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Oncogenesis is driven by germline, environmental and stochastic factors. It is unknown how these interact to produce the molecular phenotypes of tumors. We therefore quantified the influence of germline polymorphisms on the somatic epigenome of 589 localized prostate tumors. Predisposition risk loci influence a tumor’s epigenome, uncovering a mechanism for cancer susceptibility. We identified and validated 1,178 loci associated with altered methylation in tumoral but not nonmalignant tissue. These tumor methylation quantitative trait loci influence chromatin structure, as well as RNA and protein abundance. One prominent tumor methylation quantitative trait locus is associated with AKT1 expression and is predictive of relapse after definitive local therapy in both discovery and validation cohorts. These data reveal intricate crosstalk between the germ line and the epigenome of primary tumors, which may help identify germline biomarkers of aggressive disease to aid patient triage and optimize the use of more invasive or expensive diagnostic assays. Genetic variants in the germ line modulate DNA methylation in tumors and contribute to the aggressiveness of prostate cancer.

The Polymerase Associated Factor 1 complex (Paf1C) is a multifunctional regulator of eukaryotic gene expression important for the coordination of transcription with chromatin modification and post-transcriptional processes. In this study, we investigated the extent to which the functions of Paf1C combine to regulate the Saccharomyces cerevisiae transcriptome. While previous studies focused on the roles of Paf1C in controlling mRNA levels, here, we took advantage of a genetic background that enriches for unstable transcripts, and demonstrate that deletion of PAF1 affects all classes of Pol II transcripts including multiple classes of noncoding RNAs (ncRNAs). By conducting a de novo differential expression analysis independent of gene annotations, we found that Paf1 positively and negatively regulates antisense transcription at multiple loci. Comparisons with nascent transcript data revealed that many, but not all, changes in RNA levels detected by our analysis are due to changes in transcription instead of post-transcriptional events. To investigate the mechanisms by which Paf1 regulates protein-coding genes, we focused on genes involved in iron and phosphate homeostasis, which were differentially affected by PAF1 deletion. Our results indicate that Paf1 stimulates phosphate gene expression through a mechanism that is independent of any individual Paf1C-dependent histone modification. In contrast, the inhibition of iron gene expression by Paf1 correlates with a defect in H3 K36 trimethylation. Finally, we showed that one iron regulon gene, FET4, is coordinately controlled by Paf1 and transcription of upstream noncoding DNA. Together, these data identify roles for Paf1C in controlling both coding and noncoding regions of the yeast genome.

Contains fulltext : 178026.pdf (Publisher’s version ) (Closed access) In acute myeloid leukaemia, long-term survival is poor as most patients relapse despite achieving remission. Historically, the failure of therapy has been thought to be due to mutations that produce drug resistance, possibly arising as a consequence of the mutagenic properties of chemotherapy drugs. However, other lines of evidence have pointed to the pre-existence of drug-resistant cells. For example, deep sequencing of paired diagnosis and relapse acute myeloid leukaemia samples has provided direct evidence that relapse in some cases is generated from minor genetic subclones present at diagnosis that survive chemotherapy, suggesting that resistant cells are generated by evolutionary processes before treatment and are selected by therapy. Nevertheless, the mechanisms of therapy failure and capacity for leukaemic regeneration remain obscure, as sequence analysis alone does not provide insight into the cell types that are fated to drive relapse. Although leukaemia stem cells have been linked to relapse owing to their dormancy and self-renewal properties, and leukaemia stem cell gene expression signatures are highly predictive of therapy failure, experimental studies have been primarily correlative and a role for leukaemia stem cells in acute myeloid leukaemia relapse has not been directly proved. Here, through combined genetic and functional analysis of purified subpopulations and xenografts from paired diagnosis/relapse samples, we identify therapy-resistant cells already present at diagnosis and two major patterns of relapse. In some cases, relapse originated from rare leukaemia stem cells with a haematopoietic stem/progenitor cell phenotype, while in other instances relapse developed from larger subclones of immunophenotypically committed leukaemia cells that retained strong stemness transcriptional signatures. The identification of distinct patterns of relapse should lead to improved methods for disease management and monitoring in acute myeloid leukaemia. Moreover, the shared functional and transcriptional stemness properties that underlie both cellular origins of relapse emphasize the importance of developing new therapeutic approaches that target stemness to prevent relapse.

