Global nucleosome occupancy in yeast

BACKGROUND: Although eukaryotic genomes are generally thought to be entirely chromatin-associated, the activated PHO5 promoter in yeast is largely devoid of nucleosomes. We systematically evaluated nucleosome occupancy in yeast promoters by immunoprecipitating nucleosomal DNA and quantifying enrichment by microarrays. RESULTS: Nucleosome depletion is observed in promoters that regulate active genes and/or contain multiple evolutionarily conserved motifs that recruit transcription factors. The Rap1 consensus was the only binding motif identified in a completely unbiased search of nucleosome-depleted promoters. Nucleosome depletion in the vicinity of Rap1 consensus sites in ribosomal protein gene promoters was also observed by real-time PCR and micrococcal nuclease digestion. Nucleosome occupancy in these regions was increased by the small molecule rapamycin or, in the case of the RPS11B promoter, by removing the Rap1 consensus sites. CONCLUSIONS: The presence of transcription factor-binding motifs is an important determinant of nucleosome depletion. Most motifs are associated with marked depletion only when they appear in combination, consistent with a model in which transcription factors act collaboratively to exclude nucleosomes and gain access to target sites in the DNA. In contrast, Rap1-binding sites cause marked depletion under steady-state conditions. We speculate that nucleosome depletion enables Rap1 to define chromatin domains and alter them in response to environmental cues

Evolutionary rescue of phosphomannomutase deficiency in yeast models of human disease

The most common cause of human congenital disorders of glycosylation (CDG) are mutations in the phosphomannomutase gene PMM2, which affect protein N-linked glycosylation. The yeast gene SEC53 encodes a homolog of human PMM2. We evolved 384 populations of yeast harboring one of two human-disease-associated alleles, sec53-V238M and sec53-F126L, or wild-type SEC53. We find that after 1000 generations, most populations compensate for the slow-growth phenotype associated with the sec53 human-disease-associated alleles. Through whole-genome sequencing we identify compensatory mutations, including known SEC53 genetic interactors. We observe an enrichment of compensatory mutations in other genes whose human homologs are associated with Type 1 CDG, including PGM1, which encodes the minor isoform of phosphoglucomutase in yeast. By genetic reconstruction, we show that evolved pgm1 mutations are dominant and allele-specific genetic interactors that restore both protein glycosylation and growth of yeast harboring the sec53-V238M allele. Finally, we characterize the enzymatic activity of purified Pgm1 mutant proteins. We find that reduction, but not elimination, of Pgm1 activity best compensates for the deleterious phenotypes associated with the sec53-V238M allele. Broadly, our results demonstrate the power of experimental evolution as a tool for identifying genes and pathways that compensate for human-disease-associated alleles

Using Expression and Genotype to Predict Drug Response in Yeast

Personalized, or genomic, medicine entails tailoring pharmacological therapies according to individual genetic variation at genomic loci encoding proteins in drug-response pathways. It has been previously shown that steady-state mRNA expression can be used to predict the drug response (i.e., sensitivity or resistance) of non-genotyped mammalian cancer cell lines to chemotherapeutic agents. In a real-world setting, clinicians would have access to both steady-state expression levels of patient tissue(s) and a patient's genotypic profile, and yet the predictive power of transcripts versus markers is not well understood. We have previously shown that a collection of genotyped and expression-profiled yeast strains can provide a model for personalized medicine. Here we compare the predictive power of 6,229 steady-state mRNA transcript levels and 2,894 genotyped markers using a pattern recognition algorithm. We were able to predict with over 70% accuracy the drug sensitivity of 104 individual genotyped yeast strains derived from a cross between a laboratory strain and a wild isolate. We observe that, independently of drug mechanism of action, both transcripts and markers can accurately predict drug response. Marker-based prediction is usually more accurate than transcript-based prediction, likely reflecting the genetic determination of gene expression in this cross

Joint Genetic Analysis of Gene Expression Data with Inferred Cellular Phenotypes

Even within a defined cell type, the expression level of a gene differs in individual samples. The effects of genotype, measured factors such as environmental conditions, and their interactions have been explored in recent studies. Methods have also been developed to identify unmeasured intermediate factors that coherently influence transcript levels of multiple genes. Here, we show how to bring these two approaches together and analyse genetic effects in the context of inferred determinants of gene expression. We use a sparse factor analysis model to infer hidden factors, which we treat as intermediate cellular phenotypes that in turn affect gene expression in a yeast dataset. We find that the inferred phenotypes are associated with locus genotypes and environmental conditions and can explain genetic associations to genes in trans. For the first time, we consider and find interactions between genotype and intermediate phenotypes inferred from gene expression levels, complementing and extending established results

