Metabolic assessment of E. coli as a Biofactory for commercial products

Synergistic application of atmospheric and room temperature plasma mutagenesis and adaptive laboratory evolution improves the tolerance of Escherichia coli to L-cysteine

L-Cysteine production through fermentation stands as a promising technology. However, excessive accumulation of L-cysteine poses a challenge due to the potential to inflict damage on cellular DNA. In this study, we employed a synergistic approach encompassing atmospheric and room temperature plasma mutagenesis (ARTP) and adaptive laboratory evolution (ALE) to improve L-cysteine tolerance in Escherichia coli. ARTP-treated populations obtained substantial enhancement in L-cysteine tolerance by ALE. Whole-genome sequencing, transcription analysis, and reverse engineering, revealed the pivotal role of an effective export mechanism mediated by gene eamB in augmenting L-cysteine resistance. The isolated tolerant strain, 60AP03/pTrc-cysEf , achieved a 2.2-fold increase in L-cysteine titer by overexpressing the critical gene cysEf during batch fermentation, underscoring its enormous potential for L-cysteine production. The production evaluations, supplemented with L-serine, further demonstrated the stability and superiority of tolerant strains in L-cysteine production. Overall, our work highlighted the substantial impact of the combined ARTP and ALE strategy in increasing the tolerance of E. coli to L-cysteine, providing valuable insights into improving L-cysteine overproduction, and further emphasized the potential of biotechnology in industrial production.

Genome-wide identification of overexpression and downregulation gene targets based on the sum of covariances of the outgoing reaction fluxes

In metabolic engineering, predicting gene overexpression targets remains challenging because both endogenous and heterologous genes in a large metabolic space can be candidates, in contrast to gene knockout targets that are confined to endogenous genes. We report the development of iBridge that identifies positive and negative metabolites exerting positive and negative impacts on product formation, respectively, based on the sum of covariances of their outgoing (consuming) reaction fluxes for a target chemical. Then, "bridge" reactions converting negative metabolites to positive metabolites are identified as overexpression targets, while the opposites as downregulation targets. Using iBridge, overexpression and downregulation targets are suggested for the production of 298 chemicals and validated for 36 chemicals experimentally demonstrated in previous studies. Finally, iBridge is employed to engineer Escherichia coli strains capable of producing 10.3 g/L of D-panthenol, a compound not previously produced, as well as putrescine and 4-hydroxyphenyllactate at enhanced titers, 63.7 and 8.3 g/L, respectively.

Rewiring cell-free metabolic flux in E. coli lysates using a block-push-pull approach

Cell-free systems can expedite the design and implementation of biomanufacturing processes by bypassing troublesome requirements associated with the use of live cells. In particular, the lack of survival objectives and the open nature of cell-free reactions afford engineering approaches that allow purposeful direction of metabolic flux. The use of lysate-based systems to produce desired small molecules can result in competitive titers and productivities when compared to their cell-based counterparts. However, pathway crosstalk within endogenous lysate metabolism can compromise conversion yields by diverting carbon flow away from desired products. Here, the 'block-push-pull' concept of conventional cell-based metabolic engineering was adapted to develop a cell-free approach that efficiently directs carbon flow in lysates from glucose and toward endogenous ethanol synthesis. The approach is readily adaptable, is relatively rapid and allows for the manipulation of central metabolism in cell extracts. In implementing this approach, a block strategy is first optimized, enabling selective enzyme removal from the lysate to the point of eliminating by-product-forming activity while channeling flux through the target pathway. This is complemented with cell-free metabolic engineering methods that manipulate the lysate proteome and reaction environment to push through bottlenecks and pull flux toward ethanol. The approach incorporating these block, push and pull strategies maximized the glucose-to-ethanol conversion in an Escherichia coli lysate that initially had low ethanologenic potential. A 10-fold improvement in the percent yield is demonstrated. To our knowledge, this is the first report of successfully rewiring lysate carbon flux without source strain optimization and completely transforming the consumed input substrate to a desired output product in a lysate-based, cell-free system.

