*Institute of Evolution, University of Haifa, Haifa, Israel;
and
Institute of Plant Sciences and Crop Plant Research, Gatersleben, Germany
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Abstract |
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Introduction |
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It has been suggested that the evolution of noncoding, tandemly repetitive DNAs is driven by slippage during DNA replication (Levinson and Gutman 1987
; Wolff et al. 1991
; Tautz and Schlötterer 1994
; Stephan and Kim 1998
), unequal crossing over (Harding, Boyce, and Clegg 1992
), or gene conversion (Drake, Glickman, and Ripley 1983
; Tautz and Renz 1984
). Among these, replication slippage seems to play a more important role in producing new alleles at SSR loci (Levinson and Gutman 1987
; Wolff et al. 1991
; Innan, Terauchi, and Miyashita 1997
). Stephan and Cho (1994)
simulated SSR evolution using a model of replication slippage, unequal crossing over, mutation, and natural selection. This model suggested that (1) unequal crossing over is a dominant long-range ordering force keeping these arrays homogeneous even in regions of very low recombination rates; (2) replication slippage may cause SSR variation, but unequal crossing over does not act on very short tracts of SSRs; and (3) natural selection plays an essential role in controlling the length of a repeat.
However, the relationship between repeat number and amount of expected diversity according to this mechanism was not consistent among studies (e.g., Weber 1990
; Hudson et al. 1992
; Valdes, Slatkin, and Freimer 1993
; Bell and Ecker 1994
; Plaschke, Ganal, and Röder 1995
). In Arabidopsis thaliana, chromosome effect was significant for the number of alleles, but the effects of motif and unequal crossing over were not significant for SSR diversity (Innan, Terauchi, and Miyashita 1997
). In other species, no clear conclusions have been reached on the genetic effects of SSR diversity.
Wild emmer wheat, Triticum dicoccoides (Poaceae), is the tetraploid progenitor of cultivated wheats (Zohary 1970
) and carries two genomes (A and B) with 2n = 28 chromosomes. Genome A was derived from Triticum monococcum ssp. urartu, and genome B was derived from an ancient S genome of a species that was similar to the extant Triticum speltoides (Friebe and Gill 1996
). Triticum dicoccoides is distributed over the Near East Fertile Crescent, but its center of distribution is found in the Upper Jordan Valley and surrounding areas. In this area, wild emmer grows as a highly selfing annual grass in several steppe-like herbaceous formations in Quercus ithaburensis or in Quercus brantii open park forest belts (Zohary 1973
; Nevo and Beiles 1989
). It grows mainly on basaltic and terra rossa soil types. The previous microgeographic studies using allozyme and random amplified polymorphic DNA (RAPD) markers for T. dicoccoides showed significant nonrandom adaptive molecular genetic differentiation at single- and multilocus levels in contrasting soils, topographies, and climates (Nevo, Beiles, and Krugman 1988a, 1988b
; Nevo and Beiles 1989
; Nevo et al. 1991
; Fahima et al. 1999
; Li et al. 1999
).
Recently, 93 and 115 SSR loci were mapped on genomes A and B, respectively (Röder et al. 1998
). The markers are randomly distributed along the linkage map, with clustering in several centromeric regions. However, we are still short of evidence on intra- and intergenomic variation of SSR in natural populations. Similar genetic patterns were observed at allozyme, RAPD, and SSR loci for an Ammiad population (Li et al. 2000
). However, the ecological effects, including the edaphic effect on SSR diversity, are poorly known. The present study investigated SSR diversity of three natural populations of T. dicoccoides and examined genetic and ecological effects on SSR diversity in three populations of T. dicoccoides from Ammiad, Tabigha, and Yehudiyya in north Israel.
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Materials and Methods |
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The three populations were classified into two edaphic groups according to soil type. The terra rossa (TR) group included the Ammiad population plus plants from the terra rossa microniche at Tabigha. The basalt (BA) group included the Yehudiyya population plus plants from the basalt microniche at Tabigha. The sample sizes were 151 and 184 individuals for the TR and BA groups, respectively.
