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+ | <li>Team: Fudan-TSI</li> | ||
+ | <li><div class="collapsible-header">Project</div> | ||
+ | <div class="collapsible-body"><ul> | ||
+ | <li><a href="/Team:Fudan-TSI/Description">Background</a></li> | ||
+ | <li><a href="/Team:Fudan-TSI/Design">Design</a></li> | ||
+ | <li><a href="/Team:Fudan-TSI/Applied_Design">Applied Design</a></li> | ||
+ | <li><a href="/Team:Fudan-TSI/Experiments">Experiments</a></li> | ||
+ | <li><a href="/Team:Fudan-TSI/Judging">Judging</a></li> | ||
+ | </ul></div> | ||
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+ | <li><div class="collapsible-header">Results</div> | ||
+ | <div class="collapsible-body"><ul> | ||
+ | <li><a href="/Team:Fudan-TSI/Results#ReverseTranscription">Reverse Transcription</a></li> | ||
+ | <li><a href="/Team:Fudan-TSI/Results#Recombination">Recombination</a></li> | ||
+ | <li><a href="/Team:Fudan-TSI/Demonstrate">Demonstration</a></li> | ||
+ | <li><a href="/Team:Fudan-TSI/Measurement">Measurement</a></li> | ||
+ | <li><a href="/Team:Fudan-TSI/Notebook">Notebook</a></li> | ||
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+ | <li><a href="/Team:Fudan-TSI/Software">Software</a></li> | ||
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+ | <li><div class="collapsible-header">Parts</div> | ||
+ | <div class="collapsible-body"><ul> | ||
+ | <li><a href="/Team:Fudan-TSI/Basic_Part">Basic parts</a></li> | ||
+ | <li><a href="/Team:Fudan-TSI/Composite_Part">Composite parts</a></li> | ||
+ | <li><a href="/Team:Fudan-TSI/Improve">Improved parts</a></li> | ||
+ | <li><a href="/Team:Fudan-TSI/Part_Collection">Part collection</a></li> | ||
+ | </ul></div> | ||
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+ | <li><a href="/Team:Fudan-TSI/Integrated_Human_Practice">Integrated human practice</a></li> | ||
+ | <li><a href="/Team:Fudan-TSI/Collaborations">Collaborations</a></li> | ||
+ | <li><a href="/Team:Fudan-TSI/Safety">Safety</a></li> | ||
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+ | <div class="collapsible-body"><ul> | ||
+ | <li><a href="/Team:Fudan-TSI/Team">Members</a></li> | ||
+ | <li><a href="/Team:Fudan-TSI/Attributions">Attributions</a></li> | ||
+ | <li><a href="https://2018.igem.org/Team:Fudan/Heritage" target=_blank>Heritage</a></li> | ||
+ | <li><a href="/Team:Fudan-TSI">© 2019</a></li> | ||
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− | <p style="margin: 0"> | + | <p style="margin: 0">Mutation library generation is critical for biological and medical research, but current methods cannot mutate a specific sequence continuously without manual intervention. Here we present a toolbox for <i>in vivo</i> continuous mutation library construction. First, the target DNA is transcribed into RNA. Next, our reverse transcriptase reverts RNA into cDNA, during which the target is randomly mutated by enhanced error-prone reverse transcription. Finally, the mutated version replaces the original sequence through recombination. These steps will be carried out iteratively, generating a random mutation library of the target with high efficiency as mutations accumulate along with bacterial growth. Our toolbox is orthogonal and provides a wide range of applications among various species. R-Evolution could mutate coding sequences and regulatory sequences, which enables the <i>in vivo</i> evolution of individual proteins or multiple targets at a time, promotes high-throughput research, and serves as a foundational advance to synthetic biology. |
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</a><a href="http://www.fudan.edu.cn/en/" target="_blank"><img class="col s3 m6 l3" alt="Fudan University" src="https://static.igem.org/mediawiki/2018/f/f7/T--Fudan--schoolLogo.png"> | </a><a href="http://www.fudan.edu.cn/en/" target="_blank"><img class="col s3 m6 l3" alt="Fudan University" src="https://static.igem.org/mediawiki/2018/f/f7/T--Fudan--schoolLogo.png"> | ||
</a><a href="http://life.fudan.edu.cn/" target="_blank"><img class="col s3 m6 l3" style="margin-bottom: 4%;/* 该图比其他小一点,排版需要 */" alt="School of Life Sciences, Fudan University" src="https://static.igem.org/mediawiki/2018/1/1d/T--Fudan--schoolOfLifeSciencesIcon.png"> | </a><a href="http://life.fudan.edu.cn/" target="_blank"><img class="col s3 m6 l3" style="margin-bottom: 4%;/* 该图比其他小一点,排版需要 */" alt="School of Life Sciences, Fudan University" src="https://static.igem.org/mediawiki/2018/1/1d/T--Fudan--schoolOfLifeSciencesIcon.png"> | ||
</a><a href="http://www.yfc.cn/en/" target="_blank"><img class="col s3 m6 l3" style="padding: 0.15rem 0.9rem;" alt="Yunfeng Capital" src="https://static.igem.org/mediawiki/2018/e/e2/T--Fudan--yunfengLogo.png"> | </a><a href="http://www.yfc.cn/en/" target="_blank"><img class="col s3 m6 l3" style="padding: 0.15rem 0.9rem;" alt="Yunfeng Capital" src="https://static.igem.org/mediawiki/2018/e/e2/T--Fudan--yunfengLogo.png"> | ||
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− | <h3 class="col s12" style="text-align: left; color: rgba(255, 255, 255, 0.8); font-size: | + | <h3 class="col s12" style="text-align: left; color: rgba(255, 255, 255, 0.8); font-size:12px">R-Evolution: an <i>in vivo</i> sequence-specific toolbox for continuous mutagenesis</h3> |
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Revision as of 04:53, 15 September 2019
Demonstration
Video abstract
Transmembrane Logic
Sensing and integrating various transmembrane signals is a key aspect of cellular decision making.
