iGEM 2025 — Team heidelberg

{% extends "layout.html" %}
{% block title %}Notebook{% endblock %}
{% block header %}

NOTEBOOKS

{% endblock %}
{% block page\_content %}

This section presents our complete wet lab documentation in the form of detailed laboratory notebooks. Each PDF provides day-by-day records of experimental procedures, including specific protocols followed, materials used, quantitative results, qualitative observations, and analytical interpretations. Our notebooks encompass both molecular cloning and functional characterization assays. They represent a transparent account of our methodology, offering insight into both successful outcomes and the troubleshooting strategies we employed when experiments didn't proceed as planned.

{% include 'sidenav.html' %}

## Receptor

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Receptor Notebook 1:

[PDF not displaying? Click here to download](https://static.igem.wiki/teams/5775/pdfs/01-receptor1-notebook.pdf)

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Receptor Notebook 2:

[PDF not displaying? Click here to download](https://static.igem.wiki/teams/5775/pdfs/02-receptor2-notebook.pdf)

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Receptor Notebook 3:

[PDF not displaying? Click here to download](https://static.igem.wiki/teams/5775/pdfs/03-receptor3-notebook.pdf)

## Intracellular Signaling

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Intracellular Signaling Notebook:

[PDF not displaying? Click here to download](https://static.igem.wiki/teams/5775/pdfs/04-ics-notebook.pdf)

## Effector

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Effector Notebook:

[PDF not displaying? Click here to download](https://static.igem.wiki/teams/5775/pdfs/05-effector-notebook.pdf)

## Lab Duties

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Lab Duties Notebook:

[PDF not displaying? Click here to download](https://static.igem.wiki/teams/5775/pdfs/07-labduties-notebook.pdf)

## Side Projects

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Side Projects Notebook 1:

[PDF not displaying? Click here to download](https://static.igem.wiki/teams/5775/pdfs/08-sideprojects1-notebook.pdf)

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Side Projects Notebook 2:

[PDF not displaying? Click here to download](https://static.igem.wiki/teams/5775/pdfs/09-sideprojects2-notebook.pdf)

{% endblock %}

{% extends "layout.html" %}
{% block title %}Engineering{% endblock %}
{% block header %}

ENGINEERING CYCLE

{% endblock %}
{% block page\_content %}

#### Wet Lab

Explore our step-by-step **development of a modular phosphorylation-based signaling
platform**
for programmable cancer cell therapies. Follow our engineering cycles as we engineer and validate
custom
receptors, expression systems and protein secretion platforms, building a **standardized part
collection** that functions both independently and in integrated circuits. Learn how we
achieved
proof-of-concept by demonstrating **sensing of tumor-relevant ligands like VEGF and
TNF-α** and
coupling them to therapeutic outputs such as **secretion of IL-12.**

#### Dry Lab

Learn how we developed SPARC, our comprehensive digital companion for PHOENICS cell design. Discover
how we
integrated BindCraft for **de novo binder design**, implemented **molecular
dynamics
simulations** for binding affinity characterization, and built **mathematical
models of
receptor dimerization and circuit dynamics**. Find out how we created accurate
dose-response
predictions validated against wet-lab data, and developed a **digital database** that
maps the
behavior space of all possible circuit architectures for rapid prototyping.

#### HP

Discover our mission of a science-based, sustainable, and holistic approach to bridging the
translational
gap. Explore how we evolved from **identifying crucial needs in translational
education** over
hosting a **high-profile panel discussion** to developing a comprehensive
**roadmap of the
regional innovation landscape**. Learn about our journey through stakeholder engagement
and dive into
our Summer School program, School Workshops and an **interactive learning platform**
that
empowers young scientists with the knowledge and network to turn discovery into real-world impact.

{% include 'sidenav.html' %}

## Processing

### Characterization of the PHOENICS Circuit Components

We directly validated phosphorylation of the SynSubstrate and confirmed that interaction between
SynSubstrate and SynPhosphobinder depends on phosphorylation and increases with SynKinase
levels. Finally,
we seeked to connect the SH2 domain to a transcriptional reporter and identified promising
constructs for
future phosphorylation assays.

Iteration 1

Iteration 2

Iteration 3

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/ics/cyc1-fig0-ola-wb-schematic.svg)

---

**Figure 1:** Schematic representation of
SynSubstrate and
SynKinase constructs designed the Western Blot.

#### DESIGN

The PHOENICS circuit relies on the phosphorylation of a tyrosine residue within a
synthetic
substrate (SynSubstrate) composed of three CD3ζ-derived ITAM repeats. Both the
SynSubstrate and the
truncated ABL kinase (SynKinase) catalyzing its phosphorylation were adapted
from Yang et al.
(2025). In this system, phosphorylation occurs through proximity-induced
interaction mediated by
complementary coiled-coils. SynKinase is a truncated ABL kinase fused to one
half of the coiled-coil
(ABL-bZipEE) and the SynSubstrate is fused to the other coiled-coil
(CD3ζ-bZipRR).
This kinase truncation
prevents intrinsic binding between the enzyme and the substrate, ensuring that
phosphorylation
occurs exclusively upon their coiled-coil-mediated proximity. To validate
these
literature-derived components, we first designed a Western blot experiment to
confirm protein
expression in the cells and phosphorylation. We fused the SynSubstrate to triple
Myc tag (3×Myc) to
enable the detection of both phosphorylation and expression via immunostaining
in a single
experiment.

#### BUILD

For experimental validation, cells in a 12-well plate were transfected either
with the substrate
fused to a triple Myc tag (3×Myc) or with both the substrate and truncated ABL
kinase carrying the
complementary coiled-coil (Fig. 1). 48 hours post transfection, cells were
lysed and total
protein content was quantified. Equal amounts of protein (17 µg) from each
sample were loaded into
the wells of an SDS-PAGE gel and transferred on a membrane after running the
PAGE. The blot was
first probed with primary antibodies specific to phosphorylated tyrosine (pY)
within the ITAM motif
of the substrate, followed by secondary staining. Subsequently, an anti-Myc
antibody was used to
confirm the presence of the substrate in the samples.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/ics/cyc1-fig1-wb.svg)

---

**Figure 2: Western blot verification of
SynSubstrate
phosphorylation by SynKinase.** HEK293T cells were transfected
with 60 ng of SynSubstrate
either alone or together with 55 ng of SynKinase. Phosphorylation was
detected using a
phospho-tyrosine-specific antibody. A strong phospho-tyrosine signal was
observed only in the
presence of SynKinase, confirming kinase-dependent phosphorylation of the
SynSubstrate.

#### TEST

The immunoblot demonstrated the phosphorylation of the SynSubstrate only when
SynKinase is
co-expressed. In the α-pY staining (top panel), no phosphorylation was observed
for the substrate
expressed alone (lane 1), whereas a vivid phospho-tyrosine signal was detected
only when SynKinase
was present (lane 2). This suggests that the tyrosine on the substrate was
phosphorylated by the
SynKinase. In the α-Myc staining (bottom panel), it is not possible to directly
compare the levels
of expressed substrate between conditions, as the signal is influenced by
residual anti-pY signal
due to the reuse of the same secondary antibody without stripping. However, the
presence of a
distinct Myc band in both lanes demonstrates that SynSubstrate is present in
both samples.
Importantly, the sensitive anti-pY staining detects phosphorylation only in the
SynKinase
co-transfection, confirming that the substrate is not phosphorylated when
SynKinase is absent
(Fig. 2). This confirms that SynKinase specifically mediates tyrosine
phosphorylation of the
substrate.

#### LEARN

We were able to confirm that SynSubstrate phosphorylation only occurs when
SynKinase is present.
This indicates that native cellular kinases do not phosphorylate the substrate,
confirming the
orthogonality of phosphorylation.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/ics/cyc1-fig2.svg)

---

**Figure 3: Design of the SpliFAST assay for the
fluorescence
readout of phosphorylation-mediated SynSub and SH2 interaction.**
The SynSubstrate, fused
to the N-terminal fragment of SplitFAST (RspA[N]), is phosphorylated in the
cytosol by SynKinase.
The SH2 domain, acting as SynPhosphobinder, is fused to the complementary
C-terminal fragment
(RspA[C]) of SplitFAST. Upon phosphorylation, SynSubstrate interacts with
the SH2 domain,
promoting reconstitution of SplitFAST. Addition of the HMBR fluorogen
induces fluorescence, which
visualizes the phosphorylation-dependent protein–protein interaction and can
be quantified by flow
cytometry.

#### DESIGN

After successful validation of the SynSubstrate’s phosphorylation we aimed to
investigate the
semi-physiological interaction between the phosphorylated substrate and the
signal-propagating SH2
domain, also referred to as SynPhosphobinder. It is considered
semi-physiological because the pY-SH2
interaction is native, but the phosphorylation is driven by a different cellular
kinase, not our
SynKinase. We selected a recently published switchable fluorescence
complementation reporter,
SplitFAST [(Tebo and Gautier, 2019)](#References). SplitFAST allows
rapid and reversible
detection of protein-protein interactions. The N-RspA(N) half of the split
protein was fused to the
SynSubstrate, and the complementary C-RspA(N) to the SH2 (Fig. 3). Only when the
phosphorylated
SynSubstrate interacts with the SH2 domain, the reporter protein is reassembled.
Addition of the
fluorogen HMBR triggers fluorescence in the reconstituted complex, visualizing
the
phosphorylation-mediated interaction.

#### BUILD

To evaluate the dynamic range of the SynSubstrate and SH2 interaction, cells were
transfected with
SplitFAST constructs alongside increasing amounts of ABL kinase. 100 ng of both
RspA(N)-CD3𝜁-bZipRR
and RspA(C)-SH2 were co-transfected. After 48 hours of expression, cells were
trypsinized to
generate a single-cell suspension, and the HMBR fluorogen was added. The
fluorescence from
reconstituted SplitFAST complexes was then quantified by flow cytometry,
enabling detection of
phosphorylation-dependent SynSubstrate and SH2 interactions.

#### TEST

Flow cytometry analysis revealed a clear shift toward higher fluorescence
intensity in cells
co-transfected with SynKinase, indicating successful reconstitution of the
fluorescence reporter
(Fig. 4). This shift demonstrates that phosphorylation of the SynSubstrate
enables interaction with
SH2, leading to SplitFAST complementation and fluorescence. Without SynKinase,
fluorescence remained
at background levels, confirming the specificity of the interaction.
Additionally, increasing
SynKinase expression produced a corresponding increase in fluorescence,
indicative of a
dose-dependent effect on phosphorylation and dimerization. At the highest
SynKinase amounts,
fluorescence plateaued, consistent with saturation of SynSubstrate
phosphorylation and SH2 binding.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/ics/cyc1-fig3.svg)

---

**Figure 4: Validation of the
phosphorylation-dependent dimerization
using SplitFAST.**Y-axis description corresponds to the ng
amounts of SynKinase (0-250
ng). 48 hours post-transfection, dimerization-dependent SplitFAST
fluorescence was quantified by
flow cytometry, employing excitation lasers at 488 nm and 561 nm. Emission
was recorded at 525 nm
for splitFAST (Ligand: TF Lime) and at 610 nm for constitutively expressed
mCherry, serving as a
transfection control. Data is depicted as the geometric mean of fluorescence
+/- SEM with ~30000
single cells.

#### LEARN

These results demonstrate that phosphorylation induces a specific interaction
between SynSubstrate
and the SH2 domain (SynPhosphobinder). This provides a strong foundation for
extending the SH2
module with additional signal transduction functionalities in future designs.

#### DESIGN

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/ics/cyc1-fig5a-b.svg)

---

**Figure 5: Improved design of the
phosphorylation-dependent
transcription activator cleavage assay. Panel A: Final system design;
Panel B:
Rapalog-induced.** Panel A: Upon phosphorylation-induced
dimerization SynSubstrate, and
SH2 fused to nTEV and cTEV fragments are brought together, restoring TEV
protease activity. This
enables cleavage of a membrane-tethered transcriptional activator at a TEV
site, thereby releasing
the activator. The released activator translocates to the nucleus to bind
the 5x UAS sequence and
drive NanoLuc expression.Panel B: Design of validation of TEV proteolytic
activity upon
rapalog-induced reconstitution.

Having successfully validated SynSubstrate phosphorylation and the
phosphorylation-dependent
interaction with the SH2 domain, we sought to translate a phosphorylation event
into a quantifiable,
high-throughput reporter output. To achieve a cost-effective and scalable
readout, we engineered a
system in which reporter gene expression is conditionally activated upon the
interaction between the
phosphorylated substrate and SH2.

We designed a membrane-anchored transcriptional activator coupled to a
phosphorylation-dependent
cleavage system. Specifically, the C-terminus of the split TEV protease (cTEV)
was fused to the
Synthetic Substrate (3xCD3ζ), and the N-terminus (nTEV) was fused to the SH2
domain. Upon
phosphorylation, the substrate binds SH2, bringing the two parts of the splitTEV
protease into
proximity and reconstituting its enzymatic activity. The active TEV protease
then cleaves the Gal4
DNA-binding domain (Gal4DBD) fused to a VP64 transcription factor (Gal4DBD-VP64)
from the membrane
tether. The released Gal4DBD-VP64 translocates to the nucleus and activates
expression of the nano
luciferase (NanoLuc) reporter gene under the 5x UAS binding site with minimal
CMV promoter. This
system would convert substrate phosphorylation into a measurable luminescent
signal (Fig. 5A).

To validate rapalog-dependent reconstitution of TEV protease activity, we first
tested whether
rapalog-induced dimerization could restore proteolytic function. For this
purpose, we fused the cTEV
to FRB and the nTEV to FKBP, following designs reported in a recent publication
[(Vlahos et al., 2022)](References). This construct enables
chemically induced proximity
between FKBP and FRB upon addition of rapalog, which is expected to bring
together the splitTEV
fragments and reconstitute enzymatic activity (Fig. 5B).

#### BUILD

After confirming successful cloning by sequencing, HEK293T cells were transfected
with the
constructs. 24 hours post-transfection, cells were induced with 200 nM AP21967
(Rapalog) or vehicle
control (ethanol). 48 hours after transfection, cells were lysed and subjected
to the Nano-Glo assay
to measure luminescence. The NanoLuc luminescence readout was normalized to
constitutively expressed
firefly luciferase (FFLuc) levels present in all conditions to control for
variation in transfection
efficiency and cell number.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/ics/cyc1-fig6.svg)

---

**Figure 6: Quantification of reporter
expression activation using
the Nano-Glo luminescence readout of membrane tethered transcription
activator assay.**
For the Rapa-inducible Split TEV condition, HEK293T cells were transfected
24 hours post-seeding,
with FRB-cTEV, FKBP-nTEV, and 5xUAS-NanoLuc plasmids, each at 20 ng per
well. The positive control
consisted of a construct encoding a fused Gal4DBD-VP64 transcription factor
to provide
constitutive NanoLuc expression. Additional control groups included a
negative control lacking the
Gal4DBD-VP64 membrane-tethered construct, a No TEV condition, and a Full TEV
construct. All
transfection conditions incorporated a FFLuc plasmid to normalize results.
Luminescence was
measured 48 hours post-transfection with Tecan plate reader. Data are
presented as the mean ± SEM
from n = 3 technical replicates. Statistical significance was determined
with Ordinary one-way
ANOVA with Tukey’s multiple comparison test, with a single pooled variance.
\*p < 0.05, \*\*\*\*p < 0.001.

#### TEST

The transcriptional reporter assay demonstrated robust performance of the
controls, with high
luminescence observed in the positive control and no detectable signal in the
negative control,
confirming the absence of transcriptional leakage. Comparison of the ‘No TEV’
and ‘Full TEV’
conditions revealed no significant difference in reporter activity, indicating
that the TEV protease
system as configured could not detect phosphorylation-dependent events.
Furthermore, induction with
rapalog did not produce a significant increase in luminescence in the splitTEV
group compared to the
uninduced condition, suggesting that rapalog-induced dimerization failed to
enable the desired
transcriptional activation under these experimental parameters (Fig. 7).

#### LEARN

Overall, we were satisfied that the NanoLuc reporter showed high expression in
the positive control
and demonstrated no leakage in the negative control, confirming the reliability
of the
transcriptional readout. However, to circumvent the potential issues related to
membrane embedding
and rapid degradation observed with the initial design, we decided to focus on
establishing an
alternative system as discussed in a second cycle.

### Optimization of Phosphorylation Readout Assay

We engineered a robust cytosolic phosphorylation reporter using split Gal4DBD/VP64
transcriptional
activators. Signal sensitivity and specificity were enhanced through iterative optimization of
SynKinase/SynPhosphatase concentration, nuclear-cytoplasmic localization of reporters, and
fine-tuning of
component stoichiometry, enabling simple and reliable detection of the substrate phosphorylation
level.

Iteration 1

Iteration 2

Iteration 3

Iteration 4

Iteration 5

Iteration 6

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/ics/cyc2-fig1.svg)

---

**Figure 1: Improved design of the
phosphorylation-dependent
transcriptional reporter circuit.** SynSubstrate undergoes
phosphorylation in the cytosol
by SynKinase and dephosphorylation by SynPhosphatase. Upon phosphorylation,
the substrate
translocates into the nucleus where it interacts with the nuclear-resident
SynPhosphobinder. This
interaction facilitates UAS-NanoLuc binding, producing the luminescence
signal proportional to
substrate phosphorylation.

#### DESIGN

Building on lessons from the previous engineering cycle, we moved forward with a
new design focused
on a fully cytosolic system to improve signal robustness and avoid issues
related to membrane
localization. We continued using the SynSubstrate, consisting of three
CD3ζ-derived ITAM repeats,
alongside the truncated ABL kinase (SynKinase). Additionally, a truncated form
of PTPN1
(SynPhosphatase) was adapted from [Yang et al., (2025)](#References).
In this system,
phosphorylation and dephosphorylation are controlled through proximity-induced
interactions mediated
by complementary coiled-coils. SynKinase is fused to one half of the coiled-coil
(bZipEE), and
SynPhosphatase is fused to the same bZipEE module. Both enzymes compete for the
SynSubstrate, which
is fused to the complementary coiled-coil (bZipRR), allowing proximity-dependent
phosphorylation and
dephosphorylation.

The new transcriptional readout is based on the proximity-driven assembly of a
transcriptional
activator complex composed of VP64 and the Gal4DBD. In this design, Gal4DBD is
fused to the
substrate (CD3z-Gal4DBD), while VP64 is fused to the SH2 domain (SH2-VP64,
referred to as
SynPhosphobinder). Upon phosphorylation of the substrate by the kinase, the SH2
domain binds the
phosphorylated substrate, bringing VP64 and Gal4DBD together. This assembled
complex translocates
into the nucleus, where it activates transcription of the Nanoluciferase
(NanoLuc) reporter gene
under the 5xUAS repeats (UAS-NanoLuc), producing a luminescent readout of
phosphorylation-dependent
signaling (Fig. 1).

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/ics/cyc2-fig2.svg)

---

**Figure 2: Kinase and phosphatase activity
assessment experiment.
Quantification of phosphorylation-dependent transcriptional
activation using the Nano-Glo
luminescence readout.** For the cytosolic transcriptional
readout system, HEK293T cells
were transfected with 20 ng 5xUAS-NanoLuc, 20 ng Gal4DBD-CD3ζ (substrate
fusion), and 20 ng
SH2-VP64 (transcriptional activator fusion) plasmids per well, 24 hours
post-seeding.
Experimental conditions included the addition of 30 ng kinase plasmid
or/and 20 ng
SynPhosphatase plasmid as indicated. The positive control contained
Gal4DBD-VP64 fusion
constructs for constitutive NanoLuc expression, and the negative control
lacked the
transcriptional activator components. All samples were co-transfected
with 10 ng of FFLuc
plasmid for normalization. Luminescence was measured 48 hours
post-transfection with Tecan plate
reader. Data are presented as the mean ± SEM from n = 3 technical
replicates.

#### BUILD

After confirming successful cloning by sequencing, HEK293T cells were
transfected with the
constructs. 48 hours after transfection, cells were lysed and subjected to
the Nano-Glo assay to
measure luminescence. The NanoLuc luminescence readout was normalized to
constitutively expressed
firefly luciferase (FFLuc) levels present in all conditions to control for
variation in
transfection efficiency and cell number.

#### TEST

The SynKinase condition produced a strong luminescence signal, matching the
positive control with
constitutively activated NanoLuc, demonstrating that phosphorylation of the
substrate efficiently
induces reporter expression through proximity-driven assembly of
Gal4DBD-VP64. There was no
detectable luminescence in the negative control, confirming the system’s
switchability and
tightness. Importantly, the addition of the SynPhosphatase completely
abolished the
phosphorylation-induced signal, despite the higher amount of kinase
transfected relative to the
SynPhosphatase (Fig. 2). These results confirm that the reporter reliably
detects phosphorylation
and that this signal can be fully suppressed by SynPhosphatase-mediated
dephosphorylation,
validating effective dynamic control in the circuit.

#### LEARN

In this second iteration, we created a reliable cytosolic transcriptional
readout for
phosphorylation that distinguishes specific activation from background and
is switched off upon
dephosphorylation. Having shown robust reporter induction and switchability
by SynKinase and
SynPhosphatase addition, the next step was to characterize this system more
thoroughly.

#### DESIGN

For this titration experiment, the same plasmid components were used as before,
but this
iteration expanded the range of conditions to allow for more precise
characterization of the
dynamic range of phosphorylation-dependent reporter activation. Our goal was to
determine the
kinase concentration that produces a robust 'on' state - indicative of reporter
sensitivity, and
the minimal SynPhosphatase concentration required to fully suppress reporter
activation, defining
the 'off' state by canceling the phosphorylation signal. This enabled a
quantitative assessment of
system sensitivity, specificity, and effective thresholds for modulating the
cytosolic
transcriptional readout.

#### BUILD

Constructs for the new assay were cloned, sequenced, and transfected into HEK293T
cells. For
kinase titration, increasing amounts of kinase were used. For SynPhosphatase
titration, each
condition contained 20 ng of kinase to saturate phosphorylation, with increasing
amounts of
SynPhosphatase. The reporter system components - 20 ng 5xUAS NanoLuc, 20 ng
Gal4DBD-CD3ζ, and 20
ng SH2-VP64 - were constant in all conditions. 48 hours after transfection,
cells were lysed and
NanoLuc expression was quantified using the Nano-Glo Luciferase Assay.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/ics/cyc2-fig3.svg)

---

**Figure 3: Kinase titration experiment.
Quantification of
phosphorylation-dependent transcriptional activation using the Nano-Glo
luminescence
readout.** HEK293T cells were transfected 24 hours post-seeding,
with increasing amounts
of SynKinase plasmid (5, 10, 20, 30 ng) and increasing amounts of
SynPhosphatase (3, 5, 10, 15 ng)
with 20 ng of kinase present to measure the phosphorylation-dependent
activation of the NanoLuc
reporter system. Each condition included 20 ng of reporter components
(Gal4DBD-CD3ζ, SH2-VP64, and
UAS-NanoLuc) and 10 ng of FFLuc for normalization. Luminescence was measured
48 hours
post-transfection with Tecan plate reader. Data are presented as the mean ±
SEM from n = 3
technical replicates.

#### TEST

Phosphorylation-dependent reporter activation was detected with as little as 10
ng of kinase
plasmid, and the signal increased in a dose-dependent manner, demonstrating good
sensitivity of an
assay. Dephosphorylation became apparent with as little as 3 ng SynPhosphatase,
and full suppression
of the phosphorylation signal was achieved with 10 ng.

#### LEARN

The assay showed the expected and desirable pattern: Nano Lucsignal increased
with higher kinase
amounts, indicating successful detection of phosphorylation, and decreased as
additional
SynPhosphatase was introduced, reflecting efficient dephosphorylation recording.

#### DESIGN

With kinase and SynPhosphatase conditions optimized, our next step was to titrate
the
concentrations of 5xUAS NanoLuc, Gal4DBD-CD3ζ (substrate), and SH2-VP64,
previously held constant,
to identify the combination that maximizes reporter signal in the presence of
the 20 ng kinase.

#### BUILD

We have tested a range of CD3ζ-Gal4DBD to SH2-VP64 ratios by varying the amounts
of each component
across multiple conditions while keeping the kinase concentration constant at 20
ng. Each
transfection included 20 ng of UAS NanoLuc as the reporter. By systematically
changing the relative
abundance of CD3ζ and SH2-VP64, we aimed to identify the optimal stoichiometry
for maximum reporter
activation. 48 hours after transfection, NanoLuc activity was assessed using the
Nano-Glo Luciferase
Assay to evaluate transcriptional output across the tested ratios.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/ics/cyc2-fig4.svg)

---

**Figure 4: SH2-VP64: Gal4DBD-CD3ζ ratio tuning
experiment. Quantification of
phosphorylation-dependent transcriptional activation using the Nano-Glo
luminescence
readout.** HEK293T cells were transfected 24 hours post-seeding,
with 20 ng of SynKinase
and varying ratios of CD3ζ-Gal4DBD to SH2-VP64 constructs to identify the
conditions yielding the
strongest luminescence signal driven by kinase-mediated phosphorylation.
Each condition included
10 ng of the 5xUAS-NanoLuc reporter plasmid and 10 ng of FFLuc for
normalization. Luminescence was
measured 48 hours post-transfection with Tecan plate reader. Data are
presented as the mean ± SEM
from n = 3 technical replicates.

#### TEST

We observed large differences in reporter expression strength depending on the
CD3ζ-Gal4DBD to
SH2-VP64 ratio. In all tested conditions, a greater amount of SH2-VP64 relative
to the substrate
consistently resulted in stronger NanoLuc signal, whereas the opposite ratios
produced weaker
responses. The highest luminescence signal was recorded with 5 ng CD3ζ-Gal4DBD
and 25 ng SH2-VP64
(Fig. 4).

#### LEARN

The best signal amplification was achieved with 5 ng CD3ζ-Gal4DBD and 25 ng
SH2-VP64, indicating
this ratio provides optimal conditions for reporter activation in our system.
These amounts were
consistently used in the following experiments.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/ics/cyc2-fig5.svg)

---

**Figure 5: Schematic overview of the
transcriptional readout assay
and its successive modifications. The original and modified variants
differ by the positioning
of nuclear localization signal (NLS) and nuclear export signal (NES)
relative to Gal4DBD and
CD3ζ domains within the SynSubstrate construct.** In
modification
1, NES is fused to the
5′ end of SynSubstrate; in modification 2, NES is inserted between Gal4DBD
and CD3ζ. For both
modifications, SynSubstrate can shuttle between cytoplasm and nucleus, while
SynPhosphobinder
remains nuclear due to its NLS and absence of NES. Phosphorylation of
SynSubstrate in the cytosol
allows its interaction with nuclear SynPhosphobinder upon entry, triggering
NanoLuc reporter
activation through proximity-mediated assembly of the transcriptional
activation complex.

#### DESIGN

We revisited our construct designs, as illustrated in the attached scheme, to
address the spatial
logic of substrate and binder localization in the nucleus and cytosol. We
realized that having the
substrate permanently in the nucleus while the SH2-containing SynPhosphobinder
continually shuttles
between compartments might not be optimal for rapid and efficient signal
transduction. Instead, we
hypothesized that configuring the system so the substrate undergoes
nucleo-cytoplasmic shuttling,
SH2-VP64 remains resident in the nucleus, would allow the phosphorylated
substrate to efficiently
interact with SH2 upon nuclear entry and immediately drive transcriptional
activation. This approach
should optimize the detection process and enhance reporter responsiveness.

#### BUILD

For both modification 1 and modification 2, we engineered new substrate
constructs by inserting a
NES to promote nucleocytoplasmic shuttling. In modification 1, the NES was added
to the 5' end of
the NLS-Gal4DBD-CD3ζ-bZipRR substrate, while in modification 2, the NES was
positioned internally
between the Gal4DBD and 3xCD3ζ domains. For both designs, we removed the NES
sequence from the
SH2-VP64 construct, ensuring that SH2-VP64 remains resident in the nucleus to
interact with the
phosphorylated substrate when it enters (Fig. 5). Instead of normalizing the
NanoLuc against the
FFLuc their values were plotted separately to independently assess the signal
strength of
transcriptional readout modifications (NanoLuc signal) and to evaluate cell
viability (Firefly
signal). This allowed clear comparison of reporter expression and viability
across tested
conditions.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/ics/cyc2-fig6ab.svg)

---

**Figure 6: Quantification of
phosphorylation-dependent
transcriptional activation using the Nano-Glo luminescence readout.
Panel A: Raw NanoLuc;
Panel B: Raw FFLuc.** HEK293T cells were transfected were
transfected 24 hours
post-seeding, with the 5xUAS-NanoLuc reporter plasmid and either 15 ng
SH2-VP64 plus 15 ng
Gal4DBD-CD3ζ, or 25 ng SH2-VP64 plus 5 ng Gal4DBD-CD3ζ per well, as
indicated for each
condition. In all cases, 25 ng of kinase plasmid was included to drive
substrate
phosphorylation. All samples were also co-transfected with 10 ng of
FFLuc plasmid for
normalization of luminescence signals. Luminescence was measured 48
hours post-transfection with
Tecan plate reader. Data are presented as the mean ± SEM from n = 3
technical replicates.
Statistical significance was determined with Ordinary one-way ANOVA with
Tukey’s multiple
comparison test, with a single pooled variance. \*p < 0.05, \*\*\*\*p <
0.001.

#### TEST

Analysis of raw NanoLuc values revealed that both construct modifications
outperformed the
original assay, with modification 1 yielding the highest reporter signal
when 25 ng SH2-VP64 and 5
ng Gal4DBD-CD3ζ were co-transfected (Fig. 6). However, the Firefly
luciferase cell viability assay
indicated increased cellular stress for both modifications relative to the
original construct.

#### LEARN

We learned that, as predicted, redesigning the system so the substrate
shuttles between the
cytosol and nucleus while SH2-VP64 remains nuclear significantly improved
transcriptional readout
performance. This spatial arrangement enhanced the efficiency of
phosphorylation-dependent
reporter activation. To balance the maximized signal and cell viability, we
proceeded with
modification 1 for future experiments.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/ics/cyc2-fig8.svg)

---

**Figure 7: Kinase titration experiment for
assay modification 1.
Quantification of phosphorylation-dependent transcriptional activation
using the Nano-Glo
luminescence readout.** HEK293T cells were transfected 24 hours
post-seeding, with
increasing kinase plasmid amounts (0.4 to 60 ng), together with fixed
amounts of modified reporter
assay plasmids: 25 ng Gal4DBD-CD3ζ, 5 ng SH2-VP64, and 10 ng 5xUAS-NanoLuc.,
along with 10 ng
FFLuc. Luminescence was measured 48 hours post-transfection with Tecan plate
reader. Data are
presented as the mean ± SEM from n = 3 technical replicates.

#### DESIGN

Having optimized all aspects of the transcriptional readout for substrate
phosphorylation, we
repeated the SynKinase and SynPhosphatase titration experiment for the
best-performing modification
1 of the assay.

#### BUILD

In the kinase titration experiment, we again transfected the cells with
increasing amounts of
kinase. In SynPhosphatase titration, each condition contained 20 ng kinase to
saturate
phosphorylation, with varying SynPhosphatase levels. Amounts of the reporter
components were
constant: 25 ng SH2-VP64, 5 ng Gal4DBD-CD3ζ, and 10 ng 5xUAS-NanoLuc. 48 hours
after transfection,
cells were lysed and NanoLuc expression measured by the Nano-Glo assay.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/ics/cyc2-fig9.svg)

---

**Figure 8: Phosphatase titration experiment
for assay
modification 1.** HEK293T cells were transfected 24 hours
post-seeding, with a fixed 20
ng of kinase to saturate the phosphorylation of the substrate, and
increasing SynPhosphatase
amounts ranging from 1 ng to 20 ng. The same reporter amounts were used:
25 ng of Gla4DBD-Cd3Z,
5 ngSH2-VP64, 10 ng of UAS-NanoLuc, along with 10 ng FFLuc. Luminescence
was measured 48 hours
post-transfection with Tecan plate reader. Data are presented as the
mean ± SEM from n = 3
technical replicates.

#### TEST

Phosphorylation-dependent reporter activation was already detectable with 0.4
ng of kinase
plasmid, and NanoLuc expression reached a plateau at 15 ng of kinase,
proving high sensitivity and
a broad dynamic range of the readout system (Fig. 8).

In the SynPhosphatase titration, dephosphorylation was visible at a
concentration of 3 ng
SynPhosphatase , and complete suppression of the phosphorylation signal was
achieved with 15 ng
SynPhosphatase (Fig. 9). These results confirm that the assay is capable of
resolving
kinase-mediated 'on' states and SynPhosphatase-induced 'off' states with
well-defined thresholds.

#### LEARN

From this iteration, we learned that the transcriptional readout assay was
sensitive even at low
kinase levels, with a detectable signal starting at 0.4 ng kinase and
plateauing at 20 ng, which
we adopted for subsequent experiments to minimize cellular stress.
Similarly, phosphatase
titration revealed that dephosphorylation was evident at 3 ng SynPhosphatase
and complete
substrate dephosphorylation occurred beyond 10 ng. This defined the
functional dynamic range of
our assay for both phosphorylation and dephosphorylation activities.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/ics/cyc2-fig-10a-b.svg)

---

**Figure 9: Test of the dPhosphatase (Panel A)
and the dKinase
(Panel B) catalytic activity.** Panel A: Transfection of 20 ng
of
kinase to saturate the
phosphorylation, and either dead or active phosphatase variant (as stated on
figure).
Additionally, fixed amounts of modified reporter assay plasmids were
transfected: 25 ng
Gal4DBD-CD3ζ, 5 ng SH2-VP64, and 10 ng UAS-NanoLuc, along with 10 ng Firefly
Luciferase. Panel B:
Transfection of 25 ng Gal4DBD-CD3ζ, 5 ng SH2-VP64, 10 ng UAS-NanoLuc, and
with 10 ng Firefly
Luciferase. On Y axis the increasing amounts (20 ng, 30 ng) of either active
SynKinase or
catalytically dead SynKinase are labeled.HEK293T cells were transfected 24
hours post-seeding.
Luminescence was measured 48 hours post-transfection with Tecan plate
reader. Data are presented
as the mean ± SEM from n = 3 technical replicates. Statistical significance
was determined with
Ordinary one-way ANOVA with Tukey’s multiple comparison test, with a single
pooled variance. \*p < 0.05, \*\*\*\*p < 0.001.

#### DESIGN

Having our assay fully optimized and characterized, we introduced the final
touchups. We observed
differences in cell viability, reflected by FFLuc signal, between transfection
conditions.
Conditions with higher total amounts of coding constructs tended to have reduced
viability. Although
DNA amounts were equalized using pBlueScript filler plasmid, this did not fully
normalize cellular
stress due to differences in expressed protein load. To control for this, we
cloned catalytically
inactive variants of both SynKinase and SynPhosphatase enzymes and tested their
impact in the
optimized luminescence readout assay, checking if they produced any non-specific
signal independent
of catalytic activity. In parallel, we cloned enzyme versions without the
coiled-coils to see if
there is any affinity to the substrate that is not proximity-induced through
complementary coiled-coils
dimerization.

#### BUILD

We performed PCR deletions of the catalytic residues of the kinase, followed by
KLD circularization
to create inactive enzyme variants. To eliminate kinase catalytic activity while
preserving the
structural integrity of the ABL kinase domain, we introduced two point
mutations: K271R and Y393A.
Similarly, we generated a catalytically inactive SynPhosphatase variant by PCR
deletion and KLD
cloning. As a stringent control, we introduced D181A and R221M substitutions in
the phosphatase
domain. Similarly, we deleted the coiled-coil domains (bZipEE) from both
enzymes, resulting in
truncated SynKinase and SynPhosphatase variants. This allowed us to evaluate the
impact of
coiled-coil-mediated proximity on catalytic activity within the assay.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/ics/cyc2-fig12.svg)

---

**Figure 10: Test of the role of the bZipEE
coiled-coil in
SynKinase-mediated phosphorylation.** HEK293T cells were
transfected 24 hours
post-seeding, with increasing amounts (0, 10, 20, or 30 ng) of SynKinase
either with or without
the bZipEE coiled-coil domain, as indicated. Fixed amounts of
modified reporter assay
plasmids were also included: 25 ng Gal4DBD-CD3ζ, 5 ng SH2-VP64, and 10
ng 5xUAS-NanoLuc along
with 10 ng FFLuc for normalization. Luminescence was measured 48 hours
post-transfection with
Tecan plate reader. Data are presented as the mean ± SEM from n = 3
technical replicates.
Statistical significance was determined with Ordinary one-way ANOVA with
Tukey’s multiple
comparison test, with a single pooled variance. \*p < 0.05, \*\*\*\*p <
0.001.

#### TEST

As expected, conditions with catalytically dead phosphatase (dSynPhosphatase)
exhibited
significantly higher luminescence compared to those with active phosphatase.
This indicates
effective dephosphorylation only occurs in the presence of functional
SynPhosphatase, confirming
that the phosphatase activity was successfully abolished (Fig. 10A).
Conversely, conditions
containing catalytically inactive SynKinase (dSynKinase) showed negligible
signal compared to
robust reporter activation with active SynKinase (Fig. 10B) also confirming
successful deletion of
catalytic activity.

In all tested transfection amounts, the SynKinase lacking the bZipEE
coiled-coil domain
produced a significantly lower luminescence signal compared to the SynKinase
containing the coiled-coil
(Fig. 11). This confirms that the great majority of kinase activity in the
system requires coiled-coil-mediated proximity for efficient substrate
phosphorylation. Notably,
a significant increase
in signal from 0 to 30 ng of the kinase without coiled-coil was observed,
corresponding to a residual
background enzymatic activity, but this level remained far below that seen
with the fully
functional, coiled-coil-containing SynKinase. These results demonstrate that
loss
of the coiled-coil
domain effectively disables the engineered interaction and substrate
targeting for the kinase,
suppressing most of its activity in the assay.

#### LEARN

This iteration demonstrated that dSynPhosphatase and dSynKinase, as
catalytically dead variants,
can now serve as optimal filler constructs to balance metabolic load across
conditions, ensuring
more consistent cell viability measurements across experimental conditions.
Their lack of
luminescent signal confirms they do not contribute to phosphorylation or
dephosphorylation
activity in the assay. Furthermore, the significantly abolished activity of
kinase without coiled-coil
variants confirms that efficient substrate phosphorylation is strictly
proximity-based, mediated
by engineered coiled-coil interactions.

## Responding

### Optimization of PhosphoTEV

We designed and developed a phosphorylation inducible TEV protease variant, based on a split TEV
system.
The first two iterations focused on validating existing TEV systems working with the
NanoLuc-RELEASE
reporter. The following four iterations include the engineering of the initial split PhosphoTEV
design and
the following adaptation of a single chain version. The final iteration shows an application of
PhosphoTEV-RELEASE for the secretion of IL-12 as a proof-of-concept for the integration of the
system into
circuits including receptors and therapeutically relevant output proteins.

Iteration 1

Iteration 2

Iteration 3

Iteration 4

Iteration 5

Iteration 6

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/effector-tobi/e01-nano.svg)

---

**Figure 1:** Secreted NanoLuc of first
RELEASE
transfection. NanoLuc luminescence in 10 µL of supernatant was measured 48 hours
post-transfection.
Data is depicted as the mean +/- SEM with n = 3 technical replicates.

#### DESIGN

To validate the RELEASE system working in our hands, we started with plasmids from
the original
publication by Vlahos et al. (2022), containing the SEAP-RELEASE construct, a full
TEV and split TEV
halves fused to FKBP and FRB, both fused to a p450 sequence for ER localization.
Upon recommendation
by Dr. Leo Scheller to use a NanoLuc luciferase (NanoLuc) readout, we designed a
RELEASE construct
where we added an IL-10 secretion tag and replaced SEAP with a NanoLuc-IgG-Fc fusion
protein for which
he provided the sequence. As we wanted to test a split TEV system as early as
possible, we also
planned to use the Rapalog (AP21967) -inducible FKBP-nTEV and FRB-cTEV split TEV.
For this, we needed
to remove the p450 signal from the plasmids we already had, to keep it closer to the
final system.

#### BUILD

After acquiring the plasmids and planning the cloning, we PCR amplified the RELEASE
vector and the
NanoLuc-IgG insert and prepared the IL-10 secretion signal through oligo anneal. The
NanoLuc-RELEASE
plasmid was then assembled by golden gate assembly with BsaI. To remove the p450
signal from the
FKBP/FRB split TEV, we did a PCR to amplify everything but the signal, followed by
KLD to circularize
the PCR products. To test our reporter in a minimal system, we co-transfected it
with different
amounts of full TEV and Rapalog-inducible split TEV.

#### TEST

The conditions with rapalog-inducible split TEV were induced with 100 nM Rapalog 42
hours
post-transfection. NanoLuc luminescence in the supernatant was measured another 6
hours later. This
experiment was also used to establish our secreted NanoLuc assay protocol, reducing
the amount of
reagent used in all following experiments. Luciferase signal increased with more
transfected full TEV,
but there was no visible induction of split TEV with Rapalog induction beyond
background levels.

#### LEARN

The increasing NanoLuc signal with more protease showed that the RELEASE system with
our NanoLuc
construct worked as intended and demonstrated a fairly low leakage without protease.
As there was no
activity beyond background in both the induced and uninduced split TEV, it suggested
that either the
induction was too short, or the constructs didn’t work as intended, perhaps due to
the removal of the
p450 signal, preventing ER localization.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/effector-tobi/e02-rapa.svg)

---

**Figure 2: Rapalog induced NanoLuc
secretion** NanoLuc
luminescence measured in supernatant, 24 hours after induction with 100 nM
Rapalog. Data is depicted
as the mean +/- SEM with n = 3 technical replicates.

#### DESIGN

As the Rapalog-inducible split TEV system without p450 did not work in the previous
experiment, we
wanted to test the original plasmids with p450, as this was the exact system Vlahos
et al. (2022) used
with RELEASE before. Additionally, we planned to induce for longer than 6 hours to
allow for signal
accumulation, making an induction more visible.

#### BUILD

In order to get the inducible split TEV to work, we increased the transfected
plasmid amount from 5,
10 and 20 ng of each half to 20 and even 50 ng. Additionally, we included conditions
with the same
amount of ER-localized split TEV. To achieve more signal accumulation, we induced 24
hours after
transfection and measured 24 hours after induction.

#### TEST

In contrast to the first experiment, there was a visible change in secreted NanoLuc
for both the
induced and uninduced conditions when comparing them to the negative control without
any protease.
However, while there was no increase with induction for the ER-localized versions,
there was a 2-fold
increase in signal for the 20 ng of cytosolic split TEV condition.

