TY - JOUR
T1 - MicrobiomeCensus estimates human population sizes from wastewater samples based on inter-individual variability in gut microbiomes
AU - Zhang, Lin
AU - Chen, Likai
AU - Yu, Xiaoqian
AU - Duvallet, Claire
AU - Isazadeh, Siavash
AU - Dai, Chengzhen
AU - Park, Shinkyu
AU - Frois-Moniz, Katya
AU - Duarte, Fabio
AU - Ratti, Carlo
AU - Alm, Eric J.
AU - Ling, Fangqiong
N1 - Funding Information:
Funding:FLacknowledgessupportfromthe NationalScienceFoundationDirectorateof EngineeringFacultyEarlyCareerDevelopment Program(2047470),AlfredP.SloanFoundation MicrobiologyoftheBuiltEnvironmentPostdoctoral Fellowship(2015-14164),aswellasWashington
Funding Information:
FL acknowledges support from the National Science Foundation Directorate of Engineering Faculty Early Career Development Program (2047470), Alfred P. Sloan Foundation Microbiology of the Built Environment Postdoctoral Fellowship (2015-14164), as well as Washington University in St. Louis Mckelvey School of Engineering Faculty Startup Fund. EJA and CR acknowledge funding from the Kuwait Foundation for Advancement of Sciences (KFAS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank Cho C. Yiu, David E Hingston, and Joseph S. Monteiro from the MIT Facilities department for assistance with sewage sampling. We thank Noriko Endo, Sean Gibbons, Tami Lieberman, Xiaofang Jiang and Shijie Zhao for valuable discussions. We thank Mariana Matus and Newsha Ghaeli for acquisition and access of 24-hr sewage time series data.
Publisher Copyright:
© 2022 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2022/9
Y1 - 2022/9
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85139572630&partnerID=8YFLogxK
U2 - 10.1371/journal.pcbi.1010472
DO - 10.1371/journal.pcbi.1010472
M3 - Article
C2 - 36149894
AN - SCOPUS:85139572630
SN - 1553-734X
VL - 18
JO - PLoS computational biology
JF - PLoS computational biology
IS - 9
M1 - e1010472
ER -
