Killer Meiotic Drive and Dynamic Evolution of the wtf Gene Family

Abstract

Natural selection works best when the two alleles in a diploid organism are transmitted to offspring at equal frequencies. Despite this, selfish loci known as meiotic drivers that bias their own transmission into gametes are found throughout eukaryotes. Drive is thought to be a powerful evolutionary force, but empirical evolutionary analyses of drive systems are limited by low numbers of identified meiotic drive genes. Here, we analyze the evolution of the wtf gene family of Schizosaccharomyces pombe that contains both killer meiotic drive genes and suppressors of drive. We completed assemblies of all wtf genes for two S. pombe isolates, as well as a subset of wtf genes from over 50 isolates. We find that wtf copy number can vary greatly between isolates and that amino acid substitutions, expansions and contractions of DNA sequence repeats, and nonallelic gene conversion between family members all contribute to dynamic wtf gene evolution. This work demonstrates the power of meiotic drive to foster rapid evolution and identifies a recombination mechanism through which transposons can indirectly mobilize meiotic drivers.

Introduction

Many genes are maintained in eukaryotic genomes by natural selection because they provide a fitness benefit to the organisms that bear them. Analyses of these genes and their molecular functions constitute the bulk of molecular biology research performed today. However, not all genetic loci provide a fitness benefit to their hosts and some can even be described as parasites. There are many types of parasitic genes, which can comprise large fractions of eukaryotic genomes and can have a substantial impact on shaping genome evolution (Burt and Trivers 2006).

Killer meiotic drive loci are one such class of parasites that can be particularly harmful to fitness. These selfish loci act when heterozygous to destroy the meiotic products that do not inherit them. This killing causes the heterozygote to transmit the meiotic drive locus to up to 100% of the functional meiotic products (Lindholm et al. 2016; Bravo Núñez, Nuckolls, et al. 2018). Killer meiotic drivers have been observed throughout eukaryotes from plants to mammals, even though their selfish behavior generally decreases overall organismal fitness (Lindholm et al. 2016; Shen et al. 2017; Xie et al. 2017; Bravo Núñez, Nuckolls, et al. 2018; Yu et al. 2018). Killer meiotic drivers can directly cause infertility, and biasing allele transmission disrupts the ability of natural selection to choose the best adapted alleles at any linked loci. Genomic loci that suppress drive are therefore predicted to be favored by selection (Crow 1991). Indeed, the activity of many suppressors of meiotic drive has been observed, although only four suppressor genes have been cloned (Tao, Masly, et al. 2007; Grognet et al. 2014; Bravo Núñez, Lange, et al. 2018; Lin et al. 2018).

Detecting meiotic drive and distinguishing it from other phenomena that bias allele transmission can be experimentally challenging, even in the most tractable genetic systems (Burt and Trivers 2006). After establishing the presence of drive loci, identifying the genes responsible often takes years. In addition, the handful of meiotic drive loci that have been cloned in different systems are not homologous to each other, so sequence analysis is generally not useful in identifying novel drivers (Tao, Araripe, et al. 2007; Fishman and Saunders 2008; Phadnis and Orr 2009; Cocquet et al. 2012; Didion et al. 2015; Helleu et al. 2016; Akera et al. 2017; Shen et al. 2017; Xie et al. 2017; Bravo Núñez, Nuckolls, et al. 2018; Dawe et al. 2018; Yu et al. 2018). These factors limit our ability to efficiently analyze the possible presence or impact of meiotic drivers, especially in complex organisms with limited genetic tractability like humans.

Although meiotic drive genes generally do not share DNA sequence homology, they may share certain evolutionary signatures that could guide discovery of novel drive loci from genomic sequence data alone. For example, genetic conflict between drivers and suppressors is predicted to trigger an evolutionary arms race where both sides exhibit rapid evolution (Helleu et al. 2016; McLaughlin and Malik 2017). Similarly, evidence of analogous evolutionary arms races between viruses and host genomes has become widespread and has led to revolutionary insights in virus–host interactions (Daugherty and Malik 2012). However, due to the paucity of cloned meiotic drivers and suppressors, studies of the evolutionary signatures of genes known to cause or suppress meiotic drive are limited (Lindholm et al. 2016).

The wtf gene family from Schizosaccharomyces pombe offers an exceptional opportunity to study the evolution of meiotic drive systems (Lopez Hernandez and Zanders 2018). The genomes of S. pombe isolates contain more than 20 wtf genes, some of which are known to be killer meiotic drivers (Hu et al. 2017; Nuckolls et al. 2017). The characterized drive genes are predicted to encode transmembrane proteins, but there are no obvious orthologs outside of S. pombe and the complete molecular mechanisms of drive are unknown. However, the characterized driving wtf genes use alternate transcripts to generate both an antidote and a poison during gametogenesis. The poison acts on all gametes, whereas the antidote remains within wtf+ gametes. The combined action of the poison and antidote proteins results in the preferential death of the wtf− gametes generated by wtf+/wtf− heterozygotes and therefore preferential transmission of wtf+ alleles (Hu et al. 2017; Nuckolls et al. 2017).

The driving wtf genes impose significant fertility costs on their hosts and severely limit the ability of S. pombe isolates to reproduce sexually (Zanders et al. 2014; Hu et al. 2017). Novel genes or genetic variants that can suppress the action of wtf drivers are expected to promote fitness and should be favored by natural selection (Crow 1991). Indeed, we recently identified a suppressor of a killer wtf drive gene. Interestingly, this suppressor, wtf18-2, is a member of the wtf family and likely evolved from a wtf driver (Bravo Núñez, Lange, et al. 2018).

In this work, we assemble and annotate the wtf genes from two S. pombe isolates and compare them with the wtf genes of two previously published S. pombe isolates (Wood et al. 2002; Hu et al. 2017). We classify the wtf genes into possible functional groups based on previously characterized genes. In addition, we greatly extend previous evolutionary analyses of the wtf gene family (Bowen et al. 2003; Hu et al. 2017). Consistent with their engagement in molecular arms races, we show that wtf genes exhibit rapid evolution. In fact, wtf genes are among the most rapidly evolving genes in S. pombe. We show that intact wtf gene numbers vary between isolates. Moreover, we show that syntenic wtf genes often have much lower sequence identity than is typically observed between isolates. We show that homologous recombination, repeat expansion and contraction, and amino acid substitutions all contribute to the diversification of the wtf gene family. This work provides a case study for the evolutionary dynamics between selfish genes and their suppressors and supports the idea that signatures of rapid evolution could guide the discovery of novel drive loci.

