Tradeoffs between growth and enzyme production in bacteria strains from plant litter
Bacteria are involved in many processes that contribute to cycling of carbon, nitrogen, and phosphorus by producing extracellular enzymes that break down organic matter. Owing to the multitude of species and heterogeneity of microbial communities, it has been difficult to characterize individual species’ capacity for nutrient cycling or growth rates, both which have important implications for a species’ overall success. We hypothesize that bacteria trade off resource use efficiency with growth rate according to two distinct strategies: metabolically inefficient fast-growers or efficient slow-growers. We tested this hypothesis with fifty-nine strains of bacteria that were isolated from plant litter in a Mediterranean grassland ecosystem in Loma Ridge, CA (33.4N, 117.4W, elevation 365 m). These strains were grown individually in Miller LB. Accumulated biomass was measured with a spectrophotometer at various time points to establish growth rates. Assays for production of leucine aminopeptidase, β-xylosidase, acid phosphatase, cellobiohydrolase, β-glucosidase, α-glucosidase, and N-acetyl-β-D-glucosaminidase were performed when each strain reached stationary phase.
We found a significant positive relationship (r=0.34, p=0.0015) between overall enzyme activity and the time it takes for the bacterial strains to reach stationary phase (an inverse metric of growth rate). Enzyme activity of α-glucosidase (r=0.74, p=0.0056), acid phosphatase (r=0.74, p=0.0056), β-glucosidase (r=0.85, p=0.004), N-acetyl-β-D-glucosaminidase (r=0.74, p=0.0062), and β-xylosidase (r=0.82, p=0.0011) showed strong significant positive correlations with time to reach stationary phase. There was no significant relationship between cellobiohydrolase or leucine aminopeptidase and time to stationary phase. The significant relationship between overall enzyme activity and growth implies that bacteria do in fact trade off rapid growth and resource acquisition, in line with our hyposthesis. These results could be especially powerful when combined with genomic data from environmental samples to determine the potential for nutrient turnover in a community and inform ecological models. In addition, knowledge of these life history strategies offer clues on how microbes interact with each other, and how this might affect community assembly and functioning.