Meiotic recombination shows broad variations across species and along chromosomes and is often suppressed at and around genomic regions determining sexual compatibility such as mating type loci in fungi. Here, we show that the absence of Spo11-DSBs and meiotic recombination on Lakl0C-left, the chromosome arm containing the sex locus of the Lachancea kluyveri budding yeast, results from the absence of recruitment of the two chromosome axis proteins Red1 and Hop1, essential for proper Spo11-DSBs formation. Furthermore, cytological observation of spread pachytene meiotic chromosomes reveals that Lakl0C-left does not undergo synapsis. However, we show that the behavior of Lakl0C-left is independent of its particularly early replication timing and is not accompanied by any peculiar chromosome structure as detectable by Hi-C in this yet poorly studied yeast. Finally, we observed an accumulation of heterozygous mutations on Lakl0C-left and a sexual dimorphism of the haploid meiotic offspring, supporting a direct effect of this absence of meiotic recombination on L. kluyveri genome evolution and fitness. Because suppression of meiotic recombination on sex chromosomes is widely observed across eukaryotes, the mechanism for recombination suppression described here may apply to other species, with the potential to impact sex chromosome evolution.

Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.

Gene expression is known to vary among individuals, and this variability can impact the phenotypic diversity observed in natural populations. While the transcriptome and proteome have been extensively studied, little is known about the translation process itself. Here, we therefore performed ribosome and transcriptomic profiling on a genetically and ecologically diverse set of natural isolates of the Saccharomyces cerevisiae yeast. Interestingly, we found that the Euclidean distances between each profile and the expression fold changes in each pairwise isolate comparison were higher at the transcriptomic level. This observation clearly indicates that the transcriptional variation observed in the different isolates is buffered through a phenomenon known as post-transcriptional buffering at the translation level. Furthermore, this phenomenon seemed to have a specific signature by preferentially affecting essential genes as well as genes involved in complex-forming proteins, and low transcribed genes. We also explored the translation of the S. cerevisiae pangenome and found that the accessory genes related to introgression events displayed similar transcription and translation levels as the core genome. By contrast, genes acquired through horizontal gene transfer events tended to be less efficiently translated. Together, our results highlight both the extent and signature of the post-transcriptional buffering.

Assessing the complexity and expressivity of traits at the species level is an essential first step to better dissect the genotype-phenotype relationship. As trait complexity behaves dynamically, the classic dichotomy between monogenic and complex traits is too simplistic. However, no systematic assessment of this complexity spectrum has been carried out on a population scale to date. In this context, we generated a large diallel hybrid panel composed of 190 unique hybrids coming from 20 natural isolates representative of the S. cerevisiae genetic diversity. For each of these hybrids, a large progeny of 160 individuals was obtained, leading to a total of 30,400 offspring individuals. Their mitotic growth was evaluated on 38 conditions inducing various cellular stresses. We developed a classification algorithm to analyze the phenotypic distributions of offspring and assess the trait complexity. We clearly found that traits are mainly complex at the population level. On average, we found that 91.2% of cross/trait combinations exhibit high complexity, while monogenic and oligogenic cases accounted for only 4.1% and 4.7%, respectively. However, the complexity spectrum is very dynamic, trait specific and tightly related to genetic backgrounds. Overall, our study provided greater insight into trait complexity as well as the underlying genetic basis of its spectrum in a natural population.

Gene expression variation, an essential step between genotype and phenotype, is collectively controlled by local (cis) and distant (trans) regulatory changes. Nevertheless, how these regulatory elements differentially influence gene expression variation remains unclear. Here, we bridge this gap by analyzing the transcriptomes of a large diallel panel consisting of 323 unique hybrids originating from genetically divergent Saccharomyces cerevisiae isolates. Our analysis across 5,087 transcript abundance traits showed that non-additive components account for 36% of the gene expression variance on average. By comparing allele-specific read counts in parent-hybrid trios, we found that trans-regulatory changes underlie the majority of gene expression variation in the population. Remarkably, most cis-regulatory variations are also exaggerated or attenuated by additional trans effects. Overall, we showed that the transcriptome is globally buffered at the genetic level mainly due to trans-regulatory variation in the population.

