Genome Features of “Dark-Fly”, a Drosophila Line Reared Long-Term in a Dark Environment

Organisms are remarkably adapted to diverse environments by specialized metabolisms, morphology, or behaviors. To address the molecular mechanisms underlying environmental adaptation, we have utilized a Drosophila melanogaster line, termed “Dark-fly”, which has been maintained in constant dark conditions for 57 years (1400 generations). We found that Dark-fly exhibited higher fecundity in dark than in light conditions, indicating that Dark-fly possesses some traits advantageous in darkness. Using next-generation sequencing technology, we determined the whole genome sequence of Dark-fly and identified approximately 220,000 single nucleotide polymorphisms (SNPs) and 4,700 insertions or deletions (InDels) in the Dark-fly genome compared to the genome of the Oregon-R-S strain, a control strain. 1.8% of SNPs were classified as non-synonymous SNPs (nsSNPs: i.e., they alter the amino acid sequence of gene products). Among them, we detected 28 nonsense mutations (i.e., they produce a stop codon in the protein sequence) in the Dark-fly genome. These included genes encoding an olfactory receptor and a light receptor. We also searched runs of homozygosity (ROH) regions as putative regions selected during the population history, and found 21 ROH regions in the Dark-fly genome. We identified 241 genes carrying nsSNPs or InDels in the ROH regions. These include a cluster of alpha-esterase genes that are involved in detoxification processes. Furthermore, analysis of structural variants in the Dark-fly genome showed the deletion of a gene related to fatty acid metabolism. Our results revealed unique features of the Dark-fly genome and provided a list of potential candidate genes involved in environmental adaptation

Dynamics of Dark-Fly Genome Under Environmental Selections.

「暗黒バエ」の暗闇適応に関わるゲノム配列の解析 -ゲノムの視点から環境適応のメカニズムに迫る-. 京都大学プレスリリース. 2016-03-09.Environmental adaptation is one of the most fundamental features of organisms. Modern genome science has identified some genes associated with adaptive traits of organisms, and has provided insights into environmental adaptation and evolution. However, how genes contribute to adaptive traits and how traits are selected under an environment in the course of evolution remain mostly unclear. To approach these issues, we utilize "Dark-fly", a Drosophila melanogaster line maintained in constant dark conditions for more than 60 years. Our previous analysis identified 220, 000 single nucleotide polymorphisms (SNPs) in the Dark-fly genome, but did not clarify which SNPs of Dark-fly are truly adaptive for living in the dark. We found here that Dark-fly dominated over the wild-type fly in a mixed population under dark conditions, and based on this domination we designed an experiment for genome reselection to identify adaptive genes of Dark-fly. For this experiment, large mixed populations of Dark-fly and the wild-type fly were maintained in light conditions or in dark conditions, and the frequencies of Dark-fly SNPs were compared between these populations across the whole genome. We thereby detected condition-dependent selections toward approximately 6% of the genome. In addition, we observed the time-course trajectory of SNP frequency in the mixed populations through generations 0, 22, and 49, which resulted in notable categorization of the selected SNPs into three types with different combinations of positive and negative selections. Our data provided a list of about 100 strong candidate genes associated with the adaptive traits of Dark-fly

Data_Sheet_1_Experimental test of the contributions of initial variation and new mutations to adaptive evolution in a novel environment.zip