Results

Correcting wtf Gene Annotations in the Sp Reference Genome

The PomBase database provides annotated gene structures for 25 wtf genes, of which five are annotated as pseudogenes (Wood et al. 2012; McDowall et al. 2015). However, our previous analyses of the Sp wtf4, Sp wtf13, and Sp wtf18 loci revealed that the annotated splice sites were inconsistent with published long-read RNA sequence data (Nuckolls et al. 2017; Bravo Núñez, Lange, et al. 2018). We therefore reevaluated the remaining Sp wtf gene annotations using long-read RNA sequence data (supplementary fig. 1, Supplementary Material online) (Kuang et al. 2016). We found that our predictions were consistent with the PomBase annotations for 14 wtf genes but different for the remaining 11 genes. Our results matched those of Hu et al. (2017) who predicted the coding sequences computationally. In the updated annotations, four wtf genes that were previously predicted to be intact (Sp wtf6, wtf8, wtf12, and wtf17) are truncated by early stop codons (based on homology to other wtf genes). These genes join wtf1, wtf2, wtf3, wtf22, and wtf24 as likely pseudogenes.

wtf Gene Numbers Vary Greatly between S. pombe Isolates

The molecular arms race model predicts that genes in conflict, such as meiotic drivers and their suppressors, will evolve rapidly in order to outcompete one another (McLaughlin and Malik 2017). Gene duplication is a commonly used evolutionary strategy to facilitate rapid diversification and has been observed in the context of virus–host arms races (Daugherty and Malik 2012). The large number of wtf loci in the reference S. pombe genome assembly (25 genes, including pseudogenes) is consistent with a similar scenario occurring within the wtf family. In addition, previous limited analyses revealed differing numbers of wtf genes between different S. pombe group isolates (Hu et al. 2017; Nuckolls et al. 2017). To more globally test the possibility that recent wtf gene duplications or deletions have occurred in the S. pombe group, we first determined whether wtf gene numbers are dynamic between isolates.

The reference S. pombe isolate (972, isolated in France in 1921) was sequenced using extensive physical mapping and Sanger chemistry to yield an excellent assembly including the complete sequences of the wtf genes (Wood et al. 2002). Complete wtf sequences are also available for the CBS5557 isolate (collected in Spain, reported 1964) which was sequenced using long-read PacBio technology (Hu et al. 2017). The genomes of over 150 additional S. pombe isolates have also been sequenced, but those studies used paired-end 100-bp Illumina reads with standard insert sizes (∼300 bp) (Hu et al. 2015; Jeffares et al. 2015; Jeffares 2018). Due to the repetitive nature of the wtf genes and the fact that they are often flanked by repetitive Tf transposons or Tf long terminal repeats (LTRs), the sequences of most wtf loci could not be reliably determined in those genomes where only short reads were available.

To overcome this challenge, we sequenced five S. pombe isolates and the reference isolate using “mate-pair” libraries to capture pairs of 150-bp reads separated by 5–8 kb in the genome (supplementary fig. 2, Supplementary Material online). With this large insert sequencing approach, the distance between mate-pair reads is large enough that when one read of the pair falls within a repetitive wtf, the mate often falls in unique genomic sequence (fig. 1A). This allowed us to match wtf reads with their cognate genomic locus, even for wtf genes that share very high sequence identity. We sequenced to >80× coverage a derivative of the S. pombe reference isolate (which we will abbreviate as Sp), FY28974 (collected in Brazil in 1996), FY28989 (collected in Africa in 1921), FY29030 (collected in Indonesia in 1949), FY29033 (collected in Indonesia in 1923), and Schizosaccharomyces kambucha (abbreviated Sk, isolated in the United States, reported in 2002) (Singh and Klar 2002; Wood et al. 2002; Jeffares et al. 2015). Sk was historically given a different species name because it is reproductively isolated from Sp, but it is no more diverged from Sp than other isolates of the S. pombe group (Singh and Klar 2002; Rhind et al. 2011; Zanders et al. 2014). Like all isolates classified as S. pombe, all isolates analyzed in this work are all very closely related: They are estimated to have diverged from each other within the last ∼2,300 years and share on average >99% DNA sequence identity (Jeffares 2018).

Fig. 1.

A genomics approach identifies and assembles wtf gene sequences. (A) Schematic of the strategy we used to identify wtf gene locations. (B) Examples of three Sp loci are shown to illustrate how read pileups (from strategy described in [A]) flank loci with zero, one or two wtf genes. In each plot, the x axis shows relative position in the Sp reference genome, and the y axis shows the number of reads mapping to each base. (C) A map of wtf gene distribution in seven isolates of Schizosaccharomyces pombe. The map shows the three chromosomes of the Sp karyotype, although this karyotype is not shared by all isolates. The inset box indicates total wtf gene numbers (including pseudogenes) in each isolate. The numbers for FY28974, FY28989, and FY29030 (in black) are estimates because we did not assemble all wtf loci in those isolates. The * denotes the wtf genes found in a duplicated region. (D) Schematic of the strategy we used to assemble wtf gene sequences in Sk and FY29033.

To identify genomic loci that harbor wtf genes in each isolate, we first used our sequence data to select all read pairs in which one of the reads aligned to one or more of the 25 wtf genes in the reference genome (abbreviated as Sp here). We then isolated mates of those wtf reads, aligned them to the Sp reference genome, and visually analyzed regions where multiple wtf mate reads mapped (“pileups”). This yielded a map in which each wtf locus is flanked by pileups of mate reads that map uniquely in the genome (fig. 1A and B). To verify this approach, we applied it to the Sp data and accurately detected all wtf locations. We further observed that Sp loci containing a single wtf gene were typically flanked by ∼2.2-kb-wide pileups, slightly wider than the typical genomic width of a wtf gene (average 1.2 kb). Sp loci encoding two wtf genes were flanked by wider (∼4.4 kb) pileups (fig. 1B and supplementary fig. 3, Supplementary Material online). These data suggested that we could use the presence and width of such pileups to identify wtf loci and copy number genome-wide.

We then used this approach to identify wtf loci in each of the five isolates we sequenced and to estimate how many wtf genes each locus contains (fig. 1C). In Sk and FY29033, these estimates were confirmed (and in a few cases corrected) by assembly of the wtf loci from the mate-pair reads and by Sanger sequencing of a group of genes (described below). Unlike in Sp, in each of the other isolates we found a few loci flanked by even wider pileups (up to∼7.5 kb), suggesting that these loci each contain three wtf genes (supplementary fig. 3, Supplementary Material online). This inference was confirmed by assembling four such loci.

At most of the loci we detected, we observed a symmetrical pair of pileups that were ∼2.2 or ∼4.4 kb wide that clearly suggested one or two wtf genes within the locus. Some loci, including most of those with three wtf genes, showed more complicated or misleading patterns (examples are shown in supplementary fig. 4, Supplementary Material online). At asymmetric pileups, we used the widest pileup to predict the number of wtf genes. Assembly of these regions with complicated pileup patterns in Sk and FY29033 revealed that these inconsistencies were due to transposon insertions near the wtf loci that were not present in the reference genome. For example, a transposon insertion led to the two genes at the wtf2 locus in Sk showing a pileup pattern typical of a one gene locus, and an additional transposon led to the three genes at the wtf10 locus in Sk to show a pileup pattern typical of a two gene locus (supplementary fig. 4, Supplementary Material online). The underestimates in our FY29033 and Sk gene number predictions (at 3 out of 46 loci) were detected during the assembly of those loci to obtain wtf gene sequences (below). As we did not assemble all wtf loci in FY28974, FY28989, and FY29030, there could be similar uncorrected underestimates of wtf gene numbers in those isolates. In addition, our method would be unable to detect more than three tandem wtf genes because the locus size exceeds the insert size between our mate-pair reads (supplementary fig. 2, Supplementary Material online). Although we did not observe loci with more than three tandem wtf genes in genomes with assembled wtf loci, this limitation could also lead to an underestimate of wtf gene numbers in the genomes where we did not perform de novo assemblies.

