ArticlemSystems2025
Metagenomic estimation of absolute bacterial biomass in the mammalian gut through host-derived read normalization.
Article in mSystems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
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- Emerging Microbiological and Sensor-Based Approaches for Biofilm Detection in Meat and Poultry Processing Environments.Foods (Basel, Switzerland) · 2026Review
- MATRIX: rapid quantification of total and active microbial cells with single-cell phenotypes for environmental microbiomes.mSystems · 2026Article
- Multidisciplinary Delphi consensus statement on minimal standards for clinical metadata and end points in microbiome studies.Nature reviews. Gastroenterology & hepatology · 2026Review
- Human DNA levels in feces reflect gut inflammation and associate with presence of gut species in IBD patients across the age spectrum.Microbiome · 2026Article
- Ecological partitioning enables phage-antibiotic cooperation in a human Pseudomonas infection.Nature communications · 2026Article
- A foundation model for microbial growth dynamics.bioRxiv : the preprint server for biology · 2025Article
- Microbiome diversity of low biomass skin sites is captured by metagenomics but not 16S amplicon sequencing.bioRxiv : the preprint server for biology · 2025Article
- Mathematical models of the colonic microbiota: an evaluation of accuracy usingFrontiers in nutrition · 2025Article
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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, quantitative polymerase chain reaction (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 in stool were correlated with bacterial-to-diet (B:D) read count ratios, but B:D ratios exhibited a substantial number of outlier points. Host read depletion methods reduced the total number of human 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 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. 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.IMPORTANCEIn this study, we asked whether normalization by host reads alone was sufficient to estimate absolute bacterial biomass directly from stool metagenomic data, without the need for synthetic spike-ins, additional experimental biomass measurements, or training data. The approach assumes that the contribution of host DNA to stool is more constant or stable than biologically relevant fluctuations in bacterial biomass. We find that host read normalization is an effective method for detecting variation in gut bacterial biomass. Absolute bacterial biomass is a key metric that often gets left out of gut microbiome studies, and empowering researchers to include this measure more broadly in their metagenomic analyses should serve to improve our understanding of host-microbiota interactions.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.