Below you find the data from my experimental rounds A and B:

-> learning scores of the parental flies from experimental round A and B

-> learning scores of the trained parent´s offspring only from experimental round A

-> learning scores of the untrained parent´s offspring only from experimental round A

The results confuse me a lot and I am happy to discuss reasons :) However the offspring of the round-B will be ready for testing by the end of this week so there is still some data to collect…

I now switched the sign of the Optomotor Asymmetry Index in flies that were punished on producing right-turning torque, such that weaker punished torque shows up as a positive index. After that was done, I plotted the correlation between the optomotor index and the preference index:

I had to get rid of eight flies where the optomotor response was already asymmetric before the training started, so now I only have 33 flies. But with these flies, there is no correlation before training and a very significant correlation after training.

Any suggestions about appearance of the graphs?

Would it be useful to plot optomotor and performance indices as raincloudplots next to the regressions?

This would be the complete figure:

The results of comparing optomotor responses after self-learning remain solid. There still is a small asymmetry between the left/right groups, but nothing dramatic:

DNA Assembly in a 1:2 ratio of vector to insert with gRNAs of rsh and rut (Q5 and template concentration: 640 pg/µl) and 100ng of pCFD6 BbsI AP (using QuickCIP). Heat-shock (hs) transformation into E. coli (DH5α competent) with 10 µl Assembly Reaction and 100 µl cells.

For Crtl, pCFD6 BbsI AP was wrongly used.

DNA Assembly in a 1:2 ratio of vector to insert with the gRNAs of rsh and rut (Q5 and template concentration: 640 pg/µl) and 100ng of pCFD6 BbsI AP (using FastAP and 2 extraction steps). Heat-shock (hs) transformation into E. coli (DH5α competent) with 10 µl Assembly Reaction and 100 µl cells.

For Crtl, pCFD6 BbsI AP was used in reaction.

The offspring of my first experimental fly cohort finally hatched! Below you find a few first pre-tests I ran last week :)

First, here are the results of a quick test to see if the offspring shows a preference for the parentally trained side after the first training period:

After that I played around with the laser a little bit to find the learning threshhold. I set the laser on 2,6V but the results I got look a bit weird:

-> untrained wtb flies

-> offspring of trained wtb flies

I´m optimistic however there is still a lot of work to do…

Via gDNA analysis and PCR was the specific area of the rsh gene extracted and amplified where the nucleotide substitution: C to T (Folkers et al., 2006) should be for the rsh¹ mutation. The amplicon was Sanger sequenced which proved the nucleotide substitution.

Despite very warm weather, some flies did fly, even though the learning performance of the control flies was really poor. At least for now, it looks like all stocks are learning and that rut and rsh flies learn at least equally well as the Berlin flies. I’ve also managed to fix the positive preference problem:

Since the Canton S strain I used wasn’t a perfect genetic background strain anyway and didn’t fly properly, I kicked it out and replaced it with the wild type Berlin data I had collected just prior to the rut and rsh mutants. Now I can collect data for two projects in one go: I check the rut/rsh learning mutants if they still behave the way they should and with the wtb control strain and continue collecting data to evaluate their optomotor responses after training. So now the learning scores for the three strains look like this:

It still doesn’t look like rut is better than wtb, so I’m still skeptical that the strain is really what it should be. The rsh data also don’t look very promising, but I still need to get more flies with a negative preference before the training.