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Species-Wide Genetic Variation and Demographic History ofDrosophila sechellia, a Species Lacking Population Structure
Author(s):
Delphine Legrand ,
Maud I. Tenaillon ,
Pat Matyot ,
Justin Gerlach ,
Daniel Lachaise ,
Marie-Louise Cariou
Publication date Created:
August 20 2009
Publication date (Electronic):
June 08 2009
Journal:
Genetics
Publisher:
Genetics Society of America
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Abstract
Long-term persistence of species characterized by a reduced effective population size
is still a matter of debate that would benefit from the description of new relevant
biological models. The island endemic specialist Drosophila sechellia has received
considerable attention in evolutionary genetic studies. On the basis of the analysis
of a limited number of strains, a handful of studies have reported a strikingly depleted
level of genetic variation but little is known about its demographic history. We extended
analyses of nucleotide polymorphism in D. sechellia to a species-wide level using
10 nuclear genes sequenced in 10 populations. We confirmed that D. sechellia exhibits
little nucleotide-sequence variation. It is characterized by a low effective population
size, >10-fold lower than that of D. simulans, which ranks D. sechellia as the least
genetically diverse Drosophila species. No obvious population subdivision was detected
despite its fragmented geographic distribution on different islands. We used approximate
Bayesian computation (ABC) to test for demographic scenarios compatible with the geological
history of the Seychelles and the ecology of D. sechellia. We found that while bottlenecks
cannot account for the pattern of molecular evolution observed in this species, scenarios
close to the null hypothesis of a constant population size are well supported. We
discuss these findings with regard to adaptive features specific to D. sechellia and
its life-history strategy.
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Most cited references40
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Introduction to Conservation Genetics
Richard Frankham, Jonathan Ballou, David Briscoe … (2002)
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Odorant-Binding Proteins OBP57d and OBP57e Affect Taste Perception and Host-Plant Preference in Drosophila sechellia
Takashi Matsuo, SHIGERU SUGAYA, Jyunichiro Yasukawa … (2007)
Introduction Every animal must locate and identify sufficient food to meet its biological requirements. For herbivorous insects, this results in an endless battle with their host plants [1]. For example, some plants develop a chemical defense system that causes toxicity to generalist herbivores [2]. In response, generalist herbivores may then evolve a behavioral system to avoid such toxic plants. If an insect species acquires resistance to a plant toxin, however, it may reap an ecological advantage by gaining exclusive access to the toxic plant and may subsequently evolve as a specialist herbivore with a specific preference towards that plant. Such physiological and behavioral specialization plays an important role in the evolution of divergent ecological interactions between herbivores and their host plants. Nevertheless, it does not necessarily follow that ecological specialization for a particular host plant drives speciation of herbivores itself. Such specialization may not be sufficient to maintain divergence between populations at an early stage of speciation, in the face of potential gene flow via hybridization between evolving populations. The role of ecological specialization in speciation remains, therefore, to be proven [3]. Thus, it is necessary to identify the genes and molecular mechanisms responsible for ecological adaptation if we are to understand whether ecological adaptation can be a cause, or merely a consequence, of speciation [4]. Behavioral adaptation of herbivorous insects to their host plants involves the evolution of the chemosensory system [5–7]. With the recent identification of olfactory and gustatory receptors [8], knowledge of the genetic and molecular mechanisms of insect olfactory and gustatory system markedly progressed. Recent analysis of genomic information from several insect species has also revealed that the number of genes encoding these receptors varies considerably between species, indicating a close relationship between the genomic constitution of chemoreceptor gene families and the species-specific lifestyles of insects [9–11]. Thus, it is likely that the genes responsible for ecological adaptation are to be found among these receptor-encoding and receptor-related genes. Genetic studies of Drosophila have also contributed to a substantial amount of our knowledge of “speciation genes” [4]. However, these studies have primarily focused on genes that cause reproductive isolation, and genetic analysis of ecological adaptation is relatively rare. This is, in part, due to the surprisingly limited information about Drosophila in the wild, compared with those flies reared in the laboratory as a sophisticated model system of genetics. In fact, we know little about their natural foods in the wild, except for a few species. Drosophila sechellia is a specialist of Morinda citrifolia, which is commonly known as Tahitian Noni [12]. Although D. sechellia shows a preference for and resistance to the ripe fruit of M. citrifolia, its most closely related species, D. simulans and D. mauritiana, as well as D. melanogaster, are generalists and die upon contact with M. citrifolia, and thus avoid the fruit [13,14]. Because of genetic resources available for D. melanogaster and D. simulans, D. sechellia is an ideal organism with which to explore the genetics of ecological specialization. Analysis