David M. Presby, Summer R. Jasinski, Emily R. Capodilupo.
Abstract
A substantial fraction of the bacterial cytosol is occupied by catalysts and their substrates. While a higher volume density of catalysts and substrates might boost biochemical fluxes, the resulting molecular crowding can slow down diffusion, perturb the reactions’ Gibbs free energies, and reduce the catalytic efficiency of proteins. Due to these tradeoffs, dry mass density likely possesses an optimum that facilitates maximal cellular growth and that is interdependent on the cytosolic molecule size distribution.
Introduction:
The dry mass dissolved in the major compartment of bacterial cells, the cytosol, comprises hundreds of molecular species, including proteins, metabolites, polysaccharides, and nucleic acids. These molecules can be roughly classified into two sectors: the ribosomal sector, dominated by ribosomes and tRNA; and the non-ribosomal sector, comprising mostly metabolites, enzymes, and other proteins.
Methods:
Macromolecular crowding affects the flux of a metabolic reaction in multiple ways. It can (i) slow down diffusion; (ii) affect the free energy of substrate, catalyst, and the substrate-catalyst complex and thereby change their relative equilibrium ratios; and (iii) disturb the folding of a protein and affect the shape of the active site. In our modelling framework, we followed the derivation proposed in Minton that systematically accounts for the effects of crowding on metabolic fluxes caused by effects (i) and (ii).
Discussion:
The linear pathway model shows that reactions with larger catalysts and substrates favor a lower occupancy than reactions with smaller molecules (Fig 3). This effect explains the observation of lower optimal occupancies for decreasing pathway length N in the GBA model cell: the decrease in pathway length simulates the switch from minimal media, requiring on the order of 260 metabolic enzyme species to convert a small number of nutrients to the full range of cellular building blocks, to increasingly richer media, where progressively more biomass components can be taken up directly from the environment, requiring as few as 140 metabolic enzyme species.
Acknowledgments:
We thank Deya Alzoubi and David Heckmann for providing the environments used in the ccFBA simulations. We thank Hugo Dourado and Deniz Sezer for helpful discussions.
Citation: Pang TY, Lercher MJ (2023) Optimal density of bacterial cells. PLoS Comput Biol 19(6): e1011177. https://doi.org/10.1371/journal.pcbi.1011177
Editor: Stefan Klumpp, Georg-August-Universitat Gottingen, GERMANY
Received: November 1, 2022; Accepted: May 11, 2023; Published: June 12, 2023.
Copyright: © 2023 Pang, Lercher. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The source code used for simulations in this study are uploaded to two GItHub repositories, one for the linear model and the other for the whole cell model: 1 https://github.com/TinPang/optimalCytosolicDensity_linearModel 2 https://github.com/TinPang/optimalCytosolicDensity_wholeCellModel.
Funding: This work was supported by DFG Grants CRC 1310 to T.Y.P. and M.J.L. and by a grant of the Volkswagen Foundation in the “Life?” initiative to M.J.L.. This work was also supported, in part, by the MODS project funded from the programme “Profilbildung 2020” (grant number PROFILNRW2020-107-A), an initiative of the Ministry of Culture and Science of the State of North Rhine-Westphalia awarded to M.J.L.. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The grant from Volkswagen Foundation supported the salary of TYP.
Competing interests: The authors have declared that no competing interests exist.