The caolinitic terra rossa soil studied here developed on Middle Eocene hard limestone rocks and is unique in its characteristics: Available water is low (7%9% of weight), exchange cation level is low (2040 milliequivalent for 100 g soil), and no free Ca is found. The ratio between Ca and Mg is high (1040), pH is relatively low (67), and the amount of P (phosphate) is relatively high. The plant association on this soil in Tabigha is unique (Nevo, Beiles, and Krugman 1988a
), consisting of Andropogon hirsuta and Echinops viscosus (climax Zizyphus spina-Christi and Zizyphus lotus), Carlina corymbosa, Notobasis syriaca, Capparis spinosa, Erucaria myagroides with Ferula communis, Gundelia tournefortii, Phagnalon rupestre, Varthemia iphionoides, Dianthus multipunctatus, and the wild cereals Hordeum bulbosum, Hordeum spontaneum, and T. dicoccoides (less abundant than on basalt, and primarily the yellow spike morph).
The basalt soil is dark and clayey (montmorilonite) and developed on Upper Pleistocene basalt flows; it differs greatly from the terra rossa in its characteristics. It is poor in Ca, heavy, and rich in P, and available water is high (9%12% of weight). The pH is relatively high (7.78.0). General and available amounts of P are higher in the basalt than in the terra rossa. The plant association and even some morphological characteristics of wild emmer plants on this soil (Nevo, Beiles, and Krugman 1988a
) are different from those on the terra rossa. Plant association comprises Psoralea hirsuta and Echinops blancheanus (climax of Pistacia atlantica, Z. spina-Christi and Z. lotus), with Scolymus maculatus, Cichorium pumilum, Carthamus glaucus, Echium judaicum, Hirschfeldia incana, and Sinapis arvensis, and wild cereals Brachypodium distachyum, Phalaris bulbosum, Avena sterilis, H. bulbosom, H. spontaneum, and abundant T. dicoccoides (primarily the black spike morph). The intimate relationship between soil and vegetation was discussed in detail by Rabinovitch-Vin (1986)
.
Microsatellite PCR Analysis
Genomic DNA was extracted from seedlings using the method of Junghans and Metzlaff (1990)
. The 28 dinucleotide microsatellite DNA markers used in this study were described by Röder et al. (1995, 1998)
. These markers are located on different arms (one for each arm) of 14 chromosomes of T. dicoccoides. Table 1
presents the locus, repetitive motif, chromosomal location, and distance from the centromere (D). The procedure used to detect microsatellite polymorphism followed Fahima et al. (1998)
.
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Since almost half of the 28 SSR markers used are compound or imperfect SSRs, and such SSRs may conform more closely to an "infinite alleles" model (Goldstein et al. 1995
; Angers and Bernatchez 1997
; Feldman et al. 1997
), Nei's (1973)
gene diversity (He) was used to estimate SSR diversity. Number of alleles (NA) and He were calculated using the POPGENE program (Yeh et al. 1997
). The STATISTICA program (Statsoft 1996
) was used for statistical analyses. The Kruskal-Wallis one-way analysis of variance by rank (Siegel and Castellan 1988
) was used to test for difference in average repeat number, since the distribution of repeat number at a locus is not normal. Wilcoxon's signed rank test (Siegel and Castellan 1988
) was used to test significance of difference in SSR diversity among subpopulations by pairwise comparisons. Levene's test (Statsoft 1996
) and Hartley's F ratio (Statsoft 1996
) were used to test homogeneity of variance among the three populations and the two edaphic groups. Soil-specific alleles are those that significantly (by the
2 test) predominate in one of the two soil types (either TR or BA) at both microgeographic (Tabigha microsite) and macrogeographic (Ammiad [TR] vs. Yehudiyya [BA] populations) levels. Soil-unique alleles are those that present in only one (either TR or BA) of the two soil types simultaneously at micro- and macrogeographic levels. A permutation test was performed for the observed allele soil specificity and soil uniqueness. For this analysis, rare alleles (with less than 15 copies per population) were excluded from consideration. Multivariate analysis of variance (MANOVA) was used to estimate effects and contributions of genetic and environmental factors on SSR diversity.
To estimate determination coefficients of internal (genetic) mechanisms and external (environmental) factors to SSR variation, multiple regression analysis was performed. The independent variables included D, average repeat number (ARN), edaphic group, motif, and their interactions. The dependent variables included NA and variance in repeat number (2).
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Results |
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Among the three populations, the ARNs were significantly different at 23 polymorphic loci (Kruskal-Wallis test, P < 0.050.00005; table 2
). Wilcoxon's signed-ranks test showed that the ARN over all loci except GWM577 and 601 was significantly larger in the Y population than in the other two (z = 2.623, P < 0.01). 2 differed significantly at 25 loci among the three populations (Levene's test, P < 0.050.00005). At 15 loci, the A population showed much higher values of
2 than did the T and Y populations. Over all loci considered simultaneously, the
2 value for the A population was significantly greater (Wilcoxon's signed-ranks test, P < 0.01) than those for the T (z = 2.677) and Y (z = 2.785) populations (table 2
).
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SSR diversity (He; Nei 1973
) was estimated for the two edaphic groups (table 3
). Although the overall He was similar between the TR (0.755) and BA (0.733) groups, significant differences in allele composition were observed at some loci. For example, at five loci (GWM099, GWM162, GWM361, GWM415, and GWM429), the He values based on significantly different allele components (according to the
2 test for homogeneity of allele frequencies at each locus, P < 0.00005) were larger in the TR group than in BA group; the differences in He ranged from 0.139 to 0.501. At two other loci (GWM095 and GWM537), allele composition was significantly different (P < 0.00005), and He values were higher in the BA group than in the TR group; the differences ranged from 0.200 to 0.277 (table 3
).
Genetic Effects on SSR Variation
The ARN scores for genome B were higher than those for genome A over all three populations (table 4
). Interestingly, the ARNs for each genome were very similar among the three populations; values for genome A were 20.823.8, and those for genome B were 28.229.6. The NA was higher for genome B than for genome A for all three populations. Analysis of variance (MANOVA) indicated that the effects of genome on SSR variation (ARN, NA, and 2) differed among the three populations (table 5 ). Genome significantly (P < 0.050.00005) affected ARN in the A and T populations and NA in the T and Y populations (table 5
). Chromosome also affected (P < 0.050.00005) ARN, NA, and
2 of the A population, ARN and NA of the T population, and ARN and
2 of the Y population (table 5
). Chromosome 1 (both 1A and 1B) showed the largest ARN and
2 in all three populations (table 4
). The genome x chromosome interaction significantly (P < 0.050.0005) affected the NA values for all three populations, and ARN and
2 values for the A and Y populations, respectively. This study included seven types of motifs. Motif types significantly (P < 0.050.00005) affected the ARN values of all populations (table 5
). Compound SSRs, such as (CA)n(TA)k and (CT)n(GT)k, had higher ARN,
2, and NA values than did simple SSRs ((CA)n, (GA)n, or (CT)n) (table 4
). The locus effect was significant (P < 0.00050.00005) for ARN, NA, and
2 for all populations (table 5
). According to table 2
, locus GWM136 showed the largest ARN (48.869.9) and
2 (69.0233.6) values in the three populations. Locus GWM099 showed the smallest ARN value (8.115.6). The lowest
2 (
2 = 0) and NA (NA = 1) values were found for the monomorphic GWM601. The largest NA value was found for GWM169 in the A population, for GWM537 in the T population, and for GWM294 in the Y population (table 2
).
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Discussion |
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Natural stresses may accelerate replication error (Jackson, Chen, and Loeb 1998
) and recombinational intermediates (Afzal et al. 1995
) or decrease the ability of DNA mismatch-repair mechanisms (Radman et al. 1995
; Brentnall et al. 1996
; Jackson, Chen, and Loeb 1998
) so as to increase SSR diversity. In our study, we found that the interactions of soil x ARN and soil x D significantly affected SSR diversity. The results may also indicate that edaphic stresses affect replication and recombination of chromosomes of T. dicoccoides, hence SSR diversity. The selective targets may be either SSR loci themselves or selected regions close to the SSR loci.
