Correcting for Microbial Blooms in Fecal Samples during Room-Temperature Shipping.
Correcting for Microbial Blooms in Fecal Samples during Room-Temperature Shipping.
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DOI：
10.1128/msystems.00199-16
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发表时间：
2017-03
期刊：
影响因子：
6.4
通讯作者：
Knight R
中科院分区：
文献类型：
--
作者：
Amir A;McDonald D;Navas-Molina JA;Debelius J;Morton JT;Hyde E;Robbins-Pianka A;Knight R
在许多微生物组研究中，必须在室温下储存样品（即，远程现场工作）和运送不需要特殊处理训练的危险材料的样品的能力，例如乙醇（即，公民科学的努力，是至关重要的。然而，尽管室温储存几天已被证明不会掩盖比较组之间生理相关的微生物组差异，但特定细菌分类群仍有变化，特别是γ-变形菌纲的成员，这可能使微生物组谱难以解释。在这里，我们确定了最有问题的类群，并表明从几个快速生长的类群中删除序列足以纠正微生物组特征。使用无菌拭子是收集微生物组样本的一种方便和常见的方法，许多研究表明，室温储存的影响小于受试者之间的生理相关差异。然而，一些细菌分类群，特别是γ-变形菌纲的成员，在室温下生长，有时会混淆微生物组结果，特别是当假设稳定性时。虽然比较基准已经表明，几种保存方法，包括使用95%乙醇，粪便潜血试验（FOBT）和FTA卡，和Omnigene-GUT试剂盒，减少在室温下储存期间的类群丰度的变化，这些技术都有缺点，不能追溯应用到已经收集的样品。在这里，我们使用几种不同的微生物组样品储存条件研究进行了荟萃分析，显示了特定细菌生长的一致趋势（即，“开花”），并介绍了一个程序，以消除最扭曲的分析序列。与使用操作分类单元（OTU）的基于相似性的聚类相比，我们使用一种名为“去模糊”的新技术来识别对应于开花分类群的确切序列，大大减少了误报，也大大降低了运行时间。我们表明，将这种技术应用于为美国肠道项目（AGP）收集的样本，参与者只需将样本邮寄回来，而不使用冰袋或其他防腐剂，产生的结果与已发表的冷冻或以其他方式保存的样本进行的微生物组研究一致。重要性在许多微生物组研究中，必须在室温下储存样品（即，远程现场工作）和运送不需要特殊处理训练的危险材料的样品的能力，例如乙醇（即，公民科学的努力，是至关重要的。然而，尽管室温储存几天已被证明不会掩盖比较组之间生理相关的微生物组差异，但特定细菌分类群仍有变化，特别是γ-变形菌纲的成员，这可能使微生物组谱难以解释。在这里，我们确定了最有问题的类群，并表明从几个快速生长的类群中删除序列足以纠正微生物组特征。
In many microbiome studies, the necessity to store samples at room temperature (i.e., remote fieldwork) and the ability to ship samples without hazardous materials that require special handling training, such as ethanol (i.e., citizen science efforts), is paramount. However, although room-temperature storage for a few days has been shown not to obscure physiologically relevant microbiome differences between comparison groups, there are still changes in specific bacterial taxa, notably, in members of the class Gammaproteobacteria, that can make microbiome profiles difficult to interpret. Here we identify the most problematic taxa and show that removing sequences from just a few fast-growing taxa is sufficient to correct microbiome profiles. The use of sterile swabs is a convenient and common way to collect microbiome samples, and many studies have shown that the effects of room-temperature storage are smaller than physiologically relevant differences between subjects. However, several bacterial taxa, notably members of the class Gammaproteobacteria, grow at room temperature, sometimes confusing microbiome results, particularly when stability is assumed. Although comparative benchmarking has shown that several preservation methods, including the use of 95% ethanol, fecal occult blood test (FOBT) and FTA cards, and Omnigene-GUT kits, reduce changes in taxon abundance during room-temperature storage, these techniques all have drawbacks and cannot be applied retrospectively to samples that have already been collected. Here we performed a meta-analysis using several different microbiome sample storage condition studies, showing consistent trends in which specific bacteria grew (i.e., “bloomed”) at room temperature, and introduce a procedure for removing the sequences that most distort analyses. In contrast to similarity-based clustering using operational taxonomic units (OTUs), we use a new technique called “Deblur” to identify the exact sequences corresponding to blooming taxa, greatly reducing false positives and also dramatically decreasing runtime. We show that applying this technique to samples collected for the American Gut Project (AGP), for which participants simply mail samples back without the use of ice packs or other preservatives, yields results consistent with published microbiome studies performed with frozen or otherwise preserved samples. IMPORTANCE In many microbiome studies, the necessity to store samples at room temperature (i.e., remote fieldwork) and the ability to ship samples without hazardous materials that require special handling training, such as ethanol (i.e., citizen science efforts), is paramount. However, although room-temperature storage for a few days has been shown not to obscure physiologically relevant microbiome differences between comparison groups, there are still changes in specific bacterial taxa, notably, in members of the class Gammaproteobacteria, that can make microbiome profiles difficult to interpret. Here we identify the most problematic taxa and show that removing sequences from just a few fast-growing taxa is sufficient to correct microbiome profiles.