of quantitative trait loci (QTL) between D. sechellia and D. simulans has already identified the chromosomal regions responsible for the interspecies difference in resistance to the toxicity of M. citrifolia [15]. However, D. sechellia's preference for M. citrifolia was explained only by the transformation of olfactory sensilla resulting in an increase of the ab3 subtype that responds to the host odorant methyl hexanoate (MH) [16]. These findings successfully describe the present status of D. sechellia's specialization for M. citrifolia, but the evolutionary history, especially how an ancestral population started to use the toxic plant as its host, has been unexplained. Here, for the first time, we have identified genes involved in D. sechellia evolution. These genes are responsible for the behavioral differences between species in their responses to hexanoic acid (HA) and octanoic acid (OA), the toxins contained in the ripe fruit of M. citrifolia, which give it its characteristic odor. Having identified the genetic factors constituting D. sechellia's adaptation to M. citrifolia, we are now able to discuss more confidently whether host-plant specialization can drive D. sechellia speciation. Results Mapping of Locus Responsible for Interspecies Difference in Avoidance of HA We previously reported that the behavioral difference (preference/avoidance) between D. sechellia and D. simulans in response to HA, one of the main components of odor from the ripe fruit of M. citrifolia, is controlled by at least one gene on the second chromosome [17]. Further analysis of the introgression lines between D. sechellia and the D. simulans second chromosome marker strain (net b sd pm) indicated that the behavioral difference is linked to the marker pm, which is on the distal end of the right arm of the second chromosome (I. Higa and Y. Fuyama, unpublished data). Considering the fact that the overall structure of the second chromosome is conserved between D. simulans and D. melanogaster, we mapped the locus in more detail using a series of D. melanogaster deficiency strains lacking a terminal part of the right arm of the second chromosome. Because D. sechellia's preference for HA is a recessive trait to D. melanogaster's avoidance [17], the interspecies hybrids between D. sechellia and D. melanogaster deficiency strains that lack a region containing the responsible gene(s) were expected to show the D. sechellia–like phenotype, i.e., preference for HA. Two deficiency strains, Df(2R)exu1 and Df(2R)AA21, showed preference for HA when they were crossed with D. sechellia, defining the responsible locus within a very small chromosomal region, in combination with Df(2R)exu2, which showed avoidance to HA when crossed with D. sechellia (Figure 1A). Because the break points of these deficiency chromosomes had been deduced from cytological observations, we determined the position of these break points precisely by PCR-direct sequencing of genomic DNA from hybrids between D. melanogaster deficiency strains and D. sechellia (Figure 1B). According to the left break point of Df(2R)exu1 and the left break point of Df(2R)exu2, the locus was narrowed down within about 200 kilobases (kb) of the genomic region that contains 24 predicted genes. There is no large deleted region in the Df(2R)AA21 chromosome around this area, which is inconsistent with the result that Df(2R)AA21 also showed preference for HA when crossed with D. sechellia. While examining the marker sequences used in break-point determination of Df(2R)AA21, however, we incidentally found that this chromosome has a small, ten–base pair (bp) deletion in the first exon (open reading frame [ORF]) of the Odorant-binding protein 57e (Obp57e) gene resulting in a frame-shift mutation (Figure 1C). Insect OBP is a protein secreted into the lymph of chemosensory hairs, and it has been shown to play a crucial role in chemosensation [18]. Thus, it seemed likely that Obp57e is a gene responsible for the interspecies difference in response to HA. However, when Obp57e ORF sequences from D. melanogaster, D. simulans, and D. sechellia are compared, there is no D. sechellia–specific alteration except for L11I, which does not affect the result of signal peptide–sequence prediction (Figure 1D). Thus, D. sechellia Obp57e ORF is supposed to be functionally intact, suggesting that the interspecies difference is not in the structure of the gene product, but rather in gene expression. Altered Expression Control of Obp57e in D. sechellia Quantitative reverse-transcriptase polymerase chain reaction (RT-PCR) analysis revealed that the level of Obp57e transcripts is higher in the legs of D. sechellia than in D. melanogaster and D. simulans (Figure 2). This could be due to an elevated transcription activity in particular cells and/or a widened expression pattern. According to the lacZ reporter experiment, D. melanogaster Obp57e is expressed only in four cells associated with chemosensory hairs on the fourth and fifth segments of each tarsus, the most terminal part of an insect leg [19]. We confirmed that as short as 450 bp of the upstream region of Obp57e completely reproduces the reported expression pattern (Figure 3A–3C). We then cloned the corresponding region from D. simulans and D. sechellia, and introduced it into D. melanogaster with a green fluorescent protein (GFP) reporter gene. The D. simulans sequence successfully reproduced the same expression pattern as observed in D. melanogaster (Figure 3D). However, the D. sechellia sequence failed to drive GFP expression in any parts of the fly body (Figure 3E), indicating that the function of the D. sechellia sequence to promote gene expression is altered. Indeed, when the upstream sequence of Obp57e is compared between species, a 4-bp insertion was found in the D. sechellia