The Chlorella vulgaris S-Nitrosoproteome under Nitrogen-Replete and -Deplete Conditions

Henard, Calvin A.; Guarnieri, Michael T.; Knoshaug, Eric P.

doi:10.3389/fbioe.2016.00100

Cited by 9 publications

(10 citation statements)

References 27 publications

(36 reference statements)

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“…This adjustment allowed for an increase in biomass (final OD = 6.6 6 0.3). Nitrogen limitation causes a cascade of metabolic responses in algae that are associated with the synthesis of carbon-rich compounds; the exact nature of these responses, however, is currently not fully understood (Henard et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…This discrepancy explains why these genes cannot be accurately predicted by comparing the tendencies of predicted reactions rates (fluxes) with the expression of those enzymes. Ribo-seq data could help delineate nonlinear relationships between transcriptomics and proteomics (Latif et al, 2015;Henard et al, 2017). Furthermore, recent evidence has indicated highly active posttranslational mechanisms in C. vulgaris, which may also impact the discrepancy observed between gene and protein expression levels (Henard et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

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Predicting Dynamic Metabolic Demands in the Photosynthetic Eukaryote Chlorella vulgaris

et al. 2017

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Phototrophic organisms exhibit a highly dynamic proteome, adapting their biomass composition in response to diurnal light/dark cycles and nutrient availability. Here, we used experimentally determined biomass compositions over the course of growth to determine and constrain the biomass objective function (BOF) in a genome-scale metabolic model of UTEX 395 over time. Changes in the BOF, which encompasses all metabolites necessary to produce biomass, influence the state of the metabolic network thus directly affecting predictions. Simulations using dynamic BOFs predicted distinct proteome demands during heterotrophic or photoautotrophic growth. Model-driven analysis of extracellular nitrogen concentrations and predicted nitrogen uptake rates revealed an intracellular nitrogen pool, which contains 38% of the total nitrogen provided in the medium for photoautotrophic and 13% for heterotrophic growth. Agreement between flux and gene expression trends was determined by statistical comparison. Accordance between predicted flux trends and gene expression trends was found for 65% of multisubunit enzymes and 75% of allosteric reactions. Reactions with the highest agreement between simulations and experimental data were associated with energy metabolism, terpenoid biosynthesis, fatty acids, nucleotides, and amino acid metabolism. Furthermore, predicted flux distributions at each time point were compared with gene expression data to gain new insights into intracellular compartmentalization, specifically for transporters. A total of 103 genes related to internal transport reactions were identified and added to the updated model of, CZ946, thus increasing our knowledgebase by 10% for this model green alga.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Predicting Dynamic Metabolic Demands in the Photosynthetic Eukaryote Chlorella vulgaris

et al. 2017

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“…Frontiers in Bioengineering and Biotechnology | www.frontiersin.org (Guarnieri et al, 2011(Guarnieri et al, , 2013Sibi et al, 2014;Li et al, 2015b;Ouyang et al, 2015;Henard et al, 2017;Zuñiga et al, 2018;Mocsai et al, 2019) Chloroidium sp. Katz et al, 2007;Jia et al, 2009Jia et al, , 2016Gu et al, 2014;Ge et al, 2016;Lv et al, 2016;Zhao et al, 2016;Wei et al, 2017b;Wang et al, 2019d Emiliana (Benner et al, 2013;Rokitta et al, 2014;Hunter et al, 2015;McKew et al, 2015;Wördenweber et al, 2018) Fistulifera solaris (JPCC Wang et al, 2004;Tran et al, 2009;Peled et al, 2011;Gu et al, 2014;Recht et al, 2014;Su et al, 2014;Gao et al, 2016;Baumeister et al, (Chen et al, 2014;Ge et al, 2014;Jungandreas et al, 2014;Rosenwasser et al, 2014;Yang et al, 2014;Abida et al, 2015;Alipanah et al, 2015;Feng et al, 2015;Bai et al, 2016;…”

Section: Metabolomics and Metabolic Modelsunclassified

Bioengineering of Microalgae: Recent Advances, Perspectives, and Regulatory Challenges for Industrial Application

Kumar

Shekh

Jakhu

et al. 2020

Front. Bioeng. Biotechnol.

176

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Microalgae, due to their complex metabolic capacity, are being continuously explored for nutraceuticals, pharmaceuticals, and other industrially important bioactives. However, suboptimal yield and productivity of the bioactive of interest in local and robust wild-type strains are of perennial concerns for their industrial applications. To overcome such limitations, strain improvement through genetic engineering could play a decisive role. Though the advanced tools for genetic engineering have emerged at a greater pace, they still remain underused for microalgae as compared to other microorganisms. Pertaining to this, we reviewed the progress made so far in the development of molecular tools and techniques, and their deployment for microalgae strain improvement through genetic engineering. The recent availability of genome sequences and other omics datasets form diverse microalgae species have remarkable potential to guide strategic momentum in microalgae strain improvement program. This review focuses on the recent and significant improvements in the omics resources, mutant libraries, and high throughput screening methodologies helpful to augment research in the model and non-model microalgae. Authors have also summarized the case studies on genetically engineered microalgae and highlight the opportunities and challenges that are emerging from the current progress in the application of genome-editing to facilitate microalgal strain improvement. Toward the end, the regulatory and biosafety issues in the use of genetically engineered microalgae in commercial applications are described.

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“…The oleaginous chlorophyte Chlorella vulgaris represents a promising model microalgal system and production host, due to its ability to synthesize and accumulate large quantities of fuel intermediates in the form of storage lipids (Guarnieri et al, 2011 , 2012 ; Gerken et al, 2013 ; Griffiths et al, 2014 ; Zuñiga et al, 2016 ). Recent omic analyses have identified transcriptional, post-transcriptional and -translational mechanisms governing lipid accumulation in this alga (Guarnieri et al, 2011 , 2013 ), including active protein nitrosylation (Henard et al, 2017 ). Here we report the draft nuclear genome and annotation of C. vulgaris UTEX 395.…”

Section: Introductionmentioning

confidence: 99%

Genome Sequence of the Oleaginous Green Alga, Chlorella vulgaris UTEX 395

Guarnieri

Levering

Henard

et al. 2018

Front. Bioeng. Biotechnol.

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The Chlorella vulgaris S-Nitrosoproteome under Nitrogen-Replete and -Deplete Conditions

Cited by 9 publications

References 27 publications

Predicting Dynamic Metabolic Demands in the Photosynthetic Eukaryote Chlorella vulgaris

Predicting Dynamic Metabolic Demands in the Photosynthetic Eukaryote Chlorella vulgaris

Bioengineering of Microalgae: Recent Advances, Perspectives, and Regulatory Challenges for Industrial Application

Genome Sequence of the Oleaginous Green Alga, Chlorella vulgaris UTEX 395

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