Abstract
Selecting appropriate metabolic engineering targets to build efficient cell factories maximizing the bioconversion of industrial by-products to valuable compounds taking into account time restrictions is a significant challenge in industrial biotechnology. Microbial metabolism engineering following a rational design has been widely studied. However, it is a cost-, time-, and laborious-intensive process because of the cell network complexity; thus, it is important to use tools that allow predicting gene deletions. An in silico experiment was performed to model and understand the metabolic engineering effects on the cell factory considering a second complexity level by transcriptomics data integration. In this study, a systems-based metabolic engineering target prediction was used to increase glycerol bioconversion to succinic acid based on Escherichia coli. Transcriptomics analysis suggests insights on how to increase cell glycerol utilization to further design efficient cell factories. Three E. coli models were used: a core model, a second model based on the integration of transcriptomics data obtained from growth in an optimized culture media, and a third one obtained after integration of transcriptomics data from adaptive laboratory evolution (ALE) experiments. A total of 2,402 strains were obtained with fumarase and pyruvate dehydrogenase being frequently predicted for all the models, suggesting these reactions as essential to increase succinic acid production. Finally, based on using flux balance analysis (FBA) results for all the mutants predicted, a machine learning method was developed to predict new mutants as well as to propose optimal metabolic engineering targets and mutants based on the measurement of the importance of each knockout’s (feature’s) contribution. Glycerol has become an interesting carbon source for industrial processes due to biodiesel business growth since it has shown promising results in terms of biomass/substrate yields. The combination of transcriptome, systems metabolic modeling, and machine learning analyses revealed the versatility of computational models to predict key metabolic engineering targets in a less cost-, time-, and laborious-intensive process. These data provide a platform to improve the prediction of metabolic engineering targets to design efficient cell factories. Our results may also work as a guide and platform for the selection/engineering of microorganisms for the production of interesting chemical compounds.
Highlights
Shifting from petrochemical sources to renewable, abundant, and inexpensive feedstocks to obtain valuable chemicals has become a promising goal for the chemical industry (Vlysidis et al, 2011)
A classical approach for that is adaptive laboratory evolution (ALE), which is based on the selection of microorganisms with superior production capability after random mutagenesis screening
The alternative pathway consists of an oxidation step by glycerol dehydrogenase (GldA) to yield dihydroxyacetone (DHA), followed by phosphorylation by DHA kinase (DhaK) to yield dihydroxyacetone phosphate (DHAP) as well
Summary
Shifting from petrochemical sources to renewable, abundant, and inexpensive feedstocks to obtain valuable chemicals has become a promising goal for the chemical industry (Vlysidis et al, 2011). Metabolic engineering selects targets that increase productivity based on the rationality of trial-and-error development cycles and an understanding of the routes playing a significant role in the synthesis Strain design with this method has been extensively applied to use and/or produce interesting compounds (Kern A. et al, 2007; Chen et al, 2013a,b; Förster and Gescher, 2014; Woo and Park, 2014), including bio-based organic acids by substrate transport enhancement, gene overexpression, and deletion (Shams Yazdani and Gonzalez, 2008; Zhang B. et al, 2012; Buschke et al, 2013; Förster and Gescher, 2014; Yin et al, 2015; Zhu and Jackson, 2015). Making the strain industrially competitive requires much time, effort, and high cost (Rangel et al, 2020)
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