Metabolic Analysis of Solventogenic Clostridium Saccharoperbutylacetonicum N1-4 (HMT)


The market for solvent production is predicted to reach $43.4 billion by 2018 with n-butanol having over 20% market share value. Its main use is for the production of biofuel, butyl-acetate, butyl-acrylate, glycol-ethers and plasticisers.

This project focuses on the metabolic and physiologic characterisation of the acetone-butanol producing model strain N1-4. We will use a systems biology approach involving the construction of a genome scale metabolic model of the microorganism, which will be experimentally validated and informed, through the calculation of parameters associated to growth, maintenance, and solvent biosynthesis. This project has created a working GSMN for Clostridium saccharoperbutylacetonicum N1-4 (HMT) with 1296 reactions informed by the genomes sequence sources from NCBI (reference: NC_020291.1) and data from the literature and is being validated via experimental data.

Chemostat cultures will be used to determine the effects of cell stress caused by acid production in solventogenic clostridia, on bacterial growth and energy metabolism. Organic acids are initial products formed during early stages of growth, while solvent production is produced later on, possibly triggered by changes in pH..

Solventogenesis in N1-4 is under control of the sol operon, which contains genes for solvent production, including bld, ctfA, ctfB, adc, and adh. However, the solventogenic genes contained within the sol operon vary between solvent producing species. The saccharolytic species C. beijerinckii and N1-4 have an operon structure identical to the amylolytic C. acetobutylicum. It was found that a N1-4 degenerate strain carrying the wild-type genotype of sol operon, failed to fully induce butanol production. This clearly indicates that other factors, regulatory proteins, 4RNA, and/or metabolites are important for the induction of solvent production. This highlights the need to understand how different operons and Induction mechanisms are connected in order to overcome such problems.

Linking metabolic network analysis, genome analysis and metabolic and physiological observations will help to elucidate the metabolic limitations in solvent production and to design metabolic engineering strategies to overcome those limitations.