Team:BM-AMU/Design

Team:BM-AMU- 2019.igem.org


Design

The phenomenon of epithelial-mesenchymal transition (EMT) has been observed in physiological and pathological processes, including embryogenesis, inflammation, fibrosis, wound healing, and cancer progression. During EMT, cells do not necessarily exist in ‘pure’ epithelial or mesenchymal states, and some cells have hybrid features of both states. The genetic landscape of is still not well understood due to the highly dynamic and reversible nature of EMT. What we tried to achieve with our designs are:

1. A regulated EMT cell model with controller and monitor

2. A Transcriptome database for intermediate EMT states

3. A software for predicting transcriptome with EMT phenotype

For the detailed steps of each section, click on each button to read how we design the system to achieve each goal.

Design

1. A regulated EMT cell model with controller and monitor

The regulated EMT cell model should be constructed based on a well-documented EMT phenotype. Embryonic stem cells possess a pluripotent potential to give rise to cell types in three germ lines. Stem cell differentiation is related to the changes of cellular morphology and surface markers, orchestrated by tremendous molecular regulating processes accompanying with the cellular mobility changes. Through literature research, we decided to use human embryonic cell WA09 to study the intermediate cell states during differentiation.

In our system, the embryonic cells can undergo a subsequent EMT by regulating expression of EMT transcriptional factor, and the intermediate cells can be divided into different EMT states according to their sortable markers. Therefore, we improved regulated EMT cell model from two aspects: ⑴ a monitor to present intermediate EMT states, and ⑵ a controller to modulate EMT states.

1.1 How to build a “monitor”?

Designing sortable EMT “monitor”

EMT is a process in which epithelial cells lose their characteristic polarity, disassemble cell-cell junctions and become more migratory. Cadherins are important in the establishment of cell polarity and cell sorting during embryonic development. The cadherin molecules at adherens junctions have multiple isoforms. Epithelial cells typically express E-cadherin, whereas mesenchymal cells express various cadherins, including N-cadherin, R-cadherin and cadherin-11. The term cadherin switching usually refers to a switch from expression of E-cadherin to expression of N-cadherin, but also includes situations in which E-cadherin expression levels do not change significantly but the cells turn on (or increase) expression of N-cadherin. Cadherin isoform switching occurs during developmental EMT processes to allow cell types to segregate from one another.

We decided to monitor the EMT process by tagging different fluorescence protein with E-cadherin and N-cadherin. First, we constructed a td-tomato homologous recombination plasmid for E-cadherin. This vector directly integrated into the embryonic cell genome at C-terminal of E-cadherin locus by CRISPR/Cas9. Similarly, eGFP was also integrated into N-cadherin locus. The cell model was verified with DNA-seq and fluorescent “cadherin switching” could be observed during EMT process.

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1.2 How to build a “controller”?

EMT-transcriptional factor (TF)s play important roles in governing the process of developmental EMT, as well as EMT in carcinomas. Snail (SNAIL1) was identified as the first EMT-TF that directly repressed transcription of E-cadherin. Overexpression of SNAIL1 led to the loss of E-cadherin mediated cell–cell adhesion, transformed the cellular morphology from epithelial to mesenchymal, and enhanced their migratory and invasive traits. Twist was identified as another EMT-TF that inhibited E-cadherin as well as regulated other components of EMT. Silencing Twist suppressed the number of lung metastases significantly, however, did not completely inhibit them. In short, these EMT-TFs may make a cell more poised to or undergo EMT. Here, we designed a regulated expression system for SNAI1 or Twist as precise EMT controller.

Controller #1: TET-On system for SNAIL1

Tet-On system is based on the binding of the Tet repressor protein (TetR) to the tet operator (tetO) DNA sequence. We designed a new part tetR-krab ( BBa_K3120013 ) as the improvement of tetR. The chimeric transrepressor significantly prevents the leakage of target transgene.

