Microsatellite Analysis of Drosophila melanogaster Populations Along a Microclimatic Contrast at Lower Nahel Oren Canyon, Mount Carmel, Israel

Christian Schlötterer and Martin Agis

Institut für Tierzucht und Genetik, Veterinärmedizinische Universität Wien


    Abstract
 TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Acknowledgements
 References
 
Drosophila melanogaster populations collected at the south-facing slope (SFS) and north-facing slope (NFS) of lower Nahel Oren canyon, Mount Carmel, Israel display significant differences in survival and longevity at temperature, drought, and starvation stresses. Furthermore, significant assortative mating was previously observed between populations of the two slopes. We used a set of 48 microsatellite markers to analyze patterns of genetic differentiation between D. melanogaster populations from both slopes and D. simulans. Consistent with previous reports, we found D. simulans to be well differentiated from D. melanogaster. Genetic differentiation between SFS and NFS D. melanogaster populations was low (FST = 0.0012). Also a tree of individuals based on the proportion of shared alleles and a model-based clustering method provided no evidence for population substructuring.


    Introduction
 TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Acknowledgements
 References
 
Contrasts in microenvironment or differences in resources have been suggested as possible causes of sympatric speciation (Coyne 1992Citation ). In addition to the well-investigated cichlid system in African lakes (Meyer et al. 1990Citation ), the lower Nahel Oren canyon has recently been established as a site offering a microenvironment facilitating differentiation of populations (Nevo 1995Citation ). The two opposite slopes of the canyon at Mount Carmel, Israel, are separated by a distance of 100 m at the bottom and 400 m at the top. Despite a common geology and macroclimate, the south-facing slope (SFS) and north-facing slope (NFS) differ sharply in irradiation and aridity. Biotically, the NFS is Eurasian and the SFS is Afro-Asian within the Mediterranean context (Nevo 1995Citation ).

For species with a low dispersal rate, the enormous biotic differences are expected to have a significant impact on the populations living on the two slopes of the lower Nahel Oren canyon. Indeed, significant differences in mutation frequencies were reported for wild strains of the fungus Sordaris fimicola isolated from both slopes of the canyon. Most importantly, some of the between slope difference was inherited through two generations of selfing, suggesting fixed genetic differences between the two slopes (Lamb et al. 1998Citation ). Similarly, an almost threefold difference in the number of full-length BARE-1 elements was observed for wild barley individuals collected in the lower Nahel Oren canyon, possibly representing an adaptation to the different environments (Kalendar et al. 2000Citation ).

For a highly mobile species, such as Drosophila melanogaster, which could migrate several miles per day (Coyne and Milstead 1987Citation ), the predictions are less clear. One recent study demonstrated significant differences between SFS and NSF D. melanogaster isofemale lines (Nevo et al. 1998Citation ) for many traits, including survival, longevity, and behavior. Most importantly, the observed differences suggest that they represent an adaptation to the habitat. Therefore, the authors concluded that strong microclimatic natural selection is overriding migration in D. melanogaster at the lower Nahel Oren canyon (Nevo et al. 1998Citation ). Further support for the differentiation between NFS and SFS D. melanogaster populations was provided by a study on the mating behavior of flies collected at this microsite. Using two different sets of synthetic populations for each slope which were maintained in the laboratory for 12 and 48 generations, respectively, the authors showed a significant nonrandom mating pattern (Korol et al. 2000Citation ). Flies significantly preferred mating partners from the same slope. Hence, restriction in gene flow between the two slopes through the mating behavior of the two fly populations may be a crucial component of the adaptation to the different habitats on the NFS and SFS.

Provided D. melanogaster shows significant assortative mating and phenotypic divergence, the SFS and NSF D. melanogaster populations should form separate gene pools. If divergent selection between the two populations is not a recent phenomenon, then NFS and SFS populations are expected to be well differentiated by genetic drift and selection. In this report we use a large number (48) of microsatellite loci, which are highly informative markers, to quantify the genetic divergence of the SFS and NFS populations in the lower Nahel Oren canyon.


    Materials and Methods
 TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Acknowledgements
 References
 
Fly Material
Drosophila melanogaster and D. simulans flies were collected on September 17, 1997 and September 22, 1997 at the Nahal Oren Canyon, Israel, by A. Korol. Flies were obtained from station II and station VI, both located 90 m above sea level on the SFS and NFS, respectively. Freshly collected females were either sent directly or the first generation was shipped in a separate vial for each line. We used either first- or second-generation flies for our analysis (see table 1 ). Genomic DNA was extracted from single individuals using a high salt extraction method (Miller, Dykes, and Polesky 1988Citation ).


