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Genetics. Nov 2007; 177(3): 1499–1507.
PMCID: PMC2147952

An Unusually Low Microsatellite Mutation Rate in Dictyostelium discoideum, an Organism With Unusually Abundant Microsatellites

Abstract

The genome of the social amoeba Dictyostelium discoideum is known to have a very high density of microsatellite repeats, including thousands of triplet microsatellite repeats in coding regions that apparently code for long runs of single amino acids. We used a mutation accumulation study to see if unusually high microsatellite mutation rates contribute to this pattern. There was a modest bias toward mutations that increase repeat number, but because upward mutations were smaller than downward ones, this did not lead to a net average increase in size. Longer microsatellites had higher mutation rates than shorter ones, but did not show greater directional bias. The most striking finding is that the overall mutation rate is the lowest reported for microsatellites: ~1 × 10−6 for 10 dinucleotide loci and 6 × 10−6 for 52 trinucleotide loci (which were longer). High microsatellite mutation rates therefore do not explain the high incidence of microsatellites. The causal relation may in fact be reversed, with low mutation rates evolving to protect against deleterious fitness effects of mutation at the numerous microsatellites.

MICROSATELLITES, also known as simple sequence repeats, are long stretches of a short (1–6 bp), tandemly repeated DNA unit, such as the motif CAA repeated 20 times. Microsatellites are common throughout eukaryotic genomes and their lengths are often highly polymorphic, making them powerful markers for use in genetic mapping (Weber 1990; Dietrich et al. 1994; Dib et al. 1996; Roder et al. 1998), population genetics (Jarne and Lagoda 1996; Di Rienzo et al. 1998; Goldstein and Schlotterer 1999; Thuillet et al. 2002), and determination of kinship (Queller et al. 1993).

The social amoeba Dictyostelium discoideum has the highest density of microsatellite repeats of any sequenced organism, making up >11% of its genome (Eichinger et al. 2005). As is usual (Ellegren 2004), the noncoding regions are richest in microsatellites, because they are less functional. However, there is also an exceptional number of long triplet repeat loci within genes, resulting in large numbers of homopolymer amino acid strings. The most common are polyasparagine and polyglutamine; 2091 of the 13,541 predicted genes have tracts of ≥20 consecutive repeats, and some of these have multiple tracts (Eichinger et al. 2005). Microsatellites occur on average every 724 bp in exons and encode 3.3% of all amino acids (Eichinger et al. 2005). Other eukaryotic genomes also have amino acid repeats, although at a much lower density (Marcotte et al. 1998; Li et al. 2004).

In humans, certain triplet repeats that occur in or near coding regions are subject to expansions that directly cause genetic diseases (Ashley and Warren 1995; Cummings and Zoghbi 2000). Whether D. discoideum experiences such deleterious effects from its many coding-region repeats is unknown. However, unpublished work shows that these exonic microsatellites are highly variable (C. Scala, N. Mehdiabadi, J. Strassmann and D. Queller, unpublished results), suggesting that they are not tightly controlled by selection. However, selection ought to be potent in D. discoideum. It has a large geographic range [eastern North America and part of eastern Asia (Swanson et al. 1999)] and therefore should have a large effective population size. Molecular evidence suggests that it is typical of unicellular eukaryotes to have a population size (estimated as Neμ) large enough to make selection a very potent force relative to drift (Lynch and Conery 2003). This makes it harder to explain the persistence of large numbers of apparently functionless, or even deleterious, microsatellites.

Mutational changes in the number of repeats occur during DNA replication when the two DNA strands temporarily dissociate and then realign out of register, creating an unpaired repeat loop on one of the strands (Streisinger et al. 1966; Levinson and Gutman 1987; Schlötterer and Tautz 1992; Strand et al. 1993). Primary replication slippage occurring on the template strand deletes repeat units, while slippage on the nascent strand creates additional repeats. The alternative hypothesis of unequal crossing over (Smith 1973; Sia et al. 1997) is not supported by research that experimentally restricted most forms of recombination in Escherichia coli (Levinson and Gutman 1987) and yeast (Henderson and Petes 1992) without lowering microsatellite instability.

Kruglyak et al. (1998) proposed a mutation model suggesting that higher mutation rates result in more microsatellites and a shift toward longer microsatellites. High mutation rates could also account for the maintenance of high variability. So one possible explanation for the high number, long length, and variability of microsatellites in D. discoideum is that this species could have an unusually high mutation rate for microsatellites. It is this hypothesis that we test in this report.

Microsatellites mutate at rates much higher than the usual base-pair substitution rate of ~10−9/locus/generation (Ellegren 2000b; Buschiazzo and Gemmell 2006). Drosophila microsatellites have the lowest reported mutation rates: in the 10−6–10−4 range (Schlötterer et al. 1998; Schug et al. 1998; Harr and Schlötterer 2000; Vazquez et al. 2000). Mammalian mutation rates, including that of humans, fall between 10−5 and 10−2 (Serikawa et al. 1992; Weber and Wong 1993; Dietrich et al. 1994; Brinkmann et al. 1998; Sajantila et al. 1999; Xu et al. 2000), as do rates reported for plants (Udupa and Baum 2001; Thuillet et al. 2002; Vigouroux et al. 2002).

Slippage rates in vitro are 100- to 1000-fold higher than in vivo rates (Strand et al. 1993) because functional mismatch repair systems maintain drastically lower rates in the latter. Only those slippage mutations overlooked by the mismatch repair system are propagated in successive replication events. Mutations in the mismatch repair system destabilize microsatellite DNA in E. coli (Levinson and Gutman 1987), yeast (Strand et al. 1993; Wierdl et al. 1997), and humans (Kolodner 1996). Observed microsatellite mutation rates thus reflect a balance between primary replication slippage and mismatch repair efficiency.

Mutation rates are not uniform even within a genome. Most strikingly, rate of slippage increases with microsatellite length (Weber and Wong 1993; Kroutil et al. 1996; Wierdl et al. 1997; Brinkmann et al. 1998; Schlötterer 2000; Ellegren 2004), as there are more sites where slippage can occur and the conformational entropy of slippage is ~2 kcal/mol more destabilizing for long direct repeats than for shorter repetitive runs (Harvey 1997).

A simple stepwise mutation model is not stable; it leads to continual growth of microsatellites (Kruglyak et al. 1998). The size of microsatellites might be limited if large microsatellites have a downward slippage bias (Wierdl et al. 1997; Harr and Schlötterer 2000) or if large microsatellites tend to have large deletion mutations (Ellegren 2000a). Another such factor is point mutations that interrupt the repeat sequence (Petes et al. 1997; Kruglyak et al. 1998), but we did not examine this factor.

We estimated D. discoideum microsatellite mutation rates using a mutation accumulation experiment. In such experiments, lines are repeatedly passed through single-cell bottlenecks to fix mutations randomly. The cell divisions between the single-cell bottlenecks provide some opportunity for strong selection to have effects, but weakly selected mutations will be represented nearly randomly.

MATERIALS AND METHODS

Mutation accumulation:

We started each of 90 mutation accumulation lines from a common ancestor, the lab-maintained axenic D. discoideum AX4 clone. The lines grew on SM agarose plates (10 g glucose, 10 g bactopeptone, 1 g yeast, 1 g MgSO4, 1.9 g KH2PO4, 0.6 g K2HPO4, 20 g agar, 1 liter H2O) with the bacterium Klebsiella aerogenes used as a food source for the amoebas. To obtain our mutation accumulation lines, we plated out the ancestral clone and selected a single plaque to serve as the ancestor (perfectly circular clearings or plaques in the bacterial lawn derive from a single cell). This single cell line was plated out clonally, 10 single plaques were selected, and the process was repeated to obtain 10 plaques from each of these.

From the resulting 100 lines, 90 were used as mutation accumulation lines and the remaining 10 were control lines that are not part of this report. Each mutation accumulation line was put though a series of 70 single-cell bottlenecks, separated by 48-hr episodes of growth on plates as described above. The single-cell bottlenecks were accomplished by randomly selecting a clonal plaque at the end of 48 hr and transferring cells from that plaque to the next plate.

We estimate that the 48-hr growth periods encompassed an average of 14.18 cell generations. This figure is the unweighted average of estimates for the ancestral clone (14.12 ± SD 0.62, an average of eight estimates) and the 90 mutation accumulation clones at the end of the experiment (14.24 ± SD 0.71, n = 90). Each estimate was obtained by collecting and counting the cells from a single clonal plaque after 48 hr and taking the base 2 logarithm. Thus, each line went through ~14.18 × 70 = 1007 cell generations.

We extracted DNA from all 90 lines at the completion of the 10th and the 70th bottleneck. D. discoideum has a multicellular fruiting stage and we extracted DNA from the spore masses with 150 μl of a 5% chelex solution. The thousand generations of the experiment were all in the single-cell vegetative stage.

Microsatellite selection, amplification, and genotyping:

We downloaded the genomic DNA sequence of all six D. discoideum chromosomes from the online Dictyostelium database (http://www.dictybase.org). A modified version of the program Sputnik (http://espressosoftware.com/pages/sputnik.jsp) was used to compile a list of all microsatellites containing at least five perfect repeat units. We designed three sets of primers. First, we designed primers for 27 of the longest trinucleotide repeat microsatellite loci occurring within coding regions (exons) of genes (Table 1). The selected microsatellites comprised five different repeat motifs: (CAA)n, (AAT)n, (AGT)n, (TCA)n, and (GAA)n. Each motif can be read multiple ways, and 10 codons were included in the study (e.g., the CAA motif also includes ACA, AAC, TTG, TGT, and GTT codons). These microsatellites ranged in length from 33 to 76 repeat units, although 17 of the 24 were at least 50 repeats long.

TABLE 1
The 24 long trinucleotide microsatellites, categorized by codon and length

To more systematically explore the role of different repeat motifs, we selected an additional 28 microsatellites from within exonic regions of genes (Table 2), 7 from each of the motifs (TCA)n, (AGT)n, (AAT)n, and (CAA)n, again with multiple codons represented. For each motif, the 7 microsatellites had lengths of 10, 15, 20, 25, 30, 35, and 40 repeat units (with the exception of three microsatellites that differed from these stated lengths by 1 repeat unit and one case that differed by 2 repeat units; see Table 2). Due to the high density of repeated DNA within genes, the polymerase chain reaction (PCR)-amplified region sometimes contained small perfect repeats in addition to the desired microsatellite. Such additional repeats >10 bp are also listed in Tables 1 and and22.

TABLE 2
The 28 trinucleotide microsatellites chosen for seven specific lengths and grouped by motif

To explore whether our results also applied to what is usually the most common class of microsatellites in most other species, we also designed primers for 10 dinucleotide repeat loci (Table 3). These ranged from 14 to 34 repeats in the genome sequence and all were from predicted introns. Nine had AT repeats and 1 had CT repeats. Some additional repeats occurred inside the amplified regions of both dinucleotides and trinucleotides and these are also listed in Tables 1–3.

TABLE 3
The 10 dinucleotide repeat loci

We tagged one member of each primer pair with a fluorescent dye molecule at the 5′-end. The PCR amplified the microsatellite loci in all 90 mutation accumulation lines using the DNA extracted after the 70th bottleneck. Each 20-μl PCR reaction contained 2 μl of DNA extract, 8 μl of a primer mixture containing forward and reverse primers, 6 μl water, 2 μl 10× buffer, 1.2 μl magnesium chloride, 0.6 μl dNTPs, 0.25 μl BSA, and 0.3 μl Taq polymerase. A touchdown PCR procedure cycled 20 times through a range of annealing temperatures between 60° and 50°, dropping by 0.5° with each new cycle before holding at 50° for 10 additional cycles. A 10-min polymerase extension period at 72° concluded the reaction. The amplified products were cleaned and precipitated with ethanol. We determined PCR product length on an Applied Biosystems 3100 model automated genetic sequencer running the programs GeneScan 3.7 and GENOTYPER. Length differences of multiples of the repeat unit (usually 3 bp) between the PCR product and the ancestor indicated repeat mutations. We ignored differences of significantly less than the repeat unit, as they are either measurement error or real changes not attributable to misalignment and slippage in the triplet repeat microsatellite.

Microsatellites that had mutated were then amplified using DNA extracted from the 10th bottleneck. Three trinucleotide loci were discarded (and are therefore not listed in Table 1) because they had apparently non-independent mutations. This was indicated by multiple (10 or more) identical mutations, present by generation 10, that suggested that they had mutated and replicated during the grow-up phase in the establishment of the lines.

The experiment included 90 lines that had gone through 71 bottlenecks of 14.18 generations each, for a total of 90,610 meioses/locus. For a given set of loci, the mutation rate was therefore calculated as number of mutations/(number of loci × 90,610). Confidence intervals on mutation rates were calculated by first obtaining 95% confidence intervals (C.I.'s) on the number of mutations using the cumulative 0.025 and 0.975 points of the cumulative Poisson distribution and then dividing by the appropriate number of mitoses (Casella and Berger 1990).

RESULTS

Trinucleotide mutation rate:

We observed 33 changes in repeat number for the trinucleotide loci that we assayed. Each was rechecked from the DNA extracted after bottleneck 10 to see if it occurred prior to that time. Eleven of the mutations had occurred by bottleneck 10 and 22 occurred in the 60 following bottlenecks. This is significantly more mutations than expected in the period before generation 10 (χ2 = 8.02, d.f. = 1, P < 0.005). Closer examination showed that 3 of the 11 early mutations were duplicates—the same mutational change occurring twice in the same line—which might indicate that the mutation actually occurred at one single time during the grow-up generation that began the lines. One of these mutations added 1 repeat, one subtracted 1 repeat, and one added 11 repeats. It seems possible that the changes of 1 repeat unit each occurred twice, but at least the 11-repeat change seems likely to have occurred only once as such large changes are quite rare (see below). If these three mutations are deleted from the data set, the difference before and after bottleneck 10 is no longer significant (χ2 = 2.86, d.f. = 1, P > 0.05). We therefore eliminated one copy of each of these three mutations from the data set.

Dividing the number of observed mutations by the number of mitoses experienced in the mutation accumulation lineages yields an estimated mutation rate of 6.37 × 10−6 (95% C.I. 4.30 × 10−6–9.09 × 10−6). This value is quite low for a microsatellite mutation rate, contrary to the hypothesis that high slippage rates are the cause of the high density and variability of microsatellites in D. discoideum. This conclusion is unaffected by our decision to eliminate the three duplicate copies, as including them raises the mutation rate by only 10%.

Size and direction of trinucleotide mutations:

Figure 1 shows the repeat number changes observed in our 30 trinucleotide mutations. Most of the mutations—20 of 30—were changes of a single repeat. Of the remaining 10, 5 changed by 2 repeats and the remainder ranged up to a loss of 32 repeats. There was a bias toward increases in repeat numbers, with 20 increases and 10 decreases. However, largely because of the one mutation that lost 32 repeats, the average change in repeat number was not significantly >0 (0.23 ± SE 1.27). The average of the increases was 2.43 (±0.79) and the average loss of the decreases was 4.89 (±3.22). Excluding the 32-repeat loss, there is a significant upward bias (1.35 ± SE 0.63).

Figure 1.
Number of mutations observed for different changes in repeat number. The zero class (no mutation) is not shown.

Length dependence of trinucleotide mutations:

The mutation rate increased with the number of repeats in the locus (Figure 2A). For the 26 trinucleotide loci with <40 repeats, the mutation rate was 8.49 × 10−7 (95% C.I. 1.03 × 10−7–3.07 × 10−6), while for the 26 trinucleotide loci with >40 repeats, it was 1.18 × 10−5 (95% C.I. 7.90 × 10−6–1.72 × 10−5). Because of the tendency of microsatellite mutation rates to increase with length, rates are sometimes calculated on a per-repeat basis. The loci had a total of 2064 repeats, and the mutation rate per repeat was 1.60 × 10−7.

Figure 2.
Mutational changes as a function of original repeat numbers. The size of solid circles indicates the number of overlapping data points from 1 to 4. (A) Number of mutations observed (y = 0.024x − 0.38; P = 0.003). (B) Absolute value ...

The absolute sizes of mutational changes also increased with the original repeat number of the locus (Figure 2B). The regression is not significant but this is largely due to the outlier that lost 32 repeats. This point ought to support an increasing trend because it is a large change at a locus with many repeats, but it adds so much variance that it prevents significance from being obtained. Removing that point results in a significant positive slope (y = 0.067x − 1.52; P = 0.049). The loci having >60 repeats showed most of the mutations of >1 repeat and all 10 of the mutations changing >2 repeats.

