The evolving copiotrophic/oligotrophic dichotomy: From Winogradsky to physiology and genomics

The distinct cell physiology of Bradyrhizobium at the population and cellular level

The α-Proteobacteria belonging to Bradyrhizobium genus are microorganisms of extreme slow growth. Despite their extended use as inoculants in soybean production, their physiology remains poorly characterized. In this work, we produced quantitative data on four different isolates: B. diazoefficens USDA110, B. diazoefficiens USDA122, B. japonicum E109 and B. japonicum USDA6 which are representative of specific genomic profiles. Notably, we found conserved physiological traits conserved in all the studied isolates: (i) the lag and initial exponential growth phases display cell aggregation; (ii) the increase in specific nutrient concentration such as yeast extract and gluconate hinders growth; (iii) cell size does not correlate with culture age; and (iv) cell cycle presents polar growth. Meanwhile, fitness, cell size and in vitro growth widely vary across isolates correlating to ribosomal RNA operon number. In summary, this study provides novel empirical data that enriches the comprehension of the Bradyrhizobium (slow) growth dynamics and cell cycle.

r/K selection of GC content in prokaryotes

The guanine/cytosine (GC) content of prokaryotic genomes is species-specific, taking values from 16% to 77%. This diversity of selection for GC content remains contentious. We analyse the correlations between GC content and a range of phenotypic and genotypic data in thousands of prokaryotes. GC content integrates well with these traits into r/K selection theory when phenotypic plasticity is considered. High GC-content prokaryotes are r-strategists with cheaper descendants thanks to a lower average amino acid metabolic cost, colonize unstable environments thanks to flagella and a bacillus form and are generalists in terms of resource opportunism and their defence mechanisms. Low GC content prokaryotes are K-strategists specialized for stable environments that maintain homeostasis via a high-cost outer cell membrane and endospore formation as a response to nutrient deprivation, and attain a higher nutrient-to-biomass yield. The lower proteome cost of high GC content prokaryotes is driven by the association between GC-rich codons and cheaper amino acids in the genetic code, while the correlation between GC content and genome size may be partly due to functional diversity driven by r/K selection. In all, molecular diversity in the GC content of prokaryotes may be a consequence of ecological r/K selection.

Ecology theory disentangles microbial dichotomies

Microbes are often discussed in terms of dichotomies such as copiotrophic/oligotrophic and fast/slow-growing microbes, defined using the characterisation of microbial growth in isolated cultures. The dichotomies are usually qualitative and/or study-specific, sometimes precluding clear-cut results interpretation. We can unravel microbial dichotomies as life history strategies by combining ecology theory with Monod curves, a laboratory mathematical tool of bacterial physiology that relates the specific growth rate of a microbe with the concentration of a limiting nutrient. Fitting of Monod curves provides quantities that directly correspond to key parameters in ecological theories addressing species coexistence and diversity, such as r/K selection theory, resource competition and community structure theory and the CSR triangle of life strategies. The resulting model allows us to reconcile the copiotrophic/oligotrophic and fast/slow-growing dichotomies as different subsamples of a life history strategy triangle that also includes r/K strategists. We also used the number of known carbon sources together with community structure theory to partially explain the diversity of heterotrophic microbes observed in metagenomics experiments. In sum, we propose a theoretical framework for the study of natural microbial communities that unifies several existing proposals. Its application would require the integration of metagenomics, metametabolomics, Monod curves and carbon source data.