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Sequence locations of Drosophila melanogaster markers on the left arm of chromosome 2 used in this study
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Latitudinal variation of the polymorphic sn-glycerol-3-phosphate (alpha-Gpdh) locus in Drosophila melanogaster has been characterized on several continents; however, apparent clinal patterns are potentially confounded by linkage with an inversion, close associations with other genetic markers that vary clinally, and a tandem alpha-Gpdh pseudogene....
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Context 1
... sites of adult Drosophila melanogaster along the east coast of Australia in 2002 and 2004 to a region within exon 3. In(2L)t was scored using the protocol of Andolfatto et al. (1999). The cytological positions and sequence locations of all markers used in this study are shown in Fig. 1 and Table 2, respectively. ...
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... Our second novel hsr-omega finding is that heat-stimulated levels of omega-n followed a non-linear parabolic geographical pattern. While parabolic latitudinal clines in allelic frequencies or gene expression patterns are not necessarily linked directly to specific trait variations, in this and other Drosophila species several traits and allele frequencies are known to vary latitudinally in a non-linear fashion Magiafoglou et al., 2002;Sarup et al., 2006;Sgrò and Blows, 2003;Umina et al., 2006;van Heerwaarden et al., 2012). Also, at least one trait is predicted to vary non-linearly with temperature (David et al., 2003). ...
... Assoc) [20] ; CO (Microarray) [2]; HE (Microarray) [2,3,4], (Pelement insert) [21] , (QTL) [12] CG3270 151882_at 6.08E-03 HE (Microarray) [3] ,(QTL) [5,10,12] CG30035 147027_at 8.54E-03 ST (Microarray) [7], (QTL) [9] ; HE (Microarray) [3] , (QTL) [5,10] Cytochrome c proximal 143115_at 8.67E-03 HE (Microarray) [4,7] , (QTL) [10,12] CG2065 146757_at 1.05E-02 HE (Microarray) [3] ,(QTL) [5,10,12] CG6113 146165_at 1.06E-02 ST (Microarray) [1,11] , (QTL) [8,9] CG13489 147644_at 1.25E-02 ST (Microarray) [11] , (QTL) [8,9] ; DS (Microarray) [7] Lk6 151687_s_at 1.39E-02 ST (Microarray) [11] ; HE (Microarray) [7] , (QTL) [5,12] CG14935 146257_at 1.41E-02 ST (Microarray) [11] , (QTL) [8,9] ; HE (Microarray) [3] , (QTL) [12] CG1809 151956_at 1.59E-02 ST (Microarray) [11] , (QTL) [9] ; HE (Microarray) [3] , (QTL) [5,10] Glycerol 3 phosphate 153347_at 1.78E-02 ST (Assoc) [22] ; GA (Geo. Assoc) [23,24,25,26] ; CO (Assoc) [27] ; -dehydrogenase HE (Assoc) [27,28] , (Microarray) [4] CG30389 142526_at 1.90E-02 ST (Microarray) [7] , (QTL) [8,9] ; DS (Microarray) [7] crooked legs 143920_at 2.29E-02 ST (Microarray) [11] , (P-element insertions) [8] , (QTL) [8,9] Desat2 149777_at 2.86E-02 ST (Assoc) [29] ; DS (Expression) [30] ; CO (Assoc) [29] , (QTL) [5] ; HE (QTL) [5,12] CG13084 146497_at 2.95E-02 ST (Microarray) [11] , (QTL) [9] ; HE (Microarray) [7] , (QTL) 10,12 Glutathione S transferase D5 149759_at 3.11E-02 HE (Microarray) [3] , (QTL) [5,12] CG8774 152779_at 3.65E-02 ST (Microarray) [11] ; HE (Microarray) [3] , (QTL) [5,12] CG3739 150193_at 4.54E-02 ST (Microarray) [7,11] ; HE (Microarray) [3] , (QTL) [12] CG30502 146742_at 5.33E-02 HE (Microarray) [3] ,(QTL) [5,10,12] Senescence marker protein-30 149914_at 5.63E-02 ST (Exp.) [30] , DS (Exp.) ...
... HE (Microarray) [3] , (QTL) [5,12] Imaginal disc growth factor 1 152721_at 7.94E-02 ST (Microarray) [11] , (QTL) [9] ; HE (Microarray) [7] , (QTL) [10,12] CG3409 154552_at 8.27E-02 HE (Microarray) [7] , (QTL) [5,10,12] CG12813 149610_at 8.67E-02 ST (Microarray) [11] ; HE (Microarray) [3] , (QTL) [5,12] CG7882 146660_at 9.17E-02 ST (Microarray) [11] , (QTL) [9] ; HE (Microarray) [3] ,(QTL) [5,10,12] CG4266 154472_at 1.20E-01 ST (Microarray) [7] , (QTL) [8,9]; HE (Microarray) [7] Lk6 142794_at 1.26E-01 ST (Microarray) [11] ; HE (Microarray) [7] , (QTL) [5,12] Cyp313a1 149899_at 1.47E-01 ST (Microarray) [11] ; HE (Microarray) [3] ,(QTL) [5,12] CG1946 146763_at 1.48E-01 HE (Microarray) [3] ,(QTL) [5,10,12] CG9259 146569_at 1.51E-01 ST (Microarray) [11] , (QTL) [8,9] ; HE (Microarray) [3] ,(QTL) [5,10,12] dorsal 143125_at 1.80E-01 ST (Microarray) [11] , (QTL) [9] ; HE (Microarray) [4] ,(QTL) [10,12] CG10383 152901_at 1.98E-01 ST (Microarray) [1,11] , (QTL) [9] ; HE (Microarray) [3] , (QTL) [10,12] Heat shock protein 23 153583_at 2.02E-01 GA (Geo Assoc) [20] ; CO (Microarray) [2] ; HE (Microarray) [2,3,4] , (Assoc) [32] , (Exp) [30] , (QTL) [12] cul-2 154761_at 2.06E-01 HE (Microarray) [7] , (QTL) [5,10,12] methuselah 141648_at 2.07E-01 ST (Assoc) [34] , (P-element insertions) [35] ; GA (Geo Assoc) [36,37] ; HE (P-element insertions) [35] , (QTL) [5] CG12310 148781_at 2.42E-01 ST (microarray) [7] , (QTL) [8,9] ; DS (Microarray) [7] rosy 152200_at 2.50E-01 ST (Microarray) [11]; HE (Microarray) [3], (QTL) [5,12] CG10680 146526_at 3.07E-01 HE (Microarray) [3] ,(QTL) [5,10,12] Alcohol dehydrogenase 143891_at 3.14E-01 ST (Association) [22] , (QTL) [9] ; GA (Geo. Assoc) [23,24,25,26,38] ; CO (QTL) [10] ; HE (Association) [28] , (QTL) [12] Heat shock factor 141526_at 3.18E-01 CO (Exp) [39] , (Mutation) [16] ; HE (Mutation) [16,40] , (Exp) [39,41] refractory to sigma P 152056_at 3.42E-01 HE (Microarray) [3,4] , (QTL) [10,12] CG5945 146323_at 3.54E-01 ST (Microarray) [11] , (QTL) [9] ; HE (Microarray) [3] , (QTL) [10,12] CG12140 146911_at 3.94E-01 ST (Microarray) [7,11] , (QTL) [8,9] Esterase 6 143151_at 4.03E-01 ST (Microarray) [11] ; GA (Geo Assoc) [23,42,43] ; HE (Microarray) [3] Metallothionein A 143276_at 5.04E-01 ST (Microarray) [11] ; HE (Microarray) [3] , (QTL) [5,12] Cyp6a13 154318_at 6.93E-01 HE (Microarray) [3] ,(QTL) [5,10] CG18649 148783_at 8.37E-01 ST (Microarray) [7] , (QTL) [8,9] ; DS (Microarray) [7] ...
... Some studies have detected clinal patterns in molecular variants located inside inverted regions. One such example is the Drosophila melanogaster cline of alpha-Gpdh loci, located inside ln(2L)t, an inversion that also presents a clinal distribution [24]. Kennington et al. [21], for instance, found that the markers located within the ln(3R)Payne inversion were those presenting the strongest clinal variation, suggesting selection nearby. ...
... The comparison of patterns obtained in loci located inside vs. outside inverted regions might help to differentiate between effects of gene flow versus selection on clinal variation [21,26,27]. Furthermore, when analyzing clinal variation associated with chromosomal inversions the effect of the inversion itself must be taken into account [24,28]. The best approach in this case is to study clinal variation of alleles within chromosomes carrying the same gene arrangement. ...
There is increasing evidence regarding the role of chromosomal inversions in relevant biological processes such as local adaptation and speciation. A classic example of the adaptive role of chromosomal polymorphisms is given by the clines of inversion frequencies in Drosophila subobscura, repeatable across continents. Nevertheless, not much is known about the molecular variation associated with these polymorphisms. We characterized the genetic content of ca. 600 individuals from nine European populations following a latitudinal gradient by analysing 19 microsatellite loci from two autosomes (J and U) and the sex chromosome (A), taking into account their chromosomal inversions. Our results clearly demonstrate the molecular genetic uniformity within a given chromosomal inversion across a large latitudinal gradient, particularly from Groningen (Netherlands) in the north to Málaga (Spain) in the south, experiencing highly diverse environmental conditions. This low genetic differentiation within the same gene arrangement across the nine European populations is consistent with the local adaptation hypothesis for th evolutionof chromosomal polymorphisms. We also show the effective role of chromosomal inversions in maintaining different genetic pools within these inverted genomic regions even in the presence of high gene flow. Inversions represent thus an important barrier to gene flux and can help maintain specific allelic combinations with positive effects on fitness. Consistent patterns of microsatellite allele-inversion linkage disequilibrium particularly in loci within inversions were also observed. Finally, we identified areas within inversions presenting clinal variation that might be under selection.
... Sample sizes of microsatellite alleles for Florida, Pennsylvania/New Jersey, and Maine were 84, 92, 74 alleles, respectively. Twelve populations from eastern Australia were provided by Ary Hoffmann as variable numbers of isofemale lines per population and are described in Umina et al. (2006). An average of 40 alleles was sampled for estimating allele frequencies (range 10-82; two populations were excluded with low sample sizes: "I" and "T" from Umina et al.). ...
We report the results of two independent selection experiments that have exposed distinct populations of Drosophila melanogaster to different forms of thermal selection. A recombinant population derived from Arvin California and Zimbabwe isofemale lines was exposed to laboratory natural selection at two temperatures (T(AZ): 18°C and 28°C). Microsatellite mapping identified quantitative trait loci (QTL) on the X-chromosome between the replicate "Hot" and "Cold" populations. In a separate experiment, disruptive selection was imposed on an outbred California population for the "knockdown" temperature (T(KD)) in a thermal column. Microsatellite mapping of the "High" and "Low" populations also uncovered primarily X-linked QTL. Notably, a marker in the shaggy locus at band 3A was significantly differentiated in both experiments. Finer scale mapping of the 3A region has narrowed the QTL to the shaggy gene region, which contains several candidate genes that function in circadian rhythms. The same allele that was increased in frequency in the High T(KD) populations is significantly clinal in North America and is more common at the warm end of the cline (Florida vs. Maine; however, the cline was not apparent in Australia). Together, these studies show that independent selection experiments can uncover the same target of selection and that evolution in the laboratory can recapitulate putatively adaptive clinal variation in nature.
... Inversion frequencies are typically linked to climate data from weather stations but these data might not reflect conditions experienced by flies and larvae from different parts of the geographic range. Geographic studies typically consider only linear clinal patterns, whereas high density sampling of a cline might reveal a nonlinear pattern as in the case of markers within the In(2L)t inversion in D. melanogaster (Umina et al. 2006). ...
... When there are geographic patterns exhibited by genes within inversions as well as the inversions themselves, and LD is not complete, it is possible to separate out the inversion effects statistically or by examining clinal patterns of the alleles within inverted and standard arrangements. Recent examples of this approach in D. melanogaster include Fryenberg et al. (2003), who showed that clinal patterns in one of the small heat shock proteins (hsp 26) persisted when only patterns within the In(3L)P inversion were considered, and Umina et al. (2006), who separated the effects of In(2L)t from the Gpdh polymorphism. ...
There is a growing appreciation that chromosome inversions affect rates of adaptation, speciation, and the evolution of sex chromosomes. Comparative genomic studies have identified many new paracentric inversion polymorphisms. Population models suggest that inversions can spread by reducing recombination between alleles that independently increase fitness, without epistasis or coadaptation. Areas of linkage disequilibrium extend across large inversions but may be interspersed by areas with little disequilibrium. Genes located within inversions are associated with a variety of traits including those involved in climatic adaptation. Inversion polymorphisms may contribute to speciation by generating underdominance owing to inviable gametes, but an alternative view gaining support is that inversions facilitate speciation by reducing recombination, protecting genomic regions from introgression. Likewise, inversions may facilitate the evolution of sex chromosomes by reducing recombination between sex determining alleles and alleles with sex-specific effects. However, few genes within inversions responsible for fitness effects or speciation have been identified.
... The ability of a population to persist in the face of environmental change will depend on the effects of abiotic stress on individual survival, physiological performance, and reproductive output (Karlsson and Wiklund 2005; Chamaille-Jammes et al. 2006; Lester et al. 2007; Musolin 2007; Reed et al. 2007), phenotypic plasticity (Garland and Kelly 2006; Ghalambor et al. 2007; Nussey et al. 2007), interactions with other species (Leonard 2000; Bertness and Ewanchuk 2002; Petes et al. 2007; Brooker et al. 2008), and the population's genetic composition (Haag et al. 2005; Hanski and Saccheri 2006). Although effects of climate change on natural populations have been extensively investigated, researchers have only recently begun to identify specific features that might allow populations to persist in a changing environment (Helmuth et al. 2002; McLaughlin et al. 2002; Pearson and Dawson 2003; Bradshaw et al. 2004; Ellis and Post 2004; Balanya et al. 2006; Bradshaw and Holzapfel 2006; Gilman et al. 2006; Harley et al. 2006; Svensson et al. 2006; Umina et al. 2006; Tran et al. 2007). Fluctuations in environmental temperature may become more extreme as a result of climate change (Easterling et al. 2000; Diffenbaugh et al. 2005 ). ...
Understanding how climate change impacts natural systems requires investigations of the effects of environmental variation on vulnerable species and documentation of how populations respond to change. The willow beetle Chrysomela aeneicollis is ideal for such studies. It lives in California's Sierra Nevada on the southern edge of its worldwide range. Beetles experience elevated air temperatures during summertime egg laying and larval development. Exposure to these temperatures causes physiological stress, which may reduce reproductive success and endanger populations. The glycolytic enzyme phosphoglucose isomerase (PGI) is a marker of temperature adaptation in C. aeneicollis. PGI allele frequency varies across a latitudinal gradient: allele 1 is common in Rock Creek (RC), which is cooler and to the north, and allele 4 is common in Big Pine Creek (BPC), which is warmer and to the south. In populations that are intermediate in geography and climate (e.g., Bishop Creek [BC]), PGI-4 frequency increases from north to south such that alleles 1 and 4 are in relatively equal frequency in southern BC. Over the past decade, Sierra Nevada beetle populations have colonized high elevations and have become extinct at lower elevations where they were once common. In BC, the magnitude of PGI allele frequency fluctuations among life-history stages is related to maximal air temperature, with the frequency of PGI-4 increasing after the hottest part of summer. To identify mechanisms that may cause shifts in PGI allele frequency, we measured metabolic rate and fecundity for beetles collected at BC. Metabolic rate of males and females was measured at 20 degrees and 36 degrees C using flow-through respirometry. To measure laboratory fecundity, mating pairs were acclimated for 4 h each afternoon at a control temperature (20 degrees C) or at mildly elevated temperatures (26 degrees or 32 degrees C) and number of eggs laid was counted daily for 24 d, after which tissue levels of 70-kD heat shock proteins (Hsp70) were determined. Previous studies had demonstrated differences in Hsp70 expression among PGI genotypes at these temperatures. To measure field fecundity, mating pairs from BC were transplanted to similar elevations in BPC, BC, and RC and were monitored in situ for 24 d. Metabolic rate was higher for PGI 4-4 genotypes than for PGI 1-4 or PGI 1-1 individuals at 36 degrees C but not at 20 degrees C. In contrast, laboratory fecundity was greatest for females possessing PGI-1, independent of acclimation temperature. At the end of the laboratory fecundity experiment, Hsp70 expression was positively related to fecundity, suggesting minimal reproductive cost of upregulation of heat shock proteins in response to mild heat stress. In the field, fecundity was highest for PGI 1-1 and PGI 1-4 individuals in RC and PGI 4-4 individuals in BPC and was similar for all genotypes in BC. Thus, fecundity in nature was greatest for the genotypes that were most common in each area. Taken together, data reported here suggest that hot, dry summers in the Sierra Nevada may result in an increase in frequency of the PGI-4 allele and shifts to higher elevations for C. aeneicollis populations.
... Associations between genetic variation and thermal resistance may depend on the way that thermoresistance is measured, as different measures of thermoresistance are likely to reflect different underlying mechanisms (Hoffmann et al. 2003). Adh and α-Gpdh alleles exhibit clines on several continents (Van't Land et al. 2000; Umina et al. 2005; Umina et al. 2006) and are under selection. These alleles have previously been associated with thermal resistance but there are conflicting reports of levels of linkage disequilibrium between these allozymes as well as with In(2L)t (Oakeshott et al. 1984; van Delden & Kamping 1989). ...
Clinal variation in traits often reflects climatic adaptation; in Drosophila melanogaster clinal variation provides an opportunity to link variation in chromosomal inversions, microsatellite loci and various candidate genes to adaptive variation in traits. We undertook association studies with crosses from a single population of D. melanogaster from eastern Australia to investigate the association between genetic markers and traits showing clinal variation. By genotyping parents and phenotyping offspring, we minimized genotyping costs but had the power to detect association between markers and quantitative traits. Consistent with prior studies, we found strong associations between the clinal chromosomal inversion In(3R)Payne and markers within it, as well as among these markers. We also found an association between In(3L)Payne and one marker located within this inversion. Of the five predicted associations between markers and traits, four were detected (increased heat, decreased cold resistance and body size with the heat shock gene hsr-omega S, increased cold resistance with the inversion In(3L)Payne), while one was not detected (heat resistance and the heat shock gene hsp68). In a set of eight exploratory tests, we detected one positive association (between hsp23a and heat resistance) but no associations of heat resistance with alleles at the hsp26, hsp83, Desat 2, alpha-Gpdh, hsp70 loci, while cold resistance was not associated with Frost and Dca loci. These results confirm interactions between hsr-omega and thermal resistance, as well as between In(3L)Payne and cold resistance, but do not provide evidence for associations between thermal responses and alleles at other clinically varying marker genes.
... This pattern was weak in Australia, and only significant when a number of collections from high latitudes were included (Oakeshott et al. 1984). However subsequent work on recent collections (Umina et al. 2006) has shown that a-Gpdh variation exhibits a consistent non-linear cline reflecting an increase in the a-Gpdh F allele at extreme latitudes. This pattern was not influenced by the In(2L)t inversion wherein this locus is located, nor was it influenced by the presence of the a-Gpdh pseudogene, which is widespread in natural populations of D. melanogaster (Umina et al. 2006). ...
