A single-chain and fast-responding light-inducible Cre recombinase as a novel optogenetic switch.

Optogenetics enables genome manipulations with high spatiotemporal resolution, opening exciting possibilities for fundamental and applied biological research. Here, we report the development of LiCre, a novel light-inducible Cre recombinase. LiCre is made of a single flavin-containing protein comprising the AsLOV2 photoreceptor domain of Avena sativa fused to a Cre variant carrying destabilizing mutations in its N-terminal and C-terminal domains. LiCre can be activated within minutes of illumination with blue light without the need of additional chemicals. When compared to existing photoactivatable Cre recombinases based on two split units, LiCre displayed faster and stronger activation by light as well as a lower residual activity in the dark. LiCre was efficient both in yeast, where it allowed us to control the production of ß-carotene with light, and human cells. Given its simplicity and performances, LiCre is particularly suited for fundamental and biomedical research, as well as for controlling industrial bioprocesses.

In a biologist's toolkit, the Cre protein holds a special place. Naturally found in certain viruses, this enzyme recognises and modifies specific genetic sequences, creating changes that switch on or off whatever gene is close by. Genetically engineering cells or organisms so that they carry Cre and its target sequences allows scientists to control the activation of a given gene, often in a single tissue or organ. However, this relies on the ability to activate the Cre protein 'on demand' once it is in the cells of interest. One way to do so is to split the enzyme into two pieces, which can then reassemble when exposed to blue light. Yet, this involves the challenging step of introducing both parts separately into a tissue. Instead, Duplus-Bottin et al. engineered LiCre, a new system where a large section of the Cre protein is fused to a light sensor used by oats to detect their environment. LiCre is off in the dark, but it starts to recognize and modify Cre target sequences when exposed to blue light. Duplus-Bottin et al. then assessed how LiCre compares to the two-part Cre system in baker's yeast and human kidney cells. This showed that the new protein is less 'incorrectly' active in the dark, and can switch on faster under blue light. The improved approach could give scientists a better tool to study the role of certain genes at precise locations and time points, but also help them to harness genetic sequences for industry or during gene therapy.

Caenorhabditis elegans SET1/COMPASS Maintains Germline Identity by Preventing Transcriptional Deregulation Across Generations.

Chromatin regulators contribute to the maintenance of the germline transcriptional program. In the absence of SET-2, the Caenorhabditis elegans homolog of the SET1/COMPASS H3 Lys4 (H3K4) methyltransferase, animals show transgenerational loss of germline identity, leading to sterility. To identify transcriptional signatures associated with progressive loss of fertility, we performed expression profiling of set-2 mutant germlines across generations. We identify a subset of genes whose misexpression is first observed in early generations, a step we refer to as priming; their misexpression then further progresses in late generations, as animals reach sterility. Analysis of misregulated genes shows that down-regulation of germline genes, expression of somatic transcriptional programs, and desilencing of the X-chromosome are concurrent events leading to loss of germline identity in both early and late generations. Upregulation of transcription factor LIN-15B, the C/EBP homolog CEBP-1, and TGF-ß pathway components strongly contribute to loss of fertility, and RNAi inactivation of cebp-1 and TGF-ß/Smad signaling delays the onset of sterility, showing they individually contribute to maintenance of germ cell identity. Our approach therefore identifies genes and pathways whose misexpression actively contributes to the loss of germ cell fate. More generally, our data shows how loss of a chromatin regulator in one generation leads to transcriptional changes that are amplified over subsequent generations, ultimately leading to loss of appropriate cell fate.

Cell-to-cell expression dispersion of B-cell surface proteins is linked to genetic variants in humans.

Variability in gene expression across a population of homogeneous cells is known to influence various biological processes. In model organisms, natural genetic variants were found that modify expression dispersion (variability at a fixed mean) but very few studies have detected such effects in humans. Here, we analyzed single-cell expression of four proteins (CD23, CD55, CD63 and CD86) across cell lines derived from individuals of the Yoruba population. Using data from over 30 million cells, we found substantial inter-individual variation of dispersion. We demonstrate, via de novo cell line generation and subcloning experiments, that this variation exceeds the variation associated with cellular immortalization. We detected a genetic association between the expression dispersion of CD63 and the rs971 SNP. Our results show that human DNA variants can have inherently-probabilistic effects on gene expression. Such subtle genetic effects may participate to phenotypic variation and disease outcome.

Genomics of cellular proliferation in periodic environmental fluctuations.

