Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns

Abstract

AbstractMaximal growth rate is a basic parameter of microbial lifestyle that varies over several orders of magnitude, with doubling times ranging from a matter of minutes to multiple days. Growth rates are typically measured using laboratory culture experiments. Yet, we lack sufficient understanding of the physiology of most microbes to design appropriate culture conditions for them, severely limiting our ability to assess the global diversity of microbial growth rates. Genomic estimators of maximal growth rate provide a practical solution to survey the distribution of microbial growth potential, regardless of cultivation status. We developed an improved maximal growth rate estimator, and implement this estimator in an easy-to-use R package (gRodon), which outperforms the state-of-the-art growth estimator in multiple settings, including in a community context where we implement a novel species abundance correction for metagenomes. Additionally, we estimate maximal growth rates from over 200,000 genomes, metagenome-assembled genomes, and single-cell amplified genomes to survey growth potential across the range of prokaryotic diversity. We provide these compiled maximal growth rates in a publicly-available database (EGGO), which we use to illustrate how culture collections show a strong bias towards organisms capable of rapid growth. We demonstrate how this database can be used to propagate maximal growth rate predictions to organisms for which we lack genomic information, on the basis of 16S rRNA sequence alone. Finally, we observe a bias in growth predictions for extremely slow-growing organisms, ultimately leading us to suggest a novel evolutionary definition of oligotrophy based on the selective regime an organism occupies.SignificanceDespite the wide perception that microbes have rapid growth rates, many environments like seawater and soil are often dominated by microorganisms that can only grow very slowly. Our knowledge about growth is necessarily biased towards easily culturable organisms, which turn out to be those that tend to grow fast, because microbial growth rates have traditionally been measured using lab growth experiments. But how are potential growth rates distributed in nature? We developed a tool to predict maximum growth rate from an organism’s genome sequence (gRodon). We predicted the growth rates of over 200,000 organisms and compiled these predictions in a publicly-available database (EGGO), which illustrates how current collections of cultured microbes are strongly biased towards fast-growing organisms.