Engineering

Introduction

This year, our team has focused on the development of an individualized cell-based therapy that can revolutionize the field of regenerative medicine. We wanted to create an innovative approach for the enhancement of the healing process while preventing the various long-term complications following tissue injury. We focused on the restoration of normal tissue functions, especially in non-regenerative organs like neural, cardiac, and dermal tissue of the skin. After deep brainstorming, we chose burn wounds as a proof of concept for our dynamic platform, aiming to optimize burn wound care and minimize disfiguring scarring and contractures. Our vision is to change the life of all burn survivors through our stem cell-based, occlusive, nutritive gel of healing kit, consequently known as “SONG-H”.

Throughout this long journey, we encountered many challenges that regularly redefined the development of our platform. We relied on deep literature research, many experts integration, in silico software validation, and finally laboratory experimental validation. All these obstacles are discussed along our DBTL cycles which lead to essential modifications in our design.

This is a simple tutorial to handle the different stages of our DBTL cycles.

Concept Design

We started our engineering success with the preliminary challenges that we faced during the development of our concept. This development started early after our topic selection phase where we settled on the induction of healing in non-regenerative organs as our project for this year. We went through eight different iterations in order to come out with the safest, most effective, and dynamic concept design. This concept would undergo further modifications to produce our innovative stem cell-based occlusive nutritive gel of healing (SONG-H).

Iteration 1: Induction of healing in non-regenerative organs

Despite great global efforts seeking the improvement of healthcare systems, recent studies show the massive impact of irreversible organ damage on the increasing death rates worldwide.

A definitive technology is required to control the heavy burden of non-regenerative tissue injury. In our first iteration, we wanted to develop a new concept enhancing the healing ability of specific organs such as neural, myocardial, and renal tissues.

We started searching the literature for the latest technologies implemented in healing induction processes. These new techniques include stem cell therapy, biological agents, 3D printing, surgical procedures, and gene therapy.

To come up with the most optimized platform for tissue healing, we needed to dig deeper into the benefits and drawbacks of each one of the previously mentioned techniques.

Iteration 2: Induction of healing through effector genes and proteins

We wanted to induce self-renewal functions in non-regenerative tissues through a dynamic platform. This technology would be based on the potential of synthetic biology to yield new regenerative functions in irreversibly damaged organs.

We needed to find a delivery system capable of safe transportation of our effector proteins to its target tissues. We concluded that genetically engineered bacteria would be an interesting option to express and deliver our therapeutic agents.

Unfortunately, our literature research proved that bacterial delivery had multiple limitations such as the difficulty to carry large sequences of effector proteins necessary for the healing mechanism. In addition, bacterial products had many unwanted side effects including off-targeting effects, allergic reactions, and immune system suppression.

We discovered that bacterial products are not reliable as a delivery system for our platform due to their undesired limitations and unpreventable drawbacks. Hence, we needed to find another method to ensure safe and effective transportation of our effector proteins.

Figure(1) shows the limitations of bacterial delivery.

Iteration 3: Cell-based gene therapy in regenerative medicine

Technological advancements in regenerative medicine are headed towards the implementation of stem cell-based therapies in the tissue healing process. This recent technology is nowadays considered the core of several therapeutic modalities like cell-based gene therapy.

We wanted to benefit from stem cells' innate functions including tissue regeneration and immune modulation. Adding to that, the potential of stem cells to act as a prominent vehicle in intercellular communication through the delivery of different forms of therapeutic molecules such as DNA, RNA, and proteins.

After the consultation of many experts, we confirmed that stem cells would be a suitable carrier for our therapeutic agents. Taking in consideration, the features of stem cells regarding cargo flexibility, dynamic response, and target specificity. These unique features made stem cells the best option for our design compared to other available regenerative technologies.

Despite the great benefits of stem cells’ integration into our design, it would still need multiple modifications to prevent any unwanted side effects like overshooting events including thrombosis, fibrosis, and tumor formation. Additionally, we must overcome future limitations ensuring specificity and sensitivity of our platform according to the severity of the injury.

