Culture-independent meta-pangenomics enabled by long-read metagenomics reveals associations with pediatric undernutrition

The human gut microbiome is linked to child malnutrition, yet traditional microbiome approaches lack resolution. We hypothesized that complete metagenome-assembled genomes (cMAGs), recovered through long-read (LR) DNA sequencing, would enable pangenome and microbial genome-wide association study (GWAS) analyses to identify microbial genetic associations with child linear growth. LR methods produced 44-64× more cMAGs per gigabase pair (Gbp) than short-read methods, with PacBio (PB) yielding the most accurate and cost-effective assemblies. In a Malawian longitudinal pediatric cohort, we generated 986 cMAGs (839 circular) from 47 samples and applied this database to an expanded set of 210 samples. Machine learning identified species predictive of linear growth. Pangenome analyses revealed microbial genetic associations with linear growth, while genome instability correlated with declining length-for-age Z score (LAZ). This resource demonstrates the power of comparing cMAGs with health trajectories and establishes a new standard for microbiome association studies.

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Publication Details

Journal
Cell
Published
2025-09-09
DOI
https://doi.org/10.1016/j.cell.2025.08.020
Citations
6
Primary Topic
Child Nutrition and Water Access
Type
article
Field-Weighted Citation Impact
6.72
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article

Culture-independent meta-pangenomics enabled by long-read metagenomics reveals associations with pediatric undernutrition

Daniel McDonald, Kenneth Maleta, Kevin Stephenson, Allen Mamerto et al.
6 citations
Cell
Child Nutrition and Water Access
6.72
article

Culture-independent meta-pangenomics enabled by long-read metagenomics reveals associations with pediatric undernutrition

Daniel McDonald, Kenneth Maleta, Kevin Stephenson, Allen Mamerto, Rob Knight, Jeremiah J. Minich, Nolan T. Hartwick, Nicholas Allsing, M. Omar Din, Michael J. Tisza, Emily R. Murray, Todd P. Michael, Caitriona Brennan, Justin P. Shaffer, Mark Manary, Tiffany Duong, Lauren Hansen
article en
6 citations

Abstract

The human gut microbiome is linked to child malnutrition, yet traditional microbiome approaches lack resolution. We hypothesized that complete metagenome-assembled genomes (cMAGs), recovered through long-read (LR) DNA sequencing, would enable pangenome and microbial genome-wide association study (GWAS) analyses to identify microbial genetic associations with child linear growth. LR methods produced 44-64× more cMAGs per gigabase pair (Gbp) than short-read methods, with PacBio (PB) yielding the most accurate and cost-effective assemblies. In a Malawian longitudinal pediatric cohort, we generated 986 cMAGs (839 circular) from 47 samples and applied this database to an expanded set of 210 samples. Machine learning identified species predictive of linear growth. Pangenome analyses revealed microbial genetic associations with linear growth, while genome instability correlated with declining length-for-age Z score (LAZ). This resource demonstrates the power of comparing cMAGs with health trajectories and establishes a new standard for microbiome association studies.

CellVol. 188(23)
Salk Institute for Biological Studies (US), Scripps Institution of Oceanography (US), University of San Diego (US), Baylor College of Medicine (US), Washington University in St. Louis (US), Kamuzu Central Hospital (MW), University of California San Diego (US), Kamuzu University of Health Sciences (MW), California State University, Fresno (US)
Openalex Percentile: Top 3%
Child Nutrition and Water Access
6.72
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