Biofuel production: an odyssey from metabolic engineering to fermentation scale-up

Cellular variability as a driver for bioprocess innovation and optimization

Cellular heterogeneity plays a crucial role in biotechnological processes, significantly influencing metabolic activity, product yield, and process consistency. This review explores the different dimensions of cellular heterogeneity, focusing on its manifestation at both single-cell and population levels. The study examines how factors such as asymmetric cell division, age, and environmental conditions contribute to functional diversity within cell populations, with an emphasis on microorganisms like yeast. Age-related cellular heterogeneity, in particular, is highlighted for its impact on metabolic pathways, mitochondrial function, and secondary metabolite production, which directly affect bioprocess outcomes. Furthermore, the review discusses advanced techniques for detecting and managing heterogeneity, including surface marker-based approaches, which utilize proteins, polysaccharides, and lipids, and label-free methods that leverage cellular volume and physical properties for separation. Understanding and controlling cellular heterogeneity is essential for optimizing industrial bioprocesses, improving yield, and ensuring product quality. The review also underscores the potential of emerging biotechnological tools, such as real-time single-cell analysis and microfluidic devices, in enhancing separation techniques and managing cellular diversity for better process efficiency and robustness.

Flux balance analysis of metabolic networks for efficient engineering of microbial cell factories

Metabolic engineering principles have long been applied to explore the metabolic networks of complex microbial cell factories under a variety of environmental constraints for effective deployment of the microorganisms in the optimal production of biochemicals like biofuels, polymers, amino acids, recombinant proteins. One of the methodologies used for analyzing microbial metabolic networks is the Flux Balance Analysis (FBA), which employs applications of optimization techniques for forecasting biomass growth and metabolic flux distribution of industrially important products under specified environmental conditions. The in silico flux simulations are instrumental for designing the production-specific microbial cell factories. However, FBA has some inherent limitations. The present review emphasizes how the incorporation of additional kinetic, thermodynamic, expression and regulatory constraints and integration of omics data into the classical FBA platform improve the prediction accuracy of FBA. A programmed comparison of the simulated data with the experimental observations is presented for supporting the claim. The review further accounts for the successful implementation of classical FBA in biotechnological applications and identifies areas in which classical FBA fails to make correct predictions. The analysis of the predictive capabilities of the different FBA strategies presented here is expected to help researchers in finding new avenues in engineering highly efficient microbial metabolic pathways and identify the key metabolic bottlenecks during the process. Based on the appropriate metabolic network design, fermentation engineers will be able to effectively design the bioreactors and optimize large-scale biochemical production through suitable pathway modifications.

SCALiR: A Web Application for Automating Absolute Quantification of Mass Spectrometry-Based Metabolomics Data

Quantitative liquid chromatography-mass spectrometry (LC-MS)-based metabolomics is becoming an important approach for studying complex biological systems but presents several technical challenges that limit its widespread use. Computing metabolite concentrations using standard curves generated from standard mixtures of known concentrations is a labor-intensive process that is often performed manually. Currently, there are few options for open-source software tools that can automatically calculate metabolite concentrations. Herein, we introduce SCALiR (standard curve application for determining linear ranges), a new web-based software tool specifically built for this task, which allows users to automatically transform LC-MS signals into absolute quantitative data (https://www.lewisresearchgroup.org/software). SCALiR uses an algorithm that automatically finds the equation of the line of best fit for each standard curve and uses this equation to calculate compound concentrations from the LC-MS signal. Using a standard mix containing 77 metabolites, we show a close correlation between the concentrations calculated by SCALiR and the expected concentrations of each compound (R2 = 0.99 for a y = x curve fitting). Moreover, we demonstrate that SCALiR reproducibly calculates concentrations of midrange standards across ten analytical batches (average coefficient of variation 0.091). SCALiR can be used to calculate metabolite concentrations either using external calibration curves or by using internal standards to correct for matrix effects. This open-source and vendor agnostic software offers users several advantages in that (1) it requires only 10 s of analysis time to compute concentrations of >75 compounds, (2) it facilitates automation of quantitative workflows, and (3) it performs deterministic evaluations of compound quantification limits. SCALiR therefore provides the metabolomics community with a simple and rapid tool that enables rigorous and reproducible quantitative metabolomics studies.

Construction of a xylose metabolic pathway in Trichosporonoides oedocephalis ATCC 16958 for the production of erythritol and xylitol

PURPOSE

Erythritol is a valuable compound as sweetener and chemical material however cannot be fermented from the abundant substrate xylose.

METHODS

The strain Trichosporonoides oedocephalis ATCC 16958 was employed to produce polyols including xylitol and erythritol by metabolic engineering approaches.

RESULTS

The introduction of a substrate-specific ribose-5-phosphate isomerase endowed T. oedocephalis with xylose-assimilation activity to produce xylitol, and eliminated glycerol production simultaneously. A more value-added product, erythritol was produced by further introducing a homologous xylulose kinase. The carbon flux was redirected from xylitol to erythritol by adding high osmotic pressure. The production of erythritol was improved to 46.5 g/L in flasks by fermentation adjustment, and the process was scaled up in a 5-L fermentor, with a 40 g/L erythritol production after 120 h, and a time-space yield of 0.56 g/L/h.

CONCLUSION

This study demonstrated the potential of T. oedocephalis in the synthesis of multiple useful products from xylose.

