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Logo of scirepAboutEditorial BoardFor AuthorsScientific Reports
 
Sci Rep. 2016; 6: 28555.
Published online 2016 June 27. doi:  10.1038/srep28555
PMCID: PMC4921842

Prevalence and Dynamics of Ribosomal DNA Micro-heterogeneity Are Linked to Population History in Two Contrasting Yeast Species

Abstract

Despite the considerable number and taxonomic breadth of past and current genome sequencing projects, many of which necessarily encompass the ribosomal DNA, detailed information on the prevalence and evolutionary significance of sequence variation in this ubiquitous genomic region are severely lacking. Here, we attempt to address this issue in two closely related yet contrasting yeast species, the baker’s yeast Saccharomyces cerevisiae and the wild yeast Saccharomyces paradoxus. By drawing on existing datasets from the Saccharomyces Genome Resequencing Project, we identify a rich seam of ribosomal DNA sequence variation, characterising 1,068 and 970 polymorphisms in 34 S. cerevisiae and 26 S. paradoxus strains respectively. We discover the two species sets exhibit distinct mutational profiles. Furthermore, we show for the first time that unresolved rDNA sequence variation resulting from imperfect concerted evolution of the ribosomal DNA region follows a U-shaped allele frequency distribution in each species, similar to loci that evolve under non-concerted mechanisms but arising through rather different evolutionary processes. Finally, we link differences between the shapes of these allele frequency distributions to the two species’ contrasting population histories.

Recent years have seen the debate on the prevalence, indeed the existence, of ribosomal DNA (rDNA) sequence heterogeneity intensify. On the one hand, systematists exploiting the valuable properties of the Internal Transcribed Spacer (ITS, a sub-region of the rDNA) for phylogenetic inference1 have observed intra-individual rDNA sequence variation for decades2,3,4. On the other hand, a recent genome sequencing study has suggested only low levels of sequence heterogenity exist in the rDNA region5.

The rDNA, a key component of an organism’s essential protein-making machinery and therefore common to species spanning the tree of life, is arranged at one or more genomic loci. While prokaryotes and archaea package their rDNA in operon or operon-like structures6,7, in eukaryotes these loci comprise tandem arrays of rDNA units long thought by many to be homogeneous in sequence. For example, in the baker’s yeast Saccharomyces cerevisiae ~150 rDNA units of 9.1 kb in length are found in tandem at a single locus on chromosome XII8. Despite individual unit sequences being subject to evolutionary events such as point mutation, homogeneity along an entire array may be reinstated through concerted evolutionary processes such as unequal sister chromatid exchange and gene conversion9. This balance of heterogeneity-inducing mutations and homogeneity-promoting concerted evolutionary events is key to understanding micro-heterogeneity in this important genomic region. Taking a snapshot of the rDNA at any one point in time shows the nature of this balance at that moment.

Whole genome sequencing and re-sequencing projects provide the means to take such a snapshot and to characterise fully both intra- and inter-genomic rDNA sequence variation across a range of taxonomic scales, within and between the kingdoms of life. These approaches have the power to overcome sampling biases inherent to, for example, PCR-based techniques. In this study we aim to exploit an existing genome re-sequencing resource in order to finely characterise rDNA sequence variation in multiple strains of two closely related yet contrasting yeast species, S. cerevisiae and its wild relative Saccharomyces paradoxus.

We build upon recent examinations of rDNA sequence variation in relevant genome sequences. For example, Ganley and Kobayashi5 analysed Whole Genome Shotgun Sequencing (WGSS) data ranging between 1.7 and 7.7× coverage for five fungal genomes. Here, the authors identified just 13 high-stringency polymorphisms in S. paradoxus and 4 in S. cerevisiae, in line with the concept of rDNA sequence homogeneity. Simon and Weiß10 examined rDNA polymorphism in 4 plant pathogens at 3 sub-regions of the rDNA unit, discovering higher nucleotide diversity levels than Ganley and Kobayashi, notably encompassing the one species common to their analyses (Aspergillus nidulans). In a previous study11, we examined the rDNA regions of 34 strains of the baker’s yeast S. cerevisiae from WGSS data generated within the Saccharomyces Genome Resequencing Project12. Due to the greater breadth of our strain sampling than in prior studies, we were able to report high levels of sequence variation amongst individual rDNA units, ranging from 10 to 76 polymorphisms per strain across 227 variable sites. Many of the polymorphisms detected in this analysis were not fully resolved, in that for a particular strain they did not occur in all repeats of the rDNA array, and so could not be regarded as conventional single nucleotide polymorphisms (SNPs). The term partial single nucleotide polymorphism, or pSNP, was introduced for this type of intra-genomic sequence variation, in essence describing one aspect of an organism’s deviation from a perfect concerted evolutionary process. A further interesting finding from this study was an association between the number of pSNPs present in an rDNA array and the degree of genome mosaicism observed. The level of genome mosaicism attributed to each strain was denoted as either Structured or Mosaic, according to population structure analyses carried out within the first major population genetic study of yeast13. In that study, the Structured strains were believed to have been subject to a lower - but not necessarily negligible - degree of hybridisation than the Mosaic strains in their evolutionary history.

