Simulating compatible solute biosynthesis using a metabolic flux model of the biomining acidophile, Acidithiobacillus ferrooxidans ATCC 23270.

Halotolerant, acidophilic, bioleaching microorganisms are crucial to biomining operations that utilize saline water. Compatible solutes play an important role in the adaptation of these microorganisms to saline environments. Acidithiobacillus ferrooxidans ATCC 23270, an iron- and sulfur-oxidizing acidophilic bacterium, synthesizes trehalose as its native compatible solute but is still sensitive to salinity. Recently, halotolerant bioleaching bacteria were found to use ectoine as their key compatible solute. Previously, bioleaching bacteria were recalcitrant to genetic manipulation; however, recent advancements in genetic tools and techniques allow successful genetic modification of A. ferrooxidans ATCC 23270. Therefore, this study aimed to test, in silico, the effect of native and synthetic compatible solute biosynthesis by A. ferrooxidans ATCC 23270 on its growth and metabolism. Metabolic network flux modelling was used to provide a computational framework for the prediction of metabolic fluxes during production of native and synthetic compatible solutes by A. ferrooxidans ATCC 23270, in silico. Complete pathways for trehalose biosynthesis by the bacterium are proposed and captured in the updated metabolic model including a newly discovered UDP-dependent trehalose synthesis pathway. Finally, the effect of nitrogen sources on compatible solute production was simulated and showed that using nitrogen gas as the sole nitrogen source enables the ectoine-producing 'engineered' microbe to oxidize up to 20% more ferrous iron in comparison to the native microbe that only produces trehalose. Therefore, the predictive outcomes of the model have the potential to guide the design and optimization of a halotolerant strain of A. ferrooxidans ATCC 23270 for saline bioleaching operations.

A novel synthetic sRNA promoting protein overexpression in cell-free systems.

Bacterial small RNAs (sRNAs) that regulate gene expression have been engineered for uses in synthetic biology and metabolic engineering. Here, we designed a novel non-Hfq-dependent sRNA scaffold that uses a modifiable 20 nucleotide antisense binding region to target mRNAs selectively and influence protein expression. The system was developed for regulation of a fluorescent reporter in vivo using Escherichia coli, but the system was found to be more responsive and produced statistically significant results when applied to protein synthesis using in vitro cell-free systems (CFS). Antisense binding sequences were designed to target not only translation initiation regions but various secondary structures in the reporter mRNA. Targeting a high-energy stem loop structure and the 3' end of mRNA yielded protein expression knock-downs that approached 70%. Notably, targeting a low-energy stem structure near a potential RNase E binding site led to a statistically significant 65% increase in protein expression (p < 0.05). These results were not obtainable in vivo, and the underlying mechanism was translated from the reporter system to achieve better than 75% increase in recombinant diaphorase expression in a CFS. It is possible the designs developed here can be applied to improve/regulate expression of other proteins in a CFS.

Improved production of the non-native cofactor F420 in Escherichia coli.

The deazaflavin cofactor F420 is a low-potential, two-electron redox cofactor produced by some Archaea and Eubacteria that is involved in methanogenesis and methanotrophy, antibiotic biosynthesis, and xenobiotic metabolism. However, it is not produced by bacterial strains commonly used for industrial biocatalysis or recombinant protein production, such as Escherichia coli, limiting our ability to exploit it as an enzymatic cofactor and produce it in high yield. Here we have utilized a genome-scale metabolic model of E. coli and constraint-based metabolic modelling of cofactor F420 biosynthesis to optimize F420 production in E. coli. This analysis identified phospho-enol pyruvate (PEP) as a limiting precursor for F420 biosynthesis, explaining carbon source-dependent differences in productivity. PEP availability was improved by using gluconeogenic carbon sources and overexpression of PEP synthase. By improving PEP availability, we were able to achieve a ~ 40-fold increase in the space-time yield of F420 compared with the widely used recombinant Mycobacterium smegmatis expression system. This study establishes E. coli as an industrial F420-production system and will allow the recombinant in vivo use of F420-dependent enzymes for biocatalysis and protein engineering applications.

Towards a Systems Biology Approach to Understanding the Lichen Symbiosis: Opportunities and Challenges of Implementing Network Modelling.

