Xu Liang, Zakem Emily, Weissman J L
Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA.
Institute for Advanced Computational Science, Stony Brook University, Stony Brook, NY, USA.
Nat Commun. 2025 May 7;16(1):4256. doi: 10.1038/s41467-025-59558-9.
Microbial maximum growth rates vary widely across species and are key parameters for ecosystem modeling. Measuring these rates is challenging, but genomic features like codon usage statistics provide useful signals for predicting growth rates for as-yet uncultivated organisms. Here we present Phydon, a framework for genome-based maximum growth rate prediction that combines codon statistics and phylogenetic information to enhance the precision of maximum growth rate estimates, especially when a close relative with a known growth rate is available. We use Phydon to construct a large and taxonomically broad database of temperature-corrected growth rate estimates for 111,349 microbial species. The results reveal a bimodal distribution of maximum growth rates, resolving distinct groups of fast and slow growers. Our work provides insight into the predictive power of taxonomic information versus mechanistic, gene-based inference.
微生物的最大生长速率在不同物种间差异很大，是生态系统建模的关键参数。测量这些速率具有挑战性，但诸如密码子使用统计等基因组特征为预测尚未培养的生物的生长速率提供了有用信号。在此，我们展示了Phydon，这是一个基于基因组的最大生长速率预测框架，它结合了密码子统计和系统发育信息，以提高最大生长速率估计的精度，特别是在有已知生长速率的近亲存在时。我们使用Phydon构建了一个大型的、分类广泛的数据库，其中包含111,349种微生物物种的温度校正生长速率估计值。结果揭示了最大生长速率的双峰分布，区分出了快速生长者和缓慢生长者的不同群体。我们的工作深入探讨了分类信息与基于机制的、基于基因的推断的预测能力。