SCALiR: a web application for automating absolute quantification of mass spectrometry-based metabolomics data

Metabolomics is an important approach for studying complex biological systems. Quantitative liquid chromatography-mass spectrometry (LC-MS)-based metabolomics is becoming a mainstream strategy but presents several technical challenges that limit its widespread use. Computing metabolite concentrations using standard curves generated from standard mixtures of known concentrations is a labor-intensive process which is often performed manually. Currently, there are few options for open-source software tools that can automatically calculate metabolite concentrations. Herein, we introduce SCALiR (Standard Curve Application for determining Linear Ranges), a new web-based software tool specifically built for this task, which allows users to automatically transform LC-MS signal data into absolute quantitative data (https://www.lewisresearchgroup.org/software). The algorithm used in SCALiR automatically finds the equation of the line of best fit for each standard curve and uses this equation to calculate compound concentrations from their LC-MS signal. Using a standard mix containing 77 metabolites, we found excellent correlation between the concentrations calculated by SCALiR and the expected concentrations of each compound (R2 = 0.99) and that SCALiR reproducibly calculated concentrations of mid-range standards across ten analytical batches (average coefficient of variation 0.091). SCALiR offers users several advantages, including that it (1) is open-source and vendor agnostic; (2) requires only 10 seconds of analysis time to compute concentrations of >75 compounds; (3) facilitates automation of quantitative workflows; and (4) performs deterministic evaluation of compound quantification limits. SCALiR provides the metabolomics community with a simple and rapid tool that enables rigorous and reproducible quantitative metabolomics studies.

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.

Co-feeding enhances the yield of methyl ketones

The biotechnological production of methyl ketones is a sustainable alternative to fossil-derived chemical production. To date, the best host for microbial production of methyl ketones is a genetically engineered Pseudomonas taiwanensis VLB120 ∆6 pProd strain, achieving yields of 101 mgg-1 on glucose in batch cultivations. For competitiveness with the petrochemical production pathway, however, higher yields are necessary. Co-feeding can improve the yield by fitting the carbon-to-energy ratio to the organism and the target product. In this work, we developed co-feeding strategies for P. taiwanensis VLB120 ∆6 pProd by combined metabolic modeling and experimental work. In a first step, we conducted flux balance analysis with an expanded genome-scale metabolic model of iJN1463 and found ethanol as the most promising among five cosubstrates. Next, we performed cultivations with ethanol and found the highest reported yield in batch production of methyl ketones with P. taiwanensis VLB120 to date, namely, 154 mg g-1 methyl ketones. However, ethanol is toxic to the cell, which reflects in a lower substrate consumption and lower product concentrations when compared to production on glucose. Hence, we propose cofeeding ethanol with glucose and find that, indeed, higher concentrations than in ethanol-fed cultivation (0.84 g Laq-1 with glucose and ethanol as opposed to 0.48 g Laq-1 with only ethanol) were achieved, with a yield of 85 mg g-1. In a last step, comparing experimental with computational results suggested the potential for improving the methyl ketone yield by fed-batch cultivation, in which cell growth and methyl ketone production are separated into two phases employing optimal ethanol to glucose ratios.

ONE-SENTENCE SUMMARY

By combining computational and experimental work, we demonstrate that feeding ethanol in addition to glucose improves the yield of biotechnologically produced methyl ketones.

Shikimic acid biosynthesis in microorganisms: Current status and future direction

Shikimic acid (SA), a hydroaromatic natural product, is used as a chiral precursor for organic synthesis of oseltamivir (Tamiflu®, an antiviral drug). The process of microbial production of SA has recently undergone vigorous development. Particularly, the sustainable construction of recombinant Corynebacterium glutamicum (141.2 g/L) and Escherichia coli (87 g/L) laid a solid foundation for the microbial fermentation production of SA. However, its industrial application is restricted by limitations such as the lack of fermentation tests for industrial-scale and the requirement of growth-limiting factors, antibiotics, and inducers. Therefore, the development of SA biosensors and dynamic molecular switches, as well as genetic modification strategies and optimization of the fermentation process based on omics technology could improve the performance of SA-producing strains. In this review, recent advances in the development of SA-producing strains, including genetic modification strategies, metabolic pathway construction, and biosensor-assisted evolution, are discussed and critically reviewed. Finally, future challenges and perspectives for further reinforcing the development of robust SA-producing strains are predicted, providing theoretical guidance for the industrial production of SA.

Graphene oxide-based novel MOF nanohybrid for synergic removal of Pb (II) ions from aqueous solutions: Simulation and adsorption studies

Heavy metals represent a considerable threat, and the current study deals with synthesizing a novel MOF nanocomposite by intercalating graphene oxide (GO) and linker UiO-66-NDC. It was shown that UiO-66-NDC/GO had enhanced the removal efficiency of Pb (II) ions at pH 6. The adsorption kinetics data followed the PSO (Type 2) representing chemisorption. Adsorption data were also fitted with three different isotherms, namely Temkin, Freundlich, & Langmuir, and the Temkin model exhibited the best correlation (R² 0.99), representing the chemisorption nature of the adsorption process. The maximum adsorption capacity (qmax) of Pb (II) ions using Langmuir was found to be 254.45 mg/g (298 K). The Pb (II) adsorption process was confirmed to be exothermic and spontaneous as the thermodynamic parameters H° and G° were determined to have negative values. MOF nanocomposite also represents significant reusability for up to four regeneration cycles using 0.01 M HCl; for the next four, it works quite efficiently after regeneration. Meanwhile, the simulation findings confirm the superior dynamic stability (∼08 times) of the MOF nanocomposite as compared to the GO system. The removal of Pb (II) from simulated wastewater samples using a super nano-adsorbent using a MOF nanocomposite is described here for the first time.

