iGEM 2023 — Team bielefeld-cebitec

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# Our project Notebook

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* [Introduction](#introduction)
* [pASTERISK](#pasterisk)
* [EGFR sensors](#egfr)
* [IDH1 sensor](#idh1)
* [Sensor cloning](#cloning)
* [Results](#results)
* [References](#references)

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# Engineering

## Introduction

---

We set out to create a novel therapeutic for treating
[glioma](https://2023.igem.wiki/bielefeld-cebitec/prj_background) and have decided on an mRNA-based
approach to selectively kill tumor cells by expressing a toxic protein. This requires some kind of mechanism to
differentiate between healthy cells and tumor cells, and to limit the expression of the toxic payload
accordingly to the cell type. To achieve this, we utilized the DART VADAR technology to create mRNA
sensors that couple the detection of specific mRNA molecules to the conditional translation of a payload
gene.1

After deciding on the mode of action, we had to find suitable targets for the sensors, i.e. glioma-specific
mRNA transcripts that could be detected by our therapeutic mRNA. Since there are different types of glioma
with different expression profiles, we decided on two different mRNA molecules as targets after a thorough
literature research: the wild-type transcript of the epidermal growth factor receptor (EGFR), which is
commonly overexpressed in glioblastoma, and the mutant IDH1(p.R132H) transcript of the isocitrate dehydrogenase
1 gene. This mutation is observed in about 90% of astrocytoma and oligodendroglioma cases.2,3
We obtained consensus mRNA sequences of both the human EGFR and the IDH1 transcripts from the NCBI RefSeq
database (accession numbers NM\_005228.5 and NM\_005896.4), and modified the IDH1 transcript accordingly to
carry the p.R132H mutation (CGT>CAT at codon 132).2

## Engineering the pASTERISK expression plasmids

---

We envisioned our therapeutics to be transfectable mRNA packaged inside lipid nanoparticles or liposomes.
Hence, we had to find a way to produce our mRNA therapeutics in a way that would be applicable in a real
pharmaceutical production setting. We discussed the bulk expression of mRNA in E. coli with subsequent
purification due to the low cost of this approach, but dismissed it because of the risks of introducing too
many impurities. Therefore, we opted for in vitro transcription as a means of mRNA production instead.

Our first task was to create a suitable plasmid containing the open reading frame (ORF) of our therapeutic
construct under the control of a T7 promoter, so it could be linearized and transcribed in vitro using T7
polymerase. We used the published DART VADAR plasmid as a starting point for designing our own plasmid, that
we termed pASTERISK. For the backbone, we chose the commonly used pSB1C3 plasmid. We now had to design the ORF
that would encode our therapeutic construct under the control of a T7 promoter, minus the sensor sequence,
because these should be cloned into the plasmid afterwards for each target. Figure 1 shows the final design of
this construct. In fact, we designed to versions of this construct, called ASTERISK-HBA1 and ASTERISK-HBB, which we provide as
[BioBrick parts](https://2023.igem.wiki/bielefeld-cebitec/parts) to the parts registry.
The difference between these lies in the 5'- and 3'-untranslated regions (UTRs). ASTERISK-HBA1 contains UTRs
from the human alpha globin 1 gene (HBA1), while ASTERISK-HBB contains UTRs from the human beta globin
gene (HBB). We decided to try out two different UTR combinations, with the goal of improving mRNA stability.
These UTRs were chosen due to their use in mRNA vaccines and stabilizing effects on mRNA.4

![ASTERISK-HBA1 / ASTERISK-HBB + T7 promoter and terminator](https://static.igem.wiki/teams/4877/wiki/engineering/engineering-01a.svg)

**Figure 1:**
Circuit diagram of the ASTERISK-HBA1 and ASTERISK-HBB parts. Both of these constructs were cloned into pSB1C3 plasmids.

