Bioinformatics Modelling and Metabolic Engineering of the Branched Chain Amino Acid Pathway for Specific Production of Mycosubtilin Isoforms in Bacillus subtilis.

Mycosubtilin belongs to the family of lipopeptides. Different isoforms with various antifungal activities can be obtained according to the length and the isomery of the fatty acid. In this work, the activities of the mycosubtilin isoforms were first studied against the pathogen Aspergillus niger, revealing the high activity of the anteiso-C17 isoform. Modification of the mycosubtilin isoform patterns during cultures of the natural strain Bacillus subtilis ATCC 6633 was then investigated through amino acid feeding experiments. In parallel, single-gene knockouts and single-gene overexpression, leading to the overproduction of the anteiso-C15 fatty acid chains, were predicted using informatics tools which provide logical reasoning with formal models of reaction networks. In this way, it was in silico predicted that the single overexpression of the ilvA gene as well as the single knockout of the codY gene may lead to the overproduction of anteiso-C15 fatty acid chains. For the first time, it has been demonstrated that overexpression of ilvA helps to enhance the furniture of odd anteiso fatty acids leading to a favored mycosubtilin anteiso-C17 production pattern (+41%). Alternatively, a knock-out codY mutant led to a higher furniture of even iso fatty acids, leading to a favored mycosubtilin iso-C16 production pattern (+180%). These results showed that increased selective synthesis of particular isoforms of mycosubtilin through metabolic engineering is feasible, disclosing the interest of these approaches for future development of lipopeptide-producing strains.

The Surfactin-Like Lipopeptides From Bacillus spp.: Natural Biodiversity and Synthetic Biology for a Broader Application Range.

Surfactin is a lipoheptapeptide produced by several Bacillus species and identified for the first time in 1969. At first, the biosynthesis of this remarkable biosurfactant was described in this review. The peptide moiety of the surfactin is synthesized using huge multienzymatic proteins called NonRibosomal Peptide Synthetases. This mechanism is responsible for the peptide biodiversity of the members of the surfactin family. In addition, on the fatty acid side, fifteen different isoforms (from C12 to C17) can be incorporated so increasing the number of the surfactin-like biomolecules. The review also highlights the last development in metabolic modeling and engineering and in synthetic biology to direct surfactin biosynthesis but also to generate novel derivatives. This large set of different biomolecules leads to a broad spectrum of physico-chemical properties and biological activities. The last parts of the review summarized the numerous studies related to the production processes optimization as well as the approaches developed to increase the surfactin productivity of Bacillus cells taking into account the different steps of its biosynthesis from gene transcription to surfactin degradation in the culture medium.

Genetic engineering of the branched fatty acid metabolic pathway of Bacillus subtilis for the overproduction of surfactin C14 isoform.

Surfactin, a lipopeptide produced by Bacillus subtilis, is one of the most powerful biosurfactants known. This molecule consists of a cyclic heptapeptide linked to a ß-hydroxy fatty acid chain. The isomery and the length of the fatty acid (FA) chain are responsible for the surfactin's activities. In this study, the gene codY, which encode for the global transcriptional regulator and the gene lpdV, located in the bkd operon (lpdV, bkdAA, bkdAB and bkdB genes), which is responsible for the last step of the branched chain amino acid (BCAA) degradation in acyl-CoA were deleted. The influence of these deletions on the quantitative and qualitative surfactin production was analysed. The surfactin production was quantified by RP-HPLC and the surfactin isoforms were characterized using LC-MS-MS and GC-MS analysis. The results obtained in the mutants showed an enhancement of surfactin specific production by a factor of 5.8 for the codY mutant and 1.4 for lpdV mutant. Moreover qualitative analysis of the lpdV mutant reveals that it mainly produced surfactin C14 isoform (2 fold more than the wild type) with linear FA chain. Complete analysis of the extracellular metabolites using 1 H quantitative NMR reveals a reduced production of acetoin in this mutant. This work demonstrates for the first time an original approach to overproduce specifically surfactin with C14 FA chain.

Predicting changes of reaction networks with partial kinetic information.

We wish to predict changes of reaction networks with partial kinetic information that lead to target changes of their steady states. The changes may be either increases or decreases of influxes, reaction knockouts, or multiple changes of these two kinds. Our prime applications are knockout prediction tasks for metabolic and regulation networks. In a first step, we propose a formal modeling language for reaction networks with partial kinetic information. The modeling language has a graphical syntax reminiscent to Petri nets. Each reaction in a model comes with a partial description of its kinetics, based on a similarity relation on kinetic functions that we introduce. Such partial descriptions are able to model the regulation of existing metabolic networks for which precise kinetic knowledge is usually not available. In a second step, we develop prediction algorithms that can be applied to any reaction network modeled in our language. These algorithms perform qualitative reasoning based on abstract interpretation, by which the kinetic unknowns are abstracted away. Given a reaction network, abstract interpretation produces a finite domain constraint in a novel class. We show how to solve these finite domain constraints with an existing finite domain constraint solver, and how to interpret the solution sets as predictions of multiple reaction knockouts that lead to a desired change of the steady states. We have implemented the prediction algorithm and integrated it into a prediction tool. This journal article extends the two conference papers John et al. (2013) and Niehren et al. (2015) while adding a new prediction algorithm for multiple gene knockouts. An application to single gene knockout prediction for surfactin overproduction was presented in Coutte et al. (2015). It illustrates the adequacy of the model-based predictions made by our algorithm in the wet lab.

Structural simplification of chemical reaction networks in partial steady states.

We study the structural simplification of chemical reaction networks with partial steady state semantics assuming that the concentrations of some but not all species are constant. We present a simplification rule that can eliminate intermediate species that are in partial steady state, while preserving the dynamics of all other species. Our simplification rule can be applied to general reaction networks with some but few restrictions on the possible kinetic laws. We can also simplify reaction networks subject to conservation laws. We prove that our simplification rule is correct when applied to a module of a reaction network, as long as the partial steady state is assumed with respect to the complete network. Michaelis-Menten's simplification rule for enzymatic reactions falls out as a special case. We have implemented an algorithm that applies our simplification rules repeatedly and applied it to reaction networks from systems biology.

Modeling leucine's metabolic pathway and knockout prediction improving the production of surfactin, a biosurfactant from Bacillus subtilis.

A Bacillus subtilis mutant strain overexpressing surfactin biosynthetic genes was previously constructed. In order to further increase the production of this biosurfactant, our hypothesis is that the surfactin precursors, especially leucine, must be overproduced. We present a three step approach for leucine overproduction directed by methods from computational biology. Firstly, we develop a new algorithm for gene knockout prediction based on abstract interpretation, which applies to a recent modeling language for reaction networks with partial kinetic information. Secondly, we model the leucine metabolic pathway as a reaction network in this language, and apply the knockout prediction algorithm with the target of leucine overproduction. Out of the 21 reactions corresponding to potential gene knockouts, the prediction algorithm selects 12 reactions. Six knockouts were introduced in B. subtilis 168 derivatives strains to verify their effects on surfactin production. For all generated mutants, the specific surfactin production is increased from 1.6- to 20.9-fold during the exponential growth phase, depending on the medium composition. These results show the effectiveness of the knockout prediction approach based on formal models for metabolic reaction networks with partial kinetic information, and confirms our hypothesis that precursors supply is one of the main parameters to optimize surfactin overproduction.