Balaji Balagurunathan

Reconstruction and analysis of a genome-scale metabolic model for Scheffersomyces stipitis

BackgroundFermentation of xylose, the major component in hemicellulose, is essential for economic conversion of lignocellulosic biomass to fuels and chemicals. The yeast Scheffersomyces stipitis (formerly known as Pichia stipitis) has the highest known native capacity for xylose fermentation and possesses several genes for lignocellulose bioconversion in its genome. Understanding the metabolism of this yeast at a global scale, by reconstructing the genome scale metabolic model, is essential for manipulating its metabolic capabilities and for successful transfer of its capabilities to other industrial microbes.ResultsWe present a genome-scale metabolic model for Scheffersomyces stipitis, a native xylose utilizing yeast. The model was reconstructed based on genome sequence annotation, detailed experimental investigation and known yeast physiology. Macromolecular composition of Scheffersomyces stipitis biomass was estimated experimentally and its ability to grow on different carbon, nitrogen, sulphur and phosphorus sources was determined by phenotype microarrays. The compartmentalized model, developed based on an iterative procedure, accounted for 814 genes, 1371 reactions, and 971 metabolites. In silico computed growth rates were compared with high-throughput phenotyping data and the model could predict the qualitative outcomes in 74% of substrates investigated. Model simulations were used to identify the biosynthetic requirements for anaerobic growth of Scheffersomyces stipitis on glucose and the results were validated with published literature. The bottlenecks in Scheffersomyces stipitis metabolic network for xylose uptake and nucleotide cofactor recycling were identified by in silico flux variability analysis. The scope of the model in enhancing the mechanistic understanding of microbial metabolism is demonstrated by identifying a mechanism for mitochondrial respiration and oxidative phosphorylation.ConclusionThe genome-scale metabolic model developed for Scheffersomyces stipitis successfully predicted substrate utilization and anaerobic growth requirements. Useful insights were drawn on xylose metabolism, cofactor recycling and mechanism of mitochondrial respiration from model simulations. These insights can be applied for efficient xylose utilization and cofactor recycling in other industrial microorganisms. The developed model forms a basis for rational analysis and design of Scheffersomyces stipitis metabolic network for the production of fuels and chemicals from lignocellulosic biomass.

Size of gene specific inverted repeat--dependent gene deletion In Saccharomyces cerevisiae.

We describe here an approach for rapidly producing scar-free and precise gene deletions in S. cerevisiae with high efficiency. Preparation of the disruption gene cassette in this approach was simply performed by overlap extension-PCR of an invert repeat of a partial or complete sequence of the targeted gene with URA3. Integration of the prepared disruption gene cassette to the designated position of a target gene leads to the formation of a mutagenesis cassette within the yeast genome, which consists of a URA3 gene flanked by the targeted gene and its inverted repeat between two short identical direct repeats. The inherent instability of the inverted sequences in close proximity facilitates the self-excision of the entire mutagenesis cassette deposited in the genome and promotes homologous recombination resulting in a seamless deletion via a single transformation. This rapid assembly circumvents the difficulty during preparation of disruption gene cassettes composed of two inverted repeats of the URA3, which requires the engineering of unique restriction sites for subsequent digestion and T4 DNA ligation in vitro. We further identified that the excision of the entire mutagenesis cassette flanked by two DRs in the transformed S. cerevisiae is dependent on the length of the inverted repeat of which a minimum of 800 bp is required for effective gene deletion. The deletion efficiency improves with the increase of the inverted repeat till 1.2 kb. Finally, the use of gene-specific inverted repeats of target genes enables simultaneous gene deletions. The procedure has the potential for application on other yeast strains to achieve precise and efficient removal of gene sequences.

In silico design of anaerobic growth-coupled product formation in Escherichia coli: experimental validation using a simple polyol, glycerol

Integrated approaches using in silico model-based design and advanced genetic tools have enabled efficient production of fuels, chemicals and functional ingredients using microbial cell factories. In this study, using a recently developed genome-scale metabolic model for Escherichia coli iJO1366, a mutant strain has been designed in silico for the anaerobic growth-coupled production of a simple polyol, glycerol. Computational complexity was significantly reduced by systematically reducing the target reactions used for knockout simulations. One promising penta knockout E. coli mutant (E. coliΔadhEΔldhAΔfrdC ΔtpiAΔmgsA) was selected from simulation study and was constructed experimentally by sequentially deleting five genes. The penta mutant E. coli bearing the Saccharomyces cerevisiae glycerol production pathway was able to grow anaerobically and produce glycerol as the major metabolite with up to 90% of theoretical yield along with stoichiometric quantities of acetate and formate. Using the penta mutant E. coli strain we have demonstrated that the ATP formation from the acetate pathway was essential for growth under anaerobic conditions. The general workflow developed can be easily applied to anaerobic production of other platform chemicals using E. coli as the cell factory.