Innan, Terauchi, and Miyashita (1997)
suggested that there was no reason to reject some forms of natural selection, particularly if SSRs are involved in some biological role. In our study, soil-unique SSR diversity and strong motif and locus effects might suggest that the repeated tracts assayed are functionally important. The observed pattern might be an adaptation to environmental edaphic stresses. King and Soller (1999)
also suggested that many SSRs were functionally integrated into the genome, so such changes in tract length can exert a quantitative regulatory effect on gene transcription activity, and thereby on adaptive fitness. It was proved that (TC)n, (GA)n, and (TA)n control transcription activity of Ultrabithorax, hsp26, and actin5C genes in Drosophila; the (TG)n repeat modulates the transcription activity of the gene prolactin in rat (reviewed in Kashi, King, and Soller 1997
). In humans, mice, and rats, (GT)n repeats can enhance gene activity not only of the GT repeats closer to promoter sequences, but also of those from distant positions, such as introns and 3'-flanking regions (Stallings et al. 1991
). It was also suggested that SSRs might be involved in chromosome organization of Triticum (Cuadrado and Schwarzacher 1998
). Genes associated with SSRs may be favored by indirect selection whenever quantitative variation in the affected traits can provide a population with genetic resiliency for adaptation, especially in fluctuating or heterogeneous environments (King and Soller 1999
). Another interpretation results from the fact that T. dicoccoides is a highly selfing plant. Correspondingly, differential selection on fitness-related target loci will affect many other sites in the genome, either linked or unlinked to the selected loci (Korol, Preygel, and Preygel 1994
). This may generate multiple niche-specific linkage disequilibria, and it is not an easy task to discriminate such a scenario and direct selection.
Microgeographic genetic divergence, despite gene flow, following ecological heterogeneity has been shown in Avena (Allard et al. 1972
; Hamrick and Allard 1972
) and other plants (reviewed in Brown 1979
), as well as in the Triticum aegilops, H. spontaneum and T. dicoccoides, in the same transects at Tabigha and elsewhere (overviewed in Nevo [1988
] and studied spatiotemporally by Nevo et al. [1991
]). Such genetic divergence can occur over very short distances and also seems to be adaptive at the level of allozyme polymorphism. It suggests the operation of edaphic diversifying selection, in accordance with the ecological niche model of Levene (1953)
and the migration-selection model of Karlin (1982)
. It remains, however, to identify the specific selective soil components and the precise target of selection. Two experimental strategies can be proposed for that.
First, one can build a mapping population (say, F2) by crossing genotypes from the alternative niches carrying niche-unique SSR alleles and defining the quantitative traits that differ between the soil types. Such a population can be used for quantitative trait loci (QTL) mapping analysis of a multivariate fitness-related trait complex (Korol, Ronin, and Kirzhner 1995
). This will allow testing of the significance of linkage between the formerly detected soil-unique (-specific) SSR(s) and fitness-related traits. But the final proof of the SSR being the target of selection should be based on the following cloning and sequencing of the detected QTL. The second approach uses a very different strategy, but the start is the same. The foregoing F2 population may be subjected to a series of further random intercrosses. This should destroy linkage disequilibria between linked loci. Then, the resulting populations should be subjected for a few generations to the alternative edaphic conditions and then scored for allele frequencies at the SSR. Only loci involved in response to edaphic selection or closely linked markers will show directed changes in allele frequencies. Any resolution can be achieved with a sufficient number of intercrosses. In fact, the two approaches can be combined into one (see Darvasi and Soller 1995
; Xiong and Guo 1997
).
SSR Divergence in Populations
This study demonstrated that SSRs significantly diverged in ARN, 2, and NA of three populations of T. dicoccoides from Ammiad, Tabigha, and Yehudiyya, located at different altitudes and under different levels of annual rainfall (Nevo, Beiles, and Krugman 1988a, 1988b;
Nevo et al. 1991
). The A population showed the largest ARN,
2, and NA per locus across all SSR loci assayed. Particularly, in the A population, the GWM361 locus was monomorphic, suggesting that some forces either eliminate alleles or limit mutations of this locus. Locus GWM577 could only be amplified for three individuals of the A population, suggesting that some mutations may occur in the flanking regions. However, in the T and Y populations, these two loci were polymorphic. In the Y population, ARN across all the loci was the largest among the three populations. Locus GWM415 was monomorphic only in the Y population. These results may indicate environmental effects at the three sites. The Ammiad site covers four microhabitats across north-facing slope, valley, ridge with various slopes and aspects, and karst with deeply dissected rock relief (Nevo et al. 1991
; Noy-Meir et al. 1991
). The four microhabitats vary in rock relief, soil moisture, nitrogen content, and grazing pressure. The observed distinct results of the A population, in contrast to those of the other two populations, may arise from the more heterogeneous environment. Interestingly, although the three populations are located separately, they were monomorphic at GWM601 with the same repeat number. This may suggest that this (CT)n tract at this locus may be involved in some biological role in maintaining some characteristic of T. dicoccoides.
