iGEM 2024 — Team hku-hongkong


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#### **Table of contents**

* [Multi-Modal RNA](#multi-modal-rna)
* [Chimeric Antigen Receptor](#chimeric-antigen-receptor)
* [Self Replicating RNA](#self-replicating-rna)
* [TMP Control System](#tmp-control-system)
* [SIRPα Knockout](#sirp-knockout)
* [Transient Gene Modulation](#transient-gene-modulation)
* [Nanoparticle Delivery](#nanoparticle-delivery)
* [Car\_Ma's Engineering Cycles](#carma-engineering-cycles)
* [CAR\_Ma Parts List](#carma-parts-list)
* [References](#references)

## Multi-Modal RNA

Given the inherent heterogeneity of cancer in its causation and progression, CAR\_Ma was
designed to be multi-modal in attempts to fully address the many facets of the complex
disease, whilst at the same time optimizing the resource utilization and function of
transfected macrophages against cancer cells. This year’s CAR\_Ma was designed with elements
that require three modal functions: transience, replicative, and release. The VEE
replication complex designed into CAR\_Ma facilitates the functioning of the different
modalities, allowing for different sections of the RNA construct to be transient, repeated,
or released.

**Cancer is a Complex Problem**
Cancer in itself is unilaterally complex. A multifactorial disease capable of both sporadic
and familial origins (Fridovich et al., 1998). Cancer has and will eventually evolve both
within the body and alongside humanity. Throughout a tumor’s development from initiation to
late metastasis, the tumor itself evolves to sustain itself and avoid detection and
destruction. Tumors modulate and protect themselves by maintaining complex relationships
with immune cells enabling tumor evasion and shielding itself within its own tumor
microenvironment (Baghban et al., 2020; De Visser & Joyce, 2023; Kim & Cho, 2022). To tackle
such a multifaceted and ever-evolving enemy, any solution needs to be numerous in its
methods and modalities as well.

**Enabling Multimodality**
Multimodality in RNA depends on the ability of different genes and elements to be active or
translated whilst on the same, singular RNA strand - requiring polycistronic behavior.
However, mammalian mRNA molecules are typically monocistronic (Karginov et al., 2017). To
enable multimodality, CAR\_Ma was designed with the addition of 26S subgenomic promoters
(26S), as well as internal ribosome entry sites (IRES), which effectively convert
monocistronic RNA to polycistronic RNA (Karginov et al., 2017). hat as many people globally
will be able to receive the most advanced therapeutics.

26S is a subgenomic promoter that directs transcription for the subsequent downstream gene,
resulting in the formation of mRNA for the single gene (Kamrud et al., 2007). IRES elements
are responsible for assembling functional ribosomes at a start codon, allowing for 5’
cap-independent translation initiation, thus allowing for genes downstream of IRES to be
translated (Kamrud et al., 2007). Our design adds several 26S and IRES elements in our
multimodal RNA, allowing for multiple genes to be present, co-transcribable, and
co-translatable.

**Transience**
The transient modality of CAR\_Ma focuses on improving therapeutic efficiency while retaining
clinical applicability and safety. CAR\_Ma itself is a transient system. By relying on
transience, we are able to avoid safety risks and risks of malignancies with DNA or
viral-based treatments, which may lead to random insertional mutagenesis. Such issues have
plagued even the latest CAR immunotherapies, which caused the CAR-T cells to become
malignant and are required to come with boxed warnings (U.S. Food and Drug Administration,
2024). Further uses of the transient system facilitate the elimination of immune evasion by
tumor cells using CRISPR/Cas systems, or RNAi pipelines. Many cancers hijack systems such as
SIRPα-CD47 or PD-1/PD-L1 to evade detection and subsequent targeting by the immune system,
preventing clearance of cancerous cells. Knockout of SIRPα expression on macrophages at the
genomic level by the CRISPR/Cas systems using Cas9-GFP or Cas12 prevents the SIRPα-CD47
interaction, inhibiting immune evasion via this pathway. By knocking out these evasion
pathways, immune cells are able to better recognize their immune targets and destroy the
tumors (Ray et al., 2018).

Given knockout by a CRISPR/Cas system occurs at the genomic level, the expression of Cas is
not required to be continuously maintained, as a genomic knockout only needs to occur once
to result in permanent changes in the expression. Thus, the permanence of genomic changes
caused by CRISPR/Cas systems implies the need for a transient modality to avoid unnecessary
expenditure of cellular resources. Alternatively, this action can also be carried out via
RNAi instead of CRISPR using the release cycle.

**Replicative**
Replicative refers to the need for sustained expression via constant replication. CAR\_Ma’s
replicative modality contains elements that must be continuously maintained to allow for
safe and sustained expression and efficacy of our immunotherapy: a 5th generation
nanobody-powered Chimeric Antigen Receptor (CAR), the self-replicating RNA (srRNA)
components, and the TMP small-molecule mediated control system.

The self-replicative ability of srRNA components enables persistent and maintained
expression of CAR on the cell surface, replenishing receptors lost during receptor-mediated
phagocytosis. Continued and tightly controlled expression of the 5th generation
nanobody-powered CAR facilitates improved recognition and targeting of hepatocellular
carcinoma (HCC) tumor cells, by preventing depletion and stress during receptor-mediated
phagocytosis. Simultaneously, the small-molecule mediated control system was implemented to
ensure the self-replicating system could be regulated when necessary to avoid depletion of
cellular resources. In combination, these elements of CAR\_Ma act together to maintain and
control the expression of CAR on macrophages.

**Release**
Elements in the release modality consist of RNA molecules that need to be released to confer
a function for CAR\_Ma, typically when used in conjunction with proteins encoded in other
modalities. This includes the guide RNA (gRNA) of CRISPR/Cas systems, as well as
short-hairpin RNA (shRNA) acting as an alternative method for SIRPα silencing, based on the
principle of RNA interference (RNAi). The release of gRNA enables the targeting of a Cas
protein for permanent genomic knockout, while the release of shRNA enables entirely
transient transcriptomic silencing of immune evasion (Karim et al., 2018) during the active
period of the multimodal RNA. Rather than relying on a permanent method targeting the genome
like CRISPR, the use of RNAi and shRNA will allow the system to be completely transient.
This means that after its controlled shutdown, the cell will be able to return to a
completely native state, enhancing long-term safety and stability for clinical uses. The
strategic release of RNA molecules ensures CAR\_Ma can effectively modulate gene expression,
enabling both transient and permanent solutions to the problems of immune evasion.

## Chimeric Antigen Receptor (CAR)

**CAR-M vs CAR-T**
For the next generation CAR-M (CAR\_MultiModal Advanced or CAR\_Ma) developed by our HKU iGEM
2024 team, the chimeric antigen receptor is expressed on macrophages instead of the most
common T-cells, effectively addressing significant challenges in cancer therapy.

One major advantage of this approach is that macrophages can penetrate the tumor
microenvironment (TME) more efficiently, overcoming the physical barriers that generate
chemokines suppressing T-cell functionality (Bule et al., 2021; Nagarsheth et al., 2017).
Additionally, this strategy mitigates the adverse effects of M2-like tumor-associated
macrophages (TAMs) and the chemokines that often promote tumor growth and lead to CAR-T
immunosuppression (Li et al., 2019; Rodriguez-Garcia et al., 2021). Macrophages can adopt
various functional states, enabling them to adapt to the TME and exert anti-tumor effects
while reducing immune evasion (Mantovani et al., 2022). This flexibility enhances the
persistence of CAR-M cells and improves coordination with other immune cells, resulting in a
more robust immune response.

By harnessing the innate capabilities of macrophages for tumor migration and phagocytosis in
synergy with the power of vector multi-modality, CAR\_Ma not only enhances the potential for
a robust anti-tumor response by addressing the limitations of traditional CAR-T systems, but
also offers a more adaptable and resilient therapeutic strategy in the fight against cancer.

**The Heart of CAR\_Ma: Crafting the 5th Generation CAR**
The CAR receptor introduced this year is an advanced iteration of the one developed by the
HKU iGEM team last year, marking the launch of the fifth-generation CAR.

5th Generation CAR: A Nanobody-Based Approach
The driving force behind our novel fifth-generation CAR is our humanized nanobody, derived
from Camelidae antibodies. This project presents an innovative single-body nanobody-based
design that moves beyond the conventional use of single-chain fragment variable (scFv)
antibodies. By harnessing the unique properties of nanobodies, this strategy offers key
advantages, including increased stability, reduced immunogenicity, enhanced infiltration
into the TME of solid tumors, and a more efficient humanization process. Moreover, it
offers great benefits over scFvs, such as lower immunogenicity, decreased non-specific
binding, and lower immunogenicity (Maali et al., 2023). These characteristics enable
precise targeting of glypican-3 (GPC3), a cell-surface glycoprotein that is highly
expressed in diverse cancers, including hepatocellular carcinoma – the second leading
cause of cancer deaths globally in 2020 (Guo et al. 2020). GPC3, along with other
nanobodies, serve as a reliable immunohistochemical marker given its high expression,
where nanobodies – which are light chain-free modular domains that can easily be switched
or multiplexed – enable the simultaneous detection of multiple antigens, thereby allowing
for the targeted treatment of a wide array of cancers (Naidoo & Chuturgoon, 2021; Yang &
Shah, 2020).

