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Team:Tsinghua-A/pr
From 2013.igem.org
Background
What is Gene Regulatory Network (GRN)?
Gene expression is the most basic but complicated process in living system. The interactional connection and regulatory effect among genes make up a Gene Regulatory Network (GRN). People have done great research to figure out the mechanism and rules behind gene regulatory network. Typical gene regulatory networks can achieve bistability, state-switch, biooscillation and so on. Also, some specific network structures have been designed and synthesized to realize artificial control.
What is Adaptation?
In order to function reliably, any network needs to keep expression efficiency and accuracy when molecular components fluctuate. So the ability to robustly function and keep the same level of expression despite the fluctuation is called adaptation. In our project, the specific fluctuation is the DNA template abundance (copy number) variation.
How did the idea occur to us ?
Our project initially originated from medical treatment of cancer. Specific therapy to kill cancer cells greatly demands an effective method to recognize cancer cell. Hela cell is a kind of cancer cell that contains a large amount of endogenic mir-21, while the amount of this microRNA in normal cells is rather low. This remarkable difference inspired us to design a network of which the output protein value is related to the input value of mir-21. So a rational GRN is needed to sense input signal and to distinguish cancer cell from normal cell.
Previous research has offered some functional GRNs successfully sensing the difference between high input and low input. We hoped to find out similar network topologies that may also achieve corresponding high-low switch.
However, there exist many complicated factors that may arouse noise during the expression of gene, which leads to instability and lack of efficiency. We noticed that the number of DNA template (copy number) has a great effect as a noise on the expression intensity. Since the actual amount of copy number involved in gene expression process is hard to measure or control precisely, we have to consider adaptation to DNA copy number variation as a key character of a network.
So our goal is to search for a rational GRN that can sense the input signal, realize the switch function and show adaptation to copy number.
Besides, GRN consists of different kinds of regulatory motifs that may play an important role in functioning. We are also motivated to search for certain motifs which contribute to adaptation.
Why are we interested ?
Cells with different mir-21 amount response differently towards the input signal and their response may represent state-switch. Therefore, cells of 2 different types can be distinguished according to their response. So an adaptive and robust network topology can be regarded as a sensitive switch and a simple sorter, which is really exciting. In addition, possible functional motifs and its mechanism are also fun.
Construction
We constructed the following circuit A ,B and C .The circuit A corresponds to the network A ,while the circuit B is the implementation of network B. Circuit C is used as a control design to testify the function of A and B. In circuit A, as we can see, the input is miR-21, which can repress the plasmid pz371 and K1116002(The plasmid’s information can be found in parts). K1116002 induced by rtTA and Dox, serves as an auxiliary node, producing the LacI gene to inhibit the expression of EYFP. EYFP(Enhance Yellow Fluorescent Protein )is used as output. Besides, the miR-FF3 restrains the expression of LacI. The reason that we get the most of post-transcriptional control can be seen in Supplementary text. In circuit B, however, the plasmid K1116003 does not have FF3 target, leading to the contrast between circuit A and B. We can see miR-21 can’t target at pZ349 and pZ331 in circuit C , that is, there is no input in circuit C. The miR-21, used to distinguish cancer cell from normal cells ,is endogenous in Hela cell. Supplementary text
miRNAs function as posttranscriptional regulators which have distinguished features compared to transcriptional regulators, intervening late in gene expression process, with the capability to counteract variation from the upstream processes (Margaret et al., 2012). Research shows that while conducting experiment on an incoherent feedforward motif in mammalian cells, posttranscriptional regulation results in superior adaptation behavior, higher absolute expression levels and lower intrinsic fluctuations (Bleris et al., 2011). miRNAs can serve as buffers against variation during gene expression; transient increases in transcription factor activity would propagate to increases in target miRNA transcription while would be counteracted by increased miRNA and vice versa. Therefore, under the miRNA posttranscriptional regulation, protein output can be uncoupled from fluctuations in transcription factor concentration or activity (Margaret et al., 2012). miRNAs also possess good stability which, consistent with theoretical constraints, meets the need for enough molecules of a regulator to achieve a small reduction in the noise of a target gene (Lestas et al., 2010).
Experimental Characterization
We took advantage of another fluorescent protein(mkate ) as reference gene, which has no influence in our design. Published literature generally supports the view that in transient transfections, fluorescence depends linearly on the copy number of transfected plasmids (Tseng et al, 1997; Pollard et al, 1998;Cohenet al,2009; Schwakeet al, 2010). While strictly speaking, this reporter level also depends on many other potentially fluctuating parameters such as global synthesis and degradation rates(Leonidas Bleris et al,2011),it is more legitimately use the normalized quotient to instead of the value of EYFP.
Apparently, the output of circuit C is lower than circuit B .Having the negative feedback compared with circuit B, the expression of EYFP in circuit A is strongest.
Then we analysed the output of constructed designs varies with the DNA copy number. Facing with the difficulty of counting the copy number directly, we employed the reference gene to reflect .We think the copy number is high when the expression intensity of mkate is strong. One hundred thousand positive Hela cells was collected to obtain the relationship between EYFP and make.
From the figure 2, we learned with the increase of the expression intensity of makte, the specific valve of EYFP and mkate decreases. In another word, the circuit A’s output reaches saturation fast with the increase of copy number. We came to a conclusion circuit A’s adaptation to DNA copy number is higher than circuit B’s. So, the negative feedback works.
