Structural variants (SVs) are a powerful source of genetic variation that fuel the evolution of genomes and species (Gorkovskiy and Verstrepen, 2021). Structural variation is a term that encompasses a broad variety of large-scale sequence alterations that can have profound consequences for organismal phenotypes and genome evolution. For example, aneuploidies can underlie the emergence of antifungal resistance in fungal pathogens (Selmecki et al., 2006) and drive tumorigenesis in mammalian cells (Shoshani et al., 2021; Trakala et al., 2021). Polyploidization is frequently linked to speciation and adaptation, notably in plants (Otto and Whitton, 2000). Mitotic recombination can yield loss of heterozygosity (LOH) events that drive adaptation in heterozygous hybrid genomes (Smukowski Heil et al., 2017), but also the somatic fixation of oncogene mutations (Cavenee et al., 1983). Repeated sequences are known to play a leading role in the generation of SVs (Lower et al., 2019; Todd et al., 2019). Notably, transposable elements (TEs) are sequences capable of semi-autonomous replication, producing families of repeats spread within genomes (Bourque et al., 2018). Because of their replicative nature, TEs are major generators of SVs: not only do they create interspersed duplications of themselves, but they can also trigger genome rearrangements through ectopic recombination (Dunham et al., 2002; Mieczkowski et al., 2006; Startek et al., 2015). While TEs can cause SVs, various types of SVs can also affect the genomic landscape of TEs by producing gains and losses of individual copies (Figure 1).

Figure 1

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TEs are inherent components of virtually all eukaryotic genomes and owe their evolutionary success to their own replicative ability. However, this selfish propagation conflicts with the fitness of the host and can have profound deleterious impacts (Bucheton et al., 1984; Pasyukova et al., 2004; Wilke and Adams, 1992). The abundance of TEs per genome (or TE load) is an important population genetic parameter as it is expected to correlate with the fitness cost for the host. TE load in a population is most often modeled as a quantity reflecting the balance between the gain of novel copies by transposition and the decrease in copies by loss (e.g., by excision or loss of function through mutation) and purifying selection against insertion alleles (Charlesworth et al., 1994; Charlesworth and Langley, 1989). However, TE load is not only defined by the copy number (CN) of TEs in the genome, but also where TE insertions fall and, consequently, how they interact with neighboring host sequences. The multidimensional nature of TE load makes its full characterization a complex task. Recent advances in long-read sequencing make it increasingly possible to decompose TE load by resolving the genomic context specific to individual insertion loci (Oggenfuss and Croll, 2023; Rech et al., 2022). Additionally, accurate genome resolution opens unprecedented opportunities for a comprehensive assessment of the role of SVs in shaping TE load. As such, ongoing technological and computational advances in genome inference, including long-read sequencing, will certainly be key to getting a detailed understanding of the dynamics of TEs and the underpinning evolutionary forces.

One long-standing hypothesis postulates that hybridization can cause a genomic shock that triggers TE reactivation through increased mobilization (McClintock, 1984), with the resulting elevated load having major potential implications for speciation and hybrid evolution. This hypothesis was tested in many systems, some of which provided evidence for reactivation (Dion-Côté et al., 2014; O’Neill et al., 1998; Ungerer et al., 2006). In some cases, the prevailing mechanism of TE repression by the host explained the molecular basis of reactivation. For example, in Drosophila melanogaster, the absence of maternally inherited piRNAs is responsible for the failed epigenetic repression of the I and P elements, leading to the expression of a hybrid dysgenesis syndrome (Brennecke et al., 2008). However, this level of mechanistic understanding is not often matched in other systems. The reactivation hypothesis remained untested in fungi until recent works in budding yeasts of the genus Saccharomyces and in the fission yeast Schizosaccharomyces pombe (Drouin et al., 2021; Hénault et al., 2020; Smukowski Heil et al., 2021; Tusso et al., 2022).