Obp57e upstream sequence (Figure 3H). GFP expression was restored by removing the inserted 4-bp nucleotides from the D. sechellia sequence, showing that this 4-bp insertion abolishes the function of the D. sechellia Obp57e promoter sequence in D. melanogaster (Figure 3F and 3G). Nevertheless, the results of GFP reporter experiments are inconsistent with that of quantitative RT-PCR analysis, thus, the exact expression pattern of Obp57e in D. sechellia remains unclarified. Therefore, it is necessary to evaluate using more direct methods whether Obp57e is truly responsible for the interspecies difference in behavioral response to HA. Targeted Mutagenesis of Obp57d/e Knock-Out Flies We generated D. melanogaster knock-out flies for Obp57e, as well as for its neighbor Obp57d, and for both Obp57d and Obp57e, by gene targeting (Figure 4). The ends-out method was employed to achieve precise gene replacement in the gene-dense Obp57d/e region (Figure 4A). To avoid side effects on transcription of surrounding genes, the marker gene (3 kb) was excised by Cre recombinase, leaving only 34 bp of the loxP sequence. Each donor construct was designed such that the ORF was removed exactly from the ATG translation initiation site, but a putative poly-A additional signal was left intact, ensuring the termination of residual transcription that may affect the expression of downstream genes via read-through events (Figure 4B). The loss of transcripts from the targeted gene was confirmed by quantitative RT-PCR in each knock-out strain (Figure 2). We observed, however, an unexpected interaction between Obp57d and Obp57e in their expression control. The amount of Obp57e transcripts was higher in Obp57dKO flies than in the w1118 control strain. On the other hand, the amount of Obp57d transcripts decreased in the legs of Obp57eKO flies. Because each knock-out strain lacks the intron and the ORF, these regions may contain elements that influence the expression of the other gene. Altered Behavioral Responses to HA and OA in the Knock-Out Flies Each knock-out strain responded to HA differently from the control strain in the trap assay (Figure 5). Obp57dKO and Obp57eKO avoided HA, whereas females of Obp57d/eKO preferred it, suggesting that not only Obp57e, but also Obp57d, is involved in the behavioral difference observed in the screening assay. In fruit flies, host plants are largely determined by the oviposition site preference of adults. Thus, we also examined the oviposition site preference of knock-out flies in response to HA. Indeed, Obp57eKO and Obp57d/eKO seem to prefer lower concentrations of HA than the control flies, although the difference is not statistically significant (Figure 6, Tables 1–4). The direction of behavioral alteration was, however, not the same as that found in the trap assay for Obp57d/eKO . We also examined oviposition site preference in response to OA, the main toxic component in Morinda fruit. Because of its toxicity at high concentrations, the oviposition assay was carried out at concentrations lower than those of HA. Obp57dKO and Obp57eKO preferred higher concentrations of OA. This preference was particularly obvious for Obp57dKO, which was comparable to that of D. sechellia. Contrary to the responses to HA and OA, knock-out strains preferred concentrations of acetic acid and butyric acid similar to those preferred by control flies, showing that the alteration of behavioral responses in these knock-out strains is specific to HA and OA. Our observation of the behavior of Obp57dKO, Obp57eKO, and Obp57d/eKO revealed that these strains are qualitatively different from each other in their responses to HA and OA. This strongly suggests that Obp57d, as well as Obp57e, is involved in D. sechellia's behavioral adaptation to M. citrifolia. Nevertheless, none of these knock-out strains was identical to D. sechellia in behavior. This is consistent with the results of quantitative RT-PCR analysis in which no knock-out strain exhibited an expression profile identical to that of D. sechellia, proving that this species is not a simple null mutant of Obp57d and/or Obp57e. Rather, D. sechellia seems to be a neomorphic mutant with an altered expression control of these genes. Replacement of Obp57d/e Region Altered Oviposition Behavior To examine the functions of Obp57d and Obp57e in D. simulans and D. sechellia, we cloned these genes from D. simulans and D. sechellia and introduced them into the D. melanogaster Obp57d/eKO strain. Because an interaction between the two genes was observed with respect to their expression control, a genomic fragment spanning both Obp57d and Obp57e was used for genetic transformation. The resulting transformant flies showed altered responses to HA and OA in the oviposition site–preference assay (Figure 6; Tables 3 and 4). Obp57d/eKO; simObp57d/e flies avoided HA as D. simulans does. Conversely, Obp57d/eKO; secObp57d/e flies preferred high concentrations of OA as D. sechellia does. These results clearly showed that the Obp57d/e genomic region contains genetic information responsible for, at least in part, the interspecies differences in behavioral responses to HA and OA. However, these transgenic flies are not complete mimicries of the original species. Although D. simulans avoided OA, as well as HA, the response of Obp57d/eKO; simObp57d/e flies to OA was not significantly different from that of the D. melanogaster control strain (Figure 6; Table 4). The responses of these two transgenic strains in the trap assay were also different from that of the original species (Figure 5). Consistent with the results of the oviposition assay, D. simulans avoided HA and D. sechellia preferred it. Obp57d/eKO; simObp57d/e females, however, did not avoid HA, and both sexes of Obp57d/eKO; secObp57d/e flies did not prefer