Team:IISER Kolkata/Partnership

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About UCSC iGEM 2021

The 2021 team of UCSC iGEM Team has developed the project Progenie to devise a method to stop the spread of foodborne pathogens at their source. UCSC iGEM Team is from the University of California, Santa Cruz located in Santa Cruz, California. Our teams first met on 19th July through a video conference platform to discuss our projects. We found that we shared a similar goal of detecting and treating a problem and wanted to tackle the issue of antibiotic resistance in pathogens. We further went on to discuss our projects in detail and decided to help each other throughout our project. Unfortunately, the COVID Pandemic had affected our team heavily in gaining access to the wet lab and we got access to the lab quite late. Progenie here extended their helping hand to support our Namooste project and conduct one of our wet-lab experiments. We in turn offered to help them build their computational model for their Progenie project.

First meeting with UCSC iGEM Team

COVID 19 pandemic made it difficult for us to complete all our wet lab experiments as we got access to our laboratory quite late. Therefore the UCSC iGEM Team helped us to do the set of experiments from the Detection section of our project.

Biomodelling members discussing about the design of EiCsm6

What to work upon

To develop our detection kit we are using the technique of SHERLOCKv2 where a combined system of CRISPR-Cas13 and Csm6 is used to detect the miRNA biomarkers of subclinical bovine mastitis in the milk sample. Cas13a enzyme is well characterized in the iGEM parts registry but Csm6 hasn’t been done yet even in the light of SHERLOCKv2. Characterization of Csm6 is important for specific and sensitive detection of the biomarkers of the disease. UCSC iGEM Team characterized the Csm6 protein using its activating species which are RNA molecules having 2’-3’ cyclic phosphate hexadenylate ends, at varying concentrations. They took up this section of wet lab work as they had expertise in protein purification and characterization which our team lacked due to limited access to labs and time. This allowed us to carry out other experiments related to the treatment aspect of our project with more focus. They also planned to characterize different Csm6 proteins from different species.

Cas13 is activated when it recognises a target sequence, allowing it to cleave fluorescently labelled RNA reporters non-specifically. The cleavage products are linear A>p, which can activate Csm6, leading in signal amplification from non-specific reporter cleavage. Image designed by Tanya Ivanov and made by Torrey of UCSC igem 2021 team.

Experimental Design

Zhang et al. showed that Enterococcus italicus bacteria’s Csm6 protein has the highest activity when activated by the collateral cleavage products of Cas13a than TtCsm6 (Thermus Thermophilus) and LsCsm6. Research through literature survey showed that there are no studies to date about the activation level of Csm6 protein of Mycobacterium tuberculosis. UCSC Team characterized the activation of EiCsm6(previously characterized), TtCsm6(heat purified, so easy to purify), MtCsm6(new characterization).

Through several regular weekly meetings between the wet lab teams of UCSC and IISER Kolkata, the protocol for the whole experiment was finalized.

Csm6 Cloning

They employed gene blocks with a 6x-His-tag, tobacco etch virus protease cut site, and restriction enzyme cut sites on the 5' and 3' end for NcoI and BlpI, respectively, for EiCsm6, TtCsm6, and MtCsm6. After digesting the gene segments with restriction enzymes, they were ligated to the pET-52b expression vector.

Both the teams met regularly to decide upon the exact sequences and design of the plasmid for the protein expression and purification.

Producing Csm6 activator species

EiCsm6 is activated by cyclic hexadenylates (cA6), TtCsm6 is activated by cyclic tetradenylate (cA4) [1]. They purchased cyclic adenylate oligos from Biolog Science Institute and synthesized the linear adenylates in their lab. 2’-3’ cyclic phosphate ends were produced by hepatitis delta virus ribozyme (HDVR obtained from BBa_K1614002) and hydroxylated 5’ end on adenine homopolymers were produced by a hammerhead ribozyme (HHR obtained from BBa_K598000). They used flagged primers which can add Golden Gate sites on either side to amplify the HDVR region from the part. Using inverse PCR they added Golden Gate sites to the HHR containing plasmid and added tetra and hexadenine sequences at the HHR sequence’s 3’ end. The HDVR amplification product was cloned at the 3’ end of the adenine sequence. After mRNA transcription, the ribozymes get activated and cleave the sequences at 5’ and 3’ end that produce the 5’ OH and 2’-3’ cyclic phosphate linear adenine homopolymers. These Csm6 activator sequences are then extracted from the gel.

Csm6 Assay

Csm6 cleavage as templates in qPCR and TaqMan assay using digital PCR was used by them to measure the level of activation. The presence or absence of a target sequence is quantified by TaqMan. The TaqMan assay was used by them to quantify the amount of RNA cleaved by Csm6. The Csm6 was incubated with its activator species and target sequence and the products of the TaqMan assay were added in the PCR machine. Proper activation of Csm6 will lead to cleavage of the target sequence and no fluorescence. If fluorescence appears, that signifies that Csm6 wasn’t activated properly.


Csm6 gene blocks were synthesized and ordered from IDT and cloned into the protein expression vector pET-52b by restriction cloning following the Csm6 assembly protocol. Assembled plasmid was transformed into a C41 pLysS cell line. Colonies were grown on a selective media to screen the cells containing transformed plasmids containing Csm6 gene. Colony PCR was performed to make sure that the transformed plasmids were correct in insertion and orientation. The UCSC team has shared the following results of the experiment with us.