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Table 1 Population Samples Used in the Study

 
Microsatellite Typing
The chromosomal location of the loci used is given in table 2 , and further information can be obtained from our web page (http://i122server.vu-wien.ac.at). All microsatellite loci were selected without a priori information about variability in the SFS and NFS populations. Microsatellite loci were amplified in 10 µl reactions (1.5 mM MgCl2, 2 µM of each primer and 0.5 U Taq polymerase) following standard protocols (Schlötterer 1998Citation ). A 5-min initial denaturation at 94°C was followed by 30 cycles of 50 s at 94°C, 50 s at 40–55°C (depending on the primer combination), and 50 s at 72°C. We used a final extension at 72°C for 45 min to assure a quantitative terminal transferase activity of the Taq polymerase. PCR products were separated on a 7% denaturing polyacrylamide gel (32% formamide, 5.6 M urea). PCR products were sized by running a sizing ladder next to the amplified microsatellites (Schlötterer and Zangerl 1999Citation ).


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Table 2 Variability Measurements

 
Data Analysis
General measures of genetic variation, such as heterozygosity, variance in repeat number, and number of alleles were calculated using the MSanalyzer software Version 1.35 (D. Dieringer and C. Schlötterer, unpublished data). To estimate population differentiation, pairwise {Theta}-values, as an unbiased estimate of FST were generated with the F-stat software package (Goudet 1995Citation ). In the following we will refer to {Theta}-values as FST values. Although the standard analysis used information from both chromosomes, we also developed a strategy to account for the fact that some samples were second-generation flies, which could show some deviation in allelic composition from first-generation flies because of unpredictable effects in captivity. To account for this, one allele was randomly selected and subsequently doubled to create a diploid data set. Significance levels of pairwise FST values were tested by permuting genotypes among populations (2,000 times) because this method does not rely on Hardy-Weinberg assumptions (Goudet et al. 1996Citation ). The significance level, {alpha}, of pairwise FST values was adjusted for multiple testing using the Bonferroni correction (Sokal and Rohlf 1995Citation ).

Genetic distances were calculated using the proportion of shared alleles implemented in the MICROSAT software (Minch et al. 1995Citation ). The distance matrix obtained was converted into a dendrogram using the Neighbor-Joining algorithm (Saitou and Nei 1987Citation ) provided with the PHYLIP software package (Felsenstein 1991Citation ) and graphically displayed with TREEVIEW (Page 1996Citation ).

The model-based clustering method of multilocus genotypes was performed with the software STRUCTURE (Pritchard, Stephens, and Donnelly 2000Citation ). One critical parameter for Markov Chain Monte Carlo methods, such as those used in STRUCTURE, is the number of performed iterations. We followed the recommendation of Pritchard, Stephens, and Donnelly (2000)Citation and used 10,000 iterations for burn-in followed by 100,000 iterations for data collection (Pritchard, Stephens, and Donnelly 2000Citation ). To test whether lnP(X|K) had converged, we made multiple runs and performed additional runs with 500,000 iterations. STRUCTURE assumes Hardy-Weinberg equilibrium within populations and linkage equilibrium among loci.


    Results
 TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Acknowledgements
 References
 
Three measurements of variability, gene diversity, variance in repeat number, and number of alleles, were found to be almost identical between SFS and NFS populations (table 2 ). Gene diversity and variance in repeat number were similar to D. simulans (table 2 ). The lower number of alleles observed for D. simulans reflects the smaller sample size used for D. simulans (table 1 ).

Genetic differentiation measured by FST was determined for SFS, NFS, and D. simulans. FST values for each pairwise comparison of D. simulans with SFS and NFS D. melanogaster populations were high and statistically significant (0.415 and 0.410, respectively). The FST value between SFS and NFS populations was very small (0.0012) and not statistically significant when {alpha} was adjusted for multiple testing (Sokal and Rohlf 1995Citation ). Despite the lack of statistical significance, some differentiation between SFS and NFS groups was observed (FST = 0.0012). To further investigate this, we extended our analysis and grouped our samples not only according to site of collection but also according to shipment (table 1 ). Only those groups which consisted of at least three individuals were considered. Interestingly, this analysis was not consistent with the hypothesis of a genetic differentiation between slopes. Only the pairwise comparison of SFS II and SFS III was statistically significant after Bonferroni correction for multiple testing (table 3 ). Notably, these two samples were collected on different days, and SFS-III flies were second generation rather than first-generation individuals (table 1 ). The identical result was obtained when the analysis was repeated with a data set, which was haploidized by randomly discarding one allele at each locus and individual (data not shown). Hence, we could rule out that the significant differentiation between SFS II and III is an artifact produced by the usage of first- and second-generation flies.


