Integrative analysis across metagenomic taxonomic classifiers: A case study of the gut microbiome in aging and longevity in the Integrative Longevity Omics Study

TL;DR

This study compares two methods for analyzing gut microbes and finds that combining their results reveals more age-related changes than using either method alone.

Contribution

The study introduces consensus and meta-analytic approaches to integrate results from multiple metagenomic taxonomic classifiers.

Findings

Classifier-specific inferences are lost when using a single taxonomic classifier.

Both classifiers captured similar age-associated changes in diversity across cohorts.

A meta-analysis approach identified 17 taxa robustly associated with age across cohorts.

Abstract

There are various well-validated taxonomic classifiers for profiling shotgun metagenomics data, with two popular methods, MetaPhlAn (marker-gene-based) and Kraken (k-mer-based), at the forefront of many studies. Despite differences between classification approaches and calls for the development of consensus methods, most analyses of shotgun metagenomics data for microbiome studies use a single taxonomic classifier. In this study, we compare inferences from two broadly used classifiers, MetaPhlAn4 and Kraken2, applied to stool metagenomic samples from participants in the Integrative Longevity Omics study to measure associations of taxonomic diversity and relative abundance with age, replicating analyses in an independent cohort. We also introduce consensus and meta-analytic approaches to compare and integrate results from multiple classifiers. While many results are consistent across the two classifiers, we find classifier-specific inferences that would be lost when using one classifier alone. Both classifiers captured similar age-associated changes in diversity across cohorts, with variability in species alpha diversity driven by differences by classifier. When using a correlated meta-analysis approach (AdjMaxP) across classifiers, differential abundance analysis captures more age-associated taxa, including 17 taxa robustly age-associated across cohorts. This study emphasizes the value of employing multiple classifiers and recommends novel approaches that facilitate the integration of results from multiple methodologies.

The human gut contains communities of microbes that play crucial roles in health and disease. Identifying these microbes from their sequenced DNA is essential for understanding their contributions to a variety of conditions and diseases including aging. Metagenomics studies use computational tools (“classifiers”) to identify microbial species and quantify their abundances based on the DNA present in a sample. However, the tools developed use different strategies and can produce results that disagree, presenting challenges for identifying consistent findings. To address these challenges, we performed an analysis in which we used two different classifiers to investigate changes in gut microbial communities with age in two studies of extreme human longevity. Our study suggests ways to compare and mathematically combine results from multiple classifiers. One such method is a new type of meta-analysis—an analysis that combines evidence from multiple studies—which we used to account for different measurements across classifiers from the same individuals. Overall, we show that, while different classifiers generally produce results that agree, using multiple methods and combining their results enables the discovery of age-associated microbes that would not be found with a single classifier alone.

Figures