Difference between revisions of "Part:BBa K4815000"
(→Loci) |
|||
(28 intermediate revisions by the same user not shown) | |||
Line 3: | Line 3: | ||
− | + | ==Introduction== | |
===Description=== | ===Description=== | ||
<div style="text-align:justify;"> | <div style="text-align:justify;"> | ||
Line 14: | Line 14: | ||
===Loci=== | ===Loci=== | ||
PYPH1 consists of two parts: the core promoter and the scaffold. The core promoter is an 80 bp sequence and is seated at approximately -170 to -90 upstream to the codon (which is the presumed transcription start site-TSS and is where most transcription factors binding sites lie). | PYPH1 consists of two parts: the core promoter and the scaffold. The core promoter is an 80 bp sequence and is seated at approximately -170 to -90 upstream to the codon (which is the presumed transcription start site-TSS and is where most transcription factors binding sites lie). | ||
− | The scaffold is a preserved sequence in all PYPHs. It is a structure that we learned and utilized from previous research that can link the core promoter with the codon and provide restriction sites of BamH I and Xho I which make it possible for the plasmids with the scaffold to be inserted by various core promoter sequences at ease. | + | The scaffold is a preserved sequence in all PYPHs. It is a structure that we learned and utilized from previous research that can link the core promoter with the codon and provide restriction sites of BamH I and Xho I which make it possible for the plasmids with the scaffold to be inserted by various core promoter sequences at ease. |
− | + | ==Usage and Biology== | |
To begin with, PYPH1 can drive <html><a style="font-weight:900;color:red">extremely high expression</a> </html> of downstream products as is predicted by our excellent AI model Pymaker. Furthermore, our experiments have abundantly validated the ability of PYPH1. Under same circumstance, PYPH1 can drive expression rate<html><a style="font-weight:900;color:red"> 5 times</a> </html> that of natural high promoters (pTEF, pGAP, pADH), which are now most widely used high expression constitutive promoters in entrepreneurship. | To begin with, PYPH1 can drive <html><a style="font-weight:900;color:red">extremely high expression</a> </html> of downstream products as is predicted by our excellent AI model Pymaker. Furthermore, our experiments have abundantly validated the ability of PYPH1. Under same circumstance, PYPH1 can drive expression rate<html><a style="font-weight:900;color:red"> 5 times</a> </html> that of natural high promoters (pTEF, pGAP, pADH), which are now most widely used high expression constitutive promoters in entrepreneurship. | ||
Meanwhile, our experiments proof that this ability of driving extremely high expression is preserved among different downstream products, which make PYPH1 an excellent and practical substitute for natural promoters. | Meanwhile, our experiments proof that this ability of driving extremely high expression is preserved among different downstream products, which make PYPH1 an excellent and practical substitute for natural promoters. | ||
− | Besides, PYPH1 is <html><a style="font-weight:900;color:red">anti-mutant</a> </html> as our excellent AI model predicts (as is represented by the red line labeled 'high' in the figure bellow). Our AI model Pymaker outperforms competitors on all sample size datasets, and its ability to predict the specific expression rate of the core promoters is proved by experiments. On this basis, we are very confident that by introducing 1-3 random mutations per generation and imitating 100 generations, those promoters, whose expression rates predicted by Pymaker remain extremely high, are highly anti-mutant. This character of anti-mutant makes PYPH1 a highly competitive merchant among promoter parts used in yeasts fermentation. | + | Besides, PYPH1 is <html><a style="font-weight:900;color:red">anti-mutant</a> </html> as our excellent AI model predicts (as is represented by the <html><a style="color:red">red</a> </html> line labeled 'high' in the figure bellow). Our AI model Pymaker outperforms competitors on all sample size datasets, and its ability to predict the specific expression rate of the core promoters is proved by experiments. On this basis, we are very confident that by introducing 1-3 random mutations per generation and imitating 100 generations, those promoters, whose expression rates predicted by Pymaker remain extremely high, are highly anti-mutant. This character of anti-mutant makes PYPH1 a highly competitive merchant among promoter parts used in yeasts fermentation. |
<html> | <html> | ||
<figure><center> | <figure><center> | ||
Line 31: | Line 31: | ||
What’s more, PYPH1 shows a <html><a style="font-weight:900;color:red">highly constant expression rate</a> </html> among yeast population, which is not the case considering the natural promoters. This character of PYPH1 is totally out of expectation but is of vital importance in yeasts fermentation on a large commercial scale. | What’s more, PYPH1 shows a <html><a style="font-weight:900;color:red">highly constant expression rate</a> </html> among yeast population, which is not the case considering the natural promoters. This character of PYPH1 is totally out of expectation but is of vital importance in yeasts fermentation on a large commercial scale. | ||
− | + | ||
− | + | ||
+ | |||
