The infant intestinal microbiome plays an important role in metabolism and immune development with impacts on lifelong health. The linkage between the taxonomic composition of the microbiome and its metabolic phenotype is undefined and complicated by redundancies in the taxon-function relationship within microbial communities. To inform a more mechanistic understanding of the relationship between the microbiome and health, we performed an integrative statistical and machine learning-based analysis of microbe taxonomic structure and metabolic function in order to characterize the taxa-function relationship in early life. Stool samples collected from infants enrolled in the New Hampshire Birth Cohort Study (NHBCS) at approximately 6-weeks (n = 158) and 12-months (n = 282) of age were profiled using targeted and untargeted nuclear magnetic resonance (NMR) spectroscopy as well as DNA sequencing of the V4-V5 hypervariable region from the bacterial 16S rRNA gene. There was significant inter-omic concordance based on Procrustes analysis (6 weeks: p = 0.056; 12 months: p = 0.001), however this association was no longer significant when accounting for phylogenetic relationships using generalized UniFrac distance metric (6 weeks: p = 0.376; 12 months: p = 0.069). Sparse canonical correlation analysis showed significant correlation, as well as identifying sets of microbe/metabolites driving microbiome-metabolome relatedness. Performance of machine learning models varied across different metabolites, with support vector machines (radial basis function kernel) being the consistently top ranked model. However, predictive R2 values demonstrated poor predictive performance across all models assessed (avg: − 5.06% -- 6 weeks; − 3.7% -- 12 months). Conversely, the Spearman correlation metric was higher (avg: 0.344–6 weeks; 0.265–12 months). This demonstrated that taxonomic relative abundance was not predictive of metabolite concentrations. Our results suggest a degree of overall association between taxonomic profiles and metabolite concentrations. However, lack of predictive capacity for stool metabolic signatures reflects, in part, the possible role of functional redundancy in defining the taxa-function relationship in early life as well as the bidirectional nature of the microbiome-metabolome association. Our results provide evidence in favor of a multi-omic approach for microbiome studies, especially those focused on health outcomes.

中文翻译：

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生命早期肠道微生物组和代谢组之间的关联

婴儿肠道微生物群在新陈代谢和免疫发育中发挥着重要作用，影响终生健康。微生物组的分类组成与其代谢表型之间的联系是不确定的，并且由于微生物群落内分类与功能关系的冗余而变得复杂。为了更机械地理解微生物组与健康之间的关系，我们对微生物分类结构和代谢功能进行了综合统计和基于机器学习的分析，以表征生命早期的分类单元与功能关系。使用靶向和非靶向核磁共振 (NMR) 光谱对参加新罕布什尔州出生队列研究 (NHBCS) 的大约 6 周 (n = 158) 和 12 个月 (n = 282) 婴儿的粪便样本进行分析以及细菌 16S rRNA 基因 V4-V5 高变区的 DNA 测序。根据 Procrustes 分析，存在显着的组间一致性（6 周：p = 0.056；12 个月：p = 0.001），但是，当使用广义 UniFrac 距离度量来解释系统发育关系时，这种关联不再显着（6 周：p = 0.376；12 个月：p = 0.069）。稀疏典型相关分析显示出显着的相关性，并确定了驱动微生物组-代谢组相关性的微生物/代谢物组。不同代谢物的机器学习模型的性能各不相同，支持向量机（径向基函数内核）始终是排名最高的模型。然而，预测 R2 值表明所有评估模型的预测性能都很差（平均值：− 5.06% - 6 周；− 3.7% - 12 个月）。相反，Spearman 相关性指标较高（平均：0.344-6 周；0.265-12 个月）。这表明分类相对丰度不能预测代谢物浓度。我们的结果表明分类学特征和代谢物浓度之间存在一定程度的总体关联。然而，粪便代谢特征预测能力的缺乏在一定程度上反映了功能冗余在定义生命早期分类群-功能关系以及微生物组-代谢组关联的双向性质中可能发挥的作用。我们的结果提供了支持微生物组研究多组学方法的证据，特别是那些关注健康结果的研究。