Most recently, we conducted a reanalysis of this S. cerevisiae dataset, alongside that of 26 SGRP strains of S. paradoxus14. Using the TURNIP software15 for SNP and pSNP variation discovery we showed, for both species, that the phylogenetic signal contained within these rDNA-based variants was consistent with that found in genome-wide SNP datasets. Furthermore, we refined the categorisation of genome mosaicism to include three levels: Structured clean, Structured mosaic and Mosaic. This subdivision of the former Structured strains into two groups followed the observation that levels of genome hybridisation were believed to be minimal for strains classified at Structured clean whereas they were thought to be moderate for Structured mosaic strains, though still less than strains classified as Mosaic.

Here, we delve further into this two-species dataset, identifying a wealth of inter-genomic variants while also uncovering and describing a wider range of intra-genomic rDNA sequence variation, all at an unprecedented level of discrimination. We show that S. cerevisiae and S. paradoxus both possess high levels of sequence heterogenetity within their respective rDNA loci yet exhibit rather different mutational profiles. We characterise, for the first time, unresolved insertion and deletion polymorphisms in the rDNA arrays of both species, and introduce the terms pINS and pDEL to describe these intra-genomic polymorphism types respectively. We further suggest that pINS and pDEL polymorphisms may each be further characterised into two subtypes. We discover that patterns of pINS and pDEL polymorphisms, like pSNPs, are an indicator of genome mosaicism. We show for the first time that for strains within each species set, allele frequencies of unresolved polymorphisms exhibit U-shaped distributions, such as do loci evolving under non-concerted mechanisms, and that the shapes of these distributions relate to the species’ population history. Finally, we discuss how new mathematical models of rDNA evolution, when fitted to these and other similar datasets, could vastly increase our current understanding of concerted evolutionary processes across a wide range of organisms.

Results

rDNA sequence variation is abundant in both species

Our use of the TURNIP software for variation discovery enabled us to uncover a wide range of polymorphisms in both species. In addition to fixed polymorphisms, manifested as variants present in all rDNA units within a strain, unresolved polymorphisms, instead present in only a subset of rDNA units, were also identified. Whilst we have previously categorised and analysed partial single nucleotide polymorphisms (pSNPs)11,14, here we introduce partial insertions and partial deletions along with their respective abbreviations, pINSs and pDELs. We also recorded a small number of cases where distinct polymorphism classes coincided at the same sequence site within different rDNA units of the same strain, and which we term complex mutations (CX). In summary therefore, our TURNIP analysis identified three classes of fixed, inter-genomic variants (SNPs, insertions, deletions) and their respective partial, intra-genomic forms (pSNPs, pINSs, pDELs), along with complex mutations that could involve combinations of two or more of these six polymorphism types.

Tables 1 and and22 present a breakdown of the sequence variants (type and total) found in each strain of S. paradoxus and S. cerevisiae respectively. Overall, 970 polymorphisms were identified across the entire set of 26 S. paradoxus strains and 1,068 across the 34 S. cerevisiae strains. The levels of sequence variation (fixed and partial variants combined) identified in the rDNA arrays of the 60 strains examined were found to vary markedly, in S. paradoxus ranging from a single polymorphism in the European strain Q89.8 to 114 in the American strain DBVPG6304, and in S. cerevisiae from 4 polymorphisms in the W303 strain to 49 in YS4 and DBVPG1853 (see Supplementary Table S1 for strain information for both species and Supplementary Table S2 for details of variants).