Our mate-pair pileup approach could also miss additional wtf gene copies if they were found within larger recently duplicated regions of the genome. To look for such wtf genes, we aligned all sequence reads for each isolate to the Sp reference genome and looked for regions containing wtf loci where sequencing coverage was at least twice as high as the rest of the genome. We found two duplicated regions that include a wtf gene (supplementary fig. 5, Supplementary Material online). In FY29030, there is a ∼14-kb duplication of the wtf23 region (between chromosome 3 reference genome positions 2,145,417 and 2,159,329). In FY28989, there is a ∼95-kb duplication of the wtf33 region between positions 1,838,980 and 1,933,773 on chromosome 3 (supplementary fig. 5, Supplementary Material online). These duplications both appear to be very young, as we do not detect increased sequence variation in those regions compared with the flanking sequence. We therefore conclude that FY29030 contains two nearly identical copies of wtf23 and FY28989 contains two nearly identical copies of wtf33 (indicated with asterisks in fig. 1C).

After identifying all the genomic loci encoding wtf genes in the five isolates, we combined our data with the previously identified wtf landscapes in CBS5557 and Sp (McDowall et al. 2015; Hu et al. 2017). Altogether, we found that the total number of wtf genes (including pseudogenes) varied greatly between isolates, ranging from 25 in Sp to 38 in FY29033 (fig. 1C). Each locus can contain between zero and three wtf genes. Overall, the locations of wtf genes were quite similar between isolates: We found only four wtf loci that were not shared among all isolates. Most of the variation in wtf number between isolates can be explained by expansion/contraction of wtf gene numbers within each locus (fig. 1C), although without a clear outgroup it is unclear what the relative contributions of duplications and deletions are. Given that all isolates encode at least one wtf gene at 20 shared loci, it is likely that the ancestral genome of these isolates contained at least 20 wtf genes.

Assembling wtf Genes from Sk and FY29033 Yields Many Unique Gene Sequences

Within Sp, there is extensive sequence diversity among the wtf genes. Some, like Sp wtf4 and Sp wtf13, are very similar (>90% amino acid identity), whereas others, like Sp wtf4 and Sp wtf7, are not (<30% amino acid identity). We wanted to know whether the gene repertoire of Sp reflects the full range of wtf diversity, or whether it instead represents a limited sample. To test this, we used our sequencing data to assemble all wtf genes from two additional genomes, Sk and FY29033. We assembled each wtf locus separately, first selecting all read pairs in which one of the reads aligned to a unique wtf-flanking region (i.e., the pileup regions discussed above; fig. 1A, B, and D). We then assembled those read pairs to generate a contig with the wtf gene(s) in the center (fig. 1D).

To validate this approach, we also used it to assemble Sk wtf genes we had previously Sanger sequenced (wtf4, wtf5, wtf6, wtf13, wtf18, wtf21, wtf26, and wtf28; Nuckolls et al. 2017; Bravo Núñez, Lange, et al. 2018). We also Sanger sequenced Sk wtf9, wtf17, wtf19, wtf20, wtf23, wtf27, wtf29, wtf33, and wtf35 in addition to FY29033 wtf1, wtf18, wtf35, and wtf36 (naming scheme described below). We found that our assemblies matched the Sanger sequencing except at the Sk wtf2 locus. Our mate-pair sequencing revealed that the Sanger sequencing of Sk wtf2 missed a Tf transposon and a second wtf gene (wtf34) in the region, likely due to template switching during polymerase chain reaction amplification. These results suggest that our assembly approach is robust.

We then predicted wtf coding sequences based on possible open reading frames and homology to annotated Sp wtf genes. Our analyses (discussed below) found additional wtf gene variation not represented in the wtf genes found in Sp or CBS5557.

Naming wtf Genes

There are currently three reported phenotypic classes of intact wtf genes: killer meiotic drivers, suppressors of drive, and one essential gene (Sp wtf21) (Kim et al. 2010; Hu et al. 2017; Nuckolls et al. 2017; Bravo Núñez, Lange, et al. 2018). It is unknown whether there are other phenotypic classes of wtf genes, but it would not be surprising given their vast diversity. To facilitate answering this question and to guide future phenotypic classification of wtf genes, we assigned gene names to each wtf gene from Sk, FY29033, and CBS5557.

For Sp, we used existing gene names, and for each other genome, we named genes according to their genomic synteny by comparison with Sp. We use Sk as an example to explain our naming scheme. At the loci where both Sk and Sp have one wtf gene, we gave the Sk gene the same number as Sp (e.g., Sk wtf1), regardless of sequence identity. For loci where Sp has one gene and Sk has two genes (e.g., at the Sp wtf8 locus), we gave the same gene number to the Sk gene that was most similar to the Sp gene and gave the remaining Sk wtf genes increasing numbers (26–35) depending on their order in the Sk genome. We followed the same convention for naming the FY29033 and CBS5557 wtf genes to facilitate comparisons between isolates (the genes of CBS5557 were already named by Hu et al. [2017] as cw1–cw29; we provide name translations in supplementary table 1, Supplementary Material online). Supplementary figure 6, Supplementary Material online, shows wtf gene names and locations in the four isolates.

Pervasive Nonallelic Gene Conversion between wtf Genes

To examine wtf gene evolution, we aligned their coding sequences and generated a maximum-likelihood phylogenetic tree. Naively, we expected that sets of genes from the four sequenced isolates that are found in syntenic loci would group together in well-supported clades on the tree. However, syntenic genes grouped with one another in only a few clades of the tree. The wtf7, wtf11, wtf14, and wtf15 genes each form well-supported clades that do not include genes from other loci (bootstrap values >95%; supplementary data and supplementary fig. 7, Supplementary Material online). Each of these genes is quite distinct from other wtf genes (separated by long branches). The alleles of the wtf12 and wtf17 genes also form well-supported clades (>80% support), albeit less diverged from their nearest neighbors (supplementary fig. 7, Supplementary Material online). These genes, however, appear to be losing function in at least some isolates: Shared inactivating mutations in the wtf12 gene in all four isolates indicate that it pseudogenized prior to the divergence of the isolates, and the Sp and Sk sequences of wtf17 also appear pseudogenized.

Despite clear synteny and a very short time (∼2,300 years) since these yeast isolates shared a common ancestor (Jeffares 2018), none of the remaining syntenic wtf gene sets forms well-supported clades that exclude wtf genes from other loci. Furthermore, there are clear examples of well-supported clades containing genes from different loci. For example, one well-supported clade includes the following genes: Sk wtf29 and wtf30; Sp wtf19 and wtf23; FY29033 wtf8, wtf30, and wtf38; and CBS5557 wtf29 (highlighted in supplementary fig. 7, Supplementary Material online). Finally, the tree contains two well-supported terminal nodes in which gene pairs at distinct loci from the same isolate (Sp wtf19 and wtf23 as well as FY29033 wtf1 and wtf35) form a clade, whereas syntenic genes from other isolates are in distinct clades. These observations are consistent with gene conversion within the wtf gene family.

To analyze whether entire wtf coding sequences might be overwriting one another by gene conversion, or whether only portions of the genes are involved, we performed GARD (genetic algorithm for recombination detection) analysis on our coding sequence alignment to test for recombination between wtf genes (supplementary fig. 8, Supplementary Material online) (Kosakovsky Pond et al. 2006). This algorithm tests the hypothesis that the same phylogenetic tree represents the entire alignment or whether different trees best represent different segments due to recombination. GARD analysis found that the hypothesis of multiple trees best describing different segments was >100 times more likely than the hypothesis of a single tree. In addition, GARD identified three likely segments (P < 0.01, supplementary fig. 8, Supplementary Material online). Together, our observations are consistent with widespread nonallelic gene conversion between members of the wtf gene family. Such gene conversion obscures the evolutionary history of the wtf gene family and means that functional inferences can often not be made across isolates based on shared synteny. This work confirms and expands observations made by Hu et al. (2017) who previously described gene conversion among Sp and CBS5557 wtf genes. The wtf7, wtf11, wtf14, and wtf15 genes are likely not participating in this nonallelic gene conversion because the alleles of each of these genes group together in clades in all the trees (supplementary figs. 7 and 8, Supplementary Material online).

To further explore wtf nonallelic gene conversion, we divided the genes into segments (described below) and compared their evolutionary histories. We excluded wtf7, wtf11, wtf14, and wtf15 from these analyses because, as described above, they do not appear to have undergone nonallelic gene conversion. Most wtf genes have either five or six exons. For ease of comparison, we named the exons 1–6 based on the longest wtf genes (fig. 2). The five-exon genes are missing “exon 4,” but the remaining exons can be aligned to those of the six-exon genes (fig. 2). After excluding repetitive regions in exons 3 and 6 (discussed below), we generated alignments and trees (supplementary figs. 9–16 and supplementary data, Supplementary Material online) for each exon separately. We also generated alignments and trees for a conserved region (133–289 bp) upstream of the start codon and for intron 1, regions that in intact wtf meiotic drive genes presumably contain the promoters for the antidote and poison transcripts, respectively (Bowen et al. 2003; Nuckolls et al. 2017). The division between segments along intron/exon boundaries was arbitrary: There is no reason that gene conversion should show breakpoints at these boundaries.

Fig. 2.

Classification of wtf genes based on sequence, and evidence of nonallelic gene conversion. (A) Although they are quite diverged from each other, wtf7, wtf11, wtf14, and wtf15 were placed in a shared class because their sequences are unlike any functionally characterized genes. For the remaining genes, individual gene segments (each exon, intron 1, and the upstream region) from all genes were aligned and classified based on the major clades in maximum likelihood trees (see text for details). Each segment’s clades were color-coded for display purposes (i.e., black/white, black/green coding), and genes were grouped based on gene segment patterns. On the left, we display cartoons of gene structures for each group: boxes indicate exons, “M” indicates in-frame start codons, “/” indicates frameshift mutations, and “*” indicates in-frame stop codons. The repeat regions found in exons 3 and 6 are shown in brown. The names of genes in each class are listed on the right, with the gene illustrated in the cartoon underlined, and pseudogenes denoted with asterisks after gene names. The predicted function of each gene class is shown on the far right. The Class 1 genes are predicted to be meiotic drivers and the Class 2 genes are predicted to be suppressors of drive. Genes with experimentally verified phenotypes have their names outlined with purple boxes. (B) Pairwise four-gamete test for recombination (gene conversion) between all pairs of wtf gene segments for the genes in Classes 1 and 2. Orange boxes indicate that recombination likely occurred because all four segment combinations were observed. Purple boxes indicate that not all segment combinations were observed.

Strikingly, trees made from different gene segments do not show the same topology as one another (supplementary figs. 9–16 and supplementary data, Supplementary Material online). This is consistent with nonallelic gene conversion between genes. Although the short length of each segment means that bootstrap support values are generally low throughout the trees, each tree shows a broad subdivision between two main clades of wtf genes. For all but the shortest segment (exon 5), these two main clades are separated by a node with high bootstrap support. However, for different gene segments, the two main clades group different subsets of genes together. For example, the first two exons of Sp and Sk wtf9 are very different and are in different main clades. Starting within exon 3, however, the two alleles are much more similar and group in the same main clades (supplementary figs. 10, 11, and 17, Supplementary Material online). One possible explanation for this pattern is that their high similarity in exons 3–6 reflects their original syntenic relationship, but that relationship has been obscured in exons 1 and 2 by gene conversion from another wtf gene overwriting sequence in one or both of the isolates.

We used the broad clade divisions defined by the trees for each segment to generate a cartoon representation of this “patchwork” evolutionary history. In the cartoon, each color represents one of the two well-supported clades for each gene segment (fig. 2A). We used the color coding to guide grouping the full-length wtf genes as shown (fig. 2A). We then carried out four-gamete tests to look for evidence of gene conversion between the gene segments (Hudson and Kaplan 1985). Briefly, we considered each of the two major clades for each segment as alternate “alleles.” We then did pairwise comparisons of all gene segments to assay how many of the four possible allele (clade) combinations were observed. The four-gamete test is positive when all four combinations are present; although a simple accumulation of individual sequence changes could explain up to three combinations, the fourth combination can only be explained by recombination (fig. 2B). We found that 18/28 comparisons yielded a positive four-gamete test. Although we cannot reconstruct the full history and exact boundaries of gene conversion among wtf genes in each isolate, it is clear that the gene family has experienced rampant sequence exchange that could have facilitated rapid functional divergence of the gene family by bringing together new combinations of sequence variants.

Crossing over between syntenic loci likely also contributed to the shuffling of sequence blocks between wtf genes. For example, uneven crossover events between loci with multiple distinct wtf genes could generate novel hybrid wtf genes. Additionally, after a nonallelic gene conversion event has occurred at one locus, allelic recombination at that site can generate additional wtf diversity.

DNA Double-Strand Break Hotspots Are Enriched Near wtf Genes

The high level of nonallelic gene conversion among wtf genes is surprising because nonallelic homologous recombination (also known as ectopic recombination) is thought to be generally suppressed (Sasaki et al. 2010). This suppression is important because recombination events between nonallelic loci can result in genetic exchanges (crossovers) that cause deleterious chromosome rearrangements (Sasaki et al. 2010). The gene conversion among wtf genes we observe could be caused by increased frequency of nonallelic homologous recombination among these genes, or due to selection favoring the products of gene conversion events. The two explanations are not mutually exclusive and both could contribute. The latter idea is difficult to test, so we focused on the first idea. Gene conversion results from the repair of DNA double-strand breaks (DSBs). The initiating DSB could happen near or within the gene converted locus itself, or the break could happen in a different (donor) site that shares homology with the gene converted locus (e.g., another similar wtf gene) (Sasaki et al. 2010). DSBs arise at low frequencies in vegetative cells, but are dramatically induced (∼58 breaks per cell in Sp) during meiosis (Fowler et al. 2014). Due to their greater numbers and the fact that they have been mapped, we focused our analyses on meiotic DSBs.

Meiotic DSBs do not form randomly and are instead enriched in regions called “hotspots.” Sp has 602 DSB hotspots that are generally conserved between Sp and Sk, so it is reasonable to assume that the Sp hotspot map represents the S. pombe group (Fowler et al. 2014; Zanders et al. 2014). The wtf genes could have elevated gene conversion frequencies if all or a subset of them are near DSB hotspots. A factor known as the “gene conversion tract length” would affect how near to a break wtf genes must be in order to be involved in gene conversion events as either donors or recipients. This tract length specifies the amount of DNA that may be incorporated in the DSB repair event and potentially involved in gene conversion. The gene conversion tract length has only been coarsely measured in Sp for allelic meiotic recombination at one locus (ade6). The observed gene conversion tract lengths were generally <1 kb and occasionally >2 kb (Grimm et al. 1994). It is unknown whether gene conversion varies by locus, and whether tract length is different for allelic repair than for nonallelic recombination. Given this high level of uncertainty, we designated hotspots within 2.5 kb of a wtf gene as potential sources of initiating gene conversion events.

We looked for an association between the 602 previously defined Sp DSB hotspots and wtf loci by calculating the distance between each end of the wtf coding sequences and the nearest DSB hotspot (Fowler et al. 2014). There was no DSB hotspot 5′ to the first wtf gene on chromosome 3, so we only considered the hotspot 3′ of this coding sequence yielding 47 data points (two ends of each of the 24 loci containing wtf genes minus 1). We did the same comparison for all annotated coding sequences (McDowall et al. 2015). We found that DSB hotspots were significantly enriched within 2.5 kb of wtf loci as compared with all coding sequences. This enrichment was also significant if we only considered hotspots within 1 kb (table 1, G-test P < 0.01). Overall, we found that 14 of the 24 wtf loci are within 2.5 kb of one or more hotspots.

Table 1.

DSB Hotspots Are Enriched Near wtf Loci.

Distance to DSB	≤2,500 bp	>2,500 bp	≤1,000 bp	>1,000 bp
All CDS	1,764 (17.4%)	8,395 (82.6%)	1,103 (10.9%)	9,056 (89.1%)
wtf loci	18 (38.3%)	29 (61.7%)	13 (27.7%)	34 (72.3%)
	P < 0.001		P = 0.0015

The genes found within 1 kb of a hotspot include wtf1, wtf9, wtf12, wtf22, wtf25, and wtf27. These genes all show some evidence of participating in nonallelic gene conversion events (i.e., not all alleles of the genes are found on the same row in fig. 2A). wtf7, wtf11, wtf14, and wtf15 are also found within 1 kb of a hotspot, but as discussed above, these genes show no signs of nonallelic gene conversion. This lack of gene conversion is likely because these genes are highly diverged from each other and all other members of the wtf gene family (<55% DNA sequence identity).

These analyses suggest that close proximity of some genes to DSB hotspots likely contributes to the high levels of recombination within the wtf gene family. Interestingly, we observed no chromosome rearrangements with breakpoints in wtf genes in the four isolates with assembled wtf genes despite the hotspots and evidence of gene conversion. This suggests that nonallelic homologous recombination events are either preferentially repaired as gene conversions, as opposed to crossovers, or that isolates resulting from such crossovers have been removed by selection because they often generate chromosomes missing essential genes and/or with inviable duplications.

High Diversity of Intragenic Repeats in wtf Proteins

Insertions and deletions within genes can be an additional source of evolutionary novelty that can result from errors during DNA replication or from recombination (Verstrepen et al. 2005). We looked for evidence of such changes within wtf genes and found two repetitive regions that have frequently expanded and contracted during wtf evolution. The first of these is a region containing a well-conserved 33-bp repeat sequence near the beginning of exon 3 in most wtf genes (fig. 3A). Not all of the repeat units are complete. The first repeat is routinely truncated to 21 nucleotides, whereas the last repeat is truncated to between 14 and 26 nucleotides. The wtf genes have between 0 and 224 bp of sequence derived from this repeat (fig. 3B). A second dynamic repeat region occurs at the start of exon 6 in most genes (fig. 3C). This 21 bp repeat unit is less conserved and not all repeat units are complete. This repeat comprises between 0 and 84 bp of sequence in wtf genes (fig. 3D). Both repeat regions appear unstable in that wtf alleles that are otherwise similar can vary in the number of repeat units. For example, the Sp and Sk alleles of wtf4 are 93% identical outside of the repeats but have different copy numbers of both repeat segments.

Fig. 3.

Expansion and contraction of repeat sequences contribute to rapid wtf gene evolution. (A) DNA (top) and amino acid (bottom) sequence logos representing the repeat region found in exon 3. (B) The distribution of exon 3 repeat region size across all assembled wtf genes. The sizes are presented in base pairs instead of repeat units because the terminal repeats are not always full length. (C) DNA (top) and amino acid (bottom) sequence logos representing the exon 6 repeat region found in many wtf genes. (D) The distribution of exon 6 repeat sizes in all assembled wtf genes.

The changes in repeat numbers may be caused by replication slippage or imprecise repair of breaks via allelic homologous recombination (Lovett 2004; Verstrepen et al. 2005). They could also be caused by nonallelic gene conversion with a wtf containing a different number of repeats. These changes in repeat numbers may be functionally important because the repeats often overlap predicted transmembrane domains. The function of these repeats is currently unknown, but the number of repeats found in exon 6 can be important for conferring specificity between poison and antidote proteins (Bravo Núñez, Lange, et al. 2018).

wtf7, wtf11, and wtf15 Exhibit High Nonsynonymous Nucleotide Diversity

It is clear that gene duplication, deletion, gene conversion, and changes in repeat units have all acted to generate extensive diversity in the wtf gene family. We also wondered whether individual amino acid changes have also played a role in increasing wtf diversity. We were limited in the types of molecular evolutionary analyses we could conduct because S. pombe does not have sister species that are closely related enough to accurately determine the number of nonsynonymous and synonymous mutations that have occurred along each lineage. The closest relatives to S. pombe are S. octosporus and S. cryophilus, which each share only ∼66% amino acid identity with S. pombe (Rhind et al. 2011). We therefore analyzed nonsynonymous (π_N) and synonymous (π_s) nucleotide diversity among S. pombe isolates (Nei and Li 1979). Ratios of π_N/π_s ≪1 are consistent with a selective disadvantage for variants that change protein sequences. Ratios of π_N/π_s ≫1 are consistent with balancing selection to maintain allele polymorphism or relaxed selective constraint.

Fawcett et al. (2014) previously calculated π genome-wide average for S. pombe genes. They found that average π_N is 0.00096 whereas π_s is 0.006, giving a π_N/π_s ratio of 0.16. We calculated π_N/π_s in the four wtf genes that appear not to have participated in nonallelic gene conversion: wtf7, wtf11, wtf14, and wtf15 (these genes also lack internal repeats that, like gene conversion, could confound analyses). These genes were slightly too short to be included in the analyses of Fawcett et al. (2014), as they only considered genes with >200 nonsynonymous sites.

To calculate π_N/π_s, we first assembled the sequences of the four genes from 53 additional S. pombe isolates using published 100-bp paired-end read data (Jeffares et al. 2015). This was possible due to the large divergence between each of these genes and all other wtf genes. These genes all appear to be single copy in each genome as sequence coverage of these genes was similar to that of single-copy genes on chromosome 3. In many cases, the sequences of orthologous genes were identical between isolates. We found a total of 9 different alleles of wtf7, 14 alleles of wtf11, 8 alleles of wtf14, and 9 alleles of wtf15. We found 32, 25, 8, and 19 polymorphisms segregating in these genes, respectively (supplementary table 2, Supplementary Material online). Despite their shorter length, the numerous polymorphisms of at least three of these four genes make it seem unlikely that any unusual π_N/π_s ratios have occurred by chance. We calculated π_N and π_s using the DNA Sequence Polymorphism (DnaSP) software (Rozas et al. 2017).

We found that all four genes exhibited π_N/π_s ratios greater than the genome average of 0.16 (supplementary table 2, Supplementary Material online). For wtf7, we observed a π_N/π_s ratio of 5.7. The π_s value for wtf7 was almost 2-fold lower than the genome-wide average, but π_N was nearly 21-fold higher than the genome-wide average, indicating that the high ratio is driven largely by an excess of nonsynonymous polymorphisms. The high ratios for wtf11 and wtf15 (0.77 and 1.3) were also largely driven by an increased π_N, and thus seem likely to indicate balancing selection to maintain multiple protein variants. The high ratio for wtf14 (0.67), however, was largely driven by a low π_s.

Although these analyses are limited, they are consistent with the idea that there is not a single fitness optimum for wtf7, wtf11, and wtf15 within the S. pombe population. Rather, it may be that the fitness of these alleles depends on the context (genetic or environmental) in which they are found.

Some wtf Genes Show Characteristics of Poison-Antidote Systems, Whereas Others May Encode Antidote-Only Suppressors

In addition to facilitating visualization of gene conversion, we grouped the wtf genes into the three major classes shown in figure 2 to guide future functional analyses. Briefly, we dubbed the genes that contain in-frame start codons just upstream or near the beginning of exon 2 “Class 1” genes. These exon 2 ATG codons encode the start of Wtf poison protein isoforms and are shared by all of the previously known drivers (fig. 2) (Hu et al. 2017; Nuckolls et al. 2017; Bravo Núñez, Lange, et al. 2018). In addition, we used published long-read RNA sequences to confirm that all the Sp Class 1 genes have an alternate transcriptional start site within intron 1 (supplementary fig. 1, Supplementary Material online) that could encode poison transcript isoforms (Kuang et al. 2016). We therefore predict that Class 1 genes are intact meiotic drivers in which transcripts that include all exons encode antidote proteins, and transcripts which exclude exon 1 encode poison proteins.

Most other genes lack both a transcriptional isoform that excludes exon 1 and an in-frame ATG near the start of exon 2: We classify these as Class 2 genes. Due to similarity between the Class 2 genes and the antidote proteins produced by known drivers, we predict that these genes are suppressors of wtf drive genes and lack the poison isoform (Lopez Hernandez and Zanders 2018). Indeed, Class 2 contains the only known wtf drive suppressor, Sp wtf18-2 (Bravo Núñez, Lange, et al. 2018). Consistent with the predicted lack of a poison isoform, the Sk wtf5 and wtf6 genes do not cause drive in Sp (Nuckolls et al. 2017). Notably, we found no wtf genes that lack exon 1 that would encode only poison isoforms: It would have been very surprising to find such genes as we predict that they would encode “suicide” alleles unless they were very closely linked to a completely effective suppressor.

Class 3 consists of the remaining genes: wtf7, wtf11, wtf14, and wtf15. These genes are diverse and are grouped together only because they all have unknown functions. These genes do have an in-frame start codon near the start of exon 2, like known drivers. However, long-read RNA sequencing data showed no evidence of alternate transcripts for these genes beginning in intron 1 (supplementary fig. 1, Supplementary Material online), so we cannot make a clear prediction about whether they actually encode poison isoforms (Kuang et al. 2016). Furthermore, their increased sequence divergence from the rest of the wtf family could suggest divergent functions.

Discussion

Our study extends previous evolutionary analyses to demonstrate extremely dynamic evolution of the wtf gene family in multiple lineages of S. pombe (Hu et al. 2017). Although the genomes of different isolates of the S. pombe group are nearly identical (>99.5% DNA sequence identity) (Jeffares 2018), the number of wtf genes (including pseudogenes) found in the different isolates we studied is variable and the sequences of syntenic genes can be very diverged. This rapid evolution scenario is consistent with molecular arms race models that predict rapid evolution of meiotic drivers and their suppressors (McLaughlin and Malik 2017). It also supports the idea that rapid evolution could be a hallmark of these genes that could be used, along with other features like germline expression and lineage restriction, to facilitate their discovery.

Rapid Evolution of wtf Genes

We observe three mechanisms driving innovation in wtf gene sequences. First, as observed by Hu et al. (2017) who previously assayed Sp and CBS5557, we found pervasive nonallelic gene conversion affecting most wtf genes. We demonstrated that this nonallelic gene conversion was not restricted to a specific portion of the genes and included promoters. The forces driving this gene conversion will require further investigation. It is possible that the wtf genes inherently undergo gene conversion at a high rate due to some intrinsic property. For example, the close proximity of a subset of wtf loci to meiotic DSB hotspots could facilitate nonallelic recombination within the family. It is also possible that the novel wtf sequences generated by gene conversion are frequently advantageous. For example, novel variants could drive or suppress other drivers and thus be maintained by selection.

Second, we found that the number of units of repeat sequences within exons 3 and 6 varies greatly. Such repetitive sequences are known to be unstable and several wtf alleles that are otherwise very similar vary in repeat copy number. Although the function of these repeat regions is not clear, the repeats often overlap predicted transmembrane domains, and repeat number can be functionally important. For example, Sp wtf18 antidote alleles were only able to neutralize Sp wtf13 poison alleles that had the same number of exon 6 repeats (Bravo Núñez, Lange, et al. 2018). It is possible that the presence of these repeats in wtf genes is maintained, at least in part, due to their hypermutability. A high capacity to facilitate rapid gene diversification could be beneficial in genes involved in genetic conflicts.

The third contributor to rapid wtf gene evolution we observed in wtf7, wtf11, and wtf15 (but not wtf14) was an excess of nonsynonymous polymorphisms compared with genome averages (supplementary table 2, Supplementary Material online). Unfortunately, extensive gene conversion limited our analyses to four genes. The wtf7, wtf11, and wtf15 genes have no known functions and are all highly diverged from the experimentally characterized wtf genes and each other. The high level of amino acid polymorphism (high π_N/π_s) we observed in these genes, however, is consistent with the idea that the fitness of a given allele is context dependent. We have observed this experimentally for wtf meiotic drivers and suppressors. Specifically, we observed that the transmission frequency of wtf13 meiotic drive alleles into viable gametes was different in the presence of a compatible wtf18 drive suppressor allele (Bravo Núñez, Lange, et al. 2018). We therefore speculate that wtf7, wtf11, and wtf15 genes could also be meiotic drivers and/or act as modifiers of meiotic drive.

Consequences of Rapid Evolution

The rapid evolution of wtf genes has led each of the isolates we assayed here to contain a unique suite of wtf alleles. The consequences of this wtf diversity on S. pombe fitness are profound when the organism outcrosses. Although S. pombe can grow clonally, it mates and undergoes meiosis to form spores when starved. Although outcrossing occurs in the wild (Fawcett et al. 2014; Farlow et al. 2015; Jeffares et al. 2015; Tusso et al. 2018), determining how often S. pombe outcrosses is complicated because the genetic hallmarks of outcrossing are obscured by drive (Lopez Hernandez and Zanders 2018). When nonclonal isolates of S. pombe do mate to produce diploids, it is very likely there will be heterozygosity at one or more wtf loci. When these diploids undergo meiosis to generate gametes, wtf heterozygosity can lead to dramatic loss of fertility due to meiotic drive. This wtf heterozygosity is a major cause of the infertility observed in both Sp/Sk and Sp/CBS5557 heterozygous diploids and likely contributes to the generally low fertility of outcrossed (i.e., heterozygous) S. pombe diploids (Avelar et al. 2013; Hu et al. 2017; Jeffares et al. 2017; Nuckolls et al. 2017; Lopez Hernandez and Zanders 2018). Driving wtf genes are thus limiting the ability of S. pombe to enjoy all the fitness benefits of sexual reproduction, perhaps putting this species on a path to extinction.

Model for wtf Family Expansion on Chromosome 3

As noted by Bowen et al. (2003), the introns found in all wtf genes argue against gene family expansion by retrotransposition. These authors also suggested that some wtf genes coduplicated with their associated LTRs. In other words, wtf genes took advantage of the ubiquity of distributed transposon sequences to spread within the genome via nonallelic gene conversion to preexisting LTRs, a process known as segmental duplication (Dennis and Eichler 2016). As most wtf loci contain at least one wtf gene in the majority of the seven isolates analyzed here, we propose that the segmental duplications of wtf genes largely occurred prior to the divergence of these isolates and perhaps the S. pombe group.

The exploitation of distributed transposon sequences to facilitate the spread of meiotic drivers may not be specific to wtf genes. Transposon sequences are also found near Spok genes, a different family of single-gene killer meiotic drivers in the fungus Podospora anserina. Spok genes are found in as many as 11 copies per genome in some species of fungi (Grognet et al. 2014). Although it is unknown whether Spok genes are associated with transposons in other species, segmental duplication to preexisting transposon sequences may have also facilitated growth of the Spok gene family.

In addition to segmental duplication, tandem duplications (and deletions) also appear to have contributed to the expansion (and contraction) of the wtf gene family. Nonallelic recombination and slippage during DNA replication could be contributing to duplications and deletions. These events appear to have continued after the divergence of the isolates analyzed here because the number of wtf genes at any given locus varies (fig. 1C). For example, Hu et al. (2017) found that wtf27, wtf33, and wtf35 genes were all apparently lost in the Sp isolate due to recombination between two LTRs in the same orientation that flanked the genes.

Interestingly, like in the reference genome (Sp), the wtf genes in all the isolates assayed are highly enriched on what is chromosome 3 in Sp. Bowen et al. (2003) proposed that this enrichment in Sp could reflect a different evolutionary origin for chromosome 3, suggesting that it was introgressed from a diverged isolate with many wtf genes throughout the genome. If this is true, such an introgression event must have preceded the divergence of the isolates analyzed here (fig. 1). We have proposed an alternative hypothesis that the segmental duplication events spreading wtf genes occur genome-wide, but that the duplicates on chromosome 3 are preferentially maintained, because S. pombe can tolerate aneuploidy of only chromosome 3 and not the other chromosomes (Lopez Hernandez and Zanders 2018). This could be important because when two or more distinct wtf drivers compete (i.e., they are linked on opposite haplotypes), nearly all haploid gametes are expected to be destroyed. This was, in fact, observed when CBS5557 wtf9 and wtf33 were competed at an allelic locus in Sp (Hu et al. 2017). Heterozygous aneuploid or heterozygous diploid gametes, however, inherit both drivers and should be immune to both Wtf poison proteins. Sp (and presumably other isolates) only tolerates aneuploidy of chromosome 3, so that the fitness costs of competing drivers could be uniquely offset on chromosome 3 (Lopez Hernandez and Zanders 2018).

It is not clear why antidote-only wtf genes that act as suppressors of drive should specifically spread or be maintained on chromosome 3. Loci on this chromosome bear the greatest fitness cost of drivers. This is because loci on chromosome 3 are more likely to be linked in repulsion (i.e., on opposite haplotypes) to drivers that will destroy gametes that inherit them instead of the driver in heterozygous crosses. However, suppressors of drive are predicted to be favored at any unlinked locus because they increase fertility (Crow 1991). It is therefore surprising that antidote-only wtf genes have not spread throughout the genome. We favor a model in which the frequent gene conversion among wtf genes likely leads to toggling between driving and suppressing wtf genes at any given locus. For example, we predict that the wtf18 gene in FY29033 is a driver, but the wtf18 alleles in Sp are suppressors of drive (fig. 2A) (Bravo Núñez, Lange, et al. 2018). This toggling could lead to selective maintenance of wtf suppressor loci on chromosome 3 due to the mechanism described above for drivers.

Lessons for the Design of Gene Drives

The themes we describe for wtf gene evolution may be instructive for designing gene drives. Gene drives are engineered drive systems used to control natural populations. The general idea is that natural or artificial drivers can be used to spread traits (e.g., disease resistance) throughout a population or to eliminate a population, for example, by generating extreme sex ratio imbalances (Burt 2014). Analyses of natural drivers and drive suppressors, such as those of the wtf family, may prove useful for predicting how engineered gene drives (particularly gamete killers) may evolve if released in natural populations. For example, compact gene drives may duplicate to novel loci within a genome. This risk may be particularly high if the gene drives are integrated next to transposons or other dispersed repetitive elements.

