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粪便采集方法比较用于宏基因组学和代谢组学研究。

Comparison of Fecal Collection Methods for Microbiome and Metabolomics Studies.

机构信息

Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, United States.

Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, United States.

出版信息

Front Cell Infect Microbiol. 2018 Aug 28;8:301. doi: 10.3389/fcimb.2018.00301. eCollection 2018.


DOI:10.3389/fcimb.2018.00301
PMID:30234027
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6127643/
Abstract

Integrated microbiome and metabolomics analyses hold the potential to reveal interactions between host and microbiota in relation to disease risks. However, there are few studies evaluating how field methods influence fecal microbiome characterization and metabolomics profiling. Five fecal collection methods [immediate freezing at -20°C without preservative, OMNIgene GUT, 95% ethanol, RNA, and Flinders Technology Associates (FTA) cards] were used to collect 40 fecal samples from eight healthy volunteers. We performed gut microbiota 16S rRNA sequencing, untargeted metabolomics profiling, and targeted metabolomics focusing on short chained fatty acids (SCFAs). Metrics included α-diversity and β-diversity as well as distributions of predominant phyla. To evaluate the concordance with the "gold standard" immediate freezing, the intraclass correlation coefficients (ICCs) for alternate fecal collection systems were calculated. Correlations between SCFAs and gut microbiota were also examined. The FTA cards had the highest ICCs compared to the immediate freezing method for α-diversity indices (ICCs = 0.96, 0.96, 0.76 for Shannon index, Simpson's Index, Chao-1 Index, respectively), followed by OMNIgene GUT, RNA, and 95% ethanol. High ICCs (all >0.88) were observed for all methods for the β-diversity metric. For untargeted metabolomics, in comparison to immediate freezing which detected 621 metabolites at ≥75% detectability level, 95% ethanol showed the largest overlapping set of metabolites ( = 430; 69.2%), followed by FTA cards ( = 330; 53.1%) and OMNIgene GUT ( = 213; 34.3%). Both OMNIgene GUT (ICCs = 0.82, 0.93, 0.64) and FTA cards (ICCs = 0.87, 0.85, 0.54) had acceptable ICCs for the top three predominant SCFAs (butyric acid, propionic acid and acetic acid). Nominally significant correlations between bacterial genera and SCFAs ( < 0.05) were observed in fecal samples collected by different methods. Of note, a high correlation between the genus (known butyrate producer) and butyric acid was observed for both immediate freezing ( = 0.83) and FTA cards ( = 0.74). Four alternative fecal collection methods are generally comparable with immediate freezing, but there are differences in certain measures of the gut microbiome and fecal metabolome across methods. Choice of method depends on the research interests, simplicity of fecal collection procedures and ease of transportation to the lab, especially for large epidemiological studies.

摘要

综合微生物组和代谢组学分析有可能揭示宿主与微生物群之间与疾病风险相关的相互作用。然而,很少有研究评估野外方法如何影响粪便微生物组特征和代谢组学分析。我们使用 5 种粪便采集方法[立即在-20°C 下不添加防腐剂冷冻、OMNIgene GUT、95%乙醇、RNA 和 Flinders Technology Associates (FTA) 卡]从 8 名健康志愿者中采集了 40 份粪便样本。我们进行了肠道微生物组 16S rRNA 测序、非靶向代谢组学分析以及靶向代谢组学分析,重点关注短链脂肪酸 (SCFA)。评估指标包括 α 多样性和β 多样性以及主要菌群的分布。为了评估与“黄金标准”立即冷冻的一致性,计算了替代粪便采集系统的组内相关系数 (ICC)。还检查了 SCFA 与肠道微生物群之间的相关性。与立即冷冻法相比,FTA 卡的 α 多样性指数 ICC 最高(Shannon 指数、Simpson 指数、Chao-1 指数的 ICC 分别为 0.96、0.96、0.76),其次是 OMNIgene GUT、RNA 和 95%乙醇。所有方法的 β 多样性指标的 ICC 均较高(均>0.88)。对于非靶向代谢组学,与立即冷冻相比,95%乙醇检测到 621 种≥75%可检测水平的代谢物,显示出最大的重叠代谢物集(=430;69.2%),其次是 FTA 卡(=330;53.1%)和 OMNIgene GUT(=213;34.3%)。OMNIgene GUT(ICC=0.82、0.93、0.64)和 FTA 卡(ICC=0.87、0.85、0.54)对前三种主要 SCFA(丁酸、丙酸和乙酸)的 ICC 均具有可接受性。在不同方法采集的粪便样本中观察到细菌属与 SCFA 之间存在名义上显著的相关性( < 0.05)。值得注意的是,在立即冷冻(=0.83)和 FTA 卡(=0.74)中,属(已知的丁酸盐生产者)与丁酸之间存在高度相关性。四种替代粪便采集方法通常与立即冷冻法相当,但在不同方法之间存在肠道微生物组和粪便代谢组的某些测量值的差异。方法的选择取决于研究兴趣、粪便采集程序的简单性以及对实验室的运输便利性,特别是对于大型流行病学研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0a6/6127643/9fd3972036d3/fcimb-08-00301-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0a6/6127643/8d1b94cac412/fcimb-08-00301-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0a6/6127643/efe7dfc063c4/fcimb-08-00301-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0a6/6127643/390a86132c69/fcimb-08-00301-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0a6/6127643/9fd3972036d3/fcimb-08-00301-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0a6/6127643/8d1b94cac412/fcimb-08-00301-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0a6/6127643/efe7dfc063c4/fcimb-08-00301-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0a6/6127643/390a86132c69/fcimb-08-00301-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0a6/6127643/9fd3972036d3/fcimb-08-00301-g0004.jpg

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本文引用的文献

[1]
Computational Approaches for Integrative Analysis of the Metabolome and Microbiome.

Metabolites. 2017-11-18

[2]
Collection of non-meconium stool on fecal occult blood cards is an effective method for fecal microbiota studies in infants.

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FEMS Microbiol Rev. 2017-8-1

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Comparison of Fecal Collection Methods for Microbiota Studies in Bangladesh.

Appl Environ Microbiol. 2017-5-1

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Am J Epidemiol. 2017-1-15

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Sci Rep. 2016-2-26

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