Results of the colony PCR performed on the transformants. The Csm6 insert that includes the 6x His tag and the TEV protease site is expected to be around 1.4kb for both TtCsm6 and EiCsm6. Shown are 10 colonies from EiCsm6 and TtCsm6 transformants run on a 0.7% gel.

As the results of the colony PCR for TtCsm6 is a bit unclear, both the teams decided to run a PCR amplification of the isolated plasmid from the positive colonies with the primers same as that of the colony PCR. This would confirm the presence of insert in the isolated plasmids.

Results of the PCR performed on the plasmids isolated from the positive colonies.

As we can see, both the isolated plasmids have the intended lengths of insert reflecting successful cloning and plasmid isolation. UCSC Igem team 2021 used those colonies to purify protein by stimulating expression overnight with IPTG, followed by cell lysis and sonication, but the final results were not available before the wiki freeze.

UCSC Drylab

About Team UCSC’s project

The Beginnings

We got in touch with team UCSC over Instagram and the motivation of approaching each other the similarities of our project based on their respective links to antimicrobial resistance. Despite the staggering 12 hours of time difference, we were able to have several meetings over the course of our iGEM endeavour and furthered ur partnership.

The initial goal of the dry-lab component of the partnership was to help iGEM Team UCSC make a population dynamics model delineate the following aspects of their probiotic before they could arrive at the trials phase.

  • Timescales of action of probiotic
  • Delineating the spread of the probiotic population in the presence of other competing factors
  • Optimisation of parameters relevant to the population dynamics

The Initial model proposed by the UCSC team was a fork of the standard susceptible-exposed-infectious-recovered (SEIR) model.

Initial modelling efforts

When the team walked us through the model goals, we suggested a few modifications to the model that would make the model more akin to a real-world scenario involving stochastic fluctuations and competition between participating elements. In order to add stochasticity to the model, we suggested team UCSC incorporate aspects of the Lotka-Volterra model in the components under direct competition. We also tried to simulate using kinetic Monte Carlo (KMC) in Simulink to further this idea.

We commented that the phase population should have a degradation coefficient for long time scale dynamics. We also pointed out that the probability of individuals that acquired plasmid through bacterial conjugation, moving from the susceptible to the infected group, would be very low and marginal compared to other coefficients in the system.

Taking cues from these pointers, we established a carrying capacity for the system and incorporated a death coefficient to the phage population. Using the KMC simulation, we were very able to visualise a simplistic system with and without stochasticity introduced due to the Lotka-Volterra implementation. However, we ultimately decided to do away with stochasticity in the model and went ahead with a deterministic system.

The parameter set for simulating or model was extensive, and the range of values was exceedingly wide. For example, the conjugation rates were spanning over 11 orders of magnitude, creating a sensitivity issue for any stochastic model when it came to scaling for different systems. In light of the same decided to model Progenie using a series of deterministic ordinary differential equations and not have stochastic interactions.

UCSC population dynamics model: Before (left) and after (right) initial modifications

Drawing inferences

Once we had a model established, we were able to simulate it in python. We suggested team UCSC iterate the model over an array of conjugation rates to perform a kind of sensitivity analysis. This is especially important as the conjugation rates for the system has close to 11 orders of magnitude difference between the lowest and highest rates. The results of this sensitivity analysis were rather exciting and helped us determine optimised parameters.

We are extremely thankful to team Tuebingen for their incessant support and insights into our project and also for the friendships that evolved from this.


We got in touch with the iGEM Tuebingen team over Instagram and we were excited to explore their project as it was based on anti-microbial cyclotides and since we were also working on antimicrobial peptides. Throughout several meetings we got to learn about each other’s projects, and by that, how we could help each other out. Throughout several meetings for planning and designing of the models and simulations, it became obvious that what in the beginning was planned as a short collaboration developed into a partnership with mutual contributions of expertise and time from both teams.

Team Tuebingen had set out to use molecular dynamics simulations to explore the efficacy of their cyclotide molecules as antimicrobial agents. They had already selected prospective cyclotide candidates through bioinformatics exploration of sequence databases. The IISER Kolkata team was also looking into performing molecular dynamics simulations to explore the dynamics of our antimicrobial peptide (Nisins). However, we were lacking adequate computational resources to carry out simulations of large systems. Team Tubinge offered to help out with providing computational resources via the Binac supercomputing cluster.

Meeting with Tubingen

We helped team Tuebingen set up their membrane protein assemblies for molecular dynamics simulations. TeamIISER Kolkata had initially not planned to perform simulations of Nisin with membrane systems, however, Team Tuebingen suggested that it would be rather interesting to see how our antimicrobial peptides interacted. We are glad that we heeded this and went ahead with simulating Nisgs with model bacterial membranes as we gained spectacular insights from this exercise (as mentioned in the Membrane Protein Interaction section of our Molecular model.

In the end, we also discussed methods to analyse the simulation trajectories and derive relevant information from the same.

We are extremely thankful to team Tuebingen for their incessant support and insights into our project and also for the friendships that evolved from this.

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