MicrobiomeCensus estimates human population sizes from wastewater samples based on inter-individual variability in gut microbiomes
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journal.pcbi.1010472.pdf
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Published version
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Author(s) • • • • • • • • •
Zhang, Lin
Chen, Likai
Yu, Xiaoqian Annie
Duvallet, Claire
Isazadeh, Siavash
Dai, Chengzhen
Park, Shinkyu
Frois-Moniz, Katya
Duarte, Fabio
Ratti, Carlo
Date Issued
2022
Journal
PLoS Computational Biology
Publisher
Public Library of Science (PLoS)
Citation
Zhang, Lin, Chen, Likai, Yu, Xiaoqian Annie, Duvallet, Claire, Isazadeh, Siavash et al. 2022. "MicrobiomeCensus estimates human population sizes from wastewater samples based on inter-individual variability in gut microbiomes." PLoS Computational Biology, 18 (9).
Version
Final published version
Abstract
The metagenome embedded in urban sewage is an attractive new data source to understand urban ecology and assess human health status at scales beyond a single host. Analyzing the viral fraction of wastewater in the ongoing COVID-19 pandemic has shown the potential of wastewater as aggregated samples for early detection, prevalence monitoring, and variant identification of human diseases in large populations. However, using census-based population size instead of real-time population estimates can mislead the interpretation of data acquired from sewage, hindering assessment of representativeness, inference of prevalence, or comparisons of taxa across sites. Here, we show that taxon abundance and sub-species diversisty in gut-associated microbiomes are new feature space to utilize for human population estimation. Using a population-scale human gut microbiome sample of over 1,100 people, we found that taxon-abundance distributions of gut-associated multi-person microbiomes exhibited generalizable relationships with respect to human population size. Here and throughout this paper, the human population size is essentially the sample size from the wastewater sample. We present a new algorithm, MicrobiomeCensus, for estimating human population size from sewage samples. MicrobiomeCensus harnesses the inter-individual variability in human gut microbiomes and performs maximum likelihood estimation based on simultaneous deviation of multiple taxa’s relative abundances from their population means. MicrobiomeCensus outperformed generic algorithms in data-driven simulation benchmarks and detected population size differences in field data. New theorems are provided to justify our approach. This research provides a mathematical framework for inferring population sizes in real time from sewage samples, paving the way for more accurate ecological and public health studies utilizing the sewage metagenome.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1371/JOURNAL.PCBI.1010472