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Title: Predicting proteome allocation, overflow metabolism, and metal requirements in a model acetogen}
Article:
Abstract:
The unique capability of acetogens to ferment a broad range of substrates renders them ideal candidates for the biotechnological production of commodity chemicals. In particular the ability to grow with H2:CO2 or syngas( a mixture of H2/CO/CO2) makes these microorganisms ideal chassis for sustainable bioproduction. However, advanced design strategies for acetogens are currently hampered by incomplete knowledge about their physiology and our inability to accurately predict phenotypes. Here we describe the reconstruction of a novel genome-scale model of metabolism and macromolecular synthesis( ME-model) to gain new insights into the biology of the model acetogen Clostridium ljungdahlii. The model represents the first ME-model of a Gram-positive bacterium and captures all major central metabolic, amino acid, nucleotide, lipid, major cofactors, and vitamin synthesis pathways as well as pathways to synthesis RNA and protein molecules necessary to catalyze these reactions, thus significantly broadens the scope and predictability. Use of the model revealed how protein allocation and media composition influence metabolic pathways and energy conservation in acetogens and accurately predicted secretion of multiple fermentation products. Predicting overflow metabolism is of particular interest since it enables new design strategies, e. g. the formation of glycerol, a novel product for C. ljungdahlii, thus broadening the metabolic capability for this model microbe. Furthermore, prediction and experimental validation of changing secretion rates based on different metal availability opens the window into fermentation optimization and provides new knowledge about the proteome utilization and carbon flux in acetogens.
Introduction:
Acetogens have been investigated as promising alternative to convert waste gases containing CO2, H2, and CO( i. e., syngas) into multi-carbon commodities[1, 2]. The Wood-Ljungdahl pathway( WLP) enables acetogens to use either H2 or CO as an electron donor with accompanied reduction of CO2, thereby making WLP the only known CO2-fixing pathway coupled to energy conservation[3]. Energetics of autotrophic growth was poorly understood for a long time as no ATP was gained at the substrate level, and not all acetogens contain cytochrome-encoding genes to maintain the proton motive force. It was recently discovered that proton exportation could be coupled to ferredoxin oxidation and NAD+ reduction by the Rnf complex[4]. Models like constraint-based genome-scale models of metabolism( i. e., M-models) have been useful for gaining insight to possible routes of energy flux[5–8]. While M-models have enabled much progress in elucidating cofactor fluxes, other critical components of the cell( e. g., production of macromolecules and mechanistic utilization of metals, vitamins, and cofactors) are usually absent in these models, thereby limiting in-depth understanding of cellular life. So-called metabolic and gene expression models( ME-models) contain not only metabolic reactions, but represent all major cellular processes like macromolecular synthesis and basic transcriptional regulation, significantly broadening the scope and predictability of microbial systems biology[9, 10]. In ME-models, both RNA and protein abundances are explicitly predicted, which means that cofactor requirements can now be explored. ME-models can compute the optimal molecular constitution of a cell as a function of genetic and environmental parameters, providing new inroads for advanced engineering designs. Trace metals, fundamental for all living organisms, are required for catalytic processes essential to energy conservation, metabolism, replication, and maintenance. Yet metals pose a unique challenge for standard computational models as they are neither produced nor consumed biochemically in the model and are generally treated as a lumped sum in the biomass objective function[11], which prevents their proper integration into reactions[12]. ME-models change this paradigm because protein modifications are incorporated into these models. Protein modifications account for the presence of metals in biochemical reactions, thereby enabling predictions of optimal distribution of resources in response to limited metal availability. Thus, ME-models provide a robust, genome-wide approach to define how transition metals affect an organism’s functional network, which addresses the need to bridge chemistry and biology in a systematic way[12, 13]. For acetogens, understanding the role of trace metals is particularly important, as metals are crucial for the WLP[14]. Insights into such requirements provide an opportunity to rationally manipulate the WLP and other pathways for improved biotechnological outcomes[15–17]. Here, we reconstructed and deployed the first ME-model of a Gram-positive bacterium. The completed Clostridium ljungdahlii ME-model, named iJL965-ME, captures all major central metabolic, amino acid, nucleotide, lipid, major cofactors, and vitamin synthesis pathways as well as pathways to synthesis RNA and protein molecules necessary to catalyze these reactions. Furthermore, the reconstruction includes WLP, with updated cofactors, and its associated mechanisms for energy conservation. The model accurately predicted secretion of acetate, ethanol, and glycerol during changing carbon and metal availability and revealed how protein allocation and media composition influence metabolic pathways and energy conservation in this model acetogen.
Discussion:
We showed that the incorporation of the E-matrix into constraint-based genome-scale models significantly widens the scope of their application, including prediction of overflow metabolism and optimal expression levels, as well as media optimization strategies. Such capabilities proved useful for exploring and understanding system responses of C. ljungdahlii. The reconstructed C. ljungdahlii ME-model( iJL965-ME) was not only more accurate than the M-model at predicting growth rates and acetate secretion rates, but was also capable of predicting secretion of ethanol and until now unknown secretion of glycerol( Figs 2 and 3). Furthermore, in silico predictions of gene/subsystem expression were highly comparable to in vivo transcriptomics for three separate conditions, bolstering confidence in predicting macromolecular responses to environmental changes( Fig 4A–4C). C1 metabolism under both autotrophic and mixotrophic conditions was examined in more depth, and the potential of controlling WLP activity through media composition was explored( Figs 4–6). Although the lack of CODH_ACS activity( achieved by removing nickel from the media) may not cease WLP activity entirely, it may stop acetate production( as in vivo nickel depletion results suggest), leading to ethanol production as the main fermentation end product( Fig 6). However, the discrepancy between in silico and in vivo growth rates of nickel-depleted cells grown on fructose implied that WLP was more important than predicted for maximizing growth in mixotrophic conditions( Fig 6) and could be due to regulatory effects. In contrast, nickel was essential for CO-growth, but had no effect on the acetate:ethanol ratio( Fig 5). ME-models provide a comprehensive, genome-scale, systems biology approach to link the environment with macronutrient metabolism. In particular, the combination of C1 metabolism, multi-omics predictions, and cofactor integration into iJL965-ME is an important milestone for a holistic understanding of metals in metabolism. Although nickel was the only trace metal to be investigated here, iJL965-ME invites further studies elucidating specific effects of concurrent metal limitations and genetic perturbations. The ME-model represents an inclusive method that unites analysis and integration of multiple data types.
Acetogens are renowned for their potential biotechnological applications . The model acetogen Clostridium ljungdahlii has been studied intensively for its ability to produce biofuels from sustainable resources , like syngas . We describe a novel genome-scale model of metabolism and gene expression ( ME-model ) to gain insights into this model acetogen . This first ME-model for a Gram-positive bacterium contains all major metabolic and biosynthetic pathways and calculates accurate proteome allocations under diverse growth conditions , thereby significantly broadening the scope of predictability of metabolic models . Furthermore , the ME-model enables rational medium design for improved production . Our experimental validation implies wide applicability to others strains for rapid improvement of yield and titer in biotechnology-relevant applications .