Experimental evolution is an approach that allows researchers to study organisms as they evolve in controlled environments. Despite the growing popularity of this approach, there are conceptual gaps among projects that use different experimental designs. One such gap concerns the contributions to adaptation of genetic variation present at the start of an experiment and that of new mutations that arise during an experiment. The primary source of genetic variation has historically depended largely on the study organisms. In the long-term evolution experiment (LTEE) using Escherichia coli, for example, each population started from a single haploid cell, and therefore, adaptation depended entirely on new mutations. Most other microbial evolution experiments have followed the same strategy. By contrast, evolution experiments using multicellular, sexually reproducing organisms typically start with preexisting variation that fuels the response to selection. New mutations may also come into play in later generations of these experiments, but it is generally difficult to quantify their contribution in these studies. Here, we performed an experiment using E. coli to compare the contributions of initial genetic variation and new mutations to adaptation in a new environment. Our experiment had four treatments that varied in their starting diversity, with 18 populations in each treatment. One treatment depended entirely on new mutations, while the other three began with mixtures of clones, whole-population samples, or mixtures of whole-population samples from the LTEE. We tracked a genetic marker associated with different founders in two treatments. These data revealed significant variation in fitness among the founders, and that variation impacted evolution in the early generations of our experiment. However, there were no differences in fitness among the treatments after 500 or 2,000 generations in the new environment, despite the variation in fitness among the founders. These results indicate that new mutations quickly dominated, and eventually they contributed more to adaptation than did the initial variation. Our study thus shows that preexisting genetic variation can have a strong impact on early evolution in a new environment, but new beneficial mutations may contribute more to later evolution and can even drive some initially beneficial variants to extinction.</p

Summary of genome sequencing.

<p>The results of genome sequencing using an Illumina Genome Analyzer II are summarized. Flybase Dmel 5.22 genome (168,736,537 bases) was used as a reference genome.</p

Survival curves of Dark-fly and Oregon-R-S.

<p>The viability of male flies (A) and female flies (B) reared together is plotted versus time (days). Dark-fly (red lines) and Oregon-R-S (blue lines) were reared under LD (dotted lines) and DD (solid lines) conditions. The viability of virgin females (C) was also measured in a similar manner. n = 92–100 flies. Oregon-R-S virgin females showed longer longevity than the mated ones, whereas Dark-fly virgin females showed shorter longevity than the mated ones.</p

History of Dark-fly.

<p>In 1954, a fly population derived from one pair of Oregon-R-S flies was divided into 6 populations. Three of them (aL, bL and cL populations) were reared in normal light-dark cycling conditions and the remaining three populations (dD, eD, and fD populations) were reared in constant dark conditions. Unfortunately, all of the L lines were lost by 2002. The dD and eD lines were lost in 1965 and 1967, and only the fD line has been maintained until now. In 2008, we started to rear the fD line and designated it “Dark-fly”. We have maintained Dark-fly in a minimum medium as done before (black lines), and in a standard cornmeal medium (white lines) in parallel. The population size of Dark-fly has not been controlled but has usually been about 100 flies each in several culture vials.</p

SNP, InDel and CNV analyses.

<p>These data represent a summary of our analyses of SNPs, InDels and CNVs for the Dark-fly and Oregon-R-S genomes. ND means not determined.</p

Homozygosity and ROH regions.

<p>Mean homozygosity of SNPs in a sliding window (200-kb window at 100-kb steps) was plotted versus the location on 2L (A), 2R (B), 3L (C), 3R (D) and X (E) chromosomes. The Oregon-R-S genome (blue lines) displayed higher homozygosity than the Dark-fly genome (red lines) in most of the regions. Thick horizontal bars represent ROH regions identified by PLINK software for Oregon-R-S (blue bars) and Dark-fly (red bars) and are plotted above the graph without homozygosity values.</p

Genes carrying nsSNPs and cInDels in the Dark-fly ROH regions.

<p>The chromosomal position and length of the Dark-fly ROH regions showing significantly high homozygosity are listed. Genes carrying nsSNPs and InDels in each ROH region are shown. Details regarding nsSNPs and cInDels are presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033288#pone.0033288.s004" target="_blank">File S1</a>.</p

Alignment of read sequences around CG4594 gene.

<p>A view of Integrated Genomics Viewer around the CG4594 gene. The numerous small gray bars represent reads of genome sequencing. A region of about 500 bases in the CG4594 gene (red thick bar) was not covered by any read sequences of the Dark-fly genome (upper), but was fully covered by the sequences of the Oregon-R-S genome (lower). Numbers on a horizontal line indicate nucleotide position on chromosome 2L. Numbers on vertical alignment indicate read depth.</p