4th Generation CAR: TAM Repolarization A continuation from our 2023
project, low-dose of interferon-gamma (IFN-γ) is included to address the tumor associated
macrophages (TAMs), the dominant immune cell in the TME and a major defense mechanism for
solid tumors that provides nutrition to tumor ells and maintaining an environment that
suppresses immune responses (Bied et al., 2023). IFN-γ, a pleiotropic cytokine known for
its cytostatic, pro-apoptotic, antiproliferative, and stimulatory effects on M1
proinflammatory macrophages – acts as a potent polarization factor, steering transfected
macrophages and TAMs toward an anti-cancer, pro-inflammatory state (Jorgovanovic et al.,
2020).

To further improve phagocytosis, trogocytosis (the engulfment of large targets) and
coordination within the broader immune system, the alternate costimulatory domains CD19,
FcγR and MegF10 are used. Together, they promote antigen presentation to T-cells, promote
phagocytosis and trogocytosis, improve macrophage migration, inflammatory response and
survival (Morrissey et al., 2018).

The Specifics – Chimeric Antigen Receptor (CAR) Construct Design:
Typical CARs are composed of four main components: a) an extracellular target
antigen-binding domain, b) a hinge region that provides flexibility, c) a transmembrane
domain that anchors the CAR in the cell membrane, and d) one or more intracellular
signaling (co-stimulatory) domains that activate host cell responses upon antigen
recognition (Sterner & Sterner, 2021).

The newly designed fifth-generation CAR features a sophisticated assembly of components:
2x K-Turns, a signal peptide, an anti-GPC3 nanobody, a CD8 hinge, a CD28 transmembrane
domain, CD19, FcγR, MegF10, P2A sequences, IFN-gamma, additional P2A sequences, DD-L7Ae,
and mCherry2 – a design that draws inspiration from previous research on CARs for
phagocytosis (Morrissey et al., 2018).

The kink-turn (k-turn) is a prevalent structural motif in RNA that introduces a tight bend
into the helical axis, serving as an element of RNA architectural stability and a crucial
binding site for various proteins (Huang & Lilley, 2016; Schroeder et al., 2010). In
cancer research and our CAR development, k-turns regulate gene expression to modulate CAR
component levels, thereby enhancing the transcription and translation efficiency of
therapeutic proteins and improving CAR functionality (Morrissey et al., 2018). In our
case, we use our k-turn motifs as the foundation for achieving variable control within an
in vivo expression system. By co-expressing DD-L7Ae, a fusion of a degradation domain to a
k-turn binding protein (Mc Cafferty et al., 2021), we can use Trimethoprim (TMP) to
stabilize a desired proportion of the DD-L7Ae. This will in turn trigger the strong
conformational change of the k-turns in a desired proportion of the mRNA. The effect of
this allows us to control the expression of the CAR receptor itself, allowing for variable
immune response and control over the usage of intracellular resources, with the aim of
extending the longevity of the CAR cells.

The CD8 hinge region, an extracellular structural component, enhances the functionality of
CAR\_Ma by promoting the longevity of CARs in the TME (Qin et al., 2017). It provides the
necessary flexibility to navigate steric hindrance and optimizes the length and
composition of the hinge region for greater access of the antigen-binding domain to the
targeted epitope. This ultimately influences overall CAR signaling efficacy, expression,
strength of activation outputs, and epitope recognition (Sterner & Sterner, 2021).

The CD28 and CD19 transmembrane domains, recognized for their high stability, anchor the
CAR to the macrophage membrane and influence CAR expression levels, stability, signaling
activities, and synapse formation (Sterner & Sterner, 2021). Furthermore, they dimerize
with endogenous signaling molecules, enhancing overall functionality, and may also serve
as biomarkers for specific types of cancer (Zhang et al., 2017).

In CAR models, Fc gamma receptors (FcγRs) facilitate anti-cancer and anti-tumor activities
by mediating antibody-dependent cellular cytotoxicity, polarizing macrophages and other
immune cells in TME toward a more proinflammatory state, and potentially serving as
biomarkers for treatment efficacy (Caratelli et al., 2017).

## Self Replicating RNA (srRNA)

Synthetic mRNA has become a major area of interest when designing gene editing systems with
clinical applications, especially considering the development of methods for in vitro
synthesis and in vivo delivery of RNA (Wagner & Mutschler, 2023). The non-integrative nature
of RNA-based gene editing systems allows for transient, more regulatable changes, rather
than permanent genomic ones associated with risks of tumorigenicity (Warren & Lin, 2019).
However, RNA is transient due to constant enzymatic degradation - with foreign RNAs
suffering further degradation through the innate immune response - causing RNA-based
applications to require frequent re-transfections to maintain effective expression of the
system (Wagner & Mutschler, 2023). With the complications brought about by frequent
transfection, would it be possible to optimize RNA-based systems to increase their stability
and longevity after transfection?*(Correction: the orange box at 1:02 should say “nsP1234 Replicase Complex” instead of “nsP123 Replicase Complex”)*

Mechanisms of srRNA
Self-replicating RNA (srRNA) offers a solution to the short life span of traditional
mRNA-based therapies with the ability of in vivo replication, effectively mitigating the
degradation experienced by RNA in the body (Wagner & Mutschler, 2023). As a result,
introducing a srRNA module to an engineered RNA-based therapy - such as our CAR\_Ma -
allows the system's expression to increase to the medium or long-term (Wagner & Mutschler,
2023). For use in genetic engineering, srRNA is commonly sourced from the Venezuelan
Equine Encephalitis (VEE) alphavirus due to being effective and safe (Lundstrom, 2016).

During a viral infection, the initial invasion of a cell is facilitated by viral
structural proteins. The virus disassembles in the cytoplasm, releasing viral RNA encoding
structural and nonstructural proteins (nsPs) (Kim & Diamond, 2022). While the structural
proteins are responsible for viral pathogenicity and virulence to other cells, nsPs are
responsible for effectively increasing the lifespan of the virus in the cell by
replicating viral genetic material continuously (Kim & Diamond, 2022). Alphaviruses encode
for four nsPs: nsP1, nsP2, nsP3, and nsP4; which form a replicase complex that allows for
continuous replication of viral RNA, thus increasing its expression in the cell (Kim &
Diamond, 2022). Incorporating these nsPs into an RNA construct effectively grants it this
same ability.

Engineering srRNA
CAR\_Ma is designed to utilize an evolved srRNA vector, with a VEE replicase complex
modified to enable significantly longer expression of our RNA-based immunotherapy, as well
as a small-molecule modulated kill-switch control system to enhance safety by providing
greater control to a self-replicative cancer immunotherapy. The basis of CAR\_Ma’s srRNA
production is the T7-VEE-GFP plasmid, containing the necessary nsPs for in vivo
replication (Yoshioka et al., 2013). Using NEBuilder, the vector has been modified and
designed to act as the foundation for CAR\_Ma’s immunotherapy.

To enhance the inherent ability of the nsP123 replicase complex, an 18bp insertion
mutation was introduced to allow for more efficient and long-term replication (Li et al.,
2019). Thus, its addition to a sRNA vector allows for increased persistence in the cell,
which provides stable and robust expression for a significantly longer timeframe.

To enhance the inherent ability of the nsP123 replicase complex, an 18bp insertion
mutation was introduced to allow for more efficient and long-term replication (Li et al.,
2019). Thus, its addition to a sRNA vector allows for increased persistence in the cell,
which provides stable and robust expression for a significantly longer timeframe.

Furthermore, two additional nsPs: nsP14 and nsP10 were introduced to the srRNA construct
to facilitate the maintenance of the replicating RNA. While srRNA is capable of
self-replication, the duration of its efficacy is limited by the in vivo replicative
fidelity of VEE’s nsP1, nsP2, nsP3, and nsP4, and the lack of a proofreading domain to
counteract the errors of the replicase complex (Kautz et al., 2018). To overcome this
limitation and increase the potential persistence of the srRNA construct in the cell,
nsP14 and nsP10 were added to the design.

Originating from Coronaviruses, nsP14 is a viral proofreading exonuclease domain and nsP10
is its associated cofactor, which together decreases the mutational error rate of
RNA-dependent RNA polymerases, allowing for enhanced maintenance of the RNA genome (Rona
et al., 2021). Adding these nsPs to the VEE vector lends the replicase the same decreased
mutation rate, increasing fidelity and effectively improving persistence, stability, and
strength of expression.

