Enhancing acid tolerance of Escherichia coli via viroporin-mediated export of protons and its application for efficient whole-cell biotransformation

Escherichia coli-based whole-cell biocatalysts are widely used for the sustainable production of value-added chemicals. However, weak acids present as substrates and/or products obstruct the growth and fermentation capability of E. coli. Here, we show that a viroporin consisting of the influenza A matrix-2 (M2) protein, is activated by low pH and has proton channel activity in E. coli. The heterologous expression of the M2 protein in E. coli resulted in a significant increase in the intracellular pH and cell viability in the presence of various weak acids with different lengths of carbon chains. In addition, the feasibility of developing a robust and efficient E. coli-based whole-cell biocatalyst via introduction of the proton-selective viroporin was explored by employing (Z)-11-(heptanolyoxy)undec-9-enoic acid (ester) and 2-fucosyllactose (2′-FL) as model products, whose production is hampered by cytosolic acidification. The engineered E. coli strains containing the proton-selective viroporin exhibited approximately 80% and 230% higher concentrations of the ester and 2′-FL, respectively, than the control strains without the M2 protein. The simple and powerful strategy developed in this study can be applied to produce other valuable chemicals whose production involves substrates and/or products that cause cytosolic acidification.     

Communities of Niche-optimized Strains (CoNoS) – Design and creation of stable,genome-reduced co-cultures

Current bioprocesses for production of value-added compounds are mainly based on pure cultures that are composed of rationally engineered strains of model organisms with versatile metabolic capacities. However, in the comparably well-defined environment of a bioreactor, metabolic flexibility provided by various highly abundant biosynthetic enzymes is much less required and results in suboptimal use of carbon and energy sources for compound production. In nature, non-model organisms have frequently evolved in communities where genome-reduced, auxotrophic strains cross-feed each other, suggesting that there must be a significant advantage compared to growth without cooperation. To prove this, we started to create and study synthetic communities of niche-optimized strains (CoNoS) that consists of two strains of the same species Corynebacterium glutamicum that are mutually dependent on one amino acid. We used both the wild-type and the genome-reduced C1* chassis for introducing selected amino acid auxotrophies, each based on complete deletion of all required biosynthetic genes. The best candidate strains were used to establish several stably growing CoNoS that were further characterized and optimized by metabolic modelling, microfluidic experiments and rational metabolic engineering to improve amino acid production and exchange. Finally, the engineered CoNoS consisting of an l-leucine and l-arginine auxotroph showed a specific growth rate equivalent to 83% of the wild type in monoculture, making it the fastest co-culture of two auxotrophic C. glutamicum strains to date. Overall, our results are a first promising step towards establishing improved biobased production of value-added compounds using the CoNoS approach.     

Metabolic engineering of <Emphasis Type="Italic">Escherichia coli</Emphasis> for L-tryptophan production

The review summarizes the main approaches applied during the creation of L-tryptophan producing strains based on Escherichia coli for the industrial production of this amino acid. In addition, some prospects for the further improvement of tryptophan producers to increase their productivity and improve their technological characteristics based on systems metabolic engineering approaches are outlined in the review. These approaches can be used to obtain the producers of other aromatic amino acids and tryptophan precursors or derivatives.     

A Powerful Molecular Engineering Tool Provided Efficient Chlamydomonas Mutants as Bio-Sensing Elements for Herbicides Detection

This study was prompted by increasing concerns about ecological damage and human health threats derived by persistent contamination of water and soil with herbicides, and emerging of bio-sensing technology as powerful, fast and efficient tool for the identification of such hazards. This work is aimed at overcoming principal limitations negatively affecting the whole-cell-based biosensors performance due to inadequate stability and sensitivity of the bio-recognition element. The novel bio-sensing elements for the detection of herbicides were generated exploiting the power of molecular engineering in order to improve the performance of photosynthetic complexes. The new phenotypes were produced by an in vitro directed evolution strategy targeted at the photosystem II (PSII) D1 protein of Chlamydomonas reinhardtii, using exposures to radical-generating ionizing radiation as selection pressure. These tools proved successful to identify D1 mutations conferring enhanced stability, tolerance to free-radical-associated stress and competence for herbicide perception. Long-term stability tests of PSII performance revealed the mutants capability to deal with oxidative stress-related conditions. Furthermore, dose-response experiments indicated the strains having increased sensitivity or resistance to triazine and urea type herbicides with I₅₀ values ranging from 6×10⁻⁸ M to 2×10⁻⁶ M. Besides stressing the relevance of several amino acids for PSII photochemistry and herbicide sensing, the possibility to improve the specificity of whole-cell-based biosensors, via coupling herbicide-sensitive with herbicide-resistant strains, was verified.     

