Metabolomic analysis and lipid accumulation in a glucose tolerant Crypthecodinium cohnii strain obtained by adaptive laboratory evolution

Li, Xingrui; Pei, Guangsheng; Liu, Liangsen; Chen, Lei; Zhang, Weiwen

doi:10.1016/j.biortech.2017.03.049

Cited by 51 publications

(35 citation statements)

References 33 publications

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“…3a). The inhibition to DHA synthesis by high glucose was also found in other C. cohnii studies [22,23]. For example, the DHA percentage of TFAs in C. cohnii ATCC 30556 was reduced to 44.7% from 53.4%, when glucose concentration was increased to 20 g/L from 5 g/L [23].…”

Section: Effects Of Glucose Concentration On Dha Production In Repeatsupporting

confidence: 77%

“…For example, the DHA percentage of TFAs in C. cohnii ATCC 30556 was reduced to 44.7% from 53.4%, when glucose concentration was increased to 20 g/L from 5 g/L [23]. The total lipid in DCW and DHA percentage of TFAs in C. cohnii ATCC 30556 were all gradually decreased with the increase of glucose concentration from 9 to 54 g/L [22]. One possible reason of high glucose inhibition might be the increased osmotic pressure of the culture medium, which results in more energy consumption required for cellular maintenance [25].…”

Section: Effects Of Glucose Concentration On Dha Production In Repeatmentioning

confidence: 95%

“…Metabolomic analysis is able to provide a metabolitelevel representation of the phenotype for organisms due to its capability to detect a large array of small molecule metabolites qualitatively and quantitatively [18,19]. In recent years, both liquid chromatography mass spectrometry (LC-MS) and gas chromatography mass spectrometry (GC-MS) based metabolomics have been widely applied to explore biology of various microalgae, including C. cohnii [20][21][22]. For example, the lipid accumulation mechanisms of C. cohnii responded to chemical modulators were explored using LC-MS based metabolomic analysis [21]; the glucose tolerance mechanisms of C. cohnii mutants obtained by adaptive laboratory evolution were analyzed by using GC-MS metabolomic analysis [22]; and the mechanisms of antioxidant on elevating lipid accumulation in C. cohnii were analyzed using integrated LC-and GC-MS metabolomic analyses [20].…”

Section: Introductionmentioning

confidence: 99%

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Repeated fed-batch strategy and metabolomic analysis to achieve high docosahexaenoic acid productivity in Crypthecodinium cohnii

et al. 2020

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Background: Docosahexaenoic acid (DHA) is essential for human diet. However, high production cost of DHA using C. cohnii makes it currently less competitive commercially, which is mainly caused by low DHA productivity. In recent years, repeated fed-batch strategies have been evaluated for increasing the production of many fermentation products. The reduction in terms of stability of culture system was one of the major challenges for repeated fed-batch fermentation. However, the possible mechanisms responsible for the decreased stability of the culture system in the repeated fedbatch fermentation are so far less investigated, restricting the efforts to further improve the productivity. In this study, a repeated fed-batch strategy for DHA production using C. cohnii M-1-2 was evaluated to improve DHA productivity and reduce production cost, and then the underlying mechanisms related to the gradually decreased stability of the culture system in repeated fed-batch culture were explored through LC-and GC-MS metabolomic analyses. Results: It was discovered that glucose concentration at 15-27 g/L and 80% medium replacement ratio were suitable for the growth of C. cohnii M-1-2 during the repeated fed-batch culture. A four-cycle repeated fed-batch culture was successfully developed and assessed at the optimum cultivation parameters, resulting in increasing the total DHA productivity by 26.28% compared with the highest DHA productivity of 57.08 mg/L/h reported using C. cohnii, including the time required for preparing seed culture and fermentor. In addition, LC-and GC-MS metabolomics analyses showed that the gradually decreased nitrogen utilization capacity, and down-regulated glycolysis and TCA cycle were correlated with the decreased stability of the culture system during the long-time repeated fed-batch culture. At last, some biomarkers, such as Pyr, Cit, OXA, FUM, l-tryptophan, l-threonine, l-leucine, serotonin, and 4-guanidinobutyric acid, correlated with the stability of culture system of C. cohnii M-1-2 were identified. Conclusions: The study proved that repeated fed-batch cultivation was an efficient and energy-saving strategy for industrial production of DHA using C. cohnii, which could also be useful for cultivation of other microbes to improve productivity and reduce production cost. In addition, the mechanisms study at metabolite level can also be useful to further optimize production processes for C. cohnii and other microbes.

