At Year’s End: Staff Editors’ Favorite PLOS ONE Articles of 2014

2014 has been an exciting year for PLOS ONE. We saw the journal reach a milestone, publishing its 100,000th article. PLOS ONE also published thousands of new research articles this year, including some ground-breaking discoveries, as well as some unexpected … Continue reading »

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Breaking in order to Build: Part 2

Image Courtesy of Michael Schmidt

Image Courtesy of Michael Schmidt

I recently wrote about how breaks in neural DNA may be part of the process our neurons use to generate new memories. About the same time, I found a new study in Science that addressed the role of the genome in neurons from a different angle. It turns out that Drosophila (fruit flies) have particularly heterogeneous genomes in the neurons associated with learning and memory. Now let me back up and explain exactly what I mean by heterogeneous genomes and how that can affect learning and memory.

In fruit flies, one of the parts of the brain dedicated to learning and memory is called the mushroom body (MB). In this new study, the mushroom body was labeled with green fluorescent protein (GFP) so that when a fruit fly’s brain is broken down into individual cells the green ones can be sorted out. Once the cells are sorted, differences in gene expression between the green MB cells and other brain cells can be compared. It turns out that the cells focused on learning and memory, have a much higher level of expression of many types of transposons. Transposons are little bits of DNA that are able to hop around the genome inserting themselves and then stealing bits of DNA when they hop out again. Most of the time, they disrupt gene expression either by inserting themselves right into a gene or taking pieces of a gene with them when they hop out. This results in heterogeneous genomes in the brain because a subset of cells have a highly variant genome compared to other cells in the same brain.

Why are there preferentially more of these “disruptive” elements in the parts of the brain critical for learning and memory? It turns out that the genes that normally prevent transposons from hopping around are expressed at a much lower level in the MB. Low expression of the genes that repress transposons may be necessary for learning and memory in some way that is totally unrelated to their role in regulating transposon hopping. The transposon hopping may be a tolerable side effect of the process of memory building. Alternatively, transposons hopping in and out of the genome ends up results in a lot of genetic variance, and this high level of genetic variance may be important for the learning and memory activity of these specific cells. The risk associated with  excessive transposon mobility may be inextricably linked to making memories in these cells. Eventually, the high level of sequence exchange will result in cumulative deleterious changes similar to the situation found with double strand breaks in mammalian neural DNA. As of now, it’s not clear exactly how the high genetic variability in only a subset of neurons is really contributing to learning and memory. It is intriguing to compare the fly data with the mouse data. What is the advantage of high genetic variability in these learning and memory centers in the brain?

 


New method inferring natural selection published today

I am pleased to report that my new paper "A population genetics-phylogenetics approach to inferring natural selection" is published today in PLoS Genetics. This is the culmination of two years work at the University of Chicago with Molly Przeworski, plus a good deal of follow-up since I moved to Oxford. In the paper we introduce a new way of combining population genetics and phylogenetics models of natural selection, and a statistical method (gammaMap) for estimating parameters under the model. From a collection of sequences within one or more species - in the paper, we use 100 X-linked coding sequences that Peter Andolfatto produced in Drosophila melanogaster and D. simulans - the method allows you to estimate the distribution of fitness effects within each lineage, and localize the signal of selection using a Bayesian sliding window approach. Using Ryan Hernandez's simulator SFSCODE we tested the method for robustness to demographic change and linkage disequilbrium, and we investigated the effect that common assumptions concerning spatial variation in selection coefficients (sitewise, genewise and sliding window approaches) have on inference of selection. During the winter break I will work on compiling the program for different platforms and writing the documentation, with a view to releasing the software early in the New Year. Subscribe to this blog for updates or - if you are too impatient to wait - send me an email.

Discovering the distribution of fitness effects

At this year's Society for Molecular Biology and Evolution meeting in Lyon I presented ongoing work estimating the distribution of fitness effects, which is a collaborative venture with Molly Przeworski and Peter Andolfatto. Earlier versions of this research appeared in talks I presented at Chicago in December (Ecology and Evolution Departmental seminar) and Liverpool in January (UK Population Genetics Group meeting), and it follows on from last year's SMBE presentation in which I discussed methods to tease out sub-genic variation in selection pressure.

There is intrinsic interest in the fitness effects of novel mutations in coding regions of the genome, especially the relative frequency of occurrence of neutral, beneficial and deleterious variants. Yet estimating the distribution of fitness effects (the DFE) is also of practical use when localizing the signal of adaptive evolution. The reason is that in Bayesian analyses, the assumed DFE can influence the strength of evidence for or against adaptation at a particular site. Consequently it is preferably to estimate the DFE at the same time as detecting adaptation at individual sites to avoid prior assumptions unduly influencing the results.

Having estimated the DFE, it is of use in quantifying the relative contribution of adaptation versus drift to genome evolution. The figure, taken from my talk in Lyon (slides here), illustrates the idea when a normal distribution is used to estimate the DFE; the relative area of the green to the yellow shaded regions represents the respective contribution of adaptation versus drift in amino acid substitutions accrued along the Drosophila melanogaster lineage.