CAR\_Ma was designed to be an RNA-based immunotherapy to leverage the unique advantages of
RNA. RNA molecules are genomically non-integrative, which removes concerns of genetic
permanence and insertional mutagenesis attributed to plasmid and viral platforms, and
mitigates the problem of potential malignancies observed in recent BMCA-mediated CAR-T
therapies (U.S. Food and Drug Administration, 2023). With modifications such as the
introduction of nsPs to produce a higher fidelity, persistent, self-amplifying RNA
molecule, our design improves upon the inherent benefits of RNA, by addressing the
problems of persistence and fidelity observed with other RNA therapies.

## TMP Control System

While (sr)RNA has emerged at the forefront of gene therapy, it has generally lacked the
small-molecule expression control systems that DNA-based systems have (Wagner et al., 2018).
Given CAR\_Ma’s in vivo human therapeutic goal, a reliable and robust expression control
system incorporated in the RNA is absolutely essential. To address this, a variable
expression control system based on trimethoprim (TMP), a common human antibiotic, is added
in the mmRNA to allow for rapid expression shutdown of self-replicating functions and
various genes of interest like the CAR construct and shRNA functions (Wagner et al., 2018).
The system can be expanded into a variable switch, allowing adjustments in expression and
therapeutic intensity even after administration.

The system is based on two main components, the first being DD-L7Ae, a protein complex
encoded in the mmRNA. DDd is a destabilization domain protein taken from Escherichia coli,
which will be automatically targeted for degradation by the proteasome if left untreated.
Yet when stabilized by binding to TMP, DDd can disrupt the function of the protein it is
bound to. This is combined with L7Ae, a protein from Archaeoglobus fulgidus that binds to
RNA kink-turns (k-turns), a common RNA motif and the second component of the control system.
When L7Ae binds to a K-turn, it initiates a sharp conformational change that in the mmRNA,
blocks the expression of downstream open reading frames (Turner et al., 2005; Wagner et al.,
2018). By adding 2x k-turns into our mmRNA, an off-switch for CAR\_Ma expression is achieved.

In our mmRNA, 2x k-turns are placed in 3 positions: before nsP1 (before all replicons),
between nsP3 and nsP4 (since srRNA replicons can either work as nsP1234 or nsP123 and nsP4),
and before our genes of interest. Due to the complexity and unknown mechanisms of the
replicons, the multiple 2x k-turns inserted ensure that we can shut down both the
self-replicating functions and genes of interest, providing reliable and uncompromising
control over CAR\_Ma expression. The control system is assembled along with other parts of
the plasmid with NEBuilder HiFi DNA Assembly.

Furthermore, akin to an antibiotic kill curve experiment, a TMP expression curve can be
conducted to map how TMP concentration affects CAR\_Ma expression by tracking fluorescence
signals present after THP-1 transfection. This turns the off-switch into a variable switch,
capable of easy adjustment and personalisation of the therapeutic at the point-of-care. This
provides the flexibility that healthcare professionals require to better respond to
different patient needs. Additionally, the widespread oral administration of TMP and its low
cost makes it an ideal small molecule for the control system, ensuring the accessibility of
this therapeutic (Kemnic, 2022).

## SIRPα Knockout

CAR-T cell therapy has revolutionized the cancer immunotherapy field, especially in treating
some hematological malignancies (Chen, 2024). However, in the case of solid tumors, CAR-T
cells face crucial challenges. These CAR-T cells grapple to penetrate the dense and
immunosuppressive tumor microenvironment (TME) and their survivability in this hostile
environment is very limited (Chen, 2024). This brings up an important question: Could there
be a more effective immune cell for targeting solid tumors?

The Promise of CAR-Macrophages
Unlike T-cells, macrophages are natural inhabitants of the TME. They can navigate and
survive the harsh environment of TME more efficiently than other immune cells. According
to Brempelis et al. (2020), macrophages have been proven to survive longer in the TME than
any other cells. Their engineered GFP-expressing macrophages were found to persist in the
tumor cells for 22 days post-injection, which is 39-fold greater than the initial levels
(Brempelis et al., 2020). Our 5th Gen CAR construct not only targets specific solid tumor
cells but also has the ability to repolarize tumor-associated macrophages (TAMs) back to
the M1 stage, which has been shown to exert significant anti-tumor and inflammatory
effects. Additionally, TAMs are found in nearly all solid tumors providing numerous
therapeutic targets and enhancing the potential of Car\_Ma.

The Importance of SIRPα-CD47 Pathway
When CAR-Macrophages enter the TME, they must bind to tumor cells through antigen
recognition before exerting its effects. However, this process is often overtaken by tumor
cells, leading to reduced phagocytosis and evasion from the immune system. One prominent
pathway the tumor cells use is the SIRPα-CD47 signaling pathway. A key mechanism it uses
is the overexpression of CD47, a protein on tumor cells that produces a “Don’t Eat Me”
signal when interacting with the SIRPα protein on macrophages (Jiang et al., 2024). This
triggers the phosphorylation of Immunoreceptor Tyrosine-Based Inhibitory Motifs (ITIMs)
within SIRPα’s cytoplasmic domain (Qu et al., 2022). This in turn sends inhibitory signals
that reduce phagocytosis and tumor-antigen presentation (Qu et al., 2022). Therefore,
targeting the SIRPα-CD47 pathway is a promising strategy to induce the elimination of
tumor cells.

CRISPR-mediated SIRPα Knockout
In order to address the inhibition by the SIRPα-CD47 pathway, CRISPR genome editing can be
used to knock out the SIRPα domain within the macrophages, namely with the Cas9 and Cas12a
mechanisms. Through preventing the SIRPα-CD47 interaction, the phagocytic abilities of the
macrophages are able to increase by 4-fold (Ray et al., 2018).

The Cas9 mechanism involves the formation of a Cas9 complex, consisting of the Cas9
protein and a guide RNA (gRNA). The gRNA is composed of the target-specific crRNA, whose
sequence is complementary to the target domain, and the scaffolding tracrRNA, which allows
for binding with the Cas9 endonuclease. When formed, the gRNA is used to direct the Cas9
enzyme to a specific gene of interest, where the Cas9 enzyme is then able to induce a
double-strand break at the specific site, being the SIRPα domain (Kim et al., 2014). With
the use of cell’s natural DNA repair pathways, such as non-homologous end joining, the
excised DNA is repaired, however, small insertions and deletions (indels) are introduced
at the junction. As such, the error-prone DNA sequence results in disrupted protein
expression (Ishibashi et al., 2020). The final product is a SIRPα-deficient macrophage,
able to interrupt the SIRPα-CD47 pathway and thus preventing the immune evasion of tumor
cells, while allowing for phagocytosis and an overall increase in the efficacy of CAR
macrophages.

The Cas12a mechanism works similarly to Cas9, with the difference of four separate guide
RNAs composed of only the crRNA, thus being able to knock out up to 4 targets, rather than
just 1 target with Cas9 (Esmaeili Anvar et al., 2024). In our design, aside from the SIRPα
domain, three other targets have been chosen to increase the efficacy of our CAR
macrophage. The PD1-PDL1 pathway, which acts as a T-cell activation checkpoint, STAB1,
which involves inhibition of phagocytosis, as well as the VTCN1 which plays a role in
T-cell inhibition. Targeting these domains allows our macrophage to increase phagocytic
ability and boost T-cell activity, overall targeting immune evasion with an increased
efficacy.

Limitations of CRISPR
While CRISPR/Cas systems like Cas9 and Cas12a offer a powerful means to achieve permanent
knockout of genes at the genomic level, they come with numerous limitations including
potential off-target effects, and the prolonged expression of Cas9 or Cas12a enzymes.
These off-targets occur when the CRISPR system unintentionally edits DNA sequences that
are similar but not identical to the target sequence, which might lead to unforeseen
genetic modifications causing undesirable phenotypic changes or other mutations (Yang et
al., 2021). Prolonged expression of the Cas9 enzyme can increase the chances of having
off-target effects and our body may also recognize the Cas9 protein as a foreign property,
leading to an immune response that can have adverse effects. Our immune system recognizes
the Cas9 protein as a non-self entity, triggering the production of antibodies against it
(Wienert et al., 2018). Moreover, the gRNAs used in conjugation with Cas9 can activate
innate immune pathways such as the RIG-I pathway producing type-1 interferons and
inflammatory cytokines (Xu, 2024). The type-1 IFNs can interfere with and affect the
proper functioning of the gRNA-Cas9 complex, leading to a much lower efficiency in terms
of its ability to knock out genes (Wienert et al., 2018). Initially, CAR\_Ma was built with
CRISPR in mind, but these design concerns made us rethink and redesign CAR\_Ma.

## Transient Gene Modulation

Short Hairpin RNA (shRNA)
To prevent the immune evasion strategy mediated by the SIRPα/CD47 pathway, we propose
using shRNA to knock down the expression of SIRPα, by which the CD47 on tumor cells cannot
bind to SIRPα and therefore exposing itself to our immune system. Unlike CRISPR, which
induces permanent genetic changes, shRNA-mediated knockdown reduces gene expression
without altering the DNA sequence. It knocks down genes on a transcriptomic level by
following the working principle of RNA interference (RNAi) rather than CRISPR’s
genomic-level gene knockout.

