KLINIKUM PART

After 2 immunization on the mice we made an ELISA test in order to see if there is an immunogenic response in the serum. There is a response for both the peptides, the only problem is that is not really specific: the mice immunized with one peptide (URE-9) show a response also for the other peptide (URE-10), even if slightly lower, and viceversa.

Also the response is not great enough, therefore a 3rd immunization is required.

We also finally have the recombinant protein (FoxP IsoB-FLAG). Here´s the picture of the western blot with the band of the appropriate size (60 kD) on the right. On the left is the same band from the cell lysate.

After performing EMD to 6 fly traces of 20000 data points (that is 1000 sec flight) for each group (tntXwtb; c105;c232>tnt; c105;c232Xwtb). This data size was chosen to reduce computing time of the SMAP procedure. The EMD decomposes the trace into different time scales in nonstationary data. It seems that the nonlinear behavior occurs at the first IMF (the fastest time scale) and a bit in the second IMF. The potential conclusion to this is that the behavior of the the fly is only unpredictable at the fast movements whereas slow movements are very predictable. Nevertheless, to be cautious it could be that this fastest timescale is just noise, and that this noise is nonlinear. I would say that there is no difference at any time scale between groups (pay attention to the different ranges in the Y-axes), so the ring neurons R1, R3, R4d do not have any effect.

As a groundtruth I have used the same analysis pipeline for the traces in the uniform arena from Maye et al. 2007. Here the effect is even more pronounced at the fastest time scales. So I will conclude that this is real fly behavior and not noise that is shared among both setups: the Ping pong ball machine and the torquemeter.In order to gain more insights into the underlying flight structure I took one random flight trace to explain a few observations. The x-axis is the theta (that actually goes from 0-4 in steps of 0.2 and therefore we see the 21 points), in the y-axis is the correlation of the prediction to groundtruth. We see that IMF has a bigger slope, but not only that, also that its prediction correlation is around 0.88, whereas lower timescales prediction is basically perfect. That is, fast time scales are not only more nonlinear but also less unpredictable. This pattern is repeated in every fly measured

To have an impression of how these IMF resultant traces look like: IMF1, IMF2 and IMF8

This is for the sake of playing and curiosity. I made out of these two traces a modelling of their flying trace in a 2D world. Direction is right wing amplitude – left wing amplitude and distance flown is dependent on the sum of both (more amplitude of both, more forward thrust). Funny enough, the second one looks kind of fractal, which is characteristic of chaotic behaviors. If there is any comment to add to this new visualization, all ears!

Trace segment from Maye et al. 2007 in the uniform arena

Trace after filtering by selecting the first 8 IMFs (intrinsic mode functions) from EMD (empirical mode decomposition). Since the signal should be quite clean I do not take out the first IMFs. The last IMFs, however, are too slow and change the baseline to much

Some examples of the IMFs obtained from EMD:

Since this is separating behavior adapting to the data intrinsic time scales I am now thinking of analysing with the SMAP algorithm to see if the behavior is more or less nonlinear at certain time scales.

In addition, I have thought of using ICA (Independent component analysis), that is an algorithm famous for the blind source separation problem by extracting the most independent signals from the input signals (in this case the IMFs which are different time scales of the behavior). So the ICs should consist of mixes of different time scales that are correlated together and thus belong (but not necessarily) to the same action/movement module/… Here a few ICs (from 10). My idea is that muscles might coordinate independently between ICs but coordinated within ICs. However to prove that is not that easy I guess

UNIVERSITY PART

The image shows the presence of the HomI insert (upper bands) in a vector where we have already ligated the HomII insert for the FoxP isoform B. This is the construct that will allow us to insert the Gal4 in the ORF. This construct is thus ready to be injected

UNIVERSITY PART

How the technique works: tRNA based vectors for producing multiple CRISPR sgRNA from a single transcript. Using multiple sgRNA will augment gene repression. In this technique the endogenous tRNA processing machinery liberates multiple functional sgRNA from a single precursor transcript in the nucleus. The vector used (pCFD6) permits cloning of several (three in this case) sgRNA flanked by Drosophila tRNA downstream of a single promoter.

The conditional KO can be done by using UAS-tRNA-sgRNA constructs.

PCR amplification product of three different FoxP fragments, those fragment will be ligated in only one appropriate vector.

PCR of the tentative of ligation of all the 3 fragments above: green arrows indicate clones that should be having incorporated all 3 fragment, orange arrows indicate clones that can be positive to all of 3 fragments but have a strange double band (the sequencing step will tell us the results).

KLINIKUM PART

Protocol for mice immunization for creating monoclonal Ab

UNIVERSITY PART

Map of the phd-ds-Red-attP plasmid used for cloning: 1 desired construct is aiming to target all dFoxP isoforms with one homolgy domain starting at exon 1 to exon 3, the second homology domain starting at exon 3 to exon 6. The second construct is aimed to target just dFoxP IsoB, with both the homology domain at the end of the protein. This plasmid is aimed to knock down the FoxP gene without the Gal-4 knock-in.

PCR product of the homology domains 1 and 2 for FoxP (upper part of the image, left and right), and homology domains 1 and 2 for FoxP-isoformB (lower part of the picture, left and right): amplification of the desired homology domains via PCR (5 replicates for each construct in order to test different annealing temperatures). This products are going to be used for the subsequent cloning in the plasmid.

UNIVERSITY PART

Maps of the FoxP gene and of the pTGem plasmid used for cloning: 1 desired construct is aiming to target all dFoxP isoforms with one homolgy domain starting at exon 1 to exon 3, the second homology domain starting at exon 3 to exon 6. The second construct is aimed to target just dFoxP IsoB, with both the homology domain at the end of the protein.

PCR product of the homology domains 1 and 2 for FoxP (upper part of the picture, left and right), and homology domains 1 and 2 for FoxP-isoformB (lower part of the picture, left and right): amplification of the desired homology domains via PCR (4 replicates for each construct in order to test different annealing temperatures). The products will be used for subsequent cloning in the vector.

Plasmid with Hom1 Hom2 FoxP ready: the image shows the presence of the homology domain 2 for dFoxP in a plasmid where we already ligated the homolgy domani 1. This construct is thus ready to be injected.

KLINIKUM PART

We transfected E.coli cells with the plasmids for IsoA and B from the Schaff lab (Berlin) and subsequently we sequenced them with 4 primers each (1 FW primer sequence in the plasmid upstream the ORF and 1 RV, 1 FW primer sequence in the middle of the ORF and 1 RV).

pcDNA plasmid IsoA correctly amplified in E.coli

pcDNA plasmid IsoB correctly amplified in E.coli

Some traces from this week just so that you have an idea how do they look like. To me they are not the optimal traces I expected. But one can see some signal there. I will start the screen hoping to get enough good traces without too much work.

what do you think is the best quality control for accepting a trace for the analysis or not. I was thinking the 3D mapping gives a good hint but without quantification.

In addition I was writting to Andi Straw to solve the issue of running two cameras in the same computers, but now answer

Self-learning of Rover/sitter from flight simulator

Long-term memroy from self-learning

Self-learning training/tests from transgeneic flies