+ | ==Sequence and Features== | ||
<partinfo>BBa_K4815000 SequenceAndFeatures</partinfo> | <partinfo>BBa_K4815000 SequenceAndFeatures</partinfo> | ||
+ | |||
+ | ==Experiments== | ||
===Extraction=== | ===Extraction=== | ||
− | We extract | + | We design to extract promoter sequences from synthesized whole dual-fluorescence reporter plasmids, and then insert them into the LTB-eGFP expression plasmids.We extract PYPH1-7 from our dual-fluorescence reporter plasmids, using colony PCR with designed primers (primers sequences are shown in the figure below, and spacer H1-7 stands for PYPH1-7) |
+ | <html> | ||
+ | <figure><center> | ||
+ | <img alt="" | ||
+ | src="https://static.igem.wiki/teams/4815/wiki/result/9.png" | ||
+ | width="400" | ||
+ | title=""> | ||
+ | <figcaption> The bands are in highly consistent with the length of PYPH1(223 bp),which means that our construction of the PYPH1 is a complete success. </figcaption> | ||
+ | </figure> | ||
+ | </html> | ||
+ | |||
+ | ===Validation=== | ||
+ | We synthesized the dual-fluorescence reporter plasmids where PYPH1 is already placed in, comparing to the natural promoter GAP. We transform the plasmids into targeted yeasts and use fluorescence-activated cell sorting (FACS) strategy and record the fluorescence density through a flow cytometer. The figure above shows the result. | ||
+ | |||
+ | <html> | ||
+ | <figure><center> | ||
+ | <img alt="" | ||
+ | src="https://static.igem.wiki/teams/4815/wiki/2138.jpg" | ||
+ | width="700" | ||
+ | title=""> | ||
+ | <figcaption> The figure shows that PYPH1 drives expression rate over <a style="font-weight:900;color:red">2 times</a> that of GAP. What's more, this density figure explicitly reviewed that PYHP1 drive <a style="font-weight:900;color:red">a constant high expression rate</a> regardless of the yeasts' population, which is not the case considering the natural promoters (the waveform of PYPH1 is much sharper than that of GAP). </figcaption> | ||
+ | </figure> | ||
+ | </html> | ||
+ | |||
+ | ===Proof In different scenario=== | ||
+ | PYPH 1 has shown its ability in tackling real challenges. Currently, significant breakthroughs have been achieved in the application of brewing yeast in the field of biotechnology. One of them is the production of the heat-labile toxin B subunit (LTB) of Escherichia coli using brewing yeast, an important oral vaccine adjuvant widely used to prevent various diseases such as cholera, traveler’s diarrhea, and E. coli infection. | ||
+ | We use the same plasmid framework of BBa_K4815011 and change the YeGFP with our part BBa_K4815010, aimed at using PYPH1 to drive the expression of fusion protein LTB-eGFP. | ||
+ | We test the expression rate from three different dimensions: flow cytometry, protein density through western blot, transcription accumulation through quantitative RT PCR, and the results are as follows. | ||
+ | <html> | ||
+ | <style> .image-container {display: flex; justify-content: center; } .image-container img { margin: 0 10px;} </style> <div class="image-container"> <img src="https://static.igem.wiki/teams/4815/wiki/2215.png" alt="Image 1" width = 40%> <img src="https://static.igem.wiki/teams/4815/wiki/2216.png" alt="Image 2" width=40%> </div> | ||
+ | <figure><center> | ||
+ | <img alt="" | ||
+ | src="https://static.igem.wiki/teams/4815/wiki/2228.png" | ||
+ | width="300" | ||
+ | title=""> | ||
+ | <figcaption> The figures shows the western blot result of LTB-eGFP, where H1 stands for PYPH1. Using GAPDH as an internal control ,we quantify the expression intensity of LTB-eGFP as Intensity[LTB-eGFP]/intensity[GAPDH]. Under same circumstance, PYPH1 can drive expression rate <a style="font-weight:900;color:red">5 times</a> that of natural high promoters (pTEF, pGAP, pADH)</ficaption></figure></html> | ||
+ | We then checked the quantitative gene expression levels using quantitative RT-PCR, and the results indicated that PYPH1 drive<html><a style="font-weight:900;color:red"> a much higher transcript accumulation </a></html>than natural promoters. The result gives a strong validation that it is our generated promoters that play a fundamental role in driving a extremely high promoter sequences. | ||
+ | <html> | ||
+ | <figure><center> | ||
+ | <img alt="" | ||
+ | src="https://static.igem.wiki/teams/4815/wiki/result/17.png" | ||
+ | width="400" | ||
+ | title=""></figure></html> | ||
+ | |||
+ | Also, the flow cytometry result suits the result above, expression rate of LTB-eGFP driven by PYPH1 remain the sharp waveform, which means the ability to<html><a style="font-weight:900;color:red"> drive constant expression even better than natural constitutive promoters </a></html>of PYPH1<html><a style="font-weight:900;color:red"> remains </a></html>when the downstream codon changes. | ||
+ | <html> | ||
+ | <figure><center> | ||
+ | <img alt="" | ||
+ | src="https://static.igem.wiki/teams/4815/wiki/2240.png" | ||
+ | width="400" | ||
+ | title=""></figure></html> | ||
+ | |||
+ | |||
<!-- Uncomment this to enable Functional Parameter display | <!-- Uncomment this to enable Functional Parameter display |
Latest revision as of 03:19, 12 October 2023
Contents
Introduction
Description
The part we provide is the functional promoter sequence (about 223 bp) generated by our AI model Pymaker. PYPH1 means it is predicted to be anti-mutant and to drive extremely high expression rate by our AI model.