it. Indeed, the expression profiles of Obp57d and Obp57e were not exactly the same between the transgenic strains and the corresponding original species (Figure 2). Although the genomic fragments seemed to reproduce the native expression better than the GFP reporters, there still remains significant differences in expression profile, particularly between Obp57d/eKO; simObp57d/e and D. simulans. These differences suggest a contribution of additional loci to Obp57d/e expression, and thus to the interspecies differences in behavioral responses to HA and OA. Nevertheless, the Obp57d/e genomic region from D. simulans and D. sechellia could reproduce, at least in part, the behavioral pattern of the original species in an otherwise D. melanogaster genomic background, proving that a genetic difference in this region is actually involved in interspecies differences in behavioral responses to odorants contained in M. citrifolia. It should be particularly noted that the Obp57d/e region is alone sufficient for the strong avoidance of HA by D. simulans, which is a key trait in the evolution of D. sechellia's adaptation to M. citrifolia, as discussed below. Discussion Molecular Functions of OBP57d/e LUSH (OBP76a), the best studied OBP in D. melanogaster, functions as an adaptor molecule in vaccenyl acetate (VA) recognition by an odorant receptor, OR67d [20]. Mutants lacking LUSH lose their neuronal response to VA; thus, they do not respond to VA behaviorally [18]. However, our Obp57d/eKO flies retained their behavioral responses to HA and OA, suggesting that OBP57d/e do not function as adaptors for HA and OA. Rather, they seem to modulate dose-dependent responses to HA and OA, which might be achieved by other proposed functions of OBP, such as the titration or degradation of ligands [21]. There are qualitative differences in the behavioral responses to HA and OA between Obp57dKO and Obp57eKO flies. These differences might reflect functional diversification between OBP57d and OBP57e. However, the elimination of either Obp57d or Obp57e affected the expression level of the other in these knock-out flies. Obp57d removal by gene targeting increased Obp57e expression level, and Obp57e removal repressed Obp57d expression. Thus, we cannot exclude the possibility that the behavioral differences between the knock-out strains are caused by an altered expression level of either gene. A more operative method such as the Gal4-UAS system could be used to separate promoters from ORFs, thus minimizing the interaction between these two genes in expression control. It would then be possible to examine the molecular functions of OBP57d and OBP57e independently. Expression Control of Obp57d and Obp57e The results from our GFP reporter experiments and quantitative RT-PCR analysis are inconsistent. This inconsistency is also a feature of previous studies. Galindo and Smith [19] showed that the reporter constructs with 3 kb of upstream sequence from Obp57d and Obp57e were expressed in four cells in each leg, which matches the results of our GFP reporter experiments. However, using RT-PCR analysis, Takahashi and Takano-Shimizu [22] detected the transcripts not only in tarsi, but also in labella and wings. Together with the results of our quantitative RT-PCR analysis, it is clear that the reporter constructs do not reflect the complete expression pattern of Obp57d/e. The difference could be, at least in part, due to the lack of coding region in the reporter constructs. In fact, the elimination of a coding region of either Obp57d or Obp57e affected the expression level of the other gene in Obp57dKO and Obp57eKO, suggesting the involvement of ORFs and/or an intron in expression control (Figure 2). Furthermore, the introduction of the Obp57d/e genomic region from D. simulans and D. sechellia reproduced the expression of Obp57d/e in the head as well as in the legs, which was not observed in GFP reporter experiments. Although the Obp57d/e genomic region contains a considerable part of the genetic information that controls Obp57d/e expression, it is still not sufficient to explain all the differences in the expression profile between the species; genetic factors at loci other than Obp57d/e are also likely to be responsible. There are two possibilities for such factors: (1) Trans-acting factors such as a transcription factor that is necessary for Obp57d/e expression, could control expression by determining which type of cell expresses Obp57d/e, or by determining transcription level in particular Obp57d/e-expressing cells. (2) Developmental factors determining the cell fate to become Obp57d/e-expressing cells, could increase/decrease the number of Obp57d/e-expressing cells by transforming cell fate at the expense of other cell types. In fact, ab1 and ab2 sensilla on antennae are transformed into ab3 sensilla in D. sechellia [16]. Such cell-type transformation might have occurred also in Obp57d/e-expressing cells. Genes of these two categories could be identified by, for example, screening of mutants that alter the Obp57d/e > GFP expression pattern. Genetic Factors Constituting D. sechellia's Adaptation to M. citrifolia D. sechellia's adaptation to M. citrifolia consists of genetic changes at many loci. Although there are likely to be additional genetic differences between D. sechellia and D. simulans, the present status of D. sechellia's adaptation to M. citrifolia can be explained by alterations in three classes of genetic factors, as follows. Factors responsible for resistance to the host-plant toxin OA: genes of this class are mapped to at least five loci scattered over all major chromosome arms [15], suggesting that the alleles at these loci were fixed independently from each other during the course of D. sechellia evolution. Factors responsible for the olfactory