Current sequencing-based analyses of faecal microbiota quantify microbial taxa and metabolic pathways as fractions of the sample sequence library generated by each analysis. Although these relative approaches permit detection of disease-associated microbiome variation, they are limited in their ability to reveal the interplay between microbiota and host health. Comparative analyses of relative microbiome data cannot provide information about the extent or directionality of changes in taxa abundance or metabolic potential. If microbial load varies substantially between samples, relative profiling will hamper attempts to link microbiome features to quantitative data such as physiological parameters or metabolite concentrations. Saliently, relative approaches ignore the possibility that altered overall microbiota abundance itself could be a key identifier of a disease-associated ecosystem configuration. To enable genuine characterization of host–microbiota interactions, microbiome research must exchange ratios for counts. Here we build a workflow for the quantitative microbiome profiling of faecal material, through parallelization of amplicon sequencing and flow cytometric enumeration of microbial cells. We observe up to tenfold differences in the microbial loads of healthy individuals and relate this variation to enterotype differentiation. We show how microbial abundances underpin both microbiota variation between individuals and covariation with host phenotype. Quantitative profiling bypasses compositionality effects in the reconstruction of gut microbiota interaction networks and reveals that the taxonomic trade-off between Bacteroides and Prevotella is an artefact of relative microbiome analyses. Finally, we identify microbial load as a key driver of observed microbiota alterations in a cohort of patients with Crohn’s disease, here associated with a low-cell-count Bacteroides enterotype (as defined through relative profiling).

中文翻译：

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定量微生物组分析将肠道群落变异与微生物负荷联系起来

当前基于测序的粪便微生物群分析将微生物分类群和代谢途径量化为每次分析生成的样本序列库的一部分。尽管这些相关方法允许检测与疾病相关的微生物组变异，但它们揭示微生物群与宿主健康之间相互作用的能力有限。相对微生物组数据的比较分析无法提供有关分类群丰度或代谢潜力变化程度或方向的信息。如果样本之间的微生物负荷差异很大，则相对分析将阻碍将微生物组特征与定量数据（如生理参数或代谢物浓度）联系起来的尝试。突出的是，相对的方法忽略了改变整体微生物群丰度本身可能是疾病相关生态系统配置的关键标识符的可能性。为了能够真正表征宿主-微生物群的相互作用，微生物组研究必须交换计数的比率。在这里，我们通过扩增子测序和微生物细胞流式细胞计数的并行化，构建了一个用于粪便材料定量微生物组分析的工作流程。我们观察到健康个体的微生物负荷差异高达十倍，并将这种差异与肠型分化联系起来。我们展示了微生物丰度如何支持个体之间的微生物群变异以及与宿主表型的共变。定量分析绕过了肠道微生物群相互作用网络重建中的组成效应，并揭示了拟杆菌和普氏菌之间的分类权衡是相对微生物组分析的产物。最后，我们将微生物负荷确定为克罗恩病患者队列中观察到的微生物群改变的关键驱动因素，这里与低细胞计数拟杆菌肠型（通过相对分析定义）相关。