Figure(2) illustrates the features of stem cell based delivery

Iteration 4: Mesenchymal Stem Cells (MSCs) role in regenerative medicine

In this iteration, we needed to select a certain type of stem cell to be implemented in our platform. This specific type should meet the following criteria:

• Differentiation ability into multiple cell lines
• Accessibility and ease of extraction of this specific stem cell line
• Affordability of the stem cell line used to ensure the cost-effectiveness of our platform
• Lower incidence of immune rejection compared to other types
• Previous experimental and clinical validation in similar regenerative approaches

After meeting with Dr. Zomer, we decided to choose MSCs as a core element in our design. This specific type of stem cells could successfully fulfill the previously mentioned criteria. This choice also relied on the multiple characteristics of MSCs which include immune-regulatory functions, exosome-based intercellular communication, and targeted delivery to specific organs. All these features made it hard to choose between the integration of whole MSCs or only their purified secretomes in our design.

To perform effective transgene delivery, we reviewed several research papers addressing our previously mentioned concern. These papers compared the performance of the two previous options according to their regenerative functions, transgene capacity, and the rate of immune rejection. Therefore, we discovered that using whole MSCs guarantees an immune modulatory effect which cannot be achieved by exosomes alone. However, Exosomes offer indirect intracellular communication necessary for the transportation of our effector genes.

We concluded that combining both approaches by using the whole functional cell and its extracellular vesicles would achieve the best regenerative outcomes.

Figure(3) shows the increased regenerative function when using both MSCs and its exosomes.

Iteration 5: Selection of a therapeutic agent to induce tissue regeneration

After our meeting with Dr. Mohamed Abou-el-Enein, we discovered the necessity of the optimization of the regenerative functions of our MSCs. Consequently, Dr. Mohamed advised us to look up the literature for effector molecules regulating the internal pathway responsible for stem cell proliferation, differentiation, and regeneration.

Through deep literature research, we found a certain agent that would fit ideally in our design, compared to other candidates. This agent is called Yes-Associated Protein 1 (YAP-1), which is a transcription activator regulating stem cells' regenerative functions.

By manipulating YAP-1 activity, several studies managed to control stem cell differentiation and promote tissue regeneration. These studies reported healing enhancement in various non-regenerative tissues like cardiac, neural, hepatic, and skin healing.

Featuring YAP-1 in our design helped us overcome the limitations of MSCs regeneration under different circumstances of tissue injury. However, uncontrolled overexpression of YAP-1 may carry relative risks including tissue hyper-proliferation sometimes leading to tumor formation. These major safety concerns must be handled throughout upcoming iterations in order to implement YAP-1 into the final design of our platform.

Figure(4) illustrates the risk of uncontrolled proliferation associated with YAP-1.

Iteration 6: Selection of target organ for the proof of concept

To narrow the scope of our scientific validation, we needed to choose a specific organ upon which we could prove the effectiveness of our approach as a platform for tissue repair.

Following the recommendations of our PIs, we decided to target skin wounds as an initial step towards building a personalized modular self-contained regenerative platform for different tissue injuries. This choice was based upon the accessibility and simplicity of the skin as one of the largest non-regenerative organs in the body.

According to many recent studies, there are several types of skin injury that could be targeted by our regenerative platform. Following our constant vision to improve the quality of life of all patients, we decided to choose a medical accident that endangers the lives of millions of people around the world every year, which is burn injuries. These tragic accidents lack efficient management even with all the technological advancements in different medical fields. In addition, burns present a heavy burden on the healthcare system, economics, and the environment globally. This makes burns the ideal target of our project.

After visiting many burn care centers and consulting plastic surgeons about burns’ pathophysiology, we discovered some limitations for the topical application of our platform on skin to induce wound healing in case of burn injuries. These limitations should be addressed in upcoming iterations.

Iteration 7: Mode of delivery of our platform

The topical application of MSCs at the injured site would make them vulnerable due to different microenvironment stresses. These injured-site hazards would cause stem cells’ Senescence, which is a state of cellular aging characterized by a decline in cellular regenerative abilities.

To overcome the previously mentioned barriers in our design, we consulted Dr. Galal who recommended introducing our MSCs into a protective scaffold which provides structural nutritive support to enhance stem cells’ resilience in wound microenvironment.

We came through many options from which we chose the Hydrogel Scaffold as the mode of delivery of our topical therapeutic technology of healing. Hydrogel offers several important features that reinforce the local application of MSCs on injured skin, as it ensures favorable conditions by protecting MSCs from environmental stresses, waste removal, and also promotion of specific cell behavior required for tissue development and differentiation.

Despite the integration of such an effective mode of delivery in our design, Hydrogel Scaffold would still need some important modifications in order to ensure accurate implantation of MSCs within injured skin.

Figure(5) shows the enhanced function of MSCs after using hydrogel as a method of delivery.