Pangenomes provide access to an accurate representation of the genetic diversity of species, both in terms of sequence polymorphisms and structural variants (SVs). Here we generated the Saccharomyces cerevisiae Reference Assembly Panel (ScRAP) comprising reference-quality genomes for 142 strains representing the species’ phylogenetic and ecological diversity. The ScRAP includes phased haplotype assemblies for several heterozygous diploid and polyploid isolates. We identified circa (ca.) 4,800 nonredundant SVs that provide a broad view of the genomic diversity, including the dynamics of telomere length and transposable elements. We uncovered frequent cases of complex aneuploidies where large chromosomes underwent large deletions and translocations. We found that SVs can impact gene expression near the breakpoints and substantially contribute to gene repertoire evolution. We also discovered that horizontally acquired regions insert at chromosome ends and can generate new telomeres. Overall, the ScRAP demonstrates the benefit of a pangenome in understanding genome evolution at population scale.

Copy number variants (CNVs), duplications and deletions of genomic sequences, contribute to evolutionary adaptation but can also confer deleterious effects and cause disease. Whereas the effects of amplifying individual genes or whole chromosomes (i.e., aneuploidy) have been studied extensively, much less is known about the genetic and functional effects of CNVs of differing sizes and structures. Here, we investigated Saccharomyces cerevisiae (yeast) strains that acquired adaptive CNVs of variable structures and copy numbers following experimental evolution in glutamine-limited chemostats. Although beneficial in the selective environment, CNVs result in decreased fitness compared with the euploid ancestor in rich media. We used transposon mutagenesis to investigate mutational tolerance and genome-wide genetic interactions in CNV strains. We find that CNVs increase mutational target size, confer increased mutational tolerance in amplified essential genes, and result in novel genetic interactions with unlinked genes. We validated a novel genetic interaction between different CNVs and BMH1 that was common to multiple strains. We also analyzed global gene expression and found that transcriptional dosage compensation does not affect most genes amplified by CNVs, although gene-specific transcriptional dosage compensation does occur for ∼12% of amplified genes. Furthermore, we find that CNV strains do not show previously described transcriptional signatures of aneuploidy. Our study reveals the extent to which local and global mutational tolerance is modified by CNVs with implications for genome evolution and CNV-associated diseases, such as cancer.

Gene expression variation can provide an overview of the changes in regulatory networks that underlie phenotypic diversity. Certain evolutionary trajectories such as polyploidization events can have an impact on the transcriptional landscape. Interestingly, the evolution of the yeast species Brettanomyces bruxellensis has been punctuated by diverse allopolyploidization events leading to the coexistence of a primary diploid genome associated with various haploid acquired genomes. To assess the impact of these events on gene expression, we generated and compared the transcriptomes of a set of 87 B. bruxellensis isolates, selected as being representative of the genomic diversity of this species. Our analysis revealed that acquired subgenomes strongly impact the transcriptional patterns and allow discrimination of allopolyploid populations. In addition, clear transcriptional signatures related to specific populations have been revealed. The transcriptional variations observed are related to some specific biological processes such as transmembrane transport and amino acids metabolism. Moreover, we also found that the acquired subgenome causes the overexpression of some genes involved in the production of flavor-impacting secondary metabolites, especially in isolates of the beer population.

DetecDiv, a generalist deep-learning platform for automated cell division tracking and survival analysis

Théo Aspert, Didier Hentsch, Gilles Charvin. eLife, (2022) - DOI: 10.7554/eLife.79519

The process of domestication has variable consequences on genome evolution leading to different phenotypic signatures. Access to the complete genome sequences of a large number of individuals makes it possible to explore the different facets of this domestication process. Here, we sought to explore the genome evolution of Kluyveromyces lactis, a yeast species well-known for its involvement in dairy processes but also present in natural environments. Using a combination of short and long-read sequencing strategies, we investigated the genomic variability of 41 K. lactis isolates and found that the overall genetic diversity of this species is very high (θw = 3.3 × 10−2) compared to other species such as Saccharomyces cerevisiae (θw = 1.6 × 10−2). However, the domesticated dairy population shows a reduced level of diversity (θw = 1 × 10−3), probably due to a domestication bottleneck. In addition, this entire population is characterized by the introgression of the LAC4 and LAC12 genes, responsible for lactose fermentation and coming from the closely related species, Kluyveromyces marxianus, as previously described. Our results also highlighted that the LAC4/LAC12 gene cluster was acquired through multiple and independent introgression events. Finally, we also identified several genes that could play a role in adaptation to dairy environments through copy number variation. These genes are involved in sugar consumption, flocculation and drug resistance, and may play a role in dairy processes. Overall, our study illustrates contrasting genomic evolution and sheds new light on the impact of domestication processes on it.