Table 1
rDNA Sequence Variation Uncovered Within the S. paradoxus Dataset.
Table 2
rDNA Sequence Variation Uncovered Within the S. cerevisiae Dataset.

Fixed polymorphisms

This class of variant is key to understanding the inter-relationships amongst the strains under analysis, being proportional to the genetic divergence between them. Of the 970 polymorphisms identified within the S. paradoxus strains (see Table 1), 853 (87.9%) were found to be fixed, with SNPs clearly the largest contributor to this group (82.6% of fixed polymorphisms). Compared to CBS 432, the species type strain and European S. paradoxus reference strain, the 16 European strains were found to possess the fewest number of fixed polymorphisms (64; 4.0 per strain). The four Far Eastern strains exhibited almost nine times as many polymorphisms per strain (143; 35.8 per strain), and the six American strains were the most diverse group with almost twenty seven times as many polymorphisms per strain (646; 107.7 per strain), the polymorphism number clearly growing with geographic divergence from the reference strain (Fig. 1). The major exception to this trend was the Far Eastern strain N-45, which possessed just 5 fixed polymorphisms, a number more akin to strains within the European group.

Figure 1
Geographical Location of the S. paradoxus Strain Collection.

We also identified 569 fixed polymorphisms in the 34 S. cerevisiae strains (see Table 2), comprising only just over half (53.3%) of the 1068 variants observed in this species. While SNPs were again the most prevalent fixed variant (59.6%), deletions were also frequently observed within this species (37.8%). As expected from their complex genome structure, the 15 mosaic strains were found to possess the fewest number of fixed polymorphisms (159; 10.6 per strain). Fixed variants were found to be slightly more prevalent in the 12 structured mosaic strains (200; 16.7 per strain) and most frequent in the 7 structured clean strains (210; 30 per strain). The clearest exception to this trend was the Structured mosaic strain K11, which was found to possess 37 fixed polymorphisms, the second highest number for all strains in this analysis.

Partial polymorphisms

This class of variant provides an indication of the distance from a state of complete sequence homogeneity amongst a strain’s rDNA units. We believe this distance to be the result of a complex interplay of factors such as the degree of genome hybridisation, rDNA copy number and genomic ploidy (see Discussion for more on these points). While the various concerted evolutionary processes that act on the rDNA array may lead to eventual fixation or loss of a partial polymorphsim, we showed recently that pSNPs are found in the rDNA array of both S. cerevisiae and S. paradoxus strains14 and that they provide information on genetic divergence that may be used in combination with that provided by fixed polymorphisms.

In S. paradoxus, pSNPs tend to be possessed in modest numbers (Table 1; an average of 2.8 pSNPs per strain) with occasional high frequencies, as for strains N-17 (European strain; 22.2% of all pSNPs) and N-45 (Far Eastern strain; 50.0% pSNPs), a potential indicator of a genome hybridisation event. In contrast, pSNPs are generally more prevalent amongst S. cerevisiae strains (Table 2, Fig. 2; an average of 9.1 pSNPs per strain), most notably in industrial strains, with their greater numbers hypothesised to be a feature of a higher level of hybridisation between strains of that species.

Figure 2
Mutational Profiles of S. paradoxus and S. cerevisiae.

This study has also shown that unresolved insertions (pINS) and deletions (pDELs) are a feature of the variation possessed by both species. It is interesting to observe, for example, that unresolved insertions are more frequent than fixed insertions in S. cerevisiae. We also noted earlier the prevalence of deletions in S. cerevisiae rDNA arrays when compared to S. paradoxus (Fig. 2). A closer inspection reveals the majority of S. cerevisiae deletions (61.7%) are found in the non-coding IGS1 region, with the majority of IGS1 deletions specific to just five small regions, all of which are homopolymeric poly(dA).poly(dT) tracts (see Supplementary Figure S1 for an example). Approximately 40% of all deletions in S. cerevisiae are unresolved. We can further characterise these unresolved deletions into two types. Type 1 pDELs correspond to tracts of a fixed size that are found to be deleted in a subset of rDNA array units. Type 2 pDELs are more complex, with tracts also varying in size between the units in which they have been deleted. Examples of Type 1 and Type 2 pINS and pDEL variants are described in more detail in Supplementary Figure S1. Long homopolymeric tracts, particularly poly(dA).poly(dT) tracts, are known to be unstable and prone to slip-strand replication errors, which in turn can give rise to tract length variation16. While Ganley and Kobayashi5 noted that a high number of deletions may be indicative of genome size reduction17, the limited size and scope of the rDNA deletions means we are likely observing a different phenomenon. The relative lack of insertions in homopolymeric tracts is also puzzling, though the overall lack of such events in all regions of the rDNA suggest this may be a general mutational preference in S. cerevisiae strains, the reason for which is currently unclear.

