Mining for genotype-phenotype relations in Saccharomyces using partial least squares

<p>Abstract</p> <p>Background</p> <p>Multivariate approaches are important due to their versatility and applications in many fields as it provides decisive advantages over univariate analysis in many ways. Genome wide association studies are rapidly emerging, but approaches in hand pay less attention to multivariate relation between genotype and phenotype. We introduce a methodology based on a BLAST approach for extracting information from genomic sequences and Soft- Thresholding Partial Least Squares (ST-PLS) for mapping genotype-phenotype relations.</p> <p>Results</p> <p>Applying this methodology to an extensive data set for the model yeast <it>Saccharomyces cerevisiae</it>, we found that the relationship between genotype-phenotype involves surprisingly few genes in the sense that an overwhelmingly large fraction of the phenotypic variation can be explained by variation in less than 1% of the full gene reference set containing 5791 genes. These phenotype influencing genes were evolving 20% faster than non-influential genes and were unevenly distributed over cellular functions, with strong enrichments in functions such as cellular respiration and transposition. These genes were also enriched with known paralogs, stop codon variations and copy number variations, suggesting that such molecular adjustments have had a disproportionate influence on <it>Saccharomyces </it>yeasts recent adaptation to environmental changes in its ecological niche.</p> <p>Conclusions</p> <p>BLAST and PLS based multivariate approach derived results that adhere to the known yeast phylogeny and gene ontology and thus verify that the methodology extracts a set of fast evolving genes that capture the phylogeny of the yeast strains. The approach is worth pursuing, and future investigations should be made to improve the computations of genotype signals as well as variable selection procedure within the PLS framework.</p

Extracting expression modules from perturbational gene expression compendia

<p>Abstract</p> <p>Background</p> <p>Compendia of gene expression profiles under chemical and genetic perturbations constitute an invaluable resource from a systems biology perspective. However, the perturbational nature of such data imposes specific challenges on the computational methods used to analyze them. In particular, traditional clustering algorithms have difficulties in handling one of the prominent features of perturbational compendia, namely partial coexpression relationships between genes. Biclustering methods on the other hand are specifically designed to capture such partial coexpression patterns, but they show a variety of other drawbacks. For instance, some biclustering methods are less suited to identify overlapping biclusters, while others generate highly redundant biclusters. Also, none of the existing biclustering tools takes advantage of the staple of perturbational expression data analysis: the identification of differentially expressed genes.</p> <p>Results</p> <p>We introduce a novel method, called ENIGMA, that addresses some of these issues. ENIGMA leverages differential expression analysis results to extract expression modules from perturbational gene expression data. The core parameters of the ENIGMA clustering procedure are automatically optimized to reduce the redundancy between modules. In contrast to the biclusters produced by most other methods, ENIGMA modules may show internal substructure, i.e. subsets of genes with distinct but significantly related expression patterns. The grouping of these (often functionally) related patterns in one module greatly aids in the biological interpretation of the data. We show that ENIGMA outperforms other methods on artificial datasets, using a quality criterion that, unlike other criteria, can be used for algorithms that generate overlapping clusters and that can be modified to take redundancy between clusters into account. Finally, we apply ENIGMA to the Rosetta compendium of expression profiles for <it>Saccharomyces cerevisiae </it>and we analyze one pheromone response-related module in more detail, demonstrating the potential of ENIGMA to generate detailed predictions.</p> <p>Conclusion</p> <p>It is increasingly recognized that perturbational expression compendia are essential to identify the gene networks underlying cellular function, and efforts to build these for different organisms are currently underway. We show that ENIGMA constitutes a valuable addition to the repertoire of methods to analyze such data.</p

Variations in Stress Sensitivity and Genomic Expression in Diverse S. cerevisiae Isolates