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Table 3. Pairwise FST Values for Different Drosophila melanogaster Samples Collected at the Lower Nahel Oren Canyon

 
Given that our results were dependent on the assignment of individuals to a given sample, we pursued further approaches, which focus on individuals rather than groups. First, we constructed a tree of individuals using the proportion of shared alleles as genetic distance measurement. Although this measurement does not account for microsatellite mutations, it has been shown to be informative for phylogenetic reconstruction (Harr et al. 1998aCitation ). Consistent with the observations of Harr et al. (1998a)Citation , all D. simulans individuals were well separated from D. melanogaster individuals. In contrast, no differentiation among the NFS and SFS individuals was observed (fig. 1 ). Hence, the tree of individuals further supports the results of the FST-based analysis, of no genetic differentiation between SFS and NFS individuals.



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Fig. 1.—Neighbor-Joining tree of individuals based on the proportion of shared alleles

 
Recently, it has been suggested that trees of individuals may not be the most sensitive approach to uncover population structure in a set of individuals (Pritchard, Stephens, and Donnelly 2000Citation ). The authors introduced a model-based clustering method for multilocus genotype data to infer population structure and to assign individuals to populations. Furthermore, an estimate of lnP(X|K) is given. Hence, when different hypotheses about the number of populations included in a sample of individuals are specified, the program provides the probability of the data for each hypothesis. Table 4 indicates that the highest probability is obtained for the hypothesis that all D. melanogaster individuals constitute a single population. Furthermore, all runs with K > 1 assigned all individuals to the same population, further supporting the absence of population structure in the D. melanogaster sample.


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Table 4. Inferring the Number of Drosophila melanogaster Populations in the Lower Nahel Oren Canyon

 

    Discussion
 TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Acknowledgements
 References
 
Previous studies of D. melanogaster at the lower Nahel Oren canyon, Mount Carmel, Israel, indicated assortative mating of SFS and NFS flies in combination with selection for slope-specific phenotypes. Despite that assortative mating of SFS and NFS populations was not very strong (62.2%–64.2% rather than the expected 50%), it was statistically significant (Korol et al. 2000Citation ). On the basis of these results, we started with the hypothesis that a clear genetic separation of SFS and NFS flies should be revealed with neutral microsatellite loci. Although we observed a clear separation between D. melanogaster and D. simulans samples collected at the same canyon at the same time, no significant genetic differentiation could be detected between the SFS and NFS individuals. If at all, our data set provided stronger support for differentiation between temporal samples than between spatial samples.

In the following, we will discuss several scenarios which could potentially reconcile the lack of genetic differentiation for neutral microsatellites in our data set with previous observations.

Genetic Differentiation is Limited to Genomic Regions Subjected to Selection
Microsatellites are highly informative neutral markers (Schlötterer 2000Citation ). In addition to mutation and genetic drift, microsatellite allele frequencies are also affected by linkage to a selected genomic region. If recombination rates are high and population sizes are large, only very tightly linked microsatellites are affected by selection. Assuming that selection has shaped the phenotypic differences between SFS and NFS D. melanogaster populations, genomic regions not targeted by selection may not be strongly affected, as long as population sizes are large and recombination rates are high. Hence, if microsatellites are decoupled from selected regions, they are not expected to show differentiation between SFS and NFS.

In the presence of assortative mating, however, genetic drift will gradually result in genetic differentiation between the two slopes. To reconcile assortative mating and phenotypic differences with the absence of genetic differentiation for neutral microsatellites, one has to assume that the separation of the populations has occurred only recently. Unfortunately, in the absence of information about selection coefficients, the number of selected loci, dominance effects, epistasis, and migration rates it remains speculative whether this scenario is plausible. One prediction could be made, however, that a small number of microsatellite loci, which are linked to selected chromosomal regions, should differentiate SFS and NFS very well. A genome scan using informative markers such as microsatellites could be highly instrumental to identify such genomic regions (Schlötterer and Wiehe 1999Citation ).

Temporal Heterogeneity
A sample of 11 flies which were collected 5 days after the initial collection were found to be significantly differentiated from a sample consisting of 15 individuals from the initial collection. Interestingly, all samples were collected at the same slope, suggesting a larger temporal differentiation than spatial differentiation between SFS and NFS flies. Given that only a difference of a few days resulted in a significant differentiation among samples, one could assume that samples collected at larger time intervals may have even more pronounced differences. On the other hand, the differentiation was modest between temporal samples (FST = 0.03) and could only be detected if individuals were grouped for the analysis. Neither a tree of individuals nor model-based clustering of multilocus genotypes supported the presence of more than a single population. A recent analysis of microsatellite variability in synthetic NFS and SFS populations, which were established from isofemale lines collected in the same year but at different days than our lines, revealed strong population differentiation between the two populations (Michalak et al. 2001Citation ). Hence, unless the different sampling regime in both studies could account for the contrasting microsatellite data (see subsequently), temporal heterogeneity remains an important explanation for the discrepancy between our microsatellite-based results and previous results.

