Genome-Wide Analysis of Nucleotide-Level Variation in Commonly Used Saccharomyces cerevisiae Strains

Ten years have passed since the genome of Saccharomyces cerevisiae–more precisely, the S288c strain–was completely sequenced. However, experimental work in yeast is commonly performed using strains that are of unknown genetic relationship to S288c. Here, we characterized the nucleotide-level similarity between S288c and seven commonly used lab strains (A364A, W303, FL100, CEN.PK, ∑1278b, SK1 and BY4716) using 25mer oligonucleotide microarrays that provide complete and redundant coverage of the ∼12 Mb Saccharomyces cerevisiae genome. Using these data, we assessed the frequency and distribution of nucleotide variation in comparison to the sequenced reference genome. These data allow us to infer the relationships between experimentally important strains of yeast and provide insight for experimental designs that are sensitive to sequence variation. We propose a rational approach for near complete sequencing of strains related to the reference using these data and directed re-sequencing. These data and new visualization tools are accessible online in a new resource: the Yeast SNPs Browser (YSB; http://gbrowse.princeton.edu/cgi-bin/gbrowse/yeast_strains_snps) that is available to all researchers

DAF-12 Regulates a Connected Network of Genes to Ensure Robust Developmental Decisions

The nuclear receptor DAF-12 has roles in normal development, the decision to pursue dauer development in unfavorable conditions, and the modulation of adult aging. Despite the biologic importance of DAF-12, target genes for this receptor are largely unknown. To identify DAF-12 targets, we performed chromatin immunoprecipitation followed by hybridization to whole-genome tiling arrays. We identified 1,175 genomic regions to be bound in vivo by DAF-12, and these regions are enriched in known DAF-12 binding motifs and act as DAF-12 response elements in transfected cells and in transgenic worms. The DAF-12 target genes near these binding sites include an extensive network of interconnected heterochronic and microRNA genes. We also identify the genes encoding components of the miRISC, which is required for the control of target genes by microRNA, as a target of DAF-12 regulation. During reproductive development, many of these target genes are misregulated in daf-12(0) mutants, but this only infrequently results in developmental phenotypes. In contrast, we and others have found that null daf-12 mutations enhance the phenotypes of many miRISC and heterochronic target genes. We also find that environmental fluctuations significantly strengthen the weak heterochronic phenotypes of null daf-12 alleles. During diapause, DAF-12 represses the expression of many heterochronic and miRISC target genes, and prior work has demonstrated that dauer formation can suppress the heterochronic phenotypes of many of these target genes in post-dauer development. Together these data are consistent with daf-12 acting to ensure developmental robustness by committing the animal to adult or dauer developmental programs despite variable internal or external conditions

IMPROVE-PD Finder: A Web-Based Platform to Search and Share Peritoneal Dialysis Biobank, Registry, and Clinical Trial Metadata

Peritoneal dialysis (PD) is a life-sustaining kidney replacement therapy for the increasing number of people with permanent kidney failure across all age groups worldwide. Although PD potentially offers socioeconomic and performance benefits over hemodialysis, both treatments severely accelerate complications of chronic kidney disease, in particular atherosclerotic disease progression that worsens outcomes when compared with non-dialysis patients.1 Improved understanding of the underlying molecular pathogenic mechanisms should help in the design of interventions that improve outcomes.2 Current state of the art in PD research, however, faces major limitations. Although there are numerous in vitro and ex vivo studies on complex cellular and molecular networks active in PD3, 4, 5 and in vivo animal models of PD6, 7, 8 that provide in-depth pathomechanistic insights and allow identification of promising therapeutic targets,9,S1,S2 translation into clinical studies is a major challenge.S3 Patient studies that aim to substantiate experimental findings with definitive clinical outcome data are mostly small. As a result, they have not provided sufficient power to derive meaningful or clinically implementable conclusions.2 Basic PD technique has hardly changed over decades, despite high PD-related complication rates. Randomized prospective trials with hard clinical end points studied with adequate power are difficult to realize in a multifactorial setting with low patient numbers (360,000 worldwide) and are associated with high costs. To overcome these barriers intermediate end points such as PD effluent biomarkers associated (but not necessarily causally related) with hard clinical end points and composite end points are often studied.S4,S5 Equally, combining analyses of existing cohort studies and trial data through collaborative sharing might be of considerable benefit