Engineering of phenylalanine dehydrogenase from Thermoactinomyces intermedius for the production of a novel homoglutamate

The dramatic increase in healthcare costs has become a significant burden to this era. Many patients are unable to access medication because of the high price of drugs. Genetic engineering has made advances to increase the yield, titer, and productivity in the bio-based production of chemicals, materials of interest, and identification of innovative targets for drug discovery. Currently, the production of homoglutamate (α-Aminoadipic acid) involves petrochemical routes that are costly with low yield and often not suitable for industrial production. Here, we established the development of NADH-dependent homoglutamate by engineering NADH-dependent phenylalanine dehydrogenase (PDH) from Thermoactinomyces intermedius, which provides a novel tool for in-vivo metabolic engineering and in-vitro catalysis. Based on computational insight into the structure, we proposed the site-specific directed mutagenesis of the two important residues of PDH through docking simulations by AutoDock Vina which elucidated the binding mode of PDH with α-Ketoadipic acid and ligands. Our results demonstrated that the catalytic efficiency K_m/K_cat of the final mutant Ala135Arg showed a 3-fold increase amination activity towards the ketoadipic acid as compared to the other mutant Gly114Arg, a double mutant Gly114Arg/Ala135Arg, and wild type TiPDH. Furthermore, we have introduced formate dehydrogenase as a cofactor regenerative system in this study which further made this study economically viable. Our study unfolds the possibility of biosynthesis of other non-proteinogenic amino acids that might be valuable pharmaceutical intermediaries.

Chemical-damage MINE: A database of curated and predicted spontaneous metabolic reactions

Spontaneous reactions between metabolites are often neglected in favor of emphasizing enzyme-catalyzed chemistry because spontaneous reaction rates are assumed to be insignificant under physiological conditions. However, synthetic biology and engineering efforts can raise natural metabolites' levels or introduce unnatural ones, so that previously innocuous or nonexistent spontaneous reactions become an issue. Problems arise when spontaneous reaction rates exceed the capacity of a platform organism to dispose of toxic or chemically active reaction products. While various reliable sources list competing or toxic enzymatic pathways' side-reactions, no corresponding compilation of spontaneous side-reactions exists, nor is it possible to predict their occurrence. We addressed this deficiency by creating the Chemical Damage (CD)-MINE resource. First, we used literature data to construct a comprehensive database of metabolite reactions that occur spontaneously in physiological conditions. We then leveraged this data to construct 148 reaction rules describing the known spontaneous chemistry in a substrate-generic way. We applied these rules to all compounds in the ModelSEED database, predicting 180,891 spontaneous reactions. The resulting (CD)-MINE is available at https://minedatabase.mcs.anl.gov/cdmine/#/home and through developer tools. We also demonstrate how damage-prone intermediates and end products are widely distributed among metabolic pathways, and how predicting spontaneous chemical damage helps rationalize toxicity and carbon loss using examples from published pathways to commercial products. We explain how analyzing damage-prone areas in metabolism helps design effective engineering strategies. Finally, we use the CD-MINE toolset to predict the formation of the novel damage product N-carbamoyl proline, and present mass spectrometric evidence for its presence in Escherichia coli.

CRISPRi enables fast growth followed by stable aerobic pyruvate formation in Escherichia coli without auxotrophy

CRISPR interference (CRISPRi) was applied to enable the aerobic production of pyruvate in Escherichia coli MG1655 under glucose excess conditions by targeting the promoter regions of aceE or pdhR. Knockdown strains were cultivated in aerobic shaking flasks and the influence of inducer concentration and different sgRNA binding sites on the production of pyruvate was measured. Targeting the promoter regions of aceE or pdhR triggered pyruvate production during the exponential phase and reduced expression of aceE. In lab‐scale bioreactor fermentations, an aceE silenced strain successfully produced pyruvate under fully aerobic conditions during the exponential phase, but loss of productivity occurred during a subsequent nitrogen‐limited phase. Targeting the promoter region of pdhR enabled pyruvate production during the growth phase of cultivations, and a continued low‐level accumulation during the nitrogen‐limited production phase. Combinatorial targeting of the promoter regions of both aceE and pdhR in E. coli MG1655 pdCas9 psgRNA_aceE_234_pdhR_329 resulted in the stable aerobic production of pyruvate with non‐growing cells at Y_P/S  =  0.36 ± 0.029 g_Pyruvate/g_Glucose in lab‐scale bioreactors throughout an extended nitrogen‐limited production phase.