#### LEARN

We could clearly see an increase in secreted NanoLuc with both the ER-tethered and
the cytosolic
split TEV. As there was no induction for the ER variants, we hypothesized that the
system was
saturated, because we transfected significantly more plasmid than in the original
publication (Vlahos
et al., 2022). As we saw a 2-fold increase in signal with Rapalog induction, we
concluded that our
split TEV itself worked and we moved on towards tailoring a split TEV towards our
phosphorylation
circuit.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/effector-tobi/tobi-eng-3.svg)

---

**Figure 3: Schematic of the split PhosphoTEV design
and Kinase dose
response of split PhosphoTEV.** (A) The CD3ζ-domain fused to cTEV
can
be phosphorylated by
the SynKinase through the dimerization of bZipEE and -RR. Thus, SH2 fused to
nTEV binds to the
phosphorylated substrate, reconstituting the split TEV. (B) HEK293T cells were
co-transfected with
NanoLuc RELEASE, 20 ng of each TEV half or 10 ng of full TEV, and increasing
amounts of SynKinase.
Supernatant with secreted NanoLuc was measured 48 h post transfection. Data is
depicted as the mean
+/- SEM with n = 3 technical replicates. Ordinary Two-way ANOVA with
Tukey's multiple
comparison's
test. \*P<0.05, \*\*P<0.01, \*\*\*P<0.001, \*\*\*\*P<0.0001; ns, not
significant.

#### DESIGN

With our split TEV validated, we next designed an initial version of a
phosphorylation-inducible
TEV. For this, we planned to exchange the FKBP and FRB fused to nTEV and cTEV with
the 2xSH2
(SynPhosphobinder) and 3xCD3ζ-bZipRR (SynSubstrate), respectively. This design
allows phosphorylation
of SynSubstrate through the bZip-mediated proximity to SynKinase which in turn leads
to the
phosphorylation-dependent binding of SH2 to the SynSubstrate, reconstituting the
split TEV halves
(Fig. 3). The resulting split TEV system is referred to as split PhosphoTEV.

#### BUILD

We cloned the proposed split PhosphoTEV by PCR amplifying the FKBP-nTEV and FRB-cTEV
vectors so that
we excluded the Rapalog-responsible domains. Then we amplified the 2xSH2 and
bZipRR-3xCD3ζ domains
from the already cloned expression system plasmids and assembled both the 2xSH2-nTEV
and
bZipRR-3xCD3ζ-cTEV by golden gate assembly with BsaI.

#### TEST

To test our first split PhosphoTEV constructs, they were co-transfected with
increasing amounts of
cytosolic bZipEE-ABL (SynKinase). NanoLuc luminescence in the supernatant was
measured 48 hours
post-transfection (Fig. 4). There was a significant increase in TEV activity already
at 5 ng of
SynKinase and the NanoLuc signal increased further with more kinase. At the same
time, the amount of
SynKinase did not have a significant impact on the negative control where no TEV was
transfected and
there was no significant difference in signal between the negative control and the
split TEV condition
without kinase.

#### LEARN

We could successfully induce the activation of split PhosphoTEV with a cytosolic
kinase. This opens
the door to the integration of RELEASE as a post-translational output module into
phosphorylation
circuits. However, the overall activity of the split PhosphoTEV was rather low when
compared to the
negative control. Therefore, further efforts focused on improving on this.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/effector-tobi/tobi-eng-4.svg)

---

**Figure 4: Schematic of the single chain PhosphoTEV
design and Effect of hinge composition on PhosphoTEV performance**
(A) The CD3ζ-domain can be phosphorylated by the SynKinase through the
dimerization of bZipEE and -RR. Thus, SH2 binds to the phosphorylated substrate,
reconstituting the previously separated TEV halves. The hinge can be rather
rigid or flexible. (B) Six hinge variants were tested in the presence of 0, 20,
or 40 ng of cytosolic ABL. Secreted NanoLuc signal was measured 48 h after
transfection. Data is depicted as the mean +/- SEM with n = 2 independent
biological replicates. Ordinary Two-way ANOVA with Tukey’s multiple comparison’s
test. \*P < 0.05, \*\*P < 0.01, \*\*\*P < 0.001, \*\*\*\*P < 0.0001; ns, not significant.

#### DESIGN

After establishing the successful engineering of the split PhosphoTEV, we wanted to
design a new version that could achieve higher protease activity. For this, we
attempted to integrate results from a recent preprint by Cao & Toettcher (2025)
where they developed a phosphorylation dependent allosteric switch and inserted it
into the Gal4 transcription factor. Inspired by this, we planned a design where the
two split PhosphoTEV constructs would be fused into one construct by linking the SH2
and SynSubstrate domains with a variable hinge (Fig. 5). As shown by Cao &
Toettcher, the composition plays an important role in the switching behavior of the
resulting fusion protein, which is why we designed the construct with a GS-rich, a
more rigid AP-repeat linker and a helical, very rigid EAAAK linker and each of them
of length 7 and 17.

#### BUILD

Assembly of the different PhosphoTEV required the reordering of SH2 and nTEV. For
this, the bZipRR-3xCD3ζ-cTEV vector, the nTEV and 2xSH2 fragments were PCR amplified
and the variable hinges were prepared by oligo anneal. These four fragments were
then assembled using golden gate assembly with BsaI. This resulted in six plasmids,
each with a different hinge between SH2 and bZipRR, referred to by the composition
and length of the hinge.

#### TEST

The six PhosphoTEV variants were each co-transfected with NanoLuc-RELEASE and 0,
20 and 40 ng of SynKinase to screen them for switching behavior and find the optimal
variant. 48 hours after transfection, NanoLuc luminescence in the supernatant was
measured (Fig. 6). All variants except 17EAK could successfully be activated using
SynKinase. However, the switching behavior is different between them, with the 7AP
variant showing the highest induction and coming closer to the full TEV positive
control, while having a background on the same level as the negative control without
protease.

#### LEARN

Due to these results, we selected the 7AP variant as the lead candidate and as the
canonical PhosphoTEV. Compared to the split PhosphoTEV, the 7AP variant resulted in
a higher secretion in the activated state. Despite both TEV halves being located on
the same construct, the background did not significantly differ from the background
of the RELEASE system itself. This may be due to the large size of the
2xSH2-7AP-bZipRR-3xCD3ζ insertion (867 amino acids) resulting in a lot of sterical
hindrance.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/effector-tobi/prodomino-eng-5.svg)

---

**Fig. 5: Trace showing the ProDomino predictions
for insertion
tolerance and Functional evaluation of TEV domain insertion
variants.** (A) Higher values
indicate better predicted insertion tolerance. The previously used insertion
position is indicated
with a dashed line, the 6 newly chosen positions with orange lines. (B) HEK293T
cells were
co-transfected with NanoLuc RELEASE, 10 ng SynKinase and §§ ng of the PhosphoTEV
insertion variants
containing the 7AP-PhosphoDomain inserted at different positions predicted by
ProDomino (H28, F100,
D136, N174, L190, K215) or the original site T118. Secreted NanoLuc was measured
48 h post
transfection. Data is depicted as the mean +/- SEM with n = 3 technical
replicates. Ordinary Two-way
ANOVA with Tukey's multiple comparison's test. \*P<0.05,
\*\*P<0.01, \*\*\*P<0.001,
\*\*\*\*P<0.0001;
ns, not significant.

#### DESIGN

Instead of a split protein system, as in the split PhosphoTEV, the 7AP-PhosphoTEV is
rather a
domain-insertion system, albeit a very large insertion. In such systems, the
activity of a protein is
allosterically controlled by inserting a sensing domain into the protein. However,
the properties of
the allostery are strongly affected by the site where the sensing domain is inserted
within the
effector protein. To optimize the insertion site and further improve switching
behavior of the
PhosphoTEV, we used the machine learning model ProDomino, which predicts the domain
insertion
tolerance of proteins at every residue (Wolf et al., 2024). Based on the
predictions, we selected six
new insertion sites: H28, F100, D136, N174, L190 and K215.

#### BUILD

The 2xSH2-7AP-bZipRR-3xCD3ζ fusion domain was PCR amplified to generate the
insertion for all
variants. Then the full TEV plasmid was opened at the different positions through
PCR amplification
and the six plasmids were assembled by golden gate assembly with BsaI.

#### TEST

To benchmark the performance of the new constructs, we co-transfected the six new
insertion sites,
as well as the original insertion into T118, each with 0 and 10 ng of SynKinase and
NanoLuc-RELEASE.
NanoLuciferase in the supernatant was measured 48 hours-post transfection (Fig. 8).
Among the active
variants, D136, L190, and K215 showed moderate induction with fold changes of 3.3x,
3.1x, and 3.4x,
respectively. . L190 and K215 had a higher induced activity than D136, but also
displayed increased
background levels. In contrast, the H28 insertion variant showed the strongest
induction (7.6x),
exceeding the previously used T118 variant (6x). However, this was due to a slightly
reduced
background secretion compared to the original T118 PhosphoTEV.

#### LEARN

We could successfully identify four tolerated insertion sites into the TEV protease.
While they did
not result in a better PhosphoTEV version, judging by overall activity, they are a
promising result,
especially H28. Future efforts may try different insertion sites in close proximity
to it and use
these results as a starting point.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/effector-tobi/f47-il12-release.svg)

---

**Figure 9: Secreted IL-12 can be significantly
increased by SalB
induction.** HEK293T cells were co-transfected with IL-12-RELEASE,
PhosphoTEV and KORD-ABL
plasmids. Cells were induced with 3 µM SalB 24h post-transfection. IL-12 in 100
µL of supernatant
was measured by ELISA 48h post-transfection. Exact quantification of IL-12 was
not possible,
therefore, no conclusive fold change could be calculated, due to experimental
setup. Data is
depicted as the mean +/- SEM with n = 3 technical replicates. Ordinary Two-way
ANOVA with
Sidak's
multiple comparisons test. \*P<0.05, \*\*P<0.01, \*\*\*P<0.001,
\*\*\*\*P<0.0001; ns, not
significant.
Negative control was excluded from tests and is only shown for reference.

#### DESIGN

The goal of all previous iterations of this cycle was to optimize the
PhosphoTEV-RELEASE system
using NanoLuc-RELEASE for ease of readout. Including these optimized conditions, we
sought to
demonstrate the system with a therapeutically relevant protein instead. For this, we
replaced NanoLuc
in the RELEASE construct with a single chain IL-12. The amino acid sequence for the
linked IL-12 was
taken from BBa\_K554005 and codon optimized for expression in mammalian cells.

#### BUILD

After planning out the cloning and waiting for the IL-12 gene fragment, we PCR
amplified the RELEASE
vector and inserted IL-12 through golden gate assembly with BsaI.

#### TEST

To test the IL-12-RELEASE construct, we co-transfected it with the 7AP-PhosphoTEV
and either 50 or
70 ng of KORD-ABL, which could induce phosphorylation and thus secretion in
dependence of SalB in
previous experiments. Cells were induced with SalB 24 hours post-transfection and
secreted IL-12 in
100 µL of supernatant was measured through ELISA with a chemiluminescent readout.
There was a
significant increase with SalB induction for both the 50 and 70 ng ABL-KORD
conditions, with a better
induction at 70 ng.

#### LEARN

With this experiment, we could successfully show that we can secrete a
therapeutically relevant
cytokine in IL-12 and even use it in a ligand-inducible system with KORD-ABL.
However, we faced
several limitations in the methodology as we did not have the exact material
recommended by the
manufacturer, for example the horse radish peroxidase substrate. For this reason,
and because we could
not get a proper standard curve to work, we were unable to exactly quantify the
IL-12 concentration in
mg/mL.

## Sensing — GPCR

### Controlling Phosphorylation in a Switch-like Manner Using a GPCR-based Synthetic Receptor

Starting from the PAGERTF, we engineered switch like synthetic receptors that is able
to
regulate cellular behaviour by sensitively and specifically phosphorylating the CD3𝜁 of our
proessing core
module. We established two receptor classes, one sensitive to a small molecule and the other
sensing
both a
small molecule and a peptide ligand in an AND logic gate like manner.

Iteration 1

Iteration 2

Iteration 3

Iteration 4

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/maxi/first-kord-abl.svg)

---

**Figure 1: Initial Validation of KORD-ABL
functionality.**
HEK293T cells were transfected 24 hours after seeding. NanoLuc and Firefly
Luciferase expression was
measured using the Dual Luciferase assay 24 hours after induction. Induction
occurred 24h after
transfection with either DMSO, or SalB [500 nM] . Two negative controls are
shown.
Negative 1
corresponds to 40 ng of KORD-ABL without 𝛽-Arrestin2-bZipEE. Negative 2
corresponds
to 40 ng of
𝛽-Arrestin2-bZipEE without KORD-ABL. Test conditions contained 20 ng of
KORD-ABL +
20 ng of
𝛽-Arrestin2-bZipEE and 40 ng of KORD-ABL + 40 ng of 𝛽-Arrestin2-bZipEE from
left
to right. Data is
depicted as the mean +/- SEM with n = 3 technical replicates. Two-way ANOVA with
Sidak's
multiple
comparison test. \*\*\*\*P<0.0001

#### DESIGN

To increase the modularity of the sensing layer of our phosphorylation circuit, we
wanted
to
establish an alternative receptor architecture to the modular extracellular sensing
architecture
(MESA). By choosing synthetic GPCRs as a scaffold we consciously selected a receptor
that does not
depend on dimerization, but instead on recruitment of an accessory protein.
Furthermore,
as GPCRs are
highly studied, with many different variants, they represent a good starting point
for
engineering. To
develop a modular, but most importantly tightly regulated receptor, we wanted to
adapt
the
programmable antigen-gated G protein coupled engineered receptor (PAGER) as our
scaffold.

#### BUILD

We assembled the KORD-ABL receptor, by amplifying the GPCR backbone of a
PAGERTF
construct. We then performed Golden Gate Assembly between our truncated ABL and the
KORD. Primers were
chosen to achieve a 22 amino acid long linker between KORD and ABL. We initially
chose
to omit the
extracellular domains to facilitate easier testing and troubleshooting of the
functionality of our
SynKinase, a truncated ABL. We also assembled a modified version of the 𝛽-Arrestin2
used by
Kalogriopoulos et al. (2025). We replaced the C-terminally fused TEV by the bZipEE
coiled coil and
removed the N-terminally fused NanoLuciferase.To investigate the ability of KORD-ABL
to
facilitate
transfection by interacting with our phosphorylation circuit upon SalB induction, we
transfected
HEK293T cells 24 hours after seeding with two different amounts but identical ratios
between KORD-ABL
and 𝛽-Arrestin2-bZipEE. Previously determined, ideal amounts of our phosphorylation
circuit were
co-transfected.

#### TEST

Our test conditions were induced with SalB [500 nM] 24 hours after transfection.
Nanoluciferase
luminescence of all conditions was measured 48 hours after transfection using the
Nano-Glo Dual
Luciferase Reporter Assay System. The results are depicted in the associated figure.
Induction of both
amounts of KORD-ABL and 𝛽-Arrestin2-bZipEE resulted in a significant (p <
0.0001)
increase in
expression level of 15.4-fold and 20.1-fold for 20/20 ng and 40/40 ng, respectively
(Figure 1).

#### LEARN

The first measurement provided a valuable proof-of-concept for combining our
phosphorylation
construct with a modified version of the KORD-ABL construct. As expression
activation
upon
receptor activation was not yet satisfactory, future assays will focus on improving
this. A
valuable insight was the already existing tightness of the system, with no
significant
expression
over background in uninduced conditions.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/maxi/second-kord-abl.svg)

---

**Figure 2: Further ratio optimization of KORD-ABL
to
𝛽-Arrestin2-bZipEE**  HEK293T cells were transfected 24 hours after
seeding. NanoLuc and
Firefly Luciferase expression was measured using the Dual Luciferase assay 24
hours
after induction.
Induction occurred 24 h after transfection with either DMSO, or SalB [1 µM] .
The
first value of
each test conditions corresponds to the ng amount of KORD-ABL and the second
value
corresponds to
the ng amount of of 𝛽-Arrestin2-bZipEE, from left to right. Data is depicted as
the
mean +/- SEM
with n = 3 technical replicates. Two-way ANOVA with Sidak's multiple
comparison
test.\*\*\*P<0.0001

#### DESIGN

One of the key limitations we suspected was that overexpression of 𝛽-Arrestin2
facilitated increased
receptor internalization, reducing the dynamic range of the receptor by reducing the
total achievable
phosphorylation and subsequent expression. After validating that of increasing
𝛽-Arrestin2 amounts
decrease total signal, likely due to enhanced GPCR internalization we wanted to
explore
viable ratios
between KORD-ABL and 𝛽-Arrestin2.

#### BUILD

For this, we co-transfected varying ratios of KORD-ABL and 𝛽-Arrestin2-bZipEE along
with our
phosphorylation circuit and a cell viability normalization control. While we kept
𝛽-Arrestin2 amount
constant at 5 ng per well, we increased KORD-ABl concentration from 30 ng to 80 ng.
The
starting point
at 30 ng was chosen due to a previous assay achieving significantly higher fold
change
with it.

#### TEST

Increasing KORD-ABL amount in relation to 𝛽-Arrestin2-bZipEE is able to
significantly
increase
total output (p<0.0001) and also increases the dynamic range of the receptor,
achieving up to a
149-fold induction of gene expression (Figure 2).

#### LEARN

This highlights, that the ratio between KORD-ABL and 𝛽-Arrestin2-bZipEE, as well as
the
total
amount of KORD-ABL can be modified to effectively tune receptor activity.
Interestingly, the background signal of uninduced conditions stayed consistent even
at
high
amounts of KORD-ABL transfected. Overall, having shown consistent functionality of
ABL
fused to
the C-terminal tail of the KORD, the receptor architecture can be expanded to be
able to
sense
extracellular peptide ligands using a fused nanobody.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/maxi/engineering-abl-figure-3.webp)

---

**Figure 3: SDS-PAGE of different Steps during GFP
purification and
expressed NanoLuc (NLuc) as induced by GFP-PAGER-ABL activation.**
(A)
SDS-PAGE showing
protein fractions at different steps during the purification process. Labels
above
the plot indicate
the purification step at which the sample was aliquoted. (B) NanoLuc and Firefly
Luciferase
expression was measured with Dual Luciferase assay and using a plate reader 48
hours
after
transfection. HEK293T cells were transfected 24 hours after seeding. Induction
occurred 24h after
transfection with either DMSO, DMSO + GFP [1 µM], SalB [500 nM] or SalB [500 nM]
+
GFP [1 µM].
Conditions from left to right: Negative control with only 35 ng of
GFP-PAGER-ABL; 35
ng of
GFP-PAGER-ABL with 5 ng of 𝛽-Arrestin2-bZipEE; 45 ng of GFP-PAGER-ABL with 5 ng
of
𝛽-Arrestin2-bZipEE; 50 ng of GFP-PAGER-ABL with 5 ng of 𝛽-Arrestin2-bZipEE.
Data
is depicted as
the mean +/- SEM with n = 3 technical replicates. Two-way ANOVA with
Tukey's
multiple
comparison
test was performed \*\*\*\*P<0.0001; NS, not significant

#### DESIGN

After validating the functionality of the truncated ABL on the GPCR scaffold, we
wanted
to expand our
design to facilitate extracellular antigen sensing in addition to SalB.Thus, we
wanted
to adapt the
original PAGERTF system in a similar manner to KORD. We selected the
PAGER
platform for its
high modularity, which allows for the detection of diverse extracellular antigens
through nanobody
interfaces. Moreover, given the thorough characterization by Kalogriopoulos et al.
(2025), we
anticipate that other computationally designed binders could also be integrated into
this system. For
our initial tests, we chose to utilize a PAGER with the anti-GFP nanobody Lag2. Lag2
was
selected due
to the higher accessibility and improved experimental workflow when working with GFP
compared to
disease-associated proteins.

#### BUILD

Using Golden Gate Assembly, we replaced the intracellular signaling machinery of
Lag2-PAGERTF with our SynKinase. Similarly to KORD-ABL, the truncated ABL
is
linked to KORD
by a 22 amino acid linker. The engineered construct now contains an N-terminal
Arodyn
fused to Lag2.
The anti-GFP nanobody is fused to the KORD by a flexible GS linker. C-terminally
KORD is
fused to the
SynKinase. To ensure cost-effectiveness during engineering and characterization of
our
receptor
construct, we also purified GFP after bacterial expression.

#### TEST

To purify GFP, we transformed BL21 E. coli cells and inoculated 2 L of growth
medium. Following
incubation, the cells were lysed, and the lysate was centrifuged to remove debris.
The
clarified
supernatant was applied to an affinity column, washed to remove non-specific
proteins,
and GFP was
subsequently eluted. Protein concentration was determined using a Bradford assay,
and
purity was
assessed by SDS-PAGE (Figure 3A).Transfection of three different GFP-PAGER-ABL
amounts was done
to
investigate dose-dependency similarly to KORD-ABL. Compared to the negative control,
GFP-PAGER-ABL was
inducible by SalB only as well as by SalB+GFP (Figure 3B). Although the leakiness in
the
absence of
its peptide ligands shows limited AND-gate functionality, significant induction of
up to
2.6-fold
between SalB only and SalB+GFP was promising. With no, or only the peptide ligand
present, gating
remains highly effective.

#### LEARN

The GFP-PAGER-ABL shows interesting behaviour. It appears to exhibit significant
leakiness in the
presence of SalB but without GFP. Simultaneously, some gating occurs, as shown by
the
significant, up
to 2.6-fold induction between SalB and SalB+GFP. To improve gating, several
strategies
are promising.
First and foremost, titrating different ligand concentrations would give a good
insight
into gating
and saturation behaviour. Engineering the Arodyn also offers a strategy that has the
potential to
improve auto-inhibition. Moreover, swapping the nanobody could also impact gating
behaviour and thus
affect leakiness.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/maxi/second-pager-abl-gfp-salb.svg)

---

**Figure 4: SalB and GFP titration with the
GFP-PAGER-ABL.**
NanoLuc and Firefly Luciferase expression was measured with Dual Luciferase
assay
and using a plate
reader 48 hours after transfection. HEK293T cells were transfected 24 hours
after
seeding. (A)
Induction occurred 24h after transfection with either DMSO, DMSO + GFP [1 µM],
SalB
or SalB + GFP [1
µM]. Each condition contained 50 ng of GFP-PAGER-ABL with 5 ng of

#### DESIGN

To better characterize the leakiness of GFP-PAGER-ABL, and thus gain a better
understanding about the
receptor behaviour, we titrated both GFP and its small molecule ligand SalB. Ligand
titration was done
in large steps from 10 nM to 1 µM to observe receptor behaviour at both extremes.
The
maximum
concentration was chosen according to observation from an initial SalB titration on
KORD-ABL
(BBa\_25K9JYDQ).

#### BUILD

Both titrations were transfected with 50 ng of GFP-PAGER-ABL and 5 ng 𝛽-Arrestin2.

#### TEST

Figure (4A) shows the SalB titration with constant GFP on GFP-PAGER-ABL. Noticeably,
at
intermediate
SalB concentrations (50 nM; 100 nM), full binary switch-like behaviour can be
observed
with
nonsignificant expression above background when induced with just SalB, but
13.8-fold
and 11.6-fold
increase in expression upon both SalB and GFP induction, respectively. GFP titration
showed
significant inducibility down to a concentration of 10 nM GFP (Figure 4B).
Expression
upon only SalB
induction is significantly higher than background for all conditions.

#### LEARN

Titrating both ligands showed that GFP-PAGER-ABL is able to show full AND logic gate
functionality
at low SalB concentration. This also highlights that the inducibility of the
GFP-PAGER-ABL construct
is mainly dependent on SalB, whereas GFP concentration can determine total output
but
not affect
leakiness.
Engineering the construct further using an anti-VEGF nanobody changed the Arodyn
gating
to an extent,
where the receptor does not show significant induction in the presence of just SalB
until 500 nM
(shown in Results and BBa\_250PWRGF).

### Engineering a GPCR Based Synthetic Receptor Able to Act as a Repressor on a Post-translational Level

By adapting the GPCR scaffold, effectively utilized for inducing phosphorylation upon ligand
sensing, we
engineered a receptor that is able to directly interact with, and quench our phosphorylation
circuit
on a
fully post-translational level using our SynPhosphatase.

Iteration 1

Iteration 2

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/maxi/kord-ptpn1-titration.svg)

---

**Figure 1: Titration of KORD-PTPN1 against
KORD-ABL** HEK293
T cells were transfected 24 hours after seeding. NanoLuc and Firefly Luciferase
expression was
measured using the Dual Luciferase assay 24 hours after induction. Induction
occurred 24 h after
transfection, using either DMSO or SalB [500 nM]. Each condition was transfected
with 30 ng of
KORD-ABL and 5 ng of 𝛽-Arrestin2-bZipEE. Data is depicted as the mean +/- SEM
with
n = 3 technical
replicates. Two-way ANOVA with Sidak's multiple comparison test.
\*P<0.05,
\*\*P<0.01,
\*\*\*P<0.001, \*\*\*\*P<0.0001; NS, not significant.

#### DESIGN

To expand our modular receptor system and enable it to directly transduce an
inhibitory
signal on a
post-translational level, we wanted to engineer the KORD-PTPN1: a synthetic GPCR
fused
with our
SynPhosphatase, a truncated version of PTPN1. Similar to the rationale behind the
engineering of
KORD-ABL, we consciously selected synthetic GPCRs as one of our architectures due to
their ability to
recruit an intracellular accessory protein for signal transduction instead of
relying on
dimerization.
To develop a modular, but most importantly tightly regulated receptor, we also
wanted to
adapt the
programmable antigen-gated G protein coupled engineered receptor (PAGER) as our
scaffold
for our
inhibitory receptor.

#### BUILD

To validate the functionality of the SynPhosphatase on a membrane bound receptor, we
initially set
out to engineer the KORD-PTPN1 variant as a simplified receptor model. The construct
was
assembled by
Golden Gate Assembly from the cytosolic SynPhosphatase and the
Lag2-PAGERTF.
After fusion
of the PTPN1 phosphatase to intracellular C-terminal tail of KORD, connected by a 22
amino acid
linker, KLD was done to excise the extracellular GFP sensing domain. To test the
construct for
inducible phosphatase activity, it was first titrated against the KORD-ABL
construct, as
the cytosolic
enzyme variants exhibit significantly stronger activity.

#### TEST

The titration showed that the Synphosphatase is active at the membrane and able to
effectively
quench KORD-ABL phosphorylation in a dose dependent manner (Figure 1). Equal ratios
of
KORD-ABL and
KORD-PTPN1 result in a 3.0-fold reduction of expressed NanoLuciferase compared to
only
KORD-ABL.

#### LEARN

The transfection successfully showed the functionality of our SynPhosphatase when
membrane bound,
and more specifically the functionality of the KORD-PTPN1 construct. To expand upon
this, the
truncated PTPN1 can be fused to the GFP-PAGER construct, allowing for a peptide
ligand
gated,
inducible receptor phosphatase.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/maxi/pager-ptpn1.svg)

---

**Figure 2: Validation of GFP-PAGER-PTPN1
functionality
on a single
cell level**  HEK293T cells were transfected 24 hours after seeding.
Induction occurred 24
hours after transfection. mCherry expression was measured 24 hours after
induction
using flow
cytometry. Excitation lasers at 488 nm and 561 nm were used, and fluorescence
was
measured at 525 nm
and 610 nm for GFP and mCherry, respectively. Induction occurred 24 h after
transfection with either
DMSO, GFP [1 µM] + SalB [500 nM], or SalB [500 nM] or GFP [1 µM]. Data is
depicted
as the geometric
mean of fluorescence +/- geometric SEM with >20000 single cells.

#### DESIGN

Expanding on the previous KORD-PTPN1 construct we wanted to facilitate extracellular
antigen sensing
in addition to SalB. Thus, we wanted to adapt the original PAGERTF system
in
a similar
manner to KORD. We selected the PAGER platform for its high modularity, which allows
for
the detection
of diverse extracellular antigens through nanobody interfaces. Moreover, given the
thorough
characterization by Kalogriopoulos et al. (2025), we anticipate that other
computationally designed
binders could also be integrated into this system.

#### BUILD

The construct was cloned using Golden Gate Assembly from the original
Lag2-PAGERTF
construct. The intracellularly fused transcription factor and AsLOV2 domain were
replaced by the
truncated PTPN1 also utilized on the KORD-PTPN1 with an identical linker length and
make-up. We used a
GFP sensing nanobody in order to retain cost-effectiveness and improve sample
handling.

#### TEST

Analyzing the ability of the GFp-PAGER-PTPN1 to dephosphorylate upon SalB + GFP
induction was
measured at single cell resolution using flow cytometry (Figure 2). Phosphatase
activity
can be
observed by a reduction in mean fluorescence intensity between the negative control
(only KORD-ABL)
and KORD-ABL with GFP-PAGER-PTPN1. It also appears to be dose dependent, with
fluorescence decreasing
further upon addition of more GFP-PAGER-PTPN1. However, AND logic gate functionality
of
the
GFP-PAGER-PTPN1 does not appear to be present, with 1.1-fold reduction between SalB
and
SalB + GFP
induction.

#### LEARN

While we assumed full functionality, and AND logic gate behaviour the construct
displayed
phosphatase activity without further inducibility from GFP. This could indicate a
reduced activation
dependent activity or an error with the construct, leading to reduced or abolished
GFP
sensitivity.
Future engineering will focus on changing linker length and rigidity, as well as
using
differently
truncated versions of PTPN1.

## Sensing — MESA

### Optimization of Kinase-based Gene Expression

We developed rapalog-sensing MESA receptors using kinase-mediated phosphorylation to drive gene
expression.
Iterative engineering of (i) intracellular circuit architecture, (ii) receptor stoichiometry,
(iii)
transmembrane domains selection (iv) extracellular/intracellular linker lengths, and (v) signal
peptide
selection achieved **3.4-fold ligand-dependent induction** with improved cell
viability.

Iteration 1

Iteration 2

Iteration 3

Iteration 4

Iteration 5

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/mesa-rapa/fig1-orimesa-rapa-mesa-engcycl.svg)

---

**Figure 1: Test of rapalog-induced
transcription-factor
cleavage.** Quantification of reporter activation using the Nano-Glo
assay. Cells were
transfected with the complete expression-coupled PHOENICS circuit, varying only
the receptor
configuration as indicated. Neg. ctrl: reporter only. Receptor conditions
contained 9:1, 3:1, and
1:1 ratios of TEV protease to transcription factor halves (20 ng total receptor
plasmid).
NanoLuciferase luminescence values (AU) are normalized by Firefly luminescence
(AU) and display
mean
± SEM. Fluorescence was analyzed by two-way ANOVA. Medium: DMEM (10% FCS). P
values: ns > 0.05; \*
≤
0.05; \*\* ≤ 0.01; \*\*\* ≤ 0.001; \*\*\*\* ≤ 0.0001.

#### DESIGN

Building on insights from [Yang et al., (2025)](#References) and direct
recommendations
from Prof. Dr. Rebecca Wade, we initiated testing of our receptor architecture using
the
well-established MESA receptor system for the rapalog AP21967. We adapted a
construct described by
[Zhou et al., (2023)](#References) in which the transcription factor
Gal4-VP64, linked to
a
TEV cleavage site, was attached to the FKBP receptor half, while a full TEV protease
was fused to
the
FRB receptor half. Upon ligand-induced dimerization, we hypothesized that the TEV
protease would
cleave the transcription factor, releasing it to activate NanoLuciferase expression,
thereby
providing
a quantifiable readout of receptor dimerization.

#### BUILD

HEK293T cells were transfected with varying ratios of the FRB and FKBP receptor
constructs (9:1,
3:1,
and 1:1, with the transcription factor-containing construct in excess). This design
was based on the
hypothesis that a single TEV protease could cleave multiple transcription factors
through cycles of
receptor dissociation and re-dimerization, making transcription factor excess
potentially
advantageous. Twenty-four hours post-transfection, cells were treated with either
200 nM rapalog or
vehicle control (ethanol). After an additional 24 hours, NanoLuciferase expression
was measured via
luminometry and normalized by Firefly luciferase, which was co-transfected on a
separate plasmid
under
a constitutive promoter to control for transfection efficiency.

#### TEST

Normalized luminescence values revealed that both induced and uninduced signals
increased as the
transcription factor ratio decreased relative to the TEV protease construct. The
highest signal was
achieved with equimolar amounts of both receptor halves. A negative control
containing only the
reporter plasmid (no transcription factor) showed negligible luminescence,
confirming assay
sensitivity. The positive control, consisting of cytosolic TEV protease (5 ng) and
transcription
factor-FKBP construct (10 ng), produced the highest luminescence levels, which were
comparable to
those achieved with equal amounts of receptor constructs.

#### LEARN

We observed that different ratios of receptor halves strongly influenced overall
NanoLuciferase
expression and luminescence intensity. However, high background prevented detection
of significant
fold-change between induced and uninduced conditions, challenging the functionality
of this
synthetic
receptor architecture. Additionally, our hypothesis that excess transcription factor
would enhance
signal output was disproved, as maximal luminescence occurred with equimolar amounts
of both
receptor
constructs. These results parallel recent findings by [Teng et
al., (2020)](#References)
who reported similar inducibility issues with MESA-based synthetic receptors. Their
work
demonstrated
that MESA receptors failed to respond to membrane-permeable rapalog despite the
ligand's ability to
cross cellular membranes, suggesting that proper receptor localization and membrane
environment may
be
critical for functionality. Multiple studies have indicated potential ER retention
of MESA receptors
rather than cell surface expression, which could compromise receptor dimerization
even with
membrane-permeable ligands due to differences in membrane composition, molecular
crowding, or
trafficking machinery in the ER versus plasma membrane [Edelstein et al.,
(2020)](#References). Given the variability in reported MESA receptor localization and the
ambiguity of our
results, we decided to investigate the subcellular localization of our receptor
constructs before
proceeding further.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/mesa-rapa/fig2-fkbp-localizaiton-60ngrapa-mesa-engcycl.webp)

---

**Figure 2: Subcellular localization of
sfGFP–Rapalog MESA fusion
receptors.** Fluorescence microscopy of HEK293T cells expressing
sfGFP–FKBP (Rapalog)
MESA
constructs. Left panel A: DAPI (nuclei); right panel B: sfGFP signal. Cells were
transfected with
60
ng FKBP-sfGFP. The fusion construct carries sfGFP at the C terminus, separated
from the
transmembrane domain by a 12-aa GS linker on the intracellular side. Medium:
DMEM (10% FCS).
Images
acquired at 100× magnification. Scale bars, 10 µm.

#### DESIGN

To investigate whether faulty ER retention or impaired receptor trafficking explained
the absence
of
ligand-induced response in Iteration 1, we designed a localization experiment. The
intracellular
domains were replaced with superfolder GFP (sfGFP), creating fluorescent fusion
proteins that would
reveal subcellular distribution in HEK293T cells.

#### BUILD

HEK293T cells were transfected with the sfGFP-tagged receptor constructs. Thirty-six
hours
post-transfection, cells were fixed with paraformaldehyde and nuclei were
counterstained with DAPI.
Fluorescence imaging was performed at 100x magnification using immersion oil.

#### TEST

Fluorescence microscopy showed sfGFP signal at the cell periphery in addition to
intracellular
accumulation, indicating that receptor fusion proteins reached the plasma membrane
and were not
retained entirely in the ER. While image quality limited precise quantification of
membrane versus
intracellular distribution, the observed peripheral localization was sufficient to
rule out complete
trafficking failure.

#### LEARN

These results confirmed plasma membrane localization of our receptor constructs,
ruling out ER
retention or other trafficking defects as the cause of failed ligand induction in
Iteration 1. Given
that receptor localization was not the issue, we hypothesized that the high
background signal
stemmed
from the TEV protease-transcription factor readout architecture itself.
Specifically, potential
constitutive TEV activity or spontaneous transcription factor release may have
obscured
ligand-dependent changes. Consequently, we transitioned to our
phosphorylation-dependent
intracellular
signaling system for subsequent iterations.

#### DESIGN

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/mesa-rapa/fig3-firstcircuit-mesarapaengcycl-v1.svg)

---

**Figure 3: Rapalog-induced reporter activation by
MESA-coupled kinase
receptor (CD28/CD28).** (A) Quantification of SH2-mediated reporter
activation using the
Nano-Glo assay. Cells were transfected with the complete expression-coupled
PHOENICS circuit,
varying
only the receptor configuration as indicatead. Neg. ctrl: RAPA–ABL receptor half
lacking the bZipEE
half. Receptor conditions contained 20, 40, 60, or 80 ng total receptor plasmid.
(B) Firefly
luciferase values indicating cell viability under the same conditions. Cells
were co-transfected
with
equimolar amounts of a Firefly construct under HSV promoter control.
NanoLuciferase luminescence
values (AU) are normalized by Firefly luminescence (AU), both are displayed as
mean ± SEM.
Fluorescence was analyzed by two-way ANOVA. Medium: DMEM (10% FCS). P values: ns
> 0.05; \* ≤ 0.05;
\*\*
≤ 0.01; \*\*\* ≤ 0.001; \*\*\*\* ≤ 0.0001.

Having confirmed membrane localization in Iteration 2, we hypothesized that the
absence of
ligand-induced response in Iteration 1 resulted from high background signal caused
by basal TEV
protease
activity or constitutive transcription factor release. To address this, we
implemented a
phosphorylation-dependent signaling circuit requiring multiple conditional
interactions for
transcriptional activation.
We coupled a truncated, synthetic kinase domain (ABL)
to the FRB
receptor
half and a coiled-coil domain (bZipEE) to the FKBP receptor half. The
complementary coiled-coil
(bZipRR) was fused to a synthetic ABL substrate (synSub) along with the Gal4
DNA-binding domain
(Gal4DBD). The bZipEE-bZipRR pair exhibits moderate affinity, creating a dynamic
equilibrium wherein
the synSub-Gal4DBD construct shuttles between a membrane-proximal state (bound to
receptor-associated
bZipEE, enabling ABL phosphorylation) and a cytosolic state (dissociated, allowing
interaction with
SH2-VP64). Upon ligand-induced receptor dimerization, this proximity-driven
phosphorylation creates a
docking site for an SH2 domain fused to the VP64 activation domain. The
phosphotyrosine-SH2
interaction
reconstitutes full Gal4DBD-VP64 transcriptional activity. Nuclear localization
signals (NLS) and
nuclear export signals (NES) on circuit components facilitate shuttling between the
cytosol and
nucleus,
ensuring that only the fully assembled, phosphorylation-activated transcription
factor accumulates in
the nucleus to drive gene expression. This multi-layered architecture was designed
to minimize
background signal through several orthogonal control mechanisms: (i) spatial
separation of the kinase
and substrate until ligand-induced dimerization, (ii) conditional transcription
factor assembly
dependent on phosphorylation state, and (iii) dynamic protein localization ensuring
signal
amplification
occurs only when all components are properly activated.

#### BUILD

HEK293T cells were transfected with varying amounts of FRB-ABL and FKBP-bZipEE
receptor constructs
(20,
40, 60, 80ng; 1:1 ratio), along with bZipRR-synSub-Gal4DBD and SH2-VP64 constructs
(20 ng each, 1:1
ratio). The induction protocol remained the same with rapalog or vehicle control
(ethanol) being added
twenty-four hours post transfection and NanoLuciferase expression being measured
after subsequent
twenty-four hours. NanoLuciferase expression was measured and normalized by
constitutively expressed
Firefly luciferase (HSV promoter). Cells transfected with the full circuit but with
the RAPA–ABL
receptor half lacking the bZipEE-domain served as a negative control.

#### TEST

A clear dose-dependent increase in luminescence was observed across conditions:
rapalog-induced
samples
always displayed elevated expression, resulting in fold changes between 1.5x and
1.8x All fold changes
with the exception of the 80ng condition, were not significant due to error bars.
With increasing
transfected plasmid amounts both uninduced and induced luminescence scaled roughly
linearly.
Admittedly
the cell viability decreased to extremely low levels as indicated by Firefly
luciferase values, with
the
exception of the negative control.

#### LEARN

The phosphorylation-dependent circuit successfully demonstrated ligand-responsive
behavior with a
clear
increase from uninduced to induced conditions, confirming that engineered cells can
sense and respond
to
environmental AP21967 through our synthetic signaling cascade. The negative control
validated that
transcriptional activation depends on receptor-mediated proximity and
phosphorylation. The titration
proved that by increasing plasmid amounts signal strength can be enhanced, while
also increasing
background. This kept the achieved fold changes consistent at 1.5x These results
validated our circuit
architecture but revealed the need for improved signal-to-noise ratio to achieve
robust, significant
fold changes after induction. Furthermore the dwindling cell viability challenged
the functionality of
our circuit. To address this, we focused on optimizing the receptor components
themselves,
specifically
investigating the effects of linker length and transmembrane domain selection on
receptor dimerization
efficiency and signal transduction in subsequent iterations.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/mesa-rapa/fig4-newtmd-mesarapaengcycl-v1.svg)

---

**Figure 4: Rapalog-induced reporter activation by
MESA-like kinase
receptor (CD28/CD28M3).** (A) Quantification of SH2-mediated
reporter
activation using the
Nano-Glo assay (Fig. 2). Cells were transfected with the complete
expression-coupled PHOENICS
circuit,
varying only the receptor configuration as indicated. Neg. ctrl: RAPA–ABL
receptor half lacking the
bZipEE half. Receptor conditions contained 1:9, 1:4, and 1:1 ratios of ABL to
bZipEE halves and
either
10 or 20 ng total receptor plasmid. (B) Firefly luciferase values indicating
cell viability under
the
same conditions. Cells were co-transfected with equimolar amounts of a Firefly
construct under HSV
promoter control. NanoLuciferase luminescence values (AU) are normalized by
Firefly luminescence
(AU),
both are displayed as mean ± SEM. Fluorescence was analyzed by two-way ANOVA.
Medium: DMEM (10%
FCS).
P values: ns > 0.05; \* ≤ 0.05; \*\* ≤ 0.01; \*\*\* ≤ 0.001; \*\*\*\* ≤ 0.0001.