Origin
PYPH1 targets at s. cerevisiae (saccharomyces cerevisiae), the most studied eukaryotic expression system in synthetic biology.
Loci
PYPH1 consists of two parts: the core promoter and the scaffold. The core promoter is an 80 bp sequence and is seated at approximately -170 to -90 upstream to the codon (which is the presumed transcription start site-TSS and is where most transcription factors binding sites lie). The scaffold is a preserved sequence in all PYPHs. It is a structure that we learned and utilized from previous research that can link the core promoter with the codon and provide restriction sites of BamH I and Xho I which make it possible for the plasmids with the scaffold to be inserted by various core promoter sequences at ease.
Usage and Biology
To begin with, PYPH1 can drive extremely high expression of downstream products as is predicted by our excellent AI model Pymaker. Furthermore, our experiments have abundantly validated the ability of PYPH1. Under same circumstance, PYPH1 can drive expression rate 5 times that of natural high promoters (pTEF, pGAP, pADH), which are now most widely used high expression constitutive promoters in entrepreneurship. Meanwhile, our experiments proof that this ability of driving extremely high expression is preserved among different downstream products, which make PYPH1 an excellent and practical substitute for natural promoters.
Besides, PYPH1 is anti-mutant as our excellent AI model predicts (as is represented by the red line labeled 'high' in the figure bellow). Our AI model Pymaker outperforms competitors on all sample size datasets, and its ability to predict the specific expression rate of the core promoters is proved by experiments. On this basis, we are very confident that by introducing 1-3 random mutations per generation and imitating 100 generations, those promoters, whose expression rates predicted by Pymaker remain extremely high, are highly anti-mutant. This character of anti-mutant makes PYPH1 a highly competitive merchant among promoter parts used in yeasts fermentation.
What’s more, PYPH1 shows a highly constant expression rate among yeast population, which is not the case considering the natural promoters. This character of PYPH1 is totally out of expectation but is of vital importance in yeasts fermentation on a large commercial scale.
Sequence and Features
- 10COMPATIBLE WITH RFC[10]
- 12INCOMPATIBLE WITH RFC[12]Illegal NheI site found at 1
- 21INCOMPATIBLE WITH RFC[21]Illegal BamHI site found at 198
- 23COMPATIBLE WITH RFC[23]
- 25COMPATIBLE WITH RFC[25]
- 1000INCOMPATIBLE WITH RFC[1000]Illegal BsaI site found at 78
Experiments
Extraction
We design to extract promoter sequences from synthesized whole dual-fluorescence reporter plasmids, and then insert them into the LTB-eGFP expression plasmids.We extract PYPH1-7 from our dual-fluorescence reporter plasmids, using colony PCR with designed primers (primers sequences are shown in the figure below, and spacer H1-7 stands for PYPH1-7)
Validation
We synthesized the dual-fluorescence reporter plasmids where PYPH1 is already placed in, comparing to the natural promoter GAP. We transform the plasmids into targeted yeasts and use fluorescence-activated cell sorting (FACS) strategy and record the fluorescence density through a flow cytometer. The figure above shows the result.
Proof In different scenario
PYPH 1 has shown its ability in tackling real challenges. Currently, significant breakthroughs have been achieved in the application of brewing yeast in the field of biotechnology. One of them is the production of the heat-labile toxin B subunit (LTB) of Escherichia coli using brewing yeast, an important oral vaccine adjuvant widely used to prevent various diseases such as cholera, traveler’s diarrhea, and E. coli infection. We use the same plasmid framework of BBa_K4815011 and change the YeGFP with our part BBa_K4815010, aimed at using PYPH1 to drive the expression of fusion protein LTB-eGFP. We test the expression rate from three different dimensions: flow cytometry, protein density through western blot, transcription accumulation through quantitative RT PCR, and the results are as follows.
Also, the flow cytometry result suits the result above, expression rate of LTB-eGFP driven by PYPH1 remain the sharp waveform, which means the ability to drive constant expression even better than natural constitutive promoters of PYPH1 remains when the downstream codon changes.