Recombinant Protein Production with Escherichia coli in Glucose and Glycerol Limited Chemostats

Recently, there has been a resurgence of interest in continuous bioprocessing as a cost-optimised production strategy, driven by a rising global requirement for recombinant proteins used as biological drugs. This strategy could provide several benefits over traditional batch processing, including smaller bioreactors, smaller facilities, and overall reduced plant footprints and investment costs. Continuous processes may also offer improved product quality and minimise heterogeneity, both in the culture and in the product. In this paper, a model protein, green fluorescent protein (GFP) mut3*, was used to test the recombinant protein expression in an Escherichia coli strain with industrial relevance grown in chemostat. An important factor in enabling stable productivity in continuous cultures is the carbon source. We have studied the viability and heterogeneity of the chemostat cultures using a chemically defined medium based on glucose or glycerol as the single carbon source. As a by-product of biodiesel production, glycerol is expected to become a sustainable alternative substrate to glucose. We have found that although glycerol gives a higher cell density, it also generates higher heterogeneity in the culture and a less stable recombinant protein production. We suggest that manipulating the balance between different subpopulations to increase the proportion of productive cells may be a possible solution for making glycerol a successful alternative to glucose.

Constraint-based metabolic control analysis for rational strain engineering

The advancements in genome editing techniques over the past years have rekindled interest in rational metabolic engineering strategies. While Metabolic Control Analysis (MCA) is a well-established method for quantifying the effects of metabolic engineering interventions on flows in metabolic networks and metabolite concentrations, it does not consider the physiological limitations of the cellular environment and metabolic engineering design constraints. We report here a constraint-based framework, Network Response Analysis (NRA), for rational genetic strain design. NRA is cast as a Mixed-Integer Linear Programming problem that integrates MCA, Thermodynamically-based Flux Analysis (TFA), biologically relevant constraints, as well as genome editing restrictions into a comprehensive platform for identifying metabolic engineering targets. We show that the NRA formulation and its core constraints are equivalent to the ones of Flux Balance Analysis (FBA) and TFA, which allows it to be used for a wide range of optimization criteria and with various physiological constraints. We also show how the parametrization and introduction of biological constraints enhance the NRA formulation compared to the classical MCA approach, and we demonstrate its features and its ability to generate multiple alternative optimal strategies given several user-defined boundaries and objectives. In summary, NRA is a sophisticated alternative to classical MCA for rational metabolic engineering that accommodates the incorporation of physiological data at metabolic flux, metabolite concentration, and enzyme expression levels.

A lysate proteome engineering strategy for enhancing cell-free metabolite production

Cell-free systems present a significant opportunity to harness the metabolic potential of diverse organisms. Removing the cellular context provides the ability to produce biological products without the need to maintain cell viability and enables metabolic engineers to explore novel chemical transformation systems. Crude extracts maintain much of a cell’s capabilities. However, only limited tools are available for engineering the contents of the extracts used for cell-free systems. Thus, our ability to take full advantage of the potential of crude extracts for cell-free metabolic engineering is constrained. Here, we employ Multiplex Automated Genomic Engineering (MAGE) to tag proteins for selective depletion from crude extracts so as to specifically direct chemical production. Specific edits to central metabolism are possible without significantly impacting cell growth. Selective removal of pyruvate degrading enzymes resulted in engineered crude lysates that are capable of up to 40-fold increases in pyruvate production when compared to the non-engineered extract. The described approach melds the tools of systems and synthetic biology to showcase the effectiveness of cell-free metabolic engineering for applications like bioprototyping and bioproduction.