Partial insertion events may also be characterised in a similar way, with both identical and non-identical insertion sequences noted for distinct pINSs. Type 2 mutations are, in general, difficult to estimate as they require both an alignment to a reference sequence, which is analysed site-by-site, and examination of the raw sequencing reads underpinning the alignment for precise estimation, as opposed to examination of just an alignment for other types of mutation. Without explicit software coding for such eventualities, accurate manual estimation would be overly costly in a high-throughput analysis. Given that such functionality has yet to be added to the TURNIP software, the numbers of Type 1 and Type 2 pINSs and pDELs in each of the two species, as shown in Table 3 and which are estimated from the alignment alone, should be considered to be a rough estimate. It is notable that Type 2 mutations, whether partial insertions or partial deletions, appear to be proportionately less frequent in S. paradoxus than in S. cerevisiae.

Table 3
Estimates of Type 1 and Type 2 partial insertions and deletions in S. paradoxus and S. cerevisiae.

Further examination of the distribution of pINSs and pDELs across the strains of the two species shows that, as for pSNPs but arguably to a lesser extent, they are indicators of putative hybridisation events. For example, in S. paradoxus we see that pDELs are most prevalent in N-17 and N-45, our two putative hybrid strains, and that pINSs are most frequent in N-45. Furthermore, in S. cerevisiae, 32 out of 42 pINSs and 98 out of 145 pDELs are seen in strains classified as mosaic, with only 1 pINS and 10 pDELs identified in structured clean strains. Calculating Pearson’s correlation coefficients between the number of pSNPs and the combined number of pINSs and pDELs in each species gives r = 0.867 and r = 0.896 in S. paradoxus and S. cerevisiae respectively, strongly supporting the observation that strains high in one type of intra-genomic variant are likely to be high in another.

Contrasting mutational profiles exist between species

Comparing the mutational profiles of the two species groups (Fig. 2; i.e. the proportions of each of the seven mutation types) indicates that single nucleotide polymorphisms, whether fixed as SNPs or unresolved as pSNPs, were more prevalent in S. paradoxus than in S. cerevisiae (80.0% compared with 60.9%). Furthermore, the balance of these two polymorphism types was very different between the two species, with approximate ratios of 10:1 and 1:1 SNPs to pSNPs in S. paradoxus and S. cerevisiae respectively.

Our new analysis has further uncovered significant numbers of insertion and deletion polymorphisms, not considered in our previous studies11,14. These mutations account for almost 30% of all the variation detected in the two Saccharomyces species. However, one of the striking differences between the variation identified in the two species (Fig. 2) is the large number of deletions found in S. cerevisiae, with 33.7% of all variation attributed to deletions (fixed or partial), compared to only 9.7% in S. paradoxus. In contrast, insertion mutations (again, both fixed or partial) though generally more rare are more prevalent in S. paradoxus, contributing to 9.8% of all variation in that species compared to 5.3% in S. cerevisiae. Overall, a comparison of the proportions of fixed variants (i.e. SNPs, INSs and DELs) in S. cerevisiae (0.533) versus S. paradoxus (0.879) strains gives a p-value < 2.2 e−16 in an independent two-sample t-test, strongly supporting a difference between the two populations.

In general, the mutational profiles show that certain types of polymorphism are favoured in each species and furthermore that these differ between species. In their earlier analysis, Ganley and Kobayashi5 also found a biased mutational profile in S. paradoxus, though they were not able to establish one in S. cerevisiae due to a lack of identified mutations.