Metabolic engineering of Escherichia coli for acetaldehyde overproduction using pyruvate decarboxylase from Zymomonas mobilis

For the sustainable production of acetaldehyde, a key raw-material for a large number of chemical products, microbial production is a promising alternative. We have engineered an Escherichia coli strain for acetaldehyde production from glucose by introducing the pyruvate decarboxylase (Pdc) from Zymomonas mobilis and NADH oxidase (Nox) from Lactococcus lactis. Acetaldehyde production was systematically improved by knocking out the competing metabolic pathways. Multiple knockout strains were created and a final acetaldehyde titre of 0.73g/L was achieved using a quadruple knockout strain E. coli MC4100 ΔadhE ΔldhA ΔfrdC ΔackA-pta. In addition to acetaldehyde, about 0.37g/L acetoin was produced by these strains due to the additional carboligase activity exhibited by pyruvate decarboxylase resulting in a total carbon yield of 0.27g/g glucose.

Transcriptomic Analysis of 3-Hydroxypropanoic Acid Stress in Escherichia coli

The stress response of Escherichia coli to 3-hydroxypropanoic acid (3-HP) was elucidated through global transcriptomic analysis. Around 375 genes showed difference of more than 2-fold in 3-HP-treated samples. Further analysis revealed that the toxicity effect of 3-HP was due to the cation and anion components of this acid and some effects-specific to 3-HP. Genes related to the oxidative stress, DNA protection, and repair were upregulated in treated cells due to the lowered cytoplasmic pH caused by accumulated cations. 3-HP-treated E. coli used the arginine acid tolerance mechanism to increase the cytoplasmic pH. Additionally, the anion effects were manifested as imbalance in the osmotic pressure. Analysis of top ten highly upregulated genes suggests the formation of 3-hydroxypropionaldehyde under 3-HP stress. The transcriptomic analysis shed light on the global genetic reprogramming due to 3-HP stress and suggests strategies for increasing the tolerance of E. coli toward 3-HP.

Graph theory augmented math programming approach to identify minimal reaction sets in metabolic networks

Abstract Bioprocesses are of growing importance as an avenue to produce chemicals. Microorganisms containing only desired catalytic and replication capabilities in their metabolic pathways are expected to offer efficient processes for chemical production. Realizing such minimal cells is the holy grail of metabolic engineering. In this paper, we propose a new method that combines graph-theoretic approaches with mixed-integer liner programming (MILP) to design metabolic networks with minimal reactions. Existing MILP based computational approaches are computationally complex especially for large networks. The proposed graph-theoretic approach offers an efficient divide-and-conquer strategy using the MILP formulation on sub-networks rather than considering the whole network monolithically. In addition to the resulting improvement in computational complexity, the proposed method also aids in identifying the key reactions to be knocked-out in order to achieve the minimal cell. The efficacy of the proposed approach is demonstrated using three case studies from two organisms, Escherichia coli and Saccharomyces cerevisiae .

Potential for Bio-based Chemicals Production in Singapore's Petrochemical Cluster

Abstract Singapores petrochemical industry plays an integral part in the nations economy. The industry is highly dependent on crude oil as the major feedstock. This could prove increasingly unsustainable with depleting fossil fuels resources and growing environmental concerns over greenhouse gases emissions. Using renewable feedstocks such as biomass is one possible solution. This paper evaluates the potential for bio-based chemicals production in Singapores petrochemical cluster, focusing on polylactic acid (PLA). Cost and carbon footprint assessment of the PLA supply chain using various bio-renewable feedstocks sourced from the region is presented.

A Graph Theory Augmented Math Programming Approach to Identify Genetic Targets for Strain Improvement

Abstract Improvement of biological strains through targeted modification of metabolism is essential for successful development of bioprocesses. The computational complexity of optimization procedures routinely used for identifying genetic targets limits their application to genome-scale metabolic networks. In this study, we combined graph theoretic approaches with mixed-integer liner programming (MILP) to reduce the search space and thus reducing computational time. Specifically, we used cut-sets (minimal set of reactions that cuts metabolic networks) as additional constraints to reduce the search space. The efficacy of proposed approach is illustrated by identifying minimal reaction set for Saccharomyces Cerevisiae.