Microbial containment device: A platform for comprehensive analysis of microbial metabolism without sample preparation

Metabolomics is a mainstream strategy for investigating microbial metabolism. One emerging application of metabolomics is the systematic quantification of metabolic boundary fluxes – the rates at which metabolites flow into and out of cultured cells. Metabolic boundary fluxes can capture complex metabolic phenotypes in a rapid assay, allow computational models to be built that predict the behavior of cultured organisms, and are an emerging strategy for clinical diagnostics. One advantage of quantifying metabolic boundary fluxes rather than intracellular metabolite levels is that it requires minimal sample processing. Whereas traditional intracellular analyses require a multi-step process involving extraction, centrifugation, and solvent exchange, boundary fluxes can be measured by simply analyzing the soluble components of the culture medium. To further simplify boundary flux analyses, we developed a custom 96-well sampling system—the Microbial Containment Device (MCD)—that allows water-soluble metabolites to diffuse from a microbial culture well into a bacteria-free analytical well via a semi-permeable membrane. The MCD was designed to be compatible with the autosamplers present in commercial liquid chromatography-mass spectrometry systems, allowing metabolic fluxes to be analyzed with minimal sample handling. Herein, we describe the design, evaluation, and performance testing of the MCD relative to traditional culture methods. We illustrate the utility of this platform, by quantifying the unique boundary fluxes of four bacterial species and demonstrate antibiotic-induced perturbations in their metabolic activity. We propose the use of the MCD for enabling single-step metabolomics sample preparation for microbial identification, antimicrobial susceptibility testing, and other metabolic boundary flux applications where traditional sample preparation methods are impractical.

The Growth Dependent Design Constraints of Transcription-Factor-Based Metabolite Biosensors

Metabolite biosensors based on metabolite-responsive transcription factors are key synthetic biology components for sensing and precisely controlling cellular metabolism. Biosensors are often designed under laboratory conditions but are deployed in applications where cellular growth rate differs drastically from its initial characterization. Here we asked how growth rate impacts the minimum and maximum biosensor outputs and the dynamic range, which are key metrics of biosensor performance. Using LacI, TetR, and FadR-based biosensors in Escherichia coli as models, we find that the dynamic range of different biosensors have different growth rate dependencies. We developed a kinetic model to explore how tuning biosensor parameters impact the dynamic range growth rate dependence. Our modeling and experimental results revealed that the effects to dynamic range and its growth rate dependence are often coupled, and the metabolite transport mechanisms shape the dynamic range-growth rate response. This work provides a systematic understanding of biosensor performance under different growth rates, which will be useful for predicting biosensor behavior in broad synthetic biology and metabolic engineering applications.

Methods for Quantifying the Metabolic Boundary Fluxes of Cell Cultures in Large Cohorts by High-Resolution Hydrophilic Liquid Chromatography Mass Spectrometry

Metabolomics is a mainstream approach for investigating the metabolic underpinnings of complex biological phenomena and is increasingly being applied to large-scale studies involving hundreds or thousands of samples. Although metabolomics methods are robust in smaller-scale studies, they can be challenging to apply to larger cohorts due to the inherent variability of liquid chromatography mass spectrometry (LC-MS). Much of this difficulty results from the time-dependent changes in the LC-MS system, which affects both the qualitative and quantitative performances of the instrument. Herein, we introduce an analytical strategy for addressing this problem in large-scale microbial studies. Our approach quantifies microbial boundary fluxes using two zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC) columns that are plumbed to enable offline column equilibration. Using this strategy, we show that over 397 common metabolites can be resolved in 4.5 min per sample and that metabolites can be quantified with a median coefficient of variation of 0.127 across 1100 technical replicates. We illustrate the utility of this strategy via an analysis of 960 strains of Staphylococcus aureus isolated from bloodstream infections. These data capture the diversity of metabolic phenotypes observed in clinical isolates and provide an example of how large-scale investigations can leverage our novel analytical strategy.

Bioengineered microbial platforms for biomass-derived biofuel production - A review

Global warming issues, rapid fossil fuel diminution, and increasing worldwide energy demands have diverted accelerated attention in finding alternate sources of biofuels and energy to combat the energy crisis. Bioconversion of lignocellulosic biomass has emerged as a prodigious way to produce various renewable biofuels such as biodiesel, bioethanol, biogas, and biohydrogen. Ideal microbial hosts for biofuel synthesis should be capable of using high substrate quantity, tolerance to inhibiting substances and end-products, fast sugar transportation, and amplified metabolic fluxes to yielding enhanced fermentative bioproduct. Genetic manipulation and microbes' metabolic engineering are fascinating strategies for the economical production of next-generation biofuel from lignocellulosic feedstocks. Metabolic engineering is a rapidly developing approach to construct robust biofuel-producing microbial hosts and an important component for future bioeconomy. This approach has been widely adopted in the last decade for redirecting and revamping the biosynthetic pathways to obtain a high titer of target products. Biotechnologists and metabolic scientists have produced a wide variety of new products with industrial relevance through metabolic pathway engineering or optimizing native metabolic pathways. This review focuses on exploiting metabolically engineered microbes as promising cell factories for the enhanced production of advanced biofuels.

