Difference between revisions of "Team:Munich/Software/Collaborations"

For TU Delft

We started collaborating early after the European iGEM Meet-Up with TU Delft in the wetlab. Since we were both employing Cas13a as the molecular heart of our project, it made sense to exchange data and experiences from wet lab experiments. At some point during a skype meeting, we then realised that we were doing similar things in the dry lab as well and thus, decided to start collaborating and exchanging ideas in that field as well.

Secondary Structure Prediction

In our drylab collaboration with Team Delft, we performed a Cas13a design verification of crRNAs they used in their project with our developed software. Since the secondary structure of the crRNA is essential for its affinity to Cas13a, one can make assumptions on the post-binding RNase activity of the protein based on secondary structure predictions. We then compared these structures to secondary structures of crRNAs that have been shown to work experimentally. For the five crRNA sequences Team Delft provided, the predicted secondary structure matched one of the secondary structures in our databank. The only one that was not recognized by neither the NUPACK nor the mFOLD databank was crRNA 2. It's predicted secondary structure in Vienna notation is depicted below:

GGAUUUAGACUACCCCAAAAACGAAGGGGACUAAAACAAGACAGUCAUAAGUGCGGCGACGAU
..(((((((((.((((.........)))).((......))..)))).))))).((....))..

Figure 1: Graphical output of the secondary structure prediction of crRNA 2 in NUPACK.

Figure 2: Cleavage experiments of crRNA 2, crRNA 3 and crRNA 4 as provided by TU Delft.

Indeed, decreased activity using crRNA 2 in comparison to other crRNA is observed when performing experiments using these crRNA designs. Though this has to be treated with caution since crRNA 4's secondary structure was predicted to be correct but the Cas13a-crRNA-complex with that crRNA does not show higher activity. We conclude that further testing of the software has to be done to show whether it can predict activity of Cas13a.

Off-target Effects

Furthermore, in order to determine off-target effects, we constructed a database from Transcriptome data obtained from the Ensembl and EnsemblBacteria databank. We tried to make the search as tailor-made to the Delft project as possible and thus considered species that were relevant to their project of detecting bacterial resistances related to cattle and milk production. The included species were:

1. Bos Taurus (Cattle)
2. Clostridium perfringens
3. Corynebacterium diphtheria
4. Fusobacterium necrophorum
5. Lactobacillus casei
6. Lactococcus lactis
7. Providencia stuartii
8. Staphylococcus aureus
9. Streptococcus pneumoniae

From TU Delft

The TU Delft team developed a simulation a simulation code for modeling the on and off-target activities for sequences on all possible frames on the genome. In this, they employ a kinetic model by Depken et al. in 2017 to determine whether off-target activities are high enough to give a false positive collateral RNase activity of the Cas13a protein. They deducted from these model cleavage probabilities for different crRNA sequences and could thus make statements of the possibility to distinguish between our two bacterial targets, the 16s rRNA of E. Coli and B. subtillis. By running the crRNA against both genomes of these species, they determined the off-target probability to be very low for all our crRNA design as shown in Figure 3. Thus, they deduced from these that our crRNA design would be able to differentiate between E. Coli and B. subtilis.

Figure 3: Off-target probability of activation of the Cas13a protein for our crRNAs sensing E.Coli (1.3) and B. subtillis. crRNA 1-3 are taken into one since the spacer sequence of the crRNA was identical for these constructs.

References

1. Klein, M., Eslami-Mossallam, B., Gonzalez Arroyo, D., Depken, M. (2017). "The kinetic basis of CRISPR-Cas off-targeting rules." doi: 10.1101/143602.