#### DESIGN

Building on the validated circuit architecture from Iteration 3, we sought to improve
signal-to-noise
ratio and reduce variability by optimizing the MESA receptor components themselves.
Guided by
strategies
described by [(Yang et al., 2025)](#References). and [(Edelstein et
al.,
(2020)](#References)we systematically implemented three receptor engineering approaches:
(i) receptor ratio
optimization, (ii) transmembrane domain (TMD) engineering, and (iii) linker length
variation.
We
replaced the original TMDs with a CD28/CD28M3 heterodimer pair to suppress
ligand-independent
dimerization and reduce background signal. The receptor ratio was explored based on
the hypothesis
that
a single FRB-ABL kinase could sequentially phosphorylate multiple FKBP-bZipEE-bound
substrates through
cycles of coiled-coil association and dissociation, suggesting that bZipEE excess
might
enhance signal
output. Finally, we optimized linker lengths between extracellular and transmembrane
domains, and
between transmembrane and intracellular domains, to balance structural flexibility
(enabling coiled-coil dimerization) with proximity (ensuring efficient substrate
phosphorylation).
Specifically, we
implemented 10×GS linkers flanking the TMD for the FKBP-bZipEE construct, and 10×GS
(extracellular)
and
5×GS (intracellular) linkers for the FRB-ABL construct, compared to the original
12×GS linkers used
previously for both receptor halves.

#### BUILD

HEK293T cells were transfected with varying ratios of FRB-ABL and FKBP-bZipEE
receptor constructs:
1:4,
1:9 (bZipEE in excess), and 1:1, at total plasmid amounts of 10 ng or 20 ng per
receptor pair. The
cytosolic signaling components (bZipRR-synSub-Gal4DBD and SH2-VP64) were
co-transfected as in
Iteration
3. Twenty-four hours post-transfection, cells were treated with 200 nM AP21967 or
vehicle control
(ethanol). After an additional 24 hours, NanoLuciferase expression was measured and
normalized by
Firefly luciferase. As a negative control the full signaling circuit with FRB-ABL
lacking the Rapalog
receptor bZipEE domain (preventing substrate recruitment) was included.

#### TEST

Receptor optimization yielded significant improvements in assay performance.
Statistically
significant
fold-changes were observed with reduced variability across replicates. The 20 ng
transfection
conditions
produced approximately 2-fold induction with rapalog treatment across all tested
receptor ratios. The
10
ng condition showed lower absolute signal (approximately half the luminescence of
the 20 ng
conditions)
but also reduced background, resulting in fold-changes of 3.4x and 2.7x. The
negative control (ABL
without bZipEE) showed minimal activation, validating that substrate recruitment to
the membrane is
essential for circuit function.

#### LEARN

Systematic optimization of TMD selection and linker lengths successfully improved
both
signal-to-noise
ratio and experimental reproducibility. The CD28/CD28M3 heterodimer effectively
reduced
ligand-independent background, while optimized linker lengths enhanced
kinase-substrate proximity and
coiled-coil-mediated recruitment dynamics. The achievement of statistically
significant
3.4x fold induction
represents a substantial improvement over Iteration 3, validating our receptor
engineering strategy.
However, cell viability inconsistencies indicated that further optimization of
experimental conditions
or receptor expression levels could yield additional improvements in subsequent
iterations.

![Results Image](
                  https://static.igem.wiki/teams/5775/images/engineering/mesa-rapa/fig5-newsp-mesarapaengcycl.svg)

---

**Figure 5: Rapalog-induced reporter activation by
MESA-like kinase
receptors with different signal peptides (Iglam2SP/IgKSP).** (A)
Quantification of
SH2-mediated reporter activation using the Nano-Glo assay (Fig. 2). Cells were
transfected with
the complete expression-coupled PHOENICS circuit, varying only the receptor
configuration as
indicated. Neg. ctrl: RAPA–ABL receptor half lacking the bZipEE half. Receptor
conditions
contained a 1:5 ratio of ABL to bZipEE halves and 30 ng total receptor plasmid.
(B) Firefly
luciferase values indicating cell viability under the same conditions. Cells
were co-transfected
with equimolar amounts of a Firefly construct under HSV promoter control.
NanoLuciferase
luminescence values (AU) are normalized by Firefly luminescence (AU), both are
displayed as mean ±
SEM. Fluorescence was analyzed by two-way ANOVA. Medium: DMEM (10% FCS). P
values: ns > 0.05; \* ≤
0.05; \*\* ≤ 0.01; \*\*\* ≤ 0.001; \*\*\*\* ≤ 0.0001.

#### DESIGN

Despite achieving statistically significant ligand-induced responses in Iteration 4,
persistent
variability in absolute signal intensity and cell viability across replicates
prompted investigation
of additional receptor optimization parameters. We hypothesized that inefficient
receptor
trafficking or protein folding stress might contribute to experimental inconsistency
and reduced
cell health. Signal peptides, which direct nascent proteins to the secretory
pathway, critically
influence membrane protein expression levels and cellular stress. Based on
literature indicating
that signal peptide selection can profoundly impact receptor surface expression and
cell viability,
we tested an alternative signal peptide (IgKSP) known for low cellular toxicity. We
compared
receptor constructs using the original signal peptide with those incorporating
IGKSP, maintaining
the optimized CD28/CD28M3 TMDs and linker configurations from Iteration 4.

#### BUILD

HEK293T cells were transfected with FRB-ABL and FKBP-bZipEE receptor constructs at a
5:25 ng ratio
(1:5, bZipEE in excess). Receptor variants were tested with either the original
signal peptide
(Iglam2SP) or the IgKSP signal peptide. Cytosolic signaling components
(bZipRR-synSub-Gal4DBD and
SH2-VP64) were co-transfected as in previous iterations. Twenty-four hours
post-transfection, cells
were treated with 200 nM AP21967 or vehicle control (ethanol). After an additional
24 hours, both
NanoLuciferase and Firefly luciferase were measured. Firefly luciferase expression
served as an
internal control for transfection efficiency and cell viability. Parallel assay
planning and
execution precluded the use of the optimal ratio and transfected amount (1:9 ratio,
10ng total
plasmid amount) determined in Iteration 4 of this engineering cycle.

#### TEST

Signal peptide optimization did not yield substantial improvements in regards to the
observed fold
changes: We detected a 1.3x Fold change for the old signal peptide (Iglam2SP) and a
2.7x fold change
for the newly adapted IgKSP signal peptide. However it must be noted that these fold
changes were
achieved with the suboptimal ratio and 30ng receptor plasmids transfected instead of
the 10ng. The
IgKSP-modified receptors demonstrated markedly improved Firefly luciferase values
compared to the
original constructs, indicating enhanced cell viability and more consistent
transfection (right
panel). Correspondingly, normalized NanoLuciferase expression showed robust
ligand-induced responses
with reduced error bars across replicates (left panel).

#### LEARN

The systematic optimization of signal peptide selection successfully addressed the
persistent cell
viability and variability issues that limited previous iterations. IGKSP
incorporation not only
improved cell health—as evidenced by elevated Firefly luciferase expression—but also
enhanced
overall assay robustness and reproducibility. These results underscore the
importance of signal
peptide selection in synthetic receptor engineering, particularly for applications
requiring
consistent, quantitative readouts. The combination of optimized TMDs (CD28/CD28M3),
linker lengths,
receptor ratios, and signal peptide (IgKSP) established a fully functional,
reproducible MESA
receptor platform capable of reliable ligand-dependent transcriptional control. This
optimized
system provides a robust foundation for subsequent applications, including testing
with alternative
ligands, integration of phosphatase-mediated signal termination, and adaptation to
therapeutic
contexts requiring precise environmental sensing and cellular response.

### Phosphatase-Mediated Quenching of Gene Expression

We engineered phosphatase-fused MESA receptors for ligand-dependent signal termination.
Systematic
optimization of transmembrane domain configurations across three iterations—testing CD28-CD28,
CD28-FGFR1,
and CD28-CD28M3 pairs—achieved **2.8-fold rapalog-induced reporter quenching**,
establishing
proof-of-concept for bidirectional cellular control.

Iteration 1

Iteration 2

Iteration 3

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/mesa-ptpn/fig1-mesa-ptpn-engcyc-v1.svg)

---

**Figure 1: Rapalog-induced reporter quenching by
MESA-coupled
phosphatase receptor.** Quantification of reporter quenching using
the Nano-Glo assay.
Cells
were transfected with the complete expression-coupled PHOENICS circuit, varying
only the receptor
configuration as indicated. Receptors contained CD28 homodimerizing
transmembrane domains. Neg.
ctrl:
reporter only. Receptor conditions contained 1:9, 1:4, and 1:1 ratios of PTPN1
phosphatase to bZipEE
coiled-coil construct (20 ng total receptor plasmid). NanoLuciferase
luminescence values (AU) are
normalized by Firefly luminescence (AU) and display mean ± SEM. Fluorescence was
analyzed by two-way
ANOVA. Medium: DMEM (10% FCS). P values: ns > 0.05; \* ≤ 0.05; \*\* ≤ 0.01; \*\*\* ≤
0.001; \*\*\*\* ≤ 0.0001.

#### DESIGN

Having established ligand-induced phosphorylation in the previous engineering cycle,
we next tested
the
complementary dephosphorylation arm of our MESA architecture. We replaced the ABL
kinase domain with a
truncated, synthetic phosphatase (PTPN1) to investigate ligand-dependent signal
termination. The
hypothesis was that rapalog-induced recruitment of PTPN1 would dephosphorylate the
synthetic
substrate,
reducing transcriptional output. To optimize system performance, we systematically
varied the
stoichiometric ratio of PTPN1-FRB to FKBP-bZipEE receptor constructs (1:9, 1:4 and
1:1). A
constitutively active cytosolic kinase was included to establish basal substrate
phosphorylation,
enabling detection of phosphatase-mediated signal reduction.

#### BUILD

HEK293T cells were transfected with PTPN1 and bZipEE receptor constructs at the
mentioned ratios. The
amount of receptor plasmids transfected was kept at 20ng for each condition.
Cytosolic signaling
components (bZipRR-synSub-Gal4DBD and SH2-VP64) were co-transfected as in previous
iterations along a
cytosolic Kinase to provide basal phosphorylation. Twenty-four hours
post-transfection, cells were
treated with 200 nM AP21967 or vehicle control (ethanol). After an additional 24
hours, both
NanoLuciferase and Firefly luciferase were measured. Firefly luciferase expression
served as an
internal
control for transfection efficiency and cell viability.

#### TEST

Rapalog treatment reduced normalized NanoLuciferase activity across all receptor
ratios compared to
uninduced controls. However, the magnitude of signal reduction was minimal and
lacked statistical
significance for all tested configurations. The 1:4 ratio exhibited the highest
absolute reporter
activity in both induced and uninduced states, while the 1:9 and 1:1 ratios showed
comparable but
lower
overall expression levels. Firefly luciferase values were consistently low across
all conditions,
suggesting reduced cell viability or transfection efficiency, but remained uniform
between receptor
configurations and treatment groups, confirming that observed NanoLuciferase
differences reflected
signaling rather than cytotoxicity.

#### LEARN

The elevated NanoLuciferase expression in uninduced conditions confirmed successful
basal
phosphorylation by the constitutive kinase. The non-significant reduction of
luminescence for Rapalog
induced conditions however suggested inefficient dephosphorylation. We hypothesized
that the homotypic
CD28-CD28 TMD pair, while preventing unwanted constitutive interaction (as evidenced
by high uninduced
signal), may not optimally position the PTPN1 phosphatase domain for substrate
access upon
ligand-induced proximity. Thus, we decided to once again optimize the TMDs for
optimal fold-changes.

Next →

#### DESIGN

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/mesa-ptpn/fig2-mesa-ptpn-engcyc.svg)

---

**Figure 2: Successful rapalog-induced reporter
quenching following
TMD
optimization.** Quantification of reporter quenching using the
Nano-Glo assay. Cells were
transfected with the complete expression-coupled PHOENICS circuit, varying only
the receptor
configuration as indicated. Receptors contained CD28/FGFR1 heterodimerizing
transmembrane domains.
Neg. ctrl: reporter only. Receptor conditions contained 1:3 ratios of PTPN1
phosphatase to bZipEE
coiled-coil construct (20 ng total receptor plasmid). NanoLuciferase
luminescence values (AU) are
normalized by Firefly luminescence (AU) and display mean ± SEM. Fluorescence was
analyzed by two-way
ANOVA. Medium: DMEM (10% FCS). P values: ns > 0.05; \* ≤ 0.05; \*\* ≤ 0.01; \*\*\* ≤
0.001; \*\*\*\* ≤ 0.0001.

Following the suboptimal performance of the CD28-CD28 homotypic TMD pair in Iteration
1, we
hypothesized that a heterotypic TMD combination might provide improved spatial
positioning for
phosphatase-substrate interaction upon ligand-induced dimerization. Based on
literature precedent and
our previous kinase receptor optimization (Rapalog-MESA-Kinase Engineering Cycle),
we selected the
CD28-FGFR1 heterotypic TMD pair.

#### BUILD

HEK293T cells were co-transfected with: (1) FRB-CD28-PTPN1 and FKBP-FGFR1-bZipEE
receptor constructs
at
a 1:3 ratio (20 ng total receptor DNA); (2) cytosolic signaling components
bZipRR-synSub-Gal4DBD and
SH2-VP64; and (3) a constitutively active cytosolic kinase to establish basal
substrate
phosphorylation.
Twenty-four hours post-transfection, cells were treated with 200 nM AP21967 or
vehicle control
(ethanol). After an additional 24 hours, NanoLuciferase and Firefly luciferase
activities were
quantified. Firefly luciferase served as an internal control for transfection
efficiency and cell
viability.

#### TEST

The CD28-FGFR1 TMD pair yielded a statistically significant 2.0-fold reduction in
normalized
NanoLuciferase expression upon rapalog treatment (p < 0.05), representing a
substantial improvement
over
the CD28-CD28 configuration (Iteration 1). However, absolute NanoLuciferase levels
in the induced
state remained notably elevated compared to the negative control, indicating
incomplete suppression of
basal kinase activity. Firefly luciferase values were reduced in
receptor-transfected conditions
relative to controls, consistent with decreased cell viability observed across
previous phosphatase
receptor iterations. Interestingly, rapalog treatment slightly increased Firefly
expression compared
to the uninduced receptor condition.

#### LEARN

The CD28-FGFR1 heterotypic TMD pair significantly outperformed the CD28-CD28
homotypic
configuration, yielding a significant reduction of expression. However, the elevated
signal in the
induced condition—well above negative control baseline—indicated that phosphatase
activity remained
insufficient to fully counteract constitutive kinase-mediated phosphorylation. The
observation that
different TMD pairs yielded markedly different functional outcomes underscored the
critical
importance of TMD selection in phosphatase-based receptor engineering. To further
improve dynamic
range, we opted to explore additional heterotypic TMD combinations with alternative
geometries to
enhance phosphatase-substrate engagement.

#### DESIGN

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/mesa-ptpn/fig3-mesa-ptpn-engcyc-v1.svg)

---

**Figure 3: Enhanced rapalog-induced reporter
quenching with further
TMD
optimization.** Quantification of reporter quenching using the
Nano-Glo assay. Cells were
transfected with the complete expression-coupled PHOENICS circuit, varying only
the receptor
configuration as indicated. Receptors contained CD28/CD28-M3 heterodimerizing
transmembrane domains.
Neg. ctrl: reporter only. Receptor conditions contained 1:4 ratios of PTPN1
phosphatase to bZipEE
coiled-coil construct (20 ng total receptor plasmid). NanoLuciferase
luminescence values (AU) are
normalized by Firefly luminescence (AU) and display mean ± SEM. Fluorescence was
analyzed by two-way
ANOVA. Medium: DMEM (10% FCS). P values: ns > 0.05; \* ≤ 0.05; \*\* ≤ 0.01; \*\*\* ≤
0.001; \*\*\*\* ≤ 0.0001.

As a last step to further optimize phosphatase-mediated signal termination, we
implemented a
CD28-CD28M3 heterotypic TMD pair, replacing the FGFR1 domain from Iteration 2. This
TMD combination
was
also tested for TNF-alpha in the following engineering as it was reported to work
reliably by [Edelstein et al., (2020)](#References). Additionally, we
reduced total receptor DNA from 20
ng to 10 ng to address the persistent cell viability concerns observed in previous
iterations.

#### BUILD

HEK293T cells were co-transfected with: (1) FRB-CD28-PTPN1 and FKBP-CD28M3-ZipEE
receptor constructs
at
a 1:4 ratio (10 ng total receptor DNA); (2) cytosolic signaling components
bZipRR-synSub-Gal4DBD and
SH2-VP64; and (3) constitutively active cytosolic kinase for basal substrate
phosphorylation.
Twenty-four hours post-transfection, cells were treated with 200 nM AP21967 or
vehicle control
(ethanol). After an additional 24 hours, NanoLuciferase and Firefly luciferase
activities were
quantified.

#### TEST

The CD28-CD28M3 TMD pair yielded a statistically significant 2.8-fold reduction in
normalized
NanoLuciferase expression upon rapalog treatment, representing a 40% improvement
over the CD28-FGFR1
configuration (2.0-fold, Iteration 2). Induced NanoLuciferase levels approached but
remained above the
negative control baseline (which lacked constitutive kinase and reflected only basal
substrate
phosphorylation). Notably, Firefly luciferase values improved substantially compared
to previous
iterations, indicating enhanced cell viability at the reduced 10 ng transfection
amount. Furthermore,
rapalog-treated samples consistently exhibited higher Firefly expression than
uninduced
receptor-transfected cells, supporting the hypothesis that receptor
overexpression—rather than ligand
or
vehicle toxicity—drives the observed viability reduction. This suggests that
ligand-induced signaling
may partially alleviate cellular stress associated with high receptor expression.

#### LEARN

The CD28-CD28M3 heterotypic TMD pair achieved the strongest phosphatase-mediated
signal termination
to
date (2.8-fold reduction), validating this configuration as the optimal among tested
TMD combinations
for PTPN1. The progressive improvement across iterations—from non-significant
reduction (CD28-CD28) to
2.0-fold (CD28-FGFR1) to 2.8-fold (CD28-CD28M3)—demonstrates that TMD selection
critically influences
phosphatase positioning and catalytic efficiency. The correlation between this TMD
pair's superior
performance in both kinase-based and phosphatase-based architectures suggests that
CD28-CD28M3
provides
favorable general properties for MESA receptors, including optimal membrane
orientation, inter-domain
spacing, and stability. While induced NanoLuciferase levels did not fully reach
negative control
baseline, the substantial signal reduction established a proof-of-concept for
phosphatase-mediated
repression within the MESA platform. These results validated the modularity of the
receptor
architecture, confirming compatibility with both kinase (signal amplification) and
phosphatase (signal
termination) effector domains—a critical capability for engineering bidirectional,
tunable cellular
responses. Residual signal in the induced state likely reflects incomplete
kinase-phosphatase
balancing
and could be further optimized through: (1) fine-tuning constitutive kinase
expression levels; (2)
exploring alternative phosphatase domains with higher catalytic efficiency; or (3)
adjusting linker
lengths to enhance substrate accessibility.

←
Previous

### Expanding Sensing Repertoire to Protein Ligands

We engineered TNF-α-responsive MESA receptors using homodimeric anti-TNF-α scFv domains to
detect
cancer-relevant protein ligands. Systematic transmembrane domain optimization across three
iterations
progressed from 1.8-fold induction to **3.7-fold TNF-α-dependent activation**,
demonstrating
platform adaptability beyond small molecules.

Iteration 1

Iteration 2

Iteration 3

#### DESIGN

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/mesa-tnf/fig1-plr071-tnf-localization.webp)

---

**Figure 1: Subcellular localization of scFv-sfGFP
MESA fusion
receptors.** Fluorescence microscopy of HEK293T cells expressing
scFv-TNFalpha MESA
constructs. Left panel A: DAPI (nuclei); right panel B: sfGFP signal. Cells were
transfected with
120
ng TNFalpha-scFv-sfGFP. The fusion construct carries sfGFP at the C terminus,
separated from the
transmembrane domain by a 12-aa GS linker on the intracellular side. Medium:
DMEM (10% FCS). Images
acquired at 100× magnification. Scale bars, 10 µm.

Following confirmation of membrane localization in Iteration 1, we proceeded to test
ligand-dependent
signaling by constructing functional TNF-α sensing receptors. We coupled the
synthetic ABL kinase
domain
to one receptor half and the bZipEE coiled-coil to the other, both carrying
identical anti-TNF-α
scFv
domains to create a homodimeric receptor pair (in regards to the extracellular
domains). Since TNF-α
is
a homotrimeric ligand, we hypothesized that TNF-α binding would induce receptor
clustering and
proximity-driven ABL-mediated phosphorylation of the bZipEE-recruited substrate. The
intracellular
signaling cascade consisted of the bZipRR-synSub-Gal4DBD fusion protein and SH2-VP64
transcriptional
activator, driving NanoLuciferase reporter expression as established in rapalog
receptor iterations.
To
identify optimal receptor stoichiometry, we tested two different DNA amounts (10 ng
and 20 ng total)
with an ABL:ZipEE ratio of 1:4 (with bZipEE in excess based on rapalog
optimization).

#### BUILD

HEK293T cells were transfected with varying amounts and ratios of TNF-scFv-ABL and
TNF-scFv-ZipEE as
described in the previous section. Twenty-four hours post-transfection cells were
induced with either
20ng/ml TNF-alpha or vehicle control (H2O). NanoLuciferase expression was measured
after subsequent
twenty-four hours and normalized to constitutively expressed Firefly luciferase (HSV
promoter).

#### TEST

Normalized NanoLuciferase values exhibited significant fold changes of 1.8x each for
induction with
TNF-α. The negative control (reporter only) showed no TNF-α response, confirming
that the signal
required both receptor halves. Firefly luciferase values remained consistent across
conditions,
indicating that observed differences reflected signaling rather than cytotoxicity.

#### LEARN

The 1.8-fold induction confirmed ligand-dependent receptor functionality, validating
our homodimeric
TNF-α sensing design. However, we observed that increasing total plasmid DNA from 10
ng to 20 ng
elevated both induced signal and basal background proportionally, resulting in
equivalent fold changes
across conditions. This suggested that receptor expression levels alone would not
improve dynamic
range
without addressing background activity. We hypothesized that alternative TMD pairs
might reduce basal
signaling while maintaining ligand responsiveness. Drawing from our rapalog receptor
optimization,
where
systematic TMD screening yielded substantial performance improvements, we identified
two promising
candidates for testing: the FGFR1-FGFR1 homodimer and CD28-CD28M3 heterodimer
configurations. We
proceeded to evaluate these TMD variants using flow cytometry to assess their impact
on receptor
expression and background signaling before committing to full functional assays.

#### DESIGN

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/mesa-tnf/fig2-mesa-tnf-engcyc.svg)

---

**Figure 2: TNF-α-dependent signaling validated with
scFv-MESA
receptors.** Quantification of reporter quenching using the Nano-Glo
assay. Cells were
transfected with the complete expression-coupled PHOENICS circuit, varying only
the receptor
configuration as indicated. Receptors contained CD28/CD28-M3 heterodimerizing
transmembrane domains.
Neg. ctrl: reporter only. Receptor conditions contained 1:4 and 1:9 ratios of
ABL to bZipEE coiled-coil constructs (10 ng and 20 ng total receptor plasmid).
NanoLuciferase
luminescence values (AU)
are normalized by Firefly luminescence (AU) and display mean ± SEM. Fluorescence
was analyzed by
two-way ANOVA. Medium: DMEM (10% FCS). P values: ns > 0.05; \* ≤ 0.05; \*\* ≤ 0.01;
\*\*\* ≤ 0.001; \*\*\*\* ≤
0.0001.

Following the suboptimal performance of the CD28-CD28 homotypic TMD pair in Iteration
1, we
hypothesized that a heterotypic TMD combination might provide improved spatial
positioning for
phosphatase-substrate interaction upon ligand-induced dimerization. Based on
literature precedent and
our previous kinase receptor optimization (Rapalog-MESA-Kinase Engineering Cycle),
we selected the
CD28-FGFR1 heterotypic TMD pair.

#### BUILD

HEK293T cells were co-transfected with: (1) FRB-CD28-PTPN1 and FKBP-FGFR1-bZipEE
receptor constructs
at
a 1:3 ratio (20 ng total receptor DNA); (2) cytosolic signaling components
bZipRR-synSub-Gal4DBD and
SH2-VP64; and (3) a constitutively active cytosolic kinase to establish basal
substrate
phosphorylation.
Twenty-four hours post-transfection, cells were treated with 200 nM AP21967 or
vehicle control
(ethanol). After an additional 24 hours, NanoLuciferase and Firefly luciferase
activities were
quantified. Firefly luciferase served as an internal control for transfection
efficiency and cell
viability.

#### TEST

The CD28-FGFR1 TMD pair yielded a statistically significant 2.0-fold reduction in
normalized
NanoLuciferase expression upon rapalog treatment (p < 0.05), representing a
substantial improvement
over
the CD28-CD28 configuration (Iteration 1). However, absolute NanoLuciferase levels
in the induced
state remained notably elevated compared to the negative control, indicating
incomplete suppression of
basal kinase activity. Firefly luciferase values were reduced in
receptor-transfected conditions
relative to controls, consistent with decreased cell viability observed across
previous phosphatase
receptor iterations. Interestingly, rapalog treatment slightly increased Firefly
expression compared
to the uninduced receptor condition.

#### LEARN

The CD28-FGFR1 heterotypic TMD pair significantly outperformed the CD28-CD28
homotypic
configuration, yielding a significant reduction of expression. However, the elevated
signal in the
induced condition—well above negative control baseline—indicated that phosphatase
activity remained
insufficient to fully counteract constitutive kinase-mediated phosphorylation. The
observation that
different TMD pairs yielded markedly different functional outcomes underscored the
critical
importance of TMD selection in phosphatase-based receptor engineering. To further
improve dynamic
range, we opted to explore additional heterotypic TMD combinations with alternative
geometries to
enhance phosphatase-substrate engagement.

#### DESIGN

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/mesa-tnf/fig3-mesa-tnf-engcyc.svg)

---

**Figure 3: FGFR1 transmembrane domains achieve
superior TNF-α
receptor
performance.** Quantification of TNFα-induced
phosphorylation-mediated CD3ζ-SH2 constitution. Cells were transfected with the
complete expression-coupled PHOENICS circuit, varying only the receptor
configuration as indicated. Neg. ctrl: No receptor. Receptor conditions
contained specified ratios of ABL to bZipEE halves (170 ng total DNA in 12-well
plate wells). SplitFast fluorescence was quantified with flow cytometry.
Dimerization dependent SplitFAST fluorescence was quantified by flow cytometry
48 hours after transfection, employing excitation lasers at 488 nm and 561 nm.
Emission was recorded at 525 nm for splitFAST (Ligand: TFLime) and at 610 nm for
constitutively expressed mCherry, serving as a transfection control. (A)
Fluorescence profiles of viable single cell populations are displayed in
ridgeline plot, (B) geometric mean fluorescence (RFU) ± geometric SEM with
~9000 single cells are shown in barplot.

We selected two TMD combinations previously demonstrated to yield high
signal-to-noise ratios in
synthetic receptor applications: the heterotypic CD28-CD28M3 pair (1:4 ABL:ZipEE
ratio) and the
homotypic FGFR1-FGFR1 pair (1:9 ABL:ZipEE ratio). These ratios were chosen based on
the
stoichiometries
reported for optimal performance with each TMD pair. We hypothesized that
appropriate TMD selection
would reduce constitutive receptor clustering while maintaining ligand-induced
proximity, thereby
improving dynamic range.

#### BUILD

HEK293T cells were transfected with either a 1:9 ratio of FGFR1-ABL to FGFR1-ZipEE or
1:4 ratio of
CD28-ABL to CD28M3-ZipEE. Twenty-four hours post-transfection cells were induced
with either 20ng/ml
TNF-alpha or vehicle control (DMSO). After subsequent twenty-four hours cells were
measured with
Fluorescence Assisted Cell Sorting (FACS).

#### TEST

Flow cytometry revealed substantial differences in signal-to-noise ratios between TMD
configurations.
The FGFR1-FGFR1 receptor pair (1:9 ratio) exhibited basal fluorescence comparable to
the negative
control in the uninduced state, indicating minimal constitutive signaling. TNF-α
treatment of this
condition yielded a 3.7-fold increase in reporter expression. In contrast, the
CD28-CD28M3 receptor
pair
(1:4 ratio) displayed elevated basal fluorescence in the uninduced state—exceeding
even the
TNF-α-induced FGFR1 condition—resulting in only a 1.5-fold induction upon ligand
treatment. Both
configurations showed TNF-α responsiveness, but the FGFR1 pair demonstrated superior
dynamic range.

#### LEARN

TMD optimization substantially improved assay performance, with the FGFR1-FGFR1
homotypic pair
outperforming the CD28-CD28M3 heterotypic pair in both fold-change (3.7× vs. 1.5×)
and background
suppression. The elevated basal activity of CD28-CD28M3 receptors likely reflects
increased
constitutive
TMD association, consistent with the heterotypic interaction promoting spontaneous
clustering
independent of ligand. Conversely, the FGFR1-FGFR1 configuration maintained low
background while
preserving TNF-α responsiveness, suggesting that homotypic FGFR1 TMDs balance weak
basal interaction
with ligand-induced proximity. Based on these results, we selected the FGFR1-FGFR1
TMD pair at a 1:9
ratio for subsequent assays. Future work would focus on further reducing variability
and improving
statistical significance through additional parameter refinement, following the
multi-dimensional
optimization strategy validated in rapalog receptor development.

## Additional Modules

### Mutational Separation of EnvZ/OmpR Enzymatic Functions

We engineered a bacterial two-component system (TCS) as an orthogonal signaling module.
Based on the
EnvZ/OmpR pair, separate kinase and phosphatase variants were created to fine-tune OmpR
phosphorylation in
mammalian cells. Reporter assays confirmed specific activation by the kinase and revealed
background
binding
of unphosphorylated OmpR, guiding further optimization of reversibility and signal strength.

Iteration 1

Iteration 2

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/ics/tcs-eng2.svg)

---

**Figure 1: Reporter assay of the synthetic
EnvZ/OmpR
system.**
HEK293T
cells were transfected with 10 ng of OmpR together with the indicated
amounts of kinase (EnvZK)
and
phosphatase (EnvZP) variants. Reporter activity was measured 48 h post
transfection using a Dual
Luciferase
assay, with Firefly luciferase normalized to constitutively expressed
Renilla luciferase. Bars
represent
mean ± SEM, n = 3 technical replicates.

#### DESIGN

To establish an alternative signaling network alongside our PHOENICS circuit, we
explored the use
of
two-component systems (TCS) as an orthogonal mechanism for intracellular
communication. The major
advantage of
TCSs is their complete orthogonality to endogenous mammalian signaling pathways,
making them ideal
for
modular
and interference-free circuit design.
TCSs are widespread bacterial signal
transduction systems
that enable
cells to sense and respond to
environmental cues such as osmolarity, pH, or nutrients. They typically consist
of a histidine
kinase
(HK) that
detects a stimulus and autophosphorylates on a conserved histidine residue. The
phosphate group is
then
transferred to an aspartate residue on a response regulator (RR), which
subsequently activates or
represses gene
expression by binding specific promoter regions.
To build a synthetic and
modular TCS in
mammalian
cells, we
adapted the EnvZ/OmpR system described by [Jones *et
al.* (2022)](#References). In
this design, the natural membrane-bound EnvZ, normally a dimeric enzyme with
both
kinase and
phosphatase activity, was truncated and converted into cytosolic monomers with
separate catalytic
functions. Two
variants were engineered: a kinase variant (EnvZK) that phosphorylates OmpR, and
a phosphatase
variant
(EnvZP)
that removes the phosphate group.
Both constructs consist of two Dimerization
and Histidine
Phosphotransfer domains (DHp; "A") and one Catalytic and ATP-binding domain (CA;
"B") to enable
modular
interactions, a configuration that can be described as an AAB-version. Upon
phosphorylation, OmpR
acts
as a
transcriptional activator, inducing expression of a Firefly luciferase reporter
under control of an
OmpR-responsive promoter, providing a quantitative luminescent readout of
signaling activity.

#### BUILD

Both enzyme variants were cloned following the design of [Jones
*et* (2022)](#References) as truncated cytosolic monomers
carrying the respective catalytic mutations. Cloning was performed using Gibson
Assembly to generate
the
AB-version constructs (residues 222-450; UniProt-ID: P0AEJ4), consisting of one
DHp domain and one
CA
domain.
For both the kinase and phosphatase variants, the duplication and
insertion of the DHp
domain were
achieved
through PCR amplification followed by Golden Gate Assembly. The kinase construct
additionally
required
the
introduction of two point mutations via PCR. The specific point mutations
introduced were T402K and
Y287D for
the kinase, and D244A and F390L for the phosphatase.

#### TEST

The reporter assay revealed measurable background activity in the absence of
enzymes, indicating
that
unphosphorylated OmpR contributes to basal expression. Increasing amounts of the
kinase variant
(EnvZK) led to a
dose-dependent rise in reporter activity, consistent with OmpR phosphorylation.
Surprisingly, the
phosphatase
variant (EnvZP) also increased reporter output compared to the no-enzyme
condition, suggesting
residual kinase
activity. Moreover, EnvZP was unable to quench the kinase-induced signal,
indicating limited or
inefficient
dephosphorylation capacity in this variant.

#### LEARN

From these experiments, we learned that OmpR exhibits measurable background
activity even in the
absence of
upstream enzymes [(Kenney & Anand, 2020)](#References). This is
likely due to its ability
to bind DNA in an unphosphorylated
state, leading to basal transcriptional activation; a phenomenon also confirmed
during our interview
with Dr.
Jones.
In the same discussion, we received valuable feedback that the
AAB-type phosphatase
variant
can retain residual
kinase activity, which aligns with our observation of elevated reporter levels
in the EnvZP
condition.
To
address this issue, we decided to adopt an alternative phosphatase design
(GCN4-EnvZP), in which two
monomers
are dimerized via N-terminal coiled-coils to maintain a certain
configuration.

![Results Image](https://static.igem.wiki/teams/5775/images/results/ics-julia/tcs-res-new.svg)

---

**Figure 2: Quantification of OmpR-dependent
reporter activation by
cytosolic
EnvZ variants.** HEK293T cells were transfected with 30 ng of
OmpR together with the
indicated
amounts of kinase (EnvZK) and phosphatase (EnvZP) variants.
The reporter plasmid contains an OmpR-binding site upstream of a Firefly
luciferase gene, enabling
transcriptional activation upon OmpR phosphorylation.
Reporter activity was measured 48 hours post transfection using a Dual
Luciferase assay, with
Firefly
luminescence normalized to constitutively expressed Renilla luciferase.
Data are shown as mean ± SEM (n = 3 technical replicates).
Statistical significance was
determined
using one-way ANOVA with Tukey’s multiple comparison test
(\*\*\*p < 0.001, \*\*\*\*p < 0.0001, ns = not significant).

#### DESIGN

Based on our previous findings, we redesigned the phosphatase component of the
EnvZ/OmpR system to
overcome the
residual kinase activity observed in the AAB-type variant. In this improved
version, the phosphatase
is
expressed as a dimer, in which two monomers are fused via N-terminal GCN4
coiled-coil domains,
forcing the DHp domains into a fixed rotational state.

#### BUILD

Starting from the AB phosphatase backbone, an N-terminal GCN4 leucine-zipper was
cloned to enforce
dimerization
via Golden Gate assembly. To create a single-chain dimer, we tandem-fused two
phosphatase monomers
by
connecting
the C-terminus of the first monomer to the N-terminus of the second via a long,
flexible GS-linker.
The
resulting construct encodes an N-terminal GCN4-Phos-Linker-Phos architecture.
All phosphatase-point
mutations
from the previous design were retained in each monomer to preserve the intended
catalytic profile.

#### TEST

Reporter activity was measured using 30 ng of OmpR under varying kinase and
phosphatase conditions.
In the
absence of enzymes, a basal signal confirmed OmpR background activity.
Increasing amounts of EnvZK
led
to a
dose-dependent rise in reporter output, with strong activation at 20 ng and
maximal signal at 40 ng.
Co-expression of the GCN4-phosphatase (EnvZP) with 20 ng kinase barely reduced
activity, indicating
inefficient
dephosphorylation. EnvZP alone had minimal impact on basal levels, confirming
that the cytosolic
kinase strongly
activates.

#### LEARN

From these experiments, we learned that the redesigned GCN4-Phos variant no
longer exhibits
residual
kinase
activity, confirming the effectiveness of the dimeric architecture. However, it
still fails to
efficiently
dephosphorylate OmpR, as reporter activity remained largely unchanged when
co-expressed with the
kinase.
Interestingly, co-expression of GCN4-Phos with OmpR alone resulted in a slightly
lower signal than
OmpR without
enzymes. This effect may arise from the formation of a non-productive complex
between OmpR and the
phosphatase,
transiently sequestering OmpR from the promoter, a behavior consistent with
known EnvZ-OmpR
interactions
reported by [Qin *et al.* (2001)](#References).

### A Tunable Third Input for the PHOENICS Circuit

Our long-term design vision for the PHOENICS circuit is to integrate Melt40 as a third,
tunable input. By
coupling Melt40 to an SH2 domain, localization can be modulated not only by phosphotyrosine
signaling but
also by temperature, enabling transcriptional output only when the positive signal from
SH2-mediated
recruitment outweighs inhibitory input. Temperature tuning adds an environmental control
layer to
engineered
cell circuits.

Iteration 1

Iteration 2

Iteration 3

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/melt/melt-mcherry.svg)

---

**Figure 1: Temperature-gated transcription
with fused Melt40 and
transcription factor (Melt40\_Gal4\_VP64).** At cooler
temperatures, Melt40 oligomerizes
and retains the fusion at the plasma membrane, preventing DNA binding.
Heating to ~40 °C
disrupts
membrane association; the SV40 NLS drives nuclear import, Gal4DBD
engages UAS sites, and VP64
activates mCherry expression. Cooling reverses the cycle. Elements are
illustrative and not to
scale.

#### DESIGN

Our long-term design vision for the PHOENICS circuit is to integrate Melt40
as a third, tunable
input. By coupling Melt40 to an SH2 domain, localization can be modulated
not only by
phosphotyrosine signaling but also by temperature, enabling transcriptional
output only when the
positive signal from SH2-mediated recruitment outweighs inhibitory input.
Temperature tuning adds
an
environmental control layer to engineered cell circuits.

To prototype this concept, we engineered a heat-gated transcription factor
that is sequestered at
the plasma membrane at baseline temperature, and released to the nucleus
upon heating (~40 °C),
applicable, for example, in a tumour-associated fever context. We used
Melt40, a BcLOV4-derived
thermal localization module reported to switch near 40 °C, and fused it to a
Gal4DBD–VP64
transcription factor flanked by an SV40 NLS and an NES to promote reversible
nuclear shuttling.
The
transcriptional activation is to be quantified by mCherry flow cytometry,
but the system is
readily
adaptable to alternative reporters (Figure 1).

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/melt/fm-melt40-svg.svg)

---

**Figure 2: Fluorescence microscopy of
Melt40.** HEK293T
cells were transfected with Melt40 constructs at 200 ng and treated with
three different
temperatures for 3 hours: 25°C (left), 37°C (middle), and 40°C (right).
For localization
visualization of Melt40, we utilized an mCherry fusion construct (red).
Nuclei were stained
using
Hoechst (grey). Images were captured using a Leica fluorescence
microscope at 20x
magnification—scale bars: 20 µm.

#### BUILD

**Module validation.** Before adding the transcription factor
(TF), we validated
Melt40 localization using fixed-cell fluorescence microscopy at temperatures
of 25 °C, 37 °C, and
40
°C. As expected, Melt40 remained membrane-bound at 25 °C, beginning to
dissociate and partly
enriching in the nucleus at 37 °C, and completely nuclear at 40 °C -
confirming the intended
thermal
translocation behavior used as the actuation basis for the TF.

**Fusion assembly.** In this configuration, temperature acts as
a gating input:
below
the activation threshold, Melt40 sequesters Gal4–VP64 at the plasma
membrane; at elevated
temperature (~40 °C), the fusion protein is released to the nucleus, where
Gal4, as a DNA binding
domain (DBD), binds UAS elements and VP64 drives transcription. We
quantified output by mCherry
flow
cytometry, but the system is readily adaptable to alternative reporters. The
constructed
architecture resulted in the first Version:
NLS\_Gal4\_BcLOV4\_STIM\_VP64\_NES\_NLS, shortly **Gal4\_Melt40\_VP64\_NES\_NLS.**

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/melt/v1-melt40.svg)

---

**Figure 3: Flow Cytometry Analysis of
Gal4\_Melt40\_VP64\_NES\_NLS
Construct.** Flow cytometry was used to assess gene
expression and subcellular
localization of the Melt40\_TF construct under different conditions. No
Melt control groups at
37°C
and 40°C show similar geometric mean red fluorescence (RFU), indicating
no positive expression
and
only background signal in the absence of Melt. The fluorescence levels
for the control (no Melt)
conditions are indistinguishable, suggesting that the construct does not
induce transcriptional
activity in the absence of Melt. Error bars represent the standard error
of the mean (SEM).

#### TEST

The flow cytometry analysis was performed to evaluate gene expression and
subcellular
localization
of the Melt40\_TF construct at two different concentrations (40 ng and 160
ng) and at two
temperatures (37°C and 40°C). The geometric mean red fluorescence (RFU)
values were measured to
quantify the level of reporter gene expression under these conditions

**Results.** The construct did not activate transcription above
background under any
of the tested conditions. At 40 ng, geometric means were ~83 RFU (37 °C) vs
~91 RFU (40 °C),
comparable to the negative control whiteout Melt. At 160 ng, signals
remained low (≈49 RFU at 37
°C
vs ≈85 RFU at 40 °C) and again tracked background, indicating no
temperature-separated activation.
Density plots corroborated the lack of a responding subpopulation (Figure
3).

Notably, when comparing mCherry expression with the expression induced by a
fused Gal4DBD-VP64
construct at 37°C and 40°C, only a minor (1.1-fold) increase of reporter
expression was detected
(Figure 4). Temperature differences alone thus do not induce a relevant
increase in reporter
expression.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/melt/pos-cnt-melt40.svg)

---

**Figure 4: Minimal temperature-induced
variation of reporter
expression without Melt.** Quantification of mCherry reporter
expression via
Gal4DBD-VP64 transcription factor. Cells were transfected with an
mCherry reporter construct
under UAS-promoter control and the Gal4DBD-VP64 transcription factor.
Transfected cells were
incubated for 16h at 37°C or 40°C before measurement. mCherry
fluorescence was quantified with
flow cytometry. Fluorescence profiles of viable single cell populations
are displayed in a
ridgeline plot on the left, geometric mean fluorescence (RFU) ±
geometric SEM are shown in a
barplot to the right. Medium: DMEM (10% FCS).

#### LEARN

A plausible explanation would be an insufficient nuclear accumulation upon
heating due to
continuous NES-driven export, despite Melt40 release from the membrane. In
other words, our
shuttling balance likely over-favored export, preventing Gal4DBD–VP64 from
reaching an effective
nuclear concentration at 40 °C. A second explanation could be dose-dependent
background or
toxicity
at higher DNA input, which can flatten fold changes.