In the model species Saccharomyces cerevisiae, the only TEs found are five families of long terminal repeat (LTR) retrotransposons named Ty1-Ty5 (Kim et al., 1998). Its undomesticated sister species Saccharomyces paradoxus comprises the related families Ty1, Ty3, and Ty5 (Yue et al., 2017), in addition to Tsu4, which was horizontally transferred from Saccharomyces uvarum (Bergman, 2018). Saccharomyces species lack most of the defense lines typical of other eukaryotes, like DNA methylation and RNA interference (Bewick et al., 2019; Drinnenberg et al., 2009). The best-known regulation mechanism in yeast is termed copy number control (CNC) and was characterized in the Ty1 family of S. cerevisiae. This mechanism is a potent copy-number-dependent negative feedback loop by which increasing the CN of Ty1 elements strengthens their repression (Czaja et al., 2020; Garfinkel et al., 2003; Saha et al., 2015). CNC was shown to regulate the mobilization of one subfamily of Ty1 elements in S. cerevisiae, but whether CNC is active in other species remains unknown. Additionally, systematic gene deletion studies revealed that multiple cellular functions can restrict or promote Ty1 transposition (reviewed in Curcio et al., 2015), setting the stage for potential genetic incompatibilities to disrupt the regulation of retrotransposition.

We previously tested the reactivation hypothesis in a wide diversity of Saccharomyces hybrids by performing a large-scale evolution experiment by mutation accumulation (MA) to investigate the near-neutral evolution of TEs in hybrid genomes (Hénault et al., 2020). We generated a collection of MA lines comprising a set of 11 hybrid genotypes created by crossing wild strains from North American populations of S. paradoxus and S. cerevisiae (Charron et al., 2014a; Kuehne et al., 2007; Leducq et al., 2014; Leducq et al., 2016) that are separated by a range of evolutionary divergence and by replicating each cross many dozen times independently (Charron et al., 2019; Hénault et al., 2020). The resulting collection was evolved by subjecting each line to periodical extreme bottlenecks by streaking for single colonies on rich medium for ~770 mitotic generations, thus amplifying the power of random genetic drift. We previously sequenced a subset of these MA lines with short reads and measured the change in Ty load after evolution by producing aggregate estimates of relative abundance for each family and found no support for the TE reactivation hypothesis (Hénault et al., 2020). However, the use of short reads alone precluded the detailed resolution of hybrid genomes and the decomposition of their Ty load. For instance, different SVs (including de novo transposition) may have affected Ty CN in opposite directions. Under this scenario, measuring Ty family abundance would yield no significant net change, and the dissection of the underlying SVs using short reads could often be challenging.

While producing collapsed assemblies for haploid or predominantly homozygous genomes is relatively straightforward, complex heterozygous genomes are much more difficult to characterize at the haplotype level. Recent studies demonstrated the feasibility of the approach (Heasley and Argueso, 2022; O’Donnell et al., 2023), but the task of resolving hybrid genomes across a wide range of parental divergence under a unified framework remains challenging. Here, we employ a phasing and assembly approach using long reads to resolve hybrid MA lines genomes. We find that de novo transposition events play a minor role in shaping the Ty load in comparison to other SV types. Since the number of de novo insertions gained during MA is low, we further dissect transposition rate variation in S. paradoxus hybrids at a higher resolution with in vivo transposition rate measurements. We identify many aspects of hybrid genotype specificity that shape transposition rates, including initial Ty load, Ty sequence variation and mitochondrial DNA (mtDNA) inheritance.

The 11 MA crosses (Charron et al., 2019; Hénault et al., 2020) comprise five crosses within S. paradoxus populations (CC, within SpC; BB, within SpB), four crosses between S. paradoxus populations (BC, SpB×SpC; BA, SpB×SpA), and two interspecific crosses (BSc, S. paradoxus SpB×S. cerevisiae) (Figure 2A). We previously selected 127 independently evolved MA lines (10–12 per cross) and sequenced their genome with Oxford Nanopore long reads (Hénault et al., 2022). To produce an accurate representation of the two distinct subgenomes of each evolved MA line, we employed a strategy consisting of (1) sorting long-read libraries using variants that discriminate the two parental genomes of each cross and (2) performing separate de novo assembly on the sorted reads, similar to trio binning approaches (Koren et al., 2018). The proportion of sequenced bases successfully classified into subgenomes varied substantially depending on the cross (Figure 2B). CC crosses had the lowest classification rate (median: 70.8%) due to the scarcity of variants that discriminate SpC genomes from each other. BB crosses had a median classification rate of 84.5%, while the remainder (BC, BA, and BSc) reached a higher rate (median: 93.7%). Unclassified reads tended to be shorter than classified reads, but the difference was narrower for libraries with lower classification success (Figure 2—figure supplement 1). The scarcity of discriminating variants in low-divergence crosses is likely causing the classification of increasingly longer reads to fail. However, unclassified reads showed no overrepresentation in specific genomic regions (Figure 2—figure supplement 2) and their exclusion is thus unlikely to bias the resulting assemblies. The ratio of classified bases per subgenome was consistent with the corresponding ploidy levels: triploid BC lines had two copies of the SpC subgenome, while tetraploid lines had both SpC subgenomes duplicated (Charron et al., 2019; Marsit et al., 2021; Figure 2B).

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Despite the variation in classification rates, the classified reads yielded highly contiguous subgenome-level assemblies, with a median of 22 contigs, a median N50 of 783 kb, and 95% of the assemblies having N50 values larger than 200 kb (Figure 2C, Supplementary file 1a). Only six assemblies were more fragmented (>100 contigs) and corresponded to the sorted libraries with the least sequenced bases (Figure 2—figure supplement 3A), which are expected to complicate the assembly. Thus, our approach yielded chromosome-level phased representations of the two subgenomes of each MA line.

We then integrated various data to infer the state of all loci containing Ty elements in the MA lines subgenomes in relation to the corresponding parental genome. First, we produced whole-genome assemblies of the 13 haploid parental strains from long reads. We annotated their Ty elements and used whole-genome alignments to determine the orthology of Ty loci between the parental strains of each cross (Figure 3—figure supplement 1). Only 10 pairs of loci were found to be orthologous in four of the lowest divergence crosses (CC1: 1; CC2: 5; CC3: 3; BB1: 1). We also annotated the Ty elements in the MA lines subgenome assemblies and defined the orthology between Ty loci by aligning subgenome assemblies against the corresponding parental assembly. We also computed the depth of coverage of sorted long-read libraries mapped on the corresponding parental genome assemblies to yield estimates of CN per genomic window. Finally, we used the ploidy level of each MA line subgenome as previously measured by flow cytometry and short-read sequencing (Charron et al., 2019; Marsit et al., 2021).

We integrated this data to classify individual Ty loci by type of SV using a hierarchy of binary rules (Figure 3—figure supplement 2). We note that the current methodology does not aim at providing an exhaustive quantification of all SVs in the MA lines, as previously done for some SV types (Marsit et al., 2021), but focuses solely on loci containing Ty elements. 89.8% of Ty loci were successfully classified, with the handful of low-contiguity assemblies contributing disproportionately to classification failure (Figure 2—figure supplement 3B). From the successfully classified loci, 79.0% had no change compared to the parental genomes. The remainder had an uneven distribution of SV types (Figure 3A and B). The largest class was polyploidy since many CC and BC lines have become tetraploid or triploid (Charron et al., 2019; Marsit et al., 2021). Triploidization was exclusive to six BC lines and was likely caused by the diploidization of SpC parental cells (creating mating-competent pseudohaploids) prior to hybridization (Charron et al., 2019), meaning that triploidization occurred before MA strictly speaking. LOH and aneuploidy also accounted for large fractions of Ty loci affected by SVs. Notably, there was a substantial impact of LOH in the low-divergence CC crosses. Ty1 is the most abundant family in SpC genomes, and despite a relatively constant load, Ty1 copies mostly occupy non-orthologous loci (Figure 3—figure supplement 1A), providing the substrate for LOH to affect the landscape of Ty1 in CC crosses. While LOH had a bidirectional effect on Ty CNs, causing gains and losses in approximately equal proportions, aneuploidies had a strong bias toward gains (Figure 3C).

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At the level of individual MA lines, the post-evolution Ty CNs previously quantified using relative depth of coverage of short-read libraries (Hénault et al., 2020) and the net CN changes determined here were globally correlated (mixed linear model with random intercepts for MA cross × Ty family combinations, p-value = 3.01 × 10^–35). In many individual cases, significant positive correlations were observed (Figure 3—figure supplement 3), indicating that our current analyses recapitulate previous results. The distributions of net Ty CN change per MA line showed that most crosses had significant gains (Figure 3D), suggesting that Ty load can often increase as a result of random genetic drift. Some (but not all) of these crosses also exhibited significant increases in genome size after evolution (Figure 3—figure supplement 4A). The net Ty CN changes per MA line subgenome were globally correlated to the corresponding changes in subgenome size (Figure 3E). Even after excluding polyploid lines (which have the largest changes in both Ty CN and genome size), we found a significant relationship between the two variables (mixed linear model with random intercepts and slopes for MA crosses, p-value = 3.71 × 10^–9; Figure 3—figure supplement 4B), indicating that SVs affecting large portions of the genome have a substantial impact on the Ty landscape.

We identified 23 candidate de novo Ty insertions as loci occupied by one full-length Ty element that was absent from the corresponding parental genome or any other MA line. From these candidates, we used strict filtering criteria to narrow down confident loci compatible with retrotransposition. First, we required the presence of characteristic target site duplications (TSDs) of 5–6 nt (Figure 4—figure supplement 1), consistent with the staggered dsDNA cut catalyzed by the Ty1 integrase (Wilhelm et al., 2005). We further confirmed the uniqueness of Ty insertion junction sequences by inspecting whole-chromosome alignments comprising the MA line subgenomes and all parental genomes. We also ensured that all de novo TSD sequences were unique and absent from the parental full-length Ty loci. Eighteen de novo loci were compatible with all these criteria (Figure 4). The quantitative impact of de novo insertions on Ty load evolution is thus minor in comparison to other SV types, with 0.14 insertions per line on average. All de novo loci belonged to the Ty1 family of S. paradoxus. Of 11 crosses, 8 exhibited de novo Ty1 retrotransposition events, half of them being found in the CC crosses. Eight insertions were found at loci with parental Ty1 elements (full-length or solo-LTR) in close proximity, highlighting the importance of the de novo assembly approach to accurately resolve these complex loci. Two retrotransposition events in BC2 lines inserted into the UWOPS-91-202 SpB subgenome that is natively devoid of full-length elements, effectively re-colonizing a genome in which all Ty families had gone extinct.

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We asked whether the de novo Ty1 insertions were associated with specific subfamilies from the parental genomes. We built a maximum likelihood phylogenetic tree comprising the de novo insertions and all parental Ty1 and Ty2 full-length sequences (Figure 4—figure supplement 2). This analysis revealed that de novo copies originated from major Ty1 clades populating SpC, SpB, and SpA genomes, indicating that Ty1 is globally active in S. paradoxus populations. However, this tree had generally poor branch support values from the bootstrap analysis. A phylogenetic network built from the same dataset showed substantial signal for reticulate evolution between Ty1 families from S. cerevisiae and S. paradoxus SpA (Figure 4—figure supplement 3), in agreement with previous studies that showed interspecific horizontal transfers (Bleykasten-Grosshans et al., 2021; Czaja et al., 2020).

Sequence resolution of de novo loci also enabled the characterization of insertions that disrupted parental genomic features. One de novo Ty1 element inserted into the left LTR of a parental full-length Ty1 copy in the MSH-587-1 subgenome of the MA line J27 (CC1 cross, Figure 4—figure supplement 4A), resulting in a nested insertion. Additionally, in a single case, a de novo Ty1 element inserted 37 bp downstream of the start codon of the host gene AIM29 in the LL2011_009 subgenome of the MA line L41 (CC3 cross, Figure 4—figure supplement 4B), introducing an early stop codon that yields a presumably non-functional peptide of 36 amino acid residues in length.

The frequency of de novo Ty1 retrotransposition events per cross tends to support our previous conclusion that TE mobilization does not scale positively with parental divergence in Saccharomyces hybrids (Hénault et al., 2020). However, the number of de novo Ty1 insertions characterized by MA is low (at most four per cross), making it difficult to confidently test for differences in transposition rate between hybrid genotypes. In addition, multiple aspects of natural genetic variation were shown to impact the mobilization of Ty1 (Czaja et al., 2020; Smukowski Heil et al., 2021), suggesting that some of these aspects could also contribute to modulate transposition rate in S. paradoxus hybrids. Many of these aspects were not controlled for in the design of the MA experiment, which was primarily focused on generating increasingly high levels of parental divergence. Thus, it would be challenging to disentangle the effect of these factors with the limited sample sizes of our MA experiment.

We investigated how multiple aspects of genotype specificity impact transposition rate in vivo in S. paradoxus hybrids by adapting an existing genetic assay that measures the retrotransposition rate of a single tester Ty1 element from S. cerevisiae (Curcio and Garfinkel, 1991). This tool was notably used by Smukowski Heil et al., 2021 to show that retrotransposition rates are not elevated in S. cerevisiae × S. uvarum hybrids. The assay relies on an antisense artificial intron (AI) that interrupts the coding sequence of a copy of the HIS3 gene. This HIS3AI construct is itself inserted in antisense orientation in the 3′ non-coding portion of the internal sequence of a plasmid-borne Ty1 element. The correct splicing of the AI is only possible from the Ty1 mRNA, and the HIS3 ORF can be restored once the spliced mRNA is reverse transcribed and integrated in the genome. When this construct is expressed in a strain auxotrophic for histidine, selection for histidine prototrophy in a fluctuation assay (Luria and Delbrück, 1943) enables the estimation of the transposition rate of the tester element. We adapted this assay to measure retrotransposition rates in multiple S. paradoxus backgrounds, and with two Ty1 sequence variants sampled from SpB and SpC genomes as tester elements (Figure 5—figure supplement 1, Supplementary file 1b–f).

The mechanism of negative copy-number-dependent self-regulation of retrotransposition (CNC) was characterized in the Ty1 family of S. cerevisiae (Garfinkel et al., 2016). This mechanism relies on the expression of an N-truncated variant of the Ty1 capsid/nucleocapsid Gag protein (p22) from two downstream alternative start codons (Nishida et al., 2015; Saha et al., 2015). The expression of p22 scales up with the CN of Ty1 elements that encode it (Tucker et al., 2015), which gradually interferes with the assembly of the viral-like particles essential for Ty1 replication (Cottee et al., 2021; Saha et al., 2015). Thus, CNC yields a steep negative relationship between the retrotransposition rate measured with a tester element and the number of Ty1 copies in the genome (Garfinkel et al., 2003; Tucker et al., 2015). We previously observed a negative relationship between Ty1 load change after MA and the initial Ty1 load, and hypothesized that CNC-like regulation could be at play (Hénault et al., 2020). While the scarcity of Ty1 mobilization in MA lines now refutes this hypothesis, transposition rates in S. paradoxus could still be impacted by the initial load. We measured Ty1 transposition rates in eight different haploid backgrounds of S. paradoxus (one SpA, four SpB, and three SpC; Supplementary file 1e and g) that exhibit a wide variation in chromosomal Ty1 CN. The transposition rate in SpB backgrounds had a strong negative relationship with Ty1 CN (Figure 5). In an SpB background with a single Ty1 copy (UWOPS-79-140, estimated CN: 1.2), the rate reached 1.73 × 10^–6 cell^–1 gen^–1. In comparison, an SpB background harboring a single additional Ty1 copy (MSH-604, estimated CN: 2.2) had a transposition rate over 15 times lower (9.77 × 10^–8 cell^–1 gen^–1). In SpC backgrounds, which all have higher Ty1 CNs (20.7–32.4), the transposition rate was below 1 × 10^–8 cell^–1 gen^–1.

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In each background, we tested two sequence variants of the tester Ty1 element: one derived from a SpB genome (MSH-604) and one derived from a SpC genome (LL2011_012). We found that the SpC variant generally yielded lower transposition rates (Figure 6A). In the UWOPS-79-140 SpB background, the transposition rate of the SpB variant (1.73 × 10^–6 cell^–1 gen^–1) was significantly higher than the SpC variant (5.34 × 10^–7 cell^–1 gen^–1), with a difference of over threefold. We crossed three of the haploid backgrounds, one from each population (SpB: UWOPS-79-140; SpC: LL2011_012; and SpA: YPS744), to generate all the possible homozygous and heterozygous diploid backgrounds (Supplementary file 1f), and measured the transposition rate of both Ty1 variant in each background (Figure 6B, Supplementary file 1h). This experiment confirmed that transposition rates are lower with the SpC Ty1 variant, with the difference being significant in three different backgrounds (SpA × SpB, SpB × SpB, and SpB × SpC). Additionally, these measurements showed that transposition rates in heterozygous hybrids are intermediate between their respective homozygous parental backgrounds (Figure 6—figure supplement 1), providing further evidence for the robustness of Ty1 regulation in hybrids (Hénault et al., 2020; Smukowski Heil et al., 2021).

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Like many other fungal species, mtDNA inheritance in Saccharomyces yeasts is biparental (Wilson and Xu, 2012), yielding a transient state of heteroplasmy that is resolved with the fixation of a single mtDNA haplotype by vegetative segregation (Birky, 2001). A recent study measured the rate of Ty1 transposition in S. cerevisiae × S. uvarum hybrids while controlling mtDNA inheritance and showed an effect of the parental mtDNA haplotype (Smukowski Heil et al., 2021). The transposition rate linked with the S. uvarum mtDNA haplotype was approximately one order of magnitude lower than the S. cerevisiae mtDNA haplotype. We investigated whether mtDNA inheritance also affected transposition rates at the intraspecific level in hybrids between S. paradoxus natural populations. For most of the diploid backgrounds described above, we tested reciprocal crosses with either parental haploid background being depleted of its mtDNA (ρ⁰). As expected, the crosses with reciprocal ρ⁰ parents in the homozygous SpC background yielded no significant difference in transposition rate (Figure 7). However, many significant differences were observed in heterozygous backgrounds. In both SpA × SpC and SpB × SpC hybrids with the SpB Ty1 tester variant, the transposition rate was significantly lower when the mtDNA was inherited from the SpC (MATα) parent (Figure 7). Although not statistically significant, the same trend was observed for the SpC Ty1 tester variant in the SpB × SpC hybrid. These results indicate a moderate but consistent effect of the SpC mtDNA in lowering the transposition rate of Ty1.

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The repetitive nature of TEs makes the full sequence-level resolution of their genomic distribution notoriously difficult (Goerner-Potvin and Bourque, 2018). As such, studies investigating the TE reactivation hypothesis in hybrids have used a variety of methods for quantifying the activity of TEs at different levels, for instance, in terms of genomic abundance (O’Neill et al., 1998; Ungerer et al., 2006) or expression level (Dion-Côté et al., 2014; Renaut et al., 2014; Drouin et al., 2021). Not all methods enable the decomposition of TE load at the level of sequence-resolved insertion loci (Tusso et al., 2022), which is an especially challenging task in heterozygous genomes. A full mechanistic understanding of how TE load evolves in hybrids requires the ability to characterize how TE copies are gained and lost, through the action of de novo transposition and other types of SVs.

Here, we used long-read sequencing to yield accurate representations of the genomes of yeast hybrids experimentally evolved under a near-neutral MA regime and investigated the mechanisms responsible for changes in the genomic landscape of Ty elements. We found that SV types like polyploidy, LOH, and aneuploidy had the largest impact on Ty CN variation. While de novo Ty1 insertions compatible with retrotransposition were found in most of the MA crosses, their contribution to CN changes was very modest compared to the transposition-unrelated SVs. The scarcity of de novo insertions in the MA lines provided insufficient resolution to dissect subtle transposition rate variation between hybrid genotypes. We thus complemented the genomic analysis of MA lines with an in vivo assay selecting for Ty1 mobilization and measured transposition rate in multiple genetic backgrounds of S. paradoxus. We found that initial Ty1 load, Ty1 sequence variation, and mtDNA inheritance modulate transposition rate. In addition, our results confirmed that unlike other yeasts (Tusso et al., 2022), Ty mobilization in Saccharomyces is not triggered by hybridization (Hénault et al., 2020; Smukowski Heil et al., 2021) and this, along a broad gradient of parental evolutionary divergence.