Short Hairpin RNA (shRNA) is indeed a single-stranded RNA that folds back on itself
forming a stem-loop structure. This loop is what gives the shRNA the appearance of a
double-stranded RNA (dsRNA). The non-complementary region of the RNA sequence forms the
loop that connects the two ends of the stem, allowing it to fold back on itself.

Once the shRNA is introduced into the host cell, the shRNA is transcribed by its promoter.
The transcribed shRNA forms a stem-loop structure which is then transported from the
nucleus to the cytoplasm by the action of Exportin 5. Then it is processed by an
endoribonuclease known as “DICER” which cleaves the loop and converts the shRNA into short
dsRNAs or small interfering RNA (siRNA).

Upon getting processed by DICER, the dsRNA is then loaded into the RNA-induced silencing
complex (RISC), where the sense (passenger) strand gets degraded, leaving the antisense
(guide) strand to guide the RISC complex towards the complementary target mRNA sequence
(O’Keefe, 2013). A crucial component of RISC that plays a central role in the gene
silencing mechanism is the presence of Argonaute 2 (Ago2) protein in RISC. Ago2 possesses
endonuclease activity which allows it to cleave the target mRNA at the target site when
the guide strand perfectly complements the target strand (Ruda et al., 2014). This
cleavage results in the degradation of the target mRNA, leading to effective silencing of
the target gene. According to GeneCards, the cleaved mRNA fragments are further degraded
by exonucleases present in our cells (GeneCards, 2024).

In our case, the guide strand of the designed shRNA binds to the mRNA of SIRPα leading to
its cleavage and degradation by RISC. This cleavage of SIRPα results in a significant
reduction in the expression levels of SIRPα which enhances the phagocytic activity

Designing shRNA for CAR\_Ma
The design of our shRNA sequence is critical to ensure specific and efficient gene
knockdown. The process begins with selecting a target sequence within the mRNA of the gene
of interest. This target sequence is typically 19-29 nucleotides long and is extremely
specific to the target gene to minimize off-target effects (Taxman et al., 2010). The
shRNA sequence is then designed to form a stem-loop structure, which includes the sense
and anti-sense strands of the target sequence (O’Keefe, 2022). In our construct, the 26s
subgenomic promoter has been incorporated as the promoter which will drive the expression
of the shRNA. This allows us to continually express our shRNA like the existing viral and
plasmid based approaches while maintaining controllability and safety with a controllable
and transient RNA based vector.

Engineering shRNA into multi-modal RNA using Ribozymes
Self-cleaving ribozymes are RNA molecules that can catalyze their own cleavage at specific
sites. By flanking the self-cleaving ribozymes around the shRNA sequence, we can
facilitate a precise and efficient expression of our shRNA construct to mediate effective
gene silencing. As we know, the presence of post-transcriptional modifications like the 5’
cap and poly(A) tail are critical features of an mRNA to enhance its stability and promote
efficient translation. However, according to McIntyre and Fanning (2006), in a shRNA, the
presence of these PTM features can hinder the effectiveness of shRNA. The 5’ cap can
interfere with the recognition and processing of the shRNA by the RNAi machinery, as it
may prevent the loading of the dsRNA into the RISC complex (McIntyre and Fanning, 2006).
The presence of these PTMs increases the overall length of the stem regions on the shRNA.
These can result in misfolding, or processing failure by DICER, leading to lowered
silencing efficiencies.

To address these issues, self-cleaving ribozymes can be flanked around the ends of our
shRNA construct. In our case, the ribozymes used were hammerhead (HH) ribozymes and
hepatitis delta virus (HDV) ribozymes which catalyze the precise cleavage of the RNA,
removing the 5’ cap and poly(A) tail respectively, and preserving the optimal shRNA
sequence.

## Nanoparticle Delivery

**Nanostructured Lipid Carrier**
In the rapidly evolving field of RNA therapeutics, srRNA has emerged as a powerful tool for
the development of various therapeutic cancer vaccines. Clinical trials have demonstrated
that srRNA is able to encode tumor-associated antigens such as human epidermal growth factor
receptor 2 (HER2), and carcinoembryonic antigen (CEA) (Davis et al, 2021).

In terms of non-viral carriers, lipid nanoparticles were selected as the most effective
delivery method due to their superior biocompatibility, reduced cytotoxicity, and enhanced
cellular uptake and internalization. Lipid nanoparticles are the most effective due to their
ability to prevent endosomal escape– the unique composition of LNPs, specifically the
incorporation of pH-sensitive lipids, allows a disruption of endosomal membranes, leading to
successful cytoplasmic delivery of our srRNA (Zhao, 2014).

While srRNA has been delivered through a variety of different methods, such as viral
vectors, the innovative use of lipid nanoparticles (LNPs) remains a large piece of uncharted
territory. Following COVID-19, the remarkable success of mRNA vaccines such as Pfizer and
Moderna, exemplifies the excellent biocompatibility of lipid nanoparticle delivery.

However, packaging srRNA is inherently more complex than its mRNA counterparts, due to its
unique structural characteristics. To ensure that the nanoparticle we are using is the most
biocompatible, while maintaining environmental biodegradability and human safety, we
examined a myriad of different lipid-based carriers. Amongst them, we decided that the
Nanostructured Lipid Carrier (NLC) fits our project the best. The imperfect crystal model of
the NLC, and its amorphous quality allows a higher drug loading capacity, controlled
release, and its own edge of higher stability during storage. NLC can be stored at room
temperature for at least 8 months and in 4°C for up to 21 months (Gerhardt et al., 2022).
Allowing us to simplify logistics and storage making this one of our ideal candidates for
CAR\_Ma enabling our simplified administration via intravenous injection simplifying and
improving accessibility of immunotherapies.

At the current moment, NLCs are currently being used in a multitude of medical trials, such
as UV protection cosmetics, dermal drug delivery, and topical nanomedicines (Ferreira et
al., 2021). NLC and rvRNAs complexes are successfully designed for Zika vaccines (Erasmus et
al., 2018). Similar ingredients were used, of which Tween 80 is replaced with DMG-PEG. They
perform similar functions in our case, for the sake of aggregation prevention.

Though NLCs are far more capable of delivery, storage and logistics enabling CAR\_Ma simple
administration and accessibility. NLCs like other nanoparticle delivery systems are
relatively indiscriminate in their transformation targets. Though recipe optimization can
better target proportions of the NLCs administered to certain tissue groups, it will be
inevitable that other tissues will be transformed. In our case, that could risk
complications with incorrect tissues expressing the CAR\_Ma system. To optimize this further
we functionalized our nanoparticles with anti-CD14 antibodies (Marques et al., 2023) or
anti-CD14 nanobodies, to ensure with maximal efficiency that we are able to specifically
transfect monocytes and macrophages. Allowing us to maximize efficiency while minimizing
unwanted off-target effects, and completing the CAR\_Ma package.

**Zein Nanoparticle**
In addition to our work with Nanostructured Lipid Carriers (NLC), our team has innovatively
developed zein nanoparticles for the targeted delivery of srRNA into macrophages. Zein,
being edible, biocompatible, and biodegradable, stands out as an ideal material for
delivering drug and genetic material (Zhang, 2021).

The production process for zein nanoparticles involves transitioning zein from an organic
phase (alcohol) to aqueous phase or via altering the pH. This allows aggregation, and
rearranges the peptide chains in zein (Zhang, 2021; Dong, 2021). Zein has shown promise in
various applications, such as the sustained release of plasmid DNA (pDNA) (Regier et al,
2012). In addition, zein nanofibers have been developed to explore their potential to
deliver siRNA for gene silencing in skin fibroblasts (Karthikeyan et al, 2015).

Despite these advancements, aggregation of zein nanoparticles poses a significant challenge,
which will limit the viability and shelf-life of zein nanoparticles. To tackle this issue,
we added Tween 80, a surfactant that promotes electrostatic repulsion amongst zein
nanoparticles. This addition effectively prevents aggregation, and enhances the stability
for our formulations.

By leveraging the unique properties of zein, and addressing any potential challenges, we are
paving the way for effective, biodegradable, and biocompatible delivery systems for srRNA
delivery for CAR\_Ma and beyond.

## CAR\_Ma’s Engineering Cycles

In many ways, the development of CAR\_Ma has been very iterative, cycling through several
rounds of design, building, testing and learning. Since CAR\_Ma is a ground-up rearchitecting
of CAR-Ma from HKU iGEM 2023, there are major engineering cycles and smaller iterative ones
that have occurred.

**Rearchitecting 2023’s CAR-Ma Proof-of-Concept**
While the work conducted by the HKU iGEM team 2023 was rudimentary, tt allowed us to gain
incredible insight into the meticulous design and short-commings of our previous work.