preference for M. citrifolia: D. sechellia can detect Morinda fruit from as far as 150 m away [23]. Consistent with this observation, the number of olfactory sensilla specifically tuned to the host odor MH is increased in D. sechellia [16] (but also note that MH is commonly found in many other plants). In contrast, however, there are no data showing that D. simulans avoids Morinda fruit purely on the basis of olfactory cues; all behavioral assays, including our trap assay, enable flies to come in direct contact with the odor source. There is also no neural response to HA and OA from the antennae of either D. melanogaster or D. sechellia [16]. We therefore assume that the olfactory cues from Morinda fruit are fundamentally attractive to Drosophila, and not repulsive even to D. simulans. D. sechellia has an enhanced preference specifically tuned to the Morinda blend of olfactory cues, in which MH is a functionally major component. Genes responsible for this enhanced preference are thought to function in cell fate determination during neuronal development [16], but the number of genes involved is not yet known. Factors responsible for the D. simulans' avoidance of Morinda fruit: we found this behavior to be based on gustatory cues, and confirmed that the introduction of the Obp57d/e region from D. simulans made D. melanogaster avoid HA in the same manner as D. simulans (Figure 6), proving that D. simulans' avoidance of HA-containing media as an oviposition site is determined by Obp57d/e. These two genes are physically close to each other and are thus treated as a single locus in the following discussions. Historical Order of Allele Fixation during the Course of D. sechellia's Evolution Here, we discuss the order of allele fixation at the loci responsible for D. sechellia's adaptation to M. citrifolia. In particular, we focus on the issue of which mutation was the first to be fixed, because it must have played a key role in D. sechellia's evolution. We speculate on this with respect to the ecological validity of each phenotype in light of natural selection. We assume that the first mutation arose at a single locus, and examine the resulting phenotype in an ecological context. (1) If the first mutation occurred at a resistance QTL, the resulting phenotype would be partially resistant to M. citrifolia. However, this phenotype is ecologically “silent” because these flies avoid Morinda fruit and may not lay eggs on it (a mutation on the resistance QTL cannot be advantageous unless a fly's behavior is changed). (2) If the first mutation was for the enhanced preference for the host odorant, flies should be attracted to Morinda fruit. This phenotype would conflict with the gustatory avoidance of Morinda fruit. The consequence of conflicting olfactory and gustatory cues is unpredictable, but we hypothesize that, at least in oviposition behavior, gustatory avoidance would override olfactory preference. Olfactory preference for a plant that is not acceptable as an oviposition site is ecologically inconsistent and obviously disadvantageous. (3) If the first mutation was at the Obp57d/e locus, the resulting phenotype would be the loss of gustatory avoidance of M. citrifolia. This seems to be also disadvantageous because flies would die upon contact with Morinda fruit because of their lack of resistance to it. However, there are circumstances that might enable an evolving population to survive and reproduce. The toxicity of Morinda fruit declines as it rots and OA degenerates [13]. Thus, there will be a point at which the toxicity is potentially low enough to be counteracted by the nutritional gain from the fruit. Moreover, because M. citrifolia flowers and fruits throughout the year, newly eclosing adults are likely to mate and reproduce on the same Morinda tree. Such conditions may not be optimal with regard to the quality of nutrients, but could potentially provide a niche with fewer competitors and may result in selection for a resistance to host toxicity. Altogether, among the three traits constituting D. sechellia's adaptation to M. citrifolia, only the loss of avoidance provides an ecologically realistic scenario for specialization without any other phenotypic changes. The above discussion, of course, does not exclude the possibility of a simultaneous fixation of the alleles responsible for D. sechellia's adaptation to M. citrifolia. Nevertheless, it is parsimonious to assume that if there was a single causative mutation at an early stage of D. sechellia's adaptation to M. citrifolia, it was the mutation at the Obp57d/e locus that led to the loss of avoidance. Conclusion D. sechellia, together with D. mauritiana, D. simulans, and D. melanogaster, serves not only as a subject of genetic analysis of reproductive isolation, but also as a good model for genetic analysis of ecological adaptation. There are more than 50 Obp genes in the D. melanogaster genome. Studies of natural variation at these loci will provide insight into the contribution of ecological interactions to the genomic constitution. Materials and Methods Fly preparation. The fly strains used were w1118 for D. melanogaster, S357 for D. simulans, and SS86 for D. sechellia [17]. Adult flies were collected immediately after eclosion, and staged for 3 d at 25 °C with a cotton plug soaked with liquid medium (5% yeast extract and 5% sucrose). Staged flies were used for the trap assay, the oviposition site–preference assay, and quantitative RT-PCR analysis. Trap assay. A 30-ml glass flask containing 20 ml of HA solution in 0.05% Triton-X and a control flask containing the same amount of 0.05% Triton-X were placed in a plastic cage covered with a lid made of wire mesh. Up to 100 staged flies were introduced into the cage and kept in a dark, ventilated chamber overnight at 25 °C. The