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PROJECT DESIGN

Overview

“Design is the distinguishing activity of synthetic biology”

This paraphrased quote, originally coined by Herbert A. Simon to describe engineering, perfectly outlines the cornerstone of synthetic biology and the iGEM competition: the rational design of biological circuits by modular parts. Fueled by pure excitement for the promising and novel field of synthetic biology, we embarked on a multi-faceted journey into the world of iGEM and designed a bimodal project. The principal pillar of our efforts has been pANDORRA; a programmable AND OR RNAi Assembly platform engineered to optimize logic circuit design and implementation. We applied our modular assembly platform to build a multi-input RNAi based logic circuit to specifically target colorectal cancer cells. Adjuvantly, we concocted a bactofection system performing cell-specific adhesion and bacterial density dependent invasion and plasmid transference.

pANDORRA pipeline
a re-invention of the engineering cycle

RNAi-based logic circuits

We aimed to develop a fully-predictable regulatory program, exploiting the distributed cellular availability of specific molecular input to differentiate various cell types by the production of a protein output. Following the engineering cycle, as described in [1], our first step was “Specification”. Our end goal at the beginning, was quite singular: create a molecular logic circuit, a biocomputer, that can trigger cell death or produce fluorescence when a certain expression profile is found in a cell. Before delving deeper into the inner working of our logic circuit design, let’s review two fundamental notions concerning the computing of such circuits:

• 1. The nature of the biological switches. Switches are the physical entities that implement a universal set of logic gates, thus enabling computation. A plethora of biomolecules can be utilized upon which to build switches. Between gene-based, RNA-based, protein-based etc. biological switches, we chose trans-acting RNA switches and specifically miRNAs. Thanks to their ability to regulate a large fraction of the human transcriptome and natural implementation NOR logic [2] when multiple ones regulate the same gene, miRNAs have been extensively studied in mammalian systems. Moreover, they are excellent internal inputs since miRNAs are found to play crucial roles in the disease spectrum [3].
• 2. The rudimentary circuit abstraction. In order for a miRNA-based cell profiling to function, in accordance with seminal papers of the field [2, 4-7], a number of miRNA markers are selected and the circuit computes an AND gate with these markers in order to perform a classification task. Since miRNAs are molecules exerting solely inhibitory effects on expression, a repressor is required to repress the output, “linking” the high miRNA-markers that inhibit (directly or indirectly) the production of the repressor and the low miRNA-markers that typically target the output gene. As a result, we needed to select the nature of the repressor, with options including a transcriptional one such as LacI, a post-transcriptional one like a synthetic miRNA or both, as well as the in-depth topology, by determining the layers of the circuit (two or more). More elaborate architectures can be employed by utilizing this basic architecture.

In conclusion, we set our “classification” task as follows:

Produce fluorescence or induce apoptosis when a specific miRNA expression profile* is found in colorectal cancer cells (Caco-2).

*The miRNA expression profile should be predetermined in order to discriminate Caco-2 cells from healthy cells.

Circuit topology optimization & miRNA Boolean expression selection

Εxperimental classifiers have been designed by trial-and-error, by tweaking the parameters of the network in order to identify the optimal architecture and Boolean expression, or in a semi-manual fashion, via ranking and manual selection of differentially expressed miRNAs retrieved from databases produced by large scale studies. [8] There are several constraints that dictated these approaches, for example the inadequacy of basic building blocks to better assemble and characterize various mammalian classifiers and the lack of powerful tools to automate logic circuit design based on miRNA molecular switches. Although daunting as a task, we set off to address both of these issues by:

-Creating pANDORRA (programmable AND OR RNAi Assembly) in order to produce a large number of mammalian parts, which can be used for a bottom-up construction of any conceivable logic circuit based on universal logic gates.

-Increasing the functionality and directionality of our assembly process by following a step-by-step cloning workflow and using standardized primers or overhangs after the integration of extensive technical feedback received by Stamatis Damalas. Click here to check it out.

-Employing a computational framework to facilitate the selection of circuit inputs (miRNAs), form the logic expression and simulate optimal circuit-performance in different topologies. Check out our model.