**CD3ζ-based CAR**
From the beginning an iterative immunotherapy was the original goal, in doing so the designs
that we used borrowed heavily from one of our advisor’s work with CAR-T, a receptor with a
CD3ζ costimulatory domain typically seen with CAR-T therapies. The benefit at the time is
that we were quickly able to work with this due to the part already existing with us,
unfortunately it was found that with macrophages CD3ζ was a costimulatory domain that did
not greatly improve phagocytosis. Our testing, review of literature by Morrissey et al.
(2018) and feedback from our PI and others from 2023 iGEM Giant Jamboree. Learning from this
undersight, we completely rearchitected our CAR based on the work by Morrissey et al.
(2018), integrating CD19-FcγR Tandem and MegF10 costimulatory domains into our CAR enhancing
phagocytosis, trogocytosis and antigen presentation resulting in a far more effective
system.

**Overwhelming cellular resources, IFN-γ overexpression and TMP shutdown**
Besides the core of our CAR being based on costimulatory domains we integrated IFN-γ as an
armouring factor to reinforce our CAR-Ma cells and repolarize tumor associated macrophages
(TAMs) disrupting the stability of the tumor microenvironment. Another system we integrated
was a small-molecule based shutdown system based on trimethoprim (TMP) to terminate the
expression upon the completion of the treatment regime. In our initial experiments from
2023, there were difficulties with cloning our control system into our plasmids, delaying
their completion. We also noted that CAR-Ma cells demonstrated an impressive effect in
initial tests but exhibited shorter than expected lifespans. After further investigation,
and discussion with our PIs and their colleagues we were able to determine that the CAR
expression with srRNA if left uncontrolled would quickly runaway and induce excessive stress
on cellular machinery and resources. One PI also noted that IFN-γ in small doses resulted in
the armoring of CAR-M and repolarization TAMs, however the sustained expression via our
self-replicating RNA (srRNA) vector would lead to severe downstream immune responses which
could risk patient health.

As it turns out, the solution to these problems all could be solved by our small-molecule
shutdown system. After these learnings, the shutdown system was broken-down and
rearchitected completely into a system that allows for variable control. In a recent paper
on the in vivo validation of the TMP-based shutdown system on mice, Cafferty et al. (2021)
found that there was a distinct curve relating the dose of TMP given to the strength of
expression. After amending our cloning scheme to streamline the integration of the shutdown
system’s functional units we designed our shutdown system to regulate both the expression of
CAR but also the replication of the srRNA itself. Our shutdown system initially designed for
termination in case of emergencies and completion of the treatment regime was now evolved to
allow for the control of the CAR expression and IFN-γ release, by simply pairing each dose
of CAR\_Ma with a corresponding dosage of TMP. With this, our new CAR\_Ma cells are able to
retain overall effectiveness, but gain increased longevity and reduce immunological
side-effects with tighter control.

**In vivo vs Ex vivo: Delivery, CRISPR and Gene Modulation**
When 2023’s proof-of-concept moved away from Sendai viral delivery to self-replicating RNA
based vectors, the auxiliary engineering aside from CAR expression was not fully adapted to
the new system. The importance of mitigating immune evasion via pathways such as SIRPα/CD47
have been well established in enhancing phagocytosis of tumor cells (Ray et al., 2018), but
since common methods currently of delivering CRISPR operations for therapeutics were
delivered ex vivo such as those via ribonucleoprotein (RNP) complexes, CAR-Ma cells were
actually engineered with a two-step process that required the knockout of SIRPα first by the
electroporation of an RNP, then the transformation with srRNA for CAR expression also via
electroporation. Though effective for use in the laboratory, this negated a significant
advantage of using RNA as a vector as the cell must still be engineered ex vivo. This was
acceptable for a proof-of-concept, but to not only be a viable but superior therapeutic,
this would be insufficient.

Ultimately, the Holy Grail of CAR\_Ma would be in vivo expression of CAR but also the in vivo
gene editing of SIRPα via CRISPR. When we rearchitected CAR\_Ma, we went even a step further
by integrating both these functions on to the same RNA molecule dramatically simplifying
production by only requiring one template but also vastly simplifying packaging and
screening by allowing for a homogenous RNA payload. The advancement we required was born out
of engineering our self-replicating RNA and using the functional blocks to allow for
transient expression of Cas proteins, sustained expression of CAR and the release of small
non-coding RNA such as gRNA on a unified multi-modal RNA vector. Once this breakthrough was
achieved, we needed a vector to preserve the goal of in vivo delivery. As the world
recovered from the COVID-19 pandemic, inspiration struck. The mRNA vaccines used for
COVID-19 used lipid nanoparticles for in vivo delivery. When discussing with our PIs and
collaborators in microfluidics, we engineered two cutting edge nanoparticles to package our
system: A nanostructured lipid carrier and a zein-based protein nanoparticle. Allowing us to
achieve the goal of an in vivo immunotherapy, while setting the stage for a large array of
gene therapies and immunotherapies by combining CRISPR, our multi-modal RNA and nanoparticle
delivery systems.

**Minor Iterations enhancing CAR\_Ma**
Aside from breakthroughs from the rearchitecting of 2023’s CAR-Ma, there were several
crucial iterations done through minor engineering cycles during the experimentation done
this year.

**CRISPR vs RNAi**
When originally conceived, CAR\_Ma and the 2023 proof-of-concept were built to use CRISPR.
Since knockout studies that facilitated the knowledge of tumor evasion, knockout improving
phagocytosis were all using CRISPR and even modern gene therapeutics based upon CRISPR it
seemed as the obvious choice. Especially since the tool sets to use CRISPR were more
comprehensive and robust. However, a tenet of CAR\_Ma was transience. With the system itself
having the ability to be modulated, shutoff and even removed. The persistence of the CRISPR
knockout long-term broke this tenet and further posed significant risks downstream. Mainly,
since CRISPR knockouts are permanent the activity of the knocked out gene in our case would
remain off even after the CAR\_Ma system expires or is shut down via TMP, this could lead to
severe risks and complications where these cells no longer ignore friendly cells leading to
an autoimmune risk. Further, by acting at a genomic level CRISPR knockout would only have a
complete shutdown of SIRPα, lacking any control or variability.

Countering this, we moved away from genomic gene modulation towards transcriptomic
regulation instead. By leveraging RNA interference, the SIRPα gene would remain untouched
producing SIRPα RNA transcripts. By transcribing shRNA from our multi-modal RNA vector, we
are able to trigger RNA interference causing the specific degradation of targeted SIRPα RNA
transcripts, effectively silencing the gene expression transiently. Furthermore, by
leveraging our TMP-modulated variable switch, we are able to adjust the expression of the
shRNA effectively controlling the strength of the SIRPα silencing granting the system even
more flexibility.

Unfortunately, the shRNA sequences are unable to directly be added onto our RNA release
system. When initially designed, our RNA release system had a severe deficiency in that the
RNA-dependent RNA polymerase from the Venezuelan equine encephalitis (VEE) virus replicase
that powers the CAR\_Ma inherently has capping and tailing capabilities. With our
Cas9-mediated knockout, the gRNA was expressed enough to allow for the knockout to occur.
But when moving to our Cas12a multiplexed gRNA and especially with shRNA, we noticed that
the knockouts were unsuccessful. Upon further investigation we discovered the capping and
tailing capability of the VEE polymerase. We quickly adapted, designing self-cleaving
ribozymes to the 5’ and 3’ ends of the shRNA. By adding a hammerhead ribozyme to the 5’ and
a HDV ribozyme to the 3’ end, we are able to perfectly preserve the sequence of the gRNA or
shRNA during the subgenomic release of the non-coding RNA, furthermore the ribozyme
sequences will not cleave as effectively on the parent multi-modal RNA due to the length and
structural characteristics of the longer RNA molecule inhibiting effective cleavage. The
resulting system allows gRNA or shRNA to be replicated, and its subgenomic release sustained
while preserving the exact sequence needed for maximal efficiency for CRISPR or RNAi
operations.

**PCR, Cloning and Transformation**
Through designing the complexities and intricacies of CAR\_Ma, there were a lot of areas for
errors to occur. The complexities required a cloning system with high-specificity and the
ability to assemble plasmids from over 8 fragments. Eventually we landed on cloning with
NEBuilder as it was a system that was both accessible to us and matched our needs perfectly.
However using NEBuilder would require designing over 30 different gene fragments to be
assembled in various ways along with over 100 different primers.

Cloning and assembly of fragments was laborious and required frequent use of PCR, oftentimes
requiring optimization as our PCRs would be run in gels, sometimes resulting in mispriming,
smearing, or in the worst case lacking amplification. By optimizing PCR recipes and run
operations: Adjusting amount of template, primers, including additives such as DMSO. Running
touchdown, using hot-start polymerases, running longer extensions. We were able to optimize
a majority of our PCR processes to create an optimal amount of fragment for cloning.
NEBuilder itself also often required optimization, adjusting incubation times or fragments
to be in equivalent molarities.

