Abstract

Streptomyces species are known for the synthesis of bioactive compounds ranging from antibiotics, antifungals and enzymes with industrial value. Streptomyces murinus CR-43 has been shown to produce antinematodal fatty acids and may be potential for future metabolic engineering for fatty acid production. Mathematical kinetic models can inform and guide metabolic engineering strategies; however, there has been no whole-cell kinetic model of S. murinus CR-43 to-date. In this study, the ab initio whole-cell kinetic model of S. murinus CR-43 is constructed. Enzymatic genes were identified from the complete genome sequence of S. murinus CR-43 and mapped to metabolic reactions using KEGG enzyme nomenclature, enabling systematic reconstruction of the organism’s metabolic reactome. The resulting simulatable model, smuLA26, comprises of 1009 metabolites and 425 unique enzymes with 1385 enzymatic reactions. This model can be a preliminary framework for S. murinus CR-43, which serves as a platform for future refinement and exploration of metabolic engineering strategies targeting fatty acid and secondary metabolite production.

Keywords: Whole-Cell Model; Kinetic Model; Antinematodal Fatty Acids; Differential Equations; Advancesyn Toolkit

Introduction

The Streptomyces species are generally known as a prolific source of antibiotics and antifungal compounds and Streptomyces murinus, a Gram-positive and filamentous soil bacteria, is one of its members [1-5]. Other isolates of the S. murinus species are known to produce pentamycin and actinomycin D-antifungal compounds that possess broad spectrum activity against plant pathogens suggesting promise in agricultural applications [1,6,7]. Due to the ability of the Streptomyces species to secrete proteins efficiently and to thrive under fermentation conditions, the species has been generally explored as hosts for industrial enzyme production [8]. S. murinus CR-43, previously known as Streptomyces costaricanus CR-43T (T = type strain), produces antinematodal fatty acids [9,10]. This suggests that S. murinus may be potential for future metabolic engineering for fatty acid production [11].

Mathematical modelling supports metabolic engineering by providing a structured way to evaluate interventions before testing them experimentally [12,13]. Within this space, Genome-Scale Models (GSMs) and Kinetic Models (KMs) serve as the two primary modelling paradigms [14,15]. While GSMs have become widely adopted, they operate mostly at the level of flux predictions. KMs provide a richer output by including yield predictions and are often simpler to modify for in-silico gene knock-ins [1617]. These strengths make KMs more suited for screening multiple engineering scenarios. As a result, there has been a noticeable rise in interest and advocacy for the construction of new and improved kinetic models [18,19].

However, there is no whole-cell KM of S. murinus to-date. Hence, this study aims to construct a KM of S. murinus CR-43 using ab initio approach by identifying enzymes from its genome and identifying the corresponding reaction from Kyoto Encyclopedia of Genes and Genomes (KEGG) [20]. The result is a whole cell KM of S. murinus CR-43, named as smuLA26, using the nomenclature proposed by Cho and Ling, which consists of 1009 metabolites, 425 enzymes with corresponding transcriptions and translations and 1385 enzymatic reactions [21].

Materials and Methods

Identification of Reactome

The genome of Streptomyces murinus CR-43 (NCBI RefSeq assembly GCF_025231465.1; NCBI GenBank Accession NZ_CP046623.1) was used as source to identify enzymatic genes using the process previously described [17,22,23]. Briefly, each enzymatic gene was identified as a presence of complete Enzyme Commission (EC) number in the GenBank record and mapped into reaction IDs via KEGG Ligand Database for Enzyme Nomenclature [20]. For example, EC 1.1.1.23 (https://www.genome.jp/entry/1.1.1.23) catalyses reactions R01158, R01163 and R03012; where the substrates and products of each rection can be identified.