As in mature engineering clades, models simplify the real work and facilitate design in ideal conditions. Other models then evaluate the proposed designs more thoroughly and either send the designers back to the drawing board or to the test bench; that is the essence of our design approach: a progressive dance where modelling and design come ever closer together.

A. THE BASIC COMPONENTS OF THE CIRCUIT

Wet Lab Input

The sequences of the plasmids used for the construction of the cell-type classifier between HeLa cells and healthy cells described in [4] were kindly provided by Prof. Xie. We've used these plasmids as a starting point to “detach” three different mammalian promoters, four different protein coding regions and two polyA signals-terminators, codon-optimize them in order to make them BioBrick compatible and order them for de novo gene synthesis. Analytically:

• 1. Promoters
  • -CMV (strong constitutive promoter from the human cytomegalovirus)
  • -TRE (inducible tetracycline response element promoter)
  • -CAGop (strong hybrid CAG promoter followed by an intron with two LacO sites [2]
• 2. Protein Coding Regions
  • -rtTA (reverse tetracycline-controlled transactivator by fusing rTetR with VP16, utilized in Tet-On systems) [10]
  • -LacI (DNA-binding transcription factor that binds to LacO sites)
  • - Transcriptional repressor
  • -DsRed (Discosoma sp. red fluorescent protein)
  • -Apoptin
• 3.  polyA signals & terminators (include the motif AAUAAA which promotes both polyadenylation and termination)
  • -SV40 polyA
  • -rbGlob polyA*

*Next to the rbGlob polyA (upstream) there is a sequence coding for a synthetic miRNA, targeting a region of firefly luciferase [11]. It is a post-transcriptional repressor, named FF4.

As a result, using these Basic Parts, a large number of circuit topologies can be envisioned.

Dry Lab Input

There is an ongoing debate regarding the choice of using transcriptional repressors (LacI) that act as a roadblock to RNA Polymerase or posttranscriptional repressors (FF4 and other synthetic intronic miRNAs) that act by a completely different mechanism by mRNA degradation. Experimental evidence [4] support the combination of transcriptional and post-trascriptional repression as a boost in circuit performance. However, we wanted to proceed to a more detailed analysis regarding the selection of an appropriate repressor. You can check the results of this analysis here.

Integrated Design Output

Thanks to this combined analysis, we have designed the Biobricks to use in the experiments/simulations and made a strong case for the case-by-case use of a transcriptional or post-trascriptional repressor according to the miRNA dataset.

B. MODULAR CIRCUIT ASSEMBLY & OPTIMIZATION ALGORITHM

Wet Lab Input

We envisage pANDORRA as a cloning toolkit with standard parts (designed in the previous step) that can be easily applicable to the construction of a wide range of transcriptional/post-transcriptional synthetic circuits. In this endeavor, we focused on two aspects, interoperability and modularity, by integrating extensive technical feedback from experts in the field.

According to Benenson et al., 2012 [9], an effective molecular switch or gate is characterized by:

• The existence of a robust digital regime (that is, input levels that produce either a very low or a very high (saturated) output).
• Gate scalability, which is the capacity to receive an increasing number of inputs without dramatic design alterations.
• Composability, which is the capacity to operate together with other gates in parallel and/or in cascades in a predictable manner.

In order to add the aforementioned features to our toolkit and to aid in the cloning process, we created various Composite Parts, that can be used to create multi-layered classifiers. At first glance, the constructs have the following general structure:

Promoter + Protein Coding Region + BBa_K515105 + polyA signal & terminator

Analytically, BBa_K515105 consists of superfolder GFP (sfGFP), a very brightly fluorescent protein under the control of the bacterial constitutive promoter J23100 and is used as a reporter to simplify the validation process during cloning.

BBa_K515105 is flanked by two recognition sites for BbsI, a type IIS restriction enzyme and two annealing sites for a universal M13 forward & reverse primer. As type IIS restriction enzymes recognize asymmetric DNA sequences and cleave outside of their recognition sequence, they are central to our approach for fusing miRNA target sequences into the 3’-untranslated region, as described in the next section. Examples of these constructs include:

Notably, the output module can include either a coding region for a fluorescent protein, DsRed or a toxin, Apoptin (BBa_K1061001), a selective cancer cell killer derived from the Chicken Anemia Virus (CAV) known to cause p53-independent apoptosis in more than 70 human cancer cell lines while leaving normal cells unharmed [12]. The use of Apoptin instead of hbax [4] was a change incorporated for increased cytotoxicity and an additional safety fail-safe after discussing with Prof. JD Keasling and interpreting OSIRIS results.

