Rational metabolic engineering through reduction of Elementary Flux Mode complexity

The development and improvement of biotechnological applications for the production of bulk chemicals, the synthesis of which has traditionally depended mainly on the chemical industry, is becoming increasingly important for sustainable industries. The design of microbial strains for this purpose can be improved through rational metabolic engineering that may involve genetic engineering and systems biology approaches. Elementary Flux Mode (EFM) analysis can be used to find a solution space containing all steady-state flux distributions in a metabolic network, aiding metabolic engineering based on gene knockout strategies. However, the task of deciphering the EFM data can be daunting, owing to the combinatorial explosion of the number of EFMs, which may exceed several thousands or millions for large networks. In this study, EFMs were calculated for a reaction network describing the central metabolism and the biosynthetic pathways of 17 amino acids in baker’s yeast. In order to minimise the large EFM solution space, we developed a novel approach based on computational reduction and clustering of EFMs into subsets. This enabled enzymatic reaction elimination (ERE) phenotype analysis in silico and, hence, gene knockout predictions on how best to modify the network for the purpose of increasing the yield of lysine. Computational extractions of EFMs were carried out based on specific biological variables of interest in the EFM datasets. Clustering analysis was then performed to classify the resultant EFMs into subsets. Our analysis indicated that computational extraction of EFMs based on biological variables can significantly reduce the dimensionality of the datasets and ultimately aid the clustering analysis. The effectiveness of our approach was demonstrated by achieving a high level of reduction of noise in the EFM data. Clustering of the dimensionally reduced EFM data enabled development of S. cerevisiae gene knockout strains, which were subsequently tested experimentally to validate predictions.

Project overview

EFM No (cluster no)

Overall reaction Lysine molar yield

4770 (cluster 1)

786·GLUC_ext + 240·L-PHE_EXT + 456·L-GLN_EXT 6·BIOM_EXT + 264·ETOH_EXT + 750·GOH_EXT + 816·CO2_EXT + 216·L-GLUT_EXT + 240·L-TYR_EXT + 240·L-ALA_EXT + 228·L-LYS_EXT

0.290

8426 (cluster 1)

78·GLUC_ext + 4·NH3_ext 3·BIOM_EXT + 40·ETOH_EXT + 59·GOH_EXT + 34·CO2_EXT + 2·L-LYS_EXT + 12·FUM_EXT 0.026

8576 (cluster 1)

95·GLUC_ext + 4·NH3_ext 3·BIOM_EXT + 40·ETOH_EXT + 59·GOH_EXT + 2·L-LYS_EXT + 46·FUM_EXT 0.021

10363 (cluster 2) 123·GLUC_ext + 247·L-TYR_EXT + 9·L-GLN_EXT 6·BIOM_EXT + 43·ETOH_EXT + 82·GOH_EXT + 163·CO2_EXT + 247·L-PHE_EXT + 14·L-ALA_EXT + 2·L-LYS_EXT

0.016

12161 (cluster 3) 318·GLUC_ext + 632·L-TYR_EXT + 24·L-GLN_EXT 16·BIOM_EXT + 108·ETOH_EXT + 212·GOH_EXT + 408·CO2_EXT + 632·L-PHE_EXT + 44·L-ALA_EXT + 2·L-LYS_EXT

0.006

The table below depicts the overall stoichiometry of four EFMs and the molar yields of lysine produced in each EFM

Olusegun Oshota1,2,3, Naglis Malys1,4,5, Evangelos Simeonidis1,6,7, Pedro Mendes1,8,9 1Manchester Centre for Integrative Systems Biology, The University of Manchester, UK | 2Doctoral Training Centre Integrative Systems Biology, The University of Manchester, UK

3Department of Veterinary Medicine, University of Cambridge, UK | 4Faculty of Life Sciences, The University of Manchester, UK | 5School of Life Sciences, The University of Warwick, Coventry, UK 6Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg | 7Institute for Systems Biology, Seattle, USA | 8School of Computer Science, The University of Manchester, UK | 9Virginia Bioinformatics Institute, Virginia Tech, USA

Pipeline and metabolic network used

Enzymatic reaction elimination (ERE) and identification of targets

Genetic intervention and flux

Six gene targets were identified: KGD1, KGD2, LSC1, LSC2, ALT1, GLT1. Corresponding yeast deletion strains were acquired and five double-gene knockout strains (kgd1∆alt1∆, alt1∆kgd2∆, lsc1∆alt1∆, alt1∆lsc2∆ and glt1∆alt1∆) were generated. All strains were grown in standard conditions and assayed for their lysine yield.

We constructed a network that ncludes the central metabolism of S. cerevisiae, biosynthetic pathways of 17 amino acids, biomass and transport reactions.

There are a total of 162 reactions and 172 metabolites in the network.

• Once we identified the most desirable EFM, we sought a set of reactions for elimination through gene knockouts• We remove genes that take out competing EFMs, which should then increase the flux directed towards lysine through the path of choice (EFM 8426)• We iteratively remove reactions and reduce the number of competing EFMs • Ideally, all competing modes should be removed, but this is not possible (many gene deletions are lethal to the organisms)• However, if the number of competing EFMs is reduced, then the flux towards the product of interest should increase• Iterative sequential removal of enzymatic reactions, followed by the determination of the number of EFMs left• The reactions with the largest appreciable EFM reductions were selected as the gene knockouts • Compiled a list of reactions that are not in EFM 8426, but found in the other EFMs of clusters 1-3. Simulations were then carried out iteratively following this

simple algorithm: • a single reaction is eliminated from the reaction network • EFMs of the new network are calculated with COPASI • while EFM 8426 remains as a possible biochemical route go back to (a), otherwise reinstate the last reaction removed and stop

Experimental validation

• Endometabolome measurements were performed applying a non-targeted metabolic profiling strategy using GC/MS

• Intracellular accumulation of lysine in single deletion strains alt1∆ and glt1∆ found to be significantly higher (4- to 5-fold, P < 0.05) than in CS

• Both mutants showed increased levels of 2-oxoglutarate, phenylalanine and fumaric acid, but contained reduced quantities of glutamate

• As expected, an increase in flux towards lysine production was observed in all five double-gene knockout strains

• Successfully redirected flux in two out of six single-gene knockout strains, implying gene dispensability for other four strains (kgd1, kgd2, lsc1 and lsc2)

• glt1∆alt1∆ showed more than six-fold increase in lysine yield

Conclusions

• A computational method for suggesting gene deletions with the objective of over-producing specific metabolites of interest

• Our approach allows for quick and efficient interpretation of the very complex EFM decomposition of the metabolic network, enabling a target gene knockout strategy

• It was possible to find biologically and economically feasible EFMs with high lysine yield from an initial set of around 15,000 EFMs

• Demonstrated that the rational approach to metabolic engineering is to use computational modelling to guide strain development

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