Elsevier

Biochemical Engineering Journal

Volume 127, 15 November 2017, Pages 229-241
Biochemical Engineering Journal

Regular article
Analyses of extracellular protein production in Bacillus subtilis – I: Genome-scale metabolic model reconstruction based on updated gene-enzyme-reaction data

https://doi.org/10.1016/j.bej.2017.07.005Get rights and content

Highlights

  • Gene-enzyme-reaction data were collected to reconstruct B. subtilis reaction network.

  • BsRN-2016 contains 1144 genes linked to 1955 reactions and 1103 metabolites.

  • Thermodynamic analyses and elimination of unconnected-reactions led to iBsu1144.

  • iBsu1144 was solved with SAP fermentation-data at three oxygen transfer conditions.

  • Engineering sites for SAP synthesis and host cell constructions were determined.

Abstract

Bacillus subtilis genome-scale model (GEM) reconstruction was stimulated by the recent sequencing and consequent re-annotations. The updated gene-enzyme-reaction data were collected from databases to reconstruct B. subtilis reaction network BsRN-2016 containing 1144 genes linked to 1955 reactions and 1103 metabolites. Thermodynamic analysis was conducted to identify reversibility and directionality of the reactions. By elimination of unconnected-reactions from BsRN-2016, reconstruction process of the first third-generation GEM iBsu1144 employing 1083 reactions linked to 719 genes was completed. The stoichiometric flux-balance based model was solved using time-profiles of serine alkaline protease fermentation-data at three different oxygen-transfer conditions creating perturbations on the intracellular reaction-network. Testing iBsu1144 dignity with three different objective functions indicated superior robustness of the GEM. Comparison of iBsu1144 results with a second-generation GEM was demonstrated. Insights obtained from flux distributions were used to determine metabolic engineering sites. Asparagine, isoleucine, threonine, and aspartic acid were determined as the primary rate-limiting amino acids to be considered as corresponding metabolic engineering sites in SAP synthesis in B. subtilis. Flux variability analysis carried out for the optimum condition reveals that 288 reactions are active and linked to 317 genes, so called substantial genes. The blocked 735 reactions linked to 533 genes, formed a platform for guided gene deletions in B. subtilis to generate simplified host strains.

Introduction

Bacillus subtilis and its relatives are prodigious producers of industrial enzymes, such as proteases and amylases. Thus, B. subtilis has been the model of Firmiculates for decades, since the absence of an outer membrane combined with an efficient Sec-dependent secretion pathway [1] means that proteins can be secreted directly into the culture medium at high concentrations [2]. In recent years, considerable effort has been aimed at developing B. subtilis as host for the production of heterologous proteins. Meanwhile, the studies on: (i) promoters and suitable ribosome-binding sites [3], [4], [5], (ii) signal peptides [6], [7], (iii) secretion pathway [5], [8], [9], [10], (iv) proteolytic degradation of proteins [11], and (v) genome reductions for host generation [12], [13] underpin the crucial aim to increase recombinant protein (r-protein) production in B. subtilis and stimulate more research directed to metabolic and bioreactor engineering. Moreover, being sequenced and updated totally, B. subtilis [14], [15], [16] has also been shown to be an excellent model organism for systems biological analyses [2].

Intracellular reaction fluxes can be calculated by solving flux-balance based stoichiometric models based on intracellular reaction networks together with elaborate fermentation data [17]. Therefore, based on reliable models calculation of reaction fluxes is an important tool in metabolic engineering, allowing detailed quantification of in vivo fluxes which provide valuable information on cellular physiology that can be applied for engineering metabolic and regulatory pathways, dignity of which is dependent on the quantity and reliability of intracellular reactions.