Application of a Genetically Encoded Biosensor for Live Cell Imaging of L-Valine Production in Pyruvate Dehydrogenase Complex-Deficient Corynebacterium glutamicum Strains

The majority of biotechnologically relevant metabolites do not impart a conspicuous phenotype to the producing cell. Consequently, the analysis of microbial metabolite production is still dominated by bulk techniques, which may obscure significant variation at the single-cell level. In this study, we have applied the recently developed Lrp-biosensor for monitoring of amino acid production in single cells of gradually engineered L-valine producing Corynebacterium glutamicum strains based on the pyruvate dehydrogenase complex-deficient (PDHC) strain C. glutamicum ΔaceE. Online monitoring of the sensor output (eYFP fluorescence) during batch cultivation proved the sensor''s suitability for visualizing different production levels. In the following, we conducted live cell imaging studies on C. glutamicum sensor strains using microfluidic chip devices. As expected, the sensor output was higher in microcolonies of high-yield producers in comparison to the basic strain C. glutamicum ΔaceE. Microfluidic cultivation in minimal medium revealed a typical Gaussian distribution of single cell fluorescence during the production phase. Remarkably, low amounts of complex nutrients completely changed the observed phenotypic pattern of all strains, resulting in a phenotypic split of the population. Whereas some cells stopped growing and initiated L-valine production, others continued to grow or showed a delayed transition to production. Depending on the cultivation conditions, a considerable fraction of non-fluorescent cells was observed, suggesting a loss of metabolic activity. These studies demonstrate that genetically encoded biosensors are a valuable tool for monitoring single cell productivity and to study the phenotypic pattern of microbial production strains.     

Isolation and selection of plant growth-promoting rhizobacteria as inducers of systemic resistance in melon

Backgroud and aims

Powdery mildew elicited by Podosphaera fusca is an important threat to cucurbits. In order to find alternatives to the current use of chemicals, we examined the potential use of plant growth-promoting rhizobacteria (PGPR) for controlling the disease by induction of systemic resistance in the host plant.

Methods

A collection of Bacillus and Pseudomonas strains from different origins was studied, including strains isolated from roots of disease-free melon plants obtained from a greenhouse plagued by powdery mildew. The selection of best candidates was based on the evaluation of different traits commonly associated with PGPR, such as antifungal and siderophore production, swimming and swarming motilities, biofilm formation, auxin production and promotion of root development.

Results

Three Bacillus strains, B. subtilis UMAF6614 and UMAF6639 and B. cereus UMAF8564, and two Pseudomonas fluorescens strains, UMAF6031 and UMAF6033, were selected after ranking the strains using a nonparametric statistics test. Applied to melon seedlings, the selected strains were able to promote plant growth, increasing fresh weight up to 30%. Furthermore, these strains provided protection against powdery mildew and also against angular leaf spot caused by Pseudomonas syringae pv. lachrymans, with disease reductions of up to 60%.

Conclusions

These results suggest that the use of ISR-promoting PGPR could be a promising strategy for the integrated control of cucurbit powdery mildew and other cucurbit diseases.     

Genetic selection for a highly functional cysteine-less membrane protein using site saturation mutagenesis

We describe an efficient method for generating highly functional membrane proteins with variant amino acids at defined positions that couples a modified site saturation strategy with functional genetic selection. We applied this method to the production of a cysteine-less variant of the Crithidia fasciculata inosine-guanosine permease CfNT2 to facilitate biochemical studies using thiol-specific modifying reagents. Of 10 endogenous cysteine residues in CfNT2, two cannot be replaced with serine or alanine without loss of function. High-quality single- and double-mutant libraries were produced by combining a previously reported site saturation mutagenesis scheme based on the Stratagene Quikchange method with a novel gel purification step that effectively eliminated template DNA from the products. Following selection for functional complementation in Saccharomyces cerevisiae cells auxotrophic for purines, several highly functional noncysteine substitutions were efficiently identified at each desired position, allowing the construction of cysteine-less variants of CfNT2 that retained wild-type affinity for inosine. This combination of an improved site saturation mutagenesis technique and positive genetic selection provides a simple and efficient means to identify functional and perhaps unexpected amino acid variants at a desired position.     

