Accurate prediction of absolute prokaryotic abundance from DNA concentration Journal Article


Authors: Wirbel, J.; Andermann, T. M.; Brooks, E. F.; Evans, L.; Groth, A.; Dvorak, M.; Chakraborty, M.; Palushaj, B.; Reynolds, G. Z. M.; Porter, I. E.; Al Malki, M.; Rezvani, A.; Gooptu, M.; Elmariah, H.; Runaas, L.; Fei, T.; Martens, M. J.; Bolaños-Meade, J.; Hamadani, M.; Holtan, S.; Jenq, R.; Peled, J. U.; Horowitz, M. M.; Poston, K. L.; Saber, W.; Kean, L. S.; Perales, M. A.; Bhatt, A. S.
Article Title: Accurate prediction of absolute prokaryotic abundance from DNA concentration
Abstract: Quantification of the absolute microbial abundance in a human stool sample is crucial for a comprehensive understanding of the microbial ecosystem, but this information is lost upon metagenomic sequencing. While several methods exist to measure absolute microbial abundance, they are technically challenging and costly, presenting an opportunity for machine learning. Here, we observe a strong correlation between DNA concentration and the absolute number of 16S ribosomal RNA copies as measured by digital droplet PCR in clinical stool samples from individuals undergoing hematopoietic cell transplantation (BMT CTN 1801). Based on this correlation and additional measurements, we trained an accurate yet simple machine learning model for the prediction of absolute prokaryotic load, which showed exceptional prediction accuracy on an external cohort that includes people living with Parkinson's disease and healthy controls. We propose that, with further validation, this model has the potential to enable accurate absolute abundance estimation based on readily available sample measurements. © 2025 The Authors
Keywords: controlled study; human tissue; human cell; major clinical study; case control study; nonhuman; accuracy; randomized controlled trial; cohort analysis; prediction; dna; multicenter study; allogeneic hematopoietic stem cell transplantation; parkinson disease; rna 16s; feces analysis; prokaryote; machine learning; microbiome; metagenomics; human; article; population abundance; droplet digital polymerase chain reaction; cp: microbiology; cp: systems biology; absolute microbial abundance; digital droplet pcr
Journal Title: Cell Reports Methods
Volume: 5
Issue: 5
ISSN: 2667-2375
Publisher: Cell Press  
Date Published: 2025-05-19
Start Page: 101030
Language: English
DOI: 10.1016/j.crmeth.2025.101030
PUBMED: 40300608
PROVIDER: scopus
PMCID: PMC12146642
DOI/URL:
Notes: Article -- MSK Cancer Center Support Grant (P30 CA008748) acknowledged in PDF -- Source: Scopus
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  1. Miguel-Angel Perales
    918 Perales
  2. Jonathan U Peled
    155 Peled
  3. Teng Fei
    40 Fei