Difference between revisions of "Global Transcriptome Machinery Engineering"
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===Using gTME to Engineer a Complex Phenotype=== | ===Using gTME to Engineer a Complex Phenotype=== | ||
| − | The ethanol resistance | + | The amount of ethanol (and resistance to ethanol) by a yeast strain is determined by many different genes; some of these genes are known, but most of these genes are unknown. The fundamental lack of knowledge regarding the genes that contribute to these phenotypes meant that rational engineering could not be used achieve the goal of these investigators. |
| + | To circumvent this problem, the researchers developed a new technique to elicit the desired phenotype from the yeast. This method, which they have termed global transcriptome machinery engineering (gTME), is a way of using directed evolution to engineer complex phenotypes regulated by multiple genes. Unlike methods of directed evolution that are used to alter the function of individual proteins, this method also does not require any prior knowledge of gene function contributing to the desired phenotype. | ||
| − | + | The major difference between the approach used here and that used in other papers is that mutatgenesis was used to create a mutant library of a selected transcription factor instead of using the same process to create a mutant library of a single gene. | |
| − | The | + | The rationale behind this approach is that each mutant transcription factor will change the expression levels of not one but rather many genes in a cell. The strains containing these many changes in gene expression levels can then be run through a selection scheme that selects for the desired phenotype (in this case, ethanol production and resistance). |
===In the Lab=== | ===In the Lab=== | ||
| − | After an initial screening of transcription factors, the researchers chose to create a mutant library of the TATA-binding protein ''SPT15''. This mutant library was then inserted | + | After an initial screening of transcription factors, the researchers chose to create a mutant library of the TATA-binding protein ''SPT15''. This mutant library was then inserted into expression vectors and transferred into yeast cells. Expression vectors were used instead of gene replacement vectors to prevent complete disruption of normal cell function resulting from the altered transcription factors. The cloned yeast carrying the mutant library were diluted into multiple cultures and grown at elevated levels of ethanol and glucose. The mutant that grew most vigorously under these conditions, ''spt15-300,'' was isolated. |
| − | The team ran tests on the ''spt15-300'' mutant to quantify | + | The team ran tests on the ''spt15-300'' mutant strain to quantify increases in ethanol tolerance and production. When the mutant was grown at high ethanol concentrations (greater than 10%), it consistantly showed greater ethanol production than the wild-yeast yeast ('''Fig. 2a'''). Comparing fermentation analysis of the ''spt15-300'' mutant with wild-type yeast also revealed higher total production in ethanol ('''Fig. 2b'''.) |
[[Image:FIGURE ETHANOL.jpg]] | [[Image:FIGURE ETHANOL.jpg]] | ||
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'''Figure 2''' - ('''a.''') Cell counts of the evolved ''spt15-300'' mutant compared with wild-type yeast during growth in 12.5% ethanol. The ''spt15-300'' shows higher cell viability under "stressed" fermentation conditions when compared to the control. Cell counts measured by optical density. Error bars represent the standard deviation between replicate experiments. Initial cells counts were -3.5 x 10^6 cells/mL. ('''b''') Comparing the ethanol productivity of the ''spt15-300'' mutant with that of the control after growth in culture with 100 g glucose/L | '''Figure 2''' - ('''a.''') Cell counts of the evolved ''spt15-300'' mutant compared with wild-type yeast during growth in 12.5% ethanol. The ''spt15-300'' shows higher cell viability under "stressed" fermentation conditions when compared to the control. Cell counts measured by optical density. Error bars represent the standard deviation between replicate experiments. Initial cells counts were -3.5 x 10^6 cells/mL. ('''b''') Comparing the ethanol productivity of the ''spt15-300'' mutant with that of the control after growth in culture with 100 g glucose/L | ||
| − | ==Advantages== | + | ==Advantages of the Method== |
# Global transcriptome engineering was a successful technique for engineering increased yeast viability under fermentation conditions. | # Global transcriptome engineering was a successful technique for engineering increased yeast viability under fermentation conditions. | ||
| − | # The method | + | # The method achieved these goals in a fast, efficient manner that did not require constructing a model of yeast metabolism before changes could be made. |
| − | # Hypothetically, using the gTME method creates | + | # Hypothetically, using the gTME method creates the opportunity to discover new genes and pathways that affect the desired pheontype. For example, if a three previously-undescribed genes are upregulated in a selected mutant, these three genes might play key roles in this particular phenotype, which further research would illuminate. |
| − | ==Disadvantages== | + | ==Disadvantages of the Method== |
| − | # The primary limitation of gTME is that it can only test how changes gene expression affect the phenotype being selected for. The method does not test how multiple changes to genes themselves affect the organism. | + | # The primary limitation of gTME is that it can only test how changes to gene expression affect the phenotype being selected for. The method does not test how multiple changes to genes themselves affect the organism. |
| − | # | + | # A choice must be made about which transcription factor is subjected to random mutatgenesis. A mutant library of ''SPT15'' worked in this experiment, but this using transcription factor might not be sufficient in the directed evolution of other complex phenotypes. The large number of transcription factors further complicates this issue. |
==Sources== | ==Sources== | ||
[http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.] | [http://www.bio.davidson.edu/Courses/Synthetic/papers/Alper_etal.pdf Alper, H., J. Moxley, E. Nevoigt, G.R. Fink & G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. ''Science'' 314: 1565-1568.] | ||
Latest revision as of 17:57, 6 December 2007
The Experiment
Goals
Researchers Alper et al. (2006) were interested in creating a strain of yeast with improved ethanol tolerance and production. Specifically, their goal was to devolope a yeast strain with increased efficiency of ethanol fermentation for biofuel production.