Engineering Escherichia coli for Increased Productivity of Serine-Rich Proteins Based on Proteome Profiling

Variations in proteome profiles of Escherichia coli in response to the overproduction of human leptin, a serine-rich (11.6% of total amino acids) protein, were examined by two-dimensional gel electrophoresis. The levels of heat shock proteins increased, while those of protein elongation factors, 30S ribosomal protein, and some enzymes involved in amino acid biosynthesis decreased, after leptin overproduction. Most notably, the levels of enzymes involved in the biosynthesis of serine family amino acids significantly decreased. Based on this information, we designed a strategy to enhance the leptin productivity by manipulating the cysK gene, encoding cysteine synthase A. By coexpression of the cysK gene, we were able to increase the cell growth rate by approximately twofold. Also, the specific leptin productivity could be increased by fourfold. In addition, we found that cysK coexpression can improve the production of another serine-rich protein, interleukin-12 β chain, suggesting that this strategy may be useful for the production of other serine-rich proteins as well. The approach taken in this study should be useful in designing a strategy for improving recombinant protein production.     

Development of bifunctional biosensors for sensing and dynamic control of glycolysis flux in metabolic engineering

Glycolysis is the primary metabolic pathway in all living organisms. Maintaining the balance of glycolysis flux and biosynthetic pathways is the crucial matter involved in the microbial cell factory. Few regulation systems can address the issue of metabolic flux imbalance in glycolysis. Here, we designed and constructed a bifunctional glycolysis flux biosensor that can dynamically regulate glycolysis flux for overproduction of desired biochemicals. A series of positive-and negative-response biosensors were created and modified for varied thresholds and dynamic ranges. These engineered glycolysis flux biosensors were verified to be able to characterize in vivo fructose-1,6-diphosphate concentration. Subsequently, the biosensors were applied for fine-tuning glycolysis flux to effectively balance the biosynthesis of two chemicals: mevalonate and N-acetylglucosamine. A glycolysis flux-dynamically controlled Escherichia coli strain achieved a 111.3 g/L mevalonate titer in a 1L fermenter.     

Plug-in repressor library for precise regulation of metabolic flux in Escherichia coli

In metabolic engineering, enhanced production of value-added chemicals requires precise flux control between growth-essential competing and production pathways. Although advances in synthetic biology have facilitated the exploitation of a number of genetic elements for precise flux control, their use requires expensive inducers, or more importantly, needs complex and time-consuming processes to design and optimize appropriate regulator components, case-by-case. To overcome this issue, we devised the plug-in repressor libraries for target-specific flux control, in which expression levels of the repressors were diversified using degenerate 5′ untranslated region (5’ UTR) sequences employing the UTR Library Designer. After we validated a wide expression range of the repressor libraries, they were applied to improve the production of lycopene from glucose and 3-hydroxypropionic acid (3-HP) from acetate in Escherichia coli via precise flux rebalancing to enlarge precursor pools. Consequently, we successfully achieved optimal carbon fluxes around the precursor nodes for efficient production. The most optimized strains were observed to produce 2.59 g/L of 3-HP and 11.66 mg/L of lycopene, which were improved 16.5-fold and 2.82-fold, respectively, compared to those produced by the parental strains. These results indicate that carbon flux rebalancing using the plug-in library is a powerful strategy for efficient production of value-added chemicals in E. coli.     

Genetic selection system for improving recombinant membrane protein expression in E. coli

A major barrier to the physical characterization and structure determination of membrane proteins is low yield in recombinant expression. To address this problem, we have designed a selection strategy to isolate mutant strains of Escherichia coli that improve the expression of a targeted membrane protein. In this method, the coding sequence of the membrane protein of interest is fused to a C‐terminal selectable marker, so that the production of the selectable marker and survival on selective media is linked to expression of the targeted membrane protein. Thus, mutant strains with improved expression properties can be directly selected. We also introduce a rapid method for curing isolated strains of the plasmids used during the selection process, in which the plasmids are removed by in vivo digestion with the homing endonuclease I‐CreI. We tested this selection system on a rhomboid family protein from Mycobacterium tuberculosis (Rv1337) and were able to isolate mutants, which we call EXP strains, with up to 75‐fold increased expression. The EXP strains also improve the expression of other membrane proteins that were not the target of selection, in one case roughly 90‐fold.     