response index was calculated as RI = (Nh − Nw)/(Nh + Nw), where Nh is the number of flies trapped in HA solution and Nw is that of flies in the control trap. Determination of break points in deficiency chromosomes. The PCR primers used are listed in Table 5. To amplify a fragment of about 300–600 bp from genomic DNA extracted from the interspecies hybrids between D. melanogaster deficiency strains and D. sechellia, each primer was designed within the ORF of predicted genes, with the expectation that there is enough conservation of sequences between the two species. PCR products were subjected to direct sequencing with the same primer used for amplification. The deficiency chromosome was considered to cover the position when the sequence derived from D. melanogaster or those from both D. melanogaster and D. sechellia were detected, and it was not considered to cover when only the D. sechellia sequence was detected. Signal peptide–sequence prediction. Signal peptide sequence was predicted using SignalP 3.0 [24]. GFP reporter analysis. The genomic sequence upstream of Obp57e was PCR amplified with the primer pair 5′-(NotI) GCGGCCGC-GCGGTGGCACCCAAAATCAAT-3′ and 5′-(BamHI) AAAGGATCC-ACTTGCTATATTCCTAGGGAA-3′. PCR products were cloned into pGreenPelican [25], and then introduced into D. melanogaster by the established P element–based transformation method. To remove the inserted 4 bp from the sechellia > GFP construct, the vector was PCR amplified using the KOD-plus enzyme (Toyobo, http://www.toyobo.co.jp/e/) that does not append a T on the ends with the primers 5′-GATTATCCATTATATTGAAATTTAATTGC-3′ and 5′-ACATTTTTAATTGCACACACATTCAG-3′, and self-ligated after phosphorylation. At least five independent transformant lines for each construct were examined for GFP expression. Gene targeting. Disruption of Obp57d and Obp57e was carried out by the ends-out method using the vectors provided by Dr. Sekelsky [26]. A hsp70-white marker gene was excised from pBS-70w with SphI and XhoI and subcloned into the SmaI site of pBSII after blunting to obtain pBSII-70w. The Obp57d upstream region amplified with the primer pair 5′-(EcoRI) AAAGAATTC-TTAATACGAGTATATCCCAGCAAAATCGAT-3′ (P1) and 5′-(BamHI-loxP) GGATCC-ATAACTTCGTATAGCATACATTATACGAAGTTAT-CAAACTAGTTGAAGATATCATAG −3′ and the downstream region amplified with the primer pair 5′-(PstI-loxP) CTGCAG-ATAACTTCGTATAATGTATGCTATACGAAGTTAT-GGACAAGTACTACGATACTGG −3′ and 5′-(NotI) GCGGCCGC-TATGAACACTCGCCGTGGTC-3′ (P2) were subcloned into pP{EndsOut2} with hsp70-white excised from the pBSII-70w with BamHI and PstI. The Obp57e upstream region amplified with the primer pair 5′-(BamHI-loxP) GGATCC-ATAACTTCGTATAGCATACATTATACGAAGTTAT-ACTTGCTATATTCCTAGGGAA −3′ and P1 and the downstream region amplified with the primer pair primers 5′-(PstI-loxP) CTGCAG-ATAACTTCGTATAATGTATGCTATACGAAGTTAT-GCGGCCGAGAAGTATGTTTC-3′ and P2 were subcloned into pP{EndsOut2}, similarly to the case of Obp57d. The Obp57d upstream region and the Obp57e downstream region were used for the Obp57d/e targeting vector. The fly transformation and targeting crosses were carried out as described by Sekelsky (http://rd.plos.org/pbio.0050118). Two, one, and three knock-out lines were obtained for Obp57d, Obp57e, and Obp57d/e, respectively. Each knock-out line was backcrossed to the w1118 control strain for five generations. Introduction of Obp57d/e from D. simulans and D. sechellia. Genomic fragments including Obp57d/e were PCR cloned from D. simulans and D. sechellia with the primers P1 and P2, and cloned into the pCaSpeR3 transformation vector. The w1118; Obp57d/eKO strain was transformed with these vectors by the established method. At least three independent transformant lines were obtained for each construct. Quantitative RT-PCR analysis. RNA was extracted from the legs or heads of 20 staged females using an RNeasy Micro kit (Qiagen, http://www1.qiagen.com). cDNA was made using a SuperScript III First-strand Synthesis System (Invitrogen, http://www.invitrogen.com) with the oligo(dT)20 primer. Quantitative RT-PCR was carried out with the Chromo 4 realtime PCR analysis system (BioRad, http://www.bio-rad.com) using SYBR Premix ExTaq (Takara, http://www.takara-bio.com) with primers 5′-TTATTTTGGAAATTCAATTTAGAACTGCCG-3′ and 5′-TGATTCGGCTATATCTTCGTCTATTCCTTG-3′ for D. melanogaster Obp57d, 5′-TGCGCAAATGTTCTCGCTAACACTT-3′ and 5′-ATTCTCCATCACTTGGTGGGCTTCATA-3′ for D. melanogaster Obp57e, 5′- TTATTTTGGAAATTCAGTTTAGAATTTCCG-3′ and 5′- AATTGCTTCAGCTATATCTTCGTCTATTCC-3′ (P3) for D. simulans Obp57d, 5′- TGCGCAAACGTTCTTGCTTACACTT-3′ and 5′- GGCCATTTCTCCATCACTTGGTTG-3′ (P4) for D. simulans Obp57e, 5′- TTGGAAATTCAGTTTAGAAATTCTGAATGT-3′ and P3 for D. sechellia Obp57d, 5′- TGTGCGCAAATGTTCTTGCTTACACTT-3′ and P4 for D. sechellia Obp57e, and 5′- GCTAAGCTGTCGCACAAATG-3′ and 5′- TGTGCACCAGGAACTTCTTG-3′ for rp49 of all species. Either of a primer pair was designed at an exon boundary to ensure amplification only from spliced transcripts. Oviposition site–preference assay. Staged females were individually supplied with media (1% yeast extract [Gibco, http://www.invitrogen.com/content.cfm?pageid=11040]) and 0.8% Bacto Agar [Gibco]) containing an acid at four concentrations (0 mM, 10 mM, 20 mM and 30 mM for acetic acid, butyric acid, and HA; and 0 mM, 2.5 mM, 5 mM, and 7.5 mM for OA) simultaneously, and allowed the choice of medium for oviposition in a dark, ventilated box overnight at 25 °C. The number of eggs laid on each medium was scored, and the weighted mean of acid concentration was calculated for each individual. At least 36 individuals were tested for each strain with three replications. Supporting Information Accession Numbers Obp57d/e sequence data have been deposited under the GenBank (http://www.ncbi.nlm.nih.gov/Genbank) accession numbers AB232138–AB232143.
