Optimization of nutrient utilization efficiency and productivity for algal cultures under light and dark cycles using genome-scale model process control.

Algal cultivations are strongly influenced by light and dark cycles. In this study, genome-scale metabolic models were applied to optimize nutrient supply during alternating light and dark cycles of Chlorella vulgaris. This approach lowered the glucose requirement by 75% and nitrate requirement by 23%, respectively, while maintaining high final biomass densities that were more than 80% of glucose-fed heterotrophic culture. Furthermore, by strictly controlling glucose feeding during the alternating cycles based on model-input, yields of biomass, lutein, and fatty acids per gram of glucose were more than threefold higher with cycling compared to heterotrophic cultivation. Next, the model was incorporated into open-loop and closed-loop control systems and compared with traditional fed-batch systems. Closed-loop systems which incorporated a feed-optimizing algorithm increased biomass yield on glucose more than twofold compared to standard fed-batch cultures for cycling cultures. Finally, the performance was compared to conventional proportional-integral-derivative (PID) controllers. Both simulation and experimental results exhibited superior performance for genome-scale model process control (GMPC) compared to traditional PID systems, reducing the overall measured value and setpoint error by 80% over 8 h. Overall, this approach provides researchers with the capability to enhance nutrient utilization and productivity of cell factories systematically by combining genome-scale models and controllers into an integrated platform with superior performance to conventional fed-batch and PID methodologies.

Synthetic microbial communities of heterotrophs and phototrophs facilitate sustainable growth.

Microbial communities comprised of phototrophs and heterotrophs hold great promise for sustainable biotechnology. Successful application of these communities relies on the selection of appropriate partners. Here we construct four community metabolic models to guide strain selection, pairing phototrophic, sucrose-secreting Synechococcus elongatus with heterotrophic Escherichia coli K-12, Escherichia coli W, Yarrowia lipolytica, or Bacillus subtilis. Model simulations reveae metabolic exchanges that sustain the heterotrophs in minimal media devoid of any organic carbon source, pointing to S. elongatus-E. coli K-12 as the most active community. Experimental validation of flux predictions for this pair confirms metabolic interactions and potential production capabilities. Synthetic communities bypass member-specific metabolic bottlenecks (e.g. histidine- and transport-related reactions) and compensate for lethal genetic traits, achieving up to 27% recovery from lethal knockouts. The study provides a robust modelling framework for the rational design of synthetic communities with optimized growth sustainability using phototrophic partners.

The importance and future of biochemical engineering.

Today's Biochemical Engineer may contribute to advances in a wide range of technical areas. The recent Biochemical and Molecular Engineering XXI conference focused on "The Next Generation of Biochemical and Molecular Engineering: The role of emerging technologies in tomorrow's products and processes". On the basis of topical discussions at this conference, this perspective synthesizes one vision on where investment in research areas is needed for biotechnology to continue contributing to some of the world's grand challenges.

Author Correction: Environmental stimuli drive a transition from cooperation to competition in synthetic phototrophic communities.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Environmental stimuli drive a transition from cooperation to competition in synthetic phototrophic communities.

Phototrophic communities of photosynthetic algae or cyanobacteria and heterotrophic bacteria or fungi are pervasive throughout the environment1. How interactions between members contribute to the resilience and affect the fitness of phototrophic communities is not fully understood2,3. Here, we integrated metatranscriptomics, metabolomics and phenotyping with computational modelling to reveal condition-dependent secretion and cross-feeding of metabolites in a synthetic community. We discovered that interactions between members are highly dynamic and are driven by the availability of organic and inorganic nutrients. Environmental factors, such as ammonia concentration, influenced community stability by shifting members from collaborating to competing. Furthermore, overall fitness was dependent on genotype and streamlined genomes improved growth of the entire community. Our mechanistic framework provides insights into the physiology and metabolic response to environmental and genetic perturbation of these ubiquitous microbial associations.

Utilizing genome-scale models to optimize nutrient supply for sustained algal growth and lipid productivity.

