A mycotoxin transporter (4D) from a library of deoxynivalenol-tolerant microorganisms.

New strategies are needed to mitigate the mycotoxin deoxynivalenol (DON) in feed and food products. Microbial DNA fragments were generated from a library of DON-tolerant microorganisms. These fragments were screened in DON-sensitive yeast strains for their ability to modify or transport DON. Fragments were cloned into a PCR8/TOPO vector, and recombined into the yeast vector, pYES-DEST52. Resulting yeast transformants were screened in the presence of 100 ppm DON. Transformants that were able to grow in the presence of DON were plated on a selective medium, and the cloned microbial DNA fragments were sequenced. BLAST queries of one microbial DNA fragment (4D) showed a high degree of similarity to an ABC transporter. A series of screening and inhibition assays were conducted with a transport inhibitor (propanol), to test the hypothesis that 4D is a mycotoxin transporter. DON concentrations did not change for yeast transformants expressing 4D. The ability of yeast transformants expressing 4D to transport DON was inhibited by the addition of propanol. Moreover, yeast transformants expressing a known efflux pump (PDR5) showed similar trends in propanol transport inhibition compared to 4D. Future work should consider mycotoxin transporters such as 4D to the development of transgenic plants to limit DON accumulation in seeds.

Raman spectroscopy detects phenotypic differences among Escherichia coli enriched for 1-butanol tolerance using a metagenomic DNA library.

Advances in Raman spectroscopy are enabling more comprehensive measurement of microbial cell chemical composition. Advantages include results returned in near real-time and minimal sample preparation. In this research, Raman spectroscopy is used to analyze E. coli with engineered solvent tolerance, which is a multi-genic trait associated with complex and uncharacterized phenotypes that are of value to industrial microbiology. To generate solvent tolerant phenotypes, E. coli transformed with DNA libraries are serially enriched in the presence of 0.9% (v/v) and 1.1% (v/v) 1-butanol. DNA libraries are created using degenerate oligonucleotide primed PCR (DOP-PCR) from the genomic DNA of E. coli, Clostridium acetobutylicum ATCC 824, and the metagenome of a stream bank soil sample, which contained DNA from 72 different phyla. DOP-PCR enabled high efficiency library cloning (with no DNA shearing or end-polishing) and the inclusion un-culturable organisms. Nine strains with improved tolerance are analyzed by Raman spectroscopy and vastly different solvent-tolerant phenotypes are characterized. Common among these are improved membrane rigidity from increasing the fraction of unsaturated fatty acids at the expense of cyclopropane fatty acids. Raman spectroscopy offers the ability to monitor cell phenotype changes in near real-time and is adaptable to high-throughput screening, making it relevant to metabolic engineering.

A review of metabolic and enzymatic engineering strategies for designing and optimizing performance of microbial cell factories.

Microbial cell factories (MCFs) are of considerable interest to convert low value renewable substrates to biofuels and high value chemicals. This review highlights the progress of computational models for the rational design of an MCF to produce a target bio-commodity. In particular, the rational design of an MCF involves: (i) product selection, (ii) de novo biosynthetic pathway identification (i.e., rational, heterologous, or artificial), (iii) MCF chassis selection, (iv) enzyme engineering of promiscuity to enable the formation of new products, and (v) metabolic engineering to ensure optimal use of the pathway by the MCF host. Computational tools such as (i) de novo biosynthetic pathway builders, (ii) docking, (iii) molecular dynamics (MD) and steered MD (SMD), and (iv) genome-scale metabolic flux modeling all play critical roles in the rational design of an MCF. Genome-scale metabolic flux models are of considerable use to the design process since they can reveal metabolic capabilities of MCF hosts. These can be used for host selection as well as optimizing precursors and cofactors of artificial de novo biosynthetic pathways. In addition, recent advances in genome-scale modeling have enabled the derivation of metabolic engineering strategies, which can be implemented using the genomic tools reviewed here as well.

Transport of magnesium by a bacterial Nramp-related gene.

Magnesium is an essential divalent metal that serves many cellular functions. While most divalent cations are maintained at relatively low intracellular concentrations, magnesium is maintained at a higher level (∼0.5-2.0 mM). Three families of transport proteins were previously identified for magnesium import: CorA, MgtE, and MgtA/MgtB P-type ATPases. In the current study, we find that expression of a bacterial protein unrelated to these transporters can fully restore growth to a bacterial mutant that lacks known magnesium transporters, suggesting it is a new importer for magnesium. We demonstrate that this transport activity is likely to be specific rather than resulting from substrate promiscuity because the proteins are incapable of manganese import. This magnesium transport protein is distantly related to the Nramp family of proteins, which have been shown to transport divalent cations but have never been shown to recognize magnesium. We also find gene expression of the new magnesium transporter to be controlled by a magnesium-sensing riboswitch. Importantly, we find additional examples of riboswitch-regulated homologues, suggesting that they are a frequent occurrence in bacteria. Therefore, our aggregate data discover a new and perhaps broadly important path for magnesium import and highlight how identification of riboswitch RNAs can help shed light on new, and sometimes unexpected, functions of their downstream genes.

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.

Genome-scale modeling using flux ratio constraints to enable metabolic engineering of clostridial metabolism in silico.

BACKGROUND: Genome-scale metabolic networks and flux models are an effective platform for linking an organism genotype to its phenotype. However, few modeling approaches offer predictive capabilities to evaluate potential metabolic engineering strategies in silico. RESULTS: A new method called "flux balance analysis with flux ratios (FBrAtio)" was developed in this research and applied to a new genome-scale model of Clostridium acetobutylicum ATCC 824 (iCAC490) that contains 707 metabolites and 794 reactions. FBrAtio was used to model wild-type metabolism and metabolically engineered strains of C. acetobutylicum where only flux ratio constraints and thermodynamic reversibility of reactions were required. The FBrAtio approach allowed solutions to be found through standard linear programming. Five flux ratio constraints were required to achieve a qualitative picture of wild-type metabolism for C. acetobutylicum for the production of: (i) acetate, (ii) lactate, (iii) butyrate, (iv) acetone, (v) butanol, (vi) ethanol, (vii) CO2 and (viii) H2. Results of this simulation study coincide with published experimental results and show the knockdown of the acetoacetyl-CoA transferase increases butanol to acetone selectivity, while the simultaneous over-expression of the aldehyde/alcohol dehydrogenase greatly increases ethanol production. CONCLUSIONS: FBrAtio is a promising new method for constraining genome-scale models using internal flux ratios. The method was effective for modeling wild-type and engineered strains of C. acetobutylicum.