Next
→

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/melt/v2-melt40.svg)

---

**Figure 5: Flow cytometry analysis and
representative fluorescence
microscopy image of Gal4\_Melt40\_VP64.** Left, flow cytometry with
two transfection
concentrations (40 ng, 160 ng) and two temperatures (37 °C, 40 °C). Density
plots (log RFU) show
complete distributions; the bar chart reports geometric mean red
fluorescence (RFU) with SEM.
Negative Controls (Without Melt) at 37 °C and 40 °C are comparable and low ,
indicating background
signal. At 40 ng, the construct shows a temperature response: mean RFU at 40
°C is ~1.9× higher
than
at 37 °C. At 160 ng, the effect is lower(~1.1×), consistent with dose
dependent background. Right,
a
representative fluorescence microscopy image corresponds to the condition 40
°C, with 160 ng
Gal4\_Melt40\_VP64. It shows a bright red signal (mCherry) with nuclear
accumulation consistent with
elevated fluorescence at 40 °C. Error bars represent the standard error of
the mean (SEM) for
triplicate samples.

#### DESIGN

Objective. Improve heat-ON transcription by re-ordering domains so that BcLOV4
(Melt40) sits
N-terminal, followed by STIM polybasic, SV40 NLS, Gal4DBD, and VP64. This layout
was chosen to
firstly
maximize membrane sequestration via the Melt40 module at baseline, secondly
place the NLS
immediately
upstream of Gal-4DBD to favor rapid nuclear import on heat release, and lastly
to separate Gal4DBD
from the actuator to reduce steric hindrance during DNA binding, thereby
enhancing activation at
physiologically relevant temperatures (37–40 °C).

#### BUILD

To increase heat-induced nuclear retention, the NES from the first version of the
construct was
removed. This way, once Melt40 releases from the membrane at ~40 °C, the SV40
NLS can drive
efficient
nuclear import and retention of Gal4DBD–VP64 in the nucleus. The second
version’s **Gal4\_Melt40\_VP64**  architectures are therefore, from N-terminal to
C-terminal:
NLS\_Gal4DBD\_BcLOV4\_STIM\_VP64.

#### TEST

Flow cytometry experiments were performed to evaluate gene expression and
subcellular localization
of
the Melt40\_TF construct at two different concentrations (40 ng and 160 ng) and
at two temperatures
(37°C and 40°C). The geometric mean red fluorescence (RFU) values were measured
to quantify the
level
of reporter gene expression under these conditions.

**Result**: For the higher concentration (160 ng), a high
background at both
temperatures
with minimal fold change (≈1.1×; 37 °C ≈1502 RFU, 40 °C ≈1666 RFU), indicating
low
temperature-induced
separation at high doses. For the lower concentration (40 ng), clear temperature
responsiveness is
detected. The mean RFU at 40 °C showed a nearly 2-fold change, higher than at 37
°C (Figure 5).

#### LEARN

**Interpretation**: Removing the NES successfully uncovered a
heat-ON response at
lower
DNA input (40 ng), consistent with improved nuclear retention on heating.
However, overexpression
(160
ng) produced a high background that compresses fold change, likely from a
dose-related cellular
stress
that raises baseline reporter levels.

#### DESIGN

**Objective.** Improve heat-ON transcription by re-ordering
domains so that BcLOV4
(Melt40) sits N-terminal, followed by STIM polybasic, SV40 NLS, Gal4DBD, and
VP64. This layout was
chosen to firstly maximize membrane sequestration via the Melt40 module at
baseline, secondly
place
the NLS immediately upstream of Gal-4DBD to favor rapid nuclear import on
heat release, and lastly
to separate Gal4DBD from the actuator to reduce steric hindrance during DNA
binding, thereby
enhancing activation at physiologically relevant temperatures (37–40 °C).

#### BUILD

**Optimization.** We incorporated the transcription factor with
an N-terminal,
yielding the NES-free  **Melt40\_Gal4\_VP64** . The detailed
composition of the final
construct resulting in: BcLOV\_STIM\_NLS\_Gal4DBD\_VP64. The finalized plasmid
encodes the actuator,
import cue, and TF in the optimized order to couple heat-driven membrane
release to rapid nuclear
import and UAS-dependent activation.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/melt/v3-melt40.svg)

---

**Figure 6: Flow cytometry analysis and
representative
fluorescence
microscopy image of optimized Melt40\_Gal4\_VP64.** Left, flow
cytometry at two DNA
inputs
(40 ng, 160 ng) and two temperatures (37 °C, 40 °C). Density plots (log
RFU) show full
distributions; the bar chart reports geometric mean red fluorescence
(RFU) with SEM. The
negative
control whiteout Melt at 37 °C and 40 °C remains low and comparable,
indicating background
signal.
. The optimized construct displays strong temperature separation: at 40
ng, the mean RFU at 40
°C
is ~6.6× higher than at 37 °C; at 160 ng, the 40 °C signal is ~4.9×
higher than 37 °C. Right, a
representative fluorescence microscopy image (condition 40 °C, with 160
ng Melt40\_Gal4\_VP64)
shows
a bright nuclear red signal (mCherry) consistent with the elevated 40 °C
fluorescence measured
by
flow cytometry. Error bars represent the standard error of the mean
(SEM) for triplicate
samples.

#### TEST

**Results.** The optimized construct exhibited strong
temperature separation: at 40
ng, the mean RFU at 40 °C was ~6.6× higher than at 37 °C; at 160 ng, the 40
°C signal was ~4.9×
higher than 37 °C. The distributional shifts indicate a broad population
response rather than a
small responding subfraction. A representative fluorescence microscopy image
(condition: 40 °C,
160
ng Melt40\_Gal4\_VP64) shows bright nuclear mCherry, consistent with the
elevated 40 °C fluorescence
measured by flow cytometry, which supports temperature-gated nuclear
accumulation and
transcriptional activation (Figure 6).

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/melt/melt-input.svg)

---

**Figure 7: Mechanism of temperature-gated
transcription with
Melt40.** Schematic of the working model. At cooler
temperatures, Melt40 (purple)
oligomerizes and is recruited to the plasma membrane via the STIM
polybasic segment,
sequestering
the fused Gal4DBD–VP64 away from DNA. Upon heating (red arrow), Melt
disengages from the
membrane,
and the SV40 NLS drives nuclear import of Gal4DBD–VP64. In the nucleus,
Gal4DBD binds UAS sites
and VP64 activates transcription of the mCherry reporter, which
accumulates in the cytosol
(red).
Cooling reverses the process (blue arrow), returning the fusion to the
membrane and reducing
transcriptional output. Elements are illustrative and not to scale.

#### LEARN

Further optimization, achieved by redesigning the order of the functional
parts, resulted in the
**Melt40\_Gal4\_VP64** construct, which demonstrated
significantly enhanced performance
and a clear dose-dependent effect. This dose-dependent profile confirms that
the system's dynamic
range and output can be finely tuned by plasmid concentration, offering
greater flexibility for
application-specific needs.

These results collectively demonstrate that the optimized Melt40\_Gal4\_VP64
construct functions as
an effective and tunable temperature-inducible gene expression system, with
potential applications
in synthetic biology, thermogenetic controls, and precision gene regulation
under mild
hyperthermic
conditions.

Our results establish Melt40 as a reliable heat-ON actuator for nuclear
translocation and
transcriptional control, and the engineering cycles clarified how expression
level, tag balance,
and
domain order shape performance. To realize our long-term vision, using
Melt40 as a third, tunable
input in signaling circuits via SH2-guided recruitment, we still need to do
further cloning.
Specifically, we will construct SH2–Melt40 fusion variants (testing
orientation and linkers) and
integrate these modules with our Gal4/UAS transcriptional output, allowing
temperature to tune
response strength only when SH2-mediated positive signals outweigh
inhibitory inputs. These
additional constructs will enable us to characterize the full
temperature-by-phosphotyrosine input
matrix and finalize an environmentally tunable, multi-input control layer
for the PHOENICS
circuits
(Figure 7).

Next
→

## Dry Lab

### Binder Design and Characterization Pipeline for Novel Targets

This engineering cycle describes how we progressively built a
robust
computational pipeline for designing, testing, and ranking *de novo* protein binders
for our MESA
and
GPCR receptors. From initial BindCraft generation and molecular dynamics stability tests to
optimized
umbrella sampling and thermodynamic corrections, we established a reliable workflow for
identifying the
most
promising binders based on binding affinities for further wet lab validation.

Iteration 1

Iteration 2

Iteration 3

Iteration 4

Iteration 5

#### DESIGN

We started by generating *de novo* binders for several ligands using
**BindCraft** [(Pacesa *et al.*, 2025)](#References), aiming to
explore how input
ligand
structure affects the quality of predicted binders. We also tested multiple
bindcraft settings to
find
the optimal design for our binders, as advised in the [interview
with the first author of BindCraft, Dr. Martin Pacesa](/heidelberg/human-practices).

#### BUILD

For each ligand (GDF15, OGN and LY6K), we predicted 3D structures of the ligand
using**AlphaFold3**  . For GDF15 we additionally tested binder
creation using the
available crystal structure (PDB ID: 5VT2). Then we generated 10 binders for
each ligand, with
different selected binding interfaces and various binder lengths.

#### TEST

We observed that **different ligands** yielded very
**different** numbers
and **qualities of binders**. Some ligands performed exceptionally
well and yielded
many
binders, while others resulted in no binders at all. A correlation was visible
between selected
interfaces with **high pLDDT scores** and **faster**
generation of
**more binders**  . In contrast, BindCraft was unable to generate
binders for
structures
with many flexible or poorly predicted. For the GDF15 **crystal
structure**, BindCraft
also performed poorly and generated a **low amount of binders**.

#### LEARN

We learned that **BindCraft’s success** depends heavily on the
**structural
quality** of the **input ligand**. Poorly resolved
structures or high
flexibility in AlphaFold predictions can negatively influence binder generation.
Ligand choice and
structure accuracy are therefore key factors for successful binder design. As a
result we decided to
go ahead with standard BindCraft settings (60-150 residues) with predefined
well-structured
hotspots.

#### DESIGN

In this cycle, we focused on studying the **interaction interfaces**
and
**structural stability** of binder-ligand complexes to understand
how well different
binders hold up during simulations.

#### BUILD

We performed 40 ns steered MD simulations for each binder–ligand pair, with 4
replicas per system
to
ensure reproducibility. After this, we performed trajectory analysis, where we
looked at the
**root-mean-squared deviation** (RMSD), **radius of
gyration**, as well as
**secondary structure analysis** using Defined Secondary Structure
of Proteins (DSSP)
algorithm.

#### TEST

The stability of the binding interface **varied across ligands**.
For LY6K, the
interaction interface remained well preserved, while for GDF15, we observed much
more variability.
However, the binders themselves were structurally stable across all replicas
(low RMSD and radius of
gyration).

#### LEARN

We learned that while BindCraft can produce structurally stable binders, the
interaction quality
depends strongly on the ligand. The **target structure** and
**interface**
play a major role in how consistent and strong the binding becomes. Also the
**choice of
ligand** dictates the **heterogeneity** and
**variance between
binders** for the same ligand. As a result, we sought out to better
characterize and rank
the
generated binders using a more sophisticated ranking pipeline or metric.

![Results Image](https://static.igem.wiki/teams/5775/images/model/plots/bad-plot-dry.svg)

---

**Figure 1: Binding energy estimation for
initial US run.**
Resulting PMF curve G(ξ) generated using WHAM for the umbrella
sampling simulation. The
binding free energy of -101 kJ/mol was calculated from the energy
in the bound state
Δ Gbound(ξ) and unbound state ΔGunbound(ξ).

#### DESIGN

In the interview with **Dr. Martin Pacesa**, we additionally learned
that the
BindCraft
built-in ranking system, the **i\_pTM metric**, is not a great
affinity-based predictor,
but rather a binary predictor for binding. Because of this, we wanted to
establish a **new
ranking method based on binding affinities**, which would be required
for better
characterization of binders for use in SPARC’s mathematical model. After
literature research and
consulting [**Prof. Dr. Rebacca
Wade**](/heidelberg/human-practices), we
decided to estimate binding free energies using molecular dynamic (MD)
simulations, especially
**Umbrella Sampling** (US), to identify the most promising binders
before experimental
validation.

#### BUILD

Using the **Gromacs program and the Amber99sb-ildn force field**,
which we found to be
ideal for protein complexes (Lindorff-Larsen *et al*., 2010). We carried
out **energy
minimization**, **NVT** and **NPT
equilibration**, followed by a
short 10 ns steered MD **pulling run** to generate the reaction
coordinate. Umbrella
windows were extracted every 0.1 nm for PMF reconstruction.

#### TEST

The resulting **PMF** (Potential of Mean Force) curves showed
extremely
**high** and **unrealistic energies**, indicating poor
convergence. This
led
to very unrealistic binding affinities in the picomolar range, indicating a
fault within our US
workflow.

#### LEARN

We realized that this happened because the 1D reaction coordinate (COM-distance)
was not sufficient
to capture all the relevant motions (translation, rotation, folding, etc.). The
system was simply
**undersampled**, leading to an **overestimation of the
binding
strength**.

![Results Image](https://static.igem.wiki/teams/5775/images/model/plots/free-energy-profile.svg)

---

**Figure 2: Binding energy estimation for US run
with restrained
pulling.** Resulting PMF curve G(ξ) generated using WHAM
for the umbrella sampling
simulation. The binding free energy of -25.8 kJ/mol was
calculated from the energy in
the
bound state Δ Gbound(ξ) and unbound state ΔGunbound(ξ). The binder
was
restrained in the x and y direction during the SMD.

#### DESIGN

To fix the unrealistic PMFs, we looked into literature, which had documented
established US
workflows. We realized that **adding restraints during pulling**
would reduce lateral
movement and improve convergence speed [(Aho *et
al*., 2024)](#References). For
better histogram overlap, we also sought out to alter pulling/SMD conditions.

#### BUILD

We applied **orthogonal harmonic restraints in the x and y
directions** during the
pulling simulations, pulling along the z-axis for 100 ns instead of 10
ns. The increased
pulling force to k = 1000 kJ/nm2 and reduced pull rate to k=
0.0033 kJ/nm2\*s were also intended to improve histogram overlap
and provide PMF
better
convergence.

#### TEST

This setup produced **far more realistic results**. Unnaturally high
PMFs and
increasing
pulling forces were no longer observed, and the resulting histograms showed
clear overlap. The
free-energy profiles converged much better than in the previous cycle.

#### LEARN

We learned that restraints help the system **converge faster** by
limiting unnecessary
movement. However, we realized that we also **artificially restrict
entropy**, which
can
make binding appear stronger (*i.e.*, lower ΔG) than it really
is.

#### DESIGN

To recover realistic thermodynamic values for biological interpretation and ranking,
we applied
**entropy** and **volume corrections** to the restrained
systems following
[(Govind Kumar *et al*., 2023)](#References).

#### BUILD

We calculated the final binding free energies (ΔG°) from the PMF and
applied corrections
for
both volume (ΔGV) and restraints (ΔGR), effectively restoring
lost degrees of
freedom.

#### TEST

After correction, the results became **biologically meaningful** and
**thermodynamically realistic**. We could now rank binders by their
corrected ΔG°
and KD The corrected affinities were **slightly weaker**
(more positive ΔG° as expected).

#### LEARN

We learned that **strong restraints suppress entropy** and can bias the
results.
Applying
corrections gives back the degrees of freedom and **ensures realistic
thermodynamics**,
although the corrections can become large when the restraints are too strong,
leading to higher, less
favorable ΔG°.

### Predictive Mathematical Model for Phosphorylation Circuit Design

This engineering cycle outlines our step-by-step process of constructing and refining a
mathematical
framework to simulate receptor dimerization and circuit behavior in PHOENICS cells. Through
five
design–build–test–learn iterations, we evolved from an unsolvable analytical model to a
high-throughput,
prediction pipeline capable of predicting circuit performance and guiding experimental
design.

Iteration 1

Iteration 2

Iteration 3

Iteration 4

Iteration 5

#### DESIGN

To understand the processes involved in our system and guide the development of
its architecture,
we
aimed to construct a mathematical model that could simulate the complex
interactions between the
protein components. In order to tackle this, we decided on a modular approach.
Specifically, we
identified **two distinct models**: one for receptor dimerization
in the sensing layer
and another for the phosphorylation-based computational cycle in the processing
layer. This
**modular approach** ensured that each component was thoroughly
understood and
optimized
before integrating them into a single, cohesive system. For the circuit model,
we drew from a
similar
existing design, which served as a valuable reference for adapting it to our
specific requirements
[(Yang *et al.*, 2025)](#References). To begin with, we
decided to address
receptor
dimerization by defining an **ordinary differential equation
(ODE)** and solving it
using
Python, allowing us to model the process in detail before combining it with the
other system
components.

#### BUILD

To implement the model, we began by documenting all the reactions involved in the
receptor system,
carefully accounting for each interaction as well as for background dimerization
processes. From
these
reactions, we derived a system of ODEs to describe the dynamic behavior of the
system. To reflect
realistic biological conditions, we made a **steady-state
assumption**, which allowed
us
to simplify the system into a quintic form. This simplification was achieved by
utilizing partition
functions for each reaction species, ensuring that the equilibrium behavior of
the system was
accurately captured.

#### TEST

The system of equations was solved using Python, but we faced challenges in
obtaining a solution.
Due
to the complexity of the equations, an analytical solution was **not
attainable**.
Moreover, attempts to solve the system with a numerical integrator failed to
converge, suggesting
the
need for further refinement of the model. To address this issue, we sought
guidance from
**multiple experts** in systems biology and the modeling of
biological processes,
aiming
to identify potential solutions and improve the model’s accuracy and
convergence.

#### LEARN

The initial approach for modeling receptor dimerization proved **too
complex** to
solve
analytically. Consulting with experts, including [Dr. Stefan
Kallenberger](/heidelberg/human-practices), indicated that a simpler approach would be more feasible,
guiding us to reduce
complexity while preserving the key dynamics of receptor dimerization.

#### DESIGN

Based on expert advice, we decided to pursue a **numerical
approach** to model
receptor
dimerization that explicitly considers **heterodimerization**. This
allows us to model
a
broader spectrum of receptors, which is key to ensure the modularity of our
system.

#### BUILD

To implement this approach, we took an existing mathematical model of
heterodimerization as a
reference [(Lu & Wang, 2017)](#References) and integrated it into our
existing Python
framework. This allowed us to leverage prior work while adapting it to the
**specific
requirements** of our receptor system and processing circuit.

#### TEST

Initial tests using dummy parameter values produced **rapid and plausible
results**,
confirming that the numerical model behaves as expected. These preliminary
outputs validated the
approach and demonstrated that the model could reliably simulate receptor
dimerization dynamics.

#### LEARN

From this iteration, we concluded that the numerical model **successfully
captures**
receptor heterodimerization in a way that meets our system requirements. With a
working model in
place, we are now ready to integrate it into the **broader circuit
framework** for
further simulations and analyses.

#### DESIGN

The next step involved combining the receptor and circuit models into a
**larger
framework**, with the goal of calculating phosphorylated substrate
concentrations based on
**input ligand levels**. To characterize the cell architecture, it
was important to
determine both the point at which the cell switches from the
**OFF** to the
**ON** state and the **speed** of this transition.
This required
integrating
the dynamics of receptor heterodimerization with the phosphorylation-based
computational cycle.

#### BUILD

To implement this integration, we modified the circuit model to accept the
**output**
of
the receptor model as its **input**. We duplicated the receptor
model to create two
identical copies representing the positive and negative inputs. By varying the
ratio of positive to
negative ligands and calculating the resulting phosphorylation levels, we
generated
**dose-response curves** for specific architectures. From these
curves, we inferred the
**EC50** values, representing the switching point, and the
**Hill
coefficient**, reflecting the switching speed.

#### TEST

After some fine-tuning, we were able to define a set of parameters that allowed
the phosphorylated
substrate concentration to be calculated quickly and as intended. However, when
varying the input
concentrations, we observed **discontinuities and jumps** in the
results, indicating
issues in the current solving approach.

#### LEARN

This iteration demonstrated that phosphorylation levels can, in principle, be
predicted from ligand
concentrations. However, the current method for solving the quintic equation of
the receptor model
does not correctly handle **root selection**. These discontinuities
prevent reliable
calculation of the Hill coefficient and EC50, highlighting a **key
challenge** that was
crucial to be addressed in further refinement.

#### DESIGN

Following the issues we faced, our goal was then to model dose-response curves
**confidently** and **without artifacts**. The model
needed to account for
additional factors, such as **baseline phosphorylation**, which can
influence the Hill
coefficient calculation. To improve performance and precision, we decided to
first identify the
switching point and then perform a **more detailed sampling**
around it, ensuring
accurate calculations.

#### BUILD

To achieve this, we refined the root selection process to produce smooth curves
and implemented
baseline correction to account for background phosphorylation. A
**two-layer calculation
strategy** was introduced: initial coarse sampling across a broad
range of ligand ratios
identifies the switching point, followed by finer sampling to capture the
transition in detail.
These
improvements were integrated into the existing Python framework.

#### TEST

Testing with defined parameters confirmed that the improvements were effective.
Both the Hill
coefficient and EC50 were calculated correctly, and overlaying a Hill curve
using the extracted
parameters showed **excellent agreement** with the modeled
dose-response data.

#### LEARN

Testing our established model demonstrated that it can **reliably extract
circuit
characteristics** for a given architecture. With accurate and
**high-resolution** dose-response calculations now achievable, the
framework is ready
to
be applied in a **high-throughput manner** to systematically
characterize multiple
circuit designs.

#### DESIGN

Characterizing system architectures experimentally by building them in the wet lab is
**laborious
and costly**. To circumvent this restriction, we intended to leverage the
developed
mathematical mode as a **predictive tool** to guide the cell
architecture and reduce the
number of architectures that are constructed and tested in the wet lab. Due to the
analytical
intractability of the system, we decided to create a **database**
encompassing all
possible
combinations of components and varying input concentrations, providing a
comprehensive reference for
downstream analysis.

#### BUILD

To implement this, we developed a pipeline that takes a **structured table of
parameters**
and iterates over every possible permutation to calculate the Hill coefficient and
EC50 value for each
configuration. The results are then compiled to **visualize the parameter
space**. For
efficient computation across many architectures, the pipeline was parallelized to
run on a
**high-performance computing (HPC)** cluster, enabling rapid
high-throughput analysis.

#### TEST

Initial tests with dummy parameters confirmed that the pipeline functions as
intended. Subsequently,
we
ran simulations using **biologically relevant parameters** that reflect
the components
employed in the wet lab, ensuring that the output is directly applicable to
experimental design.

#### LEARN

This pipeline provides a robust tool to systematically guide the **design of
PHOENICS
cells**. By mapping the effects of component combinations and
concentrations, it enables
informed decisions for **tailoring cell architectures** to specific
experimental needs,
streamlining the design process for future wet lab implementation.

### Conducting Functional Membrane-bound MESA Receptor Simulations

This engineering cycle outlines our step-by-step process to develop a reliable computational
workflow for
modeling MESA receptor dimerization. Through multiple design–build–test–learn iterations, we
moved from
failed AlphaFold predictions to alignment-based structure generation, leading to refined,
membrane-embedded
coarse-grained simulations of CD28/CD28 and CD28/CD28(M3) transmembrane domains.

Iteration 1

Iteration 2

Iteration 3

Iteration 4

![Results Image](https://static.igem.wiki/teams/5775/images/model/plots/modeller-bad.webp)

---

**Figure 1: Alphafold-generated tertiary
structure of FKBP-CD28
receptor half.** Extracellular FKBP domain (orange) linked to the
CD28 TM domain (red),
which is predicted as a disordered region by AF3.

#### DESIGN

We sought to computationally evaluate the propensity for
**ligand-independent dimerization of
alternative MESA transmembrane (TM) domain combinations** because
subtle differences in TM
pairing can produce substantial differences in basal signaling of synthetic
receptors [(Daringer *et al.*, 2014; Levintov *et
al.*, 2024)](#References). For these
analyses we required atomic-level models of the extracellular rapalog-responsive
halves (FRB and
FKBP)
connected via a flexible linker to either the native CD28 TM sequence or the
CD28(M3) TM variant.
The
realistic geometry at the TM–linker junction is critical because it governs
helix registry and the
relative orientations sampled when the constructs are embedded in a bilayer.
Generating a
**single, end-to-end structural model** that captures both the
folded extracellular
domains and the membrane-spanning helices was therefore the primary modelling
objective of this
cycle.

#### BUILD

As an initial strategy we attempted **whole-protein structure
prediction** with
AlphaFold-3 by providing the complete receptor sequences from the extracellular
N-termini through
the
TM segments. This approach leverages state-of-the-art **neural network
prediction** to
produce a **single atomic model**from sequence, and we hoped it
would simultaneously
resolve the globular FKBP/FRB folds and produce sensible TM helices and linkers
for downstream
simulation [(Abramson *et al.*, 2024)](#References).

#### TEST

**AlphaFold3** accurately reconstructed the globular FKBP and FRB
domains with well
folded cores that matched expected topology, but the predicted models
**systematically failed
to
produce stable, canonical TM helices** and instead returned largely
unstructured or poorly
defined conformations in the membrane region; the linker geometry was likewise
uncertain. The
absence
of clear helical registry and membrane-embedded geometry rendered these models
unsuitable as
starting
coordinates for membrane simulations where **helix tilt, rotation and
packing** are
decisive.

#### LEARN

From this iteration we concluded that end-to-end prediction in its out-of-the-box
form is
**inadequate for reliably modelling membrane-embedded receptor
segments** because it
does
not explicitly represent the lipid environment or enforce membrane-specific
helix restraints. This
limitation made it necessary to adopt a complementary, **template-guided
approach**
that
can impose helical restraints and preserve TM geometry.

![Results Image](https://static.igem.wiki/teams/5775/images/model/plots/modeller-good.webp)

---

**Figure 2: Modeller-generated tertiary
structure of FKBP-CD28
receptor half.** Extracellular FKBP domain (orange) linked to the
CD28 TM domain (red),
which is modelled with a helical structure restraint using Modeller.

#### DESIGN

To obtain starting structures with well defined TM helices and realistic linkers
we adopted
comparative modelling using **Modeller, a template-based program**
that constructs
models
by sequence alignment to experimentally determined structures and allows the
**imposition of
geometric restraints** [(Webb & Sali, 2016)](#References).
This approach is well
suited for our system because the extracellular FKBP/FRB dimer is available as
crystal structures,
and
Modeller can be instructed to treat TM segments as constrained helices while
permitting the linker
to
retain conformational flexibility.

#### BUILD

We used the crystallographic FKBP–FRB dimer (PDB ID: 3FAP) as the structural
template after
manually
removing the rapalog ligand to focus on **ligand-independent
interactions**. The full
receptor sequences were provided as targets and an alignment to the template
guided backbone
construction. During model building we applied explicit **helical
restraints** to the
CD28 and CD28(M3) TM regions and loop restraints to the interdomain linker so
that the TM segments
adopted canonical helical geometry while the linker retained **realistic
flexibility**
for later sampling in dynamics.

#### TEST

The Modeller-generated, loop-refined models displayed correctly folded
extracellular FKBP/FRB
domains
consistent with the template, **flexible but geometrically plausible
linker
conformations**, and well-formed helical TM segments for both CD28
and CD28(M3). Quality
checks and visual inspection confirmed that the TM helices had
**reasonable length, registry
and
orientation** for membrane insertion, producing a set of starting
structures that were
appropriate for coarse-graining and subsequent membrane simulation.

#### LEARN

This cycle showed that sequence-alignment guided comparative modelling can
reliably produce the
membrane-competent topologies that AlphaFold-3 failed to deliver for our
constructs. Modeller
therefore provided structurally robust inputs for the dynamical studies and
highlighted that
combining
**template information with targeted restraints** is an effective
strategy for mixed
soluble/membrane proteins.

#### DESIGN

To probe membrane-mediated dimerization on biologically relevant timescales
without excessive
computational cost, and following [consultation
with expert Dr.
Fabian Grünewald](/heidelberg/human-practices), we elected to **switch from all-atom molecular
dynamics to
coarse-grained (CG) simulations** using the **Martini 3 force
field** [(Souza *et al.*, 2021)](#References). The
coarse-grained approach reduces particle
count and smooths energy landscapes, enabling multi-microsecond sampling of
large membrane systems
where slow association events and lipid-mediated interactions are of interest.

#### BUILD

Receptor halves were converted to a Martini-compatible representation using
**martinize2** [(Kroon *et al.*,
2025)](#References), which maps
atomic
coordinates to coarse-grained beads and preserves secondary structure
constraints, and these
coarse-grained proteins were then embedded into a model plasma membrane
assembled with the
**Insane membrane builder** to generate complete protein–bilayer
systems with specified
lipid composition [(Wassenaar *et al.*, 2015)](#References).

#### TEST

Automated insertion using INSANE produced systems in which the receptor halves
were
**incorrectly oriented and incompletely embedded in the bilayer**
for both CD28/CD28
and
CD28/CD28(M3) constructs, resulting in nonphysical starting configurations that
prevented the
reliable
initiation of production CG simulations. The misorientation suggested that,
while Insane excels at
creating complex membranes, its default insertion routines are **not
always robust for
asymmetric or extended protein shapes** derived from coarse-graining.

#### LEARN

We determined that Insane reliably constructs membrane patches with desired lipid
mixtures but can
fail to place and orient nontrivial protein geometries correctly, and thus
automated insertion
cannot
be relied upon for complex synthetic receptor halves. This necessitated
developing a **manual
placement workflow** to precisely control protein orientation and
separation prior to
production runs.

![Results Image](https://static.igem.wiki/teams/5775/images/model/plots/martini-dist-5.svg)

---

**Figure 3: Coarse-grained background
dimerization of MESA receptors
for different TM domain pairs.** The distance plot shows the
center-of-mass (COM)
distance
between the Modeller-generated FKBP/FRB receptor halves over the course of
the 2 𝜇⁢𝑠 simulation
for the CD28/CD28 (orange) and CD28/CD28(M3) (purple) domain pairs.

#### DESIGN

To address the insertion problem we designed a manual preparation protocol in
which the
coarse-grained receptor halves would be **explicitly oriented relative to
the bilayer
normal** and positioned at a defined initial separation, thereby
producing a controlled and
reproducible **starting ensemble for CG dynamics** and enabling the
direct comparison
of
background dimerization between TM variants.

#### BUILD

Using PyMOL we **translated and rotated the coarse-grained
coordinate** sets to align
the TM helices with the membrane normal and corrected any tilt or rotational
offsets [(Schrödinger, LLC, 2015)](#References). The two receptor
halves were positioned with an
**initial center-of-mass separation of 10 nm** to avoid artifactual
interactions at t =
0, and these manually oriented systems were simulated for 2 μs under the Martini
3 force field to
allow sampling of **membrane-mediated approach and association**.

#### TEST

Manual insertion produced **well embedded and stably oriented receptor
halves**
throughout the 2 μs trajectories and the simulations ran without the insertion
artefacts observed
previously. Analysis of the time-dependent center-of-mass distances between
receptor halves revealed
reproducible **differences in baseline membrane-mediated
dimerization** between the
CD28/CD28 and CD28/CD28(M3) pairs, supporting the hypothesis that TM sequence
variation modulates
ligand-independent association.

#### LEARN

This final iteration confirmed that **manual orientation and placement are
required**
when dealing with complex, asymmetric coarse-grained proteins and that Martini3
CG simulations
provide
a computationally efficient, robust platform to quantify **relative
background dimerization
tendencies**. Moving forward, these results motivate systematic
replicate simulations and
free-energy calculations to quantify association energetics and support rational
design of
low-background MESA constructs.

## Human Practices

### Empowering Young Scientists to Think Translationally

From an expert panel to a regional roadmap. First, we hosted a successful **panel
discussion**
that brought together leaders in translation, regulation, and patient advocacy, opening a
vital dialogue
and
educating students on the challenges of bringing research to patients. The success of this
event and the
audience's call for "what's next?" directly led to our second initiative: the creation of a
**regional
roadmap**, a practical guide designed to help students navigate the local
innovation ecosystem
and
take concrete steps with their ideas.

Iteration 1

Iteration 2

Iteration 3

#### DESIGN

Our project began with a foundational goal: **to learn what it takes to
translate a
project.** Our method was to conduct exploratory
**interviews** with leaders
in
the field. These conversations rapidly converged on a recurring, critical theme:
the immense
challenge
of navigating the regulatory landscape. This allowed us to define a specific
focus: the **"the
translation gap."** Based on this finding, we wanted to design a
project to convene senior
regulatory experts to tackle "red tape" in Germany at a policy level.

#### BUILD

To execute this design, we created the project's initial framework. This
involved drafting a formal
proposal for the expert-level round table event and identifying a list of
high-profile stakeholders
in
regulation and policy to invite. Prof. Dr. Rienk Offringa (DKFZ Drug Discovery
Lab) gave us insights
on the practical regulatory bottlenecks in Germany.

#### TEST

We tested the core concept of our project by presenting the idea to experienced
mentors and
stakeholders. Prof. Dr. Platten challenged this idea, explaining that these
essential discussions
already occur at an extremely high level, involving seasoned policymakers,
pharmaceutical companies,
and major research organizations. He gently emphasized that the level of
expertise required to
meaningfully contribute is immense, making direct influence an unrealistic, if
admirable, goal for a
student team.

#### LEARN

We learned that while we couldn't contribute to the experts' conversation, we
could address a
massive
gap at our own level. This led to our project's foundational **pivot: we
would focus on
introducing the very topics that are not taught in the research lab, like
regulation, ethics, and
entrepreneurship—to students in the format of a panel discussion.**

#### DESIGN

Following our pivot to student education, our new goal was to create an
accessible forum for these
topics. We designed a public panel discussion to bring the conversation about
translation, ethics,
and
regulation directly to students. A key part of our design process involved
conducting preparatory
interviews, such as consulting with moderation expert Dr. Carmen Sanges for her
advice on crafting
effective questions and structuring the panel for maximum engagement.

#### BUILD

We successfully recruited our target speakers and developed a comprehensive
panel discussion
moderation guide. This guide was carefully structured to move the conversation
from high-level
problems to practical advice for young scientists in the audience. We also
prepared slides and
organized the venue.

#### TEST

We hosted the panel discussion, which drew an audience of 140 people. This
strong turnout reinforced
the notion that we had correctly identified a topic of high interest to the
student community.

#### LEARN

The Q&A and subsequent networking session gave us our most crucial insight. We
learned that while
students are eager to learn, they feel overwhelmed with how to enter the world
of scientific
translation. The recurring questions weren't about the problems themselves, but
about the solutions:
"What are the next steps I can take right now?" and "Who can I turn to for
support?" This made us
realize we could take on a guiding role in our ecosystem, and we began preparing
for our next big
project.

#### DESIGN

The panelists encouraged us to take the initiative and help shape the innovation
landscape. That was
easier said than done. Even for those of us who were well-connected, the idea of
influencing our
translational ecosystem felt overwhelming. If we felt this way, we realized it
was likely that other
young scientists and students felt the same. Our goal evolved into a new
direction: to better
understand Heidelberg's innovation ecosystem, identify its key players, and map
the support networks
available to help young scientists bridge the translational gap.

#### BUILD

First, we conducted the necessary research by **contacting
stakeholders** from every
relevant part of the ecosystem, including technology transfer offices, startup
incubators, and
venture
capitalist to gather and verify accurate information. Second, with this
validated data, we
**created the guide** itself, designing a clear layout and writing
concise, actionable
content for each section.

#### TEST

The roadmap will be tested throughout the current academic semester. We will
measure the roadmap's
success by tracking key metrics:

1. **Quantitative:** We will monitor download numbers and
   **see if
   related** **innovation workshops are fuller this
   year** compared to previous
   years.
2. **Qualitative:** We will actively **collect
   feedback** through a
   survey.

#### LEARN

This phase is currently in progress, with the results unfolding over the coming
months. Our
hypothesis is that by providing a single, comprehensive resource, we will
significantly lower the
barrier for students to engage with the translational ecosystem. We’ve already
received requests
from additional institutions to be included in a possible second edition of the
guide. The feedback
and data we gather will show us whether the roadmap is effective as is, or if a
future iteration is
needed to improve its content, design, or distribution strategy. The final
outcome remains to be
seen, and we are eager to learn from our community’s experience.

### Shaping Future Innovators

We developed a series of educational formats to make complex synthetic biology concepts
accessible to high
school students. Beginning with interactive classroom workshops on signaling pathways, we
expanded to a
Summer School where students gained hands-on laboratory experience. To ensure lasting impact
and broader
accessibility, we transformed these materials into an interactive online learning platform
for students
and educators worldwide.

Iteration 1

Iteration 2

Iteration 3

#### DESIGN

To bring synthetic biology into classrooms, we designed a creative
**workshop** to
explain signaling pathways and networks – the basic principle of our own project
– to high school
students. To make this topic more accessible, we broke it down into three key
components: sensing,
processing, and responding, giving examples for each. Step by step we explained
circuits, endgates
and different inputs to make the complexity of such systems easy to grasp.

#### BUILD

We prepared a presentation on the topics mentioned above, and included a
**Kahoot**,
summarizing and reviewing the material. We came up with group work exercises
which demanded the
students to get creative by **designing their own signaling
pathways** inside of
synthetic cells,to solve specific problems. In the process of preparing and
creating the
learning-materials, we received valuable feedback from a Heidelberg University
lecturer and local
high school teachers to lay focus on **real life applications** and
the importance of
synthetic biology in daily life.

#### TEST

We hosted our workshop for students of two different advanced biology high
school classes. We
received **very positive feedback** from both the students and the
teachers, some of
which even proposed a long-term collaboration between a local high school in
Heidelberg and the iGEM
team. During the group work, the students enjoyed thinking and discussing the
different
opportunities of genetic engineering and protein design, asking us supervisors a
lot of questions
which underscored the value of giving them an insight into synthetic biology and
biotechnology as
possible future fields of studies.

#### LEARN

Experiencing the students’ enthusiasm firsthand showed us the **power of
interactive
teaching** formats. It also revealed the need for deeper,
**hands-on
experiences** for those who wanted to learn more. That is why we
complemented the school
workshops with the Summer School where students are able to conduct their own
laboratory
experiments.

#### DESIGN

Building on the initiative established by last year’s iGEM team, we continued
our collaboration with
the DKFZ Life Science Lab (LSL), organizing and supervising a **Summer
School**. The
Summer School consisted of two weekend **laboratory workshops**
where students both
attended theoretical seminars and gained hands-on experience, carrying out
experiments in
**cell culture** and **immunocytochemistry**.

#### BUILD

The experiments conducted in the Summer School were derived from our own iGEM
experiments and
various university courses. In addition, we prepared **accompanying
seminars** to
explain the foundational knowledge in molecular biology and the theoretical
background of the
experiments. We also developed a **script** to guide students
through the laboratory
procedures.

#### TEST

Before the Summer School, we ran and optimized the experiments ourselves. This
ensured smooth
execution and availability of all necessary materials. We supervised two
**three-day
courses**, both of which were **very well received**,
with all spots filling
up immediately. The students actively engaged in the seminars, asking insightful
and
thought-provoking questions about the experiments and the underlying biology,
demonstrating both
confidence and curiosity.

#### LEARN

We discovered that Life Science Lab students already had advanced scientific
knowledge and
laboratory skills, compared to other children their age. Although the Summer
School courses received
very positive feedback, the spots were **confined to eleven
students** due to the size
of the laboratory. Taking these considerations into account, we went on to
expand our initiative
into an **online learning platform**, making our educational
material accessible to
every student and educator. With that, we ensure our efforts have a
**lasting impact**
and enlarge our target audience even more.

#### DESIGN

We decided to **digitalize** the summer school script and extend
the platform with
further modules on molecular cloning and basics of molecular biology, originally
derived from our
own lab protocols. We envisioned it to contain a real experimental workflow with
in-depth theory,
explanations to each step.

#### BUILD

To make the platform more appealing, we added a variety of **interactive
modules**,
such as calculators and quizzes, allowing the students to test their calculation
skills and
knowledge. We shared the platform with students from the Life Science Lab and
received requests for
more visual materials, leading us to integrate the **Videos** from
our Wetlab Series.

#### TEST

Furthermore, we consulted multiple high school teachers and university educators
with our work to
get their insights. We received only positive feedback, and some of the high
school teachers even
plan to integrate parts into their own lesson curricula. Our learning platform
is open for students
and educators worldwide and provides **self-learning material** for
engaged students.
Also, future iGEM teams can contribute to the platform, further expanding it
with additional
modules.