Sites of variation within the rDNA unit sequence are non-random

In addition to the varying levels of sequence variation found in the individual rDNA datasets of both species, the detected variation was found to be distributed unevenly over the rDNA repeat (Table 4). Figures 3a and and3b3b depict the locations of both fixed and partial variants identified in S. paradoxus and S. cerevisiae respectively. For both species, most of the identified polymorphisms were discovered in the non-coding ETS2, IGS1, IGS2 and ETS1 regions. High numbers of polymorphisms were observed for both species within IGS1 (45.5% in S. cerevisiae and 39.7% in S. paradoxus) and IGS2 (30.2% in S. cerevisiae and 35.9% in S. paradoxus). However, S. paradoxus strains appeared to favour mutations within ETS2 (14.8%) over ETS1 (6.0%), with the opposite situation in S. cerevisiae (3.5% in ETS2 versus 10.5% in ETS1). Furthermore, the mutation types contributing to these regional proportions differed markedly between species (e.g. the most prevalent IGS1 mutations in S. paradoxus were SNPs but in S. cerevisiae were deletions).

Figure 3
Distribution of Sequence Variation Along the rDNA Unit.
Table 4
Region-By-Region Breakdown of rDNA Variation in S. paradoxus and S. cerevisiae.

In contrast, and perhaps not surprisingly in view of functional constraints, very few mutations were found in the rRNA-encoding genes. For instance, no variants were detected in the highly conserved 5S or 5.8S rRNA genes in S. cerevisiae strains, while just a single partial deletion was observed in the 5S region of the putative hybrid N-17 S. paradoxus strain. The 26S and 18S rRNA genes appeared slightly more tolerant to mutation, with 33 and 12 variants across the two species respectively, all but four of which were SNPs or pSNPs. Interestingly, in both species pSNPs in coding regions are observed at low frequency, consistent with Ganley and Kobayashi’s idea of a tolerance threshold5, where a small number of units within the rDNA array may harbour a polymorphism without detrimental effect.

We carried out two-sample t-tests between the proportions of variants in each species, in each of the ten rDNA regions, correcting for the false discovery rate of 0.05 using the Benjamini-Hochberg procedure. The results (p < 2.2 e−16, p = 6.141 e−6, p = 0.0002375, p = 0.006765, p = 0.006883, p = 0.008046, p = 0.01406, p = 0.03142, p = 0.2939 and p = NaN for ETS2, ITS2, ETS1, 26S, IGS2, IGS1, ITS1, 18S, 5S and 5.8S respectively) rejected the supposition of no species-related difference in variant proportions for all rDNA regions except for 5S and 5.8S, unsurprising given the paucity of variants in these two regions.

Partial variant allele frequencies follow a U-shaped distribution

Plotting the intra-genomic rDNA unit occupancy frequencies of partial variants (though Type 1 variants only for pINSs and pDELs, due to the difficulty in estimating such frequencies for Type 2 variants), which can be considered as allele frequencies and which are estimated by the percentage of variant reads per strain, shows an interesting pattern (Fig. 3c). For both S. paradoxus and S. cerevisiae datasets the distribution follows a U-shaped curve, though the S. cerevisiae distribution is much flatter. U-shaped distributions are often observed in biological datasets, including both allele frequency18 and gene frequency19 datasets within populations, as predicted by mutation-drift theory. Beta distributions may be fitted to such datasets to help characterise and compare them. Fitting beta distributions, using the R statistical environment, to the pSNP/SNP datasets shown in Fig. 3c gave shape parameter Maximum Likelihood Estimates of α = 0.375, β = 0.371 with standard errors of (0.046, 0.046) for S. paradoxus and α = 0.635, β = 0.818 with standard errors of (0.039, 0.053) for S. cerevisiae, formalising the observed differences between them.

The estimated unit occupancies, or proportion of units within an rDNA array estimated to harbour a particular variant, were plotted for pSNP, pINS and pDEL variants individually (again Type 1 variants only for pINSs and pDELs) in each of the two species datasets. When examining such allele frequency plots it is important to realise that a 10% occupancy is equivalent to a 90% occupancy, where each value indicates a 10% divergence from fixation of one of two allelic forms (and more generally, an x% occupancy is equivalent to a (100  x)% occupancy). Five of the six plots revealed U-shaped distributions (Supplementary Figure S2), the single exception being pINSs in S. cerevisiae, whose right-handed peak lies in the occupancy bin 70–79% rather than the expected 90–99%. The two pSNP curves were both strongly U-shaped, unsurprising considering they formed the largest sub-section of the combined distribution and were therefore likely to be less susceptible to noise. The S. paradoxus pSNPs can effectively be subdivided into two categories, 29 with very low occupancy (<10%), and 38 with very high occupancy (>90%). Only 5 of the 72 S. paradoxus pSNPs (6.9%) were found to have an intermediate occupancy, varying only between 28.8% and 43.6%. In contrast, 183 of the 311 S. cerevisiae pSNPs (58.8%) possessed an occupancy of between 10% and 90%, spanning the entire range of values.