Sampling Regime
In this report we laid special emphasis on the use of freshly collected lines. By the exclusive analysis of first- or second-generation lines, the allele spectrum is not expected to be influenced by the propagation in the laboratory. In contrast, previous studies used special synthetic populations, which were founded by 10 females and males from each of the 25 isofemale lines established for both slopes (Korol et al. 2000Citation ; Michalak et al. 2001Citation ). These synthetic populations have then been propagated in the laboratory under standard conditions. Although this propagation regime is not expected to select for different phenotypes in the SFS and NFS synthetic population, genetic drift could substantially affect them. Without careful recording of the population size in the synthetic populations, it is difficult to account for accelerated drift by minor bottlenecks during the maintenance of the synthetic populations.

In addition to drift, sexual selection during the propagation of the synthetic population could lead to unpredictable results. A recent study demonstrated significant interaction between male and female genotypes determining the success of the sperm (Clark, Begun, and Prout 1999Citation ). Hence, genetic drift could be further accelerated by sexual selection. As a consequence, each synthetic population may have a set of male and female genotypes that have coevolved, assuring the highest fitness of each sex. Although the effect of antagonistic coevolution of males and females on mating preferences has not been tested, it is conceivable that a preference for mating partners of the same line or synthetic population could be observed. For this reason, nonrandom mating would be expected between lines that have been maintained independently for several generations. This hypothesis could account for the observed difference in mating preference between the SFS and NFS synthetic populations (Korol et al. 2000Citation ).

One way to estimate the impact of genetic drift would be a comparison of microsatellite variability in our lines and the synthetic populations. Because of the different levels of variability of D. melanogaster microsatellites (Harr et al. 1998bCitation ), it is important to use the identical loci in such a comparison. Two microsatellite loci are shared between our study and the recent report by Michalak et al. (2001)Citation . Table 5 compares the gene diversity and number of alleles for these two loci. Despite that a slightly higher number of flies were analyzed for the synthetic populations, the number of alleles was always higher in the freshly collected populations. Similarly, in almost all comparisons, gene diversity was lower in the synthetic populations. Only the NFS population had a slightly higher variability at the DROGPDHA locus in the synthetic population (0.48 vs. 0.43). Hence, although the strong genetic differentiation between SFS and NFS populations reported by Michalak et al. (2001)Citation may be the result of genetic drift during the propagation of the lines, the important question is whether the behavioral and phenotypic differences could be also attributed to the culture conditions.


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Table 5. Comparison Between Synthetic and Freshly Collected Populations

 
Most of the mating behavior analysis by Korol et al. (2000)Citation used synthetic populations, but an analysis of isofemale lines from both slopes also showed a significant difference in assortative mating only in between slope comparisons (Korol et al. 2000Citation ). Therefore, genetic drift in the synthetic populations is not sufficient to explain the observed assortative mating behavior. The analysis of phenotypic differences did not use synthetic populations but relied on isofemale lines maintained in the laboratory for several generations (Nevo et al. 1998Citation ). Thus, the differences observed by Nevo et al. (1998)Citation are difficult to explain by culture conditions.

In summary, our results suggest that on the basis of highly informative neutral markers no significant population differentiation between freshly collected SFS and NFS D. melanogaster populations could be detected. Although several scenarios could be envisioned to reconcile the lack of genetic differentiation with previously described phenotypic and behavioral differences, the most convincing one is the scenario of a recent divergence between SFS and NFS flies. However, further studies on the genetic differentiation of the synthetic populations as well as phenotypic examination of freshly collected flies are required.


    Acknowledgements
 TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Acknowledgements
 References
 
We are especially grateful to A. Korol for providing us with the freshly collected D. melanogaster and D. simulans lines. Many thanks to B. Harr, M. Kauer, M. Imhof, R. Achmann, and A. Clark for helpful discussions. D. Goldstein and M. Feder shared unpublished results. D. Dieringer provided the program MSanalyzer. This work has been supported by grants of the Fonds zur Förderung der Wissenschaften (FWF) to C.S.


    Footnotes
 
Diethard Tautz, Reviewing Editor

Address for correspondence and reprints: Christian Schlötterer, Institut für Tierzucht und Genetik, Veterinärmedizinische Universität Wien, Josef Baumann Gasse 1, 1210 Vienna, Austria. christian.schloetterer{at}vu-wien.ac.at . Back


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 Introduction
 Materials and Methods
 Results
 Discussion
 Acknowledgements
 References
 

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Accepted for publication January 2, 2002.





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