Genetic Effects on SSR Diversity
This study demonstrated that genetic factors (genome, chromosome, motif, locus, etc.) are strongly correlated with SSR diversity. Different patterns of SSR diversity were observed between compound (and/or imperfect) and simple perfect SSRs. This may be because compound or imperfect SSRs are involved in more complex evolutionary processing than perfect SSRs (Ortí, Pearse, and Avise 1997
). Some evidence suggests that compound or imperfect SSRs may conform more closely to an "infinite alleles" model because of the larger number of potentially achievable allelic states (Goldstein et al. 1995
; Angers and Bernatchez 1997
; Feldman et al. 1997
). In the present study, the observed genome effect on SSR diversity may also be explained by different evolutionary patterns between compound and simple perfect SSRs, since most loci in genome B assayed here are compound SSRs, and most loci assayed in genome A are simple perfect SSRs. Out of 95 and 115 loci mapped on genome A and B by Röder et al. (1998)
, 31.6% and 52.2% were compound (including the imperfect) SSRs, respectively. The difference in locus distribution of compound and simple perfect SSRs may arise from different evolutionary sources of genomes A and B in wheat. Genome A was derived from T. monococcum ssp. urartu in east Turkey and Armenia, and genome B was derived from an ancient S genome of a species that was similar to the extant T. speltoides in southern Turkey and the Near East (Friebe and Gill 1996
). C-band polymorphisms and structural rearrangements in wild or cultivated wheat showed greater diversity in the B genome than in the A genome (Friebe and Gill 1996
). One could expect that the repeated DNA regions are also more variable in the B genome than in the A genome, and this is precisely what we found in the present study.
The different levels of SSR variation among the seven groups of chromosomes may reflect heterogeneity of different chromosomes in T. dicoccoides. The pattern may also be explained by functional importance of SSRs in chromosome organization (Caudrado and Schwarzacher 1998
).
In summary, genetic effects appeared to act on SSR variation mainly through SSR motif types, particularly compound and perfect types, potentially due to SSR functional importance and different evolutionary processes of compound and perfect SSRs.
Internal and External Effects on SSR Diversity
The evolution of noncoding tandemly repetitive DNAs was supposed to be driven by slippage during DNA replication (Levinson and Gutman 1987
; Wolff et al. 1991
; Tautz and Schlötterer 1994
) and/or an unequal recombination (Harding, Boyce, and Clegg 1992
). If so, a longer repeat should have more variation, since the chance of replication errors is higher for a longer sequence (Levinson and Gutman 1987
; Wolff et al. 1991
). SSR loci located farther from the centromeres should also have more variation, since recombination is suppressed around the centromeres of chromosomes (Gill et al. 1996
). In other words, ARN and D may affect NA and
2 at SSR loci. In (CA)n and (GA)n SSRs, edaphic group affected NA by influencing ARN (or replication slippage). In (CT)n and (CA)n(TA)k, edaphic niche affected NA through the interaction with D; i.e., edaphic selection may affect recombination of chromosomes and then affect these SSR diversities. The D x ARN interaction significantly affected NA and
2 of (CT)n(GT)k and
2 of (CT)n and (CA)n(TA)k SSRs. This interaction can occur during DNA replication involved in recombination-dependent DNA repair: strand exchange between two homologous chromosomes may create a region of mismatched (heteroduplex) DNA. These regions undergo replication-dependent correction; hence, a slippage mechanism may also work in recombination tracts involving SSR arrays. One may further speculate that the level of slippage errors varies along the chromosome, together with the rate of recombination.
SSR variation is influenced by both genetic factors and ecological forces. We estimated ecological and genetic contributions to SSR variation of T. dicoccoides based on the three populations and two edaphic groups. The results showed different amounts of ecological and genetic contribution for different parameters of SSR variation. The results suggest that both genetic and ecological factors influence SSR variation and that their relative importance varies among different SSR motifs. Notably, the genetic factors may reflect past ecological evolution under differential environmental stresses, as emphasized earlier for genomes A and B. In other words, even the genetic factors may have been deeply affected by ecological origin.
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Conclusions |
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Acknowledgements |
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Footnotes |
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1 Abbreviations: LD, linkage disequilibrium; SSR, simple sequence repeat.
2 Keywords: microsatellite diversity
genetic mechanism
ecological effect
wild emmer wheat
Triticum dicoccoides.
3 Address for correspondence and reprints: Eviatar Nevo, Institute of Evolution, University of Haifa, Haifa 31905, Israel. E-mail: nevo{at}research.haifa.ac.il
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