The linkage between nutrient supply, intracellular enzyme abundances and bacterial growth: New evidences from the central carbon metabolism of Corynebacterium glutamicum

- Enzyme abundances in C. glutamicum in response to growth on limiting as well as changing carbon sources were studied.
- Cells maintain stable enzyme concentrations when grown on d-glucose as sole carbon and energy source.
- Switching from one carbon source to another results in highly specific enzyme regulation patterns.
- A conceptual model framework is proposed, that describes cell population growth as a nested function of nutrient supply and intracellular enzyme abundances.

Corynebacterium glutamicum serves as important production host for small molecular compounds that are derived from precursor molecules of the central carbon metabolism. It is therefore a well-studied model organism of industrial biotechnology. However, a deeper understanding of the regulatory principles underlying the synthesis of central metabolic enzymes under different environmental conditions as well as its impact on cell growth is still missing. We studied enzyme abundances in C. glutamicum in response to growth on: (i) one limiting carbon source by sampling chemostat and fed-batch cultivations and (ii) changing carbon sources provided in excess by sampling batch cultivations. The targeted quantification of 20 central metabolic enzymes by isotope dilution mass spectrometry revealed that cells maintain stable enzyme concentrations when grown on d-glucose as single carbon and energy source and, most importantly, independent of its availability. By contrast, switching from d-glucose to d-fructose, d-mannose, d-arabitol, acetate, l-lactate or l-glutamate results in highly specific enzyme regulation patterns that can partly be explained by the activity of known transcriptional regulators. Based on these experimental results we propose a simple framework for modeling cell population growth as a nested function of nutrient supply and intracellular enzyme abundances. In summary, our study extends the basis for the formulation of predictive mechanistic models of bacterial growth, applicable in industrial bioprocess development.