Recent advances and sustainable development of biofuels production from lignocellulosic biomass

Many countries in the world are facing the demand for non-renewable fossil fuels because of overpopulation and economic boom. To reduce environmental pollution and zero carbon emission, the conversion of biomass into biofuels has paid better attention and is considered to be an innovative approach. A diverse raw material has been utilized as feedstock for the production of biofuel, depending on the availability of biomass, cost-effectiveness, and their geographic location. Among the different raw materials, lignocellulosic biomass has fascinated many researchers around the world. The current review discovers the potential application of lignocellulosic biomass for the production of biofuels. Various pretreatment methods have been widely used to increase the hydrolysis rate and accessibility of biomass. This review highlights recent advances in pretreatment methodologies for the enhanced production of biofuels. Detailed descriptions of the mechanism of biomass processing pathway, optimization, and modeling study have been discussed.

Consolidated bioprocessing of lignocellulose for production of glucaric acid by an artificial microbial consortium

BACKGROUND

The biomanufacturing of D-glucaric acid has attracted increasing interest because it is one of the top value-added chemicals produced from biomass. Saccharomyces cerevisiae is regarded as an excellent host for D-glucaric acid production.

RESULTS

The opi1 gene was knocked out because of its negative regulation on myo-inositol synthesis, which is the limiting step of D-glucaric acid production by S. cerevisiae. We then constructed the biosynthesis pathway of D-glucaric acid in S. cerevisiae INVSc1 opi1Δ and obtained two engineered strains, LGA-1 and LGA-C, producing record-breaking titers of D-glucaric acid: 9.53 ± 0.46 g/L and 11.21 ± 0.63 g/L D-glucaric acid from 30 g/L glucose and 10.8 g/L myo-inositol in fed-batch fermentation mode, respectively. However, LGA-1 was preferable because of its genetic stability and its superior performance in practical applications. There have been no reports on D-glucaric acid production from lignocellulose. Therefore, the biorefinery processes, including separated hydrolysis and fermentation (SHF), simultaneous saccharification and fermentation (SSF) and consolidated bioprocessing (CBP) were investigated and compared. CBP using an artificial microbial consortium composed of Trichoderma reesei (T. reesei) Rut-C30 and S. cerevisiae LGA-1 was found to have relatively high D-glucaric acid titers and yields after 7 d of fermentation, 0.54 ± 0.12 g/L D-glucaric acid from 15 g/L Avicel and 0.45 ± 0.06 g/L D-glucaric acid from 15 g/L steam-exploded corn stover (SECS), respectively. In an attempt to design the microbial consortium for more efficient CBP, the team consisting of T. reesei Rut-C30 and S. cerevisiae LGA-1 was found to be the best, with excellent work distribution and collaboration.

CONCLUSIONS

Two engineered S. cerevisiae strains, LGA-1 and LGA-C, with high titers of D-glucaric acid were obtained. This indicated that S. cerevisiae INVSc1 is an excellent host for D-glucaric acid production. Lignocellulose is a preferable substrate over myo-inositol. SHF, SSF, and CBP were studied, and CBP using an artificial microbial consortium of T. reesei Rut-C30 and S. cerevisiae LGA-1 was found to be promising because of its relatively high titer and yield. T. reesei Rut-C30 and S. cerevisiae LGA-1were proven to be the best teammates for CBP. Further work should be done to improve the efficiency of this microbial consortium for D-glucaric acid production from lignocellulose.

Genome-scale metabolic modeling of P. thermoglucosidasius NCIMB 11955 reveals metabolic bottlenecks in anaerobic metabolism

Parageobacillus thermoglucosidasius represents a thermophilic, facultative anaerobic bacterial chassis, with several desirable traits for metabolic engineering and industrial production. To further optimize strain productivity, a systems level understanding of its metabolism is needed, which can be facilitated by a genome-scale metabolic model. Here, we present p-thermo, the most complete, curated and validated genome-scale model (to date) of Parageobacillus thermoglucosidasius NCIMB 11955. It spans a total of 890 metabolites, 1175 reactions and 917 metabolic genes, forming an extensive knowledge base for P. thermoglucosidasius NCIMB 11955 metabolism. The model accurately predicts aerobic utilization of 22 carbon sources, and the predictive quality of internal fluxes was validated with previously published ¹³C-fluxomics data. In an application case, p-thermo was used to facilitate more in-depth analysis of reported metabolic engineering efforts, giving additional insight into fermentative metabolism. Finally, p-thermo was used to resolve a previously uncharacterised bottleneck in anaerobic metabolism, by identifying the minimal required supplemented nutrients (thiamin, biotin and iron(III)) needed to sustain anaerobic growth. This highlights the usefulness of p-thermo for guiding the generation of experimental hypotheses and for facilitating data-driven metabolic engineering, expanding the use of P. thermoglucosidasius as a high yield production platform.

Microbial biotechnological approaches: renewable bioprocessing for the future energy systems

The accelerating energy demands of the increasing global population and industrialization has become a matter of great concern all over the globe. In the present scenario, the world is witnessing a considerably huge energy crisis owing to the limited availability of conventional energy resources and rapid depletion of non-renewable fossil fuels. Therefore, there is a dire need to explore the alternative renewable fuels that can fulfil the energy requirements of the growing population and overcome the intimidating environmental issues like greenhouse gas emissions, global warming, air pollution etc. The use of microorganisms such as bacteria has captured significant interest in the recent era for the conversion of the chemical energy reserved in organic compounds into electrical energy. The versatility of the microorganisms to generate renewable energy fuels from multifarious biological and biomass substrates can abate these ominous concerns to a great extent. For instance, most of the microorganisms can easily transform the carbohydrates into alcohol. Establishing the microbial fuel technology as an alternative source for the generation of renewable energy sources can be a state of art technology owing to its reliability, high efficiency, cleanliness and production of minimally toxic or inclusively non-toxic byproducts. This review paper aims to highlight the key points and techniques used for the employment of bacteria to generate, biofuels and bioenergy, and their foremost benefits.

