Accurate identification and quantification of commensal microbiota bound by host immunoglobulins.
Accurate identification and quantification of commensal microbiota bound by host immunoglobulins.
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DOI：
10.1186/s40168-020-00992-w
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发表时间：
2021-01-30
期刊：
影响因子：
15.5
通讯作者：
Lam LH
中科院分区：
文献类型：
--
作者：
Jackson MA;Pearson C;Ilott NE;Huus KE;Hegazy AN;Webber J;Finlay BB;Macpherson AJ;Powrie F;Lam LH
确定免疫球蛋白靶向哪些分类群可以揭示重要的宿主-微生物相互作用。通过从样品中分选结合的细菌并使用扩增子测序来确定它们的分类，可以测定细菌分类群的免疫球蛋白结合，这是一种最广泛应用于研究免疫球蛋白A（IgA-Seq）的技术。先前的实验已经通过比较伊加结合和未结合的分选级分中的丰度来对IgA-Seq数据集中的分类单元结合进行评分。然而，由于这些是相对丰度，这样的分数是受其他类群的水平，并代表这些影响的抽象组合。先前研究的实践方法的多样性也保证了所涉及的各个阶段的基准。在这里，我们提供了优化的IgA-Seq协议的设计策略的详细描述。结合考虑上述效应的IgA-Seq数据集的新评分方法，该平台能够准确识别和定量宿主免疫球蛋白靶向的肠道微生物群。使用无菌和Rag 1 −/−小鼠作为阴性对照，菌株特异性伊加抗体作为阳性对照，我们确定了IgA-Seq的最佳试剂和荧光激活细胞分选（FACS）参数。使用模拟的IgA-Seq数据，我们表明，现有的IgA-Seq评分方法受到预排序相对丰度的影响。这对病例对照研究的解释产生了影响，其中各组之间的微生物群组成存在固有差异。我们表明，这些影响可以使用一种新的评分方法的基础上后验概率。最后，我们通过检查来自体内疾病模型的新数据和已发表的数据，证明了IgA-Seq方案和基于概率的评分的实用性。我们提供了一个详细的IgA-Seq协议，以准确地分离IgA结合的类群从肠道样品。使用模拟和实验数据，我们展示了新的基于概率的分数，该分数根据相对丰度数据的组成性质进行调整，以准确量化分类单元水平的伊加结合。所有评分方法都可以在IgAScores R软件包中使用。这些方法应该改善IgA-Seq数据集的生成和解释，并可应用于研究其他免疫球蛋白和样品类型。视频摘要在线版本包含补充材料，可在10. 1186/s40168-020-00992-w获得。
Identifying which taxa are targeted by immunoglobulins can uncover important host-microbe interactions. Immunoglobulin binding of commensal taxa can be assayed by sorting bound bacteria from samples and using amplicon sequencing to determine their taxonomy, a technique most widely applied to study Immunoglobulin A (IgA-Seq). Previous experiments have scored taxon binding in IgA-Seq datasets by comparing abundances in the IgA bound and unbound sorted fractions. However, as these are relative abundances, such scores are influenced by the levels of the other taxa present and represent an abstract combination of these effects. Diversity in the practical approaches of prior studies also warrants benchmarking of the individual stages involved. Here, we provide a detailed description of the design strategy for an optimised IgA-Seq protocol. Combined with a novel scoring method for IgA-Seq datasets that accounts for the aforementioned effects, this platform enables accurate identification and quantification of commensal gut microbiota targeted by host immunoglobulins. Using germ-free and Rag1−/− mice as negative controls, and a strain-specific IgA antibody as a positive control, we determine optimal reagents and fluorescence-activated cell sorting (FACS) parameters for IgA-Seq. Using simulated IgA-Seq data, we show that existing IgA-Seq scoring methods are influenced by pre-sort relative abundances. This has consequences for the interpretation of case-control studies where there are inherent differences in microbiota composition between groups. We show that these effects can be addressed using a novel scoring approach based on posterior probabilities. Finally, we demonstrate the utility of both the IgA-Seq protocol and probability-based scores by examining both novel and published data from in vivo disease models. We provide a detailed IgA-Seq protocol to accurately isolate IgA-bound taxa from intestinal samples. Using simulated and experimental data, we demonstrate novel probability-based scores that adjust for the compositional nature of relative abundance data to accurately quantify taxon-level IgA binding. All scoring approaches are made available in the IgAScores R package. These methods should improve the generation and interpretation of IgA-Seq datasets and could be applied to study other immunoglobulins and sample types. Video abstract The online version contains supplementary material available at 10.1186/s40168-020-00992-w.
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影响因子：
64.5
作者：
Koch MA;Reiner GL;Lugo KA;Kreuk LS;Stanbery AG;Ansaldo E;Seher TD;Ludington WB;Barton GM
通讯作者：
Barton GM
影响因子：
16.6
作者：
Krausgruber T;Schiering C;Adelmann K;Harrison OJ;Chomka A;Pearson C;Ahern PP;Shale M;Oukka M;Powrie F
通讯作者：
Powrie F
DOI：
10.1038/ismej.2012.8
发表时间：
2012-08
期刊：
The ISME journal
影响因子：
--
作者：
通讯作者：
--
影响因子：
32.4
作者：
Bunker JJ;Flynn TM;Koval JC;Shaw DG;Meisel M;McDonald BD;Ishizuka IE;Dent AL;Wilson PC;Jabri B;Antonopoulos DA;Bendelac A
通讯作者：
Bendelac A
影响因子：
1.4
作者：
Apprill, Amy;McNally, Sean;Weber, Laura
通讯作者：
Weber, Laura