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  • Accepted Manuscript: Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns
Title: Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns
Maximal 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 predicted 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; extensions allow estimates from 16S rRNA sequences alone as well as weighted community estimates from metagenomes. We compared the growth rates of cultivated and uncultivated organisms to illustrate how culture collections are strongly biased toward organisms capable of rapid growth. Finally, we found that organisms naturally group into two growth classes and observed a bias in growth predictions for extremely slow-growing organisms. These observations ultimately led us to suggest evolutionary definitions of oligotrophy and copiotrophy based more » on the selective regime an organism occupies. We found that these growth classes are associated with distinct selective regimes and genomic functional potentials. « less
Authors:
; ;
Award ID(s):
1737409
Publication Date:
NSF-PAR ID:
10277551
Journal Name:
Proceedings of the National Academy of Sciences
Volume:
118
Issue:
12
Page Range or eLocation-ID:
e2016810118
ISSN:
0027-8424
Sponsoring Org:
National Science Foundation
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