A mixed-model approach for estimating drivers of microbiota community composition and differential taxonomic abundance.
A mixed-model approach for estimating drivers of microbiota community composition and differential taxonomic abundance.
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DOI：
10.1128/msystems.00040-23
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发表时间：
2023-08-31
期刊：
影响因子：
6.4
通讯作者：
中科院分区：
文献类型：
--
作者：
下一代测序(NGS)和元编码方法越来越多地应用于野生动物种群，但被广泛应用于研究表型变异的广义线性混合模型(GLMM)方法与群落生态学中通常应用于元编码数据的统计工具之间存在着脱节。在这里，我们描述了一种新的基于GLMM的方法用于分析来自标准元编码数据的分类单元特定序列读取计数的适用性。这种方法允许通过模型随机效应结构中的交互作用项来分解不同驱动因素对群落组成变化(例如，年龄、季节、个体)的贡献。我们提供了实现该方法的指导，并展示了这些模型如何确定特定的分类组对归因于不同驱动因素的影响的责任。我们将这种方法应用于圣基尔达索伊绵羊种群的两个横截面数据集。GLMM与基于差异的方法一致，强调了年龄对微生物群落组成的显著贡献和季节对微生物群落组成的最小贡献，并同时估计了其他技术和生物因素的贡献。我们进一步使用模型预测表明，年龄效应主要是由于拟杆菌门的分类群增加，而菲尔米丘斯门的分类群减少。这种方法为理解元编码数据所衍生的社区结构的驱动因素的影响提供了有力的手段。我们讨论了如何容易地调整我们的方法，使研究人员能够估计其他因素的贡献，如宿主或微生物系统发育，以回答围绕宿主内群落的生态和进化角色的新问题。NGS和粪便代谢编码方法为研究野生肠道微生物群提供了强大的机会。因此，在野生系统中积累了大量数据，产生了对分析方法的需求，这些方法可以适当地调查决定这些群落组成的宿主和环境尺度上的同时因素。在这里，我们描述了一种广义线性混合效应模型(GLMM)方法来分析来自肠道微生物区系元编码的读取计数数据，使我们能够量化多个宿主和环境因素对宿主内群落结构的贡献。我们的方法提供了大多数野外生态学家都熟悉的输出，并且可以使用任何标准的混合效果建模程序包来运行。我们使用圣基尔达索伊绵羊种群的两个元编码数据集来说明这种方法，这些数据集调查了年龄和季节效应作为工作示例。
Next-generation sequencing (NGS) and metabarcoding approaches are increasingly applied to wild animal populations, but there is a disconnect between the widely applied generalized linear mixed model (GLMM) approaches commonly used to study phenotypic variation and the statistical toolkit from community ecology typically applied to metabarcoding data. Here, we describe the suitability of a novel GLMM-based approach for analyzing the taxon-specific sequence read counts derived from standard metabarcoding data. This approach allows decomposition of the contribution of different drivers to variation in community composition (e.g., age, season, individual) via interaction terms in the model random-effects structure. We provide guidance to implementing this approach and show how these models can identify how responsible specific taxonomic groups are for the effects attributed to different drivers. We applied this approach to two cross-sectional data sets from the Soay sheep population of St. Kilda. GLMMs showed agreement with dissimilarity-based approaches highlighting the substantial contribution of age and minimal contribution of season to microbiota community compositions, and simultaneously estimated the contribution of other technical and biological factors. We further used model predictions to show that age effects were principally due to increases in taxa of the phylum Bacteroidetes and declines in taxa of the phylum Firmicutes. This approach offers a powerful means for understanding the influence of drivers of community structure derived from metabarcoding data. We discuss how our approach could be readily adapted to allow researchers to estimate contributions of additional factors such as host or microbe phylogeny to answer emerging questions surrounding the ecological and evolutionary roles of within-host communities. NGS and fecal metabarcoding methods have provided powerful opportunities to study the wild gut microbiome. A wealth of data is, therefore, amassing across wild systems, generating the need for analytical approaches that can appropriately investigate simultaneous factors at the host and environmental scale that determine the composition of these communities. Here, we describe a generalized linear mixed-effects model (GLMM) approach to analyze read count data from metabarcoding of the gut microbiota, allowing us to quantify the contributions of multiple host and environmental factors to within-host community structure. Our approach provides outputs that are familiar to a majority of field ecologists and can be run using any standard mixed-effects modeling packages. We illustrate this approach using two metabarcoding data sets from the Soay sheep population of St. Kilda investigating age and season effects as worked examples.
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影响因子：
4.9
作者：
Björk JR;Hui FKC;O'Hara RB;Montoya JM
通讯作者：
Montoya JM
DOI：
10.1073/pnas.0707221105
发表时间：
2008-01-15
影响因子：
11.1
作者：
Graham, Andrea L.
通讯作者：
Graham, Andrea L.
影响因子：
3.6
作者：
Amato, Katherine R.;Leigh, Steven R.;Garber, Paul A.
通讯作者：
Garber, Paul A.
影响因子：
2.4
作者：
Hayward, A. D.;Pilkington, J. G.;Kruuk, L. E. B.
通讯作者：
Kruuk, L. E. B.
影响因子：
3.7
作者：
Avramenko, Russell W.;Redman, Elizabeth M.;Gilleard, John S.
通讯作者：
Gilleard, John S.