'Particle genetics': treating every cell as unique.

Genotype-phenotype relations are usually inferred from a deterministic point of view. For example, quantitative trait loci (QTL), which describe regions of the genome associated with a particular phenotype, are based on a mean trait difference between genotype categories. However, living systems comprise huge numbers of cells (the 'particles' of biology). Each cell can exhibit substantial phenotypic individuality, which can have dramatic consequences at the organismal level. Now, with technology capable of interrogating individual cells, it is time to consider how genotypes shape the probability laws of single cell traits. The possibility of mapping single cell probabilistic trait loci (PTL), which link genomic regions to probabilities of cellular traits, is a promising step in this direction. This approach requires thinking about phenotypes in probabilistic terms, a concept that statistical physicists have been applying to particles for a century. Here, I describe PTL and discuss their potential to enlarge our understanding of genotype-phenotype relations

MyLabStocks: a web-application to manage molecular biology materials.

Open AccessLaboratory stocks are the hardware of research. They must be stored and managed with mimimum loss of material and information. Plasmids, oligonucleotides and strains are regularly exchanged between collaborators within and between laboratories. Managing and sharing information about every item is crucial for retrieval of reagents, for planning experiments and for reproducing past experimental results. We have developed a web-based application to manage stocks commonly used in a molecular biology laboratory. Its functionalities include user-defined privileges, visualization of plasmid maps directly from their sequence and the capacity to search items from fields of annotation or directly from a query sequence using BLAST. It is designed to handle records of plasmids, oligonucleotides, yeast strains, antibodies, pipettes and notebooks. Based on PHP/MySQL, it can easily be extended to handle other types of stocks and it can be installed on any server architecture. MyLabStocks is freely available from: https://forge.cbp.ens-lyon.fr/redmine/projects/mylabstocks under an open source licence. © 2014 Laboratoire de Biologie Moleculaire de la Cellule CNRS. Yeast published by John Wiley & Sons, Ltd

How does evolution tune biological noise?

International audiencePart of molecular and phenotypic differences between individual cells, between body parts, or between individuals can result from biological noise. This source of variation is becoming more and more apparent thanks to the recent advances in dynamic imaging and single-cell analysis. Some of these studies showed that the link between genotype and phenotype is not strictly deterministic. Mutations can change various statistical properties of a biochemical reaction, and thereby the probability of a trait outcome. The fact that they can modulate phenotypic noise brings up an intriguing question: how may selection act on these mutations? In this review, we approach this question by first covering the evidence that biological noise is under genetic control and therefore a substrate for evolution. We then sequentially inspect the possibilities of negative, neutral, and positive selection for mutations increasing biological noise. Finally, we hypothesize on the specific case of H2A.Z, which was shown to both buffer phenotypic noise and modulate transcriptional efficiency. The recent advances in dynamic imaging and single-cell studies have revealed the stochastic nature of biochemical reactions. Numerous factors are known to affect the degree of noise in these reactions, including temperature (Jo et al., 2005), drug treatment (Dar et al., 2014), age (Bahar et al., 2006) and, very importantly, genotypes (Raser and O'Shea, 2004; Levy and Siegal, 2008; Ansel et al., 2008; Hornung et al., 2012). If mutations can modulate a reaction without necessarily changing the average concentration of its product, then they do not fit in the traditional (often deterministic) view of genotype–phenotype control. Such mutations can change the probabilistic laws of single-cell traits, such as phenotypic noise, which may have important consequences at the multicellular level (Yvert, 2014). Noise has the property to increase disorder. In contrast, living systems are highly organized, developmental processes are under many constrains, and numerous phenotypic traits display robustness to stochastic variation. It is therefore unclear how optimization and control of noise can affect both fidelity and diversity. One way to apprehend this is to examine the mutations that were shown to increase or decrease noise levels. In this review, we first present evidence that noise is under genetic control. We then speculate on the ways by which natural selection acts on it. Finally, we hypothesize on the contribution of histone variant H2A.Z to noise evolution

Genetic Complexity and Quantitative Trait Loci Mapping of Yeast Morphological Traits

Functional genomics relies on two essential parameters: the sensitivity of phenotypic measures and the power to detect genomic perturbations that cause phenotypic variations. In model organisms, two types of perturbations are widely used. Artificial mutations can be introduced in virtually any gene and allow the systematic analysis of gene function via mutants fitness. Alternatively, natural genetic variations can be associated to particular phenotypes via genetic mapping. However, the access to genome manipulation and breeding provided by model organisms is sometimes counterbalanced by phenotyping limitations. Here we investigated the natural genetic diversity of Saccharomyces cerevisiae cellular morphology using a very sensitive high-throughput imaging platform. We quantified 501 morphological parameters in over 50,000 yeast cells from a cross between two wild-type divergent backgrounds. Extensive morphological differences were found between these backgrounds. The genetic architecture of the traits was complex, with evidence of both epistasis and transgressive segregation. We mapped quantitative trait loci (QTL) for 67 traits and discovered 364 correlations between traits segregation and inheritance of gene expression levels. We validated one QTL by the replacement of a single base in the genome. This study illustrates the natural diversity and complexity of cellular traits among natural yeast strains and provides an ideal framework for a genetical genomics dissection of multiple traits. Our results did not overlap with results previously obtained from systematic deletion strains, showing that both approaches are necessary for the functional exploration of genomes

Efficient use of DNA molecular markers to construct industrial yeast strains.

Saccharomyces cerevisiae yeast strains exhibit a huge genotypic and phenotypic diversity. Breeding strategies taking advantage of these characteristics would contribute greatly to improving industrial yeasts. Here we mapped and introgressed chromosomal regions controlling industrial yeast properties, such as hydrogen sulphide production, phenolic off-flavor and a kinetic trait (lag phase duration). Two parent strains derived from industrial isolates used in winemaking and which exhibited significant quantitative differences in these traits were crossed and their progeny (50-170 clones) was analyzed for the segregation of these traits. Forty-eight segregants were genotyped at 2212 marker positions using DNA microarrays and one significant locus was mapped for each trait. To exploit these loci, an introgression approach was supervised by molecular markers monitoring using PCR/RFLP. Five successive backcrosses between an elite strain and appropriate segregants were sufficient to improve three trait values. Microarray-based genotyping confirmed that over 95% of the elite strain genome was recovered by this methodology. Moreover, karyotype patterns, mtDNA and tetrad analysis showed some genomic rearrangements during the introgression procedure

The complex pattern of epigenomic variation between natural yeast strains at single-nucleosome resolution

International audienceBackground: Epigenomic studies on humans and model species have revealed substantial inter‑individual variation in histone modification profiles. However, the pattern of this variation has not been precisely characterized, particularly regarding which genomic features are enriched for variability and whether distinct histone marks co‑vary synergistically. Yeast allows us to investigate intra‑species variation at high resolution while avoiding other sources of variation, such as cell type or subtype. Results: We profiled histone marks H3K4me3, H3K9ac, H3K14ac, H4K12ac and H3K4me1 in three unrelated wild strains of Saccharomyces cerevisiae at single‑nucleosome resolution and analyzed inter‑strain differences statistically. All five marks varied significantly at specific loci, but to different extents. The number of nucleosomes varying for a given mark between two strains ranged from 20 to several thousands; +1 nucleosomes were significantly less subject to variation. Genes with highly evolvable or responsive expression showed higher variability; however, the variation pattern could not be explained by known transcriptional differences between the strains. Synergistic variation of distinct marks was not systematic, with surprising differences between functionally related H3K9ac and H3K14ac. Interestingly, H3K14ac differences that persisted through transient hyperacetylation were supported by H3K4me3 differences, suggesting stabilization via cross talk. Conclusions: Quantitative variation of histone marks among S. cerevisiae strains is abundant and complex. Its relation to functional characteristics is modular and seems modest, with partial association with gene expression divergences, differences between functionally related marks and partial co‑variation between marks that may confer stability. Thus, the specific context of studies, such as which precise marks, individuals and genomic loci are investigated, is primor‑ dial in population epigenomics studies. The complexity found in this pilot survey in yeast suggests that high complexity can be anticipated among higher eukaryotes, including humans

Single QTL mapping and nucleotide-level resolution of a physiologic trait in wine Saccharomyces cerevisiae strains.

International audienceNatural Saccharomyces cerevisiae yeast strains exhibit very large genotypic and phenotypic diversity. However, the link between phenotype variation and genetic determinism is still difficult to identify, especially in wild populations. Using genome hybridization on DNA microarrays, it is now possible to identify single-feature polymorphisms among divergent yeast strains. This tool offers the possibility of applying quantitative genetics to wild yeast strains. In this instance, we studied the genetic basis for variations in acetic acid production using progeny derived from two strains from grape must isolates. The trait was quantified during alcoholic fermentation of the two strains and 108 segregants derived from their crossing. A genetic map of 2212 markers was generated using oligonucleotide microarrays, and a major quantitative trait locus (QTL) was mapped with high significance. Further investigations showed that this QTL was due to a nonsynonymous single-nucleotide polymorphism that targeted the catalytic core of asparaginase type I (ASP1) and abolished its activity. This QTL was only effective when asparagine was used as a major nitrogen source. Our results link nitrogen assimilation and CO(2) production rate to acetic acid production, as well as, on a broader scale, illustrating the specific problem of quantitative genetics when working with nonlaboratory microorganisms

Overt speech decoding from cortical activity: a comparison of different linear methods

IntroductionSpeech BCIs aim at reconstructing speech in real time from ongoing cortical activity. Ideal BCIs would need to reconstruct speech audio signal frame by frame on a millisecond-timescale. Such approaches require fast computation. In this respect, linear decoder are good candidates and have been widely used in motor BCIs. Yet, they have been very seldomly studied for speech reconstruction, and never for reconstruction of articulatory movements from intracranial activity. Here, we compared vanilla linear regression, ridge-regularized linear regressions, and partial least squares regressions for offline decoding of overt speech from cortical activity.MethodsTwo decoding paradigms were investigated: (1) direct decoding of acoustic vocoder features of speech, and (2) indirect decoding of vocoder features through an intermediate articulatory representation chained with a real-time-compatible DNN-based articulatory-to-acoustic synthesizer. Participant's articulatory trajectories were estimated from an electromagnetic-articulography dataset using dynamic time warping. The accuracy of the decoders was evaluated by computing correlations between original and reconstructed features.ResultsWe found that similar performance was achieved by all linear methods well above chance levels, albeit without reaching intelligibility. Direct and indirect methods achieved comparable performance, with an advantage for direct decoding.DiscussionFuture work will address the development of an improved neural speech decoder compatible with fast frame-by-frame speech reconstruction from ongoing activity at a millisecond timescale

Cell-to-Cell Stochastic Variation in Gene Expression Is a Complex Genetic Trait

The genetic control of common traits is rarely deterministic, with many genes contributing only to the chance of developing a given phenotype. This incomplete penetrance is poorly understood and is usually attributed to interactions between genes or interactions between genes and environmental conditions. Because many traits such as cancer can emerge from rare events happening in one or very few cells, we speculate an alternative and complementary possibility where some genotypes could facilitate these events by increasing stochastic cell-to-cell variations (or ‘noise’). As a very first step towards investigating this possibility, we studied how natural genetic variation influences the level of noise in the expression of a single gene using the yeast S. cerevisiae as a model system. Reproducible differences in noise were observed between divergent genetic backgrounds. We found that noise was highly heritable and placed under a complex genetic control. Scanning the genome, we mapped three Quantitative Trait Loci (QTL) of noise, one locus being explained by an increase in noise when transcriptional elongation was impaired. Our results suggest that the level of stochasticity in particular molecular regulations may differ between multicellular individuals depending on their genotypic background. The complex genetic architecture of noise buffering couples genetic to non-genetic robustness and provides a molecular basis to the probabilistic nature of complex traits

Natural Single-Nucleosome Epi-Polymorphisms in Yeast

Epigenomes commonly refer to the sequence of presence/absence of specific epigenetic marks along eukaryotic chromatin. Complete histone-borne epigenomes have now been described at single-nucleosome resolution from various organisms, tissues, developmental stages, or diseases, yet their intra-species natural variation has never been investigated. We describe here that the epigenomic sequence of histone H3 acetylation at Lysine 14 (H3K14ac) differs greatly between two unrelated strains of the yeast Saccharomyces cerevisiae. Using single-nucleosome chromatin immunoprecipitation and mapping, we interrogated 58,694 nucleosomes and found that 5,442 of them differed in their level of H3K14 acetylation, at a false discovery rate (FDR) of 0.0001. These Single Nucleosome Epi-Polymorphisms (SNEPs) were enriched at regulatory sites and conserved non-coding DNA sequences. Surprisingly, higher acetylation in one strain did not imply higher expression of the relevant gene. However, SNEPs were enriched in genes of high transcriptional variability and one SNEP was associated with the strength of gene activation upon stimulation. Our observations suggest a high level of inter-individual epigenomic variation in natural populations, with essential questions on the origin of this diversity and its relevance to gene x environment interactions