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Inferring the Demographic History and Rate of Adaptive Substitution in Drosophila
Haipeng Li, Wolfgang Stephan (2006)
Introduction A long-standing interest in evolutionary biology has been to estimate the rate of adaptive substitution. Adaptive events can be inferred from interspecific data by comparing nonsynonymous and synonymous substitution rates [1]. A second approach has been to use a combination of both interspecific and intraspecific data in employing the McDonald-Kreitman method [2]. It has been found that positive selection could play a role in the human-chimpanzee lineages [3] and that as much as 45% of all amino-acid substitutions have been fixed by natural selection in Drosophila [4]. However, the methods that include interspecific data (in particular, the McDonald-Kreitman test) may be sensitive to fairly small fluctuations in effective population size and other demographic changes [5]. An alternative is to use only data on intraspecific variation and to explicitly model the effects of demographic changes and positive selection [6–8]. Footprints of very recent positive selection can be detected by identifying selective sweeps in the genome (in particular, valleys of reduced polymorphism). In the last few years, several methods have been proposed to detect selective sweeps [9–13]. To distinguish signatures of sweeps from those of demography and estimate the rate of adaptive substitutions, we use here a modification of the approach of Li and Stephan [12]. Reduced polymorphism due to hitchhiking will be restored after about 0.1Ne generations [9,14]. This feature enables us to detect very recent hitchhiking events and to reveal the relationship between adaptation and habitat change in a species that invaded new territory. For these purposes, the cosmopolitan species D. melanogaster serves as an appropriate model since this species, originally from Africa, expanded its population size worldwide very recently [15,16]. We analyzed DNA polymorphism at more than 250 noncoding loci on the X chromosome from two D. melanogaster populations: the Netherlands and east Africa [16–18]. The homologous sequences of D. simulans are used as outgroup data to infer the ancestral status of a polymorphic site and to estimate divergence between D. melanogaster and D. simulans. Results/Discussion Inferring Demography: General Approach Demographic change affects the genome-wide polymorphism pattern in a species or population. Thus, we used the whole dataset to infer demographic processes in the two populations. For the African population, the dataset is given in terms of the mutation frequency spectrum (MFS), where the MFS is the distribution describing the relative abundance of derived mutations occurring i = 1, 2, …, n − 1 times in n homologous sequences. Following Nielsen [19], the likelihood for the kth locus is given as , where is a set of (nk − 1) expected branch lengths [12] under the demographic scenario. The branch length is scaled so that one unit represents 2NA 0 generations, where NA 0 is the current effective population size for the X chromosome in the African population; nk is the sample size of the kth locus, ξ ik is the number of derived mutations carried by i sampled chromosomes for the kth locus, and E(lik) is the expected length of branches with i descendants for the kth locus under the demographic scenario. P(ξ ik |E(lik) is given by the Poisson probability, i.e., , with λ ik = E(lik)θ Ak /2, which is the expected number of derived mutations occurring i times in nk sampled sequences at the kth locus, where θ Ak = 4NA 0ξ ik , and μ k is the mutation rate of the kth locus. Since loci are independent given the expected branch lengths, the likelihood for all loci is , where m is the number of loci. To infer the demographic change in the derived European population, we used the joint MFS [20] (Figure 1). If the sample sizes of the African and European populations are nA and nE (nA ≥ 0 and nE ≥ 0), respectively, the joint MFS for one locus is where ω ij is the number of derived mutations carried by i sampled chromosomes in the sample from the African population and by j sampled chromosomes in the sample from the European population. The values of ω00 ω nA nE and (denoting the numbers of mutations that are not present and fixed in the sample, respectively) are not considered in the analysis. Figure 1 Demographic Models of the African and European Populations (A) The demographic histories are plotted together. (B) The demographic histories are plotted for both populations separately. (C) The joint MFS for an example genealogy where the sample size of European lines (indicated by E) is 3, and that of African lines (A) is 4. ω ij is the number of mutations carried by i chromosomes of the African sample and by j chromosomes of the European sample. Finally, we assume that the out-of-Africa migration does not affect the genetic polymorphism in the African population (Figure 1). This is reasonable because the size of the founder population is likely to be very small compared to the size of the ancestral African population. Thus, we estimated the demographic scenario of the European population conditional on the estimated demographic scenario of the African population. Under this assumption, the likelihood for the joint MFS is calculated in a similar way as described above (see Materials and Methods). Demographic History of the African Population Before entering the analysis, it is crucial to examine whether the mutation rate among the noncoding loci is homogeneous. We found that the level of genetic polymorphism of a locus (measured by Watterson's θ W ) is significantly positively correlated with divergence between D. melanogaster and D. simulans (Figure 2). Based on the Poisson distribution, we compared the mutation rate μ k of each locus k (estimated from divergence) with the average mutation rate of loci over the whole X chromosome (i.e., the average of mutation rates across loci weighted by sequence length). The null hypothesis is , which is tested using Monte-Carlo simulations. The estimated mutation rate of 62 of the 266 loci (23.3%) is significantly lower than the average (at 1% significance level, one-tailed test), while that of 51 of the 266 loci (19.2%) is significantly higher. This