Lichen associations, a classic model for successful and sustainable interactions between micro-organisms, have been studied for many years. However, there are significant gaps in our understanding about how the lichen symbiosis operates at the molecular level. This review addresses opportunities for expanding current knowledge on signalling and metabolic interplays in the lichen symbiosis using the tools and approaches of systems biology, particularly network modelling. The largely unexplored nature of symbiont recognition and metabolic interdependency in lichens could benefit from applying a holistic approach to understand underlying molecular mechanisms and processes. Together with 'omics' approaches, the application of signalling and metabolic network modelling could provide predictive means to gain insights into lichen signalling and metabolic pathways. First, we review the major signalling and recognition modalities in the lichen symbioses studied to date, and then describe how modelling signalling networks could enhance our understanding of symbiont recognition, particularly leveraging omics techniques. Next, we highlight the current state of knowledge on lichen metabolism. We also discuss metabolic network modelling as a tool to simulate flux distribution in lichen metabolic pathways and to analyse the co-dependence between symbionts. This is especially important given the growing number of lichen genomes now available and improved computational tools for reconstructing such models. We highlight the benefits and possible bottlenecks for implementing different types of network models as applied to the study of lichens.

New insights into the performance characteristics of the Planova-series hollow-fiber parvovirus filters using confocal and electron microscopy.

Virus filtration remains a critical step in the downstream process for the production of monoclonal antibodies and other mammalian cell-derived biotherapeutics. Recent studies have shown large differences in virus capture behavior of different virus filters, although the origin of these differences is still unclear. The objective of this study was to use confocal and scanning electron microscopy to directly evaluate the capture of virus-size nanoparticles in Planova 20N and BioEX hollow-fiber virus filters. Confocal images of fluorescent nanoparticles were quantified using ImageJ image processing software based on the measured fluorescence intensity of the labeled nanoparticles. Nanoparticle capture by the Planova BioEX was independent of transmembrane pressure from 10 to 45 psi. In contrast, the Planova 20N showed significant differences in nanoparticle capture profile at low pressure, consistent with literature data showing virus breakthrough under these conditions. Images obtained after a process interruption show significant migration of previously captured nanoparticles in the Planova 20N filters but not in the BioEX. These results provide important insights into the nature of virus capture in different virus filters and its dependence on the underlying structure of the virus filtration membranes.

A Prospective Study on the Fermentation Landscape of Gaseous Substrates to Biorenewables Using Methanosarcina acetivorans Metabolic Model.

The abundance of methane in shale gas and of other gases such as carbon monoxide, hydrogen, and carbon dioxide as chemical process byproducts has motivated the use of gas fermentation for bioproduction. Recent advances in metabolic engineering and synthetic biology allow for engineering of microbes metabolizing a variety of chemicals including gaseous feeds into a number of biorenewables and transportation liquid fuels. New computational tools enable the systematic exploration of all feasible conversion alternatives. Here we computationally assessed all thermodynamically feasible ways of co-utilizing CH4, CO, and CO2 using ferric as terminal electron acceptor for the production of all key precursor metabolites. We identified the thermodynamically feasible co-utilization ratio ranges of CH4, CO, and CO2 toward production of the target metabolite(s) as a function of ferric uptake. A revised version of the iMAC868 genome-scale metabolic model of Methanosarcina acetivorans was chosen to assess co-utilization of CH4, CO, and CO2 and their conversion into selected target products using the optStoic pathway design tool. This revised version contains the latest information on electron flow mechanisms by the methanogen while supplied with methane as the sole carbon source. The interplay between different gas co-utilization ratios and the energetics of reverse methanogenesis were also analyzed using the same metabolic model.

Assessing methanotrophy and carbon fixation for biofuel production by Methanosarcina acetivorans.

BACKGROUND: Methanosarcina acetivorans is a model archaeon with renewed interest due to its unique reversible methane production pathways. However, the mechanism and relevant pathways implicated in (co)utilizing novel carbon substrates in this organism are still not fully understood. This paper provides a comprehensive inventory of thermodynamically feasible routes for anaerobic methane oxidation, co-reactant utilization, and maximum carbon yields of major biofuel candidates by M. acetivorans. RESULTS: Here, an updated genome-scale metabolic model of M. acetivorans is introduced (iMAC868 containing 868 genes, 845 reactions, and 718 metabolites) by integrating information from two previously reconstructed metabolic models (i.e., iVS941 and iMB745), modifying 17 reactions, adding 24 new reactions, and revising 64 gene-protein-reaction associations based on newly available information. The new model establishes improved predictions of growth yields on native substrates and is capable of correctly predicting the knockout outcomes for 27 out of 28 gene deletion mutants. By tracing a bifurcated electron flow mechanism, the iMAC868 model predicts thermodynamically feasible (co)utilization pathway of methane and bicarbonate using various terminal electron acceptors through the reversal of the aceticlastic pathway. CONCLUSIONS: This effort paves the way in informing the search for thermodynamically feasible ways of (co)utilizing novel carbon substrates in the domain Archaea.

Reversing methanogenesis to capture methane for liquid biofuel precursors.