Genome sequencing and subsequently annotation studies enable construction of genome scale reaction networks with high number of reactions interrelated with gene- and enzyme- data. B. subtilis genome was sequenced first by Kunst et al. [14] with 4100 protein-coding genes, and the annotated genome sequence stimulated the studies for B. subtilis network constructions [18], [19], [20], which were converted into in silico genome scale models (GEMs). Consequently, the first three GEMs reported by Oh et al. [18], Goelzer et al. [19], and Henry et al. [20] which can be regarded as the first-generation B. subtilis GEMs were based on the genome annotation of Kunst et al. [14]; thereafter, the second-generation GEMs of Tanaka et al. [21] and Hao et al. [22] were reconstructed after the genome annotation of Barbe et al. [15] (Table 1a). In order to evaluate the B. subtilis GEMs, type of data used in the construction [18] and reconstructions [19], [20], [21], [22] and in validation processes of the first- and second- generation B. subtilis GEMs are summarized in Table 1b. Furthermore, the strategies applied for optimum flux distributions in model constructions and their validation are summarized in Table 1c. Also, the accuracy of the GEMs are summarized in Table 1d.

Genome-scale reaction networks need to involve all accurate reactions based on correct gene-enzyme-reaction annotations. Therefore, resequencing of a genome stimulates reconstruction of genome scale model(s). In this context, the third re-sequencing of B. subtilis genome in 2013 by Belda et al. [16] and the subsequent re-annotation studies renewed the gene-enzyme-reaction data and created an updated data platform for the field of biochemical reaction engineering to reconstruct a renewed and expanded genome scale B. subtilis reaction network. The flow of updated gene-enzyme-reaction data enabled the authors to reconstruct the updated third-generation B. subtilis reaction network, BsRN-2016. Through thermodynamic analyses and elimination of unconnected-reactions, BsRN-2016 was converted into the first third-generation GEM, so-called iBsu1144. The dignity and robustness of iBsu1144 was tested in predicting intracellular reaction fluxes in a protein fermentation process, i.e., serine alkaline protease (SAP), using elaborate experimental data used in the first flux analysis work for a protein fermentation [29], [30]. The model presents a step towards more complete description of protein production in B. subtilis which is required to understand and optimize fermentation processes. Insights obtained from the intracellular flux distributions at well-defined low-, medium-, and high-oxygen transfer conditions throughout the bioprocess during batch-fermentations were used to determine metabolic engineering sites for the protein synthesis and for host cell constructions for protein productions. The reconstructed third generation GEM iBsu1144 was compared with a second- generation GEM [21], and in the light of information gathered from the GEM iBsu1144, future perspectives were discussed.

Section snippets

Mass flux balance-based analysis

Stoichiometric analysis of metabolic flux distributions [31] with flux balance (FB) methodology aims calculation of flow of metabolites through the mathematically represented intracellular reaction network without kinetic parameters. As it does not use kinetic parameters, it cannot predict metabolite concentrations, and it is only suitable for the calculation of fluxes at a mathematically well defined state. When fermentation data are used as constraints, an allowable solution space is

Reconstruction of B. subtilis reaction network and the genome-scale model

The third-generation B. subtilis reaction network, and then the GEM were constructed manually with a high level of accuracy and consistency through five interconnected steps (Fig. 2):

1. Based on the recent resequencing work [16] the updated annotations were collected from the databases; MicroCyc [36], SEED, GenoList, MetaCyc, UniProt, SubtiList, KEGG, ExPASy, BSORF, BRENDA, and the literature. The reactions were evaluated and B. subtilis reaction network BsRN-2016 containing 1144 genes, 1955

Conclusions

We presented B. subtilis reaction network BsRN-2016 and the first third-generation genome scale model (GEM) iBsu1144 with an updated gene-enzyme-reaction data for B. subtilis based on the third-sequencing of its genome [16] in 2013 and consequent re-annotation studies, following the first-generation GEMs [18], [19], [20] based on the sequencing of Kunst et al. [14] and second-generation GEMs [21], [22] based on the re-sequencing Barbe et al. [15], within a decade. iBsu1144 was used for an

Acknowledgements

This work was supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK) through the project 109R025. PK was awarded a PhD scholarship (BIDEB: 2210) by the TÜBİTAK.

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