Multi-omic characterisation of Streptomyces hygroscopicus NRRL 30439: detailed assessment of its secondary metabolic potential.

The emergence of multidrug-resistant pathogenic bacteria creates a demand for novel antibiotics with distinct mechanisms of action. Advances in next-generation genome sequencing promised a paradigm shift in the quest to find new bioactive secondary metabolites. Genome mining has proven successful for predicting putative biosynthetic elements in secondary metabolite superproducers such as Streptomycetes. However, genome mining approaches do not inform whether biosynthetic gene clusters are dormant or active under given culture conditions. Here we show that using a multi-omics approach in combination with antiSMASH, it is possible to assess the secondary metabolic potential of a Streptomyces strain capable of producing mannopeptimycin, an important cyclic peptide effective against Gram-positive infections. The genome of Streptomyces hygroscopicus NRRL 30439 was first sequenced using PacBio RSII to obtain a closed genome. A chemically defined medium was then used to elicit a nutrient stress response in S. hygroscopicus NRRL 30439. Detailed extracellular metabolomics and intracellular proteomics were used to profile and segregate primary and secondary metabolism. Our results demonstrate that the combination of genomics, proteomics and metabolomics enables rapid evaluation of a strain's performance in bioreactors for industrial production of secondary metabolites.

Linking genotype and phenotype in an economically viable propionic acid biosynthesis process.

BACKGROUND: Propionic acid (PA) is used as a food preservative and increasingly, as a precursor for the synthesis of monomers. PA is produced mainly through hydrocarboxylation of ethylene, also known as the 'oxo-process'; however, Propionibacterium species are promising biological PA producers natively producing PA as their main fermentation product. However, for fermentation to be competitive, a PA yield of at least 0.6 g/g is required. RESULTS: A new strain able to reach the required yield was obtained using genome shuffling. To gain insight into the changes leading to the improved phenotype, the genome of the new strain was sequenced, and metabolomics and transcriptomics data were obtained. In combination, the data revealed three key mutations: (i) a mutation in the promoter region of a sugar transporter which enables an increase in the uptake rate of sucrose; (ii) a mutation in a polar amino acid transporter which improves consumption of amino acids and acid tolerance; and (iii) a mutation in a gene annotated as a cytochrome C biogenesis gene, which is likely responsible for the coupling of the Wood-Werkman cycle to ATP production were responsible for the phenotype. The bioprocess was further enhanced with a feeding strategy that achieved 70 g/L of product. The proposed bioprocess is expected to outperform the economics of the current 'oxo-process' by 2020. CONCLUSIONS: In this study, using genome shuffling, we obtained a strain capable of producing PA exceeding the commercial needs. The multiomics comparison between the novel strain and the wild type revealed overexpression of amino acid pathways, changes in sucrose transporters and an increased activity in the methylglyoxal and the glucuronate interconversion pathways. The analysis also suggests that a mutation in the cytochrome C biogenesis gene, coupled with ATP production through the Wood-Werkman cycle, may be responsible for the increased PA production.

Formulation, construction and analysis of kinetic models of metabolism: A review of modelling frameworks.

Kinetic models are critical to predict the dynamic behaviour of metabolic networks. Mechanistic kinetic models for large networks remain uncommon due to the difficulty of fitting their parameters. Recent modelling frameworks promise new ways to overcome this obstacle while retaining predictive capabilities. In this review, we present an overview of the relevant mathematical frameworks for kinetic formulation, construction and analysis. Starting with kinetic formalisms, we next review statistical methods for parameter inference, as well as recent computational frameworks applied to the construction and analysis of kinetic models. Finally, we discuss opportunities and limitations hindering the development of larger kinetic reconstructions.

A probabilistic framework for the exploration of enzymatic capabilities based on feasible kinetics and control analysis.

BACKGROUND: Analysis of limiting steps within enzyme-catalyzed reactions is fundamental to understand their behavior and regulation. Methods capable of unravelling control properties and exploring kinetic capabilities of enzymatic reactions would be particularly useful for protein and metabolic engineering. While single-enzyme control analysis formalism has previously been applied to well-studied enzymatic mechanisms, broader application of this formalism is limited in practice by the limited amount of kinetic data and the difficulty of describing complex allosteric mechanisms. METHODS: To overcome these limitations, we present here a probabilistic framework enabling control analysis of previously unexplored mechanisms under uncertainty. By combining a thermodynamically consistent parameterization with an efficient Sequential Monte Carlo sampler embedded in a Bayesian setting, this framework yields insights into the capabilities of enzyme-catalyzed reactions with modest kinetic information, provided that the catalytic mechanism and a thermodynamic reference point are defined. RESULTS: The framework was used to unravel the impact of thermodynamic affinity, substrate saturation levels and effector concentrations on the flux control and response coefficients of a diverse set of enzymatic reactions. CONCLUSIONS: Our results highlight the importance of the metabolic context in the control analysis of isolated enzymes as well as the use of statistically sound methods for their interpretation. GENERAL SIGNIFICANCE: This framework significantly expands our current capabilities for unravelling the control properties of general reaction kinetics with limited amount of information. This framework will be useful for both theoreticians and experimentalists in the field.

Improving the robustness of a low-cost insect cell medium for baculovirus biopesticides production, via hydrolysate streamlining using a tube bioreactor-based statistical optimization routine.

A critical component of an in vitro production process for baculovirus biopesticides is a growth medium that is efficacious, robust, and inexpensive. An in-house low-cost serum-free medium, VPM3, has been shown to be very promising in supporting Helicoverpa armigera nucleopolyhedrovirus (HaSNPV) production in H. zea insect cell suspension cultures, for use as a biopesticide against the Heliothine pest complex. However, VPM3 is composed of a significant number of undefined components, including five different protein hydrolysates, which introduce a challenging lot-to-lot variability to the production process. In this study, an intensive statistical optimization routine was employed to reduce the number of protein hydrolysates in VPM3 medium. Nearly 300 runs (including replicates) were conducted with great efficiency by using 50 mL TubeSpin® bioreactors to propagate insect cell suspension cultures. Fractional factorial experiments were first used to determine the most important of the five default protein hydrolysates, and to screen for seven potential substitutes for the default meat peptone, Primatone RL. Validation studies informed by the screening tests showed that promising alternative media could be formulated based on just two protein hydrolysates, in particular the YST-AMP (Yeast Extract and Amyl Meat Peptone) and YST-POT (Yeast Extract and Lucratone Potato Peptone) combinations. The YST-AMP (meat-based) and YST-POT (meat-free) variants of VPM3 were optimized using response surface methodology, and were shown to be just as good as the default VPM3 and the commercial Sf-900 II media in supporting baculovirus yields, hence providing a means toward a more reproducible and scalable production process for HaSNPV biopesticides.

Metabolic and practical considerations on microbial electrosynthesis.

The production of biofuels and biochemicals is highly electron intensive. To divert fermentative and respiratory pathways to the product of interest, additional electrons (i.e. reducing power) are often needed. Meanwhile, the past decade has seen the breakthrough of sustainable electricity sources such as solar and wind. Microbial electrosynthesis (MES) is at the nexus of both, as it uses electrical energy as source of reducing power for microorganisms. This review addresses the key opportunities and challenges for MES. While exciting as a concept, MES needs to overcome many biological, electrochemical, logistical and economic challenges. Particularly the latter is critical, as on a 'per electron basis' MES does not yet appear to deliver a substantial benefit relative to existing approaches.