... However subsequent work on recent collections (Umina et al. 2006) has shown that a-Gpdh variation exhibits a consistent non-linear cline reflecting an increase in the a-Gpdh F allele at extreme latitudes. This pattern was not influenced by the In(2L)t inversion wherein this locus is located, nor was it influenced by the presence of the a-Gpdh pseudogene, which is widespread in natural populations of D. melanogaster (Umina et al. 2006). ...
Drosophila melanogaster invaded Australia around 100 years ago, most likely through a northern invasion. The wide range of climatic conditions in eastern Australia across which D. melanogaster is now found provides an opportunity for researchers to identify traits and genes that are associated with climatic adaptation. Allozyme studies indicate clinal patterns for at least four loci including a strong linear cline in Adh and a non-linear cline in alpha-Gpdh. Inversion clines were initially established from cytological studies but have now been validated with larger sample sizes using molecular markers for breakpoints. Recent collections indicate that some genetic markers (Adh and In(3R)Payne) have changed over the last 20 years reflecting continuing evolution. Heritable clines have been established for quantitative traits including wing length/area, thorax length and cold and heat resistance. A cline in egg size independent of body size and a weak cline in competitive ability have also been established. Postulated clinal patterns for resistance to desiccation and starvation have not been supported by extensive sampling. Experiments under laboratory and semi-natural conditions have suggested selective factors generating clinal patterns, particularly for reproductive patterns over winter. Attempts are being made to link clinal variation in traits to specific genes using QTL analysis and the candidate locus approach, and to identify the genetic architecture of trait variation along the cline. This is proving difficult because of inversion polymorphisms that generate disequilibrium among genes. Substantial gaps still remain in linking clines to field selection and understanding the genetic and physiological basis of the adaptive shifts. However D. melanogaster populations in eastern Australia remain an excellent resource for understanding past and future evolutionary responses to climate change.
One of the major questions in ecology and evolutionary biology is how variation in the genome enables species to adapt to divergent environments. Here, we study footprints of thermal selection in candidate genes in six wild populations of the afrotropical butterfly Bicyclus anynana sampled along a c. 3000 km latitudinal cline. We sequenced coding regions of 31 selected genes with known functions in metabolism, pigment production, development and heat shock responses. These include genes for which we expect a priori a role in thermal adaptation and, thus, varying selection pressures along a latitudinal cline, and genes we do not expect to vary clinally and can be used as controls. We identified amino acid substitution polymorphisms in 13 genes and tested these for clinal variation by correlation analysis of allele frequencies with latitude. In addition, we used two F(ST) -based outlier methods to identify loci with higher population differentiation than expected under neutral evolution, while accounting for potentially confounding effects of population structure and demographic history. Two metabolic enzymes of the glycolytic pathway, UGP and Treh, showed clinal variation. The same loci showed elevated population differentiation and were identified as significant outliers. We found no evidence of clines in the pigmentation genes, heat shock proteins and developmental genes. However, we identified outlier loci in more localized parts of the range in the pigmentation genes yellow and black. We discuss that the observed clinal variation and elevated population divergence in UGP and Treh may reflect adaptation to a geographic thermal gradient.
Determining the genetic basis of environmental adaptation is a central problem of evolutionary biology. This issue has been fruitfully addressed by examining genetic differentiation between populations that are recently separated and/or experience high rates of gene flow. A good example of this approach is the decades-long investigation of selection acting along latitudinal clines in Drosophila melanogaster. Here we use next-generation genome sequencing to reexamine the well-studied Australian D. melanogaster cline. We find evidence for extensive differentiation between temperate and tropical populations, with regulatory regions and unannotated regions showing particularly high levels of differentiation. Although the physical genomic scale of geographic differentiation is small--on the order of gene sized--we observed several larger highly differentiated regions. The region spanned by the cosmopolitan inversion polymorphism In(3R)P shows higher levels of differentiation, consistent with the major difference in allele frequencies of Standard and In(3R)P karyotypes in temperate vs. tropical Australian populations. Our analysis reveals evidence for spatially varying selection on a number of key biological processes, suggesting fundamental biological differences between flies from these two geographic regions.