Bioprospecting of microbial strains for biofuel production: metabolic engineering, applications, and challenges

The issues of global warming, coupled with fossil fuel depletion, have undoubtedly led to renewed interest in other sources of commercial fuels. The search for renewable fuels has motivated research into the biological degradation of lignocellulosic biomass feedstock to produce biofuels such as bioethanol, biodiesel, and biohydrogen. The model strain for biofuel production needs the capability to utilize a high amount of substrate, transportation of sugar through fast and deregulated pathways, ability to tolerate inhibitory compounds and end products, and increased metabolic fluxes to produce an improved fermentation product. Engineering microbes might be a great approach to produce biofuel from lignocellulosic biomass by exploiting metabolic pathways economically. Metabolic engineering is an advanced technology for the construction of highly effective microbial cell factories and a key component for the next-generation bioeconomy. It has been extensively used to redirect the biosynthetic pathway to produce desired products in several native or engineered hosts. A wide range of novel compounds has been manufactured through engineering metabolic pathways or endogenous metabolism optimizations by metabolic engineers. This review is focused on the potential utilization of engineered strains to produce biofuel and gives prospects for improvement in metabolic engineering for new strain development using advanced technologies.

Inducible intracellular membranes: molecular aspects and emerging applications

Membrane remodeling and phospholipid biosynthesis are normally tightly regulated to maintain the shape and function of cells. Indeed, different physiological mechanisms ensure a precise coordination between de novo phospholipid biosynthesis and modulation of membrane morphology. Interestingly, the overproduction of certain membrane proteins hijack these regulation networks, leading to the formation of impressive intracellular membrane structures in both prokaryotic and eukaryotic cells. The proteins triggering an abnormal accumulation of membrane structures inside the cells (or membrane proliferation) share two major common features: (1) they promote the formation of highly curved membrane domains and (2) they lead to an enrichment in anionic, cone-shaped phospholipids (cardiolipin or phosphatidic acid) in the newly formed membranes. Taking into account the available examples of membrane proliferation upon protein overproduction, together with the latest biochemical, biophysical and structural data, we explore the relationship between protein synthesis and membrane biogenesis. We propose a mechanism for the formation of these non-physiological intracellular membranes that shares similarities with natural inner membrane structures found in α-proteobacteria, mitochondria and some viruses-infected cells, pointing towards a conserved feature through evolution. We hope that the information discussed in this review will give a better grasp of the biophysical mechanisms behind physiological and induced intracellular membrane proliferation, and inspire new applications, either for academia (high-yield membrane protein production and nanovesicle production) or industry (biofuel production and vaccine preparation).

Debottlenecking mevalonate pathway for antimalarial drug precursor amorphadiene biosynthesis in Yarrowia lipolytica

World Health Organization reports that half of the population in developing countries are at risk of malaria infection. Artemisinin, the most potent anti-malaria drug, is a sesquiterpene endoperoxide extracted from the plant Artemisia annua. Due to scalability and economics issues, plant extraction or chemical synthesis could not provide a sustainable route for large-scale manufacturing of artemisinin. The price of artemisinin has been fluctuating from 200$/Kg to 1100$/Kg, due to geopolitical and climate factors. Microbial fermentation was considered as a promising method to stabilize the artemisinin supply chain. Yarrowia lipolytica, is an oleaginous yeast with proven capacity to produce large quantity of lipids and oleochemicals. In this report, the lipogenic acetyl-CoA pathways and the endogenous mevalonate pathway of Y. lipolytica were harnessed for amorphadiene production. Gene overexpression indicate that HMG-CoA and acetyl-CoA supply are two limiting bottlenecks for amorphadiene production. We have identified the optimal HMG-CoA reductase and determined the optimal gene copy number for the precursor pathways. Amorphadiene production was improved further by either inhibiting fatty acids synthase or activating the fatty acid degradation pathway. With co-expression of mevalonate kinase (encoded by Erg12), a push-and-pull strategy enabled the engineered strain to produce 171.5 ​mg/L of amorphadiene in shake flasks. These results demonstrate that balancing carbon flux and manipulation of precursor competing pathways are key factors to improve amorphadiene biosynthesis in oleaginous yeast; and Y. lipolytica is a promising microbial host to expand nature’s biosynthetic capacity, allowing us to quickly access antimalarial drug precursors.

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Endogenous acetyl-CoA and mevalonate pathway were harnessed for amorphadiene synthesis.

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Expression of native untruncated HMG-CoA reductase (HMG1) removes rate-limiting steps.

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Balancing ADS, HMG1 and MVK activity effectively pull FPP flux toward amorphadiene.

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Activation of fatty acid degradation pushes carbon flux toward HMG-CoA pathways.

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A push-and-pull strategy boosts amorphadiene production to 171.5 ​mg/L in shake flasks.