Pancreatic cancer, a highly aggressive tumour type with uniformly poor prognosis, exemplifies the classically held view of stepwise cancer development. The current model of tumorigenesis, based on analyses of precursor lesions, termed pancreatic intraepithelial neoplasm (PanINs) lesions, makes two predictions: first, that pancreatic cancer develops through a particular sequence of genetic alterations (KRAS, followed by CDKN2A, then TP53 and SMAD4); and second, that the evolutionary trajectory of pancreatic cancer progression is gradual because each alteration is acquired independently. A shortcoming of this model is that clonally expanded precursor lesions do not always belong to the tumour lineage, indicating that the evolutionary trajectory of the tumour lineage and precursor lesions can be divergent. This prevailing model of tumorigenesis has contributed to the clinical notion that pancreatic cancer evolves slowly and presents at a late stage. However, the propensity for this disease to rapidly metastasize and the inability to improve patient outcomes, despite efforts aimed at early detection, suggest that pancreatic cancer progression is not gradual. Here, using newly developed informatics tools, we tracked changes in DNA copy number and their associated rearrangements in tumour-enriched genomes and found that pancreatic cancer tumorigenesis is neither gradual nor follows the accepted mutation order. Two-thirds of tumours harbour complex rearrangement patterns associated with mitotic errors, consistent with punctuated equilibrium as the principal evolutionary trajectory. In a subset of cases, the consequence of such errors is the simultaneous, rather than sequential, knockout of canonical preneoplastic genetic drivers that are likely to set-off invasive cancer growth. These findings challenge the current progression model of pancreatic cancer and provide insights into the mutational processes that give rise to these aggressive tumours. Funding sources for this study include grants to the Pancreatic Cancer Sequencing Initiative program from the Ontario Institute for Cancer Research (OICR), through support from the Ontario Ministry of Research and Innovation, the Canada Foundation for Innovation; research award to F.N. from the OICR and the Canadian Institutes for Health Research (CIHR); Canadian Friends of the Hebrew University, the SMGS Family Foundation, NCI grant P50 CA102701 (Mayo Clinic SPORE in Pancreatic Cancer) and NCI grant R01 CA97075 (Pancreatic Cancer Genetic Epidemiology Consortium). F.N. is supported by a fellowship award from CIHR and is a recipient of a scholar’s research award from the Ontario Institute of Cancer Research (OICR), through support from the Ontario Ministry of Research and Innovation. G.Z. is a Clinician–Scientist of the Fonds de la Recherche en Sante du Quebec. P.J.C. is a Wellcome Trust Senior Clinical Fellow. T.J.H., L.D.S., J.D.M. and S.G. are recipients of Senior or Clinician–Scientist Awards from the Ontario Institute for Cancer Research

Across diverse eukaryotes, the Paf1 complex (Paf1C) plays critical roles in RNA polymerase II transcription elongation and regulation of histone modifications. Beyond these roles, the human and Saccharomyces cerevisiae Paf1 complexes also interact with RNA 3'-end processing components to affect transcript 3'-end formation. Specifically, the Saccharomyces cerevisiae Paf1C functions with the RNA binding proteins Nrd1 and Nab3 to regulate the termination of at least two small nucleolar RNAs (snoRNAs). To determine how Paf1C-dependent functions regulate snoRNA formation, we used high-density tiling arrays to analyze transcripts in paf1Δ cells and uncover new snoRNA targets of Paf1. Detailed examination of Paf1-regulated snoRNA genes revealed locus-specific requirements for Paf1-dependent posttranslational histone modifications. We also discovered roles for the transcriptional regulators Bur1-Bur2, Rad6, and Set2 in snoRNA 3'-end formation. Surprisingly, at some snoRNAs, this function of Rad6 appears to be primarily independent of its role in histone H2B monoubiquitylation. Cumulatively, our work reveals a broad requirement for the Paf1C in snoRNA 3'-end formation in S. cerevisiae, implicates the participation of transcriptional proteins and histone modifications in this process, and suggests that the Paf1C contributes to the fine tuning of nuanced levels of regulation that exist at individual loci.

