UV-B-induced modulation of constitutive heterochromatin content in Arabidopsis thaliana.

Sunlight regulates transcriptional programs and triggers the shaping of the genome throughout plant development. Among the different sunlight wavelengths that reach the surface of the Earth, UV-B (280-315 nm) controls the expression of hundreds of genes for the photomorphogenic responses and also induces the formation of photodamage that interfere with genome integrity and transcriptional programs. The combination of cytogenetics and deep-learning-based analyses allowed determining the location of UV-B-induced photoproducts and quantifying the effects of UV-B irradiation on constitutive heterochromatin content in different Arabidopsis natural variants acclimated to various UV-B regimes. We identified that UV-B-induced photolesions are enriched within chromocenters. Furthermore, we uncovered that UV-B irradiation promotes constitutive heterochromatin dynamics that differs among the Arabidopsis ecotypes having divergent heterochromatin contents. Finally, we identified that the proper restoration of the chromocenter shape, upon DNA repair, relies on the UV-B photoreceptor, UV RESISTANCE LOCUS 8 (UVR8). These findings shed the light on the effect of UV-B exposure and perception in the modulation of constitutive heterochromatin content in Arabidopsis thaliana.

Advanced Image Analysis Methods for Automated Segmentation of Subnuclear Chromatin Domains.

The combination of ever-increasing microscopy resolution with cytogenetical tools allows for detailed analyses of nuclear functional partitioning. However, the need for reliable qualitative and quantitative methodologies to detect and interpret chromatin sub-nuclear organization dynamics is crucial to decipher the underlying molecular processes. Having access to properly automated tools for accurate and fast recognition of complex nuclear structures remains an important issue. Cognitive biases associated with human-based curation or decisions for object segmentation tend to introduce variability and noise into image analysis. Here, we report the development of two complementary segmentation methods, one semi-automated (iCRAQ) and one based on deep learning (Nucl.Eye.D), and their evaluation using a collection of A. thaliana nuclei with contrasted or poorly defined chromatin compartmentalization. Both methods allow for fast, robust and sensitive detection as well as for quantification of subtle nucleus features. Based on these developments, we highlight advantages of semi-automated and deep learning-based analyses applied to plant cytogenetics.

Extensive simulations assess the performance of genome-wide association mapping in various Saccharomyces cerevisiae subpopulations.

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. This article is part of the theme issue 'Genetic basis of adaptation and speciation: from loci to causative mutations'.

Extensive impact of low-frequency variants on the phenotypic landscape at population-scale.

Genome-wide association studies (GWAS) allow to dissect complex traits and map genetic variants, which often explain relatively little of the heritability. One potential reason is the preponderance of undetected low-frequency variants. To increase their allele frequency and assess their phenotypic impact in a population, we generated a diallel panel of 3025 yeast hybrids, derived from pairwise crosses between natural isolates and examined a large number of traits. Parental versus hybrid regression analysis showed that while most phenotypic variance is explained by additivity, a third is governed by non-additive effects, with complete dominance having a key role. By performing GWAS on the diallel panel, we found that associated variants with low frequency in the initial population are overrepresented and explain a fraction of the phenotypic variance as well as an effect size similar to common variants. Overall, we highlighted the relevance of low-frequency variants on the phenotypic variation.

Genome evolution across 1,011 Saccharomyces cerevisiae isolates.

Large-scale population genomic surveys are essential to explore the phenotypic diversity of natural populations. Here we report the whole-genome sequencing and phenotyping of 1,011 Saccharomyces cerevisiae isolates, which together provide an accurate evolutionary picture of the genomic variants that shape the species-wide phenotypic landscape of this yeast. Genomic analyses support a single 'out-of-China' origin for this species, followed by several independent domestication events. Although domesticated isolates exhibit high variation in ploidy, aneuploidy and genome content, genome evolution in wild isolates is mainly driven by the accumulation of single nucleotide polymorphisms. A common feature is the extensive loss of heterozygosity, which represents an essential source of inter-individual variation in this mainly asexual species. Most of the single nucleotide polymorphisms, including experimentally identified functional polymorphisms, are present at very low frequencies. The largest numbers of variants identified by genome-wide association are copy-number changes, which have a greater phenotypic effect than do single nucleotide polymorphisms. This resource will guide future population genomics and genotype-phenotype studies in this classic model system.