Our experiments illustrate how under weakened natural selection efficiency TE load can increase in hybrid genomes by the action of transposition-unrelated SVs. This offers a nuanced perspective on the classical interpretation of the transposition-selection balance model (Charlesworth et al., 1994; Charlesworth and Langley, 1989), in which increased TE load would be predominantly driven by the relaxation of purifying selection against TE insertions generated by de novo transposition. Our results suggest that SVs arising in the context of hybridization can act as a significant source of TE insertion polymorphisms which natural selection can purge more or less efficiently, depending on the population genetic context. This is closely related to the idea that sexual reproduction could favor the spread of TE families, contributing to their evolutionary success (Hickey, 1982; Zeyl et al., 1996). Since the insertion polymorphisms that contribute to increase TE load mostly originate from standing genetic variation, they could be less deleterious and thus harder for natural selection to purge efficiently.

Our in vivo transposition assays revealed sharp contrasts in the dynamics of Ty1 mobilization. Multiple aspects of the SpC genotypes are associated with lower mobilization rates. Ty1 transposition occurs at a very low rate in SpC backgrounds compared to other S. paradoxus populations, and using a SpC Ty1 sequence variant as a tester element showed a much lower transposition rate compared to a SpB Ty1 variant in matched backgrounds. Moreover, reciprocal crosses with controlled mtDNA inheritance suggested an effect of the SpC mtDNA haplotype in reducing Ty1 transposition rates. Despite the clear evidence for lower mobilization in vivo, half of the de novo transposition events in the MA lines genomes happened in intra-lineage SpC crosses. The SpC population harbors a low level of genetic diversity compared to SpB (Eberlein et al., 2019; Leducq et al., 2016) and has a fairly constant load of Ty1 (Hénault et al., 2020). Our new whole-genome assemblies revealed that this apparent CN stability hides major variation in terms of Ty1 insertion position, which explains how LOH events can drastically alter the Ty1 landscape in CC crosses.

We note that while the CC crosses tend to have the lowest retrotransposition rates as estimated from the de novo insertions (~1 × 10^–5 per line per generation per element; Figure 4), these values are several orders of magnitude higher than the in vivo measures in SpC backgrounds. The discrepancy between these estimates could be due to uncharacterized biases inherent to each method. They could also be linked to differences between the parental genotypes used to generate the MA crosses and the fluctuation assays. One major difference is the use of ade2 genotypes in the MA parents, a strategy that was initially adopted to provide a marker for the loss of mitochondrial respiration (Joseph and Hall, 2004; Lynch et al., 2008). It has been shown that the induction of adenine starvation through minimal adenine concentration in the medium and deletion of ADE2, which inactivates the adenine de novo biosynthesis pathway, increases Ty1 transcript levels (Todeschini et al., 2005), resulting in higher transposition rates. Rich complex medium like the one that was used for the MA experiment (YPD) can exhibit substantial variation in adenine concentration (VanDusen et al., 1997), and adenine can quickly become the limiting nutrient for ade2 strains (Kokina et al., 2014). Thus, we cannot exclude that the choice of initial ade2 genotypes could have inflated the transposition rates in the MA experiment.

The contrasted mobilization dynamics between natural S. paradoxus populations suggest that intraspecific variation affects not only the potency of Ty1 transposition, but also the strength of its regulation. SpB genomes generally have low Ty1 CNs, potent Ty1 sequence variants, and a steep negative relationship of transposition rate as a function of standing genomic Ty1 CN. This latter pattern is entirely consistent with the CNC regulation mechanism identified for Ty1 in S. cerevisiae, although the molecular demonstration of CNC function in our system remains to be done. On the other hand, SpC genomes have higher Ty1 loads, despite the observation that SpC Ty1 variants and mtDNAs are linked to markedly lower transposition rates. Our data suggest that transposition rates in SpC are much higher than what would be predicted by extrapolating the steep CNC-like negative relationship of SpB genomes (Figure 5). The less potent SpC Ty1 variant may have established a state that can maintain substantial polymorphism in the population, perhaps enabled by lower or non-functional CNC regulation. The first alternative start codon from which the p22 protein can be expressed in S. cerevisiae is conserved in both SpB and SpC Ty1 variants (M249, orthologous to M253 in the S. cerevisiae Gag protein sequence alignment from Czaja et al., 2020), suggesting that both S. paradoxus variants could express p22. Despite recent structural insights into the mechanism of p22-mediated CNC (Cottee et al., 2021), the molecular basis of the difference between CNC⁺ and CNC^- p22 variants in S. cerevisiae remains unclear (Czaja et al., 2020). Variation outside of p22 is also likely to impact CNC. For instance, the expression of CNC components in S. cerevisiae is affected by the Mediator transcriptional regulator complex (Salinero et al., 2018), providing the host genome with a mechanism to modulate the action of CNC (Curcio, 2019). As such, intraspecific variation in Ty1 mobilization rate may stem from complex interactions between Ty1 potency, Ty1 self-regulation and host regulation.

