测无法区分），而生态位分化程度适中（与NNH预测相差<20%）。该研究还分析了在测试中性和/或生态位 - 中性混合模型时的两个具有挑战性的不确定性，即缺乏完整的模型特异性——相同数据集对具有潜在不同机制假设的多个模型的非唯一拟合——以及在测试零假设（模型）时缺乏设置P值阈值（在本文中提及P值阈值时也记为α值）的明确规则。实际上，这两个不确定性可能相互依赖，这进一步使统计推断变得复杂。为了应对这些不确定性，展示了一系列P值范围从0.05到0.95时的MSN/NNH测试结果。此外，研究了P值阈值设置对模型特异性的影响，以及女性健康状况对HVM中性水平的影响。研究发现，随着P值阈值从0.05增加到0.95，MSN和NNH的重叠（非唯一）拟合从29.1%降至1.3%，而MSN模型的特异性（唯一拟合数据）保持在55.7%至82.3%之间。同样随着P值阈值的升高，健康组和BV组之间的差异变得显著。这些发现表明，传统的单一P值阈值（如标准值 = 0.05）可能不足以测试中性和/或生态位中性混合模型。