The final step in our cloning would be transformation into cloning strains of E. coli. We
found that this process has been the most troublesome throughout all of our experiments.
Initially we worked with E. coli DH10b for its capability to work with larger plasmids,
however we found that they would never be any transformants. After optimizing upstream
processes and verifying that our plasmids would successfully assemble, we narrowed down the
process to the transformation. Eventually, we were able to diagnose that the large plasmid
would be too heavy on cellular resources, thus requiring an extended recovery time of 2
hours to successfully express the relevant antibiotic resistance gene for selection, thus we
were able to finally get transformants. However, after miniprepping our transformants and
sending our plasmids for sequencing, we noticed significant portions of our plasmid were
missing. Including CAR or shRNA, essential parts of CAR\_Ma. We were able to soon diagnose
that the repeated sequences, especially with shRNA were toxic to the E. coli resulting in
the cell excising the DNA. To remedy this, we moved away from E. coli DH10b and used E. coli
Stable, which provided us with the ability to clone toxic and repeated sequences.
Eventually, we were able to optimize our overall cloning and transformation process to
enable us to get the required plasmids we needed to develop and characterize CAR\_Ma.

## CAR\_Ma Parts List

**Basic Parts**
Kozak sequence - BBa\_K4344028
Signal peptide BBa\_K2549044
CD8 hinge: BBa\_K5017003
CD28 transmembrane domain BBa\_K4803006
CD19 BBa\_K5062003
FcyR BBa\_K5062004
MegF10 Costim - BBa\_K5062005
Anti-gpc3 nanbody BBa\_K5062000
Anti-GPC3 scFv BBa\_K5062001
DDd BBa\_K4696007
L7Ae - BBa\_K3113009
2x kTurn - BBa\_K4696005
P2A - BBa\_K1442039
Ifn-gamma BBa\_K4696020
mCherry XL BBa\_K4241792
5' Hammerhead Ribozyme BBa\_K5062009
3' Hepatitis D Virus Ribozyme BBa\_K5062010
SIRPa shRNA BBa\_K5062011
PD1 shRNA BBa\_K5062012
SIRPa flank sequence BBa\_K5062013
PD1 flank sequence BBa\_K5062014
26s BBa\_K4696004
-26s BBa\_K5062033
nsP10 BBa\_K5062031
nsP14 BBa\_K5062029
Cas9 gRNA BBa\_K5062020
Cas9 gRNA flank sequence BBa\_K5062032
IRES BBa\_K5062038
Enhanced IRES BBa\_K5062037
nsP1 - BBa\_K4696000
nsP2 - BBa\_K4696001
nsP3 - BBa\_K4696002
nsP4 - BBa\_K4696003
Cas12a gRNA flank sequence - BBa\_K5062034
DR1 - BBa\_K5062023
DR2 - BBa\_K5062025
DR3 - BBa\_K5062027
SIRPa gRNA - BBa\_K5062022
PD-1 gRNA - Part:BBa\_K5062024
VTCN1 gRNA - Part:BBa\_K5062026
STAB1 gRNA - Part:BBa\_K5062028
AsCpf1 (Cas12a) - BBa\_K5062021
Puromycin resistance - BBa\_K4696009

**Composite Parts**
DD-L7Ae - BBa\_K5062002
CD19/ FcyR Tandem - BBa\_K5062006
5th Generation NanoCAR - BBa\_K5062007
5th Generation CAR - BBa\_K5062008
SIRPa Ribo-shRNA - BBa\_K5062016
PD-1 Ribo-shRNA - BBa\_K5062017
SIRPa/PD1 Multiplex shRNA - BBa\_K5062018
Cas9-GFP - BBa\_K5062030
Cas9 gRNA Ribo - BBa\_K5062039
Multiplex Cas12a gRNA - BBa\_K5062045
nsP123 - BBa\_K5062046

**Multi-modal RNA**
CAR\_Ma - BBa\_K5062047
CAR\_Ma shRNA Multiplex - BBa\_K5062048
CAR\_Ma Cas9 - BBa\_K5062049
CAR\_Ma Cas12a Multiplex - BBa\_K5062050

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{% endblock %}


{% extends "layout.html" %} {% block title %}Results{% endblock %} {% block title2 %}Results{%
endblock %} {% block page\_content %}

#### **Table of contents**

* [Molecular Biology Lab](#molecular-biology-lab)
* [Cell Biology Lab](#cell-biology-lab)
* [Microfluidics Lab](#microfluidics-lab)
* [References](#references)

## Molecular Biology Lab

*1. Plasmids fragments amplification and assembly*
We started from cloning the fragments of parts for plasmid assemblies. For some parts (like
CAR and nanoCAR systems), they are divided into several fragments during ordering. These
fragments were assembled by NEBuilder prior to plasmid assemblies.

Plasmid list

- K-turn T7-VEE-GFP

Main system: nsp system, K-turn (trimethoprim control) system At an early stage we
tried to do 5 or more fragments assembly with NEBuilder, but the amount of the
colonies after Ampicillin selection was maintained at a low level. After
troubleshooting, we decided to make an intermediary plasmid for the cloning of the nsp
system, which is the biggest fragments in our final plasmids. With the help of K-turn
T7-VEE-GFP plasmid, we can reduce the number of fragments for assembly and accurately
clone our plasmids afterwards. Colony PCR was performed with 5 randomly picked
colonies for identifying the ones with correct insert. As shown in Figure 1B, only 1
out of the 5 colonies shows an expected band of 5995 bp for nsP123 verification.
![](https://static.igem.wiki/teams/5062/pages/results-1.png)

- CAR(abc)

Main system: CAR system, nsp system, K-turn (trimethoprim control) system, Puromycin
resistance system

- nanoCAR(abc)

Main system: CAR system, nsp system, K-turn (trimethoprim control) system, Puromycin
resistance system

- ribo-shRNA

Main system: ribo-shRNA, nsp system, K-turn (trimethoprim control) system, Puromycin
resistance system

- CAR-Ma nanoCAR-Ma

Main function fragments: (nano)CAR system, ribo-shRNA, nsp system, K-turn
(trimethoprim control) system, Puromycin resistance system

*2. Transformation and miniprep*
We first transformed our assembled plasmids into DH10b, but sequencing results show that
DH10b performed not accurately on our shRNA fragments. We further chose the NEB stable for
shRNA and got our plasmids for SIRPA KO tests. To increase the efficiency for the large
plasmids(15kb+), we also troubleshooted and extended the recovery time during transformation
to two hours and obtained desired results.
![](https://static.igem.wiki/teams/5062/pages/results-2.png)

*3. Plasmid Verifications*
![](https://static.igem.wiki/teams/5062/pages/results-3.png)
![](https://static.igem.wiki/teams/5062/pages/results-4.png)
After miniprep, we use PCR to amplify specific fragments to verify the accuracy of our
plasmid. We used specific primers and NEB Q5 polymerase to make the selection master mix,
this improved our verification efficiency. Figure 3 shows the PCR verification of CAR system
and nsp system. We can observe that the plasmids from all the selected colonies have the
correct size of the nsp fragments, and plasmids from 2 of the 12 colonies have the correct
size of the CAR fragments. Figure 4 shows the PCR verification of the ribo-shRNA system,
from which we can see that 4 of the 10 colonies have the correct size of the ribo-shRNA
fragments.

We have also tried the colonies PCR to save the miniculture and miniprep resources. The
defects of colony PCR is that we need to keep half of the colony on the plate for miniprep
and this is hard to operate on small colonies.

However, verification PCR can only verify the size of the fragments and is not appropriate
for small fragments. In order to make sure our plasmids are accurate enough, we further used
sequencing to validate the plasmids performed well in PCR verification. As shown in Figure
5A and 5B, the nsP and nanoCAR system have identical sequences as expected. For ribo-shRNA
system, some insertions were still observed after several attempts of troubleshooting,
including changing bacterial strain from DH10b to Stbl.3, which is capable of cloning toxic
and repeated sequences. Indeed, this is not the best case as these insertion mutations could
bring uncertainty. Theoretically, however, a few extra bases upstream of hammerhead ribozyme
and downstream of HDV ribozyme should not affect the functionality of the self-cleaving
shRNA. The HH ribozymes, HDV ribozymes, and SIRPa shRNA are both intact, without any
mutations. Due to time constraint and limited resources, we had made the difficult decision
of continuing to use these ribo-shRNA systems.
![](https://static.igem.wiki/teams/5062/pages/results-5.png)

## Cell Biology Lab

*1.THP-1 and HepG2-GFP*
The two cell-lines we are using for our cell culture experiments include THP-1, a human
monocyte leukemia cell-line, and HepG2-GFP, an immortal hepatocellular carcinoma cell-line.
THP-1 monocytes would be our chassis for carrying srRNA that was in vitro transcribed from
our plasmids, while HepG2-GFP are used for testing the efficiency of phagocytosis with
differentiated macrophages. THP-1 are round-shaped suspension cells that would grow to
confluence in 2 to 3 days. HepG2-GFP are adherent cells with epithelial-like morphology, and
would become confluent in 3 to 5 days.