Variation of the meiotic recombination landscape and properties over a broad evolutionary distance in yeasts.

Meiotic recombination is a major factor of genome evolution, deeply characterized in only a few model species, notably the yeast Saccharomyces cerevisiae. Consequently, little is known about variations of its properties across species. In this respect, we explored the recombination landscape of Lachancea kluyveri, a protoploid yeast species that diverged from the Saccharomyces genus more than 100 million years ago and we found striking differences with S. cerevisiae. These variations include a lower recombination rate, a higher frequency of chromosomes segregating without any crossover and the absence of recombination on the chromosome arm containing the sex locus. In addition, although well conserved within the Saccharomyces clade, the S. cerevisiae recombination hotspots are not conserved over a broader evolutionary distance. Finally and strikingly, we found evidence of frequent reversal of commitment to meiosis, resulting in return to mitotic growth after allele shuffling. Identification of this major but underestimated evolutionary phenomenon illustrates the relevance of exploring non-model species.

The Hidden Complexity of Mendelian Traits across Natural Yeast Populations.

Mendelian traits are considered to be at the lower end of the complexity spectrum of heritable phenotypes. However, more than a century after the rediscovery of Mendel's law, the global landscape of monogenic variants, as well as their effects and inheritance patterns within natural populations, is still not well understood. Using the yeast Saccharomyces cerevisiae, we performed a species-wide survey of Mendelian traits across a large population of isolates. We generated offspring from 41 unique parental pairs and analyzed 1,105 cross/trait combinations. We found that 8.9% of the cases were Mendelian. Further tracing of causal variants revealed background-specific expressivity and modified inheritances, gradually transitioning from Mendelian to complex traits in 30% of the cases. In fact, when taking into account the natural population diversity, the hidden complexity of traits could be substantial, confounding phenotypic predictability even for simple Mendelian traits.

Population genomics of yeasts: towards a comprehensive view across a broad evolutionary scale.

With the advent of high-throughput technologies for sequencing, the complete description of the genetic variation that occurs in populations, also known as population genomics, is foreseeable but far from being reached. Explaining the forces that govern patterns of genetic variation is essential to elucidate the evolutionary history of species. Genetic variation results from a wide assortment of evolutionary forces, among which mutation, selection, recombination and drift play major roles in shaping genomes. In addition, exploring the genetic variation within a population also corresponds to the first step towards dissecting the genotype-phenotype relationship. In this context, yeast species are of particular interest because they represent a unique resource for studying the evolution of intraspecific genetic diversity in a phylum spanning a broad evolutionary scale. Here, we briefly review recent progress in yeast population genomics and provide some perspective on this rapidly evolving field. In fact, we truly believe that it is of interest to supplement comparative and early population genomic studies with the deep sequencing of more extensive sets of individuals from the same species. In parallel, it would be more than valuable to uncover the intraspecific variation of a large number of unexplored species, including those that are closely and more distantly related. Altogether, these data would enable substantially more powerful genomic scans for functional dissection.

Bone collected during dental implant surgery: a clinical and histological study.

Bone traps vary in design, although the effect of pore size on the nature of the debris collected in vivo has not yet been established. The aims of this study were 1) to compare the clinical performance of two bone collectors during implant surgery, ii) to establish the mass of tissue collected by each device, and iii) to characterize the nature of the collected debris. Thirty-eight patients (paired for implant site) were categorised into three clinical groups according to the site and the number of implants they were to receive. Patients underwent bone collection with the Frios bone trap or the Osseous Coagulum Trap according to a randomisation sequence. The samples were fixed in formalin, frozen, freeze-dried and weighed. Material from each sample was embedded in paraffin wax and stained with haematoxylin and eosin. All sections were examined by optical microscopy and the proportion of bone to coagulum was established histomorphometrically. During surgery, the Frios bone trap blocked once and the Osseous Coagulum Trap blocked 11 times. In all cases where blockage occurred, excess coagulum was apparent. All the samples that were collected by the Frios bone trap contained bone and coagulum, with a mean proportion of 90.6% bone. With regard to the Osseous Coagulum Trap, one sample contained no bone and two samples contained only trace amounts of bone; the remaining samples contained a mean proportion of 67.3% bone. Pore size affects both clinical performance and the histological composition of the debris collected, and this might have important implications if used as an augmentation material.