Although some features of pSNP/SNP variation, such as their quantity and location, can readily be related to characteristics of their harbouring species, others are less obvious without a deeper understanding of strain origins and inter-relationships. For example, although the overall pSNP frequency distribution for the S. cerevisiae strains is characteristically shallow, this shape is only retained for the Mosiac and Structured mosaic strain subsets (Supplementary Figure S3). In constrast, the pSNP distribution of the Structured clean strains shows a similar occupancy pattern to the S. paradoxus dataset, potentially the consequence of a common low incidence of genome hybridisation. It is interesting to note that the majority of the intermediate occupancy pSNPs in the Mosaic and Structured mosaic datasets are found in the Wine/European and Sake groups. As the majority of these strains are from a fermentation origin, we hypothesise they are more likely to have undergone hybridisation (with other fermentation strains) than their wilder cousins from the Malaysian, North American and West African groups, which possess pSNPs only at very low or very high occupancy.

Sequence read coverage is consistently high in both species

The sequence read coverage of each strain set, calculated by mapping reads to 20 bp target windows of the relevant rDNA consensus sequence, was determined to ensure our results were both supported by adequate read depth and not affected significantly by biased read location. Detailed coverage plots were produced and are shown and discussed in Supplementary Figure S4. Overall, we found the vast majority of rDNA sequence sites to be covered at good read depth in all strains in both species, lending support to the results achieved above.

Discussion

We have conducted the most detailed analysis of rDNA sequence variation to date. Within each species, we have uncovered sequence variation that is both substantial in quantity and rich in evolutionary information. Unlike many preceding studies, our analysis has encapsulated variation across the whole rDNA unit, rather than sub-regions within it, and has examined a broader range of polymorphism types across a larger number of genomes. In particular, variants found to be unresolved across an rDNA array, and which were previously undefined, have now been formalised as partial insertions (pINSs) and deletions (pDELs), and even subcategories of such variants have been brought to notice for the first time.

The population histories of S. paradoxus and S. cerevisiae are believed to differ greatly. In particular, a high frequency of genome mosaicism has previously been inferred in one species, S. cerevisiae, but not the other, S. paradoxus13. It is compelling to speculate that the hybrid, mosaic strains in S. cerevisiae may be linked to human traffic and/or industrial processes whereas they are considerably less likely to arise in the wild S. paradoxus. For such contrasting species, we might expect to see major differences in features of their rDNA variation, such as relative proportions of mutation types. Indeed, we have shown in this study that the mutational profiles of the two species are highly different, and even how some mutation types are more prevalent within certain sub-regions of the rDNA array. Furthermore, we have seen that the dearth of rDNA variants within coding regions of the rDNA unit, first observed in S. cerevisiae11, also extends to S. paradoxus and is likely a general feature of rDNA variation. Given the much higher frequencies of variants in the remaining regions, we speculate that purifying selection must act strongly on the rRNA gene sequences to maintain this pattern. Only ~50% of rDNA units are transcriptionally accessible due to their heterochromatin state, and only a fraction of the remainder are active at any point in time20. In such a situation, even deleterious partial mutations with low frequencies could perhaps be tolerated given their limited transcription, with only mutations with higher frequencies selected against. An exception to this scenario might be in cases where additional mutations that maintain the RNA hairpin structure are also present. It will be interesting to see whether we can formerly test such hypotheses with real datasets in future.

Comparisons within mutation types can also be illuminating. In S. paradoxus, we observe the vast majority of pSNPs to have variant occupancies of less than 10% or greater than 90%, and SNP variation is high within the species, suggesting that many previous pSNPs have become fixed. In contrast, over half of all identified pSNPs were found to have occupancies within the 10% to 90% range in S. cerevisiae, and the numbers of pSNPs and SNPs were considerably higher and lower respectively than those seen in S. paradoxus. We have also shown the numbers of pINS and pDEL variants to be strongly correlated with the numbers of pSNPs in both species, suggesting that these variants may also be indicators of genome mosaicism, a discovery that warrants further investigation. Furthermore, we have shown for the first time that the unit occupancy values of rDNA variants, which can be considered to be allele frequencies, follow a U-shaped distribution within each dataset. While U-shaped distributions have been identified within populations for single copy loci for some time, the concerted evolutionary processes that led to those observed here are very different from those responsible for the single copy loci distributions.