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Section: Effects Of Glucose Concentration On Dha Production In Repeatsupporting

confidence: 77%

Section: Effects Of Glucose Concentration On Dha Production In Repeatmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

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Repeated fed-batch strategy and metabolomic analysis to achieve high docosahexaenoic acid productivity in Crypthecodinium cohnii

et al. 2020

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“…As an example, high lipid accumulation was achieved in a glucose‐tolerant strain of Crypthecodinium cohnii . Metabolomics analysis showed that relevant metabolites such as glycerol, glutamic acid, succinic acid, and malonic acid were positively regulated during the ALE (Li, Pei, Liu, Chen, & Zhang, ). In a long‐term laboratory evolution experiment, the butanol tolerance was increased by 1.5‐fold in a model cyanobacterium, and time‐series metabolome data analysis contributed to identifying the key metabolites and metabolic modules related to the increased tolerance.…”

Section: Quantitative Metabolomics and Its Application In Systems Metmentioning

confidence: 99%

Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

Wang

Haringa

Tang

et al. 2019

Biotech & Bioengineering

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Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind. K E Y W O R D SCFD, Euler-Langrange, metabolic model, metabolomics, population heterogeneity, scale down

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“…To circumvent this limitation, we enhance tolerance of S. cerevisiae to higher glucose and isobutanol concentration by adaptive laboratory evolution. Adaptive laboratory evolution was served as a genome-wide method for improving desirable phenotypes without the knowledge of genetic determinants for network information about those phenotypes [22][23]. And it was successfully employed to identify biological solutions to biofuel and alcohol toxicity in S. cerevisiae [24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Improving isobutanol productivity through adaptive laboratory evolution in Saccharomyces cerevisiae

Zhang

et al. 2019

Preprint

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Background: Isobutanol is an ideal second-generation biofuels due to its lower hygroscopicity, higher energy density and higher-octane value. However, isobutanol is toxic to production organisms. To improve isobutanol productivity, adaptive laboratory evolution method was carried out to improve the tolerance of Saccharomyces cerevisiae toward higher isobutanol and higher glucose concentration.Results: We evolved the laboratory strain of S. cerevisiae W303-1A by using EMS (ethyl methanesulfonate) mutagenesis followed by adaptive laboratory evolution. The evolved strain EMS39 with significant increase in growth rate and viability in media with higher isobutanol and higher glucose concentration was obtained. Then, metabolic engineering of the evolved strain EMS39 as a platform for isobutanol production were carried out. Delta integration method was used to over-express ILV3 gene and 2μ plasmids carrying ILV2, ILV5 and ARO10 were used to over-express ILV2, ILV5 and ARO10 genes in the evolved strain EMS39 and wild type W303-1A. And the resulting strains was designated as strain EMS39V2δV3V5A10 and strain W303-1AV2δV3V5A10, respectively. Our results shown that isobutanol titers of the evolved strain EMS39 increased by 30% compared to the control strain. And isobutanol productivity of strain EMS39V2δV3V5A10 increased by 32.4% compared to strain W303-1AV2δV3V5A10. Whole genome resequencing and analysis of site-directed mutagenesis of the evolved strain EMS39 have identified important mutations. In addition, RNA-Seq-based transcriptomic analysis revealed cellular transcription profile changes resulting from EMS39.Conclusions: With the aim of increase productivity of isobutanol in S. cerevisiae, improving tolerance toward higher isobutanol and higher glucose concentration via EMS mutagenesis followed by adaptive evolutionary engineering was conducted. An evolved strain EMS39 with significant increase in growth rate and viability had been obtained. And metabolic engineering of the evolved strain as a platform for isobutanol production was carried out. Furthermore, analysis of whole genome resequencing and transcriptome sequencing were also carried out.

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Metabolomic analysis and lipid accumulation in a glucose tolerant Crypthecodinium cohnii strain obtained by adaptive laboratory evolution

Cited by 51 publications

References 33 publications

Repeated fed-batch strategy and metabolomic analysis to achieve high docosahexaenoic acid productivity in Crypthecodinium cohnii

Repeated fed-batch strategy and metabolomic analysis to achieve high docosahexaenoic acid productivity in Crypthecodinium cohnii

Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

Improving isobutanol productivity through adaptive laboratory evolution in Saccharomyces cerevisiae

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