mSystemsMicrobiologyPeer-reviewed journal article
Metagenomic estimation of absolute bacterial biomass in the mammalian gut through host-derived read normalization.
Gechlang TangiD, Alex V. Carr, Crystal Perez, Katherine Ramos SarmientoiD, Lisa LevyiD, Johanna W. LampeiD, Christian DieneriD, Sean M. GibbonsiD
Abstract
Absolute bacterial biomass estimation in the human gut is crucial for understanding microbiome dynamics and host-microbe interactions. Current methods for quantifying bacterial biomass in stool, such as flow cytometry, qPCR, or spike-ins (i.e., adding cells or DNA from an organism not normally found in a sample), can be labor-intensive, costly, and confounded by factors like water content, DNA extraction efficiency, PCR inhibitors, and other technical challenges that add bias and noise. We propose a simple, cost-effective approach that circumvents some of these technical challenges: directly estimating bacterial biomass from metagenomes using bacterial-to-host (B:H) read ratios. We compare B:H ratios to the standard methods outlined above, demonstrating that B:H ratios are useful proxies for bacterial biomass in stool and possibly in other host-associated substrates. We show how B:H ratios can be used to track antibiotic treatment response and recovery in both mice and humans, which showed 403-fold and 45-fold reductions in bacterial biomass during antibiotic treatment, respectively. Our results indicate that host and bacterial metagenomic DNA fractions in human stool fluctuate longitudinally around a stable mean in healthy individuals, and the average host read fraction varies across healthy individuals by < 8-9 fold. B:H ratios offer a convenient alternative to other absolute biomass quantification methods, without the need for additional measurements, experimental design considerations, or machine learning algorithms, enabling retrospective absolute biomass estimates from existing stool metagenomic data.
- organism: human
- organism: mouse
- assay: metagenomics
- research focus: benchmarking
- research focus: differential expression
- research focus: variant calling
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 23
- FWCI
- 6.91
- Field percentile
- top 2%
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