Evolution of Mutational Robustness in the Yeast Genome: A Link to Essential Genes and Meiotic Recombination Hotspots

Deleterious mutations inevitably emerge in any evolutionary process and are speculated to decisively influence the structure of the genome. Meiosis, which is thought to play a major role in handling mutations on the population level, recombines chromosomes via non-randomly distributed hot spots for meiotic recombination. In many genomes, various types of genetic elements are distributed in patterns that are currently not well understood. In particular, important (essential) genes are arranged in clusters, which often cannot be explained by a functional relationship of the involved genes. Here we show by computer simulation that essential gene (EG) clustering provides a fitness benefit in handling deleterious mutations in sexual populations with variable levels of inbreeding and outbreeding. We find that recessive lethal mutations enforce a selective pressure towards clustered genome architectures. Our simulations correctly predict (i) the evolution of non-random distributions of meiotic crossovers, (ii) the genome-wide anti-correlation of meiotic crossovers and EG clustering, (iii) the evolution of EG enrichment in pericentromeric regions and (iv) the associated absence of meiotic crossovers (cold centromeres). Our results furthermore predict optimal crossover rates for yeast chromosomes, which match the experimentally determined rates. Using a Saccharomyces cerevisiae conditional mutator strain, we show that haploid lethal phenotypes result predominantly from mutation of single loci and generally do not impair mating, which leads to an accumulation of mutational load following meiosis and mating. We hypothesize that purging of deleterious mutations in essential genes constitutes an important factor driving meiotic crossover. Therefore, the increased robustness of populations to deleterious mutations, which arises from clustered genome architectures, may provide a significant selective force shaping crossover distribution. Our analysis reveals a new aspect of the evolution of genome architectures that complements insights about molecular constraints, such as the interference of pericentromeric crossovers with chromosome segregation

A combination of the immunohistochemical markers CK7 and SATB2 is highly sensitive and specific for distinguishing primary ovarian mucinous tumors from colorectal and appendiceal metastases

This study is supported by research funds from Cancer Research Society of Canada (19319). NSM is supported by the NSW Ministry of Health and UNSW Sydney under the NSW Health PhD Scholarship Program, and the Translational Cancer Research Network, a translational cancer research center program funded by the Cancer Institute NSW. The Gynaecological Oncology Biobank at Westmead was funded by Cancer Institute NSW (12/RIG/1–17 and 15/RIG/1–16) and the National Health and Medical Research Council of Australia (ID310670, ID628903). FM is funded by University of Pittsburgh School of Medicine Dean's Faculty Advancement Award. The HOPE study is funded by: US National Cancer Institute (K07-CA80668, P50-CA159981, R01CA095023), US Army Medical Research and Materiel Command (DAMD17–02–1–0669) and NIH/National Center for Research Resources/General Clinical Research Center (MO1- RR000056). KS is funded by the Swedish Cancer foundation. The Generations Study thank Breast Cancer Now, the Institute of Cancer Research and Ovarian Cancer Action for support and funding. The ICR acknowledge NHS funding to the NIHR Biomedical Research Centre. Tissue samples for GER were provided by the tissue bank of the National Center for Tumor Diseases (NCT, Heidelberg, Germany) in accordance with the regulations of the tissue bank and the approval of the ethics committee of the University of Heidelberg. The Health Science Alliance (HSA) Biobank acknowledges the UNSW Biorepository, UNSW Sydney, Australia. We thank Shuhong Liu, Young Ou, and Deon Richards for immunohistochemical stains, and Thomas Kryton, BFA, digital imaging specialist for Alberta Public Lab for creating the figures. We especially thank all the study participants, health care staff and data providers internationally who have made this research possible