Team:MIT/Poster

MIT: Synthetic Mammalian Circuitry for Treatment of COVID-19 Cytokine Storms

Synthetic Mammalian Circuitry for Treatment of COVID-19 Cytokine Storms

Authors:

Stuti Khandwala, Erin Kim, Ethan Levy, Rachel Shen, Erin Shin, Sangita Vasikaran

We would like to thank Professor Ron Weiss for hosting our iGEM team at MIT! We also appreciate all the help we've received from our mentors, advisors, previous members and the general synthetic biology community.

Abstract:

The COVID-19 pandemic, caused by SARS- CoV-2, has afflicted millions of people, with one prominent feature of its lethality being an overactive immune response, or cytokine storm. We aimed to design a synthetic mammalian network to alleviate cytokine storms using powerful, switchlike endoribonucleases. By sensing changes in concentration of two biomarkers indicative of cytokine storms, our system will respond with graded output of a cytokine-sequestering single-chain variable antibody fragment in order to differentially treat patients with varying levels of disease severity. We computationally constructed a cellular and plasma-level immune response to COVID-19 through an ODE-based SimBiology model to inform the design of our sensor specifications, network topology, and tailored treatment response. This engineered system, once experimentally verified in vitro, can be used to further our current understanding of COVID-19 immunopathology, with a particular focus on IL-1.

Introduction

The COVID-19 pandemic, caused by SARS- CoV-2, has afflicted millions of people, with one prominent feature of its lethality being an overactive immune response, or cytokine storm (Ragab et al., 2020). We aimed to design a synthetic mammalian network to alleviate cytokine storms using powerful, switchlike endoribonucleases. By sensing changes in concentration of two biomarkers indicative of cytokine storms, our system will respond with graded output of a cytokine-sequestering single-chain variable antibody fragment in order to differentially treat patients with varying levels of disease severity. We computationally constructed a cellular and plasma-level immune response to COVID-19 through an ODE-based SimBiology model to inform the design of our sensor specifications, network topology, and tailored treatment response. This engineered system, once experimentally verified in vitro, can be used to further our current understanding of COVID-19 immunopathology.

Inspiration

Much of COVID-19 pathology is still being unraveled; remotely, through the power of computational experimentation, we saw the opportunity to contribute with minimal risk. We thus studied literature pointing to unique biomarker patterns found in COVID-19 cytokine storms (CCSs), as well as underlying mechanisms of other overactive immune disorders, such as rheumatoid arthritis and psoriasis. Based on statistical analyses by Yang et al., IP-10 and MCP-3, two cytokines secreted by the IFN-γ cascade, when present together in elevated concentration, served as promising biomarkers for severe CCSs.

Additionally, quantifiable levels of cytokines and other immune signatures vary greatly between patients’ unique health profiles (Yang et al., 2020). Here, we realized a critical need: mitigation of the inflammatory response, catered to the individual patient’s immune response to SARS-CoV-2 infection. This challenge was one we believed synthetically engineered mammalian cells were well suited for: their ability to continually monitor state, implement dosage-feedback regulation, and utilize native biological parts pose many advantages over traditional pharmaceuticals.

Synthetic biology requires the engineering of nonlinear biology into predictable, digital behaviors; our goal was to employ effective digital molecular mechanisms to construct an analog, or “graded”, response which is tailorable to individual patients using already available synthetic biology-designed tools. Such an approach would ensure maintenance of systemic immune homeostasis.

Engineering

Full network circuit design, responsive to COVID-19 cytokine storm biomarkers’ (IP-10 + MCP-3) nuclear transcription factors (ELK-1 + NF-κB) , using digital PERSIST “OFF” logic (CasE, Csy4, Cas6), and producing an individualized anti-inflammatory, single chain antibody fragment output (scFv).

To summarize the full circuit depicted in the figure above, we will sense the cytokines IP-10 and MCP-3 which correspond to the transcription factors ELK-1 and NF-κB respectively. CasE is expressed upon activation by ELK-1, and Csy4 is expressed upon activation by NF-kB. Using the CMV promoter, Cas6 is constitutively expressed but when either or both CasE and Csy4 are present, the mRNA for Cas6 will be cut upstream of the gene sequence, so Cas6 expression will decrease. For our output fusion protein, we have another plasmid with the constitutive promoter CMV, and likewise there is an ERN cut site upstream of the gene sequence. Thus, when both IP-10 and MCP-3 are present, scFv will be produced, inhibiting our cytokine of interest.