Using gTME to Engineer a Complex Phenotype
The amount of ethanol (and resistance to ethanol) by a yeast strain is determined by many different genes; some of these genes are known, but most of these genes are unknown. The fundamental lack of knowledge regarding the genes that contribute to these phenotypes meant that rational engineering could not be used achieve the goal of these investigators.
To circumvent this problem, the researchers developed a new technique to elicit the desired phenotype from the yeast. This method, which they have termed global transcriptome machinery engineering (gTME), is a way of using directed evolution to engineer complex phenotypes regulated by multiple genes. Unlike methods of directed evolution that are used to alter the function of individual proteins, this method also does not require any prior knowledge of gene function contributing to the desired phenotype.
The major difference between the approach used here and that used in other papers is that mutatgenesis was used to create a mutant library of a selected transcription factor instead of using the same process to create a mutant library of a single gene.
The rationale behind this approach is that each mutant transcription factor will change the expression levels of not one but rather many genes in a cell. The strains containing these many changes in gene expression levels can then be run through a selection scheme that selects for the desired phenotype (in this case, ethanol production and resistance).
In the Lab
After an initial screening of transcription factors, the researchers chose to create a mutant library of the TATA-binding protein SPT15. This mutant library was then inserted into expression vectors and transferred into yeast cells. Expression vectors were used instead of gene replacement vectors to prevent complete disruption of normal cell function resulting from the altered transcription factors. The cloned yeast carrying the mutant library were diluted into multiple cultures and grown at elevated levels of ethanol and glucose. The mutant that grew most vigorously under these conditions, spt15-300, was isolated.
The team ran tests on the spt15-300 mutant strain to quantify increases in ethanol tolerance and production. When the mutant was grown at high ethanol concentrations (greater than 10%), it consistantly showed greater ethanol production than the wild-yeast yeast (Fig. 2a). Comparing fermentation analysis of the spt15-300 mutant with wild-type yeast also revealed higher total production in ethanol (Fig. 2b.)
(Alper et al., 2006 - Permission Pending)
Figure 2 - (a.) Cell counts of the evolved spt15-300 mutant compared with wild-type yeast during growth in 12.5% ethanol. The spt15-300 shows higher cell viability under "stressed" fermentation conditions when compared to the control. Cell counts measured by optical density. Error bars represent the standard deviation between replicate experiments. Initial cells counts were -3.5 x 10^6 cells/mL. (b) Comparing the ethanol productivity of the spt15-300 mutant with that of the control after growth in culture with 100 g glucose/L
Advantages of the Method
- Global transcriptome engineering was a successful technique for engineering increased yeast viability under fermentation conditions.
- The method achieved these goals in a fast, efficient manner that did not require constructing a model of yeast metabolism before changes could be made.
- Hypothetically, using the gTME method creates the opportunity to discover new genes and pathways that affect the desired pheontype. For example, if a three previously-undescribed genes are upregulated in a selected mutant, these three genes might play key roles in this particular phenotype, which further research would illuminate.
Disadvantages of the Method
- The primary limitation of gTME is that it can only test how changes to gene expression affect the phenotype being selected for. The method does not test how multiple changes to genes themselves affect the organism.
- A choice must be made about which transcription factor is subjected to random mutatgenesis. A mutant library of SPT15 worked in this experiment, but this using transcription factor might not be sufficient in the directed evolution of other complex phenotypes. The large number of transcription factors further complicates this issue.
