Optimal proteome allocation strategies for phototrophic growth in a light-limited chemostat

Abstract

Background Cyanobacteria and other phototrophic microorganisms allow to couple the light-driven assimilation of atmospheric $$\text {CO}_{2}$$ CO 2 directly to the synthesis of carbon-based products, and are therefore attractive platforms for microbial cell factories. While most current engineering efforts are performed using small-scale laboratory cultivation, the economic viability of phototrophic cultivation also crucially depends on photobioreactor design and culture parameters, such as the maximal areal and volumetric productivities. Based on recent insights into the cyanobacterial cell physiology and the resulting computational models of cyanobacterial growth, the aim of this study is to investigate the limits of cyanobacterial productivity in continuous culture with light as the limiting nutrient. Results We integrate a coarse-grained model of cyanobacterial growth into a light-limited chemostat and its heterogeneous light gradient induced by self-shading of cells. We show that phototrophic growth in the light-limited chemostat can be described using the concept of an average light intensity. Different from previous models based on phenomenological growth equations, our model provides a mechanistic link between intracellular protein allocation, population growth and the resulting reactor productivity. Our computational framework thereby provides a novel approach to investigate and predict the maximal productivity of phototrophic cultivation, and identifies optimal proteome allocation strategies for developing maximally productive strains. Conclusions Our results have implications for efficient phototrophic cultivation and the design of maximally productive phototrophic cell factories. The model predicts that the use of dense cultures in well-mixed photobioreactors with short light-paths acts as an effective light dilution mechanism and alleviates the detrimental effects of photoinhibition even under very high light intensities. We recover the well-known trade-offs between a reduced light-harvesting apparatus and increased population density. Our results are discussed in the context of recent experimental efforts to increase the yield of phototrophic cultivation.

A Mechanistic Model of Macromolecular Allocation, Elemental Stoichiometry, and Growth Rate in Phytoplankton

“…The protein pool includes enzymes devoted to hundreds of reactions which may be coarse-grained into several major categories including those related to light-harvesting and electron transport, and those related to biosynthesis, growth, and reproduction ( Figure 3A). Recent proteomic analyses are quantifying broad-brush protein allocation (McKew et al, 2013(McKew et al, , 2015Christie-Oleza et al, 2017;Jahn et al, 2018;Zavřel et al, 2019) in ways which connect to such coarse-grained models (Scott et al, 2010;Burnap, 2015;Reimers et al, 2017;Faizi et al, 2018;Faizi and Steuer, 2019). Recent studies have revealed that a large and highly variable fraction of phytoplankton proteome is devoted to lightharvesting and electron transport (Jahn et al, 2018;Zavřel et al, 2019) and this investment increases as light intensity decreases.…”

“…Models of phytoplankton physiology have sought to relate growth rate (related to fitness) and elemental stoichiometry (related to biogeochemical impacts) to external resource availability (Riley, 1946;Monod, 1949), internal stores of resources (Caperon, 1968;Droop, 1968), and the internal allocation between functional pools and storage molecules (Shuter, 1979;Geider et al, 1998;Kooijman, 2010). Recent models also explicitly represent trade-offs associated with allocation of the resource and proteome (Bonachela et al, 2013;Burnap, 2015;Smith et al, 2016;Reimers et al, 2017;Chen and Smith, 2018;Faizi et al, 2018;Jahn et al, 2018;Faizi and Steuer, 2019). We provide a more comprehensive review of published physiological models in Supplementary Text.…”

Quantitative models of nitrogen-fixing organisms

“…These models are typically constrained by conservation constraints on elemental, electron and energy budgets [27] , [53] , [152] , [153] . Some coarse-grained models resolve macromolecular allocation [121] , [122] , [154] , which can be compared with emerging sources of macromolecular and proteomics data.…”

The Origin of Phototrophy Reveals the Importance of Priority Effects for Evolutionary Innovation

“…Rapid growth and low yield occurs on abundant resources, while slow growth and high yield occurs on scarce resources. Only recently has optimal proteome allocation been analyzed in the context of phototrophy and autotrophy in general [102][103][104] , but the principles are identical when ambient light is treated as a metabolic resource.…”