Interactions between an organism and its environment can significantly influence phenotypic evolution. A first step toward understanding this process is to characterize phenotypic diversity within and between populations. We explored the phenotypic variation in stress sensitivity and genomic expression in a large panel of Saccharomyces strains collected from diverse environments. We measured the sensitivity of 52 strains to 14 environmental conditions, compared genomic expression in 18 strains, and identified gene copy-number variations in six of these isolates. Our results demonstrate a large degree of phenotypic variation in stress sensitivity and gene expression. Analysis of these datasets reveals relationships between strains from similar niches, suggests common and unique features of yeast habitats, and implicates genes whose variable expression is linked to stress resistance. Using a simple metric to suggest cases of selection, we found that strains collected from oak exudates are phenotypically more similar than expected based on their genetic diversity, while sake and vineyard isolates display more diverse phenotypes than expected under a neutral model. We also show that the laboratory strain S288c is phenotypically distinct from all of the other strains studied here, in terms of stress sensitivity, gene expression, Ty copy number, mitochondrial content, and gene-dosage control. These results highlight the value of understanding the genetic basis of phenotypic variation and raise caution about using laboratory strains for comparative genomics

A Catalog of Neutral and Deleterious Polymorphism in Yeast

The abundance and identity of functional variation segregating in natural populations is paramount to dissecting the molecular basis of quantitative traits as well as human genetic diseases. Genome sequencing of multiple organisms of the same species provides an efficient means of cataloging rearrangements, insertion, or deletion polymorphisms (InDels) and single-nucleotide polymorphisms (SNPs). While inbreeding depression and heterosis imply that a substantial amount of polymorphism is deleterious, distinguishing deleterious from neutral polymorphism remains a significant challenge. To identify deleterious and neutral DNA sequence variation within Saccharomyces cerevisiae, we sequenced the genome of a vineyard and oak tree strain and compared them to a reference genome. Among these three strains, 6% of the genome is variable, mostly attributable to variation in genome content that results from large InDels. Out of the 88,000 polymorphisms identified, 93% are SNPs and a small but significant fraction can be attributed to recent interspecific introgression and ectopic gene conversion. In comparison to the reference genome, there is substantial evidence for functional variation in gene content and structure that results from large InDels, frame-shifts, and polymorphic start and stop codons. Comparison of polymorphism to divergence reveals scant evidence for positive selection but an abundance of evidence for deleterious SNPs. We estimate that 12% of coding and 7% of noncoding SNPs are deleterious. Based on divergence among 11 yeast species, we identified 1,666 nonsynonymous SNPs that disrupt conserved amino acids and 1,863 noncoding SNPs that disrupt conserved noncoding motifs. The deleterious coding SNPs include those known to affect quantitative traits, and a subset of the deleterious noncoding SNPs occurs in the promoters of genes that show allele-specific expression, implying that some cis-regulatory SNPs are deleterious. Our results show that the genome sequences of both closely and distantly related species provide a means of identifying deleterious polymorphisms that disrupt functionally conserved coding and noncoding sequences

GJ 3090 b: one of the most favourable mini-Neptune for atmospheric characterisation

We report the detection of GJ 3090 b (TOI-177.01), a mini-Neptune on a 2.9-day orbit transiting a bright (K = 7.3 mag) M2 dwarf located at 22 pc. The planet was identified by the Transiting Exoplanet Survey Satellite and was confirmed with the High Accuracy Radial velocity Planet Searcher radial velocities. Seeing-limited photometry and speckle imaging rule out nearby eclipsing binaries. Additional transits were observed with the LCOGT, Spitzer, and ExTrA telescopes. We characterise the star to have a mass of 0.519 ± 0.013 M⊙ and a radius of 0.516 ± 0.016 R⊙. We modelled the transit light curves and radial velocity measurements and obtained a planetary mass of 3.34 ± 0.72 ME, a radius of 2.13 ± 0.11 RE, and a mean density of 1.89−0.45+0.52 g cm−3. The low density of the planet implies the presence of volatiles, and its radius and insolation place it immediately above the radius valley at the lower end of the mini-Neptune cluster. A coupled atmospheric and dynamical evolution analysis of the planet is inconsistent with a pure H–He atmosphere and favours a heavy mean molecular weight atmosphere. The transmission spectroscopy metric of 221−46+66 means that GJ 3090 b is the second or third most favorable mini-Neptune after GJ 1214 b whose atmosphere may be characterised. At almost half the mass of GJ 1214 b, GJ 3090 b is an excellent probe of the edge of the transition between super-Earths and mini-Neptunes. We identify an additional signal in the radial velocity data that we attribute to a planet candidate with an orbital period of 13 days and a mass of 17.1−3.2+8.9 ME, whose transits are not detected

