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While genome sequencing and assembly are now routine, we do not have a full, precise picture of polyploid genomes. No existing polyploid phasing method provides accurate and contiguous haplotype predictions. We developed nPhase, a ploidy agnostic tool that leverages long reads and accurate short reads to solve alignment-based phasing for samples of unspecified ploidy ( https://github.com/OmarOakheart/nPhase ). nPhase is validated by tests on simulated and real polyploids. nPhase obtains on average over 95% accuracy and a contiguous 1.25 haplotigs per haplotype to cover more than 90% of each chromosome (heterozygosity rate ≥ 0.5%). nPhase allows population genomics and hybrid studies of polyploids.

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.

Ale brewing yeast are the result of admixture between diverse strains of Saccharomyces cerevisiae, resulting in a heterozygous tetraploid that has since undergone numerous genomic rearrangements. As a result, comparisons between the genomes of modern related ale brewing strains show both extensive aneuploidy and mitotic recombination that has resulted in a loss of intragenomic diversity. Similar patterns of intraspecific admixture and subsequent selection for one haplotype have been seen in many domesticated crops, potentially reflecting a general pattern of domestication syndrome between these systems. We set out to explore the evolution of the ale brewing yeast, to understand both polyploid evolution and the process of domestication in the ecologically relevant environment of the brewery. Utilizing a common brewery practice known as repitching, in which yeasts are reused over multiple beer fermentations, we generated population time courses from multiple breweries utilizing similar strains of ale yeast. Applying whole-genome sequencing to the time courses, we have found that the same structural variations in the form of aneuploidy and mitotic recombination of particular chromosomes reproducibly rise to detectable frequency during adaptation to brewing conditions across multiple related strains in different breweries. Our results demonstrate that domestication of ale strains is an ongoing process and will likely continue to occur as modern brewing practices develop. Competing Interest Statement We declare a financial interest in the success of the breweries associated with the authors of this manuscript. No direct funding from these breweries went into the research herein presented beyond the production of the beers sampled. Otherwise, we declare no competing interests.

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.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 variation, an essential step between genomic variation and phenotypic landscape, is collectively controlled by local ( ) and distant ( ) regulatory changes. Nevertheless, how these regulatory elements differentially influence the heritability of expression traits remains unclear. Here, we bridge this gap by analyzing the transcriptomes of a large diallel panel consisting of 323 unique hybrids originated from genetically divergent yeast isolates. We estimated the broad- and narrow-sense heritability across 5,087 transcript abundance traits and showed that non-additive components account for 36% of the phenotypic variance on average. By comparing allelic expression ratios in the hybrid and the corresponding parental pair, we identified regulatory changes in 25% of all cases, with a majority acting in . We further showed that regulation could underlie coordinated expression variation across highly connected genes, resulting in significantly higher non-additive variance and most likely in some of the missing heritability of gene expression traits.

Background Pathological conditions often arise from dysregulation of complex gene networks. MicroRNAs (miRNAs) are central modulators of these networks, with upregulation of disease-promoting miRNAs suppressing beneficial pathways, and downregulation of protective/therapeutic miRNAs normally restraining pathological programs. Because individual miRNAs coordinately regulate multiple genes, they represent powerful therapeutic targets. We hypothesized that pathology-associated gene expression imbalances could be corrected by placing downregulated protective/therapeutic miRNAs under the control of promoters driving overexpression of disease-promoting miRNAs, thereby simultaneously disabling pathogenic programs and inducing therapeutic ones. We termed this concept castling, after the chess move. Methods Candidate miRNA pairs for castling were identified as inversely regulated in CAR T cells at early and late stages of the chronic antigen stimulation eventually leading to their dysfunction. Proof-of-concept experiments with castling of selected miRNA clusters (knock in of miR-17~92 into a miR15/16 locus) was then implemented in both primary T cells and in CAR T cells using a newly developed genome-editing procedure TRIPLE (Targeted Replacement Induced by Persistent Locus Editing) that enhances homology-directed repair via sequential cleavage. This was followed by differential expression of miRNA and mRNAs resulting from castling compared to similarly treated non-castled corresponding control cells. In addition, castled CAR T cells were evaluated functionally in the chronic antigen stimulation assay. Results In primary T-cells, swapped expression patterns of the castled miR17~92 and miR15-16 clusters elicited expected bidirectional changes of expression of their predicted target mRNA subsets. In CAR T cells under chronic antigen stimulation, castling of these miRNA clusters delayed dysfunction, enhanced cytokine production, and reshaped transcriptional programs consistent with restored T cell fitness. This was accompanied by up- and downregulation of multiple supporting genes. Conclusions Castling, implemented via TRIPLE or any other suitable gene editing technology, offers a broadly applicable strategy to reprogram disease-driven gene regulatory networks by converting pathological regulatory loops into self-correcting circuits. As a conceptual therapeutic approach, castling may be broadly applicable for improvement of multiple types of cell therapies in a variety of indications as well as for manipulation of not only miRNA but also of protein coding mRNAs.Competing Interest StatementH.K., S.A., S.A.N., E.S., D.Z. are employed at Lepton Pharmaceuticals. M.H., M.P. and A.D. are employed at TAmiRNA GmbH. E.F. and C.M. are advisors to Lepton Pharmaceuticals. T.C. is an advisor to AaviGen, AstraZeneca, Cimeio Therapeutics, Excision BioTherapeutics, GenCC, and Novo Nordisk. All other authors declare no conflicts of interest.Funder Information DeclaredLepton PharmaceuticalsGerman Federal Ministry of Research, Technology and Space (BMFTR)CRACK IT Challenge