Meiotic recombination is a driving force for genome evolution, deeply characterized in a few model species, notably in the budding yeast Saccharomyces cerevisiae. Interestingly, Zip2, Zip3, Zip4, Spo16, Msh4, and Msh5, members of the so-called ZMM pathway that implements the interfering meiotic crossover pathway in S. cerevisiae, have been lost in Lachancea yeast species after the divergence of Lachancea kluyveri from the rest of the clade. In this context, after investigating meiosis in L. kluyveri, we determined the meiotic recombination landscape of Lachancea waltii. Attempts to generate diploid strains with fully hybrid genomes invariably resulted in strains with frequent whole-chromosome aneuploidy and multiple extended regions of loss of heterozygosity (LOH), which mechanistic origin is so far unclear. Despite the lack of multiple ZMM pro-crossover factors in L. waltii, numbers of crossovers and noncrossovers per meiosis were higher than in L. kluyveri but lower than in S. cerevisiae, for comparable genome sizes. Similar to L. kluyveri but opposite to S. cerevisiae, L. waltii exhibits an elevated frequency of zero-crossover bivalents. Lengths of gene conversion tracts for both crossovers and non-crossovers in L. waltii were comparable to those observed in S. cerevisiae and shorter than in L. kluyveri despite the lack of Mlh2, a factor limiting conversion tract size in S. cerevisiae. L. waltii recombination hotspots were not shared with either S. cerevisiae or L. kluyveri, showing that meiotic recombination hotspots can evolve at a rather limited evolutionary scale within budding yeasts. Finally, L. waltii crossover interference was reduced relative to S. cerevisiae, with interference being detected only in the 25 kb distance range. Detection of positive inference only at short distance scales in the absence of multiple ZMM factors required for interference-sensitive crossovers in other systems likely reflects interference between early recombination precursors such as DSBs.

Proper RNA localisation is essential for physiological gene expression. Various kinds of genome-wide approaches permit to comprehensively profile subcellular transcriptomes. Among them, cell fractionation methods, that couple RNase treatment of isolated organelles to the sequencing of protected transcripts, remain most widely used, mainly because they do not require genetic modification of the studied system and can be easily implemented in any cells or tissues, including in non-model species. However, they suffer from numerous false-positives since incompletely digested contaminant RNAs can still be captured and erroneously identified as resident transcripts. Here we introduce Controlled Level of Contamination coupled to deep sequencing (CoLoC-seq) as a new subcellular transcriptomics approach that efficiently bypasses this caveat. CoLoC-seq leverages classical enzymatic kinetics and tracks the depletion dynamics of transcripts in a gradient of an exogenously added RNase, with or without organellar membranes. By means of straightforward mathematical modelling, CoLoC-seq infers the localisation topology of RNAs and robustly distinguishes between genuinely resident, luminal transcripts and merely abundant surface-attached contaminants. Our generic approach performed well on human mitochondria and is in principle applicable to other membrane-bounded organelles, including plastids, compartments of the vacuolar system, extracellular vesicles, and viral particles.

RNA-binding proteins are central players in post-transcriptional regulation. Some of them, such as the well-studied bacterial RNA chaperones Hfq and ProQ or the eukaryotic RNAi factor Argonaute, interact with hundreds-to-thousands of different RNAs and thereby globally affect gene expression. As a shared yet limited resource, these and other RNA-binding hubs drive strong competition between their multiple ligands. This creates a ground for significant cross-communication between RNA targets, which enables them to share information, “synchronise” their behaviour, and produce interesting biochemical effects, sometimes propagating across the highly connected RNA-protein network. This property is likely universally present in hub-centred networks and plays a key role in global gene expression programmes. It is also an important factor in biotechnology and synthetic biology applications of RNA/protein-based circuits. However, few studies so-far focused on describing and explaining this phenomenon from first principles. Here we introduce an information theory-based framework to comprehensively and exactly describe information flow in hub-centred networks. We show that information sharing can achieve significant levels in relatively small networks, provided the hub is present in limiting concentrations. The transmitted information is sufficient to noticeably affect the binding probabilities of competing targets but drops exponentially along the network. Target overexpression can disrupt communication between other targets, while hub sequestration boosts the crosstalk. We also find that overlaps between the interactomes of two different hubs create both entropic challenges and new forms of long-range communication between RNAs and proteins.