The aforementioned Biobricks can be used to fuse any desired complementary miRNA binding site (target tandem repeats) into the 3’-untranslated region, between the BbsI restriction sites hardcoded into the coding and the terminator sequences to control the expression of each module by specific miRNAs. This process has the following requirements:

• (1) Composite Parts in the form of Promoter + Protein Coding Region + BBa_K515105 + polyA signal & terminator, BioBrick compatible format
• (2) A number of repeated miRNA binding sites in BioBrick compatible format
• (3) A set of standarized Extended Primers which are part of the MetaBrick Platform [13] that incorporate BsaI restriction sites to the Prefix-Suffix.

If the aforementioned requirements are fulfilled, then the following method is followed:

• (1) PCR amplification of the miRNA binding sites BioBricks
• (2) Digestion of the Promoter + Protein Coding Region + BBa_K515105 + polyA signal & terminator BioBricks with BbsI
• (3) Digestion of the PCR amplified miRNA binding sites BioBricks with BsaI
• (4) The digested products have compatible sticky ends. As a result, one example of the final constructs is:

Dry Lab Input

The multiple architectures that emerge from the pANDORRA toolkit can be evaluated by using the RNAi classifier design model in order to recognize the optimal one for every different classification task, for example for classifying Caco-2 cells. Moreover, in our model, the number of repeats for miRNA binding sites is evaluated. Another point that gets elucidated is the position of the TFF4 binding site. In our models, it is adjacent to the polyA terminator, as Haefliger et al., 2016 [14] showed that when the TFF4 is upstream of other binding sites, the downstream binding sites for other miRNAs are not affected by their miRNA mimics and thus optimal knockdown efficiency isn’t observed. You can check the results of this process here.

Integrated Design Output

By this back-and-forth approach, the optimal classifier can be computationally predicted and assembled from compartmentalized modules. The architectures that emerged through this exhaustive analysis have been characterized in 3 cell lines, Caco-2, HEK-293, A549. You can check the results of this process here.

Cancer-targeting and invasion module

Type I pilli, surface rod-shaped organelles of 7nm in diameter and 1μm in length, are the best studied system of bacterial adhesion [15].They are heteropolymers of four proteins with FimA being the main structural protein of the pilli, which polymerizes approximately 1000 times to form a right-handed helix that constitutes the main axis of the structure and includes smaller concentrations of FimG, FimF and FimH [16-18]. FimH is the functional component of the structure as it alone confers the ability to bind to a-D-mannose of various eukaryotic cells and is located at the tip and the shafts of the pilus, whereas FimF and FimG seem to be responsible for docking FimH to FimA [19-20]. To achieve selective adhesion to colorectal cancer cells using type I pilli we need to disrupt their natural ability to bind to a-D-mannose and introduce a mechanism to facilitate adhesion to CRC cells by a mannose-independent mechanism. A mutation in the 49th amino acid of FimH has been demonstrated to completely abolish mannose binding [21]. In addition, a small peptide called RPMrel has been identified through phage display assays due to its ability to bind to five different colorectal cancer cell lines as well as cancerous tissues obtained by biopsies and not to other kinds of cancer [22]. Taken together these two modifications perform both the functions specified for CRC selective binding and have been successfully used by previous iGEM teams, iGEM Harvard 2015 and iGEM Ankara 2016 to that end. We employed the same part BBa_K1850011 that was submitted by iGEM Harvard 2015, in a fimH KO strain. Having achieved selective adhesion to colorectal cancer cells we move on to the second half of our device, internalization and transference of genetic material. Strains of the bacteria E. coli can be modified so that they will express two key proteins : invasin and listeriolysin O [23-24]. Invasin gives the bacteria, the ability to enter epithelial and other non phagocytic cells [25-26]. Listeriolysin O, on the other hand, has to do with what happens to the bacteria after they enter the target-cell. This particular protein allows the bacteria to free themselves of the vesicle that was used for their phagocytosis, without damaging the plasmatic/cell membrane of the target-cell. This happens due to the low pH of the vesicle (~5.9-6) that is also the optimal pH range for the protein listeriolysin [27-28]. The modified strains of E. coli, through expressing these proteins are able to not only enter non phagocytic target-cells that express b1-integrins but also to transfer their load to them, through escaping the phagolysosome [23]. Finally, we aim to put invasin and listeriolysin O under quorum control, through the use of the lux genetic circuit of Vibrio fischeri, as this operon has been utilized to achieve cell-density dependent invasion. During our communication with the safety committee, we were extremely glad to hear that we submitted a thorough and analytical Check In form and the Committee Members advised us to focus on the safe implementation of our classifier module and then consider a transfer method. As a result, we switched our focus and put great effort to characterize the components of our classifier to demonstrate the while integrating feedback from the scientific community about the health risks of our conceptual anticancer agent, formulating what we term, a 5-STAR security system, which represents the culmination of our proposed modifications:

Level 1:
Bacteria preferentially colonize cancer tissue compared to the adjacent healthy
Level 2:
Bacteria are genetically modified to express a mutated fimH, carrying a colorectal cancer-binding peptide and a point-mutation abolishing its natural substrate binding affinity.
Level 3:
Bacteria express the invasion mechanism under the control of a lux operon, in order to achieve cell-density dependent invasion
Level 4:
The classifier circuit, transferred by the bacteria for the final bactofection goal, is meticulously designed in order to elicit a actuation (e.g. apoptosis) selectively in cancer cells
Level 5:
The therapeutic protein output of our classifier is Apoptin, which causes p53-independent apoptosis in more than 70 human cancer cell lines while leaving normal cells unharmed [12]

References

[1] Kitney, R., Calvert, J., Challis, R., Cooper, J., Elfick, A., Freemont, P. S., ... & Paterson, L. (2009). Synthetic Biology: scope, applications and implications. London: The Royal Academy of Engineering.
[2] Rinaudo, K., Bleris, L., Maddamsetti, R., Subramanian, S., Weiss, R., & Benenson, Y. (2007). A universal RNAi-based logic evaluator that operates in mammalian cells. Nature biotechnology, 25(7), 795-801.
[3] Soifer, H. S., Rossi, J. J., & Sætrom, P. (2007). MicroRNAs in disease and potential therapeutic applications. Molecular therapy, 15(12), 2070-2079.
[4] Xie, Z., Wroblewska, L., Prochazka, L., Weiss, R., & Benenson, Y. (2011). Multi-input RNAi-based logic circuit for identification of specific cancer cells. Science, 333(6047), 1307-1311.
[5] Miki, K., Endo, K., Takahashi, S., Funakoshi, S., Takei, I., Katayama, S., ... & Okubo, C. (2015). Efficient detection and purification of cell populations using synthetic microRNA switches. Cell Stem Cell, 16(6), 699-711.
[6] Li, Y., Jiang, Y., Chen, H., Liao, W., Li, Z., Weiss, R., & Xie, Z. (2015). Modular construction of mammalian gene circuits using TALE transcriptional repressors. Nature chemical biology, 11(3), 207-213.
[7] Sayeg, M.K., Weinberg, B.H., Cha, S.S., Goodloe, M., Wong, W.W., and Han, X. (2015). Rationally designed microRNA-based genetic classifiers target specific neurons in the brain. ACS Synth. Biol. 4, 788–795.
[8] Landgraf, P., Rusu, M., Sheridan, R., Sewer, A., Iovino, N., Aravin, A., ... & Lin, C. (2007). A mammalian microRNA expression atlas based on small RNA library sequencing. Cell, 129(7), 1401-1414.
[9] Benenson, Y. (2012). Biomolecular computing systems: principles, progress and potential. Nature Reviews Genetics, 13(7), 455-468.
[10] Gossen, M., Freundlieb, S., Bender, G., Muller, G., Hillen, W., & Bujard, H. (1995). Transcriptional activation by tetracyclines in mammalian cells. Science-AAAS-Weekly Paper Edition, 268(5218), 1766-1768.
[11] Leisner, M., Bleris, L., Lohmueller, J., Xie, Z., & Benenson, Y. (2010). Rationally designed logic integration of regulatory signals in mammalian cells. Nature nanotechnology, 5(9), 666-670.
[12] Backendorf, C., Visser, A. E., De Boer, A. G., Zimmerman, R., Visser, M., Voskamp, P., ... & Noteborn, M. (2008). Apoptin: therapeutic potential of an early sensor of carcinogenic transformation. Annu. Rev. Pharmacol. Toxicol., 48, 143-169.
[13] Damalas, S. (2017, June 9). The MetaBrick platform for DNA manipulation and standardization. Bridging Synthetic Biology standards for optimized interoperability.
[14] Haefliger, B., Prochazka, L., Angelici, B., & Benenson, Y. (2016). Precision multidimensional assay for high-throughput microRNA drug discovery. Nature communications, 7.
[15] Stentebjerg-Olesen, B., Chakraborty, T., & Klemm, P. (1999). Type 1 Fimbriation and Phase Switching in a Natural Escherichia coli fimB Null Strain, Nissle 1917. Journal of bacteriology, 181(24), 7470-7478.
[16] Brinton, C. C. (1965). The structure, function, synthesis and genetic control of bacterial pili and a molecular model for DNA and RNA transport in gram negative bacteria. Transactions of the New York Academy of Sciences, 27(8 Series II), 1003-1054.
[17] Klemm, P., & Christiansen, G. (1987). Three fim genes required for the regulation of length and mediation of adhesion of Escherichia coli type 1 fimbriae. Molecular and General Genetics MGG, 208(3), 439-445.
[18] Krogfelt, K. A., & Klemm, P. (1988). Investigation of minor components of Escherichia coli type 1 fimbriae: protein chemical and immunological aspects. Microbial pathogenesis, 4(3), 231-238.
[19] Jones, C. H., Pinkner, J. S., Roth, R., Heuser, J., Nicholes, A. V., Abraham, S. N., & Hultgren, S. J. (1995). FimH adhesin of type 1 pili is assembled into a fibrillar tip structure in the Enterobacteriaceae. Proceedings of the National Academy of Sciences, 92(6), 2081-2085.
[20] Krogfelt, K. A., Bergmans, H., & Klemm, P. (1990). Direct evidence that the FimH protein is the mannose-specific adhesin of Escherichia coli type 1 fimbriae. Infection and immunity, 58(6), 1995-1998.
[21] Schembri, M. A., Sokurenko, E. V., & Klemm, P. (2000). Functional flexibility of the FimH adhesin: insights from a random mutant library. Infection and immunity, 68(5), 2638-2646.
[22] Kelly, K. A., & Jones, D. A. (2003). Isolation of a colon tumor specific binding peptide using phage display selection. Neoplasia, 5(5), 437-444.
[23] Fajac, I., Grosse, S., Collombet, J. M., Thevenot, G., Goussard, S., Danel, C., & Grillot-Courvalin, C. (2004). Recombinant Escherichia coli as a gene delivery vector into airway epithelial cells. Journal of controlled release, 97(2), 371-381.
[24] Grillot-Courvalin, C., Goussard, S., Huetz, F., Ojcius, D. M., & Courvalin, P. (1998). Functional gene transfer from intracellular bacteria to mammalian cells. Nature biotechnology, 16(9), 862-866.
[25] Isberg, R. R., Voorhis, D. L., & Falkow, S. (1987). Identification of invasin: a protein that allows enteric bacteria to penetrate cultured mammalian cells. Cell, 50(5), 769-778.
[26] Pepe, J. C., & Miller, V. L. (1993). Yersinia enterocolitica invasin: a primary role in the initiation of infection. Proceedings of the National Academy of Sciences, 90(14), 6473-6477.
[27] McKAY, D. B., & Lu, C. Y. (1991). Listeriolysin as a virulence factor in Listeria monocytogenes infection of neonatal mice and murine decidual tissue. Infection and immunity, 59(11), 4286-4290.
[28] Dramsi, S., & Cossart, P. (2002). Listeriolysin O. The Journal of cell biology, 156(6), 943-946.