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A method of engineering cell-free metabolism in lysates is described.

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Method enables design of cell lysates for enhancing specific metabolic processes.

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Pyruvate consuming enzymes tagged with 6xHis tags have minimal impact on growth.

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Post-lysis pull-down of tagged enzymes enables cell-free pyruvate pooling.

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Lysate engineering strategy permits metabolic states not possible in living cells.

Targeted Growth Medium Dropouts Promote Aromatic Compound Synthesis in Crude E. coli Cell-Free Systems

Progress in cell-free protein synthesis (CFPS) has spurred resurgent interest in engineering complex biological metabolism outside of the cell. Unlike purified enzyme systems, crude cell-free systems can be prepared for a fraction of the cost and contain endogenous cellular pathways that can be activated for biosynthesis. Endogenous activity performs essential functions in cell-free systems including substrate biosynthesis and energy regeneration; however, use of crude cell-free systems for bioproduction has been hampered by the under-described complexity of the metabolic networks inherent to a crude lysate. Physical and chemical cultivation parameters influence the endogenous activity of the resulting lysate, but targeted efforts to engineer this activity by manipulation of these nongenetic factors has been limited. Here growth medium composition was manipulated to improve the one-pot in vitro biosynthesis of phenol from glucose via the expression of Pasteurella multocida phenol-tyrosine lyase in crude E. coli lysates. Crude cell lysate metabolic activity was focused toward the limiting precursor tyrosine by targeted growth medium dropouts guided by proteomics. The result is the activation of a 25-step enzymatic reaction cascade involving at least three endogenous E. coli metabolic pathways. Additional modification of this system, through CFPS of feedback intolerant AroG improves yield. This effort demonstrates the ability to activate a long, complex pathway in vitro and provides a framework for harnessing the metabolic potential of diverse organisms for cell-free metabolic engineering. The more than 6-fold increase in phenol yield with limited genetic manipulation demonstrates the benefits of optimizing growth medium for crude cell-free extract production and illustrates the advantages of a systems approach to cell-free metabolic engineering.

Effect of plasma-induced oxidative stress on the glycolysis pathway of Escherichia coli

Antibiotic resistance is one of the world's most urgent public health problems. Due to its antibacterial properties, cold atmospheric plasma (CAP) may serve as an alternative method to antibiotics. It is claimed that oxidative stress caused by CAP is the main reason of bacteria inactivation. In this work, we computationally investigated the effect of plasma-induced oxidation on various glycolysis metabolites, by monitoring the production of the biomass. We observed that in addition to the significant reduction in biomass production, the rate of some reactions has increased. These reactions produce anti-oxidant products, showing the bacterial defense mechanism to escape the oxidative damage. Nevertheless, the simulations show that the plasma-induced oxidation effect is much stronger than the defense mechanism, causing killing of the bacteria.

A Novel Biosynthetic Pathway for the Production of Acrylic Acid through β-Alanine Route in Escherichia coli

Acrylic acid (AA) is an important industrial chemical used for several applications including superabsorbent polymers and acrylate esters. Here, we report the development of a new biosynthetic pathway for the production of AA from glucose in metabolically engineered Escherichia coli through the β-alanine (BA) route. The AA production pathway was partitioned into two modules: an AA forming downstream pathway and a BA forming upstream pathway. We first validated the operation of the downstream pathway in vitro and in vivo, and then constructed the downstream pathway by introducing efficient enzymes (Act, Acl2, and YciA) screened out of various microbial sources and optimizing the expression levels. For the direct fermentative production of AA from glucose, the downstream pathway was introduced into the BA producing E. coli strain. The resulting strain could successfully produce AA from glucose in flask cultivation. AA production was further enhanced by expressing the upstream genes (panD and aspA) under the constitutive BBa_J23100 promoter. Replacement of the native promoter of the acs gene with the BBa_J23100 promoter in the genome increased AA production to 55.7 mg/L in flask. Fed-batch fermentation of the final engineered strain allowed production of 237 mg/L of AA in 57.5 h, representing the highest AA titer reported to date.