For example, activation of CD8+ cells requires co-activation of TCR and CD28 molecules, meanwhile, this activation can be inhibited by the PD-1 pathway (Hui et al., 2017). By abstracting this biological process, we can get: the activation of CD8+ cell = activated TCR AND (activated CD28 NIMPLY activated PD-1). Programming cells with predictable complex transmembrane signal inputs – customized intracellular signal outputs logic relationships are significant for expanding the widespread applications of mammalian cells, such as cellular immunotherapy (Fedorov et al., 2013; Kloss et al., 2013; Roybal et al., 2016a; Roybal et al., 2016b), tissue patterning (Morsut et al., 2016;Roybal et al., 2016; Toda et al., 2018).
a sequence-specific toolbox for continuous mutagenesis
The ingenious design of nature makes us amazing, and inspires us to explore new possibilities. By designing the ENABLE (Engineered, Across-membrane, Binary Logic in Eukaryotes) system and achieving the first complete transmembrane binary Boolean logic in mammals, we could make the pupil outdo the master, make cells even smarter.
Optimizing the Receptor, aims for reduced background activation
In order to be able to implement a custom multiplexed transmembrane signal input/output relationship, the first condition is that engineering modular receptor to enable it to recognize extracellular signals and transduce them into customized intracellular signals. SynNotch is a transmembrane receptor with high programmability. Therefore, we can use this tool to receive extracellular signals and output customized intracellular signals.
Good tools are prerequisite to the successful execution of a job. In order to make SynNotch more suitable as a tool for receiving complex extracellular signals, we have optimized it. Please visit Optimization page for more details.
Three-layer design for dual transmembrane signals
The expression of membrane proteins on the cell membrane is limited. Our modeling tells us how to make cells sensitive to the limited signal molecules, and perform function efficiently is the key to make the transmembrane logic gate system work.
Based on this, we developed the ENABLE system with a 3-layers design: Receptor, Amplifier, and Combiner. By adding an intermediate layer, the “Amplifier” layer, we are able to effectively amplify the transmembrane signal, thus enabling our intracellular components to effectively travel logic functions.
The 3-layer design principle underlying ENABLE empowers any future development of transmembrane logic circuits, thus contributes a foundational advance to synthetic biology.
Unified intracellular logic gates layout
In order to be able to receive dual transmembrane signals and produce complete binary Boolean logic, we designed a set of interactive grammar for the interaction between the "Amplifier" layer and the "Combiner" layer within the cell membrane. This grammar consists mainly of the following elements, a transcription system based on the activating-form, silencing-from or NIMPLY-form promoters (the three are collectively referred to as a synthetic transcription factor-promoter pairs based transcription system); intein-based protein in vivo fusion systems, proteolytic enzyme-based protein in vivo destruction systems (the two are collectively referred to as a protein fusion/destruction-based transcription factor logic). We have test them and report in Results page, as well as on parts.igem pages.
![](https://static.igem.org/mediawiki/2018/e/ec/T--Fudan--stack.gif)
ENABLE logic gates
Below we show experimental verifications of six (OR, NOR, AND, NAND, IMPLY, NIMPLY) of the eight truly binary logics.
Here, we use the OR gate as a proof of concept for a three-layer transmembrane binary Boolean logic during tissue patterning.
Through dynamic and static observations, we can see that the Receiver cell with transmembrane OR gate can be activated by two Sender cells expressing different membrane antigens.
![](https://static.igem.org/mediawiki/2018/9/98/T--Fudan--demon-1.png)
![](https://static.igem.org/mediawiki/2018/d/d1/T--Fudan--results-rgb2.gif)
Public engagement and education
Our human practice activities include dialogues (debate and interviews), presentations, Bio-Art display and hands-on practices, all of which centered around farseeing. We want to unlock the creativity and intelligence in as many people as possible. Fully engaged with the public, we motivated by their openness and willingness, which reminds us why we initiated the research and why we work hard in the lab - for the good of the people, including ourselves.
Project Summary
Mutation library generation is critical for biological and medical research, but current methods cannot mutate a specific sequence continuously without manual intervention. Here we present a toolbox for in vivo continuous mutation library construction. First, the target DNA is transcribed into RNA. Next, our reverse transcriptase reverts RNA into cDNA, during which the target is randomly mutated by enhanced error-prone reverse transcription. Finally, the mutated version replaces the original sequence through recombination. These steps will be carried out iteratively, generating a random mutation library of the target with high efficiency as mutations accumulate along with bacterial growth. Our toolbox is orthogonal and provides a wide range of applications among various species. R-Evolution could mutate coding sequences and regulatory sequences, which enables the in vivo evolution of individual proteins or multiple targets at a time, promotes high-throughput research, and serves as a foundational advance to synthetic biology.