Living systems control cell growth dynamically by processing information from their environment. Although responses to a single environmental change have been intensively studied, little is known about how cells react to fluctuating conditions. Here, we address this question at the genomic scale by measuring the relative proliferation rate (fitness) of 3,568 yeast gene deletion mutants in out-of-equilibrium conditions: periodic oscillations between two environmental conditions. In periodic salt stress, fitness and its genetic variance largely depended on the oscillating period. Surprisingly, dozens of mutants displayed pronounced hyperproliferation under short stress periods, revealing unexpected controllers of growth under fast dynamics. We validated the implication of the high-affinity cAMP phosphodiesterase and of a regulator of protein translocation to mitochondria in this group. Periodic oscillations of extracellular methionine, a factor unrelated to salinity, also altered fitness but to a lesser extent and for different genes. The results illustrate how natural selection acts on mutations in a dynamic environment, highlighting unsuspected genetic vulnerabilities to periodic stress in molecular processes that are conserved across all eukaryotes.

Assigning function to natural allelic variation via dynamic modeling of gene network induction.

More and more natural DNA variants are being linked to physiological traits. Yet, understanding what differences they make on molecular regulations remains challenging. Important properties of gene regulatory networks can be captured by computational models. If model parameters can be "personalized" according to the genotype, their variation may then reveal how DNA variants operate in the network. Here, we combined experiments and computations to visualize natural alleles of the yeast GAL3 gene in a space of model parameters describing the galactose response network. Alleles altering the activation of Gal3p by galactose were discriminated from those affecting its activity (production/degradation or efficiency of the activated protein). The approach allowed us to correctly predict that a non-synonymous SNP would change the binding affinity of Gal3p with the Gal80p transcriptional repressor. Our results illustrate how personalizing gene regulatory models can be used for the mechanistic interpretation of genetic variants.

Exploiting Single-Cell Quantitative Data to Map Genetic Variants Having Probabilistic Effects.

Despite the recent progress in sequencing technologies, genome-wide association studies (GWAS) remain limited by a statistical-power issue: many polymorphisms contribute little to common trait variation and therefore escape detection. The small contribution sometimes corresponds to incomplete penetrance, which may result from probabilistic effects on molecular regulations. In such cases, genetic mapping may benefit from the wealth of data produced by single-cell technologies. We present here the development of a novel genetic mapping method that allows to scan genomes for single-cell Probabilistic Trait Loci that modify the statistical properties of cellular-level quantitative traits. Phenotypic values are acquired on thousands of individual cells, and genetic association is obtained from a multivariate analysis of a matrix of Kantorovich distances. No prior assumption is required on the mode of action of the genetic loci involved and, by exploiting all single-cell values, the method can reveal non-deterministic effects. Using both simulations and yeast experimental datasets, we show that it can detect linkages that are missed by classical genetic mapping. A probabilistic effect of a single SNP on cell shape was detected and validated. The method also detected a novel locus associated with elevated gene expression noise of the yeast galactose regulon. Our results illustrate how single-cell technologies can be exploited to improve the genetic dissection of certain common traits. The method is available as an open source R package called ptlmapper.

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

BACKGROUND: 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 primordial 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.

How does evolution tune biological noise?

Part 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.

CPF-associated phosphatase activity opposes condensin-mediated chromosome condensation.

Functional links connecting gene transcription and condensin-mediated chromosome condensation have been established in species ranging from prokaryotes to vertebrates. However, the exact nature of these links remains misunderstood. Here we show in fission yeast that the 3' end RNA processing factor Swd2.2, a component of the Cleavage and Polyadenylation Factor (CPF), is a negative regulator of condensin-mediated chromosome condensation. Lack of Swd2.2 does not affect the assembly of the CPF but reduces its association with chromatin. This causes only limited, context-dependent effects on gene expression and transcription termination. However, CPF-associated Swd2.2 is required for the association of Protein Phosphatase 1 PP1(Dis2) with chromatin, through an interaction with Ppn1, a protein that we identify as the fission yeast homologue of vertebrate PNUTS. We demonstrate that Swd2.2, Ppn1 and PP1Dis2 form an independent module within the CPF, which provides an essential function in the absence of the CPF-associated Ssu72 phosphatase. We show that Ppn1 and Ssu72, like Swd2.2, are also negative regulators of condensin-mediated chromosome condensation. We conclude that Swd2.2 opposes condensin-mediated chromosome condensation by facilitating the function of the two CPF-associated phosphatases PP1 and Ssu72.

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

Laboratory 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.

'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.

Natural sequence variants of yeast environmental sensors confer cell-to-cell expression variability.

