Space is a critical and also challenging frontier in human microbiome research. It has been found that lack of consideration of scales beyond individual and ignoring of microbe dispersal are two crucial roadblocks in preventing deep understanding of the spatial heterogeneity of human microbiome. Assessing and interpreting the heterogeneity and dispersal in microbiomes explicitly are particularly challenging, but implicit approaches such as Taylor's power law (TPL) can be rather effective. Based on TPL, which achieved a rare status of ecological laws, we introduce a general methodology for characterizing the spatial heterogeneity of microbiome (i.e. characterization of microbial spatial distribution) and further apply it for investigating the heterogeneity–disease relationship (HDR) via analyzing a big dataset of 26 MAD (microbiome-associated disease) studies covering nearly all high-profile MADs including obesity, diabetes and gout. It was found that in majority of the MAD cases, the microbiome was sufficiently resilient to endure the disease disturbances. Specifically, in ∼10–16% cases, disease effects were significant—the healthy and diseased cohorts exhibited statistically significant differences in the TPL heterogeneity parameters. We further compared HDR with classic diversity–disease relationship (DDR) and explained their mechanistic differences. Both HDR and DDR cross-verified remarkable resilience of the human microbiomes against MADs.

中文翻译：

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人类微生物组相关疾病中的异质性-疾病关系。

在人类微生物组研究中，空间是至关重要的领域，也是具有挑战性的领域。已经发现，在不深入了解人类微生物组的空间异质性方面，缺乏超越个人规模的考虑和忽略微生物扩散是两个关键的障碍。明确地评估和解释微生物群落中的异质性和扩散特别具有挑战性，但是诸如泰勒幂定律（TPL）之类的隐含方法可能相当有效。基于TPL，其取得了罕见的生态规律，我们介绍了表征微生物组空间异质性的通用方法（即 表征微生物的空间分布），并通过分析26个MAD（微生物相关疾病）研究的大数据集，将其进一步用于研究异质性-疾病关系（HDR），该研究涵盖了几乎所有包括肥胖症，糖尿病和痛风在内的所有重要MAD。发现在大多数MAD病例中，微生物组具有足够的弹性以承受疾病干扰。具体而言，在约10-16％的病例中，疾病影响显着-健康和患病队列在TPL异质性参数上显示出统计学上的显着差异。我们进一步将HDR与经典的多样性-疾病关系（DDR）进行了比较，并解释了它们的机理差异。HDR和DDR都交叉验证了人类微生物群对MAD的显着复原力。