The photos below are the microscopic images (100X) of THP-1 (left) and HepG2-GFP (right) at
70% to 80% confluence.
![](https://static.igem.wiki/teams/5062/pages/results-6.png)
The photos below are the fluorescent microscopic images (400X) of HepG2-GFP, under normal
inspection (up) and under excitation with a blue filter (bottom).
![](https://static.igem.wiki/teams/5062/pages/results-7.png)

*2. Puromycin Killing Curve*
Our srRNA are designed to include a puromycin resistance gene, which gives transfected THP-1
an advantage in puromycin stress. In order to distinguish the cells with successful srRNA
transfection, puromycin incubation was necessary to kill all THP-1 that do not confer
puromycin resistance. The critical concentration of puromycin was determined by checking the
percentage of viable cells after adding different concentrations (0-10 ug/mL) of the
antibiotic to confluent THP-1 (1 to 5\*105 cells/mL) for 7 days. The initial and final cell
concentration were calculated via trypan blue cell counting at Day 1 and Day 7. Each
concentration was done in triplicate, and the error bars indicate standard deviation.
Referring to the Killing curve below, the critical concentration was found to be
**5 ug/mL**, which was chosen as the selection concentration for all future transfection.
![](https://static.igem.wiki/teams/5062/pages/results-8.png)
The figure below shows the microscopic images (100X) of THP-1 during the construction of the
killing curve. The dead cells appear to be darkened, and smaller in size. An obvious trend
of increasing proportion of dead cells can be observed when we move up to higher puromycin
concentration. The images were taken before trypan blue cell counting at Day7.
![](https://static.igem.wiki/teams/5062/pages/results-9.png)

*3. srRNA Transfection*
Three types of srRNA were transfected into the wild-type THP1 by us; here are their main
functional components.

* CAR srRNA: nsp 123 and nsp4 (which provide self-replication and TMP control function);
Chimeric Antigen Receptor (CAR) system; Puromycin resistance.* nanoCAR srRNA: nsp 123 and nsp 4; nanobody Chimeric Antigen Receptor (nanoCAR) system;
  Puromycin resistance gene.
* SIRPA KO shRNA srRNA: nsp 123 and nsp 4; SIRPA KO shRNA; Puromycin resistance gene.

The detail function of each system has already been introduced in Engineering parts.

Following in vitro transcription, the srRNA was transfected into THP-1 via either
**electroporation** (Neon™ Transfection System by Thermofisher Scientific) or
**lipofection**
(Lipofectamine™ 2000 Transfection Reagent by Thermofisher Scientific).

The parameters of electroporation were chosen after thorough literature reviews, which is
1700V, 20ms, 2 pulses based on Laoharawee et al. (2022). Not to our surprises, the cell
viability after electroporation was low, as shown in the photo below (100X), taken 18 hours
after transfection. It is obvious that the morphology of most cells do not look spherical
and regular, many of them appeared to be smaller and darker than usual. Extra 2-3 days of
recovery was given to the cells for returning to a healthy state. Regarding the high level
of apparent cell damage from electroporation, we also performed lipofection to transfect
srRNA during the extra recovery time. The cells appeared to be in normal condition, with
regular shape as shown in photo below (100X).
![](https://static.igem.wiki/teams/5062/pages/results-10.png)
As the transfection efficiency is not 100%, some of the cells died after addition of
puromycin, we centrifuged at lower speed for a longer time to remove the dead cells. After
one or two passages, most of the cells were able to survive in the puromycin added medium.
This proves that our srRNA was successfully transfected into the cells and kept expressing
in the cells. Whether the rest systems work and can enhance the immunoreaction need further
co-culture and flow cytometry tests to validate.

*4. M0-macrophage Differentiation*
Following recovery and puromycin selection, the trasnfected THP-1 underwent M0-macrophage
differentiation by incubating with PMA for 24 hours, and then switching to PMA-free medium
for another 48-72 hours.
![](https://static.igem.wiki/teams/5062/pages/results-11.png)
As shown in the microscopic images above (100X), THP-1 displayed similar morphology to
M0-macrophage after 24 hours of PMA incubation, which is commonly characterized as circular
and slightly elongated (Hourani et al., 2023). Also, the migration from suspension to
adherent nature further proves the successful differentiation. M1-macrophages are
classically activated by IFN-γ and LPS, which have a dendritic-like, elongated morphology
with protruded pseudopods (Hourani et al., 2023). Referring to the photos above, the
macrophages showed a distinct difference to M0-macrophage after 72 hours in PMA-free medium.
Under light microscope, the macrophages show similar shape and features with M1-macrophages.
This could be a very initial and rough indication that our srRNA system continuously
secretes IFN-γ to polarize M0- into M1-macrophages, since IFN-γ or LPS were not present in
PMA-free medium. However, it should be noted that some M2-macrophages, like M2a and M2b,
also shared similar elongated morphologies (Hourani et al., 2023).

Therefore, light microscope inspection of macrophage morphology can only serve as a rough
and brief hint of M1-macrophage polarization. Nevertheless, these CARma macrophages were
subjected to co-culture testing with HepG2-GFP. The promising result of co-culture, detailed
in the next section, also served as an indirect, rough indication of phagocytic, anti-tumour
M1-macrophages.

*5. Co-culture*
5.1 Phagocytosis observation
![](https://static.igem.wiki/teams/5062/pages/results-12.png)
![](https://static.igem.wiki/teams/5062/pages/results-13.gif)
![](https://static.igem.wiki/teams/5062/pages/results-14.gif)
Before quantifying co-culture, we firstly observed the phagocytosis under the fluorescence
microscope. The HepG2 we chose for co-culture contain green fluorescent protein(GFP) gene,
they will produce green fluorescent signal under blue light(about 488 nm). We observed the
cells under normal light and UV light to identify the cell type.As shown in Figure 5.1, 5.2
and 5.3, we can observe that THP1 attaches with HepG2-GFP and this can be considered as
phagocytosis. Some ongoing phagocytosis can also be observed (Figure 5.3), however, we can
only observe the cells at 25 degree environment so that THP1 performs low activities and we
cannot observe the whole phagocytosis process.

5.2 Quantifying phagocytic effect analysis
![](https://static.igem.wiki/teams/5062/pages/results-15.png)
In order to test if our CAR system can enhance the phagocytosis of cancer cells by
macrophages and whether our TMP system works, CAR/nanoCAR transfected THP1 and HepG2-GFP are
grown in a Direct Co-Culture manner, GFP signal was regularly measured as well(excited at
488 nm). As table 1 shows, we co-culture wild-type THP1 (WT), CAR srRNA lipofection
transfected THP1 (lipofection CAR group), nanoCAR srRNA electroporated THP1 (electroporation
nanoCAR group), and nanoCAR srRNA lipofection transfected THP1 (lipofection nanoCAR group)
with HepG2-GFP in wells contain 180uL of the culture medium of the same formulation
(M10:RPMI=1:2), and added 14.4 ug of the trimethoprim (TMP) to half of the wells.

To improve the confidence of our data, we did a triplicate for each group.

**A. Validation of the phagocytosis of THP1**
![](https://static.igem.wiki/teams/5062/pages/results-16.png)
Figure 5.4 shows the changes in fluorescence intensity in each well over time. The control
group is HepG2-GFP seeding in the same medium as the experimental groups. We chose
lipofection CAR in TMP free medium as the representative to see the phagocytosis condition.
We found that all of the wells in this group have lower fluorescence intensity than the
control all the time, which means the amount of HepG2-GFP in experimental groups is lower
than the control. This indicates that the cells after transfection have a good phagocytic
effect.

Moreover, we want to test whether our CAR system enhances the phagocytic effect of THP1
compared to the wild-type THP1.

**B. Validation of the effect of CAR system**
![](https://static.igem.wiki/teams/5062/pages/results-17.png)
To identify whether the CAR system improves the effect of phagocytic, we compared the change
rate of the growth of GFP signal between 0h-19h and 19h-44h. As shown in Figure 5.5, the
growth of the GFP signal decreased in the nanoCAR groups and the WT group, while nanoCAR
transfected THP1 reduced the growth of the GFP signal more significantly. We can infer that
the CAR system did improve the effect of phagocytic.
![](https://static.igem.wiki/teams/5062/pages/results-18.png)
At the 43rd hour of co-culture, we refresh the medium (keep the TMP concentration) to ensure
the cells can stay in good condition and avoid the influence of cellular metabolites. We
measured the GFP signal 23 hours later and compared the change in the growth of the GFP
signal between 66h-43h (after refreshing the medium) and 43h-19h to further prove our
previous conclusion.