Living systems may have evolved probabilistic bet hedging strategies that generate cell-to-cell phenotypic diversity in anticipation of environmental catastrophes, as opposed to adaptation via a deterministic response to environmental changes. Evolution of bet hedging assumes that genotypes segregating in natural populations modulate the level of intraclonal diversity, which so far has largely remained hypothetical. Using a fluorescent P(met17)-GFP reporter, we mapped four genetic loci conferring to a wild yeast strain an elevated cell-to-cell variability in the expression of MET17, a gene regulated by the methionine pathway. A frameshift mutation in the Erc1p transmembrane transporter, probably resulting from a release of laboratory strains from negative selection, reduced P(met17)-GFP expression variability. At a second locus, cis-regulatory polymorphisms increased mean expression of the Mup1p methionine permease, causing increased expression variability in trans. These results demonstrate that an expression quantitative trait locus (eQTL) can simultaneously have a deterministic effect in cis and a probabilistic effect in trans. Our observations indicate that the evolution of transmembrane transporter genes can tune intraclonal variation and may therefore be implicated in both reactive and anticipatory strategies of adaptation.

Single-cell phenomics reveals intra-species variation of phenotypic noise in yeast.

BACKGROUND: Most quantitative measures of phenotypic traits represent macroscopic contributions of large numbers of cells. Yet, cells of a tissue do not behave similarly, and molecular studies on several organisms have shown that regulations can be highly stochastic, sometimes generating diversified cellular phenotypes within tissues. Phenotypic noise, defined here as trait variability among isogenic cells of the same type and sharing a common environment, has therefore received a lot of attention. Given the potential fitness advantage provided by phenotypic noise in fluctuating environments, the possibility that it is directly subjected to evolutionary selection is being considered. For selection to act, phenotypic noise must differ between contemporary genotypes. Whether this is the case or not remains, however, unclear because phenotypic noise has very rarely been quantified in natural populations. RESULTS: Using automated image analysis, we describe here the phenotypic diversity of S. cerevisiae morphology at single-cell resolution. We profiled hundreds of quantitative traits in more than 1,000 cells of 37 natural strains, which represent various geographical and ecological origins of the species. We observed abundant trait variation between strains, with no correlation with their ecological origin or population history. Phenotypic noise strongly depended on the strain background. Noise variation was largely trait-specific (specific strains showing elevated noise for subset of traits) but also global (a few strains displaying elevated noise for many unrelated traits). CONCLUSIONS: Our results demonstrate that phenotypic noise does differ quantitatively between natural populations. This supports the possibility that, if noise is adaptive, microevolution may tune it in the wild. This tuning may happen on specific traits or by varying the degree of global phenotypic buffering.

Genetic modifiers of chromatin acetylation antagonize the reprogramming of epi-polymorphisms.

Natural populations are known to differ not only in DNA but also in their chromatin-associated epigenetic marks. When such inter-individual epigenomic differences (or "epi-polymorphisms") are observed, their stability is usually not known: they may or may not be reprogrammed over time or upon environmental changes. In addition, their origin may be purely epigenetic, or they may result from regulatory variation encoded in the DNA. Studying epi-polymorphisms requires, therefore, an assessment of their nature and stability. Here we estimate the stability of yeast epi-polymorphisms of chromatin acetylation, and we provide a genome-by-epigenome map of their genetic control. A transient epi-drug treatment was able to reprogram acetylation variation at more than one thousand nucleosomes, whereas a similar amount of variation persisted, distinguishing "labile" from "persistent" epi-polymorphisms. Hundreds of genetic loci underlied acetylation variation at 2,418 nucleosomes either locally (in cis) or distantly (in trans), and this genetic control overlapped only partially with the genetic control of gene expression. Trans-acting regulators were not necessarily associated with genes coding for chromatin modifying enzymes. Strikingly, "labile" and "persistent" epi-polymorphisms were associated with poor and strong genetic control, respectively, showing that genetic modifiers contribute to persistence. These results estimate the amount of natural epigenomic variation that can be lost after transient environmental exposures, and they reveal the complex genetic architecture of the DNA-encoded determinism of chromatin epi-polymorphisms. Our observations provide a basis for the development of population epigenetics.

Finding modulators of stochasticity levels by quantitative genetics.

Although bakers and wine makers constantly select, compare, and hunt for new wild strains of Saccharomyces cerevisiae, yeast geneticists have long focused on a few "standard" strains to ensure reproducibility and easiness of experimentation. And so far, the wonderful natural resource of wild genetic variation has been poorly exploited in most academic laboratories. We describe here how one can use this resource to investigate the molecular sources of stochasticity in a gene regulatory network. The approach is general enough to be applied to any network of interest, as long as the experimental read-out offers robust statistics. For a given network, a typical study first identifies two backgrounds A and B displaying different levels of stochasticity and then study the network in A × B progeny. Taking advantage of microarrays or resequencing technologies, genotyping of appropriate segregants can then lead to the genomic regions housing modulators of stochasticity. The powerful toolbox available to manipulate the yeast genome offers several ways to narrow these regions further and to unambiguously demonstrate the regulatory consequences of DNA polymorphisms.

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.

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.

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.

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

Natural 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.