The Systemic Imprint of Growth and Its Uses in Ecological (Meta)Genomics

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.

Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe

“…We estimated MGTs for bacterial communities by calculating MGTs in available whole-bacterial genomes by using the method described in ref. 57 and mapping the 16S rRNA sequences we collected to these genomes.…”

Predicting microbial traits with phylogenies

“…Although genetic data can provide precise information on cellular processes or metabolic pathways, they are generally blind to other ecologically relevant phenotypic traits such as the tolerance to certain abiotic conditions or the specific growth rate (but see Vieira-Silva and Rocha, 2010). Unlike 'macrobial' ecologists, who can directly observe phenotypic characters of plants and animals, microbial ecologists usually face situations where most of the phenotypes of their study organisms are unknown.…”

Survey of (Meta)genomic Approaches for Understanding Microbial Community Dynamics

“…Interestingly, it was established that codon usage skew is specific to metagenome as a whole and is independent of the bacterial community enriched in the metagenome data [75]. This suggested that bacterial genera in the same metagenome can exhibit variable codon usage preferences but overall the metagenome is characterized by an accumulative codon bias which differs from the other metagenome samples markedly just like an observation for single microbial species/genome [76,77].…”