Frequencies of variants across the rDNA array are likely to be affected by the number of copies of the rDNA unit and the hybridisation history of the strain. Furthermore, the number of rDNA loci within a genome and its ploidy may also be factors in our ability to determine variant frequencies, since if we cannot discriminate between different loci or sub-genomes, we will be merely averaging variant frequencies across loci/sub-genomes within a strain. In a recent study14, we estimated the rDNA copy numbers of all 60 strains included within this analysis. The average copy number across the 26 S. paradoxus strains is much smaller than that of the 34 S. cerevisiae strains, at 69 and 99 respectively. Furthermore, we believe that hybridisation has been a less significant phenomenon in the evolution of S. paradoxus than of S. cerevisiae13. Although the fairly low genome coverage of the strains analysed here prevents us from computationally estimating their ploidy, we strongly believe that all 60 strains possess a single rDNA locus. Consequently, we hypothesise that copy number and hybridisation history are the major factors underpinning the observed variant distributions uncovered in this analysis and we look forward to testing this hypothesis formally in future studies. In particular, we aim to understand how the differences in the U-shaped variant frequency distributions observed for the two species arose. U-shaped distributions are a pervasive feature of biological datasets, predicted under mutation-drift evolutionary theory. Computer simulations show that ‘steep’ U-shaped distributions, such at those seen for S. paradoxus, can form quickly from initial mutations [ref. 21, Chapter 5], whereas the analogous distributions of S. cerevisiae are considerably flatter. Within our future analyses, we aim to understand in detail the roles of rDNA copy number and hybridisation events on the establishment of these distributions and how ploidy and rDNA locus number may confound their delineation.

Comprehensive characterisation of rDNA-based variation naturally relies on our ability to sequence the whole rDNA locus (or loci). At present, we are not able to assemble rDNA sequences into ordered tandemly arranged units, due to their highly repetitive nature. A consequence of this is that we cannot be sure that our target sequence has been uniformly sampled across all of its tandem copies, and hence some variation may still be missing. However, current sequencing technologies, where we can deeply sample sequences of interest, suggest that we are approaching a complete coverage of the entire locus. Furthermore, promised technological advances, particularly those in the area of long read sequencing, mean that assembly of repetitive sequences may soon be possible. Consequently, we can be confident that with current approaches, we can make good estimates of rDNA sequence variation and that these will improve further in the near future. Furthermore, more sophisticated algorithms, particularly those employing probabilistic strategies, will be helpful for calling variants in larger strain sets. In particular, a finer characterisation of Type 2 partial insertions and deletions, which optimally require automated examination of reads in addition to read alignments, may be achieved by improved variant calling methods.

The datasets developed and analysed here offer a fascinating snapshot of concerted evolution in action. Many groups, including us, are interested in learning more about these concerted evolutionary processes by modelling them mathematically. An important recent study22 used experimental approaches to monitor the gain and loss of introduced mutations within the S. cerevisiae rDNA array, discovering important characteristics of the spatial and temporal dynamics of the concerted evolutionary process within this organism. The knowledge and datasets we have developed here are a further step towards achieving successful models that can fully exploit the rich source of evolutionary knowledge held within the rDNA array. For example, beta distribution parameter estimates derived from partial variants may be particularly useful in modelling the spread of allele frequencies by processes such as unequal sister chromatid exchange and gene conversion, determining the balance of such processes in a range of organism groups, and discovering how the spread of mutations along an rDNA array may be constrained by genome hybridisation. Furthermore, rDNA is now known to play a key role in cancer cell proliferation23, and future studies will focus on hunting for rDNA sequence or copy number variants associated with disease states. Given the ubiquity of the rDNA genomic region, the prospect of using mathematical models to analyse the genomes of a wide range of species, and optimised variant-hunting methods for associations with genetic disease, is an attractive one.

Methods

Data Preparation

Raw WGSS reads were obtained, in FASTQ format, from the SGRP S. paradoxus and S. cerevisiae datasets12. Twenty six S. paradoxus and thirty four S. cerevisiae strains formed the overall dataset, a total of 716,316 and 703,641 sequencing reads respectively. The rDNA consensus sequence for S. paradoxus was obtained from GenBank [ref. 24, Accession Number:BR000309.1] for the type strain NRRL Y-17217, also known as CBS 4325, a sequence of 9,103 bp in length. The S. cerevisiae consensus sequence was obtained for the reference strain S288c from the Saccharomyces Genome Database25, a sequence of 9,137 bp in length.