An Organic Acid Based Counter Selection System for Cyanobacteria

Cyanobacteria are valuable organisms for studying the physiology of photosynthesis and carbon fixation, as well as metabolic engineering for the production of fuels and chemicals. This work describes a novel counter selection method for the cyanobacterium Synechococcus sp. PCC 7002 based on organic acid toxicity. The organic acids acrylate, 3-hydroxypropionate, and propionate were shown to be inhibitory towards Synechococcus sp. PCC 7002 and other cyanobacteria at low concentrations. Inhibition was overcome by a loss of function mutation in the gene acsA, which is annotated as an acetyl-CoA ligase. Loss of AcsA function was used as a basis for an acrylate counter selection method. DNA fragments of interest were inserted into the acsA locus and strains harboring the insertion were isolated on selective medium containing acrylate. This methodology was also used to introduce DNA fragments into a pseudogene, glpK. Application of this method will allow for more advanced genetics and engineering studies in Synechococcus sp. PCC 7002 including the construction of markerless gene deletions and insertions. The acrylate counter-selection could be applied to other cyanobacterial species where AcsA activity confers acrylate sensitivity (e.g. Synechocystis sp. PCC 6803).     

Multi-omic based production strain improvement (MOBpsi) for bio-manufacturing of toxic chemicals

Robust systematic approaches for the metabolic engineering of cell factories remain elusive. The available models for predicting phenotypical responses and mechanisms are incomplete, particularly within the context of compound toxicity that can be a significant impediment to achieving high yields of a target product. This study describes a Multi-Omic Based Production Strain Improvement (MOBpsi) strategy that is distinguished by integrated time-resolved systems analyses of fed-batch fermentations. As a case study, MOBpsi was applied to improve the performance of an Escherichia coli cell factory producing the commodity chemical styrene. Styrene can be bio-manufactured from phenylalanine via an engineered pathway comprised of the enzymes phenylalanine ammonia lyase and ferulic acid decarboxylase. The toxicity, hydrophobicity, and volatility of styrene combine to make bio-production challenging. Previous attempts to create styrene tolerant E. coli strains by targeted genetic interventions have met with modest success. Application of MOBpsi identified new potential targets for improving performance, resulting in two host strains (E. coli NST74ΔaaeA and NST74ΔaaeA cpxP_o) with increased styrene production. The best performing re-engineered chassis, NST74ΔaaeA cpxP_o, produced ∼3 × more styrene and exhibited increased viability in fed-batch fermentations. Thus, this case study demonstrates the utility of MOBpsi as a systematic tool for improving the bio-manufacturing of toxic chemicals.     

Design and engineering of E. coli metabolic sensor strains with a wide sensitivity range for glycerate

Microbial biosensors are used to detect the presence of compounds provided externally or produced internally. The latter case is commonly constrained by the need to screen a large library of enzyme or pathway variants to identify those that can efficiently generate the desired compound. To address this limitation, we suggest the use of metabolic sensor strains which can grow only if the relevant compound is present and thus replace screening with direct selection. We used a computational platform to design metabolic sensor strains with varying dependencies on a specific compound. Our method systematically explores combinations of gene deletions and identifies how the growth requirement for a compound changes with the media composition. We demonstrate this approach by constructing a set of E. coli glycerate sensor strains. In each of these strains a different set of enzymes is disrupted such that central metabolism is effectively dissected into multiple segments, each requiring a dedicated carbon source. We find an almost perfect match between the predicted and experimental dependence on glycerate and show that the strains can be used to accurately detect glycerate concentrations across two orders of magnitude. Apart from demonstrating the potential application of metabolic sensor strains, our work reveals key phenomena in central metabolism, including spontaneous degradation of central metabolites and the importance of metabolic sinks for balancing small metabolic networks.     

Engineering improved ethanol production in Escherichia coli with a genome-wide approach

A key challenge to the commercial production of commodity chemical and fuels is the toxicity of such molecules to the microbial host. While a number of studies have attempted to engineer improved tolerance for such compounds, the majority of these studies have been performed in wild-type strains and culturing conditions that differ considerably from production conditions. Here we applied the multiscalar analysis of library enrichments (SCALEs) method and performed a growth selection in an ethanol production system to quantitatively map in parallel all genes in the genome onto ethanol tolerance and production. In order to perform the selection in an ethanol-producing system, we used a previously engineered Escherichia coli ethanol production strain (LW06; ATCC BAA-2466) (Woodruff et al., in press), as the host strain for the multiscalar genomic library analysis (>10⁶ clones for each library of 1, 2, or 4 kb overlapping genomic fragments). By testing individually selected clones, we confirmed that growth selections enriched for clones with both improved ethanol tolerance and production phenotypes. We performed combinatorial testing of the top genes identified (uspC, otsA, otsB) to investigate their ability to confer improved ethanol tolerance or ethanol production. We determined that overexpression of otsA was required for improved tolerance and productivity phenotypes, with the best performing strains showing up to 75% improvement relative to the parent production strain.     

