不依赖培养的长读长宏基因组学揭示宏泛基因组学与儿童营养不良的关联，这一成果由加州大学Todd P. Michael团队经过不懈努力而取得。相关论文于2025年9月9日发表在《细胞》杂志上。
研究人员假设，通过长读（LR） DNA测序恢复的完整宏基因组组装基因组（cMAGs）将使泛基因组和微生物全基因组关联研究（GWAS）分析能够确定微生物遗传与儿童线性生长的关联。LR方法比短读方法每千兆酶对（Gbp）多产生44 - 64倍的cMAGs， PacBio（PB）产生最准确和最经济的组装。在马拉维纵向儿科队列中，该研究组从47个样本中生成了986个cMAGs（839个圆形），并将该数据库应用于扩展的210个样本。机器学习识别物种预测线性增长。泛基因组分析显示，微生物遗传与线性生长相关，而基因组不稳定性与年龄长度Z评分（LAZ）下降相关。这种抵抗证明了将cMAGs与健康轨迹进行比较的能力，并为微生物组关联研究建立了新的标准。
据悉，人类肠道微生物组与儿童营养不良有关，但传统的微生物组方法缺乏解决方案。
附：英文原文
Title: Culture-independent meta-pangenomics enabled by long-read metagenomics reveals associations with pediatric undernutrition
Author: Jeremiah J. Minich, Nicholas Allsing, M. Omar Din, Michael J. Tisza, Kenneth Maleta, Daniel McDonald, Nolan Hartwick, Allen Mamerto, Caitriona Brennan, Lauren Hansen, Justin Shaffer, Emily R. Murray, Tiffany Duong, Rob Knight, Kevin Stephenson, Mark J. Manary, Todd P. Michael
Issue&Volume: 2025-09-09
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.
DOI: 10.1016/j.cell.2025.08.020
Source: https://www.cell.com/cell/abstract/S0092-8674(25)00975-4