Translational Metabolomics: Current Challenges and Future Opportunities

Metabolomics is one of the latest omics technologies that has been applied successfully in many areas of life sciences. Despite being relatively new, a plethora of publications over the years have exploited the opportunities provided through this data and question driven approach. Most importantly, metabolomics studies have produced great breakthroughs in biomarker discovery, identification of novel metabolites and more detailed characterisation of biological pathways in many organisms. However, translation of the research outcomes into clinical tests and user-friendly interfaces has been hindered due to many factors, some of which have been outlined hereafter. This position paper is the summary of discussion on translational metabolomics undertaken during a peer session of the Australian and New Zealand Metabolomics Conference (ANZMET 2018) held in Auckland, New Zealand. Here, we discuss some of the key areas in translational metabolomics including existing challenges and suggested solutions, as well as how to expand the clinical and industrial application of metabolomics. In addition, we share our perspective on how full translational capability of metabolomics research can be explored.

Biochemistry, genetics and biotechnology of glycerol utilization in Pseudomonas species

The use of renewable waste feedstocks is an environment‐friendly choice contributing to the reduction of waste treatment costs and increasing the economic value of industrial by‐products. Glycerol (1,2,3‐propanetriol), a simple polyol compound widely distributed in biological systems, constitutes a prime example of a relatively cheap and readily available substrate to be used in bioprocesses. Extensively exploited as an ingredient in the food and pharmaceutical industries, glycerol is also the main by‐product of biodiesel production, which has resulted in a progressive drop in substrate price over the years. Consequently, glycerol has become an attractive substrate in biotechnology, and several chemical commodities currently produced from petroleum have been shown to be obtained from this polyol using whole‐cell biocatalysts with both wild‐type and engineered bacterial strains. Pseudomonas species, endowed with a versatile and rich metabolism, have been adopted for the conversion of glycerol into value‐added products (ranging from simple molecules to structurally complex biopolymers, e.g. polyhydroxyalkanoates), and a number of metabolic engineering strategies have been deployed to increase the number of applications of glycerol as a cost‐effective substrate. The unique genetic and metabolic features of glycerol‐grown Pseudomonas are presented in this review, along with relevant examples of bioprocesses based on this substrate – and the synthetic biology and metabolic engineering strategies implemented in bacteria of this genus aimed at glycerol valorization.

Engineering Robust Production Microbes for Large-Scale Cultivation

Systems biology and synthetic biology are increasingly used to examine and modulate complex biological systems. As such, many issues arising during scaling-up microbial production processes can be addressed using these approaches. We review differences between laboratory-scale cultures and larger-scale processes to provide a perspective on those strain characteristics that are especially important during scaling. Systems biology has been used to examine a range of microbial systems for their response in bioreactors to fluctuations in nutrients, dissolved gases, and other stresses. Synthetic biology has been used both to assess and modulate strain response, and to engineer strains to improve production. We discuss these approaches and tools in the context of their use in engineering robust microbes for applications in large-scale production.

Engineered microbial host selection for value-added bioproducts from lignocellulose

Lignocellulose is a rich and sustainable globally available carbon source and is considered a prominent alternative raw material for producing biofuels and valuable chemical compounds. Enzymatic hydrolysis is one of the crucial steps of lignocellulose degradation. Cellulolytic and hemicellulolytic enzyme mixes produced by different microorganisms including filamentous fungi, yeasts and bacteria, are used to degrade the biomass to liberate monosaccharides and other compounds for fermentation or conversion to value-added products. During biomass pretreatment and degradation, toxic compounds are produced, and undesirable carbon catabolic repression (CCR) can occur. In order to solve this problem, microbial metabolic pathways and transcription factors involved have been investigated along with the application of protein engineering to optimize the biorefinery platform. Engineered Microorganisms have been used to produce specific enzymes to breakdown biomass polymers and metabolize sugars to produce ethanol as well other biochemical compounds. Protein engineering strategies have been used for modifying lignocellulolytic enzymes to overcome enzymatic limitations and improving both their production and functionality. Furthermore, promoters and transcription factors, which are key proteins in this process, are modified to promote microbial gene expression that allows a maximum performance of the hydrolytic enzymes for lignocellulosic degradation. The present review will present a critical discussion and highlight the aspects of the use of microorganisms to convert lignocellulose into value-added bioproduct as well combat the bottlenecks to make the biorefinery platform from lignocellulose attractive to the market.

Rational engineering of a malate dehydrogenase for microbial production of 2,4-dihydroxybutyric acid via homoserine pathway

A synthetic pathway for the production of 2,4-dihydroxybutyric acid from homoserine (HMS), composed of two consecutive enzymatic reaction steps has been recently reported. An important step in this pathway consists in the reduction in 2-keto-4-hydroxybutyrate (OHB) into (l)-dihydroxybutyrate (DHB), by an enzyme with OHB reductase activity. In the present study, we used a rational approach to engineer an OHB reductase by using the cytosolic (l)-malate dehydrogenase from Escherichia coli (Ec-Mdh) as the template enzyme. Structural analysis of (l)-malate dehydrogenase and (l)-lactate dehydrogenase enzymes acting on sterically cognate substrates revealed key residues in the substrate and co-substrate-binding sites responsible for substrate discrimination. Accordingly, amino acid changes were introduced in a stepwise manner into these regions of the protein. This rational engineering led to the production of an Ec-Mdh-5E variant (I12V/R81A/M85E/G179D/D86S) with a turnover number (kcat) on OHB that was increased by more than 2000-fold (from 0.03 up to 65.0 s−1), which turned out to be 7-fold higher than that on its natural substrate oxaloacetate. Further kinetic analysis revealed the engineered enzyme to possess comparable catalytic efficiencies (kcat/Km) between natural and synthetic OHB substrates (84 and 31 s−1 mM−1, respectively). Shake-flask cultivation of a HMS-overproducing E. coli strain expressing this improved OHB reductase together with a transaminase encoded by aspC able to convert HMS to OHB resulted in 89% increased DHB production as compared with our previous report using a E. coli host strain expressing an OHB reductase derived from the lactate dehydrogenase A of Lactococcus lactis.

