2024年11月14日，《细胞》杂志在线发表了德国欧洲分子生物学实验室Peer Bork等研究人员的合作发现。该研究表明，粪便微生物负荷是肠道微生物组变异的主要决定因素和疾病相关性的混杂因素。

研究人员开发了一种机器学习方法，仅凭相对丰度数据预测粪便微生物负荷（每克微生物细胞数）。将该预测模型应用于一个大规模的宏基因组数据集（n=34539），研究人员证明了微生物负荷是肠道微生物组变异的主要决定因素，并与多种宿主因素相关，包括年龄、饮食和药物使用。

研究人员进一步发现，对于某些疾病，微生物负荷的变化比疾病本身更能解释患者肠道微生物组的变化。对这一效应进行调整后，绝大多数与疾病相关的物种的统计学显著性显著降低。这些分析揭示了粪便微生物负荷是微生物组研究中的一个重要混杂因素，并突出了它在理解健康和疾病中的微生物组变异中的重要性。

研究人员表示，个体栖息地中的微生物群在相对组成和绝对丰度上均有所不同。虽然测序方法可以确定分类群和基因的相对丰度，但无法提供其绝对丰度的信息。

附：英文原文

Title: Fecal microbial load is a major determinant of gut microbiome variation and a confounder for disease associations

Author: Suguru Nishijima, Evelina Stankevic, Oliver Aasmets, Thomas S.B. Schmidt, Naoyoshi Nagata, Marisa Isabell Keller, Pamela Ferretti, Helene Bk Juel, Anthony Fullam, Shahriyar Mahdi Robbani, Christian Schudoma, Johanne Kragh Hansen, Louise Aas Holm, Mads Israelsen, Robert Schierwagen, Nikolaj Torp, Anja Telzerow, Rajna Hercog, Stefanie Kandels, Dinty H.M. Hazenbrink, Manimozhiyan Arumugam, Flemming Bendtsen, Charlotte Brns, Cilius Esmann Fonvig, Jens-Christian Holm, Trine Nielsen, Julie Steen Pedersen, Maja Sofie Thiele, Jonel Trebicka, Elin Org, Aleksander Krag, Torben Hansen, Michael Kuhn, Peer Bork, Torben Hansen, Matthias Mann, Jelle Matthijnssens, Aleksander Krag, Peer Bork, Manimozhiyan Arumugam, Jonel Trebicka, Morten Karsdal, Ema Anastasiadou, Hans Israelsen, Hans Olav Melberg, Cristina Legido-Quigley, Maja Thiele

Issue&Volume: 2024-11-14

Abstract: The microbiota in individual habitats differ in both relative composition and absolute abundance. While sequencing approaches determine the relative abundances of taxa and genes, they do not provide information on their absolute abundances. Here, we developed a machine-learning approach to predict fecal microbial loads (microbial cells per gram) solely from relative abundance data. Applying our prediction model to a large-scale metagenomic dataset (n = 34,539), we demonstrated that microbial load is the major determinant of gut microbiome variation and is associated with numerous host factors, including age, diet, and medication. We further found that for several diseases, changes in microbial load, rather than the disease condition itself, more strongly explained alterations in patients’ gut microbiome. Adjusting for this effect substantially reduced the statistical significance of the majority of disease-associated species. Our analysis reveals that the fecal microbial load is a major confounder in microbiome studies, highlighting its importance for understanding microbiome variation in health and disease.

DOI: 10.1016/j.cell.2024.10.022

Source: https://www.cell.com/cell/abstract/S0092-8674(24)01204-2