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 the Kluyveromyces lactis yeast species, a well-known species 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 Kluyveromyces lactis isolates and found that the overall genetic diversity of this species is very high (π = 2.9 x 10-2) compared to other species such as Saccharomyces cerevisiae (π = 3 x 10-3). However, the domesticated dairy population shows a reduced level of diversity (π = 7 x 10-4), 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.

While genome sequencing and assembly are now routine, we still do not have a full and precise picture of polyploid genomes. Phasing these genomes, i.e. deducing haplotypes from genomic data, remains a challenge. Despite numerous attempts, no existing polyploid phasing method provides accurate and contiguous haplotype predictions. To address this need, we developed nPhase, a ploidy agnostic pipeline and algorithm that leverage the accuracy of short reads and the length of long reads to solve reference alignment-based phasing for samples of unspecified ploidy (https://github.com/nPhasePipeline/nPhase). nPhase was validated on virtually constructed polyploid genomes of the model species Saccharomyces cerevisiae, generated by combining sequencing data of homozygous isolates. nPhase obtained on average >95% accuracy and a contiguous 1.25 haplotigs per haplotype to cover >90% of each chromosome (heterozygosity rate ≥0.5%). This new phasing method opens the door to explore polyploid genomes through applications such as population genomics and hybrid studies. Competing Interest Statement The authors have declared no competing interest. Footnotes * https://github.com/nPhasePipeline/nPhase

Abstract How natural variation affects phenotype is difficult to determine given our incomplete ability to deduce the functional impact of the polymorphisms detected in a population. Although current computational and experimental tools can predict and measure allele function, there has previously been no assay that does so in a high-throughput manner while also representing haplotypes derived from wild populations. Here, we present such an assay that measures the fitness of hundreds of natural alleles of a given gene without site-directed mutagenesis or DNA synthesis. With a large collection of diverse Saccharomyces cerevisiae natural isolates, we piloted this technique using the gene SUL1, which encodes a high-affinity sulfate permease that, at increased copy number, can improve the fitness of cells grown in sulfate-limited media. We cloned and barcoded all alleles from a collection of over 1000 natural isolates en masse and matched barcodes with their respective variants using PacBio long-read sequencing and a novel error-correction algorithm. We then transformed the reference S288C strain with this library and used barcode sequencing to track growth ability in sulfate limitation of lineages carrying each allele. We show that this approach allows us to measure the fitness conferred by each allele and stratify functional and nonfunctional alleles. Additionally, we pinpoint which polymorphisms in both coding and noncoding regions are detrimental to fitness or are of small effect and result in intermediate phenotypes. Integrating these results with a phylogenetic tree, we observe how often loss-of-function occurs and whether or not there is an evolutionary pattern to our observable phenotypic results. This approach is easily applicable to other genes. Our results complement classic genotype-phenotype mapping strategies and demonstrate a high-throughput approach for understanding the effects of polymorphisms across an entire species which can greatly propel future investigations into quantitative traits. Competing Interest Statement The authors have declared no competing interest. Footnotes * https://github.com/dunhamlab/SUL1_natural_variants * https://www.ncbi.nlm.nih.gov/bioproject/PRJNA681436