The appearance of genomic variations such as loss of heterozygosity (LOH) has a significant impact on phenotypic diversity observed in a population. Recent large-scale yeast population genomic surveys have shown a high frequency of these events in natural isolates and more particularly in polyploids. However, the frequency, extent, and spectrum of LOH in polyploid organisms have never been explored and is poorly characterized to date. Here, we accumulated 5,163 LOH events over 1,875 generations in 76 mutation accumulation (MA) lines comprising nine natural heterozygous diploid, triploid, and tetraploid natural S. cerevisiae isolates from different ecological and geographical origins. We found that the rate and spectrum of LOH are variable across ploidy levels. Of the total accumulated LOH events, 8.5%, 21%, and 70.5% of them were found in diploid, triploid, and tetraploid MA lines, respectively. Our results clearly shows that the frequency of generated LOH events increases with ploidy level. In fact, the cumulative LOH rates were estimated to be 9.3×10−3, 2.2×10−2, and 8.4×10−2 events per division for diploids, triploids, and tetraploids, respectively. In addition, a clear bias towards the accumulation of interstitial and short LOH tracts is observed in triploids and tetraploids compared to diploids. The variation of the frequency and spectrum of LOH events across ploidy level could be related to the genomic instability, characterizing higher ploidy isolates.

In natural populations, the same mutation can lead to different phenotypic outcomes due to the genetic variation that exists among individuals. Such genetic background effects are commonly observed, including in the context of many human diseases. However, systematic characterization of these effects at the species level is still lacking to date. Here, we sought to comprehensively survey background-dependent traits associated with gene loss-of-function (LoF) mutations in 39 natural isolates of Saccharomyces cerevisiae using a transposon saturation strategy. By analyzing the modeled fitness variability of a total of 4,469 genes, we found that 15% of them, when impacted by a LoF mutation, exhibited a significant gain- or loss-of-fitness phenotype in certain natural isolates compared with the reference strain S288C. Out of these 632 genes with predicted background-dependent fitness effects, around 2/3 impact multiple backgrounds with a gradient of predicted fitness change while 1/3 are specific to a single genetic background. Genes related to mitochondrial function are significantly overrepresented in the set of genes showing a continuous variation and display a potential functional rewiring with other genes involved in transcription and chromatin remodeling as well as in nuclear–cytoplasmic transport. Such rewiring effects are likely modulated by both the genetic background and the environment. While background-specific cases are rare and span diverse cellular processes, they can be functionally related at the individual level. All genes with background-dependent fitness effects tend to have an intermediate connectivity in the global genetic interaction network and have shown relaxed selection pressure at the population level, highlighting their potential evolutionary characteristics.

With the advent of high throughput sequencing technologies, genome-wide association studies (GWAS) have become a powerful paradigm for dissecting the genetic origins of the observed phenotypic variation. We recently completely sequenced the genome of 1011 Saccharomyces cerevisiae isolates, laying a strong foundation for GWAS. To assess the feasibility and the limits of this approach, we performed extensive simulations using five selected subpopulations as well as the total set of 1011 genomes. We measured the ability to detect the causal genetic variants involved in Mendelian and more complex traits using a linear mixed model approach. The results showed that population structure is well accounted for and is not the main problem when the sample size is high enough. While the genetic determinant of a Mendelian trait is easily mapped in all studied subpopulations, discrepancies are seen between datasets when performing GWAS on a complex trait in terms of detection, false positive and false negative rate. Finally, we performed GWAS on the different defined subpopulations using a real quantitative trait (resistance to copper sulfate) and showed the feasibility of this approach. The performance of each dataset depends simultaneously on several factors such as sample size, relatedness and population evolutionary history.