We developed an AND gate with five PERSIST-based plasmids. The PERSIST system was developed in the Weiss lab as "a programmable RNA regulation platform using CRISPR endoRNases" (DiAndreth et al., 2019). IP-10 and MCP-3 signal downstream phosphorylation pathways to activate nuclear transcription factors (nTFs) ELK-1 and NF-κB, respectively. Two minimal constitutive promoters (here, CytoMegaloVirus, CMV) are then induced by nTF-responsive element binding, upstream of ERN genes. In choosing our ERN components for translation, we evaluated the sensitivity with the relative abundance of the biomarker (nTFs ELK-1 and NF-κB) in plasma. In CCS, MCP-3 has been observed about 103 fold lower than IP-10; Csy4, an experimentally stronger “OFF” ERN, was thus placed downstream of the NF-KB-responsive promoter, while CasE, a slightly weaker ERN, was used for the IP-10 sginaling pathway. Cas6, the weakest ERN of the Cas family evaluated, when produced by either of two CMV-constitutively-expressed transcripts, degrades the scFv mRNA transcripts and causes decreased translation of anti-inflammatory soluble scFv with a slight buffer to allow for IP-10 and MCP-3 elevation to be reached. To further enhance the system dynamics by decreasing the time required to reach steady state, a PEST degradation tag can be fused to the Cas6 sequence. Our AND gate is thus engineered to produce a dose-responsive output when there is adequate IP-10 and MCP-3 to result in the degradation of Cas6 transcripts.

Modelling

Now that we had designed our circuit, it was time to see how our components worked together. To do this, we simulated the behavior of our system in MATLAB‘s SimBiology compartmentally and mechanistically through differential equations. By compartmental, we mean that each reaction or event takes place in a defined location in respect to our engineered cell. In our model figure here, the intracellular space is orange, while the extracellular space is blue. By mechanistic, we mean that each step of a process is represented through interactions of species in different combinations of reactions, which are depicted as lines, as you can see from the legend on the left. Let’s take a closer look at the orange intracellular compartment.

Within our engineered cell, you can see the full circuit that, here in SimBiology representation. To put together the components, we thus constructed a system of differential equations that could model the interrelationships between species. A typical differential equation for a species looks like the one in blue text above: here, we can relate the rate of change of a species by adding its basal transcription, beta, subtracting its degradation, delta, and if the species participates in a binding event, a Hill cooperativity term. In the end, by providing literature values and relevant initial concentrations across timesteps, we can start to probe trends within the reactions we care about. If you look at the bottom of our AND gate, you can see this sort of antibody shaped representation of a translated ScFv output.

We can see the ScFv being exported out of our engineered cell into the extracellular space. This means we can assess its relationship to systemic serum circulating in the body. We were able to construct a cytokine interaction map in our model based upon plasma cytokine levels reported by Yiu et al. in patients undergoing inflammatory cytokine storms. We then could take our output ScFv and have it target each specific player in the cytokine storm, and from there, analyze its effects on the cytokine storm as a whole--using this information to tailor the future design of our system.

Results

Our modelling results can be seen in the graph above. We found two distinct features in our system's cytokine modulation: differences in the peak IP-10 observed and in the steady state value of IP-10.

First, we looked for the lowest peak IP-10 values, since this would indicate which therapeutic target would diminish the cytokine storm the most during the peak of the cytokine storm. As seen in the figure above, the lowest peaks corresponded to IFN-γ and IL-1, respectively; thus we found IFN-γ and IL-1 to be highly effective therapeutic targets for our scFv.

Another state we looked into was the steady-state levels of IP-10. These are significant since our modulatory system should bring the resting cytokine levels to a minimum. As seen in the figure, IL-1 and IFN-γ’s steady state had the lowest average values. Thus, we once again identified IFN-γ and IL-1 as strong candidates for our scFv target since over the long term, they would most effectively reduce the cytokine storm.