Nutrient availability is critical for growth of algae and other microbes used for generating valuable biochemical products. Determining the optimal levels of nutrient supplies to cultures can eliminate feeding of excess nutrients, lowering production costs and reducing nutrient pollution into the environment. With the advent of omics and bioinformatics methods, it is now possible to construct genome-scale models that accurately describe the metabolism of microorganisms. In this study, a genome-scale model of the green alga Chlorella vulgaris (iCZ946) was applied to predict feeding of multiple nutrients, including nitrate and glucose, under both autotrophic and heterotrophic conditions. The objective function was changed from optimizing growth to instead minimizing nitrate and glucose uptake rates, enabling predictions of feed rates for these nutrients. The metabolic model control (MMC) algorithm was validated for autotrophic growth, saving 18% nitrate while sustaining algal growth. Additionally, we obtained similar growth profiles by simultaneously controlling glucose and nitrate supplies under heterotrophic conditions for both high and low levels of glucose and nitrate. Finally, the nitrate supply was controlled in order to retain protein and chlorophyll synthesis, albeit at a lower rate, under nitrogen-limiting conditions. This model-driven cultivation strategy doubled the total volumetric yield of biomass, increased fatty acid methyl ester (FAME) yield by 61%, and enhanced lutein yield nearly 3 fold compared to nitrogen starvation. This study introduces a control methodology that integrates omics data and genome-scale models in order to optimize nutrient supplies based on the metabolic state of algal cells in different nutrient environments. This approach could transform bioprocessing control into a systems biology-based paradigm suitable for a wide range of species in order to limit nutrient inputs, reduce processing costs, and optimize biomanufacturing for the next generation of desirable biotechnology products.

An unconventional uptake rate objective function approach enhances applicability of genome-scale models for mammalian cells.

Constraint-based modeling has been applied to analyze metabolism of numerous organisms via flux balance analysis and genome-scale metabolic models, including mammalian cells such as the Chinese hamster ovary (CHO) cells-the principal cell factory platform for therapeutic protein production. Unfortunately, the application of genome-scale model methodologies using the conventional biomass objective function is challenged by the presence of overly-restrictive constraints, including essential amino acid exchange fluxes that can lead to improper predictions of growth rates and intracellular flux distributions. In this study, these constraints are found to be reliably predicted by an "essential nutrient minimization" approach. After modifying these constraints with the predicted minimal uptake values, a series of unconventional objective functions are applied to minimize each individual non-essential nutrient uptake rate, revealing useful insights about metabolic exchange rates and flows across different cell lines and culture conditions. This unconventional uptake-rate objective functions (UOFs) approach is able to distinguish metabolic differences between three distinct CHO cell lines (CHO-K1, -DG44, and -S) not directly observed using the conventional biomass growth maximization solutions. Further, a comparison of model predictions with experimental data from literature correctly correlates with the specific CHO-DG44-derived cell line used experimentally, and the corresponding dual prices provide fruitful information concerning coupling relationships between nutrients. The UOFs approach is likely to be particularly suited for mammalian cells and other complex organisms which contain multiple distinct essential nutrient inputs, and may offer enhanced applicability for characterizing cell metabolism and physiology as well as media optimization and biomanufacturing control.

Effects of glycation on human γd-crystallin proteins by different glycation-inducing agents.

Human γd-crystallin (Hγd-crystallin), a major protein component of the human eye lens, is associated with the development of juvenile- and mature-onset cataracts. Evidence suggests that nonenzymatic protein glycation plays an important role in the aetiology of cataract and diabetic sequelae. This research compared the effects of various glycation modifiers on Hγd-crystallin aggregation, by treating samples of Hγd-crystallin with ribose, galactose, or methylglyoxal using several biophysical techniques. To measure advanced glycation end products, an Nε-(carboxyethyl)lysine enzyme-linked immunosorbent assay was performed on the glycating agent-treated Hγd-crystallin samples. Fructosamine production detection was performed for both ribose-treated and galactose-treated samples. Methylglyoxal-treated samples had the highest level of aggregation and the greatest extent of unfolding, and upon incubation for a minimum of 12 days, exhibited a marked enhancement in the amount of Nε-(carboxyethyl)lysine. The molecular profiles and morphological features of the glycated samples were highly correlated to the type of glycation agent used. These findings highlight a close connection between the type of glycation modifier and the various aggregation species that form. Thus, these results may facilitate deciphering of the molecular mechanism of diabetic cataractogenesis.