We designed the ASTERISK-HBA1 and ASTERISK-HBB parts by using the published DART VADAR ORF as a
starting point, and introduced many changes and modifications. The ORF starts with either an HBA1 or HBB 5'UTR,
followed by a Kozak sequence and an mCherry transfection marker that we substituted for the original TagBFP
marker. A so-called 2A peptide (a 'self-cleaving peptide') separates the next segment of the ORF from the
transfection marker by interrupting the peptide linkage during ribosomal translation. What follows is the
sensor cloning site, in which a 123 bp sensor sequence can be cloned into by HindIII digestion followed by
Gibson assembly. Another 2A peptide separates the cloning site from the payload sequence. We introduced
additional SphI and SalI restriction sites flanking the payload sequence, so that it can be easily changed
and modified. We used the original mNeonGreen marker as a surrogate payload for our experiments. Another 2A
peptide isolates the payload from the final coding sequence, MCP-ADAR2dd. This fusion protein, consisting of
the MS2 bacteriophage major coat protein (MCP) and the deaminase domain of ADAR2 (ADAR2dd), is responsible for
driving the positive sensor activation feedback loop that characterizes the DART VADAR circuit. Finally, we
replaced the original 3'UTR with an HBA1 or HBB 3'UTR and removed the PolyA-signal, because we wanted to add
the PolyA-tail in a subsequent step after IVT. Throughout the entire original ORF, there were several illegal
restriction sites that we silently mutated for RFC10 BioBrick conformity.

![pASTERISK-HBA1 plasmid map](https://static.igem.wiki/teams/4877/wiki/engineering/engineering-02a.svg)

**Figure 2:**
Plasmid map of the pASTERISK-HBA1 plasmid. The plasmid was constructed by cloning the ASTERISK-HBA1 part
into a pSB1C3 backbone.

Both versions of these plasmids – pSB1C3 containing either the ASTERISK-HBA1 or ASTERISK-HBB part – were
successfully cloned, giving rise to the pASTERISK-HBA1 and pASTERISK-HBB plasmids, respectively (Figure 2).
We now had suitable plasmids for cloning our sensor inserts into and for producing therapeutic mRNA using IVT.
The next step was to design the sensor inserts.

## Engineering the EGFR sensors

---

In order to selectively kill glioblastoma cells that are overexpressing the EGFR gene, the fraction of
activated sensors has to be high enough in EGFR-overexpressing cells to induce lethality, but low enough
in healthy cells to leave them unharmed. Assuming that higher levels of EGFR expression lead to a higher
fraction of activated sensor molecules, which in turn leads to a higher amount of toxic protein expression,
the tumor cells should ideally cross the lethality threshold due to their EGFR overexpression, while healthy
cells do not.

![Good and bad EGFR sensor features](https://static.igem.wiki/teams/4877/wiki/engineering/engineering-03a.svg)

**Figure 3:**
Schematic explanation of good and bad EGFR sensor features. A bad EGFR sensor would be either too insensitive
and thus neither healthy nor cancer cells would be killed by the toxic payload, or too sensitive, which means
both cell types would be killed. A good EGFR sensor responds to EGFR overexpression with lethality, but is not
lethal at normal expression levels.

First, we wanted to find out how we could tweak the different properties of the sensors to change their dynamic
range and efficiency, that is, their propensity to become activated on hybridization to their target mRNA.
Ideally, we wanted to find ways to lower the sensor efficiency. This seems counterintuitive, but as explained,
the sensor should behave in a way that only a small fraction gets activated at wild-type EGFR expression
levels. If the sensor was not efficient enough to kill the tumor cells, one could increase the dosage or choose
a more potent toxin as payload.

We chose three variables that would be independently varied to observe their effect on the sensor efficiency:

* The type of central nucleotide triplet targeted by the sensor
* The amount of matching/mismatching bases between the sensor and the trigger sequence
* The amount of in-frame stop-codons that had to be edited by ADAR

This led to the design of four EGFR sensors, as shown in table 1. Using our
[DVSensor software](https://2023.igem.wiki/bielefeld-cebitec/software),
we first generated all possible sensors against the EGFR transcript in the 3'UTR region. We picked two sensors,
EGFR\_CAA5991 (targeting a CAA triplet) and EGFR\_CCA7127 (targeting a CCA triplet), to compare the effect of
targeting different triplets (CAA vs CCA). Next, we chose EGFR\_CCA7127 as a template for designing two more
sensors, EGFR\_CCA7127\_MM and EGFR\_CCA7127\_DS. In the EGFR\_CCA7127\_MM sensor, 20 mismatches were introduced to
investigate the effect of reducing the affinity of the trigger to the sensor. The EGFR\_CCA7127\_DS sensor
contains an additional stop-codon which is in-frame with the central stop-codon, which means an additional
editing step by ADAR is required to activate the sensor.