BACKGROUND: Energy from remote methane reserves is transformative; however, unintended release of this potent greenhouse gas makes it imperative to convert methane efficiently into more readily transported biofuels. No pure microbial culture that grows on methane anaerobically has been isolated, despite that methane capture through anaerobic processes is more efficient than aerobic ones. RESULTS: Here we engineered the archaeal methanogen Methanosarcina acetivorans to grow anaerobically on methane as a pure culture and to convert methane into the biofuel precursor acetate. To capture methane, we cloned the enzyme methyl-coenzyme M reductase (Mcr) from an unculturable organism, anaerobic methanotrophic archaeal population 1 (ANME-1) from a Black Sea mat, into M. acetivorans to effectively run methanogenesis in reverse. Starting with low-density inocula, M. acetivorans cells producing ANME-1 Mcr consumed up to 9 ± 1 % of methane (corresponding to 109 ± 12 µmol of methane) after 6 weeks of anaerobic growth on methane and utilized 10 mM FeCl3 as an electron acceptor. Accordingly, increases in cell density and total protein were observed as cells grew on methane in a biofilm on solid FeCl3. When incubated on methane for 5 days, high-densities of ANME-1 Mcr-producing M. acetivorans cells consumed 15 ± 2 % methane (corresponding to 143 ± 16 µmol of methane), and produced 10.3 ± 0.8 mM acetate (corresponding to 52 ± 4 µmol of acetate). We further confirmed the growth on methane and acetate production using (13)C isotopic labeling of methane and bicarbonate coupled with nuclear magnetic resonance and gas chromatography/mass spectroscopy, as well as RNA sequencing. CONCLUSIONS: We anticipate that our metabolically-engineered strain will provide insights into how methane is cycled in the environment by Archaea as well as will possibly be utilized to convert remote sources of methane into more easily transported biofuels via acetate.

Methane oxidation by anaerobic archaea for conversion to liquid fuels.

Given the recent increases in natural gas reserves and associated drawbacks of current gas-to-liquids technologies, the development of a bioconversion process to directly convert methane to liquid fuels would generate considerable industrial interest. Several clades of anaerobic methanotrophic archaea (ANME) are capable of performing anaerobic oxidation of methane (AOM). AOM carried out by ANME offers carbon efficiency advantages over aerobic oxidation by conserving the entire carbon flux without losing one out of three carbon atoms to carbon dioxide. This review highlights the recent advances in understanding the key enzymes involved in AOM (i.e., methyl-coenzyme M reductase), the ecological niches of a number of ANME, the putative metabolic pathways for AOM, and the syntrophic consortia that they typically form.

Deriving metabolic engineering strategies from genome-scale modeling with flux ratio constraints.

Optimized production of bio-based fuels and chemicals from microbial cell factories is a central goal of systems metabolic engineering. To achieve this goal, a new computational method of using flux balance analysis with flux ratios (FBrAtio) was further developed in this research and applied to five case studies to evaluate and design metabolic engineering strategies. The approach was implemented using publicly available genome-scale metabolic flux models. Synthetic pathways were added to these models along with flux ratio constraints by FBrAtio to achieve increased (i) cellulose production from Arabidopsis thaliana; (ii) isobutanol production from Saccharomyces cerevisiae; (iii) acetone production from Synechocystis sp. PCC6803; (iv) H2 production from Escherichia coli MG1655; and (v) isopropanol, butanol, and ethanol (IBE) production from engineered Clostridium acetobutylicum. The FBrAtio approach was applied to each case to simulate a metabolic engineering strategy already implemented experimentally, and flux ratios were continually adjusted to find (i) the end-limit of increased production using the existing strategy, (ii) new potential strategies to increase production, and (iii) the impact of these metabolic engineering strategies on product yield and culture growth. The FBrAtio approach has the potential to design "fine-tuned" metabolic engineering strategies in silico that can be implemented directly with available genomic tools.

Resolving cell composition through simple measurements, genome-scale modeling, and a genetic algorithm.

The biochemical composition of a cell is very complex and dynamic. It varies greatly among different organisms and environmental conditions. Inclusion of proper cell composition data is critical for accurate genome-scale metabolic flux modeling using flux balance analysis (FBA). However, determining cell composition experimentally is currently time-consuming and resource intensive. In this chapter, a method for predicting cell composition using a genome-scale model and "easy to measure" culture data (e.g., glucose uptake rate, and specific growth rate) is presented. The method makes use of a genetic algorithm for nonlinear optimization of a biomass equation (a mathematical description of cell composition). As a case study, the method was used to optimize a biomass equation for Escherichia coli MG1655 under multiple growth environments. The availability of experimentally determined (13)C flux data allowed a direct comparison with FBA predicted fluxes through the TCA cycle. Results showed dramatic improvement upon optimization of the biomass equation. In a second case study, biomass equation optimization was also applied to Clostridium acetobutylicum, an organism with less available biochemical cell composition data in the literature. The method produced a biomass equation highly similar to one determined experimentally for the closely related Gram-positive Bacillus subtilis.