Using Stochastic Causal Trees to Augment Bayesian Networks for Modeling eQTL Datasets

<p>Abstract</p> <p>Background</p> <p>The combination of genotypic and genome-wide expression data arising from segregating populations offers an unprecedented opportunity to model and dissect complex phenotypes. The immense potential offered by these data derives from the fact that genotypic variation is the sole source of perturbation and can therefore be used to reconcile changes in gene expression programs with the parental genotypes. To date, several methodologies have been developed for modeling eQTL data. These methods generally leverage genotypic data to resolve causal relationships among gene pairs implicated as associates in the expression data. In particular, leading studies have augmented Bayesian networks with genotypic data, providing a powerful framework for learning and modeling causal relationships. While these initial efforts have provided promising results, one major drawback associated with these methods is that they are generally limited to resolving causal orderings for transcripts most proximal to the genomic loci. In this manuscript, we present a probabilistic method capable of learning the causal relationships between transcripts at all levels in the network. We use the information provided by our method as a prior for Bayesian network structure learning, resulting in enhanced performance for gene network reconstruction.</p> <p>Results</p> <p>Using established protocols to synthesize eQTL networks and corresponding data, we show that our method achieves improved performance over existing leading methods. For the goal of gene network reconstruction, our method achieves improvements in recall ranging from 20% to 90% across a broad range of precision levels and for datasets of varying sample sizes. Additionally, we show that the learned networks can be utilized for expression quantitative trait loci mapping, resulting in upwards of 10-fold increases in recall over traditional univariate mapping.</p> <p>Conclusions</p> <p>Using the information from our method as a prior for Bayesian network structure learning yields large improvements in accuracy for the tasks of gene network reconstruction and expression quantitative trait loci mapping. In particular, our method is effective for establishing causal relationships between transcripts located both proximally and distally from genomic loci.</p

Preoperative Red Cell Distribution Width and 30-day mortality in older patients undergoing non-cardiac surgery: a retrospective cohort observational study

Increased red cell distribution width (RDW) is associated with poorer outcomes in various patient populations. We investigated the association between preoperative RDW and anaemia on 30-day postoperative mortality among elderly patients undergoing non-cardiac surgery. Medical records of 24,579 patients aged 65 and older who underwent surgery under anaesthesia between 1 January 2012 and 31 October 2016 were retrospectively analysed. Patients who died within 30 days had higher median RDW (15.0%) than those who were alive (13.4%). Based on multivariate logistic regression, in our cohort of elderly patients undergoing non-cardiac surgery, moderate/severe preoperative anaemia (aOR 1.61, p = 0.04) and high preoperative RDW levels in the 3rd quartile (>13.4% and ≤14.3%) and 4th quartile (>14.3%) were significantly associated with increased odds of 30-day mortality - (aOR 2.12, p = 0.02) and (aOR 2.85, p = 0.001) respectively, after adjusting for the effects of transfusion, surgical severity, priority of surgery, and comorbidities. Patients with high RDW, defined as >15.7% (90th centile), and preoperative anaemia have higher odds of 30-day mortality compared to patients with anaemia and normal RDW. Thus, preoperative RDW independently increases risk of 30-day postoperative mortality, and future risk stratification strategies should include RDW as a factor

Implication of 4E-BP1 protein dephosphorylation and accumulation in pancreatic cancer cell death induced by combined gemcitabine and TRAIL

Pancreatic cancer cells show varying sensitivity to the anticancer effects of gemcitabine. However, as a chemotherapeutic agent, gemcitabine can cause intolerably high levels of toxicity and patients often develop resistance to the beneficial effects of this drug. Combination studies show that use of gemcitabine with the pro-apoptotic cytokine TRAIL can enhance the inhibition of survival and induction of apoptosis of pancreatic cancer cells. Additionally, following combination treatment there is a dramatic increase in the level of the hypophosphorylated form of the tumour suppressor protein 4E-BP1. This is associated with inhibition of mTOR activity, resulting from caspase-mediated cleavage of the Raptor and Rictor components of mTOR. Use of the pan-caspase inhibitor Z-VAD-FMK indicates that the increase in level of 4E-BP1 is also caspase-mediated. ShRNA-silencing of 4E-BP1 expression renders cells more resistant to cell death induced by the combination treatment. Since the levels of 4E-BP1 are relatively low in untreated pancreatic cancer cells these results suggest that combined therapy with gemcitabine and TRAIL could improve the responsiveness of tumours to treatment by elevating the expression of 4E-BP1