Model Development

The model was developed using methodology in Sim, et al. [24]. Using standard BioNumbers values, transcriptional output in Escherichia coli can be estimated from about 3000 total RNA polymerases (BioNumbers 106199) of which 25% are elongating (BioNumbers 111676) at 22 nucleotides per second (BioNumbers 104109) [25-27]. At 339.5 Daltons per nucleotide, this leads to about 5600 kDa of mRNA produced each second. Converted into mass (9.3e-10 grams per second) and normalized to the 7e-16 litres per cell environment and 4225 protein-coding genes (BioNumbers 105443), we obtain 2.92 micromolar per gene per second [28,29]. With the average transcript surviving 107.56 seconds, the decay constant is 0.0093 per second (BioNumbers 107666), yielding: d[mRNA]/dt = 0.00292 – 0.0093[mRNA] [30]. For translation, about 1000 peptides per transcript per hour correspond to 0.278 peptides/s (BioNumbers 106382) and degradation occurs at 2.78e-1 per second (BioNumbers 109924) [31,32]. Hence: d[peptide]/dt = 0.278[mRNA] – 0.00000278[peptide]. The model was implemented as an Ordinary Differential Equations (ODE) system with standard median kinetic constants (kcat = 13.7 per second, Km = 1 mM), aligned with AdvanceSyn’s modelling structure [22,33-35].

Model Simulation

The constructed model was tested for simulatability using AdvanceSyn Toolkit [35]. Initial concentrations of all mRNA and enzymes were set to 0 mM. Initial concentrations of all metabolites were set to 1 mM except the following which were set to 1000 mM: (I) C00001 (Water), (II) C00002 (ATP), (III) C00003 (NAD+), (IV) C00004 (NADH), (V) C00005 (NADPH), (VI) C00006 (NADP+), (VII) C00007 (Oxygen), (VIII) C00008 (ADP), (IX) C00011 (Carbon Dioxide), (X) C00014 (Ammonia), (XI) C00025 (L-Glutamate), (XII) C00031 (D-Glucose), (XIII) C00037 (Glycine), (XIV) C00041 (L-Alanine), (XV) C00047 (L-Lysine), (XVI) C00049 (L-Aspartate), (XVII) C00064 (L-Glutamine), (XVIII) C00065 (L-Serine), (XIX) C00073 (L-Methionine), (XX) C00097 (L-Cysteine), (XXI) C00133 (D-Alanine), (XXII) C00148 (L-Proline). The model was simulated using the fourth-order Runge-Kutta method from time zero to 3600 seconds with timestep of 0.1 second and the concentrations of metabolites were bounded between 0 millimolar and 1000 millimolar. The simulation results were sampled every 2 seconds [36,37].

Results and Discussion

The annotated genome of Streptomyces murinus CR-43 consists of 1928 genes, including 1775 protein coding sequences. 425 unique EC numbers consisting of 1385 enzymatic reactions involving 1009 metabolites were identified and developed into a model based on AdvanceSyn Model Specification [35]. In addition, 850 ODEs acting as placeholder for enzyme transcriptions and translations were added.

The smuLA26 model, when loaded into AdvanceSyn Toolkit and simulated, produced clean, error-free trajectories (Fig. 1), underscoring that the model is structurally sound and properly encoded, as previously argued in recent model constructions [17,23,36-42]. Given the complexity of a whole-cell kinetic network, this is nontrivial. That said, the simulated fluxes suggesting that the concentration of tetrahydrofolate (C00101) plateau at about half the concentration of coenzyme A (C00010) but about double the concentration of formate (C00058) may be the result of simplifying assumption that all enzymes share median kinetic parameters [34]. Those uniform parameters compress natural variation; thus, distort quantitative flux balances. Nonetheless, this model constitutes a robust starting point, a “blank-slate” whole-cell KM for S. murinus CR-43, upon which more realistic kinetics, additional metabolic pathways or regulatory and growth dynamics may be layered in future work to aid engineering efforts for metabolite productions [11,43-45].

Figure 1: Selection of simulation results.

Conclusion

Here, we present an ab initio whole cell kinetic model of Streptomyces murinus CR-43, smuLA26; comprising of 1009 metabolites, 425 enzymes with corresponding transcriptions and translations and 1385 enzymatic reactions.

Conflict of Interest

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding Statement

This research did not receive any specific grant from funding agencies in the public, commercial or non-profit sectors.

Acknowledgement

The authors wish to thank the institute, Management Development Institute of Singapore, for its support towards this work. The cost of publication fees was borne by the authors.

Data Availability Statement

Not applicable.

Ethical Statement                                                

The project did not meet the definition of human subject research under the purview of the IRB according to federal regulations and therefore, was exempt.

Informed Consent Statement

Informed consent was taken for this study.

Authors’ Contributions

All authors contributed equally to this paper.

Supplementary Materials

Reaction descriptions and model can be download from https://bit.ly/smuLA26.

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