Studying the hourglass model for the evolution of development using mutation-accumulation strains: the story behind the story
One thing I've learned about scientific work is that reading papers makes it seem easy, at least on paper (excuse the pun)... This blog tells the story behind the project, the tough moments and the challenges
I remember the day we came up with the idea for this project. It was right after a group meeting, where we discussed a recently published paper by Mirko Fracesconi and Ben Lehner (Nature, 2014). In that paper, the authors showed how developmental gene expression patterns change due to genetic variations. Being in a lab with a great interest in the evolution of development (evo-devo), we wondered how we can use such an approach to tackle an age old question - why is there an embryonic stage that is much more conserved than the rest across species of the same phylum? I am referring, of course, to the phylotypic stage, whose increased conservation is summarized by the hourglass model of development. We recognized that studying the pattern of gene expression differences throughout development would allow us to search for evidence for this mythical stage. I was very excited that in this post-genomic era we have the capability to answer old questions with new molecular tools.
The approach that we came up with was to compare gene expression (of all genes) among strains, across embryonic time, and do it with C. elegans which is the model organism in our lab. We figured that different strains would serve as a good model for divergence within a species, and the question would be – considering the variations between the genomes of different strains, how will different embryonic stages be affected? Will the phylotypic stage remain unchanged? We initially considered using the same strains as in Francesconi and Lehner's paper, but these were not a good model for divergence as these strains were made by crossing two C. elegans strains. We wanted strains with mutations that could not even be attributed to positive selection.
Instead, we realized that mutation-accumulation strains were perfect for our goal. They are all derived from a common ancestor, possessing myriad mutations, and they are easy enough to work with. So, after receiving the delivery of more than 60 strains from Charlie Baer (University of Florida), after they were stuck in Israel’s customs for about a month, all that was left for me to do was to clean the cultures, grow all of them enough so I can create a stock of frozen cultures, collect as many embryos as I can from as many different strains, process them, and construct sequencing libraries. That was the easy part.
When the fire of the wet lab work settled, I was standing in front of the ruins I created – big data! What am I supposed to do with it now? Alright, I said to myself, I’ll just need to learn some programming and I should be fine. I’ll admit that that was fun. I had never coded before, and getting into it gave me a sensation of superiority: "Look at you, big data! You're not so scary!" But looking for a signal within the data wasn't easy. We tried all kinds of methods described by others in the field, but none seemed to be appropriate for our data. So now we also had to be creative and innovative (what a pain!). We finally came up with what we call "Zavit" to aid us. So I wrote the code, I checked for bugs, I ran the code, and there it was – the signal! The phylotypic stage is highly conserved between these strains. As you can imagine, a sigh of relief filled the lab that day. From that point on, everything just seemed to fall into place. We found interesting results and I remembered why science is so awesome.