#### LEARN

Students responded positively to the learning platform and provided feedback
requesting more
pictures and illustrations. We incorporated this feedback into the current
version of the platform,
which is now available on our wiki. Nevertheless, it is important to acknowledge
that the learning
platform alone will not reach the students effectively. It must be supported by
**motivated
educators** who can spark students’ interest in synthetic biology.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

References

* Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A.,
  Ronneberger, O.,
  Willmore,
  L., Ballard, A. J., Bambrick, J., Bodenstein, S. W., Evans, D. A., Hung,
  C.-C., O’Neill, M.,
  Reiman,
  D., Tunyasuvunakool, K., Wu, Z., Žemgulytė, A., Arvaniti, E., … Jumper, J.
  M. (2024). Accurate
  structure prediction of biomolecular interactions with AlphaFold 3.
  *Nature*, 630(8016),
  493–500. <https://doi.org/10.1038/s41586-024-07487-w>
* Aho, N., Groenhof, G., & Buslaev, P. (2024). Do all paths lead to Rome? How
  reliable is umbrella
  sampling along a single path? *Journal of Chemical Theory and
  Computation*, 20(15),
  6674–6686.
  <https://doi.org/10.1021/acs.jctc.4c00134>
* Daringer, N. M., Dudek, R. M., Schwarz, K. A., & Leonard, J. N. (2014).
  Modular extracellular
  sensor architecture for engineering mammalian cell-based devices. *ACS
  Synthetic Biology*,
  3(12), 892–902. <https://doi.org/10.1021/sb400128g>
* Edelstein, H. I., Donahue, P. S., Muldoon, J. J., Kang, A. K., Dolberg, T.
  B., Battaglia, L. M.,
  Allchin, E. R., Hong, M., & Leonard, J. N. (2020). Elucidation and
  refinement of synthetic
  receptor
  mechanisms. *Synthetic Biology*, 5(1), ysaa017. <https://doi.org/10.1093/synbio/ysaa017>
* Govind Kumar, V., Polasa, A., Agrawal, S., Kumar, T. K. S., & Moradi, M.
  (2023). Binding
  affinity
  estimation from restrained umbrella sampling simulations. *Nature
  Computational Science*,
  3(1),
  59–70. <https://doi.org/10.1038/s43588-022-00389-9>
* Jones, R. D., Qian, Y., Ilia, K., Wang, B., Laub, M. T., Del Vecchio, D., &
  Weiss, R. (2022).
  Robust and tunable signal processing in mammalian cells via engineered
  covalent modification
  cycles.
  *Nature Communications*, 13(1), 1720. <https://doi.org/10.1038/s41467-022-29338-w>
* Kalogriopoulos, N. A., Tei, R., Yan, Y., Klein, P. M., Ravalin, M., Cai, B.,
  Soltesz, I., Li,
  Y.,
  & Ting, A. Y. (2025). Synthetic GPCRs for programmable sensing and control
  of cell behaviour.
  *Nature*, 637(8044), 230–239. <https://doi.org/10.1038/s41586-024-08282-3>
* Kenney, L. J., & Anand, G. S. (2020). EnvZ/OmpR two-component signaling: An
  archetype system
  that
  can function noncanonically. *EcoSal Plus*, 9(1), ESP-0001-2019. <https://doi.org/10.1128/ecosalplus.esp-0001-2019>
* Kroon, P. C., Grünewald, F., Barnoud, J., van Tilburg, M., Brasnett, C.,
  Souza, P. C. T.,
  Wassenaar, T. A., & Marrink, S.-J. (2025). Martinize2 and Vermouth: Unified
  framework for topology
  generation (No. arXiv:2212.01191). *arXiv*. <https://doi.org/10.48550/arXiv.2212.01191>
* Levintov, L., Gorai, B., & Vashisth, H. (2024). Spontaneous dimerization and
  distinct packing
  modes of transmembrane domains in receptor tyrosine kinases. *bioRxiv*,
  2024.05.09.593448. <https://doi.org/10.1101/2024.05.09.593448>
* Lindorff-Larsen, K., Piana, S., Palmo, K., Maragakis, P., Klepeis, J. L.,
  Dror, R. O., & Shaw,
  D.
  E. (2010). Improved side-chain torsion potentials for the Amber ff99SB
  protein force field.
  *Proteins*, 78(8), 1950–1958. <https://doi.org/10.1002/prot.22711>
* Lu, C., & Wang, Z.-X. (2017). Quantitative analysis of ligand-induced
  heterodimerization of two
  distinct receptors. *Analytical Chemistry*, 89(13), 6926–6930. <https://doi.org/10.1021/acs.analchem.7b01274>
* Pacesa, M., Nickel, L., Schellhaas, C., Schmidt, J., Pyatova, E., Kissling,
  L., Barendse, P.,
  Choudhury, J., Kapoor, S., Alcaraz-Serna, A., Cho, Y., Ghamary, K. H.,
  Vinué, L., Yachnin, B. J.,
  Wollacott, A. M., Buckley, S., Westphal, A. H., Lindhoud, S., Georgeon, S.,
  … Correia, B. E.
  (2025).
  One-shot design of functional protein binders with BindCraft. *Nature*,
  1–10. <https://doi.org/10.1038/s41586-025-09429-6>
* Qin, L., Yoshida, T., & Inouye, M. (2001). The critical role of DNA in the
  equilibrium between
  OmpR and phosphorylated OmpR mediated by EnvZ in *Escherichia coli*.
  *Proceedings of the
  National Academy of Sciences of the United States of America*, 98(3),
  908. <https://doi.org/10.1073/pnas.031383098>
* Schrödinger, LLC. (2015). *The PyMOL Molecular Graphics System*,
  Version 1.8.
* Souza, P. C. T., Alessandri, R., Barnoud, J., Thallmair, S., Faustino, I.,
  Grünewald, F.,
  Patmanidis, I., Abdizadeh, H., Bruininks, B. M. H., Wassenaar, T. A., Kroon,
  P. C., Melcr, J.,
  Nieto, V., Corradi, V., Khan, H. M., Domański, J., Javanainen, M.,
  Martinez-Seara, H., Reuter, N.,
  …
  Marrink, S. J. (2021). Martini 3: A general purpose force field for
  coarse-grained molecular
  dynamics. *Nature Methods*, 18(4), 382–388. <https://doi.org/10.1038/s41592-021-01098-3>
* Teng, F., Cui, T., Zhou, L., Gao, Q., Zhou, Q., & Li, W. (2024).
  Programmable synthetic
  receptors:
  The next generation of cell and gene therapies. *Signal Transduction and
  Targeted Therapy*,
  9(1), 7. <https://doi.org/10.1038/s41392-023-01680-5>
* Vlahos, A. E., Kang, J., Aldrete, C. A., Zhu, R., Chong, L. S., Elowitz, M.
  B., & Gao, X. J.
  (2022). Protease-controlled secretion and display of intercellular signals.
  *Nature
  Communications*, 13(1), 912. <https://doi.org/10.1038/s41467-022-28623-y>
* Wassenaar, T. A., Ingólfsson, H. I., Böckmann, R. A., Tieleman, D. P., &
  Marrink, S. J. (2015).
  Computational lipidomics with INSANE: A versatile tool for generating custom
  membranes for
  molecular
  simulations. *Journal of Chemical Theory and Computation*, 11(5),
  2144–2155. <https://doi.org/10.1021/acs.jctc.5b00209>
* Webb, B., & Sali, A. (2016). Comparative protein structure modeling using
  MODELLER. *Current
  Protocols in Bioinformatics*, 54, 5.6.1–5.6.37. <https://doi.org/10.1002/cpbi.3>
* Yang, X., Rocks, J. W., Jiang, K., Walters, A. J., Rai, K., Liu, J., Nguyen,
  J., Olson, S. D.,
  Mehta, P., Collins, J. J., Daringer, N. M., & Bashor, C. J. (2025).
  Engineering synthetic
  phosphorylation signaling networks in human cells. *Science*,
  387(6729), 74–81. <https://doi.org/10.1126/science.adm8485>
* Zhou, J., Ge, Q., Wang, D., Guo, Q., & Tao, Y. (2023). Engineering a modular
  double-transmembrane
  synthetic receptor system for customizing cellular programs. *Cell
  Reports*, 42(4), 112385.
  <https://doi.org/10.1016/j.celrep.2023.112385>

Iteration 1

Iteration 2

Iteration 3

Iteration 4

Iteration 5

Iteration 6

#### DESIGN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem
voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui
deleniti
quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem
voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui
deleniti
quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem
voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui
deleniti
quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem
voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui
deleniti
quod,
natus facere doloremque delectus!

![Results Image](https://static.igem.wiki/teams/5775/images/results/sensing-mesa/mesafig3.svg)

---

**Figure ###:| TNFα-induced CD3ζ-SH2 constitution via
MESA-like
kinase
receptor.** Quantification of TNFα-induced phosphorylation-mediated
CD3ζ-SH2
constitution
(Fig. ###). Cells were transfected with the complete expression-coupled PHOENICS
circuit,
varying
only
the receptor configuration as indicated. Neg. ctrl: No receptor. Receptor conditions
contained
specified ratios of ABL to bZipEE halves (170 ng total DNA in 12-well plate wells).
SplifFast
fluorescence was quantified with flow cytometry. Fluorescence profiles of viable
single cell
populations (>9000 per condition) are displayed in ridgeline plot on the left,
geometric mean
fluorescence (RFU) ± geometric SEM are shown in barplot to the right. Medium: DMEM
(10% FCS).

#### DESIGN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### DESIGN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### DESIGN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### DESIGN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### DESIGN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

## Tabs Showcase

### 7 Tabs

Iteration 1

Iteration 2

Iteration 3

Iteration 4

Iteration 5

Iteration 6

Iteration 7

#### DESIGN

ALorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

Next →

#### DESIGN

BLorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

← Previous
Next →

#### DESIGN

CLorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

← Previous
Next →

#### DESIGN

DLorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

← Previous
Next →

#### DESIGN

ELorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

← Previous
Next →

#### DESIGN

FLorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

← Previous
Next →

#### DESIGN

GLorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

← Previous

### 6 Tabs

Iteration 1

Iteration 2

Iteration 3

Iteration 4

Iteration 5

Iteration 6

#### DESIGN

ALorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

Next →

#### DESIGN

BLorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

← Previous
Next →

#### DESIGN

CLorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

← Previous
Next →

#### DESIGN

DLorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

← Previous
Next →

#### DESIGN

ELorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

← Previous
Next →

#### DESIGN

FLorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

← Previous

### 5 Tabs

Iteration 1

Iteration 2

Iteration 3

Iteration 4

Iteration 5

#### DESIGN

ALorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

Next →

#### DESIGN

BLorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

← Previous
Next →

#### DESIGN

CLorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

← Previous
Next →

#### DESIGN

DLorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

← Previous
Next →

#### DESIGN

ELorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

← Previous

### 4 Tabs

Iteration 1

Iteration 2

Iteration 3

Iteration 4

#### DESIGN

ALorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

Next →

#### DESIGN

BLorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

← Previous
Next →

#### DESIGN

CLorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

← Previous
Next →

#### DESIGN

DLorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

← Previous

### 3 Tabs

Iteration 1

Iteration 2

Iteration 3

#### DESIGN

ALorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

Next →

#### DESIGN

BLorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

← Previous
Next →

#### DESIGN

CLorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

← Previous

### 2 Tabs

Iteration 1

Iteration 2

#### DESIGN

ALorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

Next →

#### DESIGN

BLorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### BUILD

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### TEST

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

#### LEARN

Lorem ipsum dolor sit amet consectetur adipisicing elit. Unde omnis quae magnam rem voluptates
inventore aspernatur aperiam corporis ullam mollitia veritatis earum consectetur qui deleniti quod,
natus facere doloremque delectus!

← Previous

{% endblock %}

{% extends "layout.html" %}
{% block title %}Experiments{% endblock %}
{% block header %}

EXPERIMENTS

{% endblock %}
{% block page\_content %}

Our protocol repository provides a complete technical manual for the molecular biology methods central to
our iGEM project. This collection includes standardized procedures for DNA manipulation, bacterial
engineering, nucleic acid purification, and analytical techniques. This transparency enables future iGEM teams
and synthetic biology researchers to accurately reproduce our work, validate our findings, and advance the
field through collaborative innovation.

{% include 'sidenav.html' %}

## DNA Cloning

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Polymerase Chain Reaction

PCR (Polymerase Chain Reaction) amplifies template DNA through repeated cycles of
denaturation,
annealing, and extension using a thermostable DNA polymerase. This protocol uses Q5
High-Fidelity DNA
Polymerase (New England Biolabs) for accurate amplification of target sequences.

**Materials**

**Table 1: PCR reaction components.** The table shows the required
amounts of each
component for a 25 µL PCR reaction.

| Component | Volume/Amount |
| --- | --- |
| Template DNA | 1-100 ng |
| Forward Primer DNA (10 µM) | 1.25 µL |
| Reverse Primer DNA (10 µM) | 1.25 µL |
| Q5 High-Fidelity 2X Master Mix | 12.5 µL |
| Nuclease Free Water | ad 25 µL |

**Step by Step Instructions**

1. Set up the PCR reaction in a PCR tube by adding all components from the materials
   table to reach a
   total volume of 25 µL
2. Mix gently by pipetting or flicking the tube, then briefly spin down to collect all
   liquid at the
   bottom
3. Place the PCR tube in a thermocycler and run the following program: Initial
   denaturation at 98°C for
   30 s, followed by 25-40 cycles of denaturation (98°C, 10 s), annealing (60-72°C,
   10-30 s), and
   extension (72°C, 30 s/kb), then final extension at 72°C for 5 min, and hold at 4°C
4. Store the PCR product at 4°C until further use

**Notes**

Annealing temperature should be optimized based on primer Tm values (typically 60-72°C)
and can be
calculated with the NEB Calculator.

Extension time depends on amplicon length: use 30 seconds per kilobase.

Number of cycles (25-40) should be optimized to minimize off-target amplification while
ensuring
sufficient product yield. Typically 30 cycles are sufficient.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

SuperFi Polymerase DNA-PCR

If regular Q5 PCR is not sufficient to achieve desired DNA amounts (e.g. because of poor
Primer design)
SuperFi DNA Polymerase can be used. Platinum™ SuperFi™ DNA Polymerase for high-fidelity
amplification of
DNA templates minimizes non-specific amplification, making it ideal for cloning and
mutagenesis
applications.

**Materials**

| Component | Volume/Amount |
| --- | --- |
| Template DNA | 1 pg - 10 ng (plasmid) |
| Forward Primer (10 µM) | 1.25 µL |
| Reverse Primer (10 µM) | 1.25 µL |
| SuperFi 2x MasterMix | 12.5 µL |
| Nuclease Free Water | 25 µL |

**Step by Step Instructions**

1. Prepare the reaction mix by combining all components in a PCR tube to achieve a
   final volume of 25
   µL, adding template DNA and primers last
2. Mix the reaction gently by pipetting or flicking, then use a QuickSpin to briefly
   collect contents
   at the tube bottom
3. Transfer tubes to a thermocycler and run the following program: initial denaturation
   at 98°C for 30
   s, followed by 25-35 cycles of denaturation (98°C, 10 s), annealing (60°C
   regardless, no need for
   different annealing temperatures, 10 s), and extension (72°C, 30 s/kb), then final
   extension at 72°C
   for 5 min, and hold at 4°C

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Oligonucleotide Annealing

This protocol anneals complementary single-stranded oligonucleotides to form
double-stranded DNA
fragments. Starting with 100 µM oligo stocks, the procedure typically yields 10 µM
annealed products
suitable for cloning applications such as Golden Gate assembly.

**Materials**

| Component | Volume/Amount |
| --- | --- |
| Forward oligonucleotide (100 µM) | 5 µL |
| Reverse oligonucleotide (100 µM) | 5 µL |
| rCutSmart buffer (10x) | 5 µL |
| Nuclease Free Water | ad 50 µL |

**Step by Step Instructions**

1. Preheat a heating block to 95°C while preparing the reaction mixture
2. Combine all components in a 1.5 mL microcentrifuge tube for a total volume of 50 µL
3. Place the tube in the heating block and incubate at 95°C for 5 minutes to fully
   denature the
   oligonucleotides
4. Turn off the heating block and allow the tube to cool slowly inside the block for at
   least 1 hour,
   enabling gradual annealing of complementary strands
5. Store the annealed oligonucleotides at -20°C until needed for downstream
   applications

**Notes**

Volumes can be scaled proportionally if different final concentrations or volumes are
required.
As an alternative cooling method, use a thermocycler programmed to cool at 0.5°C per second
from 95°C to
room temperature for more controlled annealing.
The rCutSmart buffer provides optimal salt conditions for efficient annealing of
complementary
oligonucleotides.
Ensure oligonucleotides are designed with perfect complementarity in the regions intended to
anneal.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Golden Gate Cloning

Golden Gate Assembly allows efficient and precise joining of multiple DNA fragments in a
single tube
(single pot reaction) using type IIS restriction enzymes such as BsaI or Esp3I. These
enzymes cut
outside their recognition sites, enabling seamless, scar-free assembly of
multiple-fragments to one
construct.

**Materials**

| Component | Volume/Amount |
| --- | --- |
| Destination Plasmid DNA | Variable, ensure 75 ng of Plasmid |
| Insert DNA | Variable, ensure a Vector:Insert ratio of 1:3 |
| T4 DNA Ligase | 0.5 µL |
| T4 DNA Ligase Buffer (10x) | 2 µL |
| Type IIS Restriction Enzyme | 0.5 µL |
| Nuclease Free Water | Ad 20 µL |

**Step by Step Instructions**

1. Combine all components in a PCR tube, maintaining a 1:3 molar ratio of vector to
   insert DNA, and
   adjust the final volume to 25 µL with nuclease-free water
2. Transfer the PCR tube to a thermocycler and execute the following program: 10-30
   cycles alternating
   between restriction enzyme activation (37°C, 5 min) and ligase activation (16°C, 5
   min), followed by
   enzyme inactivation at 65°C for 5 min, then hold at 4°C
3. Store the assembled product at 4°C until transformation or further processing

**Notes**

The 1:3 vector to insert molar ratio can be adjusted if assembly efficiency is low.
Cycle number (10-30) can be optimized depending on fragment complexity and insert
number.
Ensure that all DNA fragments have compatible overhangs designed for Golden Gate
assembly.
The type IIS restriction enzyme used (e.g., BsaI, BbsI) must match the recognition sites
in your
constructs.
Add 3-5 basepairs at the ends of linear constructs containing terminal type IIS
recognition sides to
enhance enzyme activity.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Gibson Assembly

Gibson assembly enables scarless joining of multiple DNA fragments with overlapping ends
without
requiring restriction sites. This one-step isothermal reaction uses exonuclease,
polymerase, and ligase
enzymes to assemble fragments in a sequence-independent manner, making it highly
versatile for complex
cloning projects.

**Materials**

| Component | Volume/Amount |
| --- | --- |
| DNA fragments (2-3 fragments) | 0.03 - 0.5 pmol total DNA |
| Gibson Assembly Master Mix (2X) | 10 µL |
| Nuclease-free water | ad 20 µL |

**Step by Step Instructions**

1. Design DNA fragments with 15-30 bp overlapping regions. DNA fragments should be >120
   bp for this
   protocol
2. Amplify all DNA fragments by PCR, ensuring primers incorporate the designed
   overlapping sequences at
   fragment termini
3. Combine the appropriate amount of DNA fragments with Gibson Assembly Master Mix and
   nuclease-free
   water in a PCR tube to reach a final volume of 20 µL
4. Incubate the assembly reaction at 50°C for 15 minutes (2-3 fragments) or 60 minutes
   (4-6 fragments)
5. Transform 2-5 µL of the assembly reaction into competent cells or store at -20°C
   until
   transformation

**Notes**

For assembling 4-6 DNA fragments 0.2 - 0.5 pmols of total DNA should be used. The
recommended
Vector:Insert ratio is 1:2 for 2-3 fragments or 1:1 for 4-6 DNA fragments.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

KLD Ligation

KLD (Kinase, Ligase, DpnI) ligation efficiently circularizes blunt-end DNA fragments
using a
streamlined enzyme mix containing kinase for phosphorylation, ligase for joining, and
DpnI restriction
endonuclease for digesting template DNA. This rapid protocol is particularly useful for
cloning PCR
products or assembled fragments.

**Materials**

| Component | Volume/Amount |
| --- | --- |
| Blunt-end DNA fragment | 1 µL |
| KLD Reaction Buffer (2X) | 5 µL |
| KLD Enzyme Mix (10X) | 1 µL |
| Nuclease Free Water | 3 µL |

**Step by Step Instructions**

1. Combine all components in a microcentrifuge tube to achieve a total reaction volume
   of 10 µL
2. Mix thoroughly by gently pipetting the reaction up and down several times
3. Incubate the reaction at room temperature (25°C) for a minimum of 5 minutes to allow
   complete
   ligation
4. Use 5 µL of the ligation mixture directly for transformation into 50 µL of competent
   cells

**Notes**

The DpnI component digests methylated template DNA, reducing background from PCR
templates in
downstream transformations.
Extended incubation beyond 5 minutes does not significantly improve ligation efficiency
for most
applications.
The reaction can be performed at room temperature without specialized equipment, making
it convenient
for routine cloning.
Store unused ligation mixture at -20°C if transformation will be delayed, though fresh
ligations
typically yield better results.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Agarose Gel Electrophoresis

Agarose gel electrophoresis separates DNA fragments based on size, using electricity.
Smaller fragments
migrate faster through the agarose gel matrix towards the Kathode than larger fragments.
This technique
is essential for visualizing PCR products, verifying digestions, and purifying DNA
fragments of specific
sizes.

**Materials**

| Component | Volume/Amount |
| --- | --- |
| PCR Product | 25 µL |
| 6x Loading Dye | 5 µL |
| SYBR-Safe | 3 µL |
| DNA Ladder | 5 µL |

**Step by Step Instructions**

1. Pour an agarose gel with the appropriate agarose concentration typically ranging
   from 1-3% (w/v).
   Add 3 µL SYBR-Safe directly to the still liquid agarose gel and stir with a pipette
   tip until no more
   colored traces are visible
2. Allow the gel to solidify for 15-30 minutes at room temperature until firm
3. Add 6x Loading Dye to your PCR Product volume and mix thoroughly by pipetting up and
   down
4. Place the solidified gel in an electrophoresis chamber, cover with TAE buffer, load
   DNA samples and
   ladder into separate wells
5. Run the gels at 120-135V for 20-30 min for a standard gel
6. Visualize DNA bands using UV or blue light illumination, and document or excise
   bands as needed

**Notes**

Higher agarose concentrations provide better resolution for smaller fragments but
increase run time.
Recommended agarose concentrations for different fragment lengths are given in Table 2.
Gels are prepared by mixing the appropriate mass of Agarose powder with TAE buffer in an
Erlenmeyer
flask, heating it in a microwave until completely dissolved, swirling occasionally and
monitoring
carefully to prevent boiling over. The lid of the flask must be unscrewed to prevent
pressure build-up
and bursting of the flask.
Once heated the dissolved agarose can be kept liquid in a water bath at 60°C until gel
casting. This
temperature allows immediate addition of staining agents.
SYBR Safe is preferred over ethidium bromide for safety reasons and can be visualized
with blue light
transilluminators.
As a rule of thumb 10 volts per centimeter of gel are recommended.
Voltage can be adjusted for faster or slower runs, but higher voltages may cause band
distortion or gel
heating. The maximum voltage that can be applied without causing issues remains to be
determined.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Extracting DNA Fragments from Agarose Gels

Gel extraction recovers specific DNA fragments from agarose gels following
electrophoresis. This
technique is essential for isolating correctly sized PCR products, restriction
fragments, or assembly
products before downstream cloning steps.

**Materials**

| Component | Volume/Amount |
| --- | --- |
| Agarose gel with separated DNA fragments | 1 gel |
| QIAquick Gel Extraction Kit | 1 preparation per band |
| Clean scalpel or razor blade | 1 |
| Nuclease Free Water | 30-50 µL (preheated to 50°C optional) |
| UV or blue light transilluminator | for visualization |

**Step by Step Instructions**

1. Visualize DNA bands on a transilluminator and identify the fragment of interest
   based on size
   comparison with the DNA ladder
2. Using a clean scalpel, carefully excise the gel slice containing the desired DNA
   band while
   minimizing excess agarose
3. Weigh the gel slice and follow the manufacturer's protocol provided with the
   QIAquick Gel Extraction
   Kit to dissolve the agarose and purify the DNA
4. Elute purified DNA in an appropriate volume of nuclease-free water and measure
   concentration using a
   NanoDrop spectrophotometer
5. Use the extracted DNA for subsequent cloning assembly, or other applications

**Notes**

Minimize UV exposure time during band excision to prevent DNA damage—use a blue light
transilluminator
when possible.
Remove excess agarose around the band to improve extraction efficiency and reduce
contaminants.
Preheating water for elution to 50°C and performing a second elution can increase DNA yield.

## Bacterial Work

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Preparing Chemically Competent E. coli for
Transformation

Chemically competent cells acquire the ability to uptake exogenous DNA through
treatment
with calcium
chloride, which destabilizes the bacterial cell membrane. These cells can then be
transformed via heat
shock, making them essential for molecular cloning workflows.

**Materials**

| Component | Volume/Amount |
| --- | --- |
| E. coli cryo-stock (e.g. DH5α) | 1 streak |
| LB medium (no antibiotics) | 5 mL + 1 L |
| MgSO₄ solution (1 M) | 10 mL |
| MgCl₂ solution (1 M) | 10 mL |
| 10X transformation buffer\* | diluted to 1X, 100 mL |
| DMSO | 2 mL |

\*10X transformation buffer: 100 mM HEPES pH 6.7, 150 mM CaCl₂, 550 mM MnCl₂, 2.5 M
KCl

**Step by Step Instructions**

1. Streak DH5α from glycerol stock onto an LB agar plate and incubate overnight at
   37°C
   to obtain
   isolated colonies
2. Inoculate a single colony into 5 mL LB medium and grow overnight at 37°C with
   shaking
3. Supplement 1 L of LB medium with 10 mL each of 1 M MgSO₄ and 1 M MgCl₂, then
   inoculate to an initial
   OD₆₀₀ of 0.006 using the overnight culture
4. Grow the main culture at 20°C with shaking until OD₆₀₀ reaches approximately
   0.1-0.2, then increase
   temperature to 25°C and continue growth until OD₆₀₀ reaches 0.45
5. Aliquot into 50 mL tubes, and chill in an ice-water bath for 10 minutes
6. Pellet cells by centrifugation at 3000 × g for 15 minutes at 4°C, discard
   supernatant, and resuspend
   each pellet in 3 mL ice-cold 1X transformation buffer
7. Pool all suspensions, incubate on ice for 10 minutes, then pellet again at 3000
   × g
   for 15 minutes
   at 4°C
8. Resuspend the final pellet in 25 mL ice-cold 1X transformation buffer, add 2 mL
   DMSO, mix gently by
   inversion, and aliquot into pre-chilled microcentrifuge tubes
9. Store microcentrifuge tubes immediately at -80°C

**Notes**

This protocol typically requires 3-4 days from initial streaking to frozen competent
cells.
All manipulations after culture harvest must be performed at 4°C to preserve cell
viability. Work on ice
whenever possible.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Transforming Chemically Competent E. coli via Heat Shock

Heat shock transformation introduces plasmid DNA into chemically competent E. coli
cells
by briefly
disrupting the cell membrane at elevated temperature. This rapid method is suitable
for
routine cloning
and enables selection of successfully transformed cells through antibiotic
resistance
markers.

**Materials**

**Table 1: Materials for heat shock transformation.** The table
shows
the required
components and volumes for transforming chemically competent E. coli cells.

| Component | Volume/Amount |
| --- | --- |
| Chemically competent E. coli cells | 50-100 µL |
| Plasmid DNA | 1-2 µL (20 ng minimum DNA amount) |
| LB agar plates with appropriate antibiotic | 1 plate per transformation |

**Step by Step Instructions**

1. Thaw one aliquot of competent cells on ice for 15 minutes, handling gently to
   avoid
   premature
   warming.
2. Add 1-2 µL of plasmid DNA directly to the cells and incubate on ice for 30
   minutes
   to allow DNA
   binding.
3. Transfer cells to a 42°C heat block for exactly 30 seconds to induce heat shock.
4. Immediately return cells to ice and incubate for 10 minutes to allow membrane
   recovery.
5. Transfer the full volume onto LB agar plates containing the appropriate
   selection
   antibiotic, spread
   using an inoculation loop and incubate overnight at 37°C.

**Notes**

Optionally SOC medium can be added to the cells after heat shock and incubated for 45
min
before
plating. With the exception of the 30 second heat shock step, this protocol proved
very
robust, reliably
transforming bacteria even when cells were kept on ice for longer or shorter periods
before or after
heatshock.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Isolating Plasmid DNA from Bacteria (MiniPrep)

MiniPrep purification extracts plasmid DNA from bacterial cultures using alkaline
lysis
followed by
column-based purification. This method provides high-quality plasmid DNA suitable
for
sequencing,
downstream cloning applications and transfection into mammalian cells.

**Materials**

| Component | Volume/Amount |
| --- | --- |
| Transformed bacterial colony | 1-2 colonies |
| LB medium with appropriate antibiotic | 5 mL per culture |
| QIAprep Spin Miniprep Kit | 1 preparation per sample |
| Nuclease Free Water | 30-50 µL (preheated to 50°C optional) |

**Step by Step Instructions**

1. Pick a single colony from a transformation plate and inoculate into 5 mL LB
   medium
   containing the
   appropriate selection antibiotic
2. Incubate the liquid culture at 37°C with shaking for 12-16 hours until turbid
3. Pellet the bacterial cells by centrifugation at maximum speed for 1-10 minutes,
   then
   discard the
   supernatant
4. Purify plasmid DNA from the cell pellet following the manufacturer's protocol
   provided with the
   QIAprep Spin Miniprep Kit
5. Elute plasmid DNA in 30-50 µL of nuclease-free water, then measure DNA
   concentration
   using a
   NanoDrop spectrophotometer at 260 nm
6. Analyse purified plasmid DNA with sequencing or restriction digest it to
   interpret
   transformation
   success by gel electrophoresis

**Notes**

Picking two colonies allows a backup option—the second pellet can be stored at 4°C
and
processed only
if the first clone proves incorrect after sequencing.
Preheating water to 50°C and performing a second elution can increase DNA yield.
For processing multiple samples simultaneously, a vacuum-manifold instead of a
centrifuge can
substantially accelerate workflow with the QIAprep Spin columns.
Avoid over-incubating cultures beyond 16 hours, as this may reduce plasmid quality
due
to chromosomal
DNA contamination.
For rapid DNA preparation (6 hours instead of overnight) or higher yields, inoculate
two
separate 5 mL
cultures using the same colony pick, then combine the resuspended pellets after
centrifugation to
increase yield from shortened growth time.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Bacterial Protein Expression & Purification

This protocol describes how to produce a protein in bacteria and purify it via HisTag
-
Ni-NTA column.

**Buffers and Reagents**

**Table 1: Media and buffer compositions.** The table shows the
composition of media and
buffers used throughout the protein expression and purification protocol.

| Buffer/Media | Components |
| --- | --- |
| LB Media | 10 g/L Tryptone, 5 g/L Yeast extract, 10 g/L NaCl |
| 2xYT-Media | 16 g/L Tryptone, 10 g/L Yeast extract, 5 g/L NaCl |
| NaP10 buffer | 50 mM NaH2PO4, 150 mM NaCl, 10 mM Imidazol, pH 8 (at 25 °C) adjusted with NaOH For lysis step: Add lysozyme to final concentration of 1 mg/mL and protease inhibitor cocktail |
| NaP20 | 50 mM NaH2PO4, 150 mM NaCl, 20 mM Imidazol, pH 8 (at 25 °C) adjusted with NaOH |
| NaP250 | 50 mM NaH2PO4, 150 mM NaCl, 250 mM Imidazol, pH 8 (at 25 °C) adjusted with NaOH |

**Protein Expression**

*Day 0 - Overnight culture*

1. Prepare 20 mL overnight culture (ONC) of transformed bacteria in LB and
   selection
   antibiotics

*Day 1 - Recombinant protein expression*

1. Inoculate 500-2000 mL 2xYT media 1:100 with ONC and selection antibiotics
2. Incubate cells at 37 °C, shaking at 180 rpm until OD600 0.4-0.6 is
   reached
3. Add inducer for expression (for example 0.1-0.2 mM IPTG)
4. Incubate for 24h at 32 °C, shaking at 180 rpm
5. Pellet cells at 3,750 x g for 45 min at 4 °C and transfer pellet into a 50 mL
   tube
   (pellet can be
   stored at -80 °C)

**Cell Lysis**

1. Dissolve pellet in 2 mL/g (pellet weight) lysis buffer (NaP10 with lysozyme and
   protease inhibitor)
2. Sonicate samples on ice for 10 min at 40% amplitude with 20 s on and 30 s off
3. Add DNAse I (1:1000) and incubate for 15 min on ice
4. Centrifuge to clear lysate at 20,000 x g for 30 min at 4 °C (Take 20 µL aliquot)
5. Sterile filter supernatant with 0.22 µm filter (Take 20 µL aliquot)

**Purification of His-tagged Protein**

*Column Preparation*

1. Add 1 mL of Ni-NTA resin slurry (500 µL effective Ni-NTA resin) to a
   gravity-flow
   column and wait
   until storage buffer is drained (can also be done at slow speed in a centrifuge)
2. Equilibrate column twice with 2 mL of NaP10

*Purification*

1. Apply filtrate onto the column and collect flow-through in a 50 mL tube (Take 20
   µL
   aliquot)
2. Apply the flow-through onto the column again twice to increase yield
3. Wash column with 10 mL NaP10 (Take 20 µL aliquot)
4. Wash column with 10 mL NaP20 (Take 20 µL aliquot)
5. Repeat step 4
6. Apply 4 mL NaP250 (Elution buffer)
7. Seal column with cap and incubate at room temperature for 30 min, light shaking
8. Open column and collect purified protein

## Mammalian Cells

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Cell Maintenance

This protocol describes how to keep Human Embryonic Kidney 293T (HEK293T) cells in
culture for use in
experiments. It is optimized for culture in T75 flasks, volumes vary for other
formats.
Cell culture
work requires a sterile environment in a biosafety cabinet (BSC) under laminar flow.
All
liquid handling
above 1 mL should be done using serological pipettes and not by pouring.

1. Warm reagents to 37 °C in a water bath and prepare complete DMEM supplemented
with
10% fetal calf
serum (FCS), Penicillin/Streptomycin (P/S) (final concentration: 100 U/ml
penicillin, 100 µg/mL
streptomycin) and L-glutamine (final concentration: 2 mM):

**Table 1: Media composition.**
The table shows the recommended composition of cell culture media.

| Reagent | Dulbecco’s Modified Eagle Medium (DMEM) | FCS | P/S (100X) | L-glutamine (100X) |
| --- | --- | --- | --- | --- |
| Volume [mL] | 500 | 50 | 5 | 5 |

2. Take flask (T75) out of the incubator (37°C, 5% CO2) and inspect
cells under a light
microscope to assess confluency, then move into BSC. It is recommended to
split
at a confluence of
70-80%.

3. Aspirate old medium from the flask and aspirate old medium.

4. Wash with 4 mL Dulbecco’s Phosphate Buffered Saline (DPBS), tilt flask and
aspirate DPBS.

5. Add 1 mL trypsin-EDTA and let sit until cells visibly detach (takes around
3-4
minutes)

5. Add 9 mL DMEM to neutralize trypsin and thoroughly resuspend cells.

6. Count cells or decide dilution based on confluency. At 80% confluency, a 1:5
split (2 mL of old
cells) is ready to split again after 48h, 1:10 after 72h, 1:20 after 96h)

7. Dilute with DMEM to desired cell concentration.

8. Transfer 10 mL of diluted cells into a new T75 flask.

**Notes**: For splitting at 70-80% confluence, these rules of thumb work for splitting
ratios: A 1:5 split
(2 mL of cells + 8 mL DMEM) is ready to split again after 48h, 1:10 (1 mL cells + 9
mL
DMEM) after 72h,
1:20 (0.5 mL + 9.5 mL DMEM) after 96h. Ratios with less cells than 1:20 are not
recommended.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Cell Seeding

This protocol describes how to seed Human Embryonic Kidney 293T (HEK293T) cells
for
experiments,
mostly involving transfection. Cell culture work requires a sterile environment
in a
biosafety cabinet
(BSC) under laminar flow. All liquid handling above 1 mL should be done using
serological pipettes and
not by pouring.

1. Split cells until cell collection steps as described in Cell Maintenance
protocol.

**Table 1: Seeding densities and volumes for different plate
formats.**

| POlate format | 96-well | 24-well | 12-well |
| --- | --- | --- | --- |
| Cells/well | 12,500 | 75,000 | 126,0005 |

2. Seed according to the used plate format, plate should be declared as
tissue
culture treated.

3. Gently shuffle plate to disperse across wells and place cells into the
incubator.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Transfection

This protocol describes how to transfect Human Embryonic Kidney 293T
(HEK293T)
cells using
Lipofectamine 2000 (Thermo Fisher Scientific, Waltham) as a cationic
transfection reagent. The goal
of transfection is to introduce exogenous DNA into eukaryotic cells.

1. Seed cells in an appropriate well plate and incubate at 37°C and 5%
   CO2 for 24h.
2. Dilute DNA to desired concentrations in OptiMEM (serum-reduced medium)
   (Thermo Fisher
   Scientific, Waltham).
3. Dilute Lipofectamine 2000 in OptiMEM. Incubate for 5 min at room
   temperature.

**Table 1: Lipofectamine 2000 and DNA amounts per well for
different plate
formats.** The table shows the recommended amounts of DNA
and
Lipofectamine 2000
transfection reagent for different well plate formats used in
transfection experiments.

| Component | 96-well | 24-well | 12-well |
| --- | --- | --- | --- |
| DNA | 100-200 ng | 200-400 ng | 300-600 ng |
| Lipofectamine 2000 | 0.5 µL | 1.0 µL | 2.0 µL |

4. Add diluted Lipofectamine 2000 to DNA solutions, briefly vortex and spin
   down. Incubate at room
   temperature for 20 min.
5. Add DNA-Lipo-Mix to cells and incubate them until measurement or
   induction.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

NanoLuc Luciferase Assay

NanoLuc Luciferase (NanoLuc) is a 19 kDa luciferase, derived from the
deep
sea shrimp
*Oplophorus gracilirostris* that produces bioluminescence in the
presence of its substrate
furimazine. It’s commonly used as a reporter in mammalian cells as it
has a
wide dynamic range and
high sensitivity. This protocol describes how to measure its activity in
a
96 well plate format
using reduced amounts of reagent. We tested different amounts of reagent
and
the difference in
results was negligible, which is why we settled for these amounts. This
protocol explains both how
to measure secreted NanoLuc in supernatant cell culture medium, and
intracellularly expressed
NanoLuc. For intracellular expression, NanoLuc is commonly normalized by
Firefly luciferase
(FLuc), which is then co-transfected in a plasmid with a weak
constitutive
promoter such as the
Herpes simplex virus-thymidine kinase (HSV-TK) promoter.

**Materials**

96 well plate with transfected cells

Black or white non-transparent 96 well plate

NanoGlo Kit or NanoGlo Dual Luciferase Reporter (DLR) Kit (both Promega)

Passive Lysis buffer (Promega)

Phosphate buffered saline (PBS)

**Amounts per Well**

NanoGlo substrate needs to be prepared freshly before every use. For this
the
substrate needs to
be diluted in the provided buffer. In the normal NanoGlo kit, the
substrate
is of 50X
concentration, for the DLR kit it is 100X. The OneGloEX reagent for
measuring FLuc can be prepared
all at once and stored at -20 °C. Per well, 10 µL of reagent can be
mixed
with 25 µL PBS, yielding
sufficient volume to cover the bottom of the well. Passive Lysis buffer
should be diluted to 1X
with WDC water.

**Measuring secreted NanoLuc**

Prepare diluted NanoGlo reagent as described above
Transfer 10 µL of supernatant into opaque plate
Add 35 µL of NanoGlo reagent/PBS
Wait 10 min
Measure Luminescence using a plate reader

**Measuring intracellular NanoLuc and FLuc**

1. Prepare diluted NanoGlo DLR, OneGloEX and 1X Passive Lysis buffer as
   described
2. Discard medium from cell culture plate
3. Add 30 µL 1X Passive Lysis buffer to each well
4. Shake plate at 500 rpm for 30 min at room temperature
5. Transfer 10 µL of lysate to opaque plate and add 35 µL OneGloEX
   reagent
6. Wait 10 min
7. Measure luminescence using a plate reader
8. Add 35 µL of NanoGlo DLR reagent to inhibit FLuc and activate
   NanoLuc
   activity
9. Wait 10 min
10. Measure NanoLuc luminescence using plate reader

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Flow Cytometry

This protocol describes how to perform flow cytometry for transfected
Human Embryonic Kidney
293T (HEK293T) with mCherry reporter and GFP transfection
normalization
or SplitFast (RspA(N),
RspA(C)) reporter with mCherry transfection normalization. In order
to
measure sufficient cells
and accurately depict the cell population, 12 well plates are used.
For
conditions induced with
a ligand, according ligands were included in each step including the
final DPBS suspension.

1. Remove cells from the incubator 48h after transfection and wash
   each
   well twice with 1 mL
   DPBS without detaching the cells.
2. Detach cells by incubating them with 5 mM EDTA in DPBS at 37°C
   for
   10 minutes and transfer
   suspension into 1.5 mL microcentrifuge tubes. From this point on
   handle samples on ice.
3. Centrifuge suspensions (3 min., 600 g) and discard supernatants.
   Resuspend the pellets in
   500 µL DPBS and centrifuge (3 min., 600 g).
4. Discard the supernatant and resuspend the pellets in 200-400 µL
   DPBS
   depending on pellet
   size.
5. Strain cell suspensions through 40 µm cell strainers into fresh
   microcentrifuge tubes.
6. For SplitFast as reporter, add 5 µM HMBR to each sample.
7. Proceed with the measurement (Excitation lasers at 488 nm were
   used
   for SPlitFast and GFP,
   with fluorescence measurement at 525 nm. Excitation lasers at
   638 nm
   were used for mCherry,
   with fluorescence measurement at 610nm.)
8. Subsequent analysis includes size gating, single cell gating and
   transfection gating (Fig.
   1).

![Results Image](https://static.igem.wiki/teams/5775/images/methods/method-fc.svg)

---

**Figure 1: Flow cytometry
workflow.** (A) mCherry
reporter assay with GFP transfection control for quantification
of
expression. (B) SplitFast
reporter assay with mCherry normalization for quantification of
phosphorylation-dependent
constitution of CD3ζ and SH2. (C) Gating strategy: Events are
size
gated by forward (FSC-A)
and side scatter area (SSC-A). Single cells were then gated by
side
scatter area (SSC-A)
versus height (SSC-H) to yield 20000 events. Cells were gated
for
mCherry or GFP events to
identify transfected cells for subsequent analyses.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Cell-Free Two-Hybrid (CF2H) Assay
for
Binder Screening

The CF2H assay provides a rapid method for validating
computationally
designed protein
binders using a cell-free expression system. Successful binding
events reconstitute a
functional CI transcription factor dimer, activating sfGFP
expression from a pRM promoter.
This enables screening of BindCraft-designed binders within 24
hours
without requiring protein
purification or bacterial transformation.

**Materials**

Cell-free bacterial extract and cell-free buffer (prepared according
to
Capin et al., 2025, or
commercial systems such as Promega E. coli S30 System for linear
DNA)
pRM-sfGFP reporter plasmid (circular DNA)
Linear DNA fragments encoding CI-DBD fused to test binders
Biotinylated, Fc-tagged target protein (e.g., GDF-15)
Streptavidin
Low-binding 384-well plate with black walls and transparent
bottom
Nuclease-free water

**Preparation of Stock Solutions**

*Linear DNA fragments:* Resuspend to 10 ng/μL in
nuclease-free
water to create a 20X
stock (final concentration: 5 nM).
Note: To reduce synthesis costs, binder sequences can be ordered
without
CI-DBD and cloned
downstream of a reusable CI-DBD construct, followed by PCR
amplification
using high-fidelity
polymerase.

*Reporter plasmid:* Prepare as 20X stock at 100 nM (final
concentration: 5 nM).

*Biotinylated target protein:* Resuspend to 1.25 μM in
ultrapure
water with gentle
mixing. Do not vortex.
Note: Follow manufacturer guidelines for minimum aliquot volumes and
consider adding 0.1% (w/v)
BSA for protein stability.