Computation-Driven Analysis of Model Polyextremo-tolerant Fungus Exophiala dermatitidis: Defensive Pigment Metabolic Costs and Human Applications

The polyextremotolerant black yeast Exophiala dermatitidis is a tractable model system for investigation of adaptations that support growth under extreme conditions. Foremost among these adaptations are melanogenesis and carotenogenesis. A particularly important question is their metabolic production cost. However, investigation of this issue has been hindered by a relatively poor systems-level understanding of E. dermatitidis metabolism. To address this challenge, a genome-scale model (iEde2091) was developed. Using iEde2091, carotenoids were found to be more expensive to produce than melanins. Given their overlapping protective functions, this suggests that carotenoids have an underexplored yet important role in photo-protection. Furthermore, multiple defensive pigments with overlapping functions might allow E. dermatitidis to minimize cost. Because iEde2091 revealed that E. dermatitidis synthesizes the same melanins as humans and the active sites of the key tyrosinase enzyme are highly conserved this model may enable a broader understanding of melanin production across kingdoms.

CF-Targeter: A Rational Biological Cell Factory Targeting Platform for Biosynthetic Target Chemicals

Biosynthesis is a promising method for chemical synthesis. However, due to varieties between different microorganism hosts, yield and heterologous pathways needed for production of target chemical may also vary from different strains. One of the main challenges in metabolic engineering is to select an appropriate chassis host for specified target chemical production. However, with thousands of microorganisms existing in nature and extremely complicated metabolism within them, it is still time-consuming and error-prone work to achieve such a goal only through experimental methods, even with some existing computational methods. Hence, more efficient methods should be proposed to assist in selecting appropriate chassis hosts. In this article, based on symbolic reaction repositories and a pathway search algorithm which performed 1 400 000 searches for per target compound, we established a biological reasoning system for appropriate chassis host selection by coupling with various GEM-models. By using a supercomputer to calculate the biosynthetic pathways for more than 1 month, nearly 50 000 000 biosynthetic pathways are computed for production of 6026 compounds within 70 microorganisms. With retrieved organisms for specified target production, several heterologous biosynthetic pathways can be shown in length order, and then the maximum theoretical yields and thermodynamic feasibility can be calculated in real time under customized growth conditions and physiological states. From the computation results, the system not only identifies experimentally validated pathways but also outputs more efficient solutions with less heterologous steps or higher maximum possible theoretical yield by engineering other organism hosts. CF-targeter is available at http://www.rxnfinder.org/cf_targeter/.

Elucidating the potential of crude cell extracts for producing pyruvate from glucose

Living systems possess a rich biochemistry that can be harnessed through metabolic engineering to produce valuable therapeutics, fuels and fine chemicals. In spite of the tools created for this purpose, many organisms tend to be recalcitrant to modification or difficult to optimize. Crude cellular extracts, made by lysis of cells, possess much of the same biochemical capability, but in an easier to manipulate context. Metabolic engineering in crude extracts, or cell-free metabolic engineering, can harness these capabilities to feed heterologous pathways for metabolite production and serve as a platform for pathway optimization. However, the inherent biochemical potential of a crude extract remains ill-defined, and consequently, the use of such extracts can result in inefficient processes and unintended side products. Herein, we show that changes in cell growth conditions lead to changes in the enzymatic activity of crude cell extracts and result in different abilities to produce the central biochemical precursor pyruvate when fed glucose. Proteomic analyses coupled with metabolite measurements uncover the diverse biochemical capabilities of these different crude extract preparations and provide a framework for how analytical measurements can be used to inform and improve crude extract performance. Such informed developments can allow enrichment of crude extracts with pathways that promote or deplete particular metabolic processes and aid in the metabolic engineering of defined products.