Iteration 8: Hydrogel Scaffold enhancement

We wanted to facilitate the communication between our MSCs and the exterior microenvironment at the wound site. This modification should mediate proper diffusion of different agents across the microscopic pores of Hydrogel.

We reviewed relevant papers addressing alteration in Hydrogel characteristics to modulate its viscosity and its pores’ size. As a result, we could ensure sufficient communication between the Hydrogel’s MSCs occupying the wound site and the surrounding microenvironment.

We selected a certain type of Hydrogel composed of Hyaluronic Acid (HA) which is a natural polymer characterized by adequate pore size and moderate viscosity. Hyaluronic Acid also has a significant role in angiogenesis and normal tissue remodeling. Thus, HA Hydrogel would be an ideal option enhancing the therapeutic efficiency of our stem cell-based therapy.

Despite all our efforts towards the optimization of our concept design, we need to take in consideration major risks that may interrupt the healing process especially infections and hyper inflammatory states. Hence, we must incorporate multiple therapeutic substances like antimicrobials, anti-inflammatory, and anti-oxidants to guarantee safe delivery of our platform. This is considered the future plan for our project. For now, we are focusing our efforts on the promotion of skin regeneration especially in burn injuries.

Figure(6) illustrates the properties of Hyaluronic acid .

Platform Design

In the second part of our project engineering, we integrate synthetic biology tools to enhance the regenerative abilities of MSCs included in our concept design. Through this integration, we aim to transfer our scientific concept into a real-world therapeutic platform. To optimize the design of this platform, we faced many challenges that are going to be discussed in details in the upcoming iterations highlighting different dry lab and wet lab progress to overcome every challenge. For more details regarding our platform performance, please refer to our Dry lab and results pages.

Iteration 9: Conditioned expression of YAP-1

To address major safety concerns previously mentioned during our concept engineering, we realized that we need to control the expression of our effector transgene “YAP-1”. Therefore, we adopted a modular synthetic receptor known as dCas9-TF Syn-RTK. This receptor can achieve conditioned expression of multiple target genes based on CRISPR dCas technology for signal transduction. It is also important to mention the way our signal transduction model functions. Starting from the dimerization of the receptor which promotes cleavage of specific peptide sequences recognized by protease enzyme and known as Tobacco Etch Virus (TEV). Finally, reaching the signal propagation to the nucleus is mediated by the guide RNA (gRNA) sequence directing dCas9 activity towards the gene of interest to regulate its expression.

Our synthetic receptor will be built to induce the conditioned expression of YAP-1. Thus, this receptor will be activated by vascular endothelial growth factor (VEGF) which is an inflammatory mediator responsible for promoting angiogenesis at the site of skin injury. Moreover, some studies reported that VEGF concentration can reflect the severity of the injury which could enable us to induce the expression of YAP-1 in proportion to VEGF concentration.

The validation of this initial version of the receptor is based on experimental characterization obtained from the original paper. This paper revealed that the initial design has high ligand non-dependent activity that would interfere with the safety and specificity of our platform.

We needed to enhance the transition of the receptor from the ON to the OFF state. Therefore, we had to carry out some modifications to our platform’s design. These required modifications should make our platform more dynamic, specific, and sensitive while meeting its therapeutic target.

Figure(7) shows that once VEGF binds to the receptor and dimerization occurs it leads to cleavage of TCS by TEV protease to release dCas9 to reach YAP-1 encoding region.

Iteration 10: First trial to reduce basal activity through dCas splitting

To reduce the basal activity of the receptor’s initial design, we performed several modifications on the signal transduction model (dCas9-TF), as we split its amino acid sequence into N-terminal fragment and C-terminal fragment. This splitting concept is the core of the design of this iteration.

We incorporated the previous design in our platform by cloning each split into one chain of the receptor. The assembly of these splits to form an effector complex will be mediated through dimerization of both chains of the receptor. This implementation would reduce off-state background noise within our system.

We confirmed the ability of two separated fragments to be assembled following receptor dimerization. This was done through assessing the binding stability between both domains using a Prodigy Haddock software tool. Additionally, we used alpha fold 3 tool for visualization of the final construct. We have performed several splitting following the same mechanism on the protein sequence in order to select the most stable splitting variant. We calculated binding stability using Prodigy Haddock online tool.For more details, check our Dry lab page.