Figure 5.6 indicates that there is a certain degree of recovery of the growth of HepG2. The
increased extent of the growth of the GFP signal in the two nanoCAR groups is less than in
the WT group, which means both the lipofection nanoCAR group and electroporation nanoCAR
group restricted the growth of HepG2 better than the WT group. We also did the two-sample
t-test and calculated the p-value. From the result, we can infer that the CAR system can
enhance the phagocytosis of THP1 significantly. (95% confidence interval for lipofection
nanoCAR, 97.5% for electroporation nanoCAR)

**C. Validation of the TMP system**
![](https://static.igem.wiki/teams/5062/pages/results-19.png)
We want to further explore the influence of TMP on the cells. We compared the growth of the
GFP signal after 43 hours of co-culture(Figure 5.7a). Among all three experimental groups,
the growth of the GFP signal slowed down when adding 0.08 mg/mL of TMP into the co-culture
medium. From previous results, we have concluded that THP1 with the CAR system can depress
the growth of HepG2. Combining this conclusion with the data we observed in Figure 5.6, we
can infer that TMP can depress the expression of the srRNA transfected into the cells.
![](https://static.igem.wiki/teams/5062/pages/results-20.png)
As we mentioned before, we refreshed the medium at the 43rd hour of co-culture. To validate
our conclusions, we did the comparison again after 88 hours of the co-culture(Figure 5.8).
The results remain in line with our expectations. The two-sample t-tests indicate that the
difference between TMP-free and adding 0.08 mg/mL of TMP is significant. (97.5% confidence
interval for lipofection nanoCAR, 95% for electroporation nanoCAR)

**D. Validation of the effect of nanobody**
![](https://static.igem.wiki/teams/5062/pages/results-21.png)
![](https://static.igem.wiki/teams/5062/pages/results-22.png)
In addition, the impact of nanobody on the CAR system was analyzed (Figure 5.7b and Figure
5.7c). The growth of the GFP signal in both nanoCAR groups is less than in the lipofection
CAR group, and we can observe the same effect after the addition of TMP. However, the
results of the two-sample t-test were relatively non uniform. The significant difference can
only be ensured between the two lipofection groups. These data partially indicate that the
introduction of nanobody can enhance the function of the CAR system.
![](https://static.igem.wiki/teams/5062/pages/results-20.png)
As we mentioned before, we refreshed the medium at the 43rd hour of co-culture. To validate
our conclusions, we did the comparison again after 88 hours of the co-culture(Figure 5.8).
The results remain in line with our expectations. The two-sample t-tests indicate that the
difference between TMP-free and adding 0.08 mg/mL of TMP is significant. (97.5% confidence
interval for lipofection nanoCAR, 95% for electroporation nanoCAR)

**E. Limitation and further improvement**
We use DMSO to dissolve TMP and add it to our culture medium as trimethoprim(TMP) is an
organic which is hard to dissolve in the culture medium. Initially we plan to further detect
whether there is a quantitative relationship between the concentration of TMP and the
expression of srRNA. However, higher concentrations of TMP need a higher amount of DMSO to
dissolve. Due to the previous test, 1.5% of DMSO showed a 10% inhibition of cell growth
(Tunçer et al., 2018). We only analyze the data from the well with 0 mg/mL and 0.08 mg/mL
TMP concerning the toxicity of TMP to the cells. In the future we will try to find another
more capable solvent and further test the quantitative relationship, this will also help the
next step in animal testing. In addition, the results only indicate that there is a
significant effect of applying nanobody when we compare the lipofection nanoCAR group with
the lipofection CAR group. There will be a higher degree of credibility in the conclusions
if we supplement the data of the electroporation CAR group and compare it with the
electroporation CAR group.

**F. Conclusion**

1. The THP1 that we transfected CAR system srRNA in can have phagocytosis with HepG2 and
   perform better than WT THP1.
2. After the introduction of the K-turn system into srRNA, we can depress the expression of
   srRNA by addition of TMP.
3. Nanobody can improve the phagocytic effect.

Remarks: the two-sample t-test we did in our analysis.
![](https://static.igem.wiki/teams/5062/pages/results-23.png)
In order to have a better approximation of p-value, we used the following linear
interpolation:
![](https://static.igem.wiki/teams/5062/pages/results-24.png)
Where p1, p2 and t1, t2 are the value in the p-value table, p, t are the value of the
sample.

## Microfluidics Lab

*Zein Nanoparticle*
Before constructing Zein nanoparticles (ZeinNP) and beginning the RNA encapsulation with it,
we first tested the optimal conditions for ZeinNP to maintain the best condition. We have
made test samples with vast attributes on (1) pH condition of the Tween 80 solution, (2) the
concentration of Tween 80 solution and (3) time of centrifugation, in order to produce Zein
nanoparticles of higher qualities.

- Determination of optimal pH condition
  ![](https://static.igem.wiki/teams/5062/pages/results-25.png)
  ![](https://static.igem.wiki/teams/5062/pages/results-26.png)
  While Figure 6 depicts the effect of turbidity imposed by pH, Figure 7 shows a trend of
  Zeta potential under different pHs. As a result, the optimal pH is pH6, as a compromise
  between preventing extreme pH value and averting ZeinNP aggregation.
- Determination of optimal concentration of Tween 80 solution
  ![](https://static.igem.wiki/teams/5062/pages/results-27.png)
  Figure 8 illustrates the impact on Particle Sizes and PD Index by varying Tween 80
  concentration, which is a surfactant. Due to compromise, the optimal Tween concentration
  is 2 mg/mL.
- Determination of optimal time of centrifugation
  ![](https://static.igem.wiki/teams/5062/results-figure-9.jpg)
  In Figure 9, there is a plunge of the PD Index before 8 minutes, and it increases and
  levels off afterwards. Therefore, the optimal time of centrifugation shown by this graph
  is 8 minutes under 5000 RPM.

### Results with our T7 RNA produced from IVT:

Using the aforementioned optimal conditions, we prepared the ZeinNP to the next steps.
Stained with Rhodamine B in a 100:1 proportion, we measured the calibration curve between
RNA concentration to fluorescence:

![](https://static.igem.wiki/teams/5062/results-figure-10.jpg)
As the R²-value is close to 1, the data can be modeled accurately as a solid linear
relationship between RNA concentration and fluorescence.

Next, after encapsulation procedures, the RNA-encapsulated ZeinNP exhibited:

![](https://static.igem.wiki/teams/5062/results-figure-11.jpg)

Particle size maintained 71.6nm with a PD Index of 0.2902, which represents a moderately
uniform distribution of very small particle sizes. The results are acceptable.

Then, we measured the fluorescence of the RNA-encapsulated Zein NP. Fluorescence of
RNA-encapsulated Zein NP was found to be 149623824 RFUs, with the background fluorescences
of wells is relatively negligible. This shows that the Zein NP contains a significant amount
of fluorescing RNA, suggesting successful encapsulation.

Finally, evaluation of the RNA-encapsulated Zein NP:

*Input RNA = 0.4mL x 10 mg/mL = 4 mg*
*Unloaded RNA conc. = (149623824 - 2814322.363) / 314882317.7 = 0.46623609 mg/mL*
*Mass of unloaded RNA = 1.8649 mg*
*Mass of loaded RNA = 4 - 1.8649 = 2.1351 mg*
*Mass of zein = 400x(7/9) x 10 x 0.001 = 3.11 mg*

**Loading capacity = 2.1351 / 3.11 = 68.65%**
**Encapsulated efficiency = 2.1351 / 4 = 53.38%**
In light of this, we summarize that our ZeinNP possesses:

- Loading capacity (LC) = 68.65%

- Encapsulated efficiency (EE) = 53.38%

in our experiments. The results are a strong proof that our ZeinNP successfully encapsulated
one of our desired RNA.

*Nanostructured Lipid Carriers (NLC):*
![](https://static.igem.wiki/teams/5062/pages/results-30.png)
![](https://static.igem.wiki/teams/5062/pages/results-31.png)

The data obtained using the dynamic light scattering (DLS) machine to measure the
intensity were recorded and the graphs were made to determine the size of the particles
formed after the mixing of the aqueous and organic phases.

The Z-average size of the particles for the CAR and NanoCAR is 196.5 d.nm and. 178.8 d.nm
respectively. Both of the sizes are less than 200 d. n m. This shows that the particles
formed in the solution were successfully formed lipid nanoparticles.

After confirming the right particle size of nanoparticles through the DLS, the next step is to filter the solution
to remove the organic and the aqueous phases. To prove that the lipid nanoparticles have successfully encapsulated our RNA,
Quant-iT RiboGreen RNA Assay Kit was used to quantify the RNA, and the detection method used was linear fluorescence.

After this, the results obtained from the fluorescence were used to determine the concentration of the RNA successfully
encapsulated in the lipid nanoparticles. Using a standard curve based on the fluorescence results, we were able to find out
that the RNA concentration inside the lipid nanoparticle is 4.582088 ug/uL. This showed that there was successful encapsulation
of our RNA in the lipid nanoparticles.

## References

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Laoharawee, K., Johnson, M. J., Lahr, W. S., Sipe, C. J., Kleinboehl, E., Peterson, J. J.,
Lonetree, C. L., Bell, J. B., Slipek, N. J., Crane, A. T., Webber, B. R., & Moriarity, B. S.
(2022). A Pan-RNase Inhibitor Enabling CRISPR-mRNA Platforms for Engineering of Primary
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Tunçer, S., Gurbanov, R., Sheraj, I., Solel, E., Esenturk, O., & Banerjee, S. (2018). Low
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<https://doi.org/10.1038/s41598-018-33234-z>

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