Importantly, all four sensors that were designed in this step have the same GC-content of 52.8%, which
eliminates GC-bias when comparing the sensor efficiencies, and had no other in-frame start- or stop-codons
that had to be removed.

**Table 1:**
EGFR sensors designed in the first engineering cycle iteration. The two sensors EGFR\_CAA5991 and
EGFR\_CCA7127 were generated using our own software. EGFR\_CCA7127 was used as a template for designing two
more sensors, EGFR\_CCA7127\_DS and EGFR\_CCA7127\_MM, with additional stop-codons and mismatches, respectively.
Depicted are the cDNA sequences of the different EGFR sensors hybridized to their respective trigger
sequences (portions of the EGFR transcript).

| Sensor | Features | Diagram |
| EGFR\_CAA5991 | Targets the CAA 5991 triplet in the 3'UTR. | ![Sensor EGFR_CAA5991](https://static.igem.wiki/teams/4877/wiki/engineering/engineering-04a.png) |
| EGFR\_CCA7127 | Targets the CCA 7127 triplet in the 3'UTR. | ![Sensor EGFR_CCA7127](https://static.igem.wiki/teams/4877/wiki/engineering/engineering-05a.png) |
| EGFR\_CCA7127\_DS | Targets the CCA 7127 triplet in the 3'UTR, with an additional stop-codon opposite to a CAA triplet. | ![Sensor EGFR_CCA7127_DS](https://static.igem.wiki/teams/4877/wiki/engineering/engineering-06a.png) |
| EGFR\_CCA7127\_MM | Targets the CCA 7127 triplet in the 3'UTR, with 20 mismatching bases introduced. | ![Sensor EGFR_CCA7127_MM](https://static.igem.wiki/teams/4877/wiki/engineering/engineering-07a.png) |

## Designing the mutant IDH1 sensor

---

The common p.R132H hot spot mutation in the IDH1 gene is caused by a CGT>CAT codon change. Importantly, this
change leads to the introduction of a novel UCA triplet in the mRNA which can be efficiently targeted using an
appropriate sensor (see Fig. 4). Therefore, we set out to construct a sensor targeting this novel triplet in
the mutant IDH1 transcript. The sensor should in theory only get activated in cells carrying the mutation,
thereby ensuring the necessary tumor specificity. As has been demonstrated by Qu et al., the UCG triplet,
which is present in the wildtype transcript, has a neglectable efficiency for inducing an A-to-I editing event
if it lies opposite to a UAG stop codon.5 The UCA triplet in the mutant transcript however, efficiently induces
A-to-I editing and thereby would activate the sensor.5
Figure 5 shows our final design of the mutant IDH1 sensor.

![Wildtype and mutant p.R132H IDH1 transcripts](https://static.igem.wiki/teams/4877/wiki/engineering/engineering-08a.png)

**Figure 4:**
Sections of the the wildtype and the mutant IDH1 transcript with the G>A point mutation. The mutation changes
a UCG triplet into a UCA triplet, which can efficiently activate a sensor by inducing A-to-I editing of a
UAG stop-codon.

![Mutant IDH1 sensor design](https://static.igem.wiki/teams/4877/wiki/engineering/engineering-09a.png)

**Figure 5:**
The final design of the mutant IDH1 sensor. Depicted is the cDNA sequence of the sensor hybridized to the
target region of the mutant IDH1 transcript. A total of 3 in-frame start- and stop-codons other than the
central stop-codon had to be removed by changing a base, which causes the mismatches with the trigger sequence.

## Putting it all together

---

After having successfully cloned pASTERISK-HBA1 and pASTERISK-HBB, we ordered all of our designed sensor
sequences as oligonucleotides. These could now be cloned into the plasmids using a HindIII digestion at the
sensor cloning site, followed by Gibson assembly to introduce the sensor insert. The plasmids obtained this way
could then be linearized and used as IVT templates for producing our therapeutic mRNA.