suggests that the mutation rate among loci is not homogeneous. Therefore, we used two models in the following analysis: (i) a constant mutation rate model in which the mutation rate of each locus is and (ii) a varying mutation rate model in which the mutation rate of locus k is μ k . The constant mutation rate model underestimates the variance of mutation rates among loci while the varying mutation rate model overestimates this variance (because of the sampling error of the estimated mutation rates). Figure 2 Watterson's θ W versus Divergence between D. melanogaster and D. simulans Pearson's r = 0.65, p 0. It is assumed that δ is homogeneous over windows because all windows have the same length. We also assume that the windows are independent of one another. Then L(δ, f(s)) is given by . By dividing the outcomes for a window into neutral and selected cases, we have P(M w |δ, f(s)) = (1 − δ)P(M w |neutral) + δ∫f(s)P(M w |s)ds , where P(M w |neutral) and P(M w |s) are estimated by rejection sampling (described in Materials and Methods). An obvious advantage of our approach is that we do not make any assumption about f(s). Let be defined as the rate of adaptive substitution with a selection coefficient within the interval (s 1, s 2). We have . Thus, δ and f(s) are estimated as { , , …} and , respectively, where s 1 > 0, s 1 Q(s) is expected if the data are simulated under neutrality. If the hitchhiking data with selection coefficient s′ are simulated, we expect that a maximum Q(s) (where s > 0) could be obtained when s = s′. In this study, Q(s) is calculated for 18 values of s (i.e., 0.06, 0.08, 0.09, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 4, 6, 8, 10; all values in percent). Then we partition the value of s (given in percentage) into six regions, which are [0.05, 0.1), [0.1, 0.3), [0.3, 0.5), [0.5, 0.7), [0.7, 1), and [1, 20). Thus, to estimate δ and f(s), we need to estimate six parameters (δ0.05,0.1, δ0.1,0.3, δ0.3,0.5, δ0.5,0.7, δ0.7,1, and δ1,20), and δ is given by their summation. We treat the cases (s < 0.05%) as neutral because of the very low power to detect them (Figures S1 and S2). To maximize the likelihood, likelihoods in a six-dimensional parameter space are calculated, where each dimension represents one parameter. To be specific, the minimum and maximum values of a parameter are 0 and 0.0167 × 10−9 per site per generation (see above), respectively. The spacing of the grid of parameter values is 0.0003 × 10−9 per site per generation. Estimating δ and f(s) for the European population. The genetic polymorphism in the European sample could be affected by sweeps that occurred in the ancestral African population before the split. Thus, we need to consider the effect of “old” sweeps when estimating δ E and f(sE). Here, we use the indices of A and E to distinguish the parameters for the European and the African populations. For hitchhiking events that occurred in the derived European population, τ is uniformly distributed within [0, tE0 ]. To estimate δ E and f(sE), we divide the outcomes for a window in the European sample into four cases: (a) there is no sweep; (b) a sweep occurred in the European population after the split; (c) a sweep occurred in the ancestral African population before the split; and (d) a sweep occurred in the European population after the split, and another sweep in the ancestral African population before the split. Given a sweep originated in the African population, the probability that the sweep occurred before the split is η = (tA 0 − tE 0 − tE 1)/tA 0. Then, the probability is given by where and are known parameters estimated from the African sample, and Q(sE, sA ) = P(M w |sE, sA ). The related Q is estimated by the method described above. When we estimate Q(sE, sA ), we use B = 100. Supporting Information Figure S1 The Power of the LRT to Detect Sweeps in the African Sample The length of each window is 100 kb, and the power is obtained by averaging over the windows. s is the true value under which the data are simulated, and is the assigned (fixed) value in the hitchhiking model. The values of s and are given in percentage. (168 KB DOC) Click here for additional data file. Figure S2 The Power of the LRT to Detect Sweeps in the European Population (164 KB DOC) Click here for additional data file. Figure S3 The Comparison of Derived MFS under Different Population Expansion Scenarios in the African Population Maximum likelihood estimates: , and the strength of the expansion = 5.0. The other three expansion scenarios are chosen such that the parameter values are within the estimated CIs. Expansion1: , and the strength of the expansion = 4.0; Expansion2: , and the strength of the expansion = 6.0; Expansion3: , and the strength of the expansion = 8.0. The sum of the squares of the residuals (between the expected and the observed) is 0.0012, 0.0066, 0.0025 and 0.0035, respectively. (81 KB DOC) Click here for additional data file. Table S1 Evaluation of Demographic Models for the African and European Populations (26 KB DOC) Click here for additional data file. Table S2 Results of the Nonoverlapping Window Analysis of the African Sample Based on Different Hitchhiking Models (124 KB DOC) Click here for additional data file. Table S3 Results of the Nonoverlapping Window Analysis of the European Sample Based on Different Hitchhiking Models (122 KB DOC) Click here for additional data file. Table S4 List of 13 Loci Which Have High Mutation Rate but Low Diversity in the African Sample (44 KB DOC) Click here for additional data file. Accession Numbers The sequences used in this study were obtained from the EMBL Nucleotide Sequence Database (http://www.ebi.ac.uk/embl) (AJ568984 to AJ571588, AJ568984 to AJ571588) and GenBank (http://www.ncbi.nlm.nih.gov/Genbank) (AY925214 to AY926258).
Author and article information
Journal
Title:
Genetics
Abbreviated Title:
Genetics
Publisher:
Genetics Society of America
ISSN
(Print):
0016-6731
ISSN
(Electronic):
1943-2631
Publication date Created:
August
20 2009
Publication date Created:
August
2009
Publication date
(Print):
August
2009
Publication date
(Electronic):
June
08 2009
Volume: 182
Issue: 4
Pages: 1197-1206
Article
DOI: 10.1534/genetics.108.092080
PMC ID: 2728859
PubMed ID: 19506309
SO-VID: 26fa86ca-3008-478c-a2bc-d64f33fbc558
Copyright © ©
2009
History
Data availability: