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Genetics. Sep 2007; 177(1): 511–522.
PMCID: PMC2013678

The Dynamics of the roo Transposable Element In Mutation-Accumulation Lines and Segregating Populations of Drosophila melanogaster

Abstract

We estimated the number of copies for the long terminal repeat (LTR) retrotransposable element roo in a set of long-standing Drosophila melanogaster mutation-accumulation full-sib lines and in two large laboratory populations maintained with effective population size ~500, all of them derived from the same isogenic origin. Estimates were based on real-time quantitative PCR and in situ hybridization. Considering previous estimates of roo copy numbers obtained at earlier stages of the experiment, the results imply a strong acceleration of the insertion rate in the accumulation lines. The detected acceleration is consistent with a model where only one (maybe a few) of the ~70 roo copies in the ancestral isogenic genome was active and each active copy caused new insertions with a relatively high rate (~10−2), with new inserts being active copies themselves. In the two laboratory populations, however, a stabilized copy number or no accelerated insertion was found. Our estimate of the average deleterious viability effects per accumulated insert [E(s) < 0.003] is too small to account for the latter finding, and we discuss the mechanisms that could contain copy number.

THE potential of transposable elements (TEs) to spread inside the host genome renders them into primarily selfish genetic material and, in the absence of forces containing their numbers, and assuming that new inserts are active transposable elements, an exponential increase in the number of copies and in the genomic insertion rate is expected (Charlesworth and Charlesworth 1983). Thus, TEs can play a relevant role in determining the genomic size of species. Furthermore, they can also provide an important substrate for the evolutionary process (Kidwell and Lisch 2001; Biémont et al. 2006) and, certainly, a relevant source of deleterious mutation.

In Drosophila melanogaster, ~10% of the genome consists of TEs, with >1500 copies per gamete in the euchromatic part of the genome and ~2000 in its heterochromatic part (Maside et al. 2001; Kaminker et al. 2002). The average rate of new insertions per element and generation was estimated to be ~10−4, while the rate of excision is two orders of magnitude smaller and does not seem to be a leading force in the dynamics of insert numbers in natural populations (Nuzhdin et al. 1997; Maside et al. 2000). Although it has been shown that new insertions have on average small deleterious effects (Houle and Nuzhdin 2004; Pasyukova et al. 2004), the distribution of such effects is unknown and a large fraction of new inserts might be neutral. Additionally, the insertion rate for different TE families varies widely between populations. Thus, the actual contribution of transposable activity to deleterious mutation remains to be ascertained.

Even for the extensively studied D. melanogaster, there is still some debate regarding the magnitude of the overall spontaneous deleterious mutation rate and that of the corresponding deleterious effects. Mutations with deleterious effects relevant in the short-to-medium term (say with deleterious effects s > 0.001) have been studied in a number of mutation-accumulation (MA) experiments, and they have been shown to usually occur at a relatively low rate, <5% per gamete and generation (García-Dorado et al. 2004). There is an obvious interest on the contribution of TE insertion to this genomic deleterious mutation rate, and it has been proposed that transposable elements account for a substantial fraction of the mutational deleterious input and may be responsible both for the diverse deleterious mutation rates estimated in different genetic backgrounds and for the accelerated mutational decay-of-fitness components observed in some MA experiments (Mukai 1969; García-Dorado and Caballero 2002; Fry 2004; Ávila et al. 2006). Furthermore, the deleterious effect of new inserts is of great interest, not only as a potentially important component of spontaneous deleterious mutation, but also as a likely factor containing the exponential increase of insert numbers in natural populations.

A set of mutation-accumulation lines and their controls (Ávila et al. 2006) was specially suitable for inquiring about the spontaneous TE insertion rate, its likely acceleration in the absence of selection, the corresponding deleterious effects of inserts, and their evolutionary dynamics in segregating populations. The initial study of these lines (MA1 lines in Figure 1; see materials and methods for details) revealed a per-gamete viability deleterious mutation rate <5% with average deleterious effect on the order of 10% (Fernández and López-Fanjul 1996; García-Dorado 1997; Chavarrías et al. 2001; Ávila and García-Dorado 2002; Caballero et al. 2002). In a later stage of mutation-accumulation (MA2 lines in Figure 1; see materials and methods), an accelerated viability decline was detected, which was ascribed to increased spontaneous mutation rates (Ávila et al. 2006). Although many transposable families showed no mobility in the MA1 lines, high transposition activity was detected for the long terminal repeat (LTR) retrotransposable roo family, which was due to a large insertion rate, with virtually no excision (Domínguez and Albornoz 1996; Maside et al. 2001; zquez 2006).

Figure 1.
General design of the mutation-accumulation experiment.

Here, we estimate the number of roo inserts in a set of 64 full-sib lines (MA2 lines; Ávila et al. 2006) and in two large control laboratory populations (C1 and C2) using the TaqMan assay of real-time quantitative PCR (QPCR) and/or in situ hybridization. QPCR was used to obtain the number of roo elements because this technique—as opposed to Southern-blot analysis (Maside et al. 2001)—allows accurate relative quantification of copy number. Additionally, and unlike in situ hybridization, it could be applied both to the MA2 lines (for which only frozen individuals were available) and to the control populations. As our interest was to detect mainly functional elements (i.e., those capable of mobilization) a sequence included in the LTR was used. Indeed, intact and identical LTRs are a necessary condition for an LTR element to be mobilized (Havecker et al. 2004). Moreover, LTRs are the most conserved sequences in the roo elements found in the euchromatic portion of the genome (Kaminker et al. 2002). In situ hybridization was used to estimate the absolute number of roo inserts in the Canton-S strain (used as calibrator in the QPCR) and in the C1 and C2 control populations. The similar results obtained in the control populations with both techniques would validate the novel QPCR approach and its use in the experimental lines. The corresponding estimates obtained from our evaluation of the number of roo inserts are discussed in light of previous information about the insertion rates of this element, mutational estimates, viability data, and equilibrium inferences.

MATERIALS AND METHODS

Previous history of the populations and lines:

The design of the mutation-accumulation experiment analyzed is represented in Figure 1. In the first stage of the experiment (Santiago et al. 1992), a D. melanogaster line isogenic for all chromosomes, obtained by Caballero et al. (1991), was used as the base population for a large control population (C1) and for 200 full-sib mutation-accumulation lines (MA1 lines; see Chavarrías et al. 2001 for further details). At generation 265, one of those MA1 lines (line MA1-85), which had formerly shown good performance, was expanded to obtain a new large control (C2) and a new set of 150 full-sib MA2 lines, which were maintained synchronously to the control C1 of the previous experiment (Ávila et al. 2006). By generation 100, flies from the surviving MA2 lines and from the C2 control were frozen at −80°, and MA2 lines were discontinued because they became very difficult to maintain in the laboratory. Both control populations were maintained in 25 bottles (250 ml with 50 ml medium added per bottle) with ~100 potential parents per bottle (8 bottles for C1 up to generation 200), using a circular mating scheme to ensure a large population size. Using lethal complementation analysis, the effective size of these populations was estimated to be ~500 (García-Dorado et al. 2007).

Real-time quantitative PCR:

In the experimental lines, the number of roo elements was analyzed in (i) 64 MA2 lines (1 sample per line consisting of 12–14 males), (ii) C1 at generation 411 (2 male and 3 female samples, each consisting of 30 individuals), (iii) C2 at generation 100 (a single sample consisting of 30 males), and (iv) C2 at generation 146 (3 male samples and 1 female sample, each consisting of 30 individuals). A single sample of the Canton-S strain (40 individuals) was also used. Genomic DNA from each sample was isolated using the DNeasy tissue kit (QIAGEN, Valencia, CA), including a step of RNase A treatment. The concentration of extracted DNAs was spectrophotometrically quantified.

The TaqMan assay of the real-time PCR detection technique was used to quantify the number of roo LTRs, using the comparative method for relative quantitation (Applied Biosystems, Foster City, CA). This method requires using an endogenous gene (reference gene) with amplification efficiency almost identical to that of the target gene. Moreover, amplicons for target and reference genes must be designed and optimized to obtain amplification efficiencies close to one (Heid et al. 1996; Livak and Schmittgen 2001; Applied Biosystems user bulletin no. 2 at http://www.appliedbiosystems.com). We used the RpL32 gene as the reference gene. Preliminary experiments using 5 μl of a fourfold dilution series ranging from 10 to 0.039 ng of DNA/μl were performed with the Canton-S strain to establish the optimal experimental conditions (amplification efficiencies close to one for target and reference sequences). The Canton-S strain was also used as the calibrator to obtain the relative amount of roo LTRs. Threshold cycle (Ct) values (i.e., the fractional cycle number at which the amount of amplified sequence reaches the threshold; Applied Biosystems) were obtained for both the target and the reference sequences in problem samples as well as in the calibrator. The amount of target sequence (roo LTR), normalized to an endogenous reference (RpL32) and relative to a calibrator (the Canton-S sample), can be obtained from the difference between the ΔCt [= Ct(target) − Ct(reference)] values for the problem sample and the calibrator (ΔΔCt) according to the expression equation M1

PCR primers and TaqMan probes were designed using the Primer Express software (Applied Biosystems). Primers and probes for roo LTR and for the RpL32 gene were designed using the GenBank accession nos. AY180917 and AE003772, respectively. Sequences of the primers and TaqMan probes used are shown in Table 1. PCR amplifications were performed in 96-well reaction plates, using separate wells for detecting the roo LTR and RpL32 sequences, and including the calibrator in each plate. For MA2 lines, two replicates were obtained for each amplicon and sample, whereas for the control populations (C1 and C2) and the Canton-S strain multiple replicates were obtained per amplicon and sample. The reaction mixture consisted of 5 μl of DNA (3.125 ng) and 20 μl of TaqMan Universal PCR Master Mix (Applied Biosystem), including primers to a final concentration of 500 nm each, and the TaqMan probe to a final concentration of 200 nm. PCR amplification was performed in an ABI Prism 7700 instrument using the following amplification conditions: 10 min at 95°, followed by 40 cycles of 15 sec at 95° and 1 min at 60°.

TABLE 1
Oligonucleotides and Taqman probes for real-time quantitative PCR

QPCR evaluation of roo copy number:

For each QPCR evaluation, a ΔΔCt value was computed by reference to the Canton-S line as ΔΔCt = ΔCt(sample) − ΔCt(Canton-S), where ΔCt(Canton-S) was the ΔCt value averaged over the two Canton-S evaluations in the same plate. This procedure was intended to remove plate effects. In fact, plate effects on ΔΔCt were nonsignificant (P < 0.24 for males and P < 0.5 for females in two-way “sample × plate” ANOVA analyses), so that ΔΔCt values from different plates were pooled in further analyses.

To estimate the absolute number of roo-element copies in the experimental lines (MA2) and control populations from QPCR results, the number of copies in the calibrator Canton-S strain needs to be obtained. In the PCR amplification method, the initial number of target molecules (Xo) can be computed as Xo = (Xt/Rt)Roequation M2 where Ro is the initial number of reference molecules and Xt and Rt are the numbers of target and reference molecules at the threshold cycle. Assuming the latter two values are equal (i.e., Xt/Rt = 1), and knowing that the reference sequence (RpL32) is a single-copy gene (i.e., Ro = 1 copy per haploid genome), the above expression simplifies to Xo = equation M3. The number of roo-LTR copies per haploid genome (i.e., copies of target sequence) in the Canton-S strain would be equation M4 = 254.97. Considering two LTR sequences per roo element, this results in 127.49 ± 4.37 roo elements per genome in the Canton-S strain, which is in close agreement with the number of roo hybridization signals detected per nucleus in this strain (124 ± 0.26, see below). Therefore, acording to the comparative QPCR method, the number of roo elements per gamete (n) in the experimental lines by reference to the Canton-S strain was estimated as

equation M5
(1)

Since n refers to the number of roo elements per haploid genome (i.e., one set of autosomes and one X chromosome), ΔΔCt values obtained from male samples were adjusted by adding the logarithm to base 2 of the expected male to female ratio of roo elements [i.e., by adding log2(0.9); see Preliminary information in the results]. Adjusted ΔΔCt values were used in Equation 1.

In the case of MA2 lines, where the ΔΔCt replicates are obtained from a single sample, the between-line component of variance might include a fraction due to sample effects. Genetic sample effects should not be a concern for the analysis of MA lines, due to the small genetic variability expected within these full-sib lines and to the number of flies included in each sample (12–14 for MA lines); and the latter argument also applies to C1 and C2 samples, each including 30 flies. However, to rule out any relevant experimental sample effect, we performed ANOVA with samples as random effect within assays using the ΔΔCt data for the control populations (C1 assayed at generation 411 with 25 ΔΔCt values from 5 samples, C2 assayed at generation 100 with 8 ΔΔCt values from a single sample, and C2 assayed at generation 146 with 19 ΔΔCt values from 4 samples). This ANOVA gave a nonsignificant (P < 0.44) component of between-sample variance, which amounted just to 0.18% of the residual variance. A similar ANOVA performed on the roo-number estimates instead of on ΔΔCt values gave a very small between-sample component of variance (0.75). Therefore, sample effects were ignored and values from different samples of the same assay were pooled for analysis.

In situ hybridization analysis:

Polytene chromosome preparations for in situ hybridization (Montgomery et al. 1987) were obtained from 6 individual larvae of the calibrator strain (Canton-S) and from 10 individual larvae of each of the control populations (C1 at generation 420 and C2 at generation 155). Larvae were grown at 17° on standard cornmeal medium under uncrowded conditions.

The probe was prepared by PCR amplifying most of the LTR of the roo element (389 of 429 bp) using Canton-S DNA. The primer sequences (5′–3′) were forward primer, ATTTTGGGCTCCGTTCATA, and reverse primer, GTAAAATCCCAAATGAGAAGA. The PCR product was gel purified and subsequently labeled with 16-biotin-dUTP by nick translation. This probe includes the complete amplicon sequence used in the QPCR experiments. Prehybridization, hybridization, and detection conditions were as described in Segarra and Aguadé (1992). A Zeiss microscope with a Leica DFC camera was used to select on average 10 nuclei per slide and to capture the corresponding images for their subsequent analysis with the Corel Photopaint program. For each individual larva, the number of hybridization signals per chromosome was obtained upon confirming their presence and cytological location in 8–10 nuclei. The total number of hybridization signals per larva was computed by adding up the counts per chromosome.

Statistical analysis:

Statistical analysis of in situ data:

The sample size of the control populations precludes establishing which hybridization signals (hereafter bands, denoted b) correspond to segregating or fixed insertions. Furthermore, our estimate of the number of bands cannot be directly compared to previous in situ estimates of the number of ancestral bands obtained for the MA1 lines by others (Maside et al. 2001; zquez 2006), because these authors used an internal probe instead of an LTR probe. For this reason, our inferences on the rate of insertion accumulation in the control populations (λc) and on the corresponding number of ancestral in situ bands for our LTR probe are based on the estimates of the variance of the number of bands. The procedure is described in appendix a. Five hundred bootstrapped samples were used to obtain bootstrap errors and percentile confidence intervals for the variance of the number of in situ bands and to perform bootstrap tests and derive errors for the estimates.

Statistical analysis of QPCR data:

Even if the distribution of errors associated with individual ΔΔCt assays was normal, it is not reasonable, a priori, to assume normality for its exponential function n (Equation 1), unless ΔΔCt values are always very small. This raises doubts about the reliability of parametric analysis for this variable. To prevent bias, both standard parametric and nonparametric statistical analyses were performed.

Parametric analysis:

This consisted of (a) a one-way ANOVA for n values computed from individual ΔΔCt observations in the control populations, with “assay” as a fixed factor (assays C1-411, C2-100, and C2-146, respectively, where the number after the hyphen is the generation number), and (b) a one-way ANOVA for n values computed for individual ΔΔCt observations from 64 MA2 lines, with “line” as a random factor and two observations per line.

If the insertion rate does not depend on copy number and remains constant during the period considered, the number of inserts accumulated per MA2 line along this period is expected to be Poisson distributed, and the between-line component of variance equation M6(n) expected from ANOVA equals the expected number of insertions accumulated per line. Thus, equation M7(n) > m (where m is the average number of inserts accumulated per line) would suggest accelerated insertion rate in MA2.

Average n values, with their standard errors, were obtained for the two control populations and for the set of MA2 lines. These were used to estimate the differences in average n between C1 and C2 and also the rates of roo insertion in MA2 up to generation 100 by reference to control C2, together with their corresponding standard errors.

Nonparametric analysis:

A single n estimate was computed for each control sample and for each MA2 line using the corresponding average ΔΔCt value in Equation 1. This should estimate the true average roo number better than the average of n estimates obtained from individual ΔΔCt values, but it has no empirical standard error. Therefore, for the control populations, bootstrap error (BE) and bootstrap confidence intervals (BCI) based on bootstrap percentiles were computed using 500 bootstrapped n values, each estimated from Equation 1 using the ΔΔCt average value of a bootstrapped sample. For the set of 64 MA2 lines, 500 estimates of the average n were also obtained, each being equal to the average of the n values for a bootstrapped sample of 64 lines, and BE and BCI were also obtained. The 500 bootstrapped estimates for n in MA2 and C2-100 (or for C2-146 and C1-411) were randomly paired and used to estimate BE and BCI for the estimates of the rates of roo insertion in MA2 up to generation 100 by reference to C2 (or for the differences in average n between C1 and C2). Bootstrap tests with α significance for pertinent hypotheses were performed on the basis of the appropriate α-percentile on 500 bootstrapped estimates.

Of particular importance for the discussion of the data was to compare the variance of the n values observed for the MA2 lines with that expected if the true number of new roo insertions per line was Poisson distributed. Due to the lack of normality for the sampling errors (ε) of the estimates of n and to their likely dependence on the true n values, ANOVA estimates of the between-line genetic component of the variance of n (see above) may not be wholly reliable. For this reason, we simulated data with resampled residuals to infer the variance that is to be expected for the estimates of n in the lines if the true number of new roo insertions is Poisson distributed with mean m. The procedure is explained in appendix b.

RESULTS

Preliminary information:

We have investigated the distribution of roo elements in the D. melanogaster genome (release 5.1) using a BLAST search on FlyBase (http://flybase.bio.indiana.edu/) for our QPCR amplicon, consisting of a 65-nucleotide LTR sequence. In the euchromatin, 235 sequences exhibited a similarity ≥98%. Twenty-one of these sequences corresponded to solo LTRs, whereas the remaining 214 were associated by pairs at distances ranging from 1000 to 20,000 bp, indicating that they belonged to the same TE element (Rho et al. 2007). This would imply that in situ analysis using an internal probe (as in previous studies for MA1; Maside et al. 2001; zquez 2006) should detect equation M8 bands in the release 5.1 genome, while our LTR probe would be expected to detect a number 19.6% larger (equation M9). On the other hand, in the whole genome (euchromatin and heterochromatin), 258 sequences exhibited a similarity ≥98%, indicating that our QPCR approach would have estimated equation M10 roo copies. Therefore, similar roo numbers would be detected in the sequenced genome using in situ and QPCR approaches (128 vs. 129) as, using in situ hybridization, the upward bias due to the detection of LTR solos is canceled out by the downward bias due to heterochromatic copies going undetected.

The above consideration depends on the small number of roo elements found in the heterochromatin using our amplicon, and it should be noted that significant heterochromatic portions, known as repeat regions, remain to be aligned. However, TEs in these regions usually belong to clusters made of thousands of nested old fragments of different transposable elements (http://chervil.bio.indiana.edu:7092/annot/dmel-release4-notes.html#3.2update), where it is unlikely to have LTRs presenting the almost complete identity to our amplicon sequence required to be detected in the QPCR analysis. Therefore, the inclusion of these unaligned repeat regions would not likely render different QPCR estimates for roo numbers.

In the above search, 20.8% of euchromatic sequences matching the amplicon were located at chromosome X, and previous results for MA1 lines indicated that this chromosome harbored 22.2% of the ancestral MA1 roo in situ bands (Maside et al. 2001). Similarly, 21.3% of the bands that we detected for the C1 and C2 populations corresponded to chromosome X, and the number of roo elements estimated by QPCR in males for the same two controls (using nonadjusted ΔΔCt values for males in Equation 1) was 92% that for females. From these observations, the average male-to-female ratio of roo elements would equal 0.9, which was used to estimate roo numbers per haploid genome (see materials and methods).

Averages for the observed number of in situ bands and QPCR roo numbers:

The average number of in situ bands per individual and the QPCR estimates of roo numbers (based for each population or MA2 line on the corresponding ΔΔCt average) are given in Table 2. These QPCR numbers differ from the average of n values on the basis of single ΔΔCt observations by <0.5%, and their bootstrap errors differ from the standard error of average n values by <5% (results not shown).

TABLE 2
Average number of roo inserts

The remarkable similarity between in situ and QPCR estimates for assays of the same population is in agreement with the above results from the genome search, indicating that the actual biases from these estimates tend to cancel each other out and supporting the joint discussion of our in situ and QPCR estimates. Anyway, no estimate will be obtained from direct comparison between in situ and QPCR values.

It should be noted that the difference in average roo number between C1 and C2 (or MA2) depends upon the unknown number of inserts that the MA1-85 line had accumulated when it was used to derive the MA2 lines and the C2 population, as well as on any differences in the insertion rates. Furthermore, even bands common to all 10 individuals assayed in a given population need not have been present in the corresponding ancestral genome, which precludes identifying ancestral bands. Therefore, average roo numbers do not provide direct estimates for the overall insertion rates. However, it is remarkable that the difference in the average number of bands between C1 and C2 (7) was very similar to the corresponding difference in n QPCR values, supporting again the joint discussion of in situ and QPCR estimates.

The n QPCR values allow us to estimate the excess in average per-generation roo-insertion rate for MA2 lines compared to C2 up to generation t = 100 as (nMA2nC2)/100. This amounts to λMA2|C2 = 0.104 ± 0.036. Using bootstrap, the 5% low percentile for this estimate is 0.052, indicating that the insertion rate for MA2 lines is significantly larger than for MA1 lines (λMA1 = 0.031 ± 0.003 by generation 262; Maside et al. 2001). QPCR results also indicate that the average number of inserts for the C2 population was roughly stable by the end of the experiment.

Inferences based on estimates of the variance:

The variances of the per-chromosome and per-individual number of in situ bands for the C1 and C2 populations are given in Table 3. The variance is consistently larger for C2, despite the fact that this population was more recently founded from a nonsegregating origin than C1 (155 generations as opposed to 420). Using our estimates of the effective population size for C1 and C2 (Ne = 500; García-Dorado et al. 2007) and assuming equation M11(n) = σ2(b)/2 in Equation A1, the estimates of the rate of insert accumulation (i.e., the rate of neutral insertion that would account for the variances observed in C1 and C2) were λC1 = 0.009 and λC2 = 0.047, with bootstrap errors 0.0029 and 0.0190, respectively, where λC1 was significantly smaller than λC2 (P < 0.05 in a bootstrap percentile test). These estimates indicate that the average number of inserts accumulated per haploid genome (tλC, which includes both segregating and fixed accumulated inserts) were 3.78 for C1 and 7.28 for C2 by the end of the experiment. Note that, since the expected number of segregating inserts, assumed Poisson distributed, should equal the variance for insert numbers [equation M12(n), 3.13 and 6.86 for C1 and C2, respectively], the expected number of fixed inserts (nf) in each control population would be negligible even under neutrality. Estimates assuming equation M13(n) = 2σ2(b)/3 (as if the populations were at the mutation-drift balance; see appendix a) were not qualitatively different from the former (λC1 = 0.012, λC2 = 0.063).

TABLE 3
Observed variance for the number of in situ bands per chromosome and per individual with its bootstrap error

The parametric analysis of QPCR roo numbers in the MA2 lines gives an observed variance of the per-line average σ2(n) = 130.2 and a significant (P < 0.0005) between-line component of the variance equation M14 [note that these full-sib lines can be considered to lack genetic variability, so that equation M15 should be ascribed to differences in the true number of inserts per line]. If new roo insertions would occur at a constant rate and were independent of previous insertion events in each line, the true number of new inserts accumulated per MA2 line should be Poisson distributed, with mean and variance tλMA2. Then, a equation M16 value by generation 100 would imply one new insert per gamete and generation for the MA2 lines, an insertion rate exceedingly high compared to the estimates mentioned above (λMA2|C2 = 0.104 ± 0.036; λC1 = 0.009; λC2 = 0.047), indicating that the rate of new insertions might be positively correlated with the number of previously accumulated inserts. Since this conclusion is based on a parametric analysis (ANOVA) whose statistical requirements (normality and homoscedasticity of residual errors) may have not been met, this result is checked below using simulation.

Joint inferences from the different analysis:

The main estimates from this joint analysis are presented in Table 4.

TABLE 4
Joint inferences for roo inserts

First, adding up the in situ estimate for the accumulation rate in C2 (λC2 = 0.047) to the QPCR estimate of the average insertion rate for MA2 compared to C2 (λMA2|C2 = 0.104), we obtain an average rate of insertion in MA2 lines that amounts to λMA2 = 0.152 up to generation 100, which is five times the λMA1 = 0.031 roo insertion rate reported by Maside et al. (2001) for MA1 lines at generation 262.

Second, introducing in Equation A2 our estimates for the average and variance of the number of bands (from Tables 2 and and3,3, respectively) together with the above estimates for λc, we can infer that the number of ancestral bands for populations C1 and C2 should be n0C1 = 75.7 and n0C2 = 83.9. The n0C1 estimate, considerably larger than the 63 ancestral bands obtained by Maside et al. (2001) and by zquez (2006), implies that our LTR probe hybridized at a number of genomic locations that are 20.2% larger than for the internal probe [(75.7 − 63)/63 = 0.202], in narrow agreement with the corresponding percentage estimated for the euchromatin in release 5.1 (19.6%; see above), despite the different genomes involved in both analyses. Furthermore, the difference between n0C2 and n0C1 would imply that the MA1-85 line had accumulated about eight inserts at generation 265, when it was used to generate the MA2 lines and the C2 control, thus showing an average insertion rate λMA1-85 = 0.031. This value is in close agreement with the Maside et al. (2001) estimate for a sample of 16 MA1 lines at generation 262 (λMA1 = 0.031 ± 0.003).

Finally, the expected variance of n values for the MA2 lines was obtained by simulation using the empirical error distribution and assuming that the number of newly accumulated inserts was Poisson distributed with average m = tλMA2 ≈ 15. The value obtained, equation M17(n) = 29.0, was well below the observed value [σ2(n) = 129.7, for n values computed from average ΔΔCt values]. This result corroborates that the insertion rate is not uniform across MA2 lines and is consistent with a positive association between the insertion rate in MA2 lines and the number of previously accumulated inserts, a phenomenon that would induce accelerated insertion. The empirical distribution of the QPCR roo values (n) in the MA2 lines is given in Figure 2, where the expected distribution if new insertions had not occurred and all the variance was due to experimental and sampling error is also given for comparison. Figure 2 illustrates that the large variance observed in the MA2 experiment is not due to a few rare lines with large n values and suggests that acceleration is a relatively common process in the genetic background of our lines. Acceleration could have also occurred during the MA1 experiment, being only moderate up to generation 262, when the observed distribution did not significantly depart from the Poisson one (with average 8.06 and variance 11.80 for the per-line insert number, as obtained from in situ analysis of 16 lines; Maside et al. 2001). However, at generation 301, the average insertion rate had significantly increased (P < 0.008), and the variance was twice the mean (11 lines analyzed, mean number of inserts 13.38, variance 26.09; Vázquez 2006). For control populations, however, the number of inserts per genome should be roughly Poisson distributed even under accelerated insertion, due to segregation and recombination, so that no insight concerning acceleration can be obtained from the magnitude of the variance for insert numbers.

Figure 2.
Empirical distribution for the QPCR estimate of the number of roo inserts in the 64 MA2 lines (open bars) together with the corresponding expected distribution in the absence of new insertions obtained by simulation using resampled residuals (shaded bars). ...

DISCUSSION

Our results show that (i) the roo insertion rate was clearly higher in our second mutation-accumulation experiment than in the first one, (ii) this rate experienced a strong acceleration in the mutation-accumulation lines during the second experiment (MA2), and (iii) the accumulation rate for each control population was considerably lower than in the corresponding set of MA lines, the number of inserts for C2 being roughly stable by the end of the experiment.

The process of insert accumulation in inbred lines:

Our inference that the average insertion rate for the MA1-85 line when it was used to derive MA2 and C2 (0.031) was equal to the average rate for the whole set of MA1 lines indicates that MA1-85 is representative of the MA1 experiment regarding roo accumulation. Thus, we tentatively consider MA2 lines as an extended MA1 experiment in this respect. Figure 3 represents average n values for the mutation-accumulation lines of this MA1–MA2 experiment, where n values from Maside et al. (2001) and zquez (2006) were adjusted to account for the larger number of bands obtained using an LTR probe (see results). The detected increase in the number of inserts with time suggests a continuously accelerated process.

Figure 3.
Average number of roo copies per gamete in the mutation-accumulation lines through the whole MA1–MA2 experiment. Solid dots are experimental values and the lines give the prediction from Equation 2 fitted assuming different x0 values (solid thick ...

Although it has been shown that transposable families have active and inactive copies in natural populations, the theory developed to account for the population dynamics of transposable elements usually considers that the overall genomic insertion rate is proportional to the overall number of element copies in the genome. Since natural selection has been relaxed in our MA lines to a good extent, we have investigated whether the observed acceleration fits a simple generalization of that current insertion model, where we consider that (i) not all ancestral inserts are active, (ii) insert number increases by a constant factor ν of the standing number (x) of active copies, and (iii) new inserts are active in mutation-accumulation lines. Since excision rates have been found in general to be two orders of magnitude lower than insertion rates, and since roo excisions were not observed after 262 generations in the MA1 lines (Nuzhdin et al. 1997; Maside et al. 2001), ν should virtually equal the insertion rate per active copy and generation, and henceforth the effect of excision in the dynamics of transposable elements will be ignored. In this situation, the ancestral number of inactive copies is (n0 − x0), and the expected number of inserts per gamete in a given generation t is

equation M18
(2)

This model implies that, at generation t, the rate of new insertion per gamete and generation is lt = νxt. From now on, we use lt to denote the gametic insertion rate at any given generation t, while λ (with an optional subscript appropriate to the specific populations or lines) is the average of insertion rates (lt) up to a given generation. We have checked the fitting of the data to predictions from Equation 2 for different x0 integer values ranging from 1 to n0. To achieve this, we denote by y the increase up to generation t for the natural logarithm of the number of active copies y = loge(ntn0 + x0) − loge(x0), and we compute y from each available nt estimate for the MA1–MA2 lines. Then, since Equation 2 implies y = νt, we check the model fitting for each x0 value by analyzing the linear regression, forced through the origin, of y on generation number, where the regression slope ν gives the rate of new insertions per generation and active copy. For x0 values on the order of n0 (i.e., most ancestral copies are active), the model predicts an acceleration that is too slow compared to the data. Predictions for n obtained using different x0 and ν values in Equation 2 are shown in Figure 3. The best fitting corresponds to a single initial active insert (x0 = 1 in Equation 2) and gives ν = 0.0087 ± 0.0001, which, for x0 = 1, also estimates the initial insertion rate per gamete and generation [l(t=0) = ν = 0.0087]. The excellent fitting of this model (P < 8 × 10−6, explaining a fraction R2 = 0.999 of the y data dispersion) should not be taken at face value, since x0 was chosen ad hoc. Notwithstanding, it shows that the results are remarkably consistent with a situation where the insertion rate per generation and gamete is proportional to the number of active inserts by a relatively large factor (~0.009), the initial number of active inserts being very small and new inserts being active in mutation-accumulation lines. Our ν estimate implies an overall average per-copy insertion rate (0.0087/75 ≈ 0.0001) on the order of that previously estimated (~10−4 for Drosophila; Nuzhdin et al. 1997; Maside et al. 2000), but due to a small fraction of active elements with relatively high per-copy insertion rates (ν ≈ 10−2). This result is qualitatively robust against reasonable experimental or sampling errors. Thus, using a two standard error lower limit for the copy number in the reference Canton-S strain (which gives 92.8 instead of 99.7 for the average roo copy number in MA2 lines), the model fits still reasonably well, giving x0 = 3 and ν = 0.0053 (P < 0.0065, R2 = 0.974), supporting the same general view. For copia elements, results obtained by Nuzhdin et al. (1998) are also consistent with a possible small number of active copies, although, in that case, more than a single element was found to transpose (Perdue and Nuzhdin 2000).

We have estimated the average deleterious effect of new inserts [E(s)] from the regression slope (bV,n) of chromosome II viability (V, obtained by Ávila et al. 2006 for MA2 lines at generation 98) on the QPCR number of insert copies here obtained for generation 100. To achieve this, bV,n was corrected to take into account the experimental error of n estimates and for the fact that viability was assayed for chromosome II while n was assayed for the whole genome [E(s) = −CKbV,n, where C = 130.2/100.2 = 1.3 is the ratio of the variance of the n estimate in the lines to that of the true n values estimated from ANOVA, and K = 2.35 is the ratio of insertions that occur in the whole genome to those in chromosome II estimated from Table 3]. The regression estimate was positive (bV,n = 0.0021 ± 0.0019), although it was not significantly different from zero. Thus, E(s) = −3.0bV,n = −0.0063, as if mutations were advantageous on the average. The largest average deleterious effect that would be consistent with this estimate in a one-tailed test (P < 0.05) is 0.0032. This latter value can thus be taken as an upper bound for E(s), which is slightly smaller than previous point estimates for copia new insertions in mutation-accumulation lines (Houle and Nuzhdin 2004; Pasyukova et al. 2004). This small average deleterious effect per roo insert, together with our values for the number of roo inserts accumulated per line, would account for at most ~6% of the viability decline detected in our MA lines at different generations (Fernández and López-Fanjul 1996; García-Dorado 1997; Chavarrías et al. 2001; Caballero et al. 2002; Ávila et al. 2006). Thus, accelerated roo insertion alone cannot explain the accelerated viability decline. Note, however, that although the average per-generation insertion rate for the mutation-accumulation lines through the whole MA1–MA2 experiment is λMA = (99.7 − 75.7)/365 = 0.066, the per-generation insertion rate for the MA1–MA2 lines predicted by Equation 2 at the end of the experiment would be lt=365 = 0.22, i.e., >20-fold the ancestral rate, so that it could be involved in the final collapse of the lines. On the other hand, the same mechanism causing accelerated insertion for roo could have also caused acceleration for some other active transposable families. For example, a previous analysis with RAPDs suggested mobility for Idefix in our MA lines (Salgado et al. 2005), a family that has not been included in any previous studies using this material.

Finally, considering an overall haploid insertion rate of ~0.15 in the euchromatic part of the genome for the pool of TE families (Maside et al. 2001; Kaminker et al. 2002), our small estimate for the average deleterious effect for viability [E(s) < 0.0032] suggests that the rate of viability decline due to TE activity in the absence of selection (ΔMTE = 0.15 × 0.0032 = 0.00048) makes a relatively small contribution (<18%) to the rate of viability decline estimated in MA experiments (~0.0025; see García-Dorado et al. 2004).

The accumulation process in moderate-sized populations:

The rate of insertion accumulation in the control populations is clearly below the insertion rate in the corresponding MA lines. For C1 (λC1 = 0.009), it coincides with the initial per-generation insertion rate for MA1 in the model fitted above (lt=0 = 0.0087). For C2, however, the average per-generation accumulation rate (λC2 = 0.047) is smaller than the per-gamete insertion rate estimated for the MA1-85 line at generation 265, when this was used as the ancestral line for the second MA experiment (inferred from the model: lt=265 = νxt=265 = 0.0087 × 8.2 = 0.071), and insert numbers were stable by the end of the experiment. This slowed accumulation can come from any mechanism inactivating new copies in segregating populations and/or from selection against new inserts reducing the number of active copies and consequently the gametic insertion rate, so that selection against new inserts can hardly be disentangled from reduced insertion rate per active copy.

Results from theoretical studies, assuming genomic insertion rates proportional to the overall number of inserts, have shown that natural selection can control copy abundance only under synergistic effects of inserts on fitness and that equilibrium would be possible only when each insert has a selection coefficient on the order of the per-element insertion rate (Charlesworth 1991). Using published estimates for the average insertion rate per copy element and generation (Nuzhdin et al. 1997; Maside et al. 2000), this would imply selection coefficients on the order of 10−4, which would not be large enough to cause efficient purifying selection under the effective population size of our control populations (i.e., s > ~2/Ne = ~0.004). Moreover, our estimate from MA2 lines [E(s) < 0.003] implies that viability selection against new inserts should be inefficient in our control populations. Note that we estimated average deleterious effects using lines that had accumulated an extremely large between-line variance for the number of homozygous inserts, so that, in the presence of synergy, our bound estimate for E(s) should include very large synergistic effects. Therefore, our results exclude that synergistic selection on viability could account for the observed contention of insert number observed in the control populations.

On the other hand, it has been proposed that insert numbers can be stabilized by selection against chromosomal rearrangements caused by ectopic recombination between elements (Langley et al. 1988). This selection could synergistically increase with the number of heterozygous inserts (those that cannot pair properly), maximizing selection against new active copies, which are expected to segregate at low frequency, but to cause no load in case they became fixed. It should be noted that the fitness reduction from unequal exchange should occur through fertility impairment, so that it would go undetected in our viability tests. This regulatory mechanism would be inefficient in the MA lines, both because selective intensity would be small compared to drift and because of the small number of copies segregating in these brother × sister lines, unless the insertion rate is very large as occurred by the end of the experiment when the lines finally collapsed (predicted rate l = 0.22). However, it can account for the stabilization of insert numbers in our control populations. Note that our estimates predict an acceleration for the insertion that, in the absence of selection, would be much larger than that under a model where all ancestral inserts were active, with insertion rate 10−4 each. Therefore, the selection coefficient per active copy required to halt insert numbers (on the order of ν according to Charlesworth 1991, i.e., ~10−2) should be about two orders of magnitude larger than previously assumed, thus explaining insert number contention even in moderate-sized populations. However, due to synergy, this deleterious effect can be smaller when the number of copies, or the overall frequency of segregating inserts, are below the equilibrium value. Our results also imply that transposable activity for a specific line depends on the random sampling of active copies during line founding and is consistent with the observation that, despite the important copy numbers of many transposable families in natural populations, each studied laboratory line shows mobility for only a few families that vary from line to line (Nuzhdin et al. 1997).

Under the above model of selective contention, a prevalence of inactive copies could be due to the accumulation of spontaneous mutation in old elements, which are expected to drift in low-recombination regions sheltered from natural selection. This scenario is in agreement with results by Bartolomé and Maside (2004), who found that low-recombination regions harbor important numbers of fixed and degenerated transposable elements, while elements in high-recombination regions are fewer, canonical, and segregate at low frequencies. However, our inference that a large number of copies with well-conserved LTR sequences are inactive in the ancestral population would imply some inactivation mechanism, as might be RNA interference, which can silence genes at both the transcriptional level (through methylation; Kawasaki and Taira 2004) and the posttranscriptional level (Vastenhouw and Plasterk 2004; Brennecke et al. 2007). This mechanism has been proposed to cause specific TE silencing stimulated by the number of TE copies in a predator–prey-like model, leading to copy number regulation comparable to that arising from ectopic exchange (Abrusán and Krambeck 2006). In this situation, it is appealing to consider that particular TE copies could occasionally escape silencing through mutation, leading to insertion bursts. Although, in principle, this mechanism does not explain why the insertion rate was controlled in our C1 and C2 populations but not in the MA lines, it might explain the long-term inactivation of TE families in large populations.

General consequences:

Our results show a dramatic acceleration of the per-gamete insertion rate of roo elements for very small populations (N = 2), consistent with a very small number of active copies in the ancestral population, each causing the insertion of new active copies at a relatively large rate (ν ≈ 0.009). This situation should induce much higher acceleration than a scenario of many active inserts with small activity each, thus requiring a more powerful mechanism regulating insert number and accounting for deleterious effects on the order of 10−2 per active insert at the equilibrium, about two orders of magnitude larger than previously thought. In moderate-sized populations (Ne = 500) we observe insert accumulation, but it showed no acceleration for population C1 and finally halted for population C2. The estimated average homozygous deleterious effect for viability of new roo insertions is too small to account for this insert-number contention, even considering synergy. However, this contention could be due to unequal meiotic exchange causing synergistic selection for fertility. Our results indicate that most ancestral copies were not active, suggesting the operation of an inactivation mechanism like RNA interference.

Acknowledgments

We are grateful to Carlos López-Fanjul, Sergey Nuzhdin, Carmen Segarra, and to an anonymous reviewer for helpful discussion. This work was supported by Distinció per la Promoció de la Recerca Universitària from Generalitat de Catalunya (to M.A.) and by grants CGL2005-02412/BOS and BFU2004-02253 from Ministerio de Ciencia y Tecnología to A.G-D. and M.A., respectively.

APPENDIX A

To infer the rate at which new inserts accumulate in control populations (λc) from the variance of band number, we assume that the inserts segregating in these populations are those that behave as neutral for the corresponding effective size N. Let n be the number of roo copies per gamete and assume that the number of new effectively neutral inserts that accumulate per gamete and generation is Poisson distributed with mean λc. The overall number of such new inserts (fixed or not) accumulated per gamete and generation after t generations is tλc. The variance of roo copy number per gamete at generation t (equation M19(n)) is due to segregating inserts and is expected to undergo a reduction equation M20(n)/2N from generation t to t +1 due to drift and an increase λc due to the Poisson-distributed new insertions. Thus, it can be shown that we expect equation M21(n) = λcequation M22, approximately. Therefore, we estimate

equation M23
(A1)

This approach can induce some upward bias under accelerated insertion or if inserts have substantial recessive deleterious effects as, in those cases, the rate of fixation of new inserts can be smaller than expected from the number of segregating inserts. However, this bias will not be relevant until the accumulation period is so long that a substantial proportion of the neutral mutations is expected to be fixed in the population, i.e., when t [dbl greater-than sign] equation M24. In our case, equation M25, and equation M26. Therefore, even assuming neutral inserts and constant insertion rates, most of the inserts accumulated per genome are expected to be still segregating in the population and, therefore, the above bias should be small.

For fixed inserts, the number of bands per individual equals the number of inserts per gamete, but for segregating ones one band is recorded for both homozygous and heterozygous individuals. Thus, the expected number of bands per individual corresponding to segregating inserts is E(bs) = equation M27, where the sum is over the loci involved or possible insertion sites, and q = 1 − p is the frequency of a particular insert. Using diffusion theory, it can be shown that the expected value of E(bs) at equilibrium for neutral mutations equals equation M28 the number of segregating inserts per gamete (ns). However, although our populations seem to be close to the equilibrium for deleterious mutations (García-Dorado et al. 2007), they are still far from the mutation–drift balance, which is roughly attained after 6Ne generations (note that t < N in C1 and C2). Therefore, we expect q [double less-than sign] p, so that E(bs) should be about twice the number of segregating inserts per gamete. As this factor affects the probability of each band separately, the number of bands per individual corresponding to segregating inserts (bs) can still be assumed to be Poisson distributed, so that the variance of band number at each generation [V(b)] should be affected by the same factor as the corresponding mean number of bands from segregating inserts [E(bs)]. Therefore, the range for the mean number of segregating inserts per gamete E(ns) should be E(bs)/2 ≤ E(ns) ≤ E(bs)2/3, and, since the variance for the overall number of bands is due to segregating inserts, the range for the variance of insert number equation M29(n) is σ2(b)/2 ≤ equation M30(n) ≤ 2σ2(b)/3.

Since our populations are still far from the mutation–drift balance, the estimate equation M31(n) = σ2(b)/2 will be used in Equation A1, although some results assuming equation M32(n) = 2σ2(b)/3 will also be given. Thus, assuming equation M33(n) = σ2(b)/2 and E(ns) = E(bs)/2, the average number of bands detected per individual in a control population should be E(b) = n0 + E(nf) + 2E(ns), where n0, nf, and ns, stand for ancestral, nonancestral fixed, and segregating insert numbers for the population considered, respectively. Therefore, since E(ns) = σ2(ns) = σ2(b)/2 and E(nf) + E(ns) = tλc, the number of ancestral bands can be inferred as

equation M34
(A2)

APPENDIX B

Residual errors for ΔΔCt within MA2 lines were corrected according to the degrees of freedom within each line, to recover the sampling error variance observed within MA2 lines (since there are only two observations per MA2 line, this requires multiplying residuals by a equation M35 factor). Corrected residuals from different lines were averaged by pairs, to produce a sample of 64 sampling errors (ξ) for the average ΔΔCt of the MA2 lines. A sample of 64 estimates of n was simulated, each as

equation M36
(B1)

where P is a random number from a Poisson distribution with average m, and n02 is our estimate of the ancestral copy number for the MA2 experiment. We computed the variance of these simulated nsim values [σ2(nsim)], which estimates the variance for the n values observed in the MA2 lines that would be expected under the Poisson distribution assumption, including effects from nonnormal sampling errors for n estimates and from their correlation with the true roo number of the line. A value of the observed variance in the real sample of MA2 lines (one n estimate per line based on the line ΔΔCt average) larger than σ2(nsim) would imply an accelerated insertion rate in the MA2 lines.

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