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ABSTRACT Rational engineering of gas-fermenting bacteria for high yields of bioproducts is vital for a sustainable bioeconomy. It will allow the microbial chassis to renewably valorize natural resources from carbon oxides, hydrogen, and/or lignocellulosic feedstocks more efficiently. To date, rational design of gas-fermenting bacteria such as changing the expression levels of individual enzymes to obtain the desired pathway flux is challenging, because pathway design must follow a verifiable metabolic blueprint indicating where interventions should be executed. Based on recent advances in constraint-based thermodynamic and kinetic models, we identify key enzymes in the gas-fermenting acetogen Clostridium ljungdahlii that correlate with the production of isopropanol. To this extent, we integrated a metabolic model in comparison with proteomics measurements and quantified the uncertainty for a variety of pathway targets needed to improve the bioproduction of isopropanol. Based on in silico thermodynamic optimization, minimal protein requirement analysis, and ensemble modeling-based robustness analysis, we identified the top two significant flux control sites, i.e., acetoacetyl-coenzyme A (CoA) transferase (AACT) and acetoacetate decarboxylase (AADC), overexpression of which could lead to increased isopropanol production. Our predictions directed iterative pathway construction, which enabled a 2.8-fold increase in isopropanol production compared to the initial version. The engineered strain was further tested under gas-fermenting mixotrophic conditions, where more than 4 g/L isopropanol was produced when CO, CO2, and fructose were provided as the substrates. In a bioreactor environment sparging with CO, CO2, and H2 only, the strain produced 2.4 g/L isopropanol. Our work highlighted that the gas-fermenting chasses can be fine-tuned for high-yield bioproduction by directed and elaborative pathway engineering. IMPORTANCE Highly efficient bioproduction from gaseous substrates (e.g., hydrogen and carbon oxides) will require systematic optimization of the host microbes. To date, the rational redesign of gas-fermenting bacteria is still in its infancy, due in part to the lack of quantitative and precise metabolic knowledge that can direct strain engineering. Here, we provide a case study by engineering isopropanol production in gas-fermenting Clostridium ljungdahlii. We demonstrate that a modeling approach based on the thermodynamic and kinetic analysis at the pathway level can provide actionable insights into strain engineering for optimal bioproduction. This approach may pave the way for iterative microbe redesign for the conversion of renewable gaseous feedstocks.

Stable isotope based metabolic flux analysis is currently the unique methodology that allows the experimental study of the integrated responses of metabolic networks. This method primarily relies on isotope labeling and modeling, which could be a challenge in both experimental and computational biology. In particular, the algorithm implementation for isotope simulation is a critical step, limiting extensive usage of this powerful approach. Here, we introduce EMUlator a Python-based isotope simulator which is developed on Elementary Metabolite Unit (EMU) algorithm, an efficient and powerful algorithm for isotope modeling. We propose a novel adjacency matrix method to implement EMU modeling and exemplify it stepwise. This method is intuitively straightforward and can be conveniently mastered for various customized purposes. We apply this arithmetic pipeline to understand the phosphoketolase flux in the metabolic network of an industrial microbe Clostridium acetobutylicum. The resulting design enables a high-throughput and non-invasive approach for estimating phosphoketolase flux in vivo. Our computational insights allow the systematic design and prediction of isotope-based metabolic models and yield a comprehensive understanding of their limitations and potentials.

Cellulolytic bacteria have the potential to perform lignocellulose hydrolysis and fermentation simultaneously. The metabolic pathways of these bacteria, therefore, require more comprehensive and quantitative understanding. Using isotope tracer, gas chromatography-mass spectrometry, and metabolic flux modeling, we decipher the metabolic network of Clostridium thermocellum, a model cellulolytic bacterium which represents as an attractive platform for conversion of lignocellulose to dedicated products. We uncover that the Embden–Meyerhof–Parnas (EMP) pathway is the predominant glycolytic route whereas the Entner–Doudoroff (ED) pathway and oxidative pentose phosphate pathway are inactive. We also observe that C. thermocellum's TCA cycle is initiated by both Si- and Re-citrate synthase, and it is disconnected between 2-oxoglutarate and oxaloacetate in the oxidative direction; C. thermocellum uses a citramalate shunt to synthesize isoleucine; and both the one-carbon pathway and the malate shunt are highly active in this bacterium. To gain a quantitative understanding, we further formulate a fluxome map to quantify the metabolic fluxes through central metabolic pathways. This work represents the first global in vivo investigation of the principal carbon metabolism of C. thermocellum. Our results elucidate the unique structure of metabolic network in this cellulolytic bacterium and demonstrate the capability of isotope-assisted metabolite studies in understanding microbial metabolism of industrial interests.