## Engineering results

---

In order to analyze the properties of the HBA1 and HBB UTRs in isolation, both plasmids (pASTERISK-HBA1 and pASTERISK-HBB)
were used as templates for in vitro transcription using T7 polymerase with an empty sensor cloning site (i.e.
without any sensor sequences). The mRNA was purified and used to transfect HEK293T cells. We expected to see
both encoded fluorescence markers (mCherry and mNeonGreen) to be expressed by the cells, since there was no sensor
insert that would prevent the payload mCherry sequence expression. Figure 6-8 show the results 8h, 24h and 48h
after transfection.

![](https://static.igem.wiki/teams/4877/wiki/results/microscopy/hba-hbb-positiv-8h.jpg)

**Figure 6: Fluorescence microscopy images of transfected HEK293T cells after 8 hours.**

![](https://static.igem.wiki/teams/4877/wiki/results/microscopy/hba-hbb-positiv-24h.jpg)

**Figure 7: Fluorescence microscopy images of transfected HEK293T cells after 24 hours.**

![](https://static.igem.wiki/teams/4877/wiki/results/microscopy/hba-hbb-positiv-48h.jpg)

**Figure 8: Fluorescence microscopy images of transfected HEK293T cells after 48 hours.**

## References

* 1. Gayet, R. V. et al. Autocatalytic base editing for RNA-responsive translational control. Nat Commun 14, 1339 (2023).
* 2. Horbinski, C. What do we know about IDH1/2 mutations so far, and how do we use it? Acta Neuropathol 125, 621–636 (2013).
* 3. Lassman, A. B. et al. Epidermal growth factor receptor (EGFR) amplification rates observed in screening patients for randomized trials in glioblastoma. J Neurooncol 144, 205–210 (2019).
* 4. Kim, S. C. et al. Modifications of mRNA vaccine structural elements for improving mRNA stability and translation efficiency. Mol Cell Toxicol 18, 1–8 (2022).
* 5. Qu, L. et al. Programmable RNA editing by recruiting endogenous ADAR using engineered RNAs. Nat Biotechnol 37, 1059–1069 (2019).

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* [Abstract](#Abstract)
* [Safety and Security](#SaS)
* [References](#Introduction)
* Laboratory Safety

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# Part Construction

## Abstract

For our therapeutic approach we transform our Engineering Design considerations into
reality. The generation of an Entry Vector which is capable of generating mRNA sensing and
killing switches via in vitro transcription. For intraoperative mRNA therapy, we aim to develop
stable and highly efficient sensing and killing switches. By targeting glioma specific
alterations, we aim to develop our integrative treatment strategy. With our Part Construction,
we presented our results:

☑ assemble of our Entry Vectors pASTERISK for integrating sensor sequences
☑ verification of successfully cloning via Nanopore Sequencing and Sanger Sequencing experiments
☑ optimization of existing protocols by testing different experimental setups to contribute more
accessible workflows for future teams
☑ driving our Engineering Cycle to prepare mRNA synthesis via in vitro transcription

We managed to create our pASTERISK Entry Vector for modulable insertion of sensor
sequences and payloads to contribute with multifunctional usage. Our Part Pages provides
detailed information of our genetically circuit of pASTERISK. Because of the integrated T7
Promoter, the Entry Vector is capable of producing mRNA molecules. By inserting the sensor
sequences made with our Software sensor design tool, the sensor cloning site can be
opened via HindIII and the sequence can be inserted via Gibson Assembly or Restriction
enzyme cloning according to RFC10 standards. In addition, by digest pASTERISK with SphI
and SalI, the payload sequence can be changed as well. The modulable design of our
plasmids allows different fields of applications and opens more possibilities for future iGEM
teams to use adjustable synthetic biology.
TODO: add hyperlinks and figures

## Preparation of Parts Backbone Plasmids, Expression vectors and cloning fragments

At the beginning of our project, we established chemo- and electrocompetent *E. coli* for all
following transfections and cloning strategies within our project. We verify the transfection
efficiency and competenz as well as the quality and selection efficiency. To continue the
iGEM tradition of RFC10-based cloning, we decided to utilize pSB1C3 as our main
backbone plasmid. The successful transfections were verified via the chloramphenicol
resistance and outgrowth in LB media containing appropriate antibiotics resistance. We
extracted the plasmid after appropriate outgrowth with different methods including mini spin
preparation and midi silica preparation kits.