A Post-translational Metabolic Switch Enables Complete Decoupling of Bacterial Growth from Biopolymer Production in Engineered Escherichia coli

Most of the current methods for controlling the formation rate of a key protein or enzyme in cell factories rely on the manipulation of target genes within the pathway. In this article, we present a novel synthetic system for post-translational regulation of protein levels, FENIX, which provides both independent control of the steady-state protein level and inducible accumulation of target proteins. The FENIX device is based on the constitutive, proteasome-dependent degradation of the target polypeptide by tagging with a short synthetic, hybrid NIa/SsrA amino acid sequence in the C-terminal domain. Protein production is triggered via addition of an orthogonal inducer ( i.e., 3-methylbenzoate) to the culture medium. The system was benchmarked in Escherichia coli by tagging two fluorescent proteins (GFP and mCherry), and further exploited to completely uncouple poly(3-hydroxybutyrate) (PHB) accumulation from bacterial growth. By tagging PhaA (3-ketoacyl-CoA thiolase, first step of the route), a dynamic metabolic switch at the acetyl-coenzyme A node was established in such a way that this metabolic precursor could be effectively redirected into PHB formation upon activation of the system. The engineered E. coli strain reached a very high specific rate of PHB accumulation (0.4 h^-1) with a polymer content of ca. 72% (w/w) in glucose cultures in a growth-independent mode. Thus, FENIX enables dynamic control of metabolic fluxes in bacterial cell factories by establishing post-translational synthetic switches in the pathway of interest.

Metabolic engineering of Methanosarcina acetivorans for lactate production from methane

We previously demonstrated anaerobic conversion of the greenhouse gas methane into acetate using an engineered archaeon that produces methyl-coenzyme M reductase (Mcr) from unculturable microorganisms from a microbial mat in the Black Sea to create the first culturable prokaryote that reverses methanogenesis and grows anaerobically on methane. In this work, we further engineered the same host with the goal of converting methane into butanol. Instead, we discovered a process for converting methane to a secreted valuable product, L-lactate, with sufficient optical purity for synthesizing the biodegradable plastic poly-lactic acid. We determined that the 3-hydroxybutyryl-CoA dehydrogenase (Hbd) from Clostridium acetobutylicum is responsible for lactate production. This work demonstrates the first metabolic engineering of a methanogen with a synthetic pathway; in effect, we produce a novel product (lactate) from a novel substrate (methane) by cloning the three genes for Mcr and one for Hbd. We further demonstrate the utility of anaerobic methane conversion with an increased lactate yield compared to aerobic methane conversion to lactate. Biotechnol. Bioeng. 2017;114: 852-861. © 2016 Wiley Periodicals, Inc.

Enhanced production of histamine dehydrogenase by Natrinema gari BCC 24369 in a non-sterile condition

The production of histamine dehydrogenase (HADH) by Natrinema gari BCC 24369, a halophilic archeaon isolated from fish sauce, was optimized and scaled up under a non-sterile condition. Through statistical design by Plackett-Burman design (PBD), casamino acid, NaCl, MgSO4·7H2O and FeCl2·4H2O were identified as the significant medium compositions influencing HADH production. Central composite design (CCD) was employed to identify the optimal values of individual composition yielding the maximum HADH production. The analysis indicated that the optimal medium was composed of 15 g/l casamino acid, 75 g/l MgSO4·7H2O, 273 g/l NaCl, 2.5 mg/l FeCl2·4H2O, 10 g/l yeast extract, 5 g/l sodium glutamate and 5 g/l KCl. Based on the one-factor-at-a-time (OFAT) method, the optimum initial pH of the culture medium and the incubation temperature for HADH production were 7.5 and 37 °C, respectively. The production of HADH under optimal conditions was 2.2-fold higher than that under un-optimized conditions. Owing to the halophilic nature of Nnm. gari BCC 24369, a more economical and eco-friendlier HADH production was developed under a completely non-sterile condition. In a 16-l batch cultivation of Nnm. gari BCC 24369, HADH productivity under a non-sterile condition (858 ± 12 U/g cell biomass) was comparable to that under a sterile condition (878 ± 15 U/g cell biomass). These results demonstrate the feasibility and simplicity of HADH production using Nnm. gari BCC 24369 under a non-sterile condition without compromising enzyme yield and any changes in Km value.

