Abstract
Microbial minimal generation times range from a few minutes to several weeks. They are evolutionarily determined by variables such as environment stability, nutrient availability, and community diversity. Selection for fast growth adaptively imprints genomes, resulting in gene amplification, adapted chromosomal organization, and biased codon usage. We found that these growth-related traits in 214 species of bacteria and archaea are highly correlated, suggesting they all result from growth optimization. While modeling their association with maximal growth rates in view of synthetic biology applications, we observed that codon usage biases are better correlates of growth rates than any other trait, including rRNA copy number. Systematic deviations to our model reveal two distinct evolutionary processes. First, genome organization shows more evolutionary inertia than growth rates. This results in over-representation of growth-related traits in fast degrading genomes. Second, selection for these traits depends on optimal growth temperature: for similar generation times purifying selection is stronger in psychrophiles, intermediate in mesophiles, and lower in thermophiles. Using this information, we created a predictor of maximal growth rate adapted to small genome fragments. We applied it to three metagenomic environmental samples to show that a transiently rich environment, as the human gut, selects for fast-growers, that a toxic environment, as the acid mine biofilm, selects for low growth rates, whereas a diverse environment, like the soil, shows all ranges of growth rates. We also demonstrate that microbial colonizers of babies gut grow faster than stabilized human adults gut communities. In conclusion, we show that one can predict maximal growth rates from sequence data alone, and we propose that such information can be used to facilitate the manipulation of generation times. Our predictor allows inferring growth rates in the vast majority of uncultivable prokaryotes and paves the way to the understanding of community dynamics from metagenomic data.
Highlights
Maximal growth rates are central to microbial life-history strategies [1,2,3,4,5,6,7,8,9]
We found an increase in copy number of rRNA (Figure 1) and tRNA genes (Figure S1) with decreasing minimal generation times (r = 20.59 and r = 20.51, all p-value,0.0001)
While experimental work has shown the advantages of optimizing codon usage bias for expression of heterologous proteins [49], our results suggest that optimization of highly expressed genes should lead to higher growth rates
Summary
Maximal growth rates are central to microbial life-history strategies [1,2,3,4,5,6,7,8,9]. Among host-associated bacteria, competition often results in increased virulence through selection for higher growth rates as these have an outstanding role in the trade-off between rapid horizontal dissemination and slow clearance from the host [10,11]. Among free-living bacteria there is a trade-off between fast growth in copiotrophs and scavenging potential in slow-growing oligotrophs [4,14,15]. It would be extremely useful to predict maximal growth rates from sequence alone This would allow establishing generation time predictions for the vast numbers of unknown or uncultivated bacteria for which we lack such information. High growth rates result more from the increase in the production of the gene expression machinery than
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