We successfully transfected and isolated our plasmids for different
usage including:

* pSB1C3 - 2800 bp - Backbone plasmid for pASTERISK-HBA1 and pASTERISK-HBB to
  improve RFC 10 standards
* pDART VADAR - X bp - Plasmid for generating sensor construct that target IRFP720
  transcripts in vivo according to Gayet et al. 2023
* pDART VADAR\_05 - X bp - Plasmid for constitutive Expression of native IRFP720 transcript
* pDART VADAR\_06 - X bp - Entry Vector for cloning of sensor sequencing within target site
* pcDNA5 - X bp - Backbone Vector for constitutive Expression of native IDH1 and IDH1
  R132H
* pcMV3 - X bp - Backbone Vector for constitutive Expression of native EGFR plasmids
* p135 - X bp - containing Part for Poly-A-Signal (hGh)
* p147 - X bp - Backbone vector for procaryotic expression of pASTERSIK

## Amplification of cloning fragments and sensor sequences

In addition, we design our fragments and sensor sequences needed to achieve our project
goals and amplify the fragments with appropriate primer pairs designed through in silico
analyzes. After amplification of cloning fragments and sensor sequences gel electrophoresis
and Sequencing experiments verified the present of desired sequences:

* F1-HBA1 - X bp - Fragment 1 for ASTERSIK with UTR of HBA
* F2\_Entry - X bp - Fragment 2 for ASTERSIK with UTR of HBA
* F3-HBA1 - X bp - Fragment 3 for ASTERSIK with UTR of HBA
* F1-HBB1 - X bp - Fragment 1 for ASTERSIK with UTR of HBB
* F2\_Entry - X bp - Fragment 2 for ASTERSIK with UTR of HBB
* F3-HBB1 - X bp - Fragment 3 for ASTERSIK with UTR of HBB
* IDH1 wt - X bp - Sequence of wildtype IDH1
* IDH1 mt - X bp - Sequence of mutant IDH1 containing R132H
* mCherry - X bp - Sequence for Expression of mCherry (because of technical limitations
  instead of TagBFP implemented)

ÜBERSICHT - PRIMER (Erklärung: mit und ohne PstI Recovery, Hinweis: 2 Isoformen of
different UTR structures)
\* Sensor sequences overlap with in silico expectations
\* successfully amplification of sensor sequences and insert sequences
However, we tested different experimental setups, conditions, materials and different
combinations of various suppliers to improve the efficiency of our downstream experiments
for e.g. different polymerases for amplification or restriction enzymes for digestions. All
protocols contributed to the iGEM community and the synthetic biology community are
optimized to reconstruct our results and implement pASTERISK in their own workflow.

## Digestion of backbone plasmids and sequences

As part of the preparation to perform construct assemblies, all isolated vectors were digested
using appropriate restriction enzymes. In comparison to the fragments, all linearized vectors
were gel electrophoretically purified. Ether gel extraction kit or PCR cleanup kit were used to
purify and concentrate the fragments.

* pSB1C3 - digestion with EcoRI / PstI to cut GFP sequence out of cloning side- length of
  backbone after digestion: X bp
* pcDNA5 - digestion with BamHI / NotI to prohibit relegation - length of backbone after
  digestion: X bp
* p135 - digestion with X / X to extract sequence for Poly-A-Signal - length of desired
  fragment: X bp
* p147 - digestion with X / X to extract sequence for CMV promoter - length of desired
  fragment: X bp

\* successfully isolated separated PCR products for Part Construction

## Construction assemblies and cloning strategies

Using different cloning strategies including gibson assembly, restriction enzyme cloning and
biobrick assembly, we successfully integrated our fragments and sensor sequences in the
entry site of our backbone plasmids. As an alternative we improve the overlap extension
PCR to assembly fragments with appropriate overlap. The lossless integration and correct
orientation of the inserts were validated via Colony PCR and Sequencing experiments.

TODO!!!

![Main menu](https://static.igem.wiki/teams/4877/wiki/software/software-01.png)
![Main menu](https://static.igem.wiki/teams/4877/wiki/software/software-01.png)

## Sequencing Results

TODO!!!

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