*Streptavidin:* Prepare working solution at 1.25 μM in
ultrapure
water.

*Protein-streptavidin complex:* Mix streptavidin and
biotinylated
protein in 1:1 molar
ratio. Incubate at room temperature for 1 hour.

**CF2H Reaction Assembly**

Work on ice throughout assembly. Assemble reactions in a total
volume
of 22 μL according to
the following composition:

Bacterial extract: 7.3 μL (1/3 of total volume)
Cell-free buffer: 9.2 μL (5/12 of total volume)
Reporter plasmid (20X stock): 1.1 μL (final concentration: 5 nM)
Linear binder DNA (20X stock): 1.1 μL (final concentration: 5
nM)
Streptavidin-protein complex: 2.6 μL (final concentration: 150
nM)
Nuclease-free water: 0.7 μL

**Assembly Steps**

1. Combine bacterial extract and cell-free buffer in a PCR tube
   on
   ice
2. Add reporter plasmid and linear binder DNA
3. Add streptavidin-protein complex
4. Add nuclease-free water to reach final volume of 22 μL
5. Mix gently by pipetting

**Incubation and Measurement**

1. Incubate reactions at 37°C for 8 hours (Note: Fluorescence
   is
   detectable after 2 hours but
   reaches maximum intensity at 6-8 hours)
2. Dilute each reaction 1:3 with nuclease-free water and mix
   thoroughly
3. Transfer three 20 μL aliquots from each diluted reaction to
   separate wells of a 384-well
   plate (technical triplicates)
4. Measure sfGFP fluorescence using a plate reader with
   excitation
   at 485 nm and emission at
   530 nm

**Controls**

Implement the following controls for data quality:

*Blank:* Cell-free extract only (no plasmid or DNA). Subtract
mean fluorescence from all
wells to correct for autofluorescence.

*Negative control #1:* pRM-sfGFP plasmid without binder DNA
to
establish leakage
expression.

*Negative control #2:* Each binder without
streptavidin-protein
complex to detect
homodimerization or aggregation artifacts.

*Positive control:* ALFA epitope tag and anti-ALFA nanobody
(NbALFA), each fused to
CI-DBD, which bind with low picomolar affinity.

*Standard curves:* Dilution series of purified GFP or
fluorescein
measured on the same
plate for quantification.

**Notes**

BindCraft-designed binders with i\_pTM scores above 0.8 should be
prioritized, though higher
scores do not necessarily correlate with superior binding. The
assay
provides qualitative
validation and relative comparisons rather than quantitative
binding
affinities. This method
is particularly useful for pre-screening candidates before
biophysical characterization
methods such as SPR.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Fluorescence Microscopy with
Fixation
and Nuclear Staining

This protocol enables visualization of fluorescently labeled
proteins
in mammalian cells
using fluorescence microscopy. Cells are fixed with
paraformaldehyde
to preserve cellular
structures, stained with DAPI to visualize nuclei, and mounted
on
microscope slides for
imaging. This technique is essential for assessing protein
localization, expression levels,
and cellular distribution following transfection.

**Materials**

| Component | Volume/Amount/Annotation |
| --- | --- |
| Transfected cells on coverslips | in 24-well plate |
| Paraformaldehyde solution (4% PFA) | 250 µL per well |
| PBS (sterile) | 500 µL per wash |
| DAPI in PBS (1:1000 dilution) | 190 µL per well |
| Mowiol mounting medium | 10-20 µL per coverslip |
| Microscope slides | 1 per coverslip |
| Nail polish | for sealing |
| Fine forceps | for handling coverslips |
| Kimwipes or cellulose paper | for drying |

**Step by Step Instructions**

1. Seed HEK293T cells or other mammalian cells onto coverslips
   in a
   24-well plate, transfect
   according to your standard protocol, and incubate for 36
   hours.
2. Remove culture medium by vacuum aspiration under a fume hood
   and
   immediately add 250 µL of
   4% PFA solution per well, then incubate at room temperature
   for
   15-20 minutes in the dark to
   fix cells.
3. Perform three washing steps with 500 µL PBS per well,
   incubating
   for 5 minutes at room
   temperature with gentle rotation for each wash, disposing of
   all
   PFA waste in a designated
   waste bottle.
4. Add 190 µL of DAPI solution (diluted 1:1000 in PBS) to each
   well
   and incubate at room
   temperature for 5 minutes in the dark to stain nuclei.
5. Wash cells 2-3 times with PBS as before, then carefully
   remove
   each coverslip from the
   well using fine forceps.
6. Gently absorb excess liquid by touching the edge of the
   coverslip to a Kimwipe, then
   invert the coverslip and place it cell-side down onto 10-20
   µL
   of Mowiol mounting medium on
   a labeled microscope slide.
7. Seal the edges of the coverslip with nail polish to prevent
   drying and store slides
   upright in the dark until the mounting medium solidifies,
   then
   transfer to a slide box.
8. Image samples using a fluorescence microscope with
   appropriate
   excitation and emission
   filters for your fluorophore and DAPI according to the
   manufacturer's instructions.

**Notes**

All steps involving PFA must be performed under a fume hood due
to
toxicity—dispose of all
PFA-containing solutions in designated waste containers. Also
wear
appropriate personal
protection equipment.
Keep samples in darkness after fixation to prevent
photobleaching of
fluorescent signals
When mounting coverslips, ensure cells face the slide (coverslip
must be inverted) for proper
imaging distance from the objective.
Label slides immediately with date, construct information, and
initials for proper sample
tracking.
Mounted slides can be stored for several weeks at 4°C in the
dark,
though signal intensity may
decrease over time.
Colored nail polish is advantageous for sealing as it provides
better visualization of where
polish has already been applied around the coverslip edges.

## Safety

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Cobalt Chloride Stock Preparation
and
Hypoxia Induction in
HEK293 Cells

⚠️ SAFETY WARNING: Cobalt chloride is classified as a CMR
(Carcinogenic, Mutagenic,
Reprotoxic) substance. This protocol requires enhanced safety
measures including mandatory
double gloving, face mask protection, and fume hood work.

**Safety Precautions**

MANDATORY SAFETY EQUIPMENT:

Double nitrile gloves (change outer gloves after stock
preparation)
Face mask (N95 or equivalent respiratory protection)
Safety goggles
Lab coat
Work exclusively under fume hood

WASTE DISPOSAL:

Segregate all cobalt chloride-contaminated waste
Dispose of tips, tubes, and gloves in designated CMR waste
containers
Neutralize liquid waste according to institutional guidelines

**Stock Solution Preparation (3 mM)**

This protocol prepares a 3 mM cobalt chloride stock solution for
hypoxia induction
experiments.

**Materials**

Cobalt chloride hexahydrate (CoCl₂·6H₂O, MW: 237.93 g/mol)
Sterile nuclease-free water or cell culture-grade water
15 mL conical tube
Analytical balance
Vortex mixer
0.22 µm sterile filter

**Procedure**

1. Put on double gloves and face mask. Work under fume hood for
   all
   steps
2. Calculate the required mass of cobalt chloride hexahydrate:
   * For 3 mL of 3 mM solution: 2.14 mg CoCl₂·6H₂O
   * For 5 mL of 3 mM solution: 3.57 mg CoCl₂·6H₂O
3. Weigh 1-2 mg of cobalt chloride hexahydrate into a 15 mL
   conical
   tube using an analytical
   balance
4. Add sterile water to achieve the desired final volume for 3
   mM
   concentration:
   * If 1 mg weighed: add 1.4 mL water
   * If 2 mg weighed: add 2.8 mL water
5. Vortex vigorously until completely dissolved (solution
   should be
   clear pink/red)
6. Change outer gloves after stock preparation
7. Filter sterilize through a 0.22 µm filter into a sterile
   tube
8. Label clearly with "3 mM CoCl₂ - CMR HAZARD" and date
9. Store at 4°C protected from light for up to 2 weeks

**Hypoxia Induction in 96-Well Plate**

This protocol induces hypoxia-like conditions in HEK293 cells
using
100 µM final
concentration of cobalt chloride.

**Materials**

3 mM cobalt chloride stock solution
HEK293 cells seeded in 96-well plate
Cell culture medium
Multichannel pipette
Sterile pipette tips

**Procedure**

1. Ensure double gloving and face mask are in place. Work under
   fume hood
2. Calculate treatment volume:
   * For 100 µM final concentration in 150 µL well
     volume:
     add 5 µL of 3 mM stock
   * For 200 µL well volume: add 6.7 µL of 3 mM stock
3. Remove old medium from cells or prepare for direct addition
   to
   existing medium

**Table 1: Cobalt chloride treatment setup for
96-well
plate.**

| Component | Volume per well | Final concentration |
| --- | --- | --- |
| 3 mM CoCl₂ stock | 5 µL | 100 µM |
| Cell culture medium | 145 µL | - |
| Total volume | 150 µL | - |

4. Add 5 µL of 3 mM cobalt chloride stock to each designated
   well
   using a pipette
5. Add 145 µL of fresh cell culture medium or gently mix if
   adding
   to existing medium
6. Change outer gloves after treatment application
7. Incubate cells at 37°C, 5% CO₂ for desired time period
   (typically 4-24 hours for
   hypoxia-inducible factor activation)
8. Dispose of all contaminated materials in CMR waste
   containers

**Controls and Considerations**

Negative control: Wells treated with equivalent volume of sterile
water
Positive control: Cells under actual hypoxic conditions (1% O₂) if
available
Time points: Common treatment durations are 4h, 8h, 16h, and 24h
Viability: Monitor cell viability as cobalt chloride can be
cytotoxic at
high concentrations or
long exposures

**Waste Management**

All pipette tips, tubes, and gloves must go into designated CMR
waste
containers
Liquid waste should be collected separately and disposed according
to
institutional CMR waste
protocols
Decontaminate work surfaces with appropriate cleaning agents
Remove and dispose of outer gloves before leaving fume hood area

{% endblock %}

{% extends "layout.html" %}
{% block title %}Results{% endblock %}
{% block header %}

RESULTS

{% endblock %}
{% block page\_content %}

#### **ABSTRACT**

With PHOENICS, we present a **synthetic phosphorylation toolbox** to engineer intelligent
therapeutic cells. It connects diverse ligand sensing to intracellular computation and targeted protein
secretion through a
**network of kinases and phosphatases**.

We established two complementary **receptor platforms** that sense both small molecules and
tumor-associated
proteins, enabling ligand-specific activation or inhibition of intracellular signaling. Furthermore, we
developed **dual-input circuits** that compute the balance between stimulatory and inhibitory
cues. This dynamic
and reversible control confines activation to the tumor environment, providing intrinsic **safety for
therapeutic applications**.

We demonstrated phosphorylation-dependent gene expression and secretion of therapeutic proteins, achieving
rapid modulation entirely at the post-translational level. Together, these results establish PHOENICS as a
versatile signaling architecture that empowers cells to process complex environments and act with
**precision in
oncology**.

#### See our Sensing, Processing and Responding components in action by clicking on parts!

#### Main Results:

* Validated MESA & GPCR receptors
* Proved Phosphorylation-based Effector Function
* Secretion of immuno-regulatory proteins like IL12

####

{% include 'sidenav.html' %}

## Processing

Synthetic signaling circuits promise to revolutionize cell therapies by enabling engineered cells to
sense, compute, and respond with high precision. Yet, achieving orthogonal and reversible control
remains
a major challenge. Existing systems often depend on irreversible safety switches (e.g. iCasp9) or
provide
only slow, incomplete reversibility through antigen-gated or pharmacologic inhibition, limiting dynamic
regulation. To overcome these constraints, we developed PHOENICS: a synthetic phosphorylation circuit
that
enables rapid and reversible signal switching at the post-translational level. In PHOENICS, a synthetic
kinase-phosphatase pair dynamically controls the phosphorylation state of a substrate, which determines
binding to a phosphobinder (SH2) and thereby regulates downstream responses. We demonstrated that
substrate phosphorylation is specific and orthogonal, occurring exclusively in the presence of the
synthetic kinase. The SH2-substrate interaction proved fully reversible and concentration-dependent as
increasing kinase levels strengthened complex formation, while higher phosphatase expression
progressively
diminished it. These core processing units lay the foundation for programmable and dynamically regulated
signaling in future cell therapies.

## Aim

Engineering complex, interaction-dependent fusion proteins presents a significant challenge. Adopting a
bottom-up strategy, we characterized each functional core component in a simplified environment to enable
rapid Design-Build-Test-Learn cycles, facilitating thorough part optimization and improved downstream
engineering. Because effective (de-)phosphorylation and subsequent (de-)dimerization underpin our
system's processing layer, understanding their kinetics and regulatory behavior is critical to
achieve the
desired circuit response. Prior to integration, we developed assays providing a modular framework for
validating individual circuit components. After first validating phosphorylation with western blot
analysis, we investigated phosphorylation-dependent protein-protein interactions using splitFAST, which
provides dimerization-specific fluorescence at the single-cell level by flow cytometry.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Background

Cellular signaling networks coordinate how cells perceive and respond to their environment through
cascades of post-translational modifications and regulated protein interactions. Among these,
phosphorylation and dephosphorylation are central regulators of protein activity, localization, and
molecular interactions. Such signaling mechanisms control key processes including cell proliferation
(MAPK/ERK), metabolism (PI3K-Akt), immune activation (JAK-STAT), and cell cycle regulation (CDK
pathways)
(Cohen, 2000). Within these networks, modular adaptor domains translate phosphorylation states into
specific molecular assemblies and downstream outputs (Pawson & Scott, 2005).

Building on these natural systems, synthetic phosphorylation circuits have been developed to
reconstruct
or reprogram signal transduction using defined molecular components. By coupling engineered kinases
and
phosphatases to modular substrates and interaction domains, these systems allow **precise and
tunable control** of signaling dynamics while remaining **orthogonal to endogenous
pathways** (Lim & Pawson, 2010).

Based on previous work by [Yang *et al.* (2025)](#References), who established a
modular framework for
synthetic
phosphorylation networks, we implemented a synthetic phosphorylation circuit composed of four core
processing components: an engineered **ABL kinase** serving as the phosphorylation
module, a
**PTPN1 phosphatase** providing reversible control, a **CD3ζ substrate**
carrying immunoreceptor tyrosine-based activation motifs (ITAMs), and an **SH2 binder**
that
recognizes phosphorylated sites to couple signal state to downstream function.

The ABL kinase was chosen for its well-characterized activity toward tyrosine-rich motifs, including
ITAM
sequences (Moresco & Koleske, 2003). While native ABL is not inherently orthogonal to cellular
substrates,
we employed a truncated version (residues 218-511; UniProt-ID: P00519) lacking regulatory and
localization
domains.
Kinase-substrate specificity is achieved through engineered coiled-coil recruitment using
bZipEE and bZipRR motifs to spatially confine phosphorylation to the synthetic substrate (Acharya
*et
al.*, 2002). The resulting ABL-bZipEE fusion constitutes the SynKinase module.
As a
counteracting
enzyme, we used a truncated variant of PTPN1 phosphatase (residues 3-321; UniProt-ID: P18031)
comprising only the
catalytic domain. Removal of regulatory and targeting regions eliminates ER localization and reduces
interactions with endogenous substrates (Tonks, 2006). The enzyme was likewise fused to bZipEE,
forming
the SynPhosphatase, which enables reversible dephosphorylation within the synthetic network.
The
synthetic substrate (SynSubstrate) consists of three tandem repeats of the CD3ζ C-terminal ITAM motif
(residues 131-164; UniProt-ID: P20963). In natural T-cell signaling, these motifs mediate receptor
activation through site-specific phosphorylation and SH2-domain binding [(Pawson,
2004)](#References). In our design,
repeating the motif three times enhances the dynamic range and sensitivity of
phosphorylation-dependent responses. To
detect
and translate substrate phosphorylation, we used two SH2 domains from ZAP-70 (residues 2-259;
UnirProt-ID:
P43403), which specifically recognize phosphorylated CD3ζ ITAMs [(Hatada
*et al.*, 1995)](#References). This
module, termed the SynPhosphobinder, acts as a phosphorylation-dependent interaction unit that
couples
the
modified substrate to downstream synthetic effectors while remaining insulated from endogenous
signaling.

## Phosphorylation of CD3ζ

Validating functional phosphorylation of our SynSubstrate by our SynKinase is essential
to couple accuracy of dependent read-out methods. Western blotting against the phosphorylated residue in
the absence of the SynKinase also gives an insight into background noise facilitated by endogenous enzyme
activity. We verified that coiled coil mediated proximity between our SynKinase, a truncated ABL kinase,
and the SynSubstrate leads to efficient phosphorylation.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Orthogonal Phosphorylation of SynSubstrate by SynKinase

To verify the orthogonality of SynKinase-mediated phosphorylation, we performed Western blot
analysis using anti-pY antibodies specific for phosphorylated tyrosine residues within the ITAM
motifs. Substrate presence in each sample was confirmed by Myc-tag fusion and subsequent anti-Myc
staining (**Fig. 1**).

![Results Image](https://static.igem.wiki/teams/5775/images/results/ics-ola/ola-wb-schematic3.svg)

---

**Figure 1: Schematic of SynKinase and SynSubstrate constructs used
for
phosphorylation validation.** The cytosolic SynKinase consists of a truncated ABL kinase
fused to a bZipEE dimerization domain. The SynSubstrate contains three tandem CD3ζ ITAM motifs (3x
CD3ζ) fused to the complementary bZipRR domain and a C-terminal triple Myc tag for detection.

Western blot analysis confirmed that SynSubstrate phosphorylation occurs exclusively in the presence
of SynKinase. The anti-pY antibody detected no phosphorylation signal in the SynSubstrate-only
condition (**Fig. 2**, lane 1, top panel), whereas a strong signal appeared upon
SynSubstrate +
SynKinase
cotransfection (lane 2, top panel). Myc-tag staining verified substrate expression in both conditions,
confirming that the absence of phosphorylation in the control was not due to missing substrate. The
α-Myc signal cannot be quantitatively compared between samples because residual anti-pY binding
persisted after antibody reuse without stripping. Nevertheless, equal SynSubstrate plasmid amounts
were transfected in all conditions, supporting the conclusion that SynKinase specifically and
orthogonally phosphorylates SynSubstrate.

![Results Image](https://static.igem.wiki/teams/5775/images/results/ics-ola/cyc1-fig1-wb.svg)

---

**Figure 2: Western blot verification of SynSubstrate
phosphorylation
by SynKinase.**HEK293T cells were transfected with equal amounts of SynSubstrate either alone or together
with SynKinase. Phosphorylation was detected using a phospho-tyrosine-specific (anti-pY) antibody. A
phosphorylation signal was observed exclusively in the presence of SynKinase, confirming
kinase-dependent modification of the SynSubstrate.

## Dimerization

Efficient dimerization between phosphorylated SynSub and SH2, coupled with their dissociation upon
dephosphorylation,
is essential for mediating phosphorylation-dependent signaling output. This phosphorylation-dependent
dimerization
was confirmed using the split fluorescent protein system SplitFAST. SynKinase titration demonstrated a
dose-dependent increase of dimerization corresponding to phosphorylation levels, which was effectively
attenuated in
a dose-dependent manner by SynPhosphatase.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

ITAM CD3ζ and SH2 Enable Selective, Phosphorylation Dependent
Dimerization

To develop and test the basic circuit functionality, we established a split-fluorescent protein
system
enabling us to directly observe dimerization between CD3ζ and SH2. This strategy allowed us to
investigate interaction dynamics before further engineering a dimerization dependent output. SplitFAST
is a split version of RspA-FAST, a protein that fluorescences in the presence of the small molecule
HMBR
[(Rakotoarison *et al.*, 2024)](#References)(**Fig. 3**). Exhibiting
low
self-complementation and
a high
dynamic range it allows
for
effective studying of protein protein interactions.

![Results Image](https://static.igem.wiki/teams/5775/images/results/maxi/splitfast-method.svg)

---

**Figure 3: Schematic of the CD3ζ-SH2 dimerization dependent
splitFAST
reporter read-out** Upon coiled-coil interaction between RspA(N)-CD3ζ-bZipRR and the
SynKinase, tyrosine residues within the CD3ζ are phosphorylated. This increases affinity to the
SH2, allowing for phosphorylation-specific dimerization. Dimerization between
RspA(N)-CD3ζ-bZipRR and RspA(C)-SH2 brings the splitFAST halves into proximity giving a fluorescent
readout upon HMBR addition.

To demonstrate that the CD3ζ and SH2 domains dimerize in a phosphorylation dependent manner, the
RspA(N)-CD3ζ-bZipRR and RspA(C)-SH2 fusion proteins were used. To minimize steric interference,
the smaller RspA(C) domain was fused to SH2, while the larger RspA(N) domain was fused to CD3ζ.
Increasing SynKinase expression resulted in a gradual rise in splitFAST fluorescence, consistent with
phosphorylation-dependent dimerization (**Fig. 4**). In the absence of SynKinase,
fluorescence remained at
background levels, while high SynKinase amounts led to signal saturation, indicating maximal
phosphorylation of the CD3ζ substrate.

![Results Image](https://static.igem.wiki/teams/5775/images/results/maxi/cytosolic-abl-titr-flow.svg)

---

**Figure 4: Validating phosphorylation dependent dimerization
using splitFAST.** HEK293T cells were transfected 24 hours after seeding. Y-axis
description corresponds to the ng amounts of SynKinase, 100 ng of both RspA(N)-CD3𝜁-bZipRR and
RspA(C)-SH2 were co-transfected. Dimerization dependent SplitFAST fluorescence was quantified by
flow cytometry 48 hours after transfection, employing excitation lasers at 488 nm and 561 nm.
Emission was recorded at 525 nm for splitFAST (Ligand: TFLime) and at 610 nm for
constitutively
expressed mCherry, serving as a transfection control. Data is depicted as the geometric mean of
fluorescence +/- geometric SEM with ~30000 single cells

Reversibility of phosphorylation-dependent dimerization is crucial for dynamic signal processing and
logic gate computation. To test this, we assessed whether SynPhosphatase could access and
dephosphorylate CD3ζ, thereby disrupting CD3ζ-SH2 binding. Increasing SynPhosphatase expression
progressively reduced splitFAST fluorescence, indicating dissociation (**Fig. 5**). The
dose-dependent
decline in signal, reaching background levels at high SynPhosphatase concentrations, confirms
effective dephosphorylation and circuit reversibility.

![Results Image](https://static.igem.wiki/teams/5775/images/results/maxi/cytosolic-ptpn1-titration-flow.svg)

---

**Figure 5: Validating dephosphorylation dependent dissociation
using splitFAST.** HEK293T cells were transfected 24 hours after seeding. Y-axis
description corresponds to the ng amounts of SynPhosphatase, 100 ng of RspA(N)-CD3𝜁-bZipRR,
RspA(C)-SH2 and SynKinase were co-transfected. Dimerization dependent SplitFAST fluorescence was
quantified by flow cytometry 48 h post transfection, employing excitation lasers at 488 nm and 561
nm. Emission was
recorded at 525 nm for splitFAST (Ligand: TFLime) and at 610 nm for constitutively
expressed
mCherry, serving as a transfection control. Data is depicted as the geometric mean of fluorescence
+/- geometric SEM with ~30000 single cells

## Discussion

Using a bottom-up approach, we systematically characterized the functionality and
interaction dynamics of the core PHOENICS components before advancing to higher circuit integration.
Computational modeling with SPARC complemented these experiments by providing quantitative insights into the
parameter space underlying phosphorylation-dependent behavior. By establishing assays to directly validate
phosphorylation and to monitor dimerization of CD3ζ and SH2, we created a robust framework for iterative
circuit optimization.
Western blot analysis confirmed functional phosphorylation and dephosphorylation catalyzed by the SynKinase
and SynPhosphatase. SplitFAST assays further demonstrated that SynKinase activity drives CD3ζ-SH2
association, whereas SynPhosphatase expression induces dissociation, confirming the reversibility of the
interaction.
Moving forward, tuning the interaction dynamics between these components will enable fine control over
circuit performance and response kinetics. Testing coiled-coil variants of different affinities will help
determine how binding strength influences temporal behavior and the potential for switch-like responses. In
parallel, modeling these variants will guide experimental design by prioritizing configurations with desired
kinetic or ultrasensitive properties. Additional exploration of alternative tyrosine kinases, phosphatases,
and ITAM motifs may further expand the circuit's dynamic range and functional versatility.

## Responding

In cell therapies, precise temporal control of activation and deactivation is critical. Therapeutic
cells must rapidly respond to disease cues and deactivate once the signal subsides. Building on our core
processing modules, we coupled phosphorylation-dependent signaling to two effector systems, enabling
engineered cells to translate post-translational states into reversible functional responses.
First, we established a transcriptional output layer by linking substrate phosphorylation directly to
gene expression. Transcriptional activators and DNA-binding domains were fused to SH2 and CD3ζ, allowing
phosphorylation-dependent dimerization to initiate transcription.
Second, to achieve faster and dynamically reversible control, we implemented a phosphorylation-inducible
protease system in which phosphorylation activates a protease that releases pre-synthesized proteins,
enabling rapid and tunable secretion.
Together, these modules demonstrate how PHOENICS connects post-translational signaling to gene
expression and secretion, forming a reversible effector layer for precise and adaptive control of
therapeutic cell activity.

## Aim

After validating kinase-dependent phosphorylation and dimerization of CD3ζ and SH2, the next step was to
couple the phosphorylation signal to a meaningful effector function. For this, we planned to establish two
parallel output systems, each with distinct use cases.
In our expression-based system, the SynSubstrate was fused to a Gal4 DNA-Binding Domain (GAL4DBD), while
the
SH2-domain was linked to a VP64 transcriptional transactivator. Once the substrate is phosphorylated, it
is
engaged by SH2, which results in transcription factor reconstitution and subsequent gene
expression.
However, in a therapeutic
context, precision demands speed, because cellular composition can shift from malignant to
healthy within the same tissue. To create an effector platform that matches the rapid dynamics of our
phosphorylation circuit, we implemented the RELEASE system [(Vlahos *et al.*,
2022)](#references). For this, we engineered a
phosphorylation-inducible Tobacco Etch Virus protease (PhosphoTEV) to control protein secretion on a
post-translational level.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Background

#### The split Gal4DBD-VP64 Transcription System

The Gal4 DNA-binding domain (Gal4DBD) originates from the yeast transcription factor Gal4, which
regulates
galactose metabolism genes by binding specific Upstream Activating Sequences (UAS). Its zinc cluster
motif
coordinates two zinc ions and stabilizes a fold that enables precise recognition of palindromic
CGG(N11)CCG repeats. Two Gal4DBD monomers dimerize on DNA to achieve strong and specific
promoter
binding [(Hellauer *et al.*, 1996)](#References).
The VP64 activation domain is a
synthetic
construct derived from the herpes simplex virus protein VP16. It consists of four random repeats of
the minimal
VP16 activation domain [(Beerli *et al.*, 1998)](#References) and enhances
transcription by
recruiting mediator proteins and histone acetyltransferases, which promote RNA polymerase II assembly
[(Mittler *et al.*, 2003)](#References).
In synthetic systems, Gal4DBD and VP64
are often used
together to form a modular
transcriptional activator, in which Gal4DBD provides DNA-binding specificity while VP64 drives
promoter activation
[(Hu et *et al.*, 2014)](#References).

#### Protein Secretion in Mammalian Cells

The secretory pathway processes over 25% of mammalian proteins. Nascent proteins are
translated in
the rough
endoplasmic reticulum (ER) and enter the ER lumen via co-translational translocation [(Benavides-López *et al.*,
2025)](#References). Inside the ER, proteins fold with help from molecular chaperones. Misfolded proteins
undergo
ER-associated
degradation (ERAD), while correctly folded proteins proceed forward [(Benavides-López *et
al.*, 2025)](#References). COPII-coated
vesicles mediate anterograde transport from ER to the Golgi complex while COPI-coated vesicles handle
retrograde
transport from Golgi back to the ER, recycling essential components [(Benavides-López *et
al.*, 2025)](#References).

#### ER Retention and Protease-Controlled Secretion

In mammalian cells, proteins destined to remain in the endoplasmic reticulum (ER) are retained
through C-terminal
dilysine motifs, which are recognized by the COPI vesicle machinery and coatomer complex responsible
for
retrograde transport from the Golgi back to the ER [(Barlowe & Helenius, 2016;
Hoffman *et
al.*, 2003)](#References).
The Retained Endoplasmic Cleavable Secretion (RELEASE) system exploits
this mechanism
by tethering a protein of interest (POI) to the ER membrane with a dilysine or diarginine motif
downstream of a
TEV cleavage site (**Fig. 6**). Under normal conditions, the POI is retained in the ER
through
COPI-mediated
transport. Upon TEV protease cleavage, the retention signal is removed and the POI enters the
secretion pathway. A
furin cleavage site between the transmembrane domain and POI ensures proper processing in the
trans-Golgi for
final secretion [(Vlahos *et al.*, 2022)](#References).

![Results Image](https://static.igem.wiki/teams/5775/images/results/effector-tobi/release-scheme.svg)

---

**Figure 6: Visualization of the RELEASE system.** The POI
(yellow
circle) is expressed and tethered to the ER membrane with a transmembrane domain (orange). As long
as the TEV
protease (pink pacman) does not cleave off the ER retention signal at its cleavage site (pink
circle), it is
retained in the ER. Upon cleavage, it undergoes the secretory pathway through the Golgi complex and
is secreted
into the extracellular space.

## Expression

Using the split composite transcription factor of Gal4DBD and VP64, we established a robust and sensitive
output that translates phosphorylation signal into protein expression. The system is sensitive towards
(de-)phosphorylation over a wide range of both kinase and phosphatase amounts.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Phosphorylation-Dependent and Reversible Transcriptional Control

To build on the previously validated phosphorylation and dimerization events, a transcriptional
effector circuit was implemented that translates enzymatic activity into a measurable output.
Therefore,
Gal4DBD is fused to the synthetic substrate, while VP64 is attached to the SH2 domain. SynKinase,
recruited via complementary coiled-coils (bZipEE/bZipRR), phosphorylates CD3ζ and enables SH2-VP64
binding. This interaction reconstitutes a functional Gal4DBD-VP64 transcription factor that activates
NanoLuciferase expression. Conversely, SynPhosphatase dephosphorylates CD3ζ, preventing SH2-VP64
recruitment and thereby reporter activation (**Fig. 7**).

![Results Image](https://static.igem.wiki/teams/5775/images/schematics/expression-responding.svg)

---

**Figure 7: Schematic representation of the phosphorylation circuit
and Nanoluciferase reporter assay.** SynKinase (ABL-bZipEE) phosphorylates the
SynSubstrate
(Gal4DBD-CD3ζ-bZipRR) in a proximity-dependent manner, while SynPhosphatase (PTPN1-bZipEE)
counteracts
this modification through dephosphorylation. Phosphorylated SynSubstrate recruits the SH2-VP64
binder,
which, together with Gal4DBD, activates transcription from a minimal promoter and induces
NanoLuciferase expression. For clarity, CD3ζ (three ITAM repeats) and SH2 (two domains) are
illustrated as single units.

To improve circuit performance, the system was optimized for expression ratios and subcellular
localization. An SH2-VP64 to Gal4DBD-CD3ζ-bZipRR ratio of 5:1 yielded the strongest transcriptional
response. The SynSubstrate carries both an N-terminal nuclear export signal (NES) and nuclear
localization
signal (NLS) for nucleocytoplasmic shuttling, while the SynPhosphobinder contains only an N-terminal
NLS,
resulting in predominant nuclear localization. These optimized
configurations were used for the subsequent experiments.
A kinase titration revealed a
dose-dependent
increase in reporter activity that plateaued above 40 ng ABL-bZipEE, with no significant
differences
between 40, 50, and 60 ng, indicating saturation of substrate phosphorylation. Transcriptional
activation was therefore limited by SynSubstrate availability rather than kinase abundance (**Fig.
8**).

![Results Image](https://static.igem.wiki/teams/5775/images/results/ics-julia/bigabl-titr-fixed2.svg)

---

**Figure 8: Dose-response of SynKinase.** HEK293T cells were
transfected with SynSubstrate (5 ng) and SH2-VP64 (25 ng) in a 1:5 ratio together with
increasing amounts of cytosolic SynKinase. The Dual-Luciferase assay was performed 48 hours
post
transfection, with NanoLuciferase normalized to Firefly. Data are presented as the mean ± SEM
with n = 3 technical replicates. Statistical significance was determined with ordinary
one-way ANOVA and Tukey's multiple comparisons test (single pooled variance).

Conversely, PTPN1-bZipEE titration showed that increasing SynPhosphatase levels progressively reduced
reporter activity, which leveled off above 7 ng, with no further significant decrease observed
(**Fig.
9**). His demonstrates reversible dephosphorylation-dependent control, where
Gal4DBD-CD3ζ-bZipRR
dephosphorylation counteracts kinase-mediated activation and resets the circuits to its inactive state.

![Results Image](https://static.igem.wiki/teams/5775/images/results/ics-julia/bigptpn1-titr-fixed2.svg)

---

**Figure 9: Titration of SynPhosphatase.** HEK293T cells
were
transfected with 20 ng SynKinase, 25 ng SH2-VP64, and 5 ng Gal4DBD-CD3ζ-bZipRR
(SynSubstrate), while SynPhosphatase (PTPN1-bZipEE) was titrated from 1 to 20 ng as
indicated. Reporter activity was measured 48 h post transfection using a Dual Luciferase assay,
with NanoLuciferase normalized to constitutively expressed Firefly luciferase. Data are shown as
mean
± SEM (n = 3 technical replicates). Statistical significance was determined with
ordinary one-way ANOVA with Tukey's multiple comparison test (single pooled variance).

Together, the kinase and phosphatase titration experiments demonstrate that the phosphorylation
circuit
enables precise, reversible, and tunable control of transcriptional activation. Reporter expression
increased proportionally with SynKinase input until phosphorylation sites on the SynSubstrate became
saturated, while rising SynPhosphatase levels progressively reduced the signal back to baseline. These
findings confirm that circuit output directly reflects dynamic balance between kinase- and
phosphatase-mediated modification states, establishing a controllable molecular switch that links
enzymatic activity to gene expression.

## Secretion

To allow the implementation of the RELEASE system into our phosphorylation circuits, we engineered and
characterized two different designs of a phosphorylation-inducible TEV protease. This module allows for
rapid protein secretion without the delay of gene expression.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

PhosphoTEV Induces Phosphorylation-Dependent Protein Secretion

To engineer a phosphorylation-inducible TEV protease, we developed two different designs. In the
first
one we used a split TEV system where the N-terminal half was linked to an SH2-domain, while the
C-terminal half was linked to a SynSubstrate and bZipRR (**Fig. 10A**). In a second
iteration,
the
two constructs were joined into one by linking them through a semi-rigid linker between the SH2 and
bZipRR domains (**Fig. 10B**). In both versions, the association of the bZip coiled-coils
causes
phosphorylation of the SynSubstrate so that the SH2 domain binds to it, resulting in the
reconstitution
of the TEV protease. The activated protease in turn causes the secretion of a luminescent NanoLuc
luciferase (NanoLuc) reporter, which could be measured in the supernatant.

![Results Image](https://static.igem.wiki/teams/5775/images/results/effector-tobi/splittev-vs-linkedtev-001.svg)

---

**Figure 10: Different PhosphoTEV designs.** Reconstitution of
split TEV halves depends on the phosphorylation-inducible binding of SH2 to CD3ζ. (A) In the split
PhosphoTEV design, the interacting domains are located on different constructs. (B) In the linked
PhosphoTEV, SH2 and bZipRR are connected through a variable hinge.

To show the activation of the split and linked PhosphoTEV constructs, HEK293T cells were transfected
with
either of them with or without a cytosolic SynKinase, as well as the NanoLuc-RELEASE construct in all
conditions. A condition with split PhosphoTEV without Kinase was not included, as its background did not
significantly differ from the linked version's. After 48 hours, NanoLuc luminescence in the
supernatant
was measured (**Fig. 11**). Comparing the linked PhosphoTEV without SynKinase to a negative
control
with
only RELEASE construct, there was no significant increase in NanoLuc signal. With added SynKinase, the
signal increased 2.3-fold for the split, and 4.7-fold for the split and linked versions, respectively.
Between the two PhosphoTEVs, the linked variant showed a significantly higher activation.
This proved the phosphorylation-induced activation, as well as a low background of the system.

![Results Image](https://static.igem.wiki/teams/5775/images/results/effector-tobi/e24-results.svg)

---

**Figure 11: SynKinase activates PhosphoTEV and induces secretion.** HEK293T
cells were co-transfected with split and linked PhosphoTEV variants without (orange) and with
(purple)
SynKinase, and the NanLuc-RELEASE plasmid. Secreted NanoLuc in supernatant was measured 48h
post-transfection. Positive and negative controls (yellow) with a full TEV and no TEV are shown for
comparison. Data is depicted as the mean +/- SEM with n = 3 technical replicates. Statistical
significance was calculated with an ordinary One-way ANOVA with Tukey's multiple comparisons
test. \*\*P<0.01, \*\*\*\*P<0.0001; ns, not significant.

A key advantage of phosphorylation-based signaling circuits lies in the reversibility dependent on
kinase and phosphatase activity. To assess whether this behaviour applies to the RELEASE system,
different amounts of PhosphoTEV were co-transfected with SynKinase alone or in combination with
SynPhosphatase. After 48 hours, NanoLuc luminescence in the supernatant was measured
(**Fig. 12**). As
shown before, the background of PhosphoTEV without kinase or phosphatase did not differ significantly
from the RELEASE background without any TEV. With only SynKinase, there was a 2.6-fold induction in
the
luminescence signal. This induction could be quenched significantly with the addition of
SynPhosphatase,
reducing the increase by about 50 %, showing that the activation of PhosphoTEV is reversible and
sensitive to SynPhosphatase.
With the successful (de-)activation of PhosphoTEV using SynKinase and -Phosphatase, we could establish
the fully post-translational RELEASE response module, able to be integrated into more sophisticated
circuits.

![Results Image](https://static.igem.wiki/teams/5775/images/results/effector-tobi/e22-phos-result.svg)

---

**Figure 12: SynPhosphatase deactivates PhosphoTEV and reduces secretion.**
HEK293T cells were co-transfected with the linked PhosphoTEV and either no enzyme (yellow), only
SynKinase (orange), or in combination with SynPhosphatase (purple), and the NanoLuc-RELEASE plasmid.
Secreted NanoLuc in supernatant was measured 48h post-transfection. Data is depicted as the mean +/-
SEM with n = 3 technical replicates. Statistical significance was calculated with an ordinary
Two-way
ANOVA with Tukey's multiple comparison's test. \*\*\*P<0.001,
\*\*\*\*P<0.0001;
Negative and positive controls were excluded from the test and only shown for
reference.

## Discussion

These phosphorylation‑responsive systems show that intracellular kinase activity can be routed through
SH2-CD3ζ recognition to drive either a transcriptional or a proteolytic output, yielding a modular
signal-response architecture with dynamic, tunable control across molecular layers. Kinase input increases
activation until substrate or promoter occupancy saturates, while phosphatase input rapidly quenches
activity by removing the phosphorylation off ITAMs required for SH2 engagement, underscoring phosphorylation
as a universal
signaling language that integrates diverse upstream cues with programmable downstream functions.

The Gal4‑VP64 output module amplifies phosphorylation signals into gene expression by reconstituting a
composite activator only upon SH2 binding to phosphorylated CD3ζ fused to Gal4DBD at UAS sites. The observed
large dynamic range, validated by kinase and phosphatase titrations, was strengthened by stoichiometric and
localization
optimizations that increase effective
nuclear molarity. The natural rates of transcription and translation in biological circuits tend to favor
stable,
long-lasting responses rather than rapid changes. Future improvements - such as adding destabilization
domains,
degrons that target proteins for degradation, or RNA-based controls like responsive 3′ UTR decay elements
and
ribozyme switches - could speed up signal repression and enhance the temporal precision of circuit
responses,
while maintaining robust output levels.

The PhosphoTEV-RELEASE output module provides immediate, post‑translational actuation by directly coupling
phosphorylation state to ER exit and secretion. ER retention ensures low basal output by preventing
premature
reporter release. Upon phosphorylation-dependent SH2 engagement, TEV protease activity is reconstituted,
resulting in removal of the ER retention tag, permitting anterograde trafficking and thus rapid reporter
secretion.
The linked PhosphoTEV architecture improves the on‑state activtiy,
likely through higher effective molarity and favorable geometry, while ER retention buffers off‑state
leakage. The remaining background is consistent with spontaneous fragment association or partial retention
escape. Future efforts will focus on further reducing this leakage, on one hand through the optimization of
the RELEASE construct by exchanging the transmembrane domain to reduce retention escape and increase
expression levels. On the other hand, the PhosphoTEV construct will be further improved through linker
optimization, which may greatly impact background, as shown in the context of allosterically controlled
proteins.
Treating gene expression and secretion as two swappable output modules enables circuits to be adapted to
application demands: the expression arm delivers strong, programmable amplification with slower onset, while
the secretion arm delivers rapid, transient output with minimal background leakage. Layering these outputs
can
yield hierarchical, staged dynamics that better approximate natural cascades - fast post‑translational
responses leading or gating slower transcriptional programs - while maintaining orthogonality and reversible
control suitable for therapeutic contexts. Coupling both modules to ligand‑responsive kinases further
supports pharmacological control, reinforcing the practicality of phosphorylation‑gated logic in living
cells.

## Sensing

Precise recognition of extracellular cues is crucial for the safe and adaptable development of cell
therapies. Existing synthetic receptors are often limited by slow, transcription-based signaling and
poor
modularity. To overcome these constraints, we developed a phosphorylation-based sensing layer that links
ligand detection to rapid, post-translational computation within the PHOENICS framework. This system
integrates synthetic GPCR and Modular Extracellular Sensor Architecture (MESA) receptors in a
plug-and-play fashion to translate ligand binding into phosphorylation or dephosphorylation of the
synthetic substrate CD3ζ, thereby regulating SH2-mediated transcriptional reconstitution. Both receptor
scaffolds allow for complex combinatorial signal processing, facilitating both activation and
repression,
and were shown to functionally sense tumor associated proteins including VEGF and TNF-α. The GPCR
design
enables particularly fast, binary switch-like responses with minimal background and a large dynamic
range
while MESA offers enhanced modularity coupled with high output strength. Using our *in silico*
platform SPARC, we designed and validated de novo protein binders against cancer-relevant targets such
as
GDF-15, thereby extending the plug-and-play retargeting of both receptor classes. The combination of
these
features establishes a rapid, reversible, and modular input layer that bridges extracellular sensing
with
post-translational computation - providing a foundation for personalized cancer therapies capable of
responding precisely and dynamically to complex tumor environments.