Constraint-based modeling in microbial food biotechnology

Genome-scale metabolic network reconstruction offers a means to leverage the value of the exponentially growing genomics data and integrate it with other biological knowledge in a structured format. Constraint-based modeling (CBM) enables both the qualitative and quantitative analyses of the reconstructed networks. The rapid advancements in these areas can benefit both the industrial production of microbial food cultures and their application in food processing. CBM provides several avenues for improving our mechanistic understanding of physiology and genotype–phenotype relationships. This is essential for the rational improvement of industrial strains, which can further be facilitated through various model-guided strain design approaches. CBM of microbial communities offers a valuable tool for the rational design of defined food cultures, where it can catalyze hypothesis generation and provide unintuitive rationales for the development of enhanced community phenotypes and, consequently, novel or improved food products. In the industrial-scale production of microorganisms for food cultures, CBM may enable a knowledge-driven bioprocess optimization by rationally identifying strategies for growth and stability improvement. Through these applications, we believe that CBM can become a powerful tool for guiding the areas of strain development, culture development and process optimization in the production of food cultures. Nevertheless, in order to make the correct choice of the modeling framework for a particular application and to interpret model predictions in a biologically meaningful manner, one should be aware of the current limitations of CBM.

Thermodynamic analysis of computed pathways integrated into the metabolic networks of E. coli and Synechocystis reveals contrasting expansion potential

Introducing biosynthetic pathways into an organism is both reliant on and challenged by endogenous biochemistry. Here we compared the expansion potential of the metabolic network in the photoautotroph Synechocystis with that of the heterotroph E. coli using the novel workflow POPPY (Prospecting Optimal Pathways with PYthon). First, E. coli and Synechocystis metabolomic and fluxomic data were combined with metabolic models to identify thermodynamic constraints on metabolite concentrations (NET analysis). Then, thousands of automatically constructed pathways were placed within each network and subjected to a network-embedded variant of the max-min driving force analysis (NEM). We found that the networks had different capabilities for imparting thermodynamic driving forces toward certain compounds. Key metabolites were constrained differently in Synechocystis due to opposing flux directions in glycolysis and carbon fixation, the forked tri-carboxylic acid cycle, and photorespiration. Furthermore, the lysine biosynthesis pathway in Synechocystis was identified as thermodynamically constrained, impacting both endogenous and heterologous reactions through low 2-oxoglutarate levels. Our study also identified important yet poorly covered areas in existing metabolomics data and provides a reference for future thermodynamics-based engineering in Synechocystis and beyond. The POPPY methodology represents a step in making optimal pathway-host matches, which is likely to become important as the practical range of host organisms is diversified.

EcoSynther: A Customized Platform To Explore the Biosynthetic Potential in E. coli

Developing computational tools for a chassis-centered biosynthetic pathway design is very important for a productive heterologous biosynthesis system by considering enormous foreign biosynthetic reactions. For many cases, a pathway to produce a target molecule consists of both native and heterologous reactions when utilizing a microbial organism as the host organism. Due to tens of thousands of biosynthetic reactions existing in nature, it is not trivial to identify which could be served as heterologous ones to produce the target molecule in a specific organism. In the present work, we integrate more than 10,000 E. coli non-native reactions and utilize a probability-based algorithm to search pathways. Moreover, we built a user-friendly Web server named EcoSynther. It is able to explore the precursors and heterologous reactions needed to produce a target molecule in Escherichia coli K12 MG1655 and then applies flux balance analysis to calculate theoretical yields of each candidate pathway. Compared with other chassis-centered biosynthetic pathway design tools, EcoSynther has two unique features: (1) allow for automatic search without knowing a precursor in E. coli and (2) evaluate the candidate pathways under constraints from E. coli physiological states and growth conditions. EcoSynther is available at http://www.rxnfinder.org/ecosynther/ .

A review of computational tools for design and reconstruction of metabolic pathways

Metabolic pathways reflect an organism's chemical repertoire and hence their elucidation and design have been a primary goal in metabolic engineering. Various computational methods have been developed to design novel metabolic pathways while taking into account several prerequisites such as pathway stoichiometry, thermodynamics, host compatibility, and enzyme availability. The choice of the method is often determined by the nature of the metabolites of interest and preferred host organism, along with computational complexity and availability of software tools. In this paper, we review different computational approaches used to design metabolic pathways based on the reaction network representation of the database (i.e., graph or stoichiometric matrix) and the search algorithm (i.e., graph search, flux balance analysis, or retrosynthetic search). We also put forth a systematic workflow that can be implemented in projects requiring pathway design and highlight current limitations and obstacles in computational pathway design.