TOI-269 b: An eccentric sub-Neptune transiting a M2 dwarf revisited with ExTrA

We present the confirmation of a new sub-Neptune close to the transition between super-Earths and sub-Neptunes transiting the M2 dwarf TOI-269 (TIC 220 479 565, V = 14.4 mag, J = 10.9 mag, Ro = 0.40 Ro, Mo = 0.39 Mo, d = 57 pc). The exoplanet candidate has been identified in multiple TESS sectors, and validated with high-precision spectroscopy from HARPS and ground-based photometric follow-up from ExTrA and LCO-CTIO. We determined mass, radius, and bulk density of the exoplanet by jointly modeling both photometry and radial velocities with juliet. The transiting exoplanet has an orbital period of P = 3.6977104 ± 0.0000037 days, a radius of 2.77 ± 0.12 R·, and a mass of 8.8 ± 1.4 M·. Since TOI-269 b lies among the best targets of its category for atmospheric characterization, it would be interesting to probe the atmosphere of this exoplanet with transmission spectroscopy in order to compare it to other sub-Neptunes. With an eccentricity e = 0.425-0.086+0.082, TOI-269 b has one of the highest eccentricities of the exoplanets with periods less than 10 days. The star being likely a few Gyr old, this system does not appear to be dynamically young. We surmise TOI-269 b may have acquired its high eccentricity as it migrated inward through planet-planet interactions

HD 207897 b : a dense sub-Neptune transiting a nearby and bright K-type star

We present the discovery and characterization of a transiting sub-Neptune that orbits the nearby (28 pc) and bright (V = 8.37) K0V star HD 207897 (TOI-1611) with a 16.20-day period. This discovery is based on photometric measurements from the Transiting Exoplanet Survey Satellite mission and radial velocity (RV) observations from the SOPHIE, Automated Planet Finder, and HIRES high-precision spectrographs. We used EXOFASTv2 to model the parameters of the planet and its host star simultaneously, combining photometric and RV data to determine the planetary system parameters. We show that the planet has a radius of 2.50 ± 0.08 RE and a mass of either 14.4 ± 1.6 ME or 15.9 ± 1.6 ME with nearly equal probability. The two solutions correspond to two possibilities for the stellar activity period. The density accordingly is either 5.1 ± 0.7 g cm−3 or 5.5−0.7+0.8 g cm−3, making it one of the relatively rare dense sub-Neptunes. The existence of this dense planet at only 0.12 AU from its host star is unusual in the currently observed sub-Neptune (2 < RE < 4) population. The most likely scenario is that this planet has migrated to its current position

CARMENES: high-resolution spectra and precise radial velocities in the red and infrared

SPIE Astronomical Telescopes + Instrumentation (2018, Austin, Texas, United States

Dissection of genetically complex traits with extremely large pools of yeast segregants

Most heritable traits, including many human diseases 1, are caused by multiple loci. Studies in both humans and model organisms, such as yeast, have failed to detect a large fraction of the loci that underlie such complex traits 2,3. A lack of statistical power to identify multiple loci with small effects is undoubtedly one of the primary reasons for this problem. We have developed a method in yeast that allows the use of dramatically larger sample sizes than previously possible and hence permits the detection of multiple loci with small effects. The method involves generating very large numbers of progeny from a cross between two strains and then phenotyping and genotyping pools of these offspring. We applied the method to 17 chemical resistance traits and mitochondrial function, and identified loci for each of these phenotypes. We show that the range of genetic complexity underlying these quantitative traits is highly variable, with some traits influenced by one major locus and others due to at least 20 loci. Our results provide an empirical demonstration of the genetic complexity of many traits and show that it is possible to identify many of the underlying factors using straightforward techniques. Our method should have broad applications in yeast and can be extended to other organisms

Fast-acting and nearly gratuitous induction of gene expression and protein depletion in Saccharomyces cerevisiae

We developed systems to rapidly express any yeast gene or to specifically degrade any protein, each with minimal untargeted disturbance of cell physiology. We illustrate applications of these new tools for elucidating the architecture and dynamics of genetic regulatory networks