SummaryPreselection of compounds that are more likely to induce a phenotype can increase the efficiency and reduce the costs for model organism screening. To identify such molecules, we screened ∼81,000 compounds in Saccharomyces cerevisiae and identified ∼7500 that inhibit cell growth. Screening these growth-inhibitory molecules across a diverse panel of model organisms resulted in an increased phenotypic hit-rate. These data were used to build a model to predict compounds that inhibit yeast growth. Empirical and in silico application of the model enriched the discovery of bioactive compounds in diverse model organisms. To demonstrate the potential of these molecules as lead chemical probes, we used chemogenomic profiling in yeast and identified specific inhibitors of lanosterol synthase and of stearoyl-CoA 9-desaturase. As community resources, the ∼7500 growth-inhibitory molecules have been made commercially available and the computational model and filter used are provided.

Dosage suppression is a genetic interaction in which overproduction of one gene rescues a mutant phenotype of another gene. Although dosage suppression is known to map functional connections among genes, the extent to which it might illuminate global cellular functions is unclear. Here we analyze a network of interactions linking dosage suppressors to 437 essential genes in yeast. For 424 genes, we curated interactions from the literature. Analyses revealed that many dosage suppression interactions occur between functionally related genes and that the majority do not overlap with other types of genetic or physical interactions. To confirm the generality of these network properties, we experimentally identified dosage suppressors for 29 genes from pooled populations of temperature-sensitive mutant cells transformed with a high-copy molecular-barcoded open reading frame library, MoBY-ORF 2.0. We classified 87% of the 1,640 total interactions into four general types of suppression mechanisms, which provided insight into their relative frequencies. This work suggests that integrating the results of dosage suppression studies with other interaction networks could generate insights into the functional wiring diagram of a cell.

The majority of newly diagnosed prostate cancers are slow growing, with a long natural life history. Yet a subset can metastasize with lethal consequences. We reconstructed the phylogenies of 293 localized prostate tumors linked to clinical outcome data. Multiple subclones were detected in 59% of patients, and specific subclonal architectures associate with adverse clinicopathological features. Early tumor development is characterized by point mutations and deletions followed by later subclonal amplifications and changes in trinucleotide mutational signatures. Specific genes are selectively mutated prior to or following subclonal diversification, including MTOR, NKX3-1, and RB1. Patients with low-risk monoclonal tumors rarely relapse after primary therapy (7%), while those with high-risk polyclonal tumors frequently do (61%). The presence of multiple subclones in an index biopsy may be necessary, but not sufficient, for relapse of localized prostate cancer, suggesting that evolution-aware biomarkers should be studied in prospective studies of low-risk tumors suitable for active surveillance.

A library of universal Saccharomyces cerevisiae Barcoder strains for efficient tagging is presented. It is used to tag a collection of hypomorphic alleles of essential yeast genes and applied to chemical genetic screens. Also in this issue, Breslow et al. present a similar collection of hypomorphic alleles, coupled with a sensitive growth assay for improved genetic interaction studies. The ability to perform complex bioassays in parallel enables experiments that are otherwise impossible because of throughput and cost constraints. For example, highly parallel chemical-genetic screens using pooled collections of thousands of defined Saccharomyces cerevisiae gene deletion strains are feasible because each strain is bar-coded with unique DNA sequences. It is, however, time-consuming and expensive to individually bar-code individual strains. To provide a simple and general method of barcoding yeast collections, we built a set of donor strains, called Barcoders, with unique bar codes that can be systematically transferred to any S. cerevisiae collection. We applied this technology by generating a collection of bar-coded 'decreased abundance by mRNA perturbation' (DAmP) loss-of-function strains comprising 87.1% of all essential yeast genes. These experiments validate both the Barcoders and the DAmP strain collection as useful tools for genome-wide chemical-genetic assays.