Conversion of glucose-xylose mixtures to pyruvate using a consortium of metabolically engineered Escherichia coli

Two strains of Escherichia coli were engineered to accumulate pyruvic acid from two sugars found in lignocellulosic hydrolysates by knockouts in the aceE, ppsA, poxB, and ldhA genes. Additionally, since glucose and xylose are typically consumed sequentially due to carbon catabolite repression in E. coli, one strain (MEC590) was engineered to grow only on glucose while a second strain (MEC589) grew only on xylose. On a single substrate, each strain generated pyruvate at a yield of about 0.60 g/g in both continuous culture and batch culture. In a glucose-xylose mixture under continuous culture, a consortium of both strains maintained a pyruvate yield greater than 0.60 g/g when three different concentrations of glucose and xylose were sequentially fed into the system. In a fed-batch process, both sugars in a glucose-xylose mixture were consumed simultaneously to accumulate 39 g/L pyruvate in less than 24 h at a yield of 0.59 g/g.

Technological Microbiology: Development and Applications

Over thousands of years, modernization could be predicted for the use of microorganisms in the production of foods and beverages. However, the current accelerated pace of new food production is due to the rapid incorporation of biotechnological techniques that allow the rapid identification of new molecules and microorganisms or even the genetic improvement of known species. At no other time in history have microorganisms been so present in areas such as agriculture and medicine, except as recognized villains. Currently, however, beneficial microorganisms such as plant growth promoters and phytopathogen controllers are required by various agricultural crops, and many species are being used as biofactories of important pharmacological molecules. The use of biofactories does not end there: microorganisms have been explored for the synthesis of diverse chemicals, fuel molecules, and industrial polymers, and strains environmentally important due to their biodecomposing or biosorption capacity have gained interest in research laboratories and in industrial activities. We call this new microbiology Technological Microbiology, and we believe that complex techniques, such as heterologous expression and metabolic engineering, can be increasingly incorporated into this applied science, allowing the generation of new and improved products and services.

The Resistome: A Comprehensive Database of Escherichia coli Resistance Phenotypes

The microbial ability to resist stressful environmental conditions and chemical inhibitors is of great industrial and medical interest. Much of the data related to mutation-based stress resistance, however, is scattered through the academic literature, making it difficult to apply systematic analyses to this wealth of information. To address this issue, we introduce the Resistome database: a literature-curated collection of Escherichia coli genotypes-phenotypes containing over 5,000 mutants that resist hundreds of compounds and environmental conditions. We use the Resistome to understand our current state of knowledge regarding resistance and to detect potential synergy or antagonism between resistance phenotypes. Our data set represents one of the most comprehensive collections of genomic data related to resistance currently available. Future development will focus on the construction of a combined genomic-transcriptomic-proteomic framework for understanding E. coli's resistance biology. The Resistome can be downloaded at https://bitbucket.org/jdwinkler/resistome_release/overview .

Prospects of microbial cell factories developed through systems metabolic engineering

While academic‐level studies on metabolic engineering of microorganisms for production of chemicals and fuels are ever growing, a significantly lower number of such production processes have reached commercial‐scale. In this work, we review the challenges associated with moving from laboratory‐scale demonstration of microbial chemical or fuel production to actual commercialization, focusing on key requirements on the production organism that need to be considered during the metabolic engineering process. Metabolic engineering strategies should take into account techno‐economic factors such as the choice of feedstock, the product yield, productivity and titre, and the cost effectiveness of midstream and downstream processes. Also, it is important to develop an industrial strain through metabolic engineering for pathway construction and flux optimization together with increasing tolerance to products and inhibitors present in the feedstock, and ensuring genetic stability and strain robustness under actual fermentation conditions.