Moreover, the effect of the splitting modifications on signal transduction were measured through detecting the activity of the new version of dCas9(C/N)-TF Syn-VEGFR in the absence and presence of VEGFA comparing the final results with the previous iteration design. Receptor activity was detected through analyzing the EYFP activation score by Flow Cytometry (FC).

We found that our previous modifications managed to reduce the basal activity, but could not provide us with the expected safety level. Therefore, we need to make further alterations in our platform to achieve more optimized results.

Figure(8) shows the splitting of dCas9 for reduction of basal activity.

Iteration 11: Second trial to reduce basal activity through TEV integration and splitting

In this second trial, we addressed one of the causes of increased basal activity of our receptor. We assumed that TEV spontaneous uncontrolled activity would be responsible for the leakage in our system. As a result, we focused on regulating TEV activity by carrying out several modifications including TEV integration and splitting within the receptor.

In this iteration, we expect to reach a stable self-contained system with low off-state background noise. Therefore, we applied a new technique integrating TEV protease into one of chains of dCas9(C/N)-TF Syn-VEGFR. We engineered two forms of the receptor integrating TEV within the receptor’s structure. In the first one, the whole TEV protease enzyme was cloned into one chain of the receptor. While in the second form, we split TEV into two fragments and grafted each one of them into a chain of the receptor.

We used alpha fold to display the interaction between the N and C fragments. Moreover, we assessed the binding stability of the two splits by using Prodigy Haddock, an online software tool.

Through experimental testing, It was found that the split TEV version of the receptor displayed lower basal activity levels in comparison with the previous model.

Despite the reduction of basal activity of the receptor, this implemented system still displays intolerable levels of uncontrolled off-state activity.

Figure(9) illustrates TEV splitting for further reduction of basal activity.

Iteration 12: Selection of the most stable pattern on the external domains

To detect VEGFA's presence within the external environment of our MSCs, we chose VEGFR as our signal sensing domain. VEGFR is divided into many different types from which we selected VEGFR-1 and VEGFR-2 as the external domains of our system. This choice was based on their higher affinity to VEGFA compared to other types.

To choose the best pattern for our signal sensing domain, we built four different constructs of the receptor according to systematic variations in internal and external domains between different constructs. The first construct is formed of two chains carrying VEGFR-1 as signal sensing domain. The second construct is formed of two chains carrying VEGFR-2 as signal sensing domain. The third construct consists of two different chains; the first one is VEGFR-1 with dCas9(N), the second one is VEGFR-2 with dCas9(C)-TF. The last construct is the opposite of the third one; with two different chains, the first one is VEGFR-2 with dCas9(N), and the second one is VEGFR-1 with dCas9(C)-TF.

• VEGF-R2, N-TEV, NES, TCS (Q, L), HA, dCas9(N),mCherry BBa_K5036028
• VEGF-R1, C-TEV, NLS, TCS(Q,L), HA,dCas9(C),VP64,GFP BBa_K5036029
• VEGF-R2, N-TEV, NES, TCS (Q, G), HA, dCas9(N),mCherry BBa_K5036030
• dVEGF-R1, C-TEV, NLS, TCS(Q,G),HA,dCas9(N),VP64,GFP BBa_K5036031
• VEGF-R2, N-TEV, NES, TCS (Q, L), HA, dCas9(C),mCherry BBa_K5036032
• VEGF-R1, N-TEV, NLS, TCS(Q,G),HA,dCas9(C),VP64,GFP BBa_K5036033
• VEGF-R2, C-TEV, NES, TCS (Q, L), HA, dCas9(N),mCherry BBa_K5036034

We tested the binding stability of the complex resulting from ligand-dependent dimerization using a prodigy Haddock software tool. Additionally, we used alpha fold 3 tool for visualization of the final construct. Finally, we concluded that the last construct with VEGFR-2/1 has the highest level of binding stability. For more details, check our Dry lab page.

Through experimental validation, the last construct had the EYFP activation score for signal transduction which indicates its high sensitivity to VEGFA.

Regarding the latest design, we made a new assumption clarifying the cause behind the ligand non-dependent activity leakage in our system. This assumption states that there is a spontaneous dimerization between our synthetic receptor chains and native VEGFR on the cell membrane. Thus, we need to test this hypothesis in upcoming iterations.

Iteration 13: Tailoring of the receptor activity

In this iteration, we wanted to coordinate the signal transduction simultaneously with the dimerization of only two chains within our system dCas9(C/N)-TF Syn-VEGFR-1/2. Through this design, we could avoid the dimerization of our receptor with native VEGFR.