## Aim

To establish sensing capabilities with our intracellular phosphorylation logic, we wanted to engineer two
modular receptor platforms with distinct use cases. In order to develop a receptor scaffold able to
respond with all-or-nothing-behaviour, we planned to engineer the synthetic κ-opioid receptor DREADD
(KORD) [(Vardy *et al.*, 2015)](#References) and additionally adapt the PAGER platform
[(Kalogriopoulos
*et al.*, 2025)](#References). By
replacing the intracellular signaling machinery with our SynPhosphatase and -Kinase, receptor
activation
can facilitate direct (de-)phosphorylation of CD3𝜁, our SynSubstrate.
In parallel, we wanted to develop MESA receptors similarly fused to our SynKinase and SynPhosphatase. MESA
offers the distinct benefit of a stronger total activation and quenching, albeit with the tradeoff of a
diminished dynamic range.
To expand accessible tumor targets, we established a CF2H-based validation pipeline for computationally
designed de novo binders, demonstrating this approach with GDF-15-targeting constructs. Together, these
experiments aimed to deliver a modular, bidirectional sensing layer capable of processing complex tumor
microenvironment cues.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Background

Cells sense environmental cues such as small molecules, proteins, and surface signals through diverse
receptors, including G protein-coupled receptors, receptor tyrosine kinases, and ion channels. Many of
these receptors act on pathways involving **phosphorylation, a fast, reversible and tunable
post-translational process**. Kinases and phosphatases rapidly and reversibly set the
phosphorylation state of chosen substrates, turning extracellular cues into graded intracellular
responses on second-to-minute timescales [(Farahani *et al.*, 2023; Zhang
*et al.*,
2005)](#References). This
architectural logic - **co-recruitment of catalytic domains to defined substrates, tight spatial
control, and rapid off-kinetics** - makes phosphorylation an attractive approach for
engineering synthetic sense-and-respond systems that are both fast and precise.
Current synthetic receptors such as CARs and SynNotch receptors established that extracellular signals
can be coupled to customized transcriptional responses, but their activation physics (clustering and
force for CARs; proteolytic release for SynNotch) constrain antigen scope and largely commit
computation to the gene-expression layer, with inherent delays and potential basal leakiness.
Recent work has integrated phosphorylation control with the Modular Extracellular Sensing Architecture
(MESA) to engineer modular, orthogonal phosphorylation cycles in human cells thus integrating upstream
receptor activation with downstream gene expression. MESA addressed a central limitation by
introducing a self-contained, **orthogonal** receptor that dimerizes upon ligand and
triggers engineered outputs through modular intracellular cassettes; importantly, MESA's
ectodomains
and transmembrane/linker elements can be swapped or tuned to retarget specificity and performance
[(Daringer *et al.*, 2014)](#References). Improving the original MESA architecture
by
using receptor-coupled
kinases
demonstrated that phospho-circuits can be predictively tuned and interconnected while remaining
largely insulated from endogenous phosphorylation networks [(Yang
*et al.*, 2025)](#References). Yet, even in
these
systems, inhibitory control typically resides within the internal cycle rather than at the
receptor
itself.
**Synthetic GPCRs** have recently emerged as alternative modular scaffolds for
programmable sensing. The PAGER framework fuses a nanobody with an auto-inhibitory peptide to
the
𝜅-Opioid Receptor DREADD so that antigen binding relieves inhibition and permits small molecule
activation, enabling modular antigen-gated control over transgene expression, endogenous
G-protein
signaling, or real-time readouts [(Kalogriopoulos *et al.*, 2025)](#References).
PAGER highlights two
properties
essential for therapeutic translation - **retargetability** via simple binder
exchange
and **drug addressability** - yet still primarily decodes through transcriptional
modules
or native GPCR pathways. A phosphorylation-native decoder placed immediately downstream of
such GPCRs
would preserve modular antigen gating while delivering the speed and reversibility of
protein-protein
signaling.
Despite these advances, a specific capability has been missing at the sensing layer to allow the
implementation of advanced receptor construct into cellular therapies: a  **bidirectional,
ligand-programmable phosphorylation interface** that is both (i) **reversible**
- providing receptor-level ON control via a kinase and OFF control via a phosphatase on a
post-translational level - and (ii) **plug-and-play retargetable** to the soluble
cues
that characterize the tumor microenvironment (TME). Prior orthogonal phospho-systems
established
reversible cycles and model-guided tuning, and they showed coupling to receptors, but to our
knowledge
they did not provide a general **phosphatase-fused, ligand-gated receptor** to
facilitate
extracellularly mediated inhibition on a post-translational level. Filling this gap would
enable fast
N-IMPLY safety logic and drug-tunable dormancy directly at the receptor-circuit
boundary-capabilities
that are particularly valuable for oncological applications, where multi-ligand contexts and
patient-specific profiles demand flexible decoding and stringent OFF control [(Norton *et al.*,
2015;
Yang *et al.*, 2025).](#References)

## GPCR

As one of our modular sensing systems, we engineered multiple synthetic GPCR scaffolds
offering different levels of sensing capabilities and modularity in HEK293T cells. This architecture allows
for modular sensing of varied peptide and small molecule ligands and is able to enact a binary, switch-like
response.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Synthetic GPCRs Induce Gene Expression in a Switch-Like, Ligand
Dependent Manner

To effectively drive gene expression in an inducible, ligand-specific manner, a receptor should be
host-orthogonal, exhibit low background, and be readily inducible. In this context, we chose the
𝜅-Opioid receptor DREADD (KORD), an engineered GPCR inducible by the small-molecule Salvinorin B
(SalB), as a receptor scaffold for our phosphorylation circuit (**Fig. 13**). By fusing
its intracellular C-terminal tail with our SynKinase, a truncated ABL kinase able to induce specific,
proximity-based phosphorylation, we created a novel receptor construct: KORD-ABL. Together with an
engineered version of 𝛽-Arrestin2, an endogenous regulator of GPCR activity, it builds a system that
is able to specifically recruit and phosphorylate our SynSubstrate upon receptor activation.

SalB binding triggers a conformational change that enables GRK-mediated phosphorylation of residues in
the receptor's C-terminal tail (**Fig. 13**). This recruits β-Arrestin2-bZipEE that
can bind
Gal4DBD-Cd3ζ-bZipRR via a coiled-coil interaction. The resulting positioning of the CD3ζ near
the ABL kinase fused to the GPCR leads to its phosphorylation. The phosphorylated Gal4DBD-CD3ζ-bZipRR
subsequently dimerizes with SH2-VP64, forming a transcriptional activator that binds the 5xUAS motif
and
drives transgene expression.

![Results Image](https://static.igem.wiki/teams/5775/images/results/maxi/kord-expression.svg)

---

**Figure 13: Schematic overview of KORD-ABL coupled to the
phosphorylation circuit**. In the absence of SalB, 𝛽-Arrestin2-bZipEE cannot bind
KORD-ABL. Receptor activation through SalB presence leads to a conformational change, allowing GRK
binding and subsequent phosphorylation of the C-terminal tail of KORD. This allows for the binding
of 𝛽-Arrestin2-bZipEE. Connection to the intracellular PHOENICS circuit is facilitated by the fused
bZipEE domain on 𝛽-Arrestin2 and its spatial organization in the proximity of the ABL kinase upon
KORD binding. Bound 𝛽-Arrestin2-bZipEE recruits Gal4DBD-CD3𝜁-bZipRR, which is subsequently
phosphorylated by ABL. Dissociation from the KORD due to the transient coiled-coil interactions
allows dimerization between the phosphorylated CD3𝜁 and SH2, reconstituting the split transcription
factor Gal4DBD-VP64. Shuttling to the nucleus then facilitates transgene expression under control of
the 5xUAS-minCMV promoter.

To evaluate the synthetic KORD-ABL receptor, we investigated its ability to drive NanoLuciferase
expression upon induction by its small molecule ligand SalB. We co-transfected HEK293T cells with
KORD-ABL, 𝛽-Arrestin2-bZipEE, as well as [our phosphorylation circuit](#Responding) and
constitutively
expressed
Firefly luciferase to normalize to cell viability. Cells were induced with 500 nM SalB 24 hours after
transfection. Nano- and Firefly Luciferase activity was determined 48 hours after transfection using
the Nano-Glo Dual-Luciferase Reporter Assay System. As controls, we first used the entire
phosphorylation circuit without KORD-ABL, as well as separately without 𝛽-Arrestin2-bZipEE. In three
biological replicates, induction of KORD-ABL significantly increases NanoLuciferase expression by
28-fold compared to its uninduced condition (p < 0.0001), exhibiting a switch-like behavior to drive
gene expression (**Fig. 14**). Moreover, KORD-ABL does not exhibit significant
leakiness
or background noise compared to control conditions without KORD-ABL or 𝛽-Arrestin2-bZipEE (p >
0.99). This demonstrates the functionality of a novel receptor architecture that can drive transgene
expression in a switch-like manner, without exhibiting significant background or interference in
native signaling pathways. This data was further corroborated by measuring mCherry expression upon
KORD-ABL induction on a single-cell basis using flow cytometry.

![Results Image](https://static.igem.wiki/teams/5775/images/results/maxi/first-plot-kord-abl.svg)

---

**Figure 14: KORD-ABL represents a novel receptor platform that senses
and responds to extracellular cues in a switch-like manner**. HEK293T cells were
transfected 24 hours after seeding. NanoLuc and Firefly Luciferase expression was measured 48 hours
after transfection using the Dual Luciferase assay. Induction occurred 24 hours transfection using
DMSO or SalB [500 nM]. Negative 1 corresponds to KORD-ABL and the full phosphorylation-dependent
expression circuit without 𝛽-Arrestin2-bZipEE. Negative 2 corresponds to 𝛽-Arrestin2-bZipEE and
the full phosphorylation-dependent expression circuit without KORD-ABL. 30/5 ng refers to the
condition containing the full receptor construct, with 30 ng of KORD-ABL, 5 ng of
𝛽-Arrestin2-bZipEE, and the full phosphorylation-dependent expression circuit. Data is depicted as
the mean +/- SEM with n = 3 biological replicates. Two-way ANOVA with Sidak's multiple comparison
test. \*\*\*\*P < 0.0001; NS, not significant.

A critical variable in synthetic systems is the ability to respond to extracellular cues in a fast
and steady manner. To evaluate this property, we characterized the response kinetics of our combined
sensing, processing, and output system. For this purpose, SalB induction was performed in cells
co-transfected with KORD-ABL, β-Arrestin2-bZipEE, the phosphorylation circuit, and a normalization
plasmid. Induction occurred at different time points before measurement to capture dynamic changes in
system activity. NanoLuciferase expression increases significantly by 10-fold compared to its
uninduced
condition 5 hours after induction (p = 0.0049). Expression also correlates linearly with time
(R2 = 0.97), reaching a total fold change of 151-fold compared to its uninduced condition
measured 32 h after induction (**Fig. 15**). This highlights how KORD-ABL, in conjunction
with our phosphorylation circuit, drives rapid gene expression after sensing a small molecule and
processing the signal at the post-translational level.

![Results Image](https://static.igem.wiki/teams/5775/images/results/maxi/time-resolved-with-fold-change.svg)

---

**Figure 15: KORD-ABL drives fast gene expression upon
ligand sensing** . Line plot showing an increase in expression over time. HEK293T cells
were transfected 24 hours after seeding. NanoLuc and Firefly Luciferase expression was measured 48
hours after transfection using the Dual Luciferase assay. Induction occurred at the indicated time
points before measurement with either DMSO or SalB [2 µM]. Each condition was transfected with 50 ng
of KORD-ABL and 5 ng of 𝛽-Arrestin2-bZipEE, Overlaid with a linear regression curve (R2
= 0.97). Data is depicted as the mean +/- SEM with n = 3 technical replicates. Due to technical
errors during sample induction, one value is missing for all induced conditions. Two-way ANOVA with
Sidak's multiple comparison test. \*\*P < 0.01; \*\*\*\*P < 0.0001.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Synthetic GPCRs Repress Expression on a Post-Translational
Level

To expand the existing receptor repertoire to go beyond gene-expression mediated repression, we set
out to establish a receptor construct that is able to directly interact with, and quench our
phosphorylation circuit on a post-translational level. Fusing our SynPhosphatase, a truncated version
of the PTPN1 phosphatase, to the C-terminal tail of the KORD, we aimed to enable ligand-inducible
dephosphorylation and subsequent quenching of expression (**Fig. 16**)

![Results Image](https://static.igem.wiki/teams/5775/images/results/maxi/kord-ptpn1-quenching.svg)

---

**Figure 16: Schematic overview of KORD-PTPN1 coupled to the
phosphorylation circuit**. In the absence of SalB, 𝛽-Arrestin2-bZipEE is unable to bind
KORD-PTPN1. Receptor activation through SalB presence leads to a conformational change, allowing GRK
binding and subsequent phosphorylation of the C-terminal tail of KORD. This allows for the binding
of β-Arrestin2-bZipEE. Connection to the intracellular PHOENICS circuit is facilitated by the
fused
bZipEE domain on β-Arrestin2 and its spatial organization in the proximity of the PTPN1
phosphatase upon KORD binding. Bound 𝛽-Arrestin2-bZipEE recruits Gal4DBD-CD3ζ-bZipRR, which is
subsequently dephosphorylated by PTPN1. Dephosphorylation of CD3ζ leads to dissociation of
Gal4DBD-CD3ζ-bZipRR and Sh2-VP64, thereby splitting the transcription factor and quenching gene
expression.

To evaluate the ability of KORD-PTPN1 to interact with and facilitate dephosphorylation of CD3𝜁, as
well as to interrogate full circuit compatibility, we co-transfected KORD-ABL, KORD-PTPN1,
𝛽-Arrestin2-bZipEE, and our phosphorylation circuit with a 5x UAS NanoLuciferase expression cassette
as well as a normalization plasmid. Cells were induced with 500 nM SalB 24 hours after transfection.
Expressed NanoLuciferase was measured 48 hours after transfection using a plate reader. Titrating
increasing amounts of KORD-PTPN1 against KORD-ABL led to a significant decrease in
phosphorylation-mediated expression, indicating a dose-dependent ability to dephosphorylate and quench
expression starting at a 2:3 ratio (KORD-PTPN1:KORD-ABL; p = 0.004) (**Fig. 17**).
Increasing KORD-PTPN1 amounts relative to KORD-ABL further resulted in a 4.3-fold reduction in the
total expressed NanoLuciferase (p < 0.0001). Overall, this indicates that KORD-PTPN1 can facilitate
a
dose-dependent, linear suppression of total output, confirming its inhibitory role within the
circuit.

![Results Image](https://static.igem.wiki/teams/5775/images/results/maxi/kord-ptpn1-fight.svg)

---

**Figure 17: Titration of KORD-PTPN1 against KORD-ABL**.
HEK293T cells were transfected 24 hours after seeding. NanoLuc and Firefly Luciferase expression was
measured using the Dual Luciferase assay 48 hours after transfection. Induction occurred 24 h after
transfection, using either DMSO or SalB [500 nM]. Each condition was transfected with 30 ng of
KORD-ABL and 5 ng of 𝛽-Arrestin2-bZipEE, as well as the amount of KORD-PTPN1 indicated on the
X-axis.
Data is depicted as the mean +/- SEM with n = 3
technical replicates. Two-way ANOVA with Sidak's multiple comparison test. \*\*\*P<0.001,
\*\*\*\*P<0.0001.

To further evaluate the total quenching ability of KORD-PTPN1 on a single cell level, we
co-transfected KORD-ABL, KORD-PTPN1, 𝛽-Arrestin2-bZipEE, and our phosphorylation circuit with a
5x UAS mCherry expression cassette as well as a normalization plasmid. Cells were induced with 500
nM SalB 24 hours after transfection. Expressed mCherry fluorescence was determined 48 hours after
transfection using Flow cytometry. Cells were gated based on GFP fluorescence from a transfected
normalization plasmid with constitutive expression. As a control, 180 ng of KORD-ABL was
transfected together with the complete phosphorylation circuit and measured under both uninduced
(DMSO) and induced (SalB) conditions. For simplicity, only induced conditions of KORD-ABL +
KORD-PTPN1 are shown. mCherry fluorescence of DMSO control conditions was equivalent to that of
the only KORD-ABL condition, confirming minimal basal activation. Co-transfection of equal amounts
of KORD-ABL and KORD-PTPN1 with the expression circuit quenched expression by 6.8-fold. At high
KORD-PTPN1 levels (2:1 ratio of KORD-PTPN1 to KORD-ABL), fluorescence approached background
intensity (1.4-fold above full quenching achieved by the cytosolic SynPhosphatase), indicating
that KORD-PTPN1 efficiently quenches KORD-ABL–mediated signaling (**Fig. 18**).

![Results Image](https://static.igem.wiki/teams/5775/images/results/maxi/kord-ptpn1.svg)

---

**Figure 18: KORD-PTPN1 quenches expression on a post-translational
level.** HEK293T cells were transfected with the complete expression-coupled PHOENICS
circuit 24 hours after seeding, with varying amounts of KORD-ABL and KORD-PTPN1 as indicated.
Cells were induced 24 hours after transfection with DMSO or SalB [500 nM].
mCherry fluorescence was quantified with flow cytometry 48 hours after transfection. 180/0+
DMSO: Uninduced control exhibiting only background fluorescence in the absence of SalB. 180/0+
SalB: Induced positive control showing the signal achievable without quenching by KORD-PTPN1.
Test conditions show the plasmid amounts between KORD-ABL and KORD-PTPN1 (180/180: 180 ng
KORD-ABL and 180 ng KORD-PTPN1; intracellular components remain constant ). Fluorescence
profiles of viable single cell populations (>13000 per condition) are displayed in the ridgeline
plot on the left, geometric mean fluorescence (RFU) ± geometric SEM are shown in the barplot to
the right.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Novel GPCRs Sense and Respond to Tumor-Specific Antigens and Small
Molecules in a Binary Switch-Like Manner

Building on an extension of the KORD architecture, the Programmable Antigen-Gated Engineered G
Protein-coupled Receptor (PAGER) [(Kalogriopoulos *et al.*, 2025)](#References), we
engineered a receptor that
detects
both a small molecule and a soluble or surface-bound ligand. Targeting the tumor-relevant antigen
vascular epithelial growth factor (VEGF), we show the ability to sense, process, and respond to
physiologically relevant cues using the PAGER-ABL construct in conjunction with the PHOENICS
circuit.
This approach enables the PAGER architecture to overcome the existing limitations of synthetic
receptors by introducing dual modularity: the extracellular nanobody domain can be readily exchanged
to detect diverse soluble or membrane-associated ligands, while the intracellular GPCR backbone can
be
replaced with β-arrestin-recruiting receptors, ensuring signaling orthogonality.

Furthermore, this design enables flexible reprogramming of input specificity and allows combinatorial
processing of signals, while supporting adaptable rewiring of downstream outputs, allowing for
seamless integration into the PHOENICS circuit with fully reversible signaling dynamics.

The PAGER-ABL construct consists of the KORD-ABL backbone and intracellular domains. In addition,
it contains an extracellular nanobody N-terminally fused with Arodyn, a peptide antagonist of
KORD, facilitating constant auto-inhibition. Ligand binding by the nanobody displaces Arodyn from
the orthosteric site of KORD, allowing SalB to bind, triggering a conformational change that
enables GRK-mediated phosphorylation of residues in the receptor's C-terminal tail (**Fig.
19**). This recruits β-Arrestin2-bZipEE that can bind Gal4DBD-Cd3ζ-bZipRR via a
coiled-coil interaction. The resulting positioning of the CD3ζ near the ABL kinase fused to
the GPCR leads to its phosphorylation. The phosphorylated Gal4DBD-CD3ζ-bZipRR subsequently
dimerizes with SH2-VP64, forming a transcriptional activator that binds the 5xUAS motif and drives
transgene expression.

![Results Image](https://static.igem.wiki/teams/5775/images/results/maxi/vegf-pager-expression.svg)

---

**Figure 19: Schematic overview of VEGF sensing PAGER-ABL coupled to
the phosphorylation circuit**. In the absence of SalB or VEGF, 𝛽-Arrestin2-bZipEE is
unable to bind KORD-ABL. Presence of VEGF results in nanobody binding, displacing Arodyn from the
orthosteric pocket of PAGER-ABL. This sensitizes the PAGER to SalB, allowing SalB-dependent
activation. Receptor activation leads to a conformational change, allowing GRK binding and
subsequent phosphorylation of the C-terminal tail of KORD. This allows for the binding of
β-Arrestin2-bZipEE. Connection to the intracellular PHOENICS circuit is facilitated by the
fused
bZipEE domain on β-Arrestin2 and its spatial organization in the proximity of the ABL kinase
upon
KORD binding. Bound β-Arrestin2-bZipEE recruits Gal4DBD-CD3ζ-bZipRR, which ABL
subsequently
phosphorylates. Dissociation from the KORD due to the transient coiled-coil interactions allows
dimerization between the phosphorylated CD3𝜁 and SH2, reconstituting the split transcription factor
Gal4DBD-VP64. Shuttling to the nucleus then facilitates transgene expression under control of the
5xUAS-minCMV promoter.

To test whether the VEGF-sensing PAGER-ABL receptor could act as an AND gate and drive expression
through the PHOENICS circuit, we co-transfected cells with VEGF-PAGER-ABL, the phosphorylation
circuit, a NanoLuciferase reporter, and a constitutive control for normalization. HEK293T cells were
stimulated 24 hours after transfection with SalB (100 nM, 250 nM, or 500 nM) and VEGF165
(500 nM). Nano- and Firefly Luciferase activity was determined 48 hours after transfection using the
Nano-Glo Dual-Luciferase Reporter Assay System. Induction of PAGER-ABL with either DMSO, SalB, or VEGF
did not induce a significant increase in NanoLuciferase expression (p > 0.05). Induction with SalB +
VEGF resulted in significant, dose-dependent increases of NanoLuciferase expression. Compared to SalB
only, SalB + VEGF induced a 44.2-fold, 48.8-fold, and 28-fold change in expression (**Fig.
20**). This shows the functionality of a novel receptor architecture that can drive
transgene
expression in an AND logic gate, binary switch-like manner, without exhibiting significant background
or interference in native signaling pathways. Furthermore, the receptor architecture can sense
tumor-relevant antigens and induce expression in a dose-dependent manner.

![Results Image](https://static.igem.wiki/teams/5775/images/results/maxi/vegf-best-plot.svg)

---

**Figure 20: VEGF sensing PAGER-ABL represents a novel receptor
platform, sensing and responding to a surface-bound or soluble ligand and a small molecule
extracellular cues in an AND logic, switch-like manner** . Barplot showing NanoLuciferase
expression in the presence of different ligands and at different SalB concentrations. HEK293T cells
were transfected 24 hours after seeding. NanoLuc and Firefly Luciferase expression was measured 48
hours after transfection using the Dual Luciferase assay. Induction occurred 24 hours after
transfection with DMSO, DMSO + VEGF [500 nM], SalB or VEGF [500 nM] + SalB. Each condition was
transfected with 60 ng of KORD-ABL and 5 ng of 𝛽-Arrestin2-bZipEE. Data is depicted as the mean +/-
SEM with n = 3 technical replicates. Two-way ANOVA with Tukey's multiple comparisons test was
performed. \*\*\*\*P < 0.0001; NS, not significant.

## MESA

To expand the sensing layer of our system, we engineered and optimized rapalog-inducible
two-chain Modular Extracellular Sensing Architecture (MESA) receptor system in HEK293T cells. With this we
aimed to establish an additional receptor architecture as part of our PHOENICS toolbox, to enable straight
forward retargeting towards additional cancer-relevant ligands.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Rapalog-Gated MESA-Receptors Drive Gene Expression via Ligand-Inducible
Phosphorylation

Our MESA receptor design was inspired by a recent publication [(Yang
*et al.*,
2025)](#References) and consists of a rapalog binding domain-fused ABL kinase chain (FRB-ABL) and a rapalog
binding domain-attached bZipEE coiled coil chain (FKBP-bZipEE), which heterodimerize upon addition of
the rapalog (AP21967). Proximity between receptor-fused kinase and substrate-recruiting coiled coil
results in the phosphorylation of our regulatory substrate CD3ζ. This activates our previously
established [expression platform](#Responding): an SH2-Gal4DBD reader
protein and a bZipRR-VP64 activator, which is fused to CD3ζ. SH2 selective binding to phosphorylated
CD3ζ enables the reconstitution of a functional Gal4DBD-VP64 transcription factor, driving
NanoLuciferase reporter expression (**Fig. 21**).

![Results Image](https://static.igem.wiki/teams/5775/images/results/sensing-mesa/mesa-graphic1.svg)

---

**Figure 21: NanoLuciferase expression coupled PHOENICS circuit with
rapalog induction via MESA kinase receptors.** Adapted MESA-receptors consist of a bZipEE
coiled coil-carrying and a ABL kinase fused chain. Receptors bind rapalog via extracellular FRB/FKBP
domains, mediating receptor dimerization and inducing proximity between receptors fused ABL kinase
and
coiled coil bZipEE. bZipEE recruits the substrate-construct via a complementary coiled coil bZipRR.
Proximity-induced phosphorylation of CD3ζ results in binding of SH2. Gal4DBD and VP64 reconstitute.
This drives NanoLuciferase reporter expression upon nuclear shuttling of the reconstituted
transcription factor complex, producing a measurable luminescence signal.

To verify that ligand-dependent receptor-dimerization leads to substrate-recruitment and
phosphorylation, cells were co-transfected with equal amounts of FRB-ABL and FKBP-bZipEE receptor
constructs in combination with a bZipRR-fused CD3ζ-substrate. To assess ligand-dependent
substrate-phosphorylation, western blot with phosphospecific and anti-Myc Antibody-staining was
performed for rapalog-induced and uninduced conditions (**Fig. 22A**). To confirm
ligand-inducible
expression via our Nanoluciferase platform, cells were co-transfected with optimal ratios of FRB-ABL
and
FKBP-bZipEE, alongside the phosphorylation-responsive protein-circuit components and NanoLuciferase
reporter construct. NanoLuciferase activity was quantified by luminometry and normalized by Firefly
luciferase, which was co-transfected at equimolar amounts across all conditions, accounting for
possible
variations in cell viability and transfection efficiency (**Fig. 22B**).

![Results Image](https://static.igem.wiki/teams/5775/images/results/sensing-mesa/mesafig1.svg)

---

**Figure 22: Verification of rapalog-induced phosphorylation and
SH2-mediated reporter activation.** (A) Western blot of myc-tagged substrate using
anti-phosphotyrosine (pY) and anti-myc antibodies. The strong myc signal in the positive control
results from reusing the same secondary antibody without stripping after pY detection, leading to
signal accumulation. (B) Quantification of SH2-mediated reporter activation using the Nano-Glo assay
. Cells were transfected with the complete expression-coupled PHOENICS circuit, varying
only
the receptor configuration as indicated. Receptor conditions contained a 1:9 ratio of ABL to bZipEE
halves (10 ng total DNA). Pos. ctrl: cytosolic kinase fused to a coiled coil; neg. ctrl: FRB-ABL
receptor half lacking the coiled coil half. NanoLuciferase luminescence values (AU) are
normalized by Firefly luminescence (AU) and display mean ± SEM. Normalized luminescence was analyzed
by
two-way ANOVA. P values: ns > 0.05; \* ≤ 0.05; \*\* ≤ 0.01; \*\*\* ≤ 0.001; \*\*\*\* ≤
0.0001.

In the Western Blot, cytosolic ABL in combination with the coiled coil-fused substrate serves as a
positive control for robust phosphorylation. As a negative control, we used a receptor construct
without
coiled coil, disrupting substrate-recruitment to the receptor fused ABL kinase. Anti-Myc-staining
shows
clear bands for both rapalog-induced and uninduced receptors at the length of our CD3ζ substrate,
confirming successful protein loading. Anti-phosphorylation-staining results in a clear band for the
induced condition, while uninduced receptors did not exhibit detectable phosphorylation.

In luminometry, cytosolic ABL in combination with intracellular circuit components again serves as a
positive control. As a negative control, the FRB-ABL receptor chain and circuit components were
transfected without the FKBP partner to assess background phosphorylation. Rapalog induction produced
a
3.4-fold increase in NanoLuciferase expression compared to uninduced conditions, demonstrating robust
ligand-dependent transcriptional activation through our synthetic phosphorylation receptor platform.

This demonstrates ligand-dependent phosphorylation of our CD3ζ substrate using our receptor designs
and
**verifies ligand-inducible expression via our MESA-receptor-coupled phosphorylation
circuit**.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Ligand-Dependent Dephosphorylation via Rapalog-Gated MESA-Receptors
Inhibits Gene Expression

After establishing ligand-dependent activation of gene expression, we engineered what is, to our
knowledge, an entirely novel platform for **inhibitory ligand sensing** and integrated it
into our circuit. For this, we replaced the truncated ABL kinase with the truncated phosphatase PTPN1
on
the FRB receptor chain. This MESA-receptor now dephosphorylates our regulatory substrate upon
ligand-induced dimerization, which prevents SH2-mediated transcription factor reconstitution and
inhibits expression (**Fig. 23**).

![Results Image](https://static.igem.wiki/teams/5775/images/results/sensing-mesa/mesa-graphic2.svg)

---

**Figure 23: Rapalog-mediated suppression of NanoLuciferase expression via the PHOENICS
circuit coupled to MESA phosphatase receptors.**
Ligand-dependent control of proximity-based dephosphorylation was achieved by replacing the
intracellular kinase domain of one MESA receptor with a phosphatase, while retaining the bZipEE
motif
on the other receptor half. Co-transfection with a cytosolic kinase ensures constitutive
phosphorylation of the substrate, enabling detection of ligand-induced dephosphorylation events.
Upon
rapalog binding, MESA receptor dimerization brings the phosphatase into proximity with the
substrate,
promoting dephosphorylation. Loss of substrate phosphorylation disrupts SH2 binding and prevents
reconstitution of the Gal4DBD and VP64 transcriptional activator, thereby suppressing NanoLuciferase
reporter expression and reducing luminescence output.

To demonstrate ligand-dependent inhibition of expression, we co-transfected optimal ratios of our
inhibitory receptor constructs alongside cytosolic ABL to maintain basal substrate phosphorylation,
the
intracellular circuit components, and the NanoLuciferase reporter construct. NanoLuciferase activity
was
again quantified by luminometry and Firefly Luciferase normalization (**Fig. 24**).

![Results Image](https://static.igem.wiki/teams/5775/images/results/sensing-mesa/mesafig2.svg)

---

**Figure 24: Rapalog-induced reporter expression quenching by
phosphatase receptor.** Quantification of SH2-mediated reporter activation using the
Nano-Glo
assay. Cells were transfected with the complete expression-coupled PHOENICS circuit,
varying only the receptor configuration as indicated. Neg. ctrl: FKBP-bZipEE receptor half lacking
the
phosphatase-fused construct. Receptor conditions contained a 1:4 ratio of PTPN1 to bZipEE halves
(10
ng total DNA). NanoLuciferase luminescence values (AU) are normalized by Firefly luminescence (AU)
and
display mean ± SEM. Luminescence was analyzed by two-way ANOVA. P values: ns
>
0.05; \* ≤ 0.05; \*\* ≤ 0.01; \*\*\* ≤ 0.001; \*\*\*\* ≤ 0.0001.

As a negative control for background gene expression, we transfected intracellular circuit components
and our FRBP-bZipEE construct. Rapalog treatment of functional receptor conditions resulted in a
2.8-fold reduction in normalized NanoLuciferase expression compared to uninduced states, verifying
reliable, **ligand-dependent inhibition of gene expression**.

This demonstrates functionality of our phosphatase-fused receptors and enables intracellular
processing
of two opposing ligand signals with our MESA-architectures coupled to our phosphorylation circuit.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Exchange of Extracellular Binding Domains Enables Retargeting of
MESA-Receptors to Protein Ligands

After integrating stimulatory and inhibitory ligand sensing into our circuit, we set out to verify
**target flexibility** of our synthetic receptor architecture. By exchanging the
extracellular domains of our receptor constructs with anti-TNFα scFvs, we established soluble
protein-dependent activation of gene expression (**Fig. 25**).

![Results Image](https://static.igem.wiki/teams/5775/images/results/sensing-mesa/mesa-graphic3.svg)

---

**Figure 25: NanoLuciferase expression coupled PHOENICS circuit with
TNFα induciton via MESA kinase receptor.** Adaptation of the initial reporter assay to
assess
TNFα-mediated reporter expression via anti-TNFα-scFv-fused MESA-receptors. MESA-receptors
bind TNFα via extracellular anti-TNFα-scFvs. TNFα-binding mediates receptor dimerization, inducing
proximity between receptors fused ABL kinase and coiled coil bZipEE. bZipEE recruits the
substrate-construct via a complementary coiled coil bZipRR. Proximity-induced phosphorylation of
CD3ζ
results in binding of SH2. Gal4DBD and VP64 reconstitute. This drives NanoLuciferase reporter
expression upon nuclear shuttling of the reconstituted transcription factor complex, producing a
measurable luminescence signal.

To show induction of gene expression by TNFα-sensing, we co-transfected our adapted receptor
constructs, intracellular circuit components, and the NanoLuciferase reporter construct.
NanoLuciferase
activity was quantified by luminometry and Firefly Luciferase normalization (**Fig.
26**).

![Results Image](https://static.igem.wiki/teams/5775/images/results/sensing-mesa/mesafig3.svg)

---

**Figure 26: TNF-induced reporter activation by MESA kinase receptor.**
Quantification of SH2-mediated reporter activation using the Nano-Glo assay. Cells were
transfected with the complete expression-coupled PHOENICS circuit, varying only the receptor
configuration as indicated. Neg. ctrl: RAPA-ABL receptor half lacking the bZipEE half. Receptor
conditions contained a 1:4 ratio of ABL to bZipEE halves (10 ng total DNA). NanoLuciferase
luminescence values (AU) are normalized by Firefly luminescence (AU) and display mean ± SEM.
Luminescence was analyzed by two-way ANOVA. P values: ns > 0.05; \* ≤ 0.05;
\*\* ≤ 0.01; \*\*\* ≤ 0.001; \*\*\*\* ≤ 0.0001.

As a positive control, cytosolic ABL kinase was added to facilitate strong substrate phosphorylation.
Conditions including only the intracellular circuit components alone as well as the ABL receptor
monomer
served as negative controls of background gene expression and background phosphorylation byFRB-ABL
without its FKBP-bZipEE-partner. For functional receptor conditions, treatment with TNFα resulted in a
1.8-fold increase of NanoLuciferase expression, confirming **soluble-protein-dependent
activation
of our circuit**, albeit needing optimization to reduce background expression.

To **refine expression control for minimized background** activity, we utilized our dry
lab model [SPARC](/heidelberg/model) and optimized receptor transmembrane domains (TMDs)
with
coarse-grained molecular
dynamics. To reduce spontaneous dimerization we exchanged CD28 and CD28-M3 TMDs with FGFR1
TMDs. Additionally we fine tuned receptor construct ratios for precise TNFα-inducible phosphorylation
of
our regulatory substrate. To assess phosphorylation efficiency of different designs at single-cell
resolution, we adapted our circuit for a flow cytometry readout. We replaced Gal4DBD and VP64 with
halves of the splitFast fluorophore system to quantify SH2-mediated assembly of
the intracellular circuit components. Upon reconstitution of circuit components and addition of HMBR,
SplitFast becomes fluorescently active (**Fig. 27**).

![Results Image](https://static.igem.wiki/teams/5775/images/results/sensing-mesa/mesa-graphic4.svg)

---

**Figure 27: TNFα-inducted split fluorophore reconstitution in
PHOENICS circuit via MESA kinase receptor.** MESA-receptors bind TNFα via extracellular
anti-TNFα-scFvs. TNFα-binding mediates receptor
dimerization, inducing proximity between receptors fused ABL kinase and coiled coil bZipEE. bZipEE
recruits the substrate-construct via a complementary coiled coil bZipRR. Proximity-induced
phosphorylation of CD3ζ results in binding of SH2. Split fluorophore constructs RspA(C) and RspA(N)
reconstitute, yielding fluorescence upon addition of HMBR, producing a measurable reporter for
CD3ζ-SH2 association.

To demonstrate reduced background activity of our improved TNFα-MESA, we compared the previous design
with FGFR1 TMD constructs, which was transfected at optimal ratios of ABL-chain and bZipEE-chain, as
predicted by SPARC, and quantified CD3ζ phosphorylation with flow cytometry (**Fig.
28**).

![Results Image](https://static.igem.wiki/teams/5775/images/results/sensing-mesa/mesafig4.svg)

---

**Figure 28: TNFα-induced CD3ζ-SH2 constitution via MESA-like kinase
receptor.** Quantification of TNFα-induced phosphorylation-mediated CD3ζ-SH2 constitution.
Cells were transfected with the complete expression-coupled PHOENICS circuit, varying only the
receptor configuration as indicated. Neg. ctrl: No receptor. Receptor conditions contained specified
ratios of ABL to bZipEE halves (170 ng total DNA in 12-well plate wells). SplitFast fluorescence was
quantified with flow cytometry. Dimerization dependent SplitFAST fluorescence was quantified by flow
cytometry 48 hours after transfection, employing excitation lasers at 488 nm and 561 nm. Emission
was recorded at 525 nm for splitFAST (Ligand: TFLime) and at 610 nm for constitutively expressed
mCherry, serving as a transfection control. (A) Fluorescence profiles of viable single cell
populations are displayed in ridgeline plot, (B) geometric mean fluorescence (RFU) ± geometric SEM
with ~9000 single cells are shown in barplot.

As a negative control, we transfected our SplitFast-fused intracellular components without receptor
constructs to display background fluorescence without substrate-phosphorylation. For functional
receptor
conditions, FGFR1-transmembrane domains and an optimized ratio of receptor constructs for reduced
background activity exhibit a clear shift of the cell population towards higher fluorescence upon TNFα
treatment. With these changes in place we move from a 1.5-fold increase of geometric mean of
fluorescence between uninduced and induced states for the old ratios and transmembrane domains to a
3.7-fold increase. Optimized receptor construct ratios and transmembrane domains result in
significantly
lower background phosphorylation in the uninduced state, likely due to minimized spontaneous
dimerization. We expect to transfer these improvements in dynamic range to our
expression system, which is mediated by the constitution of the intracellular circuit components
measured in this assay, in the near future.

Overall, this serves as a proof-of-concept for **processing
soluble-protein ligands** in our phosphorylation circuit via our MESA-receptor
platform.
We showcase its target flexibility by **readily exchangeable extracellular binding
domains**, opening up various retargeting options to tumor-microenvironment associated
soluble
proteins.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Opposing MESA-Receptors Control Gene Expression via Dual-Ligand
Processing

After establishing both, small-molecule and soluble-protein gated synthetic MESA-receptors as a
sensing
layer, we proceeded to validate **simultaneous processing of multiple ligand-signals** in
our phosphorylation circuit. Guided by SPARCs mathematical modeling predicting optimal receptor
ratios,
we integrated both our kinase-carrying TNFα-receptors and our ralapog-gated phosphatase-receptors
within
one cell, forming a cellular processing unit: Gene expression is only triggered when the stimulatory
TNFα-signal outweighs the inhibitory rapalog input, providing an additional safety layer and enabling
precise environmental control of cellular outputs (**Fig. 29**).

![Results Image](https://static.igem.wiki/teams/5775/images/results/sensing-mesa/mesa-graphic5.svg)

---

**Figure 29: NanoLuciferase expression coupled PHOENICS circuit with
TNFα induction and rapalog inhibition via MESA receptors.** Adaptation of the initial
reporter assays to assess TNFα-induced reporter expression via anti-TNFα-scFv-fused MESA-receptors
and
rapalog-controlled quenching. ABL kinase carrying MESA-receptors bind TNFα via
extracellular anti-TNFα-scFvs. TNFα-binding mediates receptor dimerization, inducing proximity
between
ABL and coiled coil bZipEE. bZipEE recruits the substrate-construct via a complementary coiled coil
bZipRR, resulting in proximity-induced phosphorylation of CD3ζ. SH2 binds phosphorylated CD3ζ, with
subsequent Gal4DBD-VP64 transcription factor constitution and NanoLuciferase reporter expression.
Rapalog-binding of PTPN1 phosphatase-carrying receptor induced dimerization, resulting in substrate
dephosphorylation, quenching reporter expression. NanoLuciferase expression can be quantified by
luminometry.

To showcase effective expression regulation in a two-ligand-setting, we co-transfected stimulatory
and
inhibitory receptors alongside our intracellular circuit components and the NanoLuciferase reporter
construct. NanoLuciferase activity was quantified by luminometry and normalized by Firefly Luciferase
(**Fig. 30**).

![Results Image](https://static.igem.wiki/teams/5775/images/results/sensing-mesa/mesafig5.svg)

---

**Figure 30: Dual-receptor logic for processing dual-ligand-specific
reporter activation.** Quantification of SH2-mediated reporter activation using the
Nano-Glo
assay. Cells were transfected with the complete expression-coupled PHOENICS circuit,
varying only the receptor configuration as indicated. Neg. ctrl: No receptor. Receptors were
co-transfected with 10 ng total DNA using a 1:4 ratio of TNF to rapalog receptors. For the rapalog
receptor, a 1:3 ratio of phosphatase to bZipEE halves was used; for the TNF receptor, a 1:5 ratio of
TNF-ABL to bZipEE halves. NanoLuciferase luminescence values (AU) are normalized by Firefly
luminescence (AU) and display mean ± SEM. Luminescence was analyzed by two-way ANOVA. P values: ns >
0.05; \* ≤ 0.05; \*\* ≤ 0.01; \*\*\* ≤ 0.001; \*\*\*\* ≤ 0.0001.

Circuit components without receptor constructs serve as a negative control displaying only background
expression. For conditions including both, stimulatory TNFα-receptor and inhibitory rapalog-receptor,
TNFα treatment produced a 2.0-fold increase relative to the uninduced state. Simultaneous TNFα and
rapalog exposure fully suppressed activation, returning NanoLuciferase to baseline expression of the
uninduced system. This demonstrates **functional ligand-dependent AND-NOT gating of
expression**. Future experiments will focus on the reduction of background activity by
optimizing transmembrane domains and calibrating receptor stoichiometry, as shown above. By adjusting
the relative abundance of stimulatory versus inhibitory receptors, the switching threshold of
stimulatory versus inhibitory ligand concentrations can be positioned at defined ligand ratios. This
maintains dormancy in healthy tissues and robust expression of therapeutically relevant effectors when
tumor microenvironment specific ligand profiles are encountered.