The TURNIP software (version 1.3) for analysis of repetitive DNA sequences15 was used to process the SGRP reads against the relevant consensus sequence, thereby identifying rDNA-specific shotgun reads. However, on manual examination some reads that passed through this filtering process were suspected to be non-rDNA in origin. Performing a BLASTN search of selected reads, identified by TURNIP as variant but mapping to highly conserved rDNA regions, against the National Center for Biotechnology Information Nucleotide database26 resulted in identifying a possible contamination of the S. paradoxus reference strain CBS 432 with Plasmodium falciparum sequence. More stringent filtering strategies were therefore employed prior to further use with the TURNIP software.

Data Filtering

A custom Perl script (Supplementary Figure S5), employing BioPerl modules [ref. 27, BioPerl version 1.6.9, Perl version 5.12], was used to filter the dataset more stringently. Sequencing reads from each strain were aligned to the S. paradoxus consensus sequence using blastall [ref. 28, BLAST version 2.2.27+, using BioPerl module Bio::Tools::Run::StandAloneBlast]. Conditions for reads to pass the filter comprised a minimum read length of 150 bp, minimum identity of 75%, and minimum percentage of the original read involved in a High-scoring Segment Pair of 75%. BLAST parameters included an E-value of 1 × 10−10, gap opening penalty of 3, gap extension penalty of 1 and a nucleotide mismatch penalty of −1. The results of this modified filter were compared to the previous results. All reads that were complicit in a putative polymorphism in the original filtering process, but not in the more stringent modified filter, and a random selection of remaining reads were then manually checked, confirming that the final results were highly likely to retain all true hits to the rDNA sequence while removing false positive matches. Reads that passed the new filter were then converted into a BLAST searchable database using formatdb, ready for use with TURNIP. This process resulted in a total of 36,522 and 44,481 rDNA-specific sequencing reads for the 26 S. paradoxus and 34 S. cerevisiae strains respectively.

Variant Discovery and Analysis

Three categories of inter-genomic polymorphism - SNPs, insertions and deletions - and their respective intra-genomic polymorphism forms - pSNPs, pINSs and pDELs, were identified within the two strain datasets using TURNIP (see the Results section for definitions of all polymorphism types). Default parameters were used within the configuration file, with a minimum quality score of 38, and an allowed shortness of 38. The TURNIP BLAST parameters -b and -v were each set to 800, higher than the default value, to allow all reads aligning to specific rDNA regions to be stored and analysed. To ensure confidence in unresolved variation discovery, the additional criterion that pSNPs, pINSs and pDELs should be present in more than a single read in a given strain was asserted. The output was then inspected visually for complex mutations (denoted CX), i.e. nucleotide positions at which more than one type of variant was identified11. These positions were then annotated manually. Strain- and species-specific variants were analysed within the R statistical computing environment [ref. 29, version 2.15.2], using standard and MASS libraries (version 7.3-22) together with the fitdistrplus library [ref. 30, version 1.0-2] for fitting of Beta distributions to allele frequency curves.

Additional Information

How to cite this article: James, S. A. et al. Prevalence and Dynamics of Ribosomal DNA Micro-heterogeneity Are Linked to Population History in Two Contrasting Yeast Species. Sci. Rep. 6, 28555; doi: 10.1038/srep28555 (2016).

Supplementary Material

Supplementary Information:

Acknowledgments

This work was supported by the Biotechnology and Biological Sciences Research Council, through a National Capability award to the National Collection of Yeast Cultures (grant number BBS/E/F/00044440 to I.N.R.), an Institute Strategic Programme award to the Institute of Food Research (grant number BBS/E/F/00044471 to I.N.R.) and a Doctoral Training Grant award to C.W.

Footnotes

Author Contributions S.A.J. carried out manual curation and analysis of the data and also drafted the manuscript. C.W. performed all computational analyses and drafted the manuscript. R.P.D. assisted in the computational analysis and J.D. with the statistical analysis. J.D. and I.N.R. conceived the study, participated in its design and supervision and completed the manuscript. All authors read, modified, and approved the final article.

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