Production of the cyanogenic glycoside dhurrin in yeast

Kotopka, Benjamin J.; Smolke, Christina D.

doi:10.1016/j.mec.2019.e00092

Cited by 18 publications

(9 citation statements)

References 32 publications

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“…Cyanogenic glycosides are present in all parts of the plant with the leaves having the highest concentration [6]. According to Kotopka and Smolke [7], these compounds act as chemical defences produced by the plants. Cyanogenic glycosides act as a deterrent against pathogenic organisms and the activities of herbivores.…”

Section: Cyanogenic Glycosides and Cyanidementioning

confidence: 99%

Characterization of yellow root cassava and food products: investigation of cyanide and β-carotene concentrations

et al. 2020

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Objective: Cyanide is a highly toxic compound, and the consumption of products containing cyanide is a significant public health concern. Conversely, β-carotene possesses essential nutritional attributes for human health, therefore the characterisation and quantification of both compounds in food products is fundamental. Herein, cyanide and β-carotene levels in two flours produced from the roots of two varieties of cassava (Manihot esculenta crantz), namely UMUCASS-38(TMS 01/1371) and NR-8082, and their associated food products were detected and quantified. Results: The cyanide content of NR-8082 and UMUCASS-38 flours was determined at 18.01 ± 0.01 ppm and 17.02 ± 0.02 ppm (mean ± SD), respectively. These flours contained significantly higher (p < 0.05) than the residual cyanide levels determined in the cookies and cake produced therefrom with levels of 10.00 ± 0.00 ppm and 7.10 ± 0.14 ppm (mean ± SD), respectively. The levels of β-carotene determined in both the cake and cookie samples varied significantly (p < 0.05). The highest levels of β-carotene at 6.53 ± 0.02 µg/g (mean ± SD) were determined in raw roots of UMUCASS-38. While NR-8082 levels of β-carotene were less than UMUCASS-38 at 1.12 ± 0.02 µg/g (mean ± SD). Processing the roots into flour reduced the β-carotene content to 4.78 ± 0.01 µg/g and 0.76 ± 0.02 µg/g (mean ± SD) in UMUCASS-38 and NR-8082 flours, respectively. Cookies and cake produced from flour derived from the UMUCASS-38 variety had (mean ± SD) 2.15 ± 0.01 µg/g and 2.84 ± 0.04 µg/g of β-carotene, respectively.

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Section: Cyanogenic Glycosides and Cyanidementioning

confidence: 99%

Characterization of yellow root cassava and food products: investigation of cyanide and β-carotene concentrations

et al. 2020

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“…To create a strain expressing the ZEV artificial transcription factor for the P ZEV library experiment, the P ACT1 promoter and the ZEV artificial transcription factor gene were PCRamplified in a single fragment from DBY19053 15 , and a fragment containing the T CYC1 terminator and the entire plasmid backbone was PCR-amplified from pCS2657 50 . These fragments were joined by Gibson assembly 51 , yielding pCS4339; the P ACT1 -ZEV ATF -T CYC1 expression cassette was PCR-amplified and integrated into the LEU2 locus of CSY3 using the Cas9-assisted integration method 52 (using pCS4187 53 as the guide RNA plasmid), yielding strain CSY1252. CSY1252 was also used in the promoter design validation experiments.…”

Section: Yeast Strains Culture Transformation and Passaging For Famentioning

confidence: 99%

Model-driven generation of artificial yeast promoters

Kotopka

Smolke

2019

Preprint

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AbstractPromoters play a central role in controlling gene regulation; however, a small set of promoters is used for most genetic construct design in the yeast Saccharomyces cerevisiae. Generating and utilizing models that accurately predict protein expression from promoter sequences would enable rapid generation of novel useful promoters and facilitate synthetic biology efforts in this model organism. We measured the gene expression activity of over 675,000 unique sequences in a constitutive promoter library, and over 327,000 sequences in an inducible promoter library. Training an ensemble of convolutional neural networks jointly on the two datasets enabled very high (R2 > 0.79) predictive accuracies on multiple sequence-activity prediction tasks. We developed model-guided design strategies which yielded large, sequence-diverse sets of novel promoters exhibiting activities similar to current best-in-class sequences. In addition to providing large sets of new promoters, our results show the value of model-guided design as an approach for generating useful DNA parts.

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“…These fragments were joined by Gibson assembly 50 , yielding pCS4339; the P ACT1 -ZEV ATF-T CYC1 expression cassette was PCR-amplified and integrated into the LEU2 locus of CSY3 using the Cas9-assisted integration method 51 (using pCS4187 (ref. 52 ) as the guide RNA plasmid), yielding strain CSY1252. CSY1252 was also used in the promoter design validation experiments.…”

Section: Methodsmentioning

confidence: 99%

Model-driven generation of artificial yeast promoters

2020

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Promoters play a central role in controlling gene regulation; however, a small set of promoters is used for most genetic construct design in the yeast Saccharomyces cerevisiae. Generating and utilizing models that accurately predict protein expression from promoter sequences would enable rapid generation of useful promoters and facilitate synthetic biology efforts in this model organism. We measure the gene expression activity of over 675,000 sequences in a constitutive promoter library and over 327,000 sequences in an inducible promoter library. Training an ensemble of convolutional neural networks jointly on the two data sets enables very high (R 2 > 0.79) predictive accuracies on multiple sequence-activity prediction tasks. We describe model-guided design strategies that yield large, sequence-diverse sets of promoters exhibiting activities higher than those represented in training data and similar to current best-in-class sequences. Our results show the value of model-guided design as an approach for generating useful DNA parts.

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Production of the cyanogenic glycoside dhurrin in yeast

Cited by 18 publications

References 32 publications

Characterization of yellow root cassava and food products: investigation of cyanide and β-carotene concentrations

Characterization of yellow root cassava and food products: investigation of cyanide and β-carotene concentrations

Model-driven generation of artificial yeast promoters

Model-driven generation of artificial yeast promoters

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