Controller #2: Tamoxifen-Ert2 system for Twist

In tamoxifen-Ert2 system, high expression of Twist1-Ert2 fusion protein is dependent on constitutive promoter. However, the fusion protein cannot enter the nucleus and bind to downstream EMT genes without tamoxifen. The intermediate EMT states can be regulated both reversibly and quantitatively by various concentrations of tamoxifen. Then, a regulated EMT cell model with sortable “monitor” and quantitative “controller” was constructed to study intermediate states of EMT.

To better characterize the dose-effect relationship, we generated mathematical modeling of two EMT transcriptional factors (SNAIL1 and Twist).Based on this cell model, we could induce EMT cells in any intermediate state by varying concentrations of Dox and Tamoxifen.

The schematic drawings of the two systems are as follows:

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Tet-on system

In Tet-on system, tetR fusion protein is highly expressed driven by constitutive promoter. Without DOX, the fusion protein will move and locate on TRE sequence. And Krab, as a transcription inhibitor, will inhibit the transcription of Snail1. When DOX is added, the fusion protein will break away from the TRE sequence and Snail 1 gene will express. By controlling the concentration of DOX, the occurrence of EMT can be controlled at the gene level.

Tamoxifen-Ert2 system

In Tamoxifen-Ert2 system, Twist1-Ert2 fusion protein is highly expressed driven by constitutive promoter. However, without tamoxifen, the fusion protein cannot enter the nucleus, thus cannot activate or inhibit downstream gene expression. When tamoxifen was added, the changes of protein entry and EMT-related gene expression would occur in turn. By controlling the concentration of tamoxifen, the effect of TF on EMT was controlled at the protein level.

In summary, we use monitor and controller to capture different intermediate states of EMT accurately.

2. A database for intermediate EMT states
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FACS is a specialized type of flow cytometry to sort a heterogeneous mixture of biological cells based upon the specific light scattering and fluorescent characteristics of each cell. We sorted fluorescent cells of intermediate EMT states into groups and obtained the transcriptome landscape of each group by RNA-seq. We believed this phenotype-specific Transcriptome database will provide all of the following features: ⑴ comprehensive data archives of intermediate EMT states, including coding-transcript profiling, long non-coding RNA profiling and coexpression networks; ⑵ the visualization of expression profiles of intermediate EMT states; and ⑶ EMT phenotype -oriented data organization and searching.

3. A software for predicting transcriptome with EMT phenotype
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To make our database friendlier to researchers, we decided to construct software for predicting transcriptome with EMT phenotype------“cadherin switching”. The fluorescent intensity of cadherin isoforms will be analyzed as input, then phenotype-specific transcriptional data were used as training tags, and finally the most probable sketch map of transcriptional landscape could be output based on supporting vector machine (SVM)/ convolutional neural networks (CNN). Now, we planned to update the software with expanded types of input data, including immunofluorenscent images. It would bridge the gap between the fluorescent phenotype and transcriptional landscape during highly dynamic EMT process.

References:

1.Dongre, A. and R.A. Weinberg, New insights into the mechanisms of epithelial-mesenchymal transition and implications for cancer. Nat Rev Mol Cell Biol, 2019. 20(2): p. 69-84.

2.Pastushenko, I., et al., Identification of the tumour transition states occurring during EMT. Nature, 2018. 556(7702): p. 463-468.

3.Pei, D., et al., Mesenchymal-epithelial transition in development and reprogramming. Nat Cell Biol, 2019. 21(1): p. 44-53.

4.Zeisberg, M. and E.G. Neilson, Biomarkers for epithelial-mesenchymal transitions. J Clin Invest, 2009. 119(6): p. 1429-37.

5. Rhim, A.D., et al., EMT and dissemination precede pancreatic tumor formation. Cell, 2012. 148(1-2): p. 349-61.

6. Chek Ounkomol, Daniel A. Fernandes, et al.Three dimensional cross-modal image inference: label-free methods for subcellular structure prediction.bioRvix, 2017.