In conclusion, our work provides a detailed account of the alteration of TE genomic landscapes in yeast hybrids evolved under relaxed natural selection efficiency through an accurate resolution of their subgenomes by phased assembly. We highlight that retrotransposition is only a minor contributor to changes in TE load compared to transposition-unrelated SVs. While in vivo transposition rate assays revealed substantial variation that is underpinned by multiple aspects of genetic diversity at the intraspecific level, the potential for transposition rate in shaping the TE landscape in Saccharomyces hybrids is likely to be overpowered by other types of structural genomic variation.

Nanopore long-read sequencing data (basecalled reads) and parental genome assemblies are available at NCBI under accession number PRJNA828354. Illumina short-read sequencing data of the MA lines is available at NCBI under accession number PRJNA515073. Custom scripts, custom databases of reference Ty sequences and subgenome-level assemblies of MA lines used in this study are available at GitHub (copy archived at Landrylab, 2023).

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   ID PRJNA828354. ONT whole genome sequencing from MA experiment on S. paradoxus and S. cerevisiae wild yeast hybrids.

   https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA828354
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   https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA515073

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Author details

1. Mathieu Hénault

   1. Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, Canada
   2. Département de biochimie, microbiologie et bioinformatique, Université Laval, Québec, Canada
   3. Quebec Network for Research on Protein Function, Engineering, and Applications (PROTEO), Université Laval, Québec, Canada
   4. Université Laval Big Data Research Center (BDRC_UL), Québec, Canada

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Funding acquisition, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   mathieu.henault.1@ulaval.ca

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0760-7545

2. Souhir Marsit

   1. Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, Canada
   2. Département de biochimie, microbiologie et bioinformatique, Université Laval, Québec, Canada
   3. Quebec Network for Research on Protein Function, Engineering, and Applications (PROTEO), Université Laval, Québec, Canada
   4. Université Laval Big Data Research Center (BDRC_UL), Québec, Canada
   5. Département de biologie, Université Laval, Québec, Canada

   Present address

   Département de biologie, chimie et géographie, Université du Québec à, Rimouski, Canada

   Contribution

   Conceptualization, Resources, Investigation, Methodology

   Competing interests

   No competing interests declared

3. Guillaume Charron

   1. Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, Canada
   2. Quebec Network for Research on Protein Function, Engineering, and Applications (PROTEO), Université Laval, Québec, Canada
   3. Université Laval Big Data Research Center (BDRC_UL), Québec, Canada
   4. Département de biologie, Université Laval, Québec, Canada

   Present address

   Centre de foresterie des Laurentides, Ressources naturelles Canada, Québec, Canada

   Contribution

   Conceptualization, Resources, Investigation, Methodology

   Competing interests

   No competing interests declared

4. Christian R Landry

   1. Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, Canada
   2. Département de biochimie, microbiologie et bioinformatique, Université Laval, Québec, Canada
   3. Quebec Network for Research on Protein Function, Engineering, and Applications (PROTEO), Université Laval, Québec, Canada
   4. Université Laval Big Data Research Center (BDRC_UL), Québec, Canada
   5. Département de biologie, Université Laval, Québec, Canada

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Validation, Methodology, Project administration, Writing – review and editing

   Competing interests

   Senior editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0003-3028-6866

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