We found IFN-γ to most directly inhibit the cytokine storm. However, IFN-γ is also a master regulator of the immune response, and targeting this could lead to a significant drop in patient immune strength. (Kaps et. al.). Although IL-1 is also a key player in the immune response, other studies have found clinical efficacy in IL-1 inhibition (Cavalli et al., 2020). Thus, since IL-1 had the lowest steady-state IP-10 level, the second lowest peak cytokine level, and previous literature indicating its efficacy as a cytokine storm target, we found it to be the most effective target for our scFv in modulating the cytokine storm.

Future Directions

We understand that it is important to test the feasibility of our system in vitro, as well as be able to explain our computational results biologically. Further we wish to experiment with multiple parts and parameters to produce input output relations nearer to actual biology. We see our project guiding the study of COVID-19 cytokine storm pathology by focusing on specific cytokine(s) found to be have significant effect if modulated from our model. We also hope that parallels can be drawn to other inflammatory diseases from our study to help in identifying relevant cytokines and potentially using the results therapeutically.

Medal Requirements

Bronze:

Developed a computational compartmentalized cytokine network that can be modified for a variety of cytokine-targeting treatments for inflammatory diseases with ten cytokines.

Characterized 15 parts associated with the PERSIST system from the Weiss Lab (Bba_K3621000-15) which can be used by future teams in mammalian circuits.

Silver:

Hosted the international MMM (MIT Mammalian Meetup) with 18 registered teams.

Successfully simulated the cytokine storm system with ten cytokines.

Contributed one review article and one original research article to the Maastricht iGEM Journal.

Contributed to the URochester Biomarker Database for characterizing cytokine storms.

Gold:

Engineering success integrated by reiterating circuit design through discussions with Weiss Lab.

Integrated human practices through discussions of implementation with the biological engineering community including bioethicists.

Successfully modeled the cytokine storm and tested different cytokine targets to identify two therapeutic targets.

References and Acknowledgements

Acharya, D., Liu, G., & Gack, M. U. (2020). Dysregulation of type I interferon responses in COVID-19. Nat Rev Immunol 20, 397–398 doi:10.1038/s41577-020-0346-x

Cavalli, G., Luca G. D., Campochiaro C., Della-Torre E., Ripa M., Canetti M., … Dagna L. (2020). Interleukin-1 blockade with high-dose anakinra in patients with COVID-19, acute respiratory distress syndrome, and hyperinflammation: a retrospective cohort study. The Lancet Rheumatology, 2(6), 325-331. doi:10.1016/S2665-9913(20)30127-2

DiAndreth, B., Wauford, N., Hu, E., Palacios, S., & Weiss R. (2019). PERSIST: A programmable RNA regulation platform using CRISPR endoRNases. bioRxiv, 12(15) 1-17. doi:10.1101/2019.12.15.867150

Kaps, L, Labenz, C, Grimm, D, Schwarting, A, Galle, PR, Schreiner, O. Treatment of cytokine storm syndrome with IL‐1 receptor antagonist anakinra in a patient with ARDS caused by COVID‐19 infection: A case report. Clin Case Rep. 2020; 00: 1– 5. https://doi.org/10.1002/ccr3.3307

Ragab, D., Salah Eldin, H., Taeimah, M., Khattab, R., & Salem, R. (2020). The COVID-19 Cytokine Storm; What We Know So Far. Frontiers In Immunology, 11, 1446.doi:10.3389/fimmu.2020.01446

Yang, Y., Shen, C., Li, J., Yuan, J., Wei, J., Huang, F., Wang, F., Li, G., Li, Y., Xing, L., Peng, L., Yang, M., Cao, M., Zheng, H., Wu, W., Zou, R., Li, D., Xu, Z., Wang, H., Zhang, M., … Liu, Y. (2020). Plasma IP-10 and MCP-3 levels are highly associated with disease severity and predict the progression of COVID-19. The Journal of allergy and clinical immunology, 146(1), 119–127. doi:10.1016/j.jaci.2020.04.027

We would like to thank all our awesome mentors and friends for all their support throughout our iGEM project!

Mentors

Deepak Mishra, Miles George, Malik George, Melody Wu, Ben Allsup, Kristina Stoyanova, Sebastian Palacios,
past members Diani Jones + Dana Osei,

Advisors

Dr. Kenneth Oye and Dr. Françoise Baylis
Noreen Wauford, Dr. Fabio Callendo
Dr. Michael Birnbaum
BE Communications Lab
Professor Ron Weiss