Methodological approaches in application of synthetic lethality screening towards anticancer therapy

A promising direction in the development of selective less toxic cancer drugs is the usage of synthetic lethality concept. The availability of large-scale synthetic low-molecular-weight chemical libraries has allowed HTS for compounds synergistic lethal with defined human cancer aberrations in activated oncogenes or tumour suppressor genes. The search for synthetic lethal chemicals in human/mouse tumour cells is greatly aided by a prior knowledge of relevant signalling and DNA repair pathways, allowing for educated guesses on the preferred potential therapeutic targets. The recent generation of human/rodents genome-wide siRNAs, and shRNA-expressing libraries, should further advance this more focused approach to cancer drug discovery

Saccharomyces cerevisiae: Population Divergence and Resistance to Oxidative Stress in Clinical, Domesticated and Wild Isolates

BACKGROUND: Saccharomyces cerevisiae has been associated with human life for millennia in the brewery and bakery. Recently it has been recognized as an emerging opportunistic pathogen. To study the evolutionary history of S. cerevisiae, the origin of clinical isolates and the importance of a virulence-associated trait, population genetics and phenotypic assays have been applied to an ecologically diverse set of 103 strains isolated from clinics, breweries, vineyards, fruits, soil, commercial supplements and insect guts. METHODOLOGY/PRINCIPAL FINDINGS: DNA sequence data from five nuclear DNA loci were analyzed for population structure and haplotype distribution. Additionally, all strains were tested for survival of oxidative stress, a trait associated with microbial pathogenicity. DNA sequence analyses identified three genetic subgroups within the recombining S. cerevisiae strains that are associated with ecology, geography and virulence. Shared alleles suggest that the clinical isolates contain genetic contribution from the fruit isolates. Clinical and fruit isolates exhibit high levels of recombination, unlike the genetically homogenous soil isolates in which no recombination was detected. However, clinical and soil isolates were more resistant to oxidative stress than any other population, suggesting a correlation between survival in oxidative stress and yeast pathogenicity. CONCLUSIONS/SIGNIFICANCE: Population genetic analyses of S. cerevisiae delineated three distinct groups, comprising primarily the (i) human-associated brewery and vineyard strains, (ii) clinical and fruit isolates (iii) and wild soil isolates from eastern U.S. The interactions between S. cerevisiae and humans potentiate yeast evolution and the development of genetically, ecologically and geographically divergent groups

An investigation into aripiprazole's partial D(2) agonist effects within the dorsolateral prefrontal cortex during working memory in healthy volunteers

Rationale: Working memory impairments in schizophrenia have been attributed to dysfunction of the dorsolateral prefrontal cortex (DLPFC) which in turn may be due to low DLPFC dopamine innervation. Conventional antipsychotic drugs block DLPFC D2 receptors, and this may lead to further dysfunction and working memory impairments. Aripiprazole is a D2 receptor partial agonist hypothesised to enhance PFC dopamine functioning, possibly improving working memory. Objectives: We probed the implications of the partial D2 receptor agonist actions of aripiprazole within the DLPFC during working memory. Investigations were carried out in healthy volunteers to eliminate confounds of illness or medication status. Aripiprazole’s prefrontal actions were compared with the D2/5-HT2A blocker risperidone to separate aripiprazole’s unique prefrontal D2 agonist actions from its serotinergic and striatal D2 actions that it shares with risperidone. Method: A double-blind, placebo-controlled, parallel design was implemented. Participants received a single dose of either 5 mg aripiprazole, 1 mg risperidone or placebo before performing the n-back task whilst undergoing fMRI scanning. Results: Compared with placebo, the aripiprazole group demonstrated enhanced DLPFC activation associated with a trend for improved discriminability (d’) and speeded reaction times. In contrast to aripiprazole’s neural effects, the risperidone group demonstrated a trend for reduced DLPFC recruitment. Unexpectedly, the risperidone group demonstrated similar effects to aripiprazole on d’ and additionally had reduced errors of commission compared with placebo. Conclusion: Aripiprazole has unique DLPFC actions attributed to its prefrontal D2 agonist action. Risperidone’s serotinergic action that results in prefrontal dopamine release may have protected against any impairing effects of its prefrontal D2 blockade