An effective tool for the global analysis of both DNA methylation status and protein–chromatin interactions is a microarray constructed with sequences containing regulatory elements. One type of array suited for this purpose takes advantage of the strong association between CpG Islands (CGIs) and gene regulatory regions. We have obtained 20 736 clones from a CGI Library and used these to construct CGI arrays. The utility of this library requires proper annotation and assessment of the clones, including CpG content, genomic origin and proximity to neighboring genes. Alignment of clone sequences to the human genome (UCSC hg17) identified 9595 distinct genomic loci; 64% were defined by a single clone while the remaining 36% were represented by multiple, redundant clones. Approximately 68% of the loci were located near a transcription start site. The distribution of these loci covered all 23 chromosomes, with 63% overlapping a bioinformatically identified CGI. The high representation of genomic CGI in this rich collection of clones supports the utilization of microarrays produced with this library for the study of global epigenetic mechanisms and protein– chromatin interactions. A browsable database is available on-line to facilitate exploration of the CGIs in this library and their association with annotated genes or promoter elements.

G-protein coupled receptors (GPCRs) are involved in a variety of disease processes and comprise major drug targets. However, the complexity of integral membrane proteins such as GPCRs makes the identification of their interacting partners and subsequent drug development challenging. A comprehensive understanding of GPCR protein interaction networks is needed to design effective therapeutic strategies to inhibit these drug targets. Here, we developed a novel split-ubiquitin membrane yeast two-hybrid (MYTH) technology called CHIP-MYTH, which allows the unbiased characterization of interaction partners of full-length GPCRs in a drug-dependent manner. This was achieved by coupling DNA microarray technology to the MYTH approach, which allows a quantitative evaluation of interacting partners of a given integral membrane protein in the presence or absence of drug. As a proof of principle, we applied the CHIP-MYTH approach to the human β2-adrenergic receptor (β2AR), a target of interest in the treatment of asthma, chronic obstructive pulmonary disease (COPD), neurological disease, cardiovascular disease, and obesity. A CHIP-MYTH screen was performed in the presence or absence of salmeterol, a long-acting β2AR-agonist. Our results suggest that β2AR activation with salmeterol can induce the dissociation of heterotrimeric G-proteins, Gαβγ, into Gα and Gβγ subunits, which in turn activates downstream signaling cascades. Using CHIP-MYTH, we confirmed previously known and identified novel β2AR interactors involved in GPCR-mediated signaling cascades. Several of these interactions were confirmed in mammalian cells using LUminescence-based Mammalian IntERactome (LUMIER) and co-immunoprecipitation assays. In summary, the CHIP-MYTH approach is ideal for conducting comprehensive protein-protein interactions (PPI) screenings of full-length GPCRs in the presence or absence of drugs, thus providing a valuable tool to further our understanding of GPCR-mediated signaling.