The largest change, a loss of 32 repeats, was seen at a 67-repeat locus, which is consistent with control of microsatellites by large deletions. However, there was no distinct tendency for longer microsatellites to contract instead of expand (Figure 2C).

Motif dependence of trinucleotide mutations:

We assayed five different trinucleotide motifs, comprising 10 different codons. Table 4 shows the estimated mutation rates for each. The three motifs for which we sampled at least 10 loci had remarkably similar estimates. The other two had point estimates that were a little higher and lower, but the confidence intervals all overlap and do not indicate any true differences.

TABLE 4
Trinucleotide mutation rates by motif

Dinucleotide mutation rate:

Only a single mutation was observed for the 10 dinucleotide loci. It was a change from 34 to 36 repeat units in the longest repeat studied (locus DDB0234212). This gives an average mutation rate of 1.10 × 10−6 (95% C.I. 2.79 × 10−8–6.15 × 10−6), lower than that observed for the trinucleotides. The dinucleotide mutation rate could be even lower than this estimate because the mutated locus included two long mononucleotide repeats (Table 3), but a change of 4 bases is more easily explained by a mutation in the long dinucleotide locus. The mutation rate per repeat was 5.72 × 10−8.

DISCUSSION

Low mutation rate:

We initiated this study with the idea that a high mutation rate might help explain the extraordinary abundance of microsatellites in the D. discoideum genome. High abundance and also high variability could be explained if D. discoideum had an unusually high mutation rate for microsatellites, leading to a higher rate of neutral evolution at these loci. In fact, we found the opposite: D. discoideum has an unusually low microsatellite mutation rate, 6.37 × 10−6/locus/generation for the trinucleotides tested and 1.10 × 10−6 for the dinucleotides. In other species, mutation rates are usually higher—in the range of 10−2–10−5/generation (Ellegren 2000b; Buschiazzo and Gemmell 2006)—although many of these estimates are for multicellular organisms, which can have many mitoses per generation.

The D. discoideum mutation rates are most similar to those of Drosophila melanogaster and Saccharomyces cerevisiae, which are exceptional among previously studied organisms for their low microsatellite mutation rates. Two mutation accumulation studies in D. melanogaster yielded low mutation rates. One estimate from 24 D. melanogaster 10 dinucleotide, 6 trinucleotide, and 8 tetranucleotide repeat loci was 6.3 × 10−6 (Schug et al. 1997), and the later addition of 39 dinucleotide brought the dinucleotide mutation rate up to 9.3 × 10−6 (Schug et al. 1997). Another estimate from 16 dinucleotide and 12 trinucleotide loci yielded a similar rate of 5.1 × 106 (Vazquez et al. 2000). Although these rates are very similar to those of D. discoideum, the Drosophila loci studied had fewer repeats. Thus, when calculated on a per repeat basis (see Kruglyak et al. 2000), the D. discoideum rates (dinucleotide 5.7 × 10−8; trinucleotide 1.6 × 10−7) are somewhat lower than the Drosophila ones (dinucleotide 7.7 × 10−7; trinucleotide 2.7 × 10−7) and also lower than those estimated for yeast (dinucleotide 9.2 × 10−7; trinucleotide 5.0 × 10−7).

Thus, the D. discoideum point estimates appear to be similar to, or even lower than, the lowest yet recorded. The main point, however, is not which species has the lowest rate, but that we can definitively exclude the hypothesis that the high abundance and length of microsatellites in D. discoideum derives from a higher-than-normal mutation rate. D. discoideum has accumulated numerous long microsatellites for some other reason, in spite of its low mutation rate.

D. discoideum generally has low mutation rates in the presence of various mutagenizing agents, perhaps selected for because of high exposure to chemical mutagens in the soil (Deering et al. 1996). The low microsatellite mutation rates that we found may simply be one manifestation of a generally low mutation rate.

Properties of trinucleotide repeat mutations:

In addition to showing an overall low mutation rate, the data also provide information on the properties of mutations that may provide further insight into why D. discoideum has so many microsatellites relative to other species. One feature that would increase microsatellite accumulation is an upward bias in mutations, as has been observed in some species (Amos et al. 1996; Primmer et al. 1996; Vigouroux et al. 2002). At first, there appears to be an upward bias in D. discoideum trinucleotides, with 20 of 30 mutations leading to increases in repeat number. However, because decreases tended to be larger than increases, there is no significant gain of sequence unless the outlier loss of 32 repeats is excluded. Thus, this factor does not explain why D. discoideum has so many long repeat loci.