Materials and Methods

Yeast Isolates and Whole-Genome Sequencing

The Sp (SZY643) and Sk (SZY661) strains are described by Nuckolls and Bravo Núñez et al. (Nuckolls et al. 2017). We obtained all other isolates from the National BioResource Center, Japan. We prepared genomic DNA using QIAGEN Genomic-tips (catalog number 10262 and 10243) using the QIAGEN DNA buffer set (catalog number 19060). We followed the kit protocol except that we extended the lyticase treatment to 18 h and the RNase A/Proteinase K treatment to 5 h. The Stowers Institute Molecular Biology core facility prepared the sequencing libraries using the Illumina Nextera Mate-Pair Sample Prep Kit (catalog number FC-132-1001). In total, 5- to 8-kb fragments were selected using a BluePippin machine (Sage Science). The libraries were sequenced (150-bp paired-end reads) on an Illumina MiSeq using the MiSeq Reagent Kit v2 (300 cycle) (catalog number MS-102-2002). Sequence data are available in SRA (accession number PRJNA476416).

Assaying wtf Gene Numbers

We used Geneious version 10.0.7 (https://www.geneious.com; last accessed March 18, 2019) for all sequence analyses, unless otherwise stated, using the “map to reference function” for all short-read alignments. To find wtf loci in Sk, we identified read pairs from the mate-pair library in which one (or both) reads aligned to a library containing the 25 Sp wtf genes (using the default “medium-low sensitivity” aligner setting) (Steps 1 and 2 in fig. 1A). The “medium” or “medium-low sensitivity” settings are the suggested settings for next generation sequencing reads. Geneious does not recommend more sensitive settings because “using higher sensitivities is unlikely to improve results and will probably take too long and is usually unnecessary if you have sufficient coverage.” The “medium-low sensitivity” allows 20% mismatched bases, whereas the “medium sensitivity” allows 30%. For the other genomes, we also included the Sk wtf genes as reference sequences. From those wtf-matching read pairs, we then isolated any “partner” reads that did not align to wtf genes by again mapping reads to our reference set of wtf genes (“medium sensitivity” setting), this time saving only the individual reads that failed to align to any wtf gene (fig. 1A, Step 3). We then took these “wtf-partner” reads and aligned them to the Sp reference genome (“medium sensitivity” setting) (fig. 1A, Step 4). This generated pileups of reads flanking wtf loci. We inspected the pileups manually to infer the number of wtf genes at each locus based on the width and pattern of the pileups, as described in the text. For Sk and FY29033, these inferences were confirmed or corrected by assembling the wtf loci (see below).

Assembling wtf Genes

To assemble the wtf gene(s) at a given locus, we used flanking unique sequences as “bait” to identify all read pairs in the region, and then performed individual de novo assemblies for each wtf locus separately. This approach should avoid misassemblies that can occur in whole-genome assemblies at repetitive regions like wtf loci. In more detail, we first manually identified coordinates of the sequence pileups described above, adding ∼2-kb flanking sequence (fig. 1D, orange bars under the pileups). We excluded LTR sequences and other repetitive DNA sequences from these regions and denote them “orange regions.” We identified all mate-pairs that align to these orange regions (“medium-low sensitivity” setting) (fig. 1D, Step 1). We then filtered those reads so that we retained only candidate wtf locus reads, and not those from flanking regions. To do this, we defined two additional reference regions flanking the wtf locus (“green regions”) that extend the orange region to within ∼500 bp of the wtf locus and by ∼15 kb in the other direction (fig. 1D, green bars under the pileups). We then aligned the read pairs defined in Step 1 to the green regions (“medium sensitivity” setting), retaining only individual reads that failed to align to the green regions; these reads represent candidate wtf locus reads (fig. 1D, Steps 2 and 3). Finally, we assembled these candidate wtf reads using the Geneious “de novo assemble” function (default “medium sensitivity” setting) (fig. 1D, Step 4). We obtained 1–4 contigs in most of these assemblies that we were able to stitch together manually using known wtf gene orientations and sequence overlaps.

To validate our approach, we compared the assembled genes to Sanger sequencing of 22 cloned wtf genes (Nuckolls et al. 2017; Bravo Núñez, Lange, et al. 2018). For 21 genes, the sequences matched. For the Sk wtf2 locus, our assemblies detected that our published Sanger sequence of the locus was incorrect due to template switching during polymerase chain reaction (Nuckolls et al. 2017). The frequency of bases that did not match the consensus in the assembled wtf loci was similar to that observed in the regions flanking the wtf loci, suggesting that the assemblies were not collapsing nonidentical repeated sequences. In addition, we failed to identify polymorphic sites within the assemblies using the default settings in the Geneious SNP caller. Gene sequences and annotations are available in GenBank (accession numbers MH837193–MH837230 and MH837431–MH837459).

DNA Sequence Alignments, Tree Construction, and Sequence Logos

We aligned DNA sequences of the full-length wtf genes (or of wtf gene segments) in Geneious using the Geneious aligner with the “global alignment without free end gaps” setting. All alignments are provided as supplementary data, Supplementary Material online. We then generated trees in Geneious using the PHYML plugin (version 2.2.3) with the default settings (HKY85 substitution model, set to optimize tree topology branch length and substitution rate, NNI topology search) with 100 bootstraps. For exons 3 and 6, we aligned only sequences downstream of the repetitive regions found near the beginning of those exons (fig. 3). For wtf family-wide gene conversion analysis, we used ran a command-line version of the GARD algorithm (using the general discrete model of site-to-site rate variation with three rate classes) (Kosakovsky Pond et al. 2006). We used Weblogo3 (http://weblogo.threeplusone.com; last accessed March 18, 2019) to generate sequence logos of the repetitive regions (Crooks et al. 2004).

Analysis of Nucleotide Diversity

We mapped paired-end reads from 54 additional S. pombe isolates to the Sp reference genome to generate consensus sequences of wtf7, wtf11, wtf14, and wtf15 in the additional isolates (Jeffares et al. 2015). The assembled sequences of these genes are available in GenBank (accession numbers MH837181–MH837192 and MH837231–MH837430). We then codon-aligned a total of 57 sequences for each gene. We used the GARD algorithm (via the DataMonkey website www.datamonkey.com; last accessed March 18, 2019) to screen each alignment for evidence of gene conversion (using the general discrete model of site-to-site rate variation with three rate classes) (Kosakovsky Pond et al. 2006). GARD did not find evidence for gene conversion in the wtf7, wtf11, wtf14, or wtf15 alignments. We calculated π_N/π_s using DnaSP (Rozas et al. 2017).

Supplementary Material

Supplementary data are available at Molecular Biology and Evolution online.