Overall, these results serve as a **proof-of-concept** for the **computation of
opposing signals** with our phosphorylation circuit, which can be tailored to produce
**precise cellular outputs**. By exchanging extracellular binding domains on our
MESA-receptors, we have showcased **straightforward retargetability** of our receptors to
**cancer-relevant soluble-proteins**. This unlocks various potential targets that shape
and
are indicative of the tumor microenvironment. We have further expanded the accessible ligand space
with
our dry lab efforts by successfully generated several de novo protein binders for cancer-relevant
ligands with experimental validation demonstrating our [pipeline's
reliability](/heidelberg/model#Binders). This enables
personalized applications of the PHOENICS system, which can be re-tuned to programmed ligand
combinations, allowing therapeutic effector functions to be gated by ligand ratios characteristic of
specific tumor microenvironments.

## Binder Validation

To enable modular retargeting of our receptor platforms toward cancer-relevant ligands, we
established a streamlined pipeline integrating computational de novo binder design with functional
validation using cell-free two-hybrid (CF2H) screening. Screening five binders against GDF-15, a protein
tumor related protein, we identified a promising binder, validating this approach for expanding the
sensing
capabilities of our PHOENICS toolbox.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

*In Vitro* Screening of *In Silico*-Generated De Novo
Protein Binders.

Having designed protein binders for retargeting our sensory receptor platforms by exchange of
extracellular binding domains on MESAs or nanobody-replacement on our GPCRs, we set out to achieve
experimental **validation of our binder designs**. To rapidly assess computationally
designed
binders against GDF-15, a protein upregulated in cancers as shown by [(Zhao
*et al.*,
2020)](#References). We implemented the Cell-Free Two-Hybrid (CF2H) system as described by [(Capin *et al.*, 2025)](#References). This approach enables protein-protein
interaction
screening
without requiring cloning, protein purification, or specialized equipment.
This method utilizes a cell-free expression system to selectively express reporter sfGFP by
binder-mediated reconstitution of split transcription factors. Biotinylated GDF-15-Fc was complexed
with
streptavidin to induce multimerization and then spiked into cell-free reactions containing linear DNA
encoding CIopt-fused binder constructs and a pRM-sfGFP reporter plasmid. After expression,
binder-target
interaction induces CIopt dimerization, which activates pRM promoter-driven sfGFP expression,
providing
a
fluorescent readout to functionally assess binding capabilities of the binder design to its designated
target (**Fig. 31**)

![Results Image](https://static.igem.wiki/teams/5775/images/results/cf2hassay/binder-assay-method.svg)

---

**Figure 31: Cell-Free Two-Hybrid (CF2H) assay for functional binder
validation.** Functional validation of protein-binders with sfGFP expression readout.
Biotinylated target proteins complex with the streptavidin tetramer. This complex is added to a
cell-free expression system in combination with linearized DNA encoding for a CIoptDBD-fused binder
and a reporter plasmid, encoding sfGFP under pRM-promotor control. CIoptDBD-binder expression
results
in binding to target protein with subsequent CIopt-dimerization. Two dimerized CIopt domains bind to
Operon 1 and Operon 2 of the pRM-promoter inducing expression and subsequently yielding a measurable
fluorescence signal.

We observed some basal promoter leakage for the negative control, indicating moderate residual pRM
activity in the absence of CIopt activation. This was however significantly lower compared to our
positive control, confirming the viability of this assay.
**Binder B3 demonstrated highest sfGFP fluorescence upon ligand induction** out of all
tested candidates, while in the absence of biotinylated GDF-15 it produced fluorescence not higher
than
the autofluorescence of the cell-free extract (**Fig. 32**). This confirms that
**sfGFP was specifically
expressed due to binder-target interaction** rather than non-specific aggregation. Binder-3
exhibited higher fluorescence than our positive control, however direct comparison of binding
affinities
is limited by varying protein concentrations as binders were expressed in situ by the cell-free
system.
Additionally the molar amount of GDF-15 binder DNA slightly exceeded the amount of the positive
control
binder.

![Results Image](https://static.igem.wiki/teams/5775/images/results/cf2hassay/binderassay-results-normalized.svg)

---

**Fig 32: Functional validation of de novo binders with the CF2H
assay.** Functional assessment of *in silico* generated de novo binders against
GDF-15 using
the CF2H assay. Neg. ctrl: No DBD-binder encoding DNA. Pos. ctrl: DNA encoding for NbALFA nanobody
paired
cognate ALFA-tag epitope. sfGFP fluorescence [AU] was measured for induction with GDF-15 and
compared
to fluorescence without target protein, displaying mean ± SEM. Fluorescence was analyzed by two-way
ANOVA. P values: ns > 0.05; \* ≤ 0.05; \*\* ≤ 0.01; \*\*\* ≤ 0.001; \*\*\*\* ≤ 0.0001.

While quantitative comparison is thus limited, these results qualitatively confirm the binding
capabilities of our de novo designed binder to GDF-15. Binders B2 and B7 also showed detectable
binding
activity, albeit without or reduced significant difference compared to background sfGFP expression. B1
and B5 did not produce detectable sfGFP fluorescence over the autofluorescence of the extract measured
in Neg. CT2.
Overall, these results demonstrate that **our de novo binder actively targets cancer-relevant
GDF-15. This indicates the potency of our dry lab model SPARC** in designing functional
binders
for select target proteins and highlights the utility of CF2H as an accessible and rapid validation
platform for de novo designed protein binders. Going forward, this established pipeline linking in
silico design and experimental validation enables us to engineer custom binding moieties for
retargeting
our receptor platforms.

## Discussion

In this year's iGEM cycle, we established two distinct receptor platforms to connect extracellular
ligand detection to our phosphorylation circuit: a small-molecule or protein ligand-gated MESA
architecture and a synthetic GPCR-based receptor design sensing either small molecules alone or in
combination with protein cues. Both systems rely on the same intracellular principle of ligand-induced
recruitment of the regulatory CD3ζ substrate, inducing proximity to receptor-fused kinases for stimulatory
ligands or phosphatases for inhibitory signaling. Resulting phosphorylation or dephosphorylation of CD3ζ
regulates gene expression or secretion via SH2-mediated transcription factor reconstitution. Despite this
shared design logic, the two receptor classes exhibited strikingly different behaviors in terms of
background activity, dynamic range, and modularity, offering complementary advantages and limitations.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

GPCRs

We created our synthetic GPCR sensing platform by fusing a truncated version of the ABL kinase or
PTPN1
phosphatase to the κ-opioid receptor DREADD (KORD) and recruiting the CD3ζ substrate to β-Arrestin2
via
complementary coiled coils. When the GPCR is activated by ligand binding, it undergoes a
conformational
change that mediates 𝛽-Arrestin2-bZipEE binding, thus ensuring that proximity-induced
(de–)phosphorylation events occur exclusively after ligand binding.

This architecture exhibited **highly accurate** ligand-inducible control over reporter
expression with robust fold changes in both luminometry and flow cytometry. By integrating either
SynKinase or SynPhosphatase fusions, we could readily switch between ligand-inducible activation
(KORD-ABL) and inhibition (KORD-PTPN1), enabling **processing of both stimulatory and inhibitory
signals**. Motivated by these results, we further extended this GPCR platform by
implementing
PAGER (Programmable Antigen-gated G-Protein coupled Engineered Receptors) designs, where extracellular
nanobodies carrying an arodyn-blocking peptide enabled **dual-input AND-gating** of
small-molecule and protein ligands. After achieving proof-of-concept for the PAGER design as a sensory
component in our phosphorylation circuit, we retargeted PAGER to **VEGF**, successfully
establishing **cancer-relevant soluble protein sensing** coupled to an expression output.
Collectively, these results establish GPCR-based (de-)phosphorylation receptors as a highly versatile,
tight platform for precise environmental control of cellular activity in the PHOENICS circuit.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

MESA Receptors

Our MESA designs were engineered by fusing ABL kinase or PTPN1 phosphatase to receptors carrying
rapalog-binding domains and recruiting CD3ζ via a coiled coil. Ligand binding induces receptor
dimerization and brings the substrate into proximity to the kinase or phosphatase, yielding
ligand-inducible transcriptional activation. Furthermore, by exchanging extracellular domains (ECDs)
for
scFvs, we showed that MESAs are **readily retargetable to soluble proteins** such as
**TNFα**, confirming their modularity as a sensing layer. Finally, by integrating kinase-
and
phosphatase-fused MESA receptors in one cell, we demonstrated PHOENICS' ability to process
opposing
extracellular cues, resulting in flexible switching between active and dormant states.

Our current MESA designs exhibit moderate spontaneous background dimerization, indicated by elevated
reporter expression compared to baseline levels in the absence of an inducing ligand. Despite
optimization
cycles guided by our SPARC modeling, including transmembrane domain engineering and construct ratio
tuning, leaky expression in the OFF-state could only be partially suppressed. This resulted in smaller
fold changes compared to our synthetic GPCRs and limits the dynamic range of our MESA receptors. While
our
MESA platform provides a versatile and modular architecture, we plan to continue optimization efforts,
which will likely prove advantageous for applications requiring highly precise control of cellular
outputs.

Together, our results establish **two complementary receptor platforms** for
phosphorylation-based signal processing. MESAs excel in their **modularity and straightforward
retargetability**, offering a rapid path to new ligand inputs. GPCRs provide a novel receptor
class with tight OFF states and the ability to **gate multiple ligand modalities** via a
single construct. Our receptor constructs have been engineered as plug-and-play components of our
PHOENICS
circuit and can effectively enable the processing of multiple opposing signals coupled to therapeutic
gene
expression or secretion. This paves the way for customized applications of the **PHOENICS
system**, which can be retuned to programmed ligand combinations targeting
**TME-specific
characteristics.**

### Outlook

Throughout our engineering cycles, we identified stoichiometry and ligand concentration as critical
design parameters across both platforms. For GPCRs, optimal ratios of receptor to β-Arrestin2 modules and
appropriate SalB concentrations were necessary to achieve maximal induction while minimizing background.
For MESAs, tuning transmembrane domains and receptor monomer stoichiometry was essential to mitigate
spontaneous dimerization. These optimization steps were closely **guided** by our
computational model **SPARC** and highlight the importance of iterative
**Design-Build-Test-Learn** cycles in developing synthetic receptor systems with predictable
input-output behaviors. Building on these insights, our next steps focus on further suppressing MESA
background activity through alternative transmembrane scaffolds and dimerization interfaces.

In parallel, we aim to expand our sensory platform by leveraging SPARC to design MESA-ECDs and
arodyn-fused nanobodies for our GPCR architecture against additional tumor-relevant targets. Additionally,
we plan to implement **multi-input logic** by combining multiple receptor modules to enable
more sophisticated tumor microenvironment discrimination. SPARC will be instrumental in guiding these
expansions, enabling rational design of custom binding domains, optimal circuit compositions and
predictive tuning of receptor stoichiometry for **maximum therapeutic efficacy.**

## Circuit Assembly

With all system components validated and characterized, it was finally time to put it all together and
assemble the PHOENICS circuit. To demonstrate the modularity and applicability of our system, we
implemented two different circuits, applying our sense-process-respond logic with both a tumor-relevant
ligand and a therapeutically applicable output protein.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

VEGF Sensing with RELEASE

Using the previously established VEGF-PAGER-ABL, our optimized PhosphoTEV and the NanoLuc-RELEASE
reporter, we implemented a circuit to make RELEASE VEGF-inducible (**Fig. 33**).
To simulate an application in which SalB is present systematically and circuit assembly should occur
only in the presence of VEGF, NanoLuc secretion was compared between SalB alone and with VEGF.
Co-stimulation 24 hours post-transfection with SalB led to a significant increase in secretion
(p=0.03), corresponding to a modest 1.5-fold change. To estimate the basal activity of SalB alone, a
separate no-kinase RELEASE condition was included for reference. In this comparison, SalB alone
appeared to generate elevated background relative to the no-kinase baseline, suggesting partial
leakiness of PAGER gating in the absence of VEGF.

![Results Image](https://static.igem.wiki/teams/5775/images/results/effector-tobi/vegf-release-2.svg)

---

**Figure 33: VEGF-inducible NanoLuc secretion.**
VEGF-inducible NanoLuc secretion. HEK293T cells were transfected with VEGF-PAGER-ABL, PhosphoTEV,
and NanoLuc-RELEASE plasmids 24 hours post-seeding. 24 hours post-transfection, they were induced
with either SalB [500 nM] or SalB [500 nM] + VEGF [500 nM]. The data is shown as the mean +/- SEM
with n = 3 technical replicates. A 1-tailed Student's t-test was performed to test for an increase
with VEGF induction. Positive and negative controls were excluded from testing and are shown as a
reference. \*P<0.05.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Induced IL-12 Secretion with KORD-ABL

To prove the modularity of our secretion module, we exchanged the NanoLuc-RELEASE reporter with
Interleukin-12 (IL-12). IL-12 is a potent cytokine that activates natural killer and cytotoxic T cells
and exhibits anti-angiogenic effects, inhibiting tumor growth and counteracting the immune-suppressive
tumor microenvironment. We validated it on a simple circuit using two different amounts of the
KORD-ABL construct. The cells were induced with SalB after 24 hours and secreted IL-12 was measured in
the supernatant another 24 hours later using a sandwich ELISA with a chemiluminescent readout
(**Fig.
34**).
For both KORD-ABL amounts, a significant increase in secreted IL-12 could be observed. However, there
was a substantial increase without SalB induction compared to the negative control without any
protease, indicating leakage of the system. Nevertheless, this proves the system working as planned in
principle.

![Results Image](https://static.igem.wiki/teams/5775/images/results/effector-tobi/il12-release.svg)

---

**Figure 34: SalB induced IL-12 secretion using PhosphoTEV-RELEASE
and KORD-ABL.**
HEK293T cells were co-transfected with β-Arrestin2-bZipEE, IL-12-RELEASE, PhosphoTEV and KORD-ABL
plasmids. Cells were induced with 3 µM SalB 24h post-transfection. IL-12 in 100 µL of supernatant
was measured by ELISA with a chemiluminescence readout 48h post-transfection. Data is depicted as
the mean +/- SEM with n = 3 technical replicates. Statistical significance was calculated with a
Two-way ANOVA with Sidak's multiple comparisons test. \*P<0.05, \*\*P<0.01.

## Discussion

These results demonstrate a proof of concept for fully post-translational, receptor-driven protein
secretion, mediated by phosphorylation. We successfully validated all system components individually,
thoroughly characterizing them and showed them to work in conjunction. Thus, we established the PHOENICS
toolbox as a modular framework that allows reprogramming to different input signals, as well as effector
functions. The proven reversibility of our signal transduction through inducible dephosphorylation allows
our system to integrate multiple signals and thus adapt to different therapeutic contexts. We present a set
of modular receptors, as well as two output systems with different advantages, with either a superior
specificity or rapid response.

## Additional Modules

### Membrane Localization using Temperature (Melt)

To enable sensing of additional environmental cues characteristic of the tumor
microenvironment, such as elevated temperature, hypoxia, and pH, we set out to engineer complementary
sensory modules beyond small-molecule and protein ligands. While experiments for hypoxia and pH sensing
are
ongoing, we have achieved promising results for temperature-controlled gene expression.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Melt-Module Enables Temperature-Induced Gene Expression

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/melt/melt-mcherry.svg)

---

**Figure 35: Mechanism of temperature-gated transcription with
Melt40.** At cooler temperatures, Melt40 (purple)
oligomerizes and is recruited to the plasma membrane via the STIM polybasic segment, sequestering
the
fused Gal4DBD-VP64 away from DNA. Upon heating (red arrow), Melt disengages from the membrane and
the
SV40 NLS drives nuclear import of Gal4DBD-VP64. In the nucleus, Gal4DBD binds UAS sites and VP64
activates transcription of the mCherry reporter, which accumulates in the cytosol (red). Cooling
reverses the process (blue arrow), returning the fusion to the membrane and reducing transcriptional
output.

Melt is a temperature-inducible membrane translocator consisting of a mutated LOV domain that
undergoes
conformational change around 40°C [(Benman *et al.*, 2025)](#References). We
designed
a
temperature-sensing
system by
fusing it with a Gal4 DNA-binding domain (Gal4DBD) and VP64 transcriptional activator. This
construct
facilitates nuclear shuttling via a nuclear localization sequence (NLS). Our Melt-construct was
combined
with an mCherry reporter controlled by a Gal4-UAS promoter (**Fig. 35**). To measure
temperature-induced
membrane-dissociation, we incubated transfected cells at 37°C and 40°C and measured reporter
expression
with flow cytometry (**Fig. 36**).

![Results Image](https://static.igem.wiki/teams/5775/images/results/thirdinput/meltfig1.svg)

---

**Figure 36: Temperature-induced reporter expression via
Melt.**
Quantification of temperature-induced mCherry reporter expression via membrane-dissociating
transcription factor-fused Melt. Cells were transfected with mCherry reporter construct under
UAS-promotor control. Neg. ctrl: whiteout Melt. Melt conditions contained specified construct
version,
with Gal4DBD-VP64 fused C-terminally. Transfected cells were incubated for 12-16h at 37°C or 40°C
before measurement. mCherry fluorescence was quantified with flow cytometry. Fluorescence profiles
of
viable single cell populations are displayed in the ridgeline plot on the left, geometric mean
fluorescence (RFU) ± geometric SEM are shown in the barplot to the right.

As a negative control we transfected cells solely with the mCherry reporter construct, which
exhibited
minimal basal expression for both incubation temperatures. When incubated at 37°C, our Melt construct
exhibited moderate background expression, as indicated by a detectable shift of the cell population
towards higher fluorescence and an elevated geometric mean of fluorescence. However, induction at 40°C
produced a significantly higher population of fluorescent cells and a 6.6-fold increase of measured
mean
mCherry fluorescence. Notably, when comparing mCherry expression through a fused Gal4DBD-VP64
construct
at 37°C and 40°C, only a minor (1.1-fold) increase of reporter expression was detected
(**Fig. 37**).
Temperature differences alone thus do not account for the observed effect in this assay.

![Results Image](https://static.igem.wiki/teams/5775/images/results/thirdinput/meltfig2.svg)

---

**Figure 37: Minimal temperature-induced variation of reporter
expression without Melt.** Quantification of mCherry reporter expression via Gal4DBD-VP64
transcription factor. Cells were transfected with mCherry reporter construct under UAS-promotor
control and Gal4DBD-VP64 transcription factor. Transfected cells were incubated for 12-16h at 37°C
or
40°C before measurement. mCherry fluorescence was quantified with flow cytometry. Fluorescence
profiles of viable single cell populations are displayed in ridgeline plot on the left, geometric
mean
fluorescence (RFU) ± geometric SEM are shown in barplot to the right.

Overall, these results demonstrate robust temperature control of expression and suggest a promising
candidate for a temperature-sensing module in our circuit, albeit needing some optimization to
minimize
background membrane dissociation. Our next steps aim to implement Melt as another sensing platform in
PHOENICS, through SH2 domain fusion. In this configuration, temperature elevation would induce SH2
release from the membrane, freeing it to participate in transcription factor reconstitution within our
established phosphorylation-based circuit (**Fig. 38**). We also plan to tune LOV domain
variants to
reduce
background activity at physiological temperature (37°C) while achieving selective induction at
pathologically relevant elevated temperatures. Critically, this temperature-sensing capability could
serve as an externally controllable safety mechanism. It could locally confine cell effector functions
to tumor sites that frequently exhibit elevated temperature levels [(Yahara
*et al.*, 2003)](#References).
Moreover,
therapeutic activation could be spatiotemporally triggered by elevating tumor temperature through
focused ultrasound.

![Results Image](https://static.igem.wiki/teams/5775/images/engineering/melt/melt-input.svg)

---

**Figure 38: Possible implementation of Melt into PHOENICS circuit.**
Ligand sensing leads to phosphorylation or dephosphorylation of CD3ζ. For temperatures above 40°C,
the Melt40 dissociates from the membrane and allows binding of fused SH2 to phosphorylated CD3ζ,
resulting in the reconstitution of a spit TEV protease. ER retention signals are now cleaved off
the Nanoluciferase-construct, leading to its secretion.

### Two-Component System

We implemented the bacterial two-component system EnvZ/OmpR as an additional signaling layer. Using
histidine-aspartate phosphorylation, a mechanism absent in mammalian cells, it is fully orthogonal and can
function in parallel with the PHOENICS system. We successfully achieved OmpR activation through substrate
phosphorylation, demonstrating modular signal control.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

Expanding Synthetic Signaling with a Two-Component System

Bacteria employ two component systems (TCSs) to sense and adapt to environmental changes. Each
system
consists
of a sensor histidine kinase (HK) and a response regulator (RR). The HK, often membrane-bound, detects
external
cues such as changes in osmolarity, pH, or nutrient availability [(Stock *et
al.*, 2000)](#References). Most HKs form homodimers in
which each subunit phosphorylates the other in a trans-autophosphorylation reaction. Upon stimulation,
it
undergoes ATP-dependent autophosphorylation on a conserved histidine residue and transfers the
phosphoryl
group to
an aspartate residue in the RR. Phosphorylation induces conformational changes in the RR that
modulates its
activity, often altering protein-protein or protein-DNA interactions to regulate downstream processes.
Many
HKs
also act as phosphatases, dephosphorylating their cognate RR to reset signaling, enabling dynamic and
reversible
control of cellular adaptation [(Gao & Stock, 2009; Papon & Stock, 2019)](#References).

The EnvZ/OmpR system is a well characterized TCS. In *Escherichia coli*, it regulates outer
membrane
porins (OmpF, OmpC) that respond to osmotic changes. EnvZ, a membrane-bound HK, senses osmolarity
through
its periplasmic domain and phosphorylates the RR OmpR. Phosphorylated OmpR binds DNA to control
transcription of OmpF and OmpC, activating OmpF under low osmolarity and OmpC under high osmolarity.
This
graded response finetunes membrane permeability to maintain cellular homeostasis [(Kenney & Anand, 2020)](#References).

In synthetic biology, TCSs are increasingly used as modular tools to engineer programmable signaling
pathways. Their simple, reversible phosphotransfer mechanism allows precise control of signal
processing and
dynamic regulation of cellular responses. A key advantage is their complete orthogonality to mammalian
signaling, as TCS rely on histidine-aspartate phosphorylation, a chemistry absent in eukaryotic cells.
This
prevents cross-reactivity with endogenous pathways and enables the design of insulated, tunable
circuits for
synthetic sensing and gene control application [(Scheller *et al.*, 2020;
Schmidl *et al.*, 2019)](#References).

Building on these benefits, we adapted the synthetic signaling architecture described by [(Jones *et al.*, 2022)](#References).The naturally bifunctional HK EnvZ was
truncated to
remove its transmembrane domain and further mutated to yield monomeric cytosolic variants with
isolated
enzymatic activities, one acting as a kinase and the other as a phosphatase. The kinase variant
(EnvZK)
comprises two Dimerization and Histidine Phosphotransfer domains (DHp; "A") and one Catalytic and
ATP-binding domain (CA; "B"). In contrast, the phosphate (EnvZP) variant is formed
by two monomers that are held together via a GCN4 coiled-coil, forcing the DHp domains into a fixed
rotational state (**Fig. 39**)

![Results Image](https://static.igem.wiki/teams/5775/images/results/ics-julia/tcs-schem.svg)

---

**Figure 39: Schematic of the engineered EnvZ/OmpR two-component
system.** The bacterial EnvZ/OmpR signaling pair was adapted for mammalian cells by creating
separate kinase and phosphatase variants of EnvZ. The kinase (EnvZK) consists of two DHp and one CA
domain (AAB variant), enabling phosphorylation of the response regulator OmpR. The phosphatase
(EnvZP) is formed by two AB monomers, which are held together by GCN4 coiled-coil linkers (shown as
grey bars) to maintain the active conformation. For clarity, DHp and CA domains are not explicitly
depicted. When OmpR becomes phosphorylated, it binds to the OmpR-binding site (OmpR-bs) upstream of
the reporter and induces Firefly luciferase expression in a phosphorylation-dependent manner.

Reporter activity was measured under varying kinase and phosphatase conditions using 30 ng of OmpR.
In the
absence of both enzymes, a basal signal was detected, indicating background activity. Expression of
the EnvZ
kinase progressively increased reporter output, with a significant elevation already observed at 20 ng
compared to the enzyme-free condition, and 40 ng resulting in maximal activation. Co-expression of 20
ng
kinase with 20 ng of the phosphatase variant barely reduced reporter activity compared to the 20 ng
kinase
condition, suggesting that the phosphatase exhibits limited dephosphorylating efficiency under these
conditions. EnvZP alone caused only a minor, non-significant decrease relative to the enzyme-free
control
(**Fig. 40**). Together, these results demonstrate that the cytosolic kinase efficiently
phosphorylates
and
activates OmpR, while the phosphatase counteracts this effect only weakly and does not substantially
affect
basal reporter levels.

![Results Image](https://static.igem.wiki/teams/5775/images/results/ics-julia/tcs-res-new.svg)

---

Figure 40: **Quantification of OmpR-dependent reporter activation by
cytosolic EnvZ variants.** HEK293T cells were transfected with 30 ng of OmpR together with
the
indicated amounts of kinase (EnvZK) and phosphatase (EnvZP) variants. The reporter plasmid
contains
an OmpR-binding site upstream of a Firefly luciferase gene, enabling transcriptional activation
upon
OmpR phosphorylation. Reporter activity was measured 48 hours post transfection using a Dual
Luciferase assay, with Firefly luminescence normalized to constitutively expressed Renilla
luciferase. Data are shown as mean ± SEM (n = 3 technical replicates). Statistical
significance was determined using one-way ANOVA with Tukey's multiple comparison test (\*\*\*p < 0.001, \*\*\*\*p <
0.0001, ns = not significant).

In this system, reporter activity was strongly influenced by the phosphorylation state of OmpR. As
expected,
co-expression of the cytosolic EnvZ kinase domain led to robust activation of the OmpR-responsive
reporter. In
contrast, co-expression of the corresponding phosphatase variant failed to efficiently reduce reporter
activity,
indicating limited dephosphorylation of OmpR under these conditions. Additionally, a measurable basal
activity
was observed in the enzyme-free condition containing OmpR alone, suggesting that the reporter construct
is not
fully silent in the absence of phosphorylation.
This background likely originates from
unphosphorylated OmpR
binding to the promoter, as has been reported for the native EnvZ/OmpR system. Although phosphorylation
enhances
OmpR dimerization and DNA affinity, several studies have shown that unphosphorylated OmpR can still
associate
with target promoters and weakly activate transcription [(Kenney & Anand,
2020)](#References). This
agrees with observations from Dr. Jones, who confirmed that OmpR can bind DNA even in its inactive form
during
one of our interviews with him.
Interestingly, co-expression EnvZP with OmpR resulted in a slightly
reduced
signal compared to OmpR alone. One possible explanation is that the phosphatase interacts with OmpR to
form a
non-productive complex, thereby sequestering OmpR away from the promoter. EnvZ and OmpR are known to
form stable
cytoplasmic complexes independently of OmpR phosphorylation [(Qin *et al.*,
2001)](#References), and such interactions could transiently limit promoter occupancy.

As demonstrated previously for ABL-bZipEE and PTPN1-bZipEE, a logical next step would be to couple
the
OmpR/EnvZ system to synthetic receptor platforms such as MESA or PAGER architectures, enabling
extracellular
control over kinase and phosphatase activity. Further optimization will be required to minimize
background
activity, improve dephosphorylation efficiency, and explore alternative coiled-coil pairs to fine-tune
enzyme interaction and signaling strength.These improvements will be particularly important for
potential
applications in cell therapy, where unintended basal activity or incomplete dephosphorylation could
lead to
undesired cellular responses.
A major advantage of this system is its full orthogonality to
mammalian
signaling pathways, allowing implementation in a broad range of human cell types, including immune
cells,
where SH2/CD3ζ-based systems may trigger unwanted cross-reactivity. Moreover, this module could be
combined
with the PHOENICS platform to create parallel, non-interfering signaling layers that operate on
distinct
substrates, providing greater flexibility and modularity in synthetic signaling circuit design.

![Dropdown Header](https://static.igem.wiki/teams/5775/images/misc/2025-10-05-dropdown-box-header.svg)

References

* Acharya, A., Ruvinov, S. B., Gal, J., Moll, J. R., & Vinson, C. (2002). A heterodimerizing
  leucine
  zipper coiled coil system for examining the specificity of a position interactions: Amino acids I,
  V, L, N, A, and K. *Biochemistry*, 41(48), 14122-14131. <https://doi.org/10.1021/bi020486r>
* Barlowe, C., & Helenius, A. (2016). Cargo Capture and Bulk Flow in the Early Secretory Pathway.
  *Annual Review of Cell and Developmental Biology*, 32, 197–222. <https://doi.org/10.1146/annurev-cellbio-111315-125016>
* Beerli, R. R., Segal, D. J., Dreier, B., & Barbas, C. F. (1998). Toward controlling gene
  expression at will: Specific regulation of the erbB-2/HER-2 promoter by using polydactyl zinc
  finger
  proteins constructed from modular building blocks. *Proceedings of the National Academy of
  Sciences of the United States of America*, 95(25), 14628–14633. <https://doi.org/10.1073/pnas.95.25.14628>
* Benavides-López, S., Muñoz-Echeverri, L. M., Trujillo-Roldán, M. A., & Valdez-Cruz, N. A.
  (2025).
  The flow of proteins through the mammalian secretory pathway described by a phenomenological-based
  semi-physical model. *Biochemical Engineering Journal*, 220, 109759. <https://doi.org/10.1016/j.bej.2025.109759>
* Benman, W., Huang, Z., Iyengar, P., Wilde, D., Mumford, T. R., & Bugaj, L. J. (2025). A
  temperature-inducible protein module for control of mammalian cell fate. *Nature Methods*,
  22(3), 539–549. <https://doi.org/10.1038/s41592-024-02572-4>
* Capin, J., Mayonove, P., DeVisch, A., Becher, A., Ngo, G., Courbet, A., Ragotte, R. J., Cohen
  Gonsaud, M., Espeut, J., & Bonnet, J. (2025). CF2H: a Cell-Free Two-Hybrid platform for rapid
  protein binder screening. *bioRxiv*. <https://doi.org/10.1101/2025.07.16.665152>
* Chassin, H., Müller, M., Tigges, M., Scheller, L., Lang, M., & Fussenegger, M. (2019). A modular
  degron library for synthetic circuits in mammalian cells. *Nature Communications*, 10(1),
  2013.
  <https://doi.org/10.1038/s41467-019-09974-5>
* Chen, H., Ge, X., Li, C., Zeng, J., & Wang, X. (2024). Structure and assembly of the human IL-12
  signaling complex. *Structure*, 32(10), 1640–1651.e5. <https://doi.org/10.1016/j.str.2024.07.010>
* Cohen, P. (2000). The regulation of protein function by multisite phosphorylation – a 25 year
  update. *Trends in Biochemical Sciences*, 25(12), 596–601. [https://doi.org/10.1016/S0968-0004(00)01712-6](https://doi.org/10.1016/S0968-0004%2800%2901712-6)
* Daringer, N. M., Dudek, R. M., Schwarz, K. A., & Leonard, J. N. (2014). Modular Extracellular
  Sensor Architecture for Engineering Mammalian Cell-based Devices. *ACS Synthetic Biology*,
  3(12), 892–902. <https://doi.org/10.1021/sb400128g>
* Dolberg, T. B., Meger, A. T., Boucher, J. D., Corcoran, W. K., Schauer, E. E., Prybutok, A. N.,
  Raman, S., & Leonard, J. N. (2021). Computation-guided optimization of split protein systems.
  *Nature Chemical Biology*, 17(5), 531–539. <https://doi.org/10.1038/s41589-020-00729-8>
* Engstrom, M. D., & Pfleger, B. F. (2017). Transcription control engineering and applications in
  synthetic biology. *Synthetic and Systems Biotechnology*, 2(3), 176–191. <https://doi.org/10.1016/j.synbio.2017.09.003>
* Farahani, P. E., Yang, X., Mesev, E. V., Fomby, K. A., Brumbaugh-Reed, E. H., Bashor, C. J.,
  Nelson, C. M., & Toettcher, J. E. (2023). pYtags enable spatiotemporal measurements of receptor
  tyrosine kinase signaling in living cells. *eLife*, 12, e82863. <https://doi.org/10.7554/eLife.82863>
* Gao, R., & Stock, A. M. (2009). Biological insights from structures of two-component proteins.
  *Annual Review of Microbiology*, 63, 133–154. <https://doi.org/10.1146/annurev.micro.091208.073214>
* Hatada, M. H., Lu, X., Laird, E. R., Green, J., Morgenstern, J. P., Lou, M., Marr, C. S.,
  Phillips, T. B., Ram, M. K., & Theriault, K. (1995). Molecular basis for interaction of the
  protein
  tyrosine kinase ZAP-70 with the T-cell receptor. *Nature*, 377(6544), 32–38. <https://doi.org/10.1038/377032a0>
* Hellauer, K., Rochon, M. H., & Turcotte, B. (1996). A novel DNA binding motif for yeast zinc
  cluster proteins: The Leu3p and Pdr3p transcriptional activators recognize everted repeats.
  *Molecular and Cellular Biology*, 16(11), 6096. <https://doi.org/10.1128/MCB.16.11.6096>
* Hoffman, G. R., Rahl, P. B., Collins, R. N., & Cerione, R. A. (2003). Conserved Structural
  Motifs
  in Intracellular Trafficking Pathways: Structure of the γCOP Appendage Domain. *Molecular
  Cell*, 12(3), 615–625. <https://doi.org/10.1016/j.molcel.2003.08.002>
* Hu, J., Lei, Y., Wong, W.-K., Liu, S., Lee, K.-C., He, X., You, W., Zhou, R., Guo, J.-T., Chen,
  X., Peng, X., Sun, H., Huang, H., Zhao, H., & Feng, B. (2014). Direct activation of human and
  mouse
  Oct4 genes using engineered TALE and Cas9 transcription factors. *Nucleic Acids Research*,
  42(7), 4375–4390. <https://doi.org/10.1093/nar/gku109>
* Jones, R. D., Qian, Y., Ilia, K., Wang, B., Laub, M. T., Del Vecchio, D., & Weiss, R. (2022).
  Robust and tunable signal processing in mammalian cells via engineered covalent modification
  cycles.
  *Nature Communications*, 13(1), 1720. <https://doi.org/10.1038/s41467-022-29338-w>
* Kalogriopoulos, N. A., Tei, R., Yan, Y., Klein, P. M., Ravalin, M., Cai, B., Soltesz, I., Li,
  Y.,
  & Ting, A. Y. (2025). Synthetic GPCRs for programmable sensing and control of cell behaviour.
  *Nature*, 637(8044), 230–239. <https://doi.org/10.1038/s41586-024-08282-3>
* Kasembeli, M. M., Rodas, J., & Tweardy, D. J. (2025). Update on Structure and Function of SH2
  Domains: Mechanisms and Emerging Targeting Strategies. *International Journal of Molecular
  Sciences*, 26(18), 9060. <https://doi.org/10.3390/ijms26189060>
* Kenney, L. J., & Anand, G. S. (2020). EnvZ/OmpR Two-Component Signaling: An Archetype System
  That
  Can Function Noncanonically. *EcoSal Plus*, 9(1), ESP-0001–2019. <https://doi.org/10.1128/ecosalplus.ESP-0001-2019>
* Lim, W. A., & Pawson, T. (2010). Phosphotyrosine signaling: Evolving a new cellular
  communication
  system. *Cell*, 142(5), 661–667. <https://doi.org/10.1016/j.cell.2010.08.023>
* Mittler, G., Stühler, T., Santolin, L., Uhlmann, T., Kremmer, E., Lottspeich, F., Berti, L., &
  Meisterernst, M. (2003). A novel docking site on Mediator is critical for activation by VP16 in
  mammalian cells. *The EMBO Journal*, 22(24), 6494–6504. <https://doi.org/10.1093/emboj/cdg619>
* Moresco, E. M., & Koleske, A. J. (2003). Regulation of neuronal morphogenesis and synaptic
  function by Abl family kinases. *Current Opinion in Neurobiology*, 13(5), 535–544. <https://doi.org/10.1016/j.conb.2003.08.002>
* Norton, K.-A., Popel, A. S., & Pandey, N. B. (2015). Heterogeneity of chemokine cell-surface
  receptor expression in triple-negative breast cancer. *American Journal of Cancer Research*,
  5(4), 1295–1307.
* Papon, N., & Stock, A. M. (2019). Two-component systems. *Current Biology*, 29(15),
  R724–R725. <https://doi.org/10.1016/j.cub.2019.06.010>
* Pawson, T. (2004). Specificity in signal transduction: From phosphotyrosine–SH2 domain
  interactions to complex cellular systems. *Cell*, 116(2), 191–203. [https://doi.org/10.1016/S0092-8674(03)01077-8](https://doi.org/10.1016/S0092-8674%2803%2901077-8)
* Pawson, T., & Scott, J. D. (2005). Protein phosphorylation in signaling – 50 years and counting.
  *Trends in Biochemical Sciences*, 30(6), 286–290. <https://doi.org/10.1016/j.tibs.2005.04.013>
* Qin, L., Yoshida, T., & Inouye, M. (2001). The critical role of DNA in the equilibrium between
  OmpR and phosphorylated OmpR mediated by EnvZ in *Escherichia coli*. *Proceedings of the
  National Academy of Sciences of the United States of America*, 98(3), 908–913. <https://doi.org/10.1073/pnas.031383098>
* Renna, P., Ripoli, C., Dagliyan, O., Pastore, F., Rinaudo, M., Re, A., Paciello, F., & Grassi,
  C.
  (2022). Engineering a switchable single-chain TEV protease to control protein maturation in living
  neurons. *Bioengineering & Translational Medicine*, 7(2), e10292. <https://doi.org/10.1002/btm2.10292>
* Román, E., Coman, I., Prieto, D., Alonso-Monge, R., & Pla, J. (2019). Implementation of a
  CRISPR-based system for gene regulation in *Candida albicans*. *mSphere*, 4(1),
  e00001-19.
  <https://doi.org/10.1128/mSphere.00001-19>
* Scheller, L., Schmollack, M., Bertschi, A., Mansouri, M., Saxena, P., & Fussenegger, M. (2020).
  Phosphoregulated orthogonal signal transduction in mammalian cells. *Nature Communications*,
  11(1), 3085. <https://doi.org/10.1038/s41467-020-16895-1>
* Schmidl, S. R., Ekness, F., Sofjan, K., Daeffler, K. N.-M., Brink, K. R., Landry, B. P.,
  Gerhardt,
  K. P., Dyulgyarov, N., Sheth, R. U., & Tabor, J. J. (2019). Rewiring bacterial two-component
  systems
  by modular DNA-binding domain swapping. *Nature Chemical Biology*, 15(7), 690–698. <https://doi.org/10.1038/s41589-019-0286-6>
* Shah, K., Al-Haidari, A., Sun, J., & Kazi, J. U. (2021). T cell receptor (TCR) signaling in
  health
  and disease. *Signal Transduction and Targeted Therapy*, 6(1), 412. <https://doi.org/10.1038/s41392-021-00823-w>
* Stock, A. M., Robinson, V. L., & Goudreau, P. N. (2000). Two-component signal transduction.
  *Annual Review of Biochemistry*, 69, 183–215. <https://doi.org/10.1146/annurev.biochem.69.1.183>
* Szczesna-Skorupa, E., & Kemper, B. (2011). The signal-anchor sequence of CYP2C1 inserts into the
  membrane as a hairpin structure. *Biochemical and Biophysical Research Communications*,
  415(2),
  405–409. <https://doi.org/10.1016/j.bbrc.2011.10.082>
* Tonks, N. K. (2006). Protein tyrosine phosphatases: From genes, to function, to disease.
  *Nature
  Reviews Molecular Cell Biology*, 7(11), 833–846. <https://doi.org/10.1038/nrm2039>
* Vardy, E., Robinson, J. E., Li, C., Olsen, R. H. J., DiBerto, J. F., Giguere, P. M., Sassano, F.
  M., Huang, X.-P., Zhu, H., Urban, D. J., White, K. L., Rittiner, J. E., Crowley, N. A., Pleil, K.
  E., Mazzone, C. M., Mosier, P. D., Song, J., Kash, T. L., Malanga, C. J., … Roth, B. L. (2015). A
  new DREADD facilitates the multiplexed chemogenetic interrogation of behavior. *Neuron*,
  86(4),
  936–946. <https://doi.org/10.1016/j.neuron.2015.03.065>
* Vlahos, A. E., Kang, J., Aldrete, C. A., Zhu, R., Chong, L. S., Elowitz, M. B., & Gao, X. J.
  (2022). Protease-controlled secretion and display of intercellular signals. *Nature
  Communications*, 13(1), 912. <https://doi.org/10.1038/s41467-022-28623-y>
* Wolf, B., Shehu, P., Brenker, L., Bachmann, A. von, Kroell, A.-S., Southern, N., Holderbach, S.,
  Eigenmann, J., Aschenbrenner, S., Mathony, J., & Niopek, D. (2024). Rational engineering of
  allosteric protein switches by in silico prediction of domain insertion sites. *bioRxiv*,
  2024.12.04.626757. <https://doi.org/10.1101/2024.12.04.626757>
* Yahara, T., Koga, T., Yoshida, S., Nakagawa, S., Deguchi, H., & Shirouzu, K. (2003).
  Relationship
  between microvessel density and thermographic hot areas in breast cancer. *Surgery Today*,
  33(4), 243–248. <https://doi.org/10.1007/s005950300055>
* Yang, X., Rocks, J. W., Jiang, K., Walters, A. J., Rai, K., Liu, J., Nguyen, J., Olson, S. D.,
  Mehta, P., Collins, J. J., Daringer, N. M., & Bashor, C. J. (2025). Engineering synthetic
  phosphorylation signaling networks in human cells. *Science*, 387(6729), 74–81. <https://doi.org/10.1126/science.adm8485>
* Zhang, Y., Wolf-Yadlin, A., Ross, P. L., Pappin, D. J., Rush, J., Lauffenburger, D. A., & White,
  F. M. (2005). Time-resolved mass spectrometry of tyrosine phosphorylation sites in the epidermal
  growth factor receptor signaling network reveals dynamic modules. *Molecular & Cellular
  Proteomics*, 4(9), 1240–1250. <https://doi.org/10.1074/mcp.M500089-MCP200>
* Zhao, Z., Zhang, J., Yin, L., Yang, J., Zheng, Y., Zhang, M., Ni, B., & Wang, H. (2020).
  Upregulated GDF-15 expression facilitates pancreatic ductal adenocarcinoma progression through
  orphan receptor GFRAL. *Aging*, 12(22), 22564–22581. <https://doi.org/10.18632/aging.103830>

{% endblock %}