Molecular Subtypes of High-Grade Serous Ovarian Cancer across Racial Groups and Gene Expression Platforms.

BACKGROUND: High-grade serous carcinoma (HGSC) gene expression subtypes are associated with differential survival. We characterized HGSC gene expression in Black individuals and considered whether gene expression differences by self-identified race may contribute to poorer HGSC survival among Black versus White individuals. METHODS: We included newly generated RNA sequencing data from Black and White individuals and array-based genotyping data from four existing studies of White and Japanese individuals. We used K-means clustering, a method with no predefined number of clusters or dataset-specific features, to assign subtypes. Cluster- and dataset-specific gene expression patterns were summarized by moderated t-scores. We compared cluster-specific gene expression patterns across datasets by calculating the correlation between the summarized vectors of moderated t-scores. After mapping to The Cancer Genome Atlas-derived HGSC subtypes, we used Cox proportional hazards models to estimate subtype-specific survival by dataset. RESULTS: Cluster-specific gene expression was similar across gene expression platforms and racial groups. Comparing the Black population with the White and Japanese populations, the immunoreactive subtype was more common (39% vs. 23%-28%) and the differentiated subtype was less common (7% vs. 22%-31%). Patterns of subtype-specific survival were similar between the Black and White populations with RNA sequencing data; compared with mesenchymal cases, the risk of death was similar for proliferative and differentiated cases and suggestively lower for immunoreactive cases [Black population HR = 0.79 (0.55, 1.13); White population HR = 0.86 (0.62, 1.19)]. CONCLUSIONS: Although the prevalence of HGSC subtypes varied by race, subtype-specific survival was similar. IMPACT: HGSC subtypes