We constructed multiple mutated variants of TEV cleavage sites (TCS) to alter the affinity of TEV protease reaching its specific peptide sequence. As a result, the cleavage rate would be either significantly raised or severely suppressed. After this construction, we needed to evaluate the suitable pattern of recently grafted mutated variants of TCS within the receptor in order to select the ideal complex showing the least basal activity.

We tested the different constructs mentioned above which were generated by DynaMut online software tool. We also measured their binding stability to TEV protease by Prodigy Haddock online software tool. We concluded that the software-induced mutation significantly altered the binding affinity of TCS to TEV protease. We could consequently make our receptor more sensitive to the ligand concentration, which allowed the receptor to respond in a dose-sensitive manner according to the lowest concentration of the biomarker in different biological conditions.

Despite the high signal transduction activity of the receptor that is mediated by CRISPR dCas technology, off-targeting events remain a serious concern in our design so far.

Figure(10) shows variants of TCS produced in order to alter the activity of TEV protease for finding the lowest basal activity.

Iteration 14: Testing different transcription activators

In this iteration, we had to identify the cut value which we should consider safe and effective overexpression for the gene of interest. Regarding our project target gene YAP-1 we found that it can reach up to fifty-fold above the normal levels in equivalent conditions without any reported complication. As a result, we decided to incorporate multiple transcription activator models and select the most suitable construct.

We built three different constructs of transcription activator models. and we linked their activity to GFP or mcherry to measure their different activation scores.

• VEGF-R1, C-TEV, NLS, TCS(Q,G),HA, dCas9(C),VP64,GFP BBa_K5036026
• VEGF-R1, C-TEV, NLS, TCS(Q,G),HA,dCas9(C),VP64,GAL4,GFPBBa_K5036027
• UAS Trans CMV enhancerBBa_K5036021

• Modeling

Initially, we predicted their transcription activation potential through mathematical modeling. This simulation was performed based on calculated parameters reported in works of literature that reflect their transcription power.

The previous constructs were assessed by measuring the GFP activation score and the result interpretation indicates that the best candidate is CRISPR/dCas9(vp64+GAL4)-UAS trans-CMV enhancer, as it showed elevation of the GFP activation score 16 fold more than the control in the absence of VEGFA.

Despite the high activation score of CRISPR/dCas9(vp64+GAL4)-UAS trans-CMV, we had concerns regarding the off-targeting effect of CRISPR technology that may interfere with our approach safety.

Figure(11) shows the multiple transcription factors used along dCas9 for effective overexpression of YAP-1 without complications.

Iteration 15: Adding conditioned activity to the transfected YAP-1

This iteration discusses how we choose the best gRNA targeting the YAP-1 gene located within chromosome 11 to induce its expression. Moreover, we took into concern the off-targeting effects. Therefore, we generated a library of gRNA targeting YAP-1 to evaluate their thermodynamic qualities, such as Gibbs free energy, and GC content to choose the best gRNA candidate.

We generated a library of gRNAs by using CRISPR ON software online tool. This gRNA was designed based on the Nanog enhancer sequence that is located upstream of the YAP-1 gene.

We assessed the gRNA thermodynamic qualities, GC content, and specificity of the target gene through multiple online software Vienna RNA packages including RNAfold, RNAup, and RNAeval. Moreover, we assessed the off-targeting event through applying the best candidate from our gRNA library to an online software tool known as CRISPR OFF.

Despite our efforts to select the best gRNA with minimal off-targeting effects. We still can’t make sure of the software tool's predicted results. Moreover, we unfortunately can’t validate this process experimentally.

Figure(12) shows the gRNA structure generated by CRISPR ON software tool.

Iteration 16: Adding conditioned activity to the transfected YAP-1

To enhance the regenerative function of the native cells within the wound microenvironment, we decided to take advantage of MSCs’ exosomes to transfect a modified version of YAP-1 to neighboring cells. The implementation of this concept would boost the proliferation and differentiation of injured tissue benefiting from the YAP-1 effect. Consequently, we could enhance wound closure which is the main idea of our platform.

We adopted a new technology to control our transgene expression levels, this technology is known as Translation Initiation Device (TID). TID is based on reprogramming one of the native mechanisms responsible for mRNA translation regulation. This native mechanism is called Closed Loop Model. For more details on this model, check the Design page. The implementation of TID in our platform provided us with a conditioned expression of YAP-1 regarding its translation level. This conditioned expression is based on sensing specific biomarkers that would transform TID into its active form. Thus, we chose MMP9 as our intracellular marker reflecting tissue injury.