In silico and in vivo stability analysis of a heterologous biosynthetic pathway for 1,4-butanediol production in metabolically engineered E. coli

Recently, several approaches have been published in order to develop a functional biosynthesis route for the non-natural compound 1,4-butanediol (BDO) in E. coli using glucose as a sole carbon source or starting from xylose. Among these studies, there was reported as high as 18 g/L product concentration achieved by industrial strains, however BDO production varies greatly in case of the reviewed studies. Our motivation was to build a simple heterologous pathway for this compound in E. coli and to design an appropriate cellular chassis based on a systemic biology approach, using constraint-based flux balance analysis and bi-level optimization for gene knock-out prediction. Thus, the present study reports, at the "proof-of concept" level, our findings related to model-driven development of a metabolically engineered E. coli strain lacking key genes for ethanol, lactate and formate production (ΔpflB, ΔldhA and ΔadhE), with a three-step biosynthetic pathway. We found this strain to produce a limited quantity of 1,4-BDO (.89 mg/L BDO under microaerobic conditions and .82 mg/L under anaerobic conditions). Using glycerol as carbon source, an approach, which to our knowledge has not been tackled before, our results suggest that further metabolic optimization is needed (gene-introductions or knock-outs, promoter fine-tuning) to address the redox potential imbalance problem and to achieve development of an industrially sustainable strain. Our experimental data on culture conditions, growth dynamics and fermentation parameters can consist a base for ongoing research on gene expression profiles and genetic stability of such metabolically engineered E. coli strains.

Unsupervised Identification of Isotope-Labeled Peptides

In vivo isotopic labeling coupled with high-resolution proteomics is used to investigate primary metabolism in techniques such as stable isotope probing (protein-SIP) and peptide-based metabolic flux analysis (PMFA). Isotopic enrichment of carbon substrates and intracellular metabolism determine the distribution of isotopes within amino acids. The resulting amino acid mass distributions (AMDs) are convoluted into peptide mass distributions (PMDs) during protein synthesis. With no a priori knowledge on metabolic fluxes, the PMDs are therefore unknown. This complicates labeled peptide identification because prior knowledge on PMDs is used in all available peptide identification software. An automated framework for the identification and quantification of PMDs for nonuniformly labeled samples is therefore lacking. To unlock the potential of peptide labeling experiments for high-throughput flux analysis and other complex labeling experiments, an unsupervised peptide identification and quantification method was developed that uses discrete deconvolution of mass distributions of identified peptides to inform on the mass distributions of otherwise unidentifiable peptides. Uniformly (13)C-labeled Escherichia coli protein was used to test the developed feature reconstruction and deconvolution algorithms. The peptide identification was validated by comparing MS(2)-identified peptides to peptides identified from PMDs using unlabeled E. coli protein. Nonuniformly labeled Glycine max protein was used to demonstrate the technology on a representative sample suitable for flux analysis. Overall, automatic peptide identification and quantification were comparable or superior to manual extraction, enabling proteomics-based technology for high-throughput flux analysis studies.

ATP regulation in bioproduction

Adenosine-5'-triphosphate (ATP) is consumed as a biological energy source by many intracellular reactions. Thus, the intracellular ATP supply is required to maintain cellular homeostasis. The dependence on the intracellular ATP supply is a critical factor in bioproduction by cell factories. Recent studies have shown that changing the ATP supply is critical for improving product yields. In this review, we summarize the recent challenges faced by researchers engaged in the development of engineered cell factories, including the maintenance of a large ATP supply and the production of cell factories. The strategies used to enhance ATP supply are categorized as follows: addition of energy substrates, controlling pH, metabolic engineering of ATP-generating or ATP-consuming pathways, and controlling reactions of the respiratory chain. An enhanced ATP supply generated using these strategies improves target production through increases in resource uptake, cell growth, biosynthesis, export of products, and tolerance to toxic compounds.

Challenges in the production of itaconic acid by metabolically engineered Escherichia coli

Metabolic engineering allows the production of a variety of high-value chemicals in heterologous hosts. For example, itaconic acid (IA) has been produced in several microorganisms, such as Escherichia coli, Aspergillus niger, and Synechocystis sp. through the expression of cis-aconitate decarboxylase gene (cad) from Aspergillus terreus. Recently, we showed that inactivation of the isocitrate dehydrogenase gene and overexpression of the aconitase gene dramatically enhanced the production levels of IA in E. coli expressing cad. Furthermore, we demonstrated that it is possible to produce IA directly from starch by engineered E. coli that additionally expresses the α-amylase gene from Streptococcus bovis. In this study, we sum up our findings regarding the challenges of IA production in E. coli.

Engineering the fatty acid metabolic pathway in Saccharomyces cerevisiae for advanced biofuel production

Fatty acid-derived fuels and chemicals have attracted a great deal of attention in recent decades, due to their following properties of high compatibility to gasoline-based fuels and existing infrastructure for their direct utilization, storage and distribution. The yeast Saccharomyces cerevisiae is the ideal biofuel producing candidate, based on the wealth of available genetic information and versatile tools designed to manipulate its metabolic pathways. Engineering the fatty acid metabolic pathways in S. cerevisiae is an effective strategy to increase its fatty acid biosynthesis and provide more pathway precursors for production of targeted products. This review summarizes the recent progress in metabolic engineering of yeast cells for fatty acids and fatty acid derivatives production, including the regulation of acetyl-CoA biosynthesis, NADPH production, fatty acid elongation, and the accumulation of activated precursors of fatty acids for converting enzymes. By introducing specific enzymes in the engineered strains, a powerful platform with a scalable, controllable and economic route for advanced biofuel production has been established.

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Recent progress in metabolic engineering for enhanced fatty acid production.

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Regulation of acetyl-CoA, NADPH pathway for fatty acid synthesis.

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Regulation of elongation and catabolic pathway to strength fatty acid synthesis.

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Enhanced production of activated precursors for fatty acid derivatives production.