It has been suggested that repeat numbers may be regulated by a change in the direction of mutations with length: shorter loci might have an upward mutational bias while longer loci have a downward mutational bias (Garza et al. 1995). Some evidence has been found for this pattern (Lai et al. 1994; Harr and Schlötterer 1998; Xu et al. 2000). However, this model is not supported by our data; mutations in loci with many repeats are not more likely to result in losses (Figure 2C). Even including the 32-repeat-loss mutation, which occurred in a large microsatellite, there was no trend toward larger average losses with high repeat number.

We found no dependence of mutation rate on the triplet motif. This provides further evidence against the hypothesis that mutation rates are driving microsatellite abundance. Among triplet motifs, AAT is by far the most common in D. discoideum, both inside and outside of coding sequences (Eichinger et al. 2005). Apparently this does not result from unusually poor replication or proofreading of AAT tracts, because AAT mutation rates were typical among those that we measured. However, we did not test motifs that rarely appeared in long repeats, so it remains possible that their low abundance is due to still lower rates of mutation.

Selection:

The impact of repeats of amino acids on D. discoideum is unknown. Variation in triplet repeats in coding regions sometimes has some functional significance (Fondon and Garner 2004; Li et al. 2004; Hammock and Young 2005). A number of human genetic diseases arise from expansion of triplet repeats in coding regions (Ashley and Warren 1995; Cummings and Zoghbi 2000), showing that long repeats can sometimes be detrimental.

One possible explanation for the large number of long repeats in coding regions is that there is some unknown splicing mechanism that removes these repeats either from mRNA or from protein. Such repeats could become common and long because splicing out renders them harmless. However, these triplet repeats do appear in cDNA sequences and are therefore present in mRNA. A mechanism for splicing amino acid repeats out of proteins seems unlikely, and one piece of evidence argues against it. A Western blot of D. discoideum protein stained with an antibody that recognizes stretches of 25 or more glutamines shows a very broad smear, suggesting that proteins of all sizes have these repeats (W. F. Loomis, personal communication).

Future studies are needed to determine if long repeats are detrimental to fitness in D. discoideum. If they are detrimental, it could explain why D. discoideum shows microsatellite mutation rates that are so low—even lower than in the previous standard for low rates, D. melanogaster. It is possible that there is a causal relationship in the opposite direction from what we first hypothesized. We initially supposed that high mutation rates might drive the evolution of long repeats. But it is also possible that if some other factor generates many long repeats, particularly in genes, it could select for highly efficient mismatch repair mechanisms.

We conclude with a new hypothesis for what that other factor driving microsatellite abundance might be. At >77%, D. discoideum has one of the most AT-rich genomes known (Eichinger et al. 2005). This could have the effect of increasing the supply of proto-microsatellites. Microsatellites can start from duplication of nonrepeat sequences (Zhu et al. 2000; Nishizawa and Nishizawa 2002) or they can start from chance point mutations generating enough repeats to increase the chance of slippage to higher repeat numbers, perhaps with some critical threshold (Levinson and Gutman 1987; Messier et al. 1996). A natural extension of the second hypothesis is that an AT-biased genome (or a CG-biased one) would tend to accumulate more small repeat sequences by point substitution than an unbiased genome and therefore have more sequences passing the threshold where the slippage process takes over. This idea was incorporated in a null model by Dieringer and Schlötterer (2003; see Figure 1) but AT bias was not proposed as a primary explanation for differences between species. Depristo et al. (2006) proposed that AT bias accounts for variation in abundance of low-complexity regions in proteins. We suggest that the explanation will apply most strongly to microsatellites (as the units of lowest complexity and highest slippage) and that it should apply even more strongly to nonprotein sequences than to constrained coding ones.

Acknowledgments

We thank Amanda Cruess for the computer search for microsatellite sequences, Bill Loomis for sharing his unpublished Western blot, and two anonymous referees for comments. This article is based upon work supported by the National Science Foundation grant EF-0328455.

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