Genomics has benefited from continued innovations and advances in automation and information management. New technologies will continue to increase the rate of discovery; however, the future requires tools to analyze the vast amount of data collected in highly multiplexed assays that are capable of interrogating biological systems en masse. To date, high-density barcode microarray platforms have been used for the comprehensive analysis of transcription factor binding sites (ChIP-chip), gene expression, nucleosome occupancy, and fitness profiling, to name a few examples. More recently, next-generation sequencing (NGS) technologies have been applied to tackle these same applications with promising results, including RNA-seq (Nagalakshmi et al. 2008), ChIP-seq (Robertson et al. 2007), genome analysis (Bentley et al. 2008), nucleosome occupancy (Ozsolak et al. 2007), and many other applications (e.g., de novo sequencing, SNP detection). For a more detailed review of next-generation sequence applications, we refer you to MacLean et al. (2009). We previously established a genome-wide chemogenomic assay (Giaever et al. 2004; Hillenmeyer et al. 2008; Hoon et al. 2008) that uses barcoded yeast deletion strains in a competitive growth assay (combined with a barcode microarray readout) to identify the genes important for growth in the presence of compound, e.g., haploinsufficiency profiling (HIP) or homozygous profiling (HOP). Although high-density barcode microarrays are well-suited for such assays, the assay platform requires re-tooling to investigate other organisms or strain collections. For example, one may have to either design a new barcode microarray for each organism or cell type, or re-engineer strains such that they carry specific barcodes. Either case will carry significant up-front costs. Furthermore, a new array design may require a priori sequence information, whereas an NGS approach does not. We adapted a validated barcode microarray-based chemogenomic assay and directly compared the barcode microarray data to that of high-throughput sequencing. This protocol (Barcode Analysis by Sequencing, or “Bar-seq”) directly “counts” each barcode in a complex sample via sequencing. For this comparison, we used the well-characterized yeast deletion strain library and assessed its ability to identify the known targets for several well-characterized drugs. Together, Bar-seq, combined with a reannotation of the yeast deletion collection and development of methods to analyze the data, promise to make Bar-seq a powerful tool for understanding gene function.

Real-time quantitative PCR (qPCR) is a powerful tool for quantifying specific DNA target sequences. Although determination of relative quantity is widely accepted as a reliable means of measuring differences between samples, there are advantages to being able to determine the absolute copy numbers of a given target. One approach to absolute quantification relies on construction of an accurate standard curve using appropriate external standards of known concentration. We have validated the use of tissue genomic DNA as a universal external standard to facilitate quantification of any target sequence contained in the genome of a given species, addressing several key technical issues regarding its use. This approach was applied to validate mRNA expression of gene candidates identified from microarray data and to determine gene copies in transgenic mice. A simple method that can assist achieving absolute quantification of gene expression would broadly enhance the uses of real-time qPCR and in particular, augment the evaluation of global gene expression studies.

Next-generation sequencing has proven an extremely effective technology for molecular counting applications where the number of sequence reads provides a digital readout for RNA-seq, ChIP-seq, Tn-seq and other applications. The extremely large number of sequence reads that can be obtained per run permits the analysis of increasingly complex samples. For lower complexity samples, however, a point of diminishing returns is reached when the number of counts per sequence results in oversampling with no increase in data quality. A solution to making next-generation sequencing as efficient and affordable as possible involves assaying multiple samples in a single run. Here, we report the successful 96-plexing of complex pools of DNA barcoded yeast mutants and show that such ‘Bar-seq’ assessment of these samples is comparable with data provided by barcode microarrays, the current benchmark for this application. The cost reduction and increased throughput permitted by highly multiplexed sequencing will greatly expand the scope of chemogenomics assays and, equally importantly, the approach is suitable for other sequence counting applications that could benefit from massive parallelization.

We generated a high-resolution whole-genome sequence and individually deleted 5100 genes in Sigma1278b, a Saccharomyces cerevisiae strain closely related to reference strain S288c. Similar to the variation between human individuals, Sigma1278b and S288c average 3.2 single-nucleotide polymorphisms per kilobase. A genome-wide comparison of deletion mutant phenotypes identified a subset of genes that were conditionally essential by strain, including 44 essential genes unique to Sigma1278b and 13 unique to S288c. Genetic analysis indicates the conditional phenotype was most often governed by complex genetic interactions, depending on multiple background-specific modifiers. Our comprehensive analysis suggests that the presence of a complex set of modifiers will often underlie the phenotypic differences between individuals.