• Dry lab

We assessed the binding stability between TID different domains through four successive steps. Firstly we assessed the binding affinity of different nanobodies to the initiator biomarker (MMP9). The binding stability was evaluated by the Prodigy haddock online software tool and the highest score candidates were combined with TID’s RNA binding proteins (MCP and NPS3). Besides that, we measured the binding stability of MCP and NSP3A to their target site in the absence and presence of the combined Nanobodies to detect any functional alteration in MCP and NSP3A.

MCP,MMP9 Nanobody1	BBa_K5036045
MCP, MMP9 Nanobody2	BBa_K5036046
MCP, MMP9 Nanobody3	BBa_K5036047
MMP9Nanobody1,NSP3A	BBa_K5036048
MMP9Nanobody2,NSP3A	BBa_K5036049
MMP9Nanobody3,NSP3A	BBa_K5036050

• Model

We have used the previously measured kinetics to simulate all possible patterns of the TID complex to choose the most stable pattern.

TID activities were tested through the measurement of BFP activation score in the absence and presence of the initiator biomarker. The results confirmed the efficacy of TID in linking the translation activity of effector gene to the biomarker of interest. In the presence of the initiator biomarker, the reporter gene activator scored high levels of device activity. In contrast, the reporter gene activator score displayed minimal activity in absence of the initiator biomarker.

After the successful integration of TID in our platform, we have relatively achieved conditioned expression of YAP-1 in response to the presence of MMP9. However, the fact that our TID system displays minimal basal activity is considered unacceptable according to our safety measures.

Figure(13) shows the circularization of TID in presence of MMP-9 causing its activation leading to translation of YAP-1 and tissue proliferation .

Iteration 17: Significance of Poly-A tail removal

After reviewing the literature, we discovered that the Poly-A tail could be responsible for the OFF-state basal activity of TID, as the Poly-A tail mediates spontaneous ligand non-dependent circulation of our effector gene transcript. Therefore, a native closed loop model was formed and carried out the translation of our transgene regardless of the absence of the initiator biomarker.

To avoid the possible leakage caused by the Poly-A tail, we decided to excise it from our effector transgene mRNA, bearing in mind its essential role in mRNA structure stability. This is why, we added HammerHead ribozyme (HHR) (BBa_K5036039) to the 3`end of the YAP-1 mRNA upstream to the Poly-A tail to catalyze a self-cleavage reaction. This reaction would remove the Poly-A tail from YAP-1 only after stable formation of its mRNA.

We assessed the stability of TID transcript by using RNA fold online tool.We evaluated Gibbs free energy in the absence and presence of poly-A tail to ensure the stability of TID’s RNA following poly-A tail excision.

The performance of the previously mentioned construct was assessed through measuring the activity of TID in the absence of the initiator biomarker compared to the initial version which contained the Poly-A tail. This has shown a dramatic decrease in basal activity within the new modified version of TID.

To ensure optimum performance of TID, we had to make TID more concentration-sensitive by linking its activity to the initiator biomarker levels.

Figure(14) shows the HHR inducing self cleavage of Poly-A tail for minimzing basal activity of the mRNA (TID).

Iteration 18: Sensitivity modulation of our TID system

To reach a dose-dependent therapeutic response of our TID, we tuned our device to respond accurately to its corresponding initiator biomarkers.

According to several papers, the RNA binding protein -known as membrane coat protein (MCP)- changes in the binding affinity to MS2, which is a specific aptameric stem-loop for MCP. This change is caused by an alteration in the MS2 copy numbers (×N). Therefore, we tuned the TID activity by changing the copy number of MS2 at the 3`end of YAP-1 mRNA. Additionally, we built different constructs with various MS2 copy numbers (×N) including:

YAP-1-MS2(×8)	BBa_K5036025
YAP-1-MS2(×12)	BBa_K5036024
YAP-1-MS2(×16)	BBa_K5036023
YAP-1-MS2(×24)	BBa_K5036019

It was found that there is a directly proportionated relation between MS2 (×N) and TID sensitivity. The higher copy number of MS2 tested the higher reporter gene activation scored.

Thanks to this final iteration, our platform’s design has reached its peak in terms of safety, sensitivity, and specificity to achieve effective dose-dependent therapeutic response in case of tissue injury. This leads to optimum wound closure and active restoration of skin integrity, taking in consideration the physiological and cosmetic aspects of wound healing.