Identification of genome-scale metabolic network models using experimentally measured flux profiles

Genome-scale metabolic network models can be reconstructed for well-characterized organisms using genomic annotation and literature information. However, there are many instances in which model predictions of metabolic fluxes are not entirely consistent with experimental data, indicating that the reactions in the model do not match the active reactions in the in vivo system. We introduce a method for determining the active reactions in a genome-scale metabolic network based on a limited number of experimentally measured fluxes. This method, called optimal metabolic network identification (OMNI), allows efficient identification of the set of reactions that results in the best agreement between in silico predicted and experimentally measured flux distributions. We applied the method to intracellular flux data for evolved Escherichia coli mutant strains with lower than predicted growth rates in order to identify reactions that act as flux bottlenecks in these strains. The expression of the genes corresponding to these bottleneck reactions was often found to be downregulated in the evolved strains relative to the wild-type strain. We also demonstrate the ability of the OMNI method to diagnose problems in E. coli strains engineered for metabolite overproduction that have not reached their predicted production potential. The OMNI method applied to flux data for evolved strains can be used to provide insights into mechanisms that limit the ability of microbial strains to evolve towards their predicted optimal growth phenotypes. When applied to industrial production strains, the OMNI method can also be used to suggest metabolic engineering strategies to improve byproduct secretion. In addition to these applications, the method should prove to be useful in general for reconstructing metabolic networks of ill-characterized microbial organisms based on limited amounts of experimental data.     

Ultrahigh-throughput screening of industrial enzyme-producing strains by droplet-based microfluidic system

Droplet-based microfluidics has emerged as a powerful tool for single-cell screening with ultrahigh throughput, but its widespread application remains limited by the accessibility of a droplet microfluidic high-throughput screening (HTS) platform, especially to common laboratories having no background in microfluidics. Here, we first developed a microfluidic HTS platform based on fluorescence-activated droplet sorting technology. This platform allowed (i) encapsulation of single cells in monodisperse water-in-oil droplets; (ii) cell growth and protein production in droplets; and (iii) sorting of droplets based on their fluorescence intensities. To validate the platform, a model selection experiment of a binary mixture of Bacillus strains was performed, and a 45.6-fold enrichment was achieved at a sorting rate of 300 droplets per second. Furthermore, we used the platform for the selection of higher α-amylase-producing Bacillus licheniformis strains from a mutant library generated by atmospheric and room temperature plasma mutagenesis, and clones displaying over 50% improvement in α-amylase productivity were isolated. This droplet screening system could be applied to the engineering of other industrially valuable strains.     

Computational design of auxotrophy-dependent microbial biosensors for combinatorial metabolic engineering experiments

Combinatorial approaches in metabolic engineering work by generating genetic diversity in a microbial population followed by screening for strains with improved phenotypes. One of the most common goals in this field is the generation of a high rate chemical producing strain. A major hurdle with this approach is that many chemicals do not have easy to recognize attributes, making their screening expensive and time consuming. To address this problem, it was previously suggested to use microbial biosensors to facilitate the detection and quantification of chemicals of interest. Here, we present novel computational methods to: (i) rationally design microbial biosensors for chemicals of interest based on substrate auxotrophy that would enable their high-throughput screening; (ii) predict engineering strategies for coupling the synthesis of a chemical of interest with the production of a proxy metabolite for which high-throughput screening is possible via a designed bio-sensor. The biosensor design method is validated based on known genetic modifications in an array of E. coli strains auxotrophic to various amino-acids. Predicted chemical production rates achievable via the biosensor-based approach are shown to potentially improve upon those predicted by current rational strain design approaches. (A Matlab implementation of the biosensor design method is available via http://www.cs.technion.ac.il/~tomersh/tools).     

Fermentation of de-oiled algal biomass by <Emphasis Type="Italic">Lactobacillus casei</Emphasis> for production of lactic acid

De-oiled algal biomass (algal cake) generated as waste byproduct during algal biodiesel production is a promising fermentable substrate for co-production of value-added chemicals in biorefinery systems. We explored the ability of Lactobacillus casei 12A to ferment algal cake for co-production of lactic acid. Carbohydrate and amino acid availability were determined to be limiting nutritional requirements for growth and lactic acid production by L. casei. These nutritional requirements were effectively addressed through enzymatic hydrolysis of the algal cake material using α-amylase, cellulase (endo-1,4-β-d-glucanase), and pepsin. Results confirm fermentation of algal cake for production of value-added chemicals is a promising avenue for increasing the overall cost competiveness of the algal biodiesel production process.