Updating Urinary Microbiome Analyses to Enhance Biologic Interpretation.
Updating Urinary Microbiome Analyses to Enhance Biologic Interpretation.
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DOI：
10.3389/fcimb.2022.789439
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发表时间：
2022
影响因子：
5.7
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中科院分区：
文献类型：
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评估尿液微生物组的一种方法是16S rRNA基因测序，分析方法正在迅速发展。这项对现有数据集的重新分析旨在确定更新的生物信息学和统计技术是否会影响临床推断。先前的一项研究比较了123名混合性尿失禁(MUI)女性和84名对照组的尿菌群。我们从多个可变区获得了未处理的测序数据，从原始分析中处理了操作分类单元(OTU)表，并确定了临床数据。我们用DADA2对测序数据进行重新处理，生成扩增子序列变异(ASV)表。将ASV表中的分类群与原始OTU表中的类群进行比较，并对更新处理后不同可变区的类群进行比较。在调整临床协变量的同时，使用贝叶斯图形成分回归(BGCR)来测试微生物组成与临床表型(例如，MUI与对照)之间的相关性。几种技术被用来将样本聚集到微生物群落中。多变量回归被用来测试微生物群落和MUI之间的关联，同时再次调整潜在的混杂变量。在通过更新的生物信息学处理确定的分类群中，只有40%是最初确定的，尽管根据相对丰度而言，通过这两种方法确定的分类群代表了测序数据的99%。不同的16S rRNA基因区域导致了不同的回收类群。通过BGCR分析，总体微生物组成与临床表型之间存在关联的概率很低(33.7%)。然而，当微生物数据被聚集到细菌群落中时，我们确认细菌群落与MUI相关。与最初发表的分析相反，我们没有按年龄组确定不同的相关性，这可能是由于统计模型中纳入了不同的协变量。与早期的技术相比，更新的生物信息处理技术恢复了不同的分类群，尽管这些差异大多存在于低丰度的分类群中，这些分类群在整个微生物组中所占的比例很小。虽然总体微生物组成与MUI无关，但我们证实了某些细菌群落与MUI之间的关联。在多变量模型中评估细菌群落和MUI之间的关联时，合并几个与尿液微生物组相关的协变量可以改善推断。
An approach for assessing the urinary microbiome is 16S rRNA gene sequencing, where analysis methods are rapidly evolving. This re-analysis of an existing dataset aimed to determine whether updated bioinformatic and statistical techniques affect clinical inferences. A prior study compared the urinary microbiome in 123 women with mixed urinary incontinence (MUI) and 84 controls. We obtained unprocessed sequencing data from multiple variable regions, processed operational taxonomic unit (OTU) tables from the original analysis, and de-identified clinical data. We re-processed sequencing data with DADA2 to generate amplicon sequence variant (ASV) tables. Taxa from ASV tables were compared to the original OTU tables; taxa from different variable regions after updated processing were also compared. Bayesian graphical compositional regression (BGCR) was used to test for associations between microbial compositions and clinical phenotypes (e.g., MUI versus control) while adjusting for clinical covariates. Several techniques were used to cluster samples into microbial communities. Multivariable regression was used to test for associations between microbial communities and MUI, again while adjusting for potentially confounding variables. Of taxa identified through updated bioinformatic processing, only 40% were identified originally, though taxa identified through both methods represented >99% of the sequencing data in terms of relative abundance. Different 16S rRNA gene regions resulted in different recovered taxa. With BGCR analysis, there was a low (33.7%) probability of an association between overall microbial compositions and clinical phenotype. However, when microbial data are clustered into bacterial communities, we confirmed that bacterial communities are associated with MUI. Contrary to the originally published analysis, we did not identify different associations by age group, which may be due to the incorporation of different covariates in statistical models. Updated bioinformatic processing techniques recover different taxa compared to earlier techniques, though most of these differences exist in low abundance taxa that occupy a small proportion of the overall microbiome. While overall microbial compositions are not associated with MUI, we confirmed associations between certain communities of bacteria and MUI. Incorporation of several covariates that are associated with the urinary microbiome improved inferences when assessing for associations between bacterial communities and MUI in multivariable models.