Metagenomic estimation of absolute bacterial biomass in the mammalian gut through host-derived read normalization.
收藏NIAID Data Ecosystem2026-05-02 收录
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https://www.ncbi.nlm.nih.gov/sra/SRP596113
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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, 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 count ratios. We compared 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. B:H ratios were correlated with bacterial-to-diet (B:D) read count ratios, but B:D ratios exhibited a substantial number of outlier points, indicating that B:H ratios were more reliable estimates of bacterial biomass. Host read depletion methods reduced the total number of host reads in a given sample, but B:H ratios were strongly correlated before and after host read depletion, indicating that host read depletion did not reduce the utility of B:H ratios. B:H ratios showed expected variation between health and disease states (e.g., a decline in IBD relative to healthy controls), and were generally stable in healthy individuals over time. Finally, we showed how B:H and B:D ratios can be used to track antibiotic treatment response and recovery in both mice and humans. 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, enabling robust absolute biomass estimates directly from stool metagenomic data.
创建时间:
2025-07-18