Mimicking lichens: incorporation of yeast strains together with sucrose-secreting cyanobacteria improves survival, growth, ROS removal, and lipid production in a stable mutualistic co-culture production platform.

BACKGROUND: The feasibility of heterotrophic-phototrophic symbioses was tested via pairing of yeast strains Cryptococcus curvatus, Rhodotorula glutinis, or Saccharomyces cerevisiae with a sucrose-secreting cyanobacterium Synechococcus elongatus. RESULTS: The phototroph S. elongatus showed no growth in standard BG-11 medium with yeast extract, but grew well in BG-11 medium alone or supplemented with yeast nitrogen base without amino acids (YNB w/o aa). Among three yeast species, C. curvatus and R. glutinis adapted well to the BG-11 medium supplemented with YNB w/o aa, sucrose, and various concentrations of NaCl needed to maintain sucrose secretion from S. elongatus, while growth of S. cerevisiae was highly dependent on sucrose levels. R. glutinis and C. curvatus grew efficiently and utilized sucrose produced by the partner in co-culture. Co-cultures of S. elongatus and R. glutinis were sustained over 1 month in both batch and in semi-continuous culture, with the final biomass and overall lipid yields in the batch co-culture 40 to 60% higher compared to batch mono-cultures of S. elongatus. The co-cultures showed enhanced levels of palmitoleic and linoleic acids. Furthermore, cyanobacterial growth in co-culture with R. glutinis was significantly superior to axenic growth, as S. elongatus was unable to grow in the absence of the yeast partner when cultivated at lower densities in liquid medium. Accumulated reactive oxygen species was observed to severely inhibit axenic growth of cyanobacteria, which was efficiently alleviated through catalase supply and even more effectively with co-cultures of R. glutinis. CONCLUSIONS: The pairing of a cyanobacterium and eukaryotic heterotroph in the artificial lichen of this study demonstrates the importance of mutual interactions between phototrophs and heterotrophs, e.g., phototrophs provide a carbon source to heterotrophs, and heterotrophs assist phototrophic growth and survival by removing/eliminating oxidative stress. Our results establish a potential stable production platform that combines the metabolic capability of photoautotrophs to capture inorganic carbon with the channeling of the resulting organic carbon directly to a robust heterotroph partner for producing biofuel and other chemical precursors.

Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions.

The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine.

Comparative analysis of human γD-crystallin aggregation under physiological and low pH conditions.

Cataract, a major cause of visual impairment worldwide, is the opacification of the eye's crystalline lens due to aggregation of the crystallin proteins. The research reported here is aimed at investigating the aggregating behavior of γ-crystallin proteins in various incubation conditions. Thioflavin T binding assay, circular dichroism spectroscopy, 1-anilinonaphthalene-8-sulfonic acid fluorescence spectroscopy, intrinsic (tryptophan) fluorescence spectroscopy, light scattering, and electron microscopy were used for structural characterization. Molecular dynamics simulations and bioinformatics prediction were performed to gain insights into the γD-crystallin mechanisms of fibrillogenesis. We first demonstrated that, except at pH 7.0 and 37°C, the aggregation of γD-crystallin was observed to be augmented upon incubation, as revealed by turbidity measurements. Next, the types of aggregates (fibrillar or non-fibrillar aggregates) formed under different incubation conditions were identified. We found that, while a variety of non-fibrillar, granular species were detected in the sample incubated under pH 7.0, the fibrillogenesis of human γD-crystallin could be induced by acidic pH (pH 2.0). In addition, circular dichroism spectroscopy, 1-anilinonaphthalene-8-sulfonic acid fluorescence spectroscopy, and intrinsic fluorescence spectroscopy were used to characterize the structural and conformational features in different incubation conditions. Our results suggested that incubation under acidic condition led to a considerable change in the secondary structure and an enhancement in solvent-exposure of the hydrophobic regions of human γD-crystallin. Finally, molecular dynamics simulations and bioinformatics prediction were performed to better explain the differences between the structures and/or conformations of the human γD-crystallin samples and to reveal potential key protein region involved in the varied aggregation behavior. Bioinformatics analyses revealed that the initiation of amyloid formation of human γD-crystallin may be associated with a region within the C-terminal domain. We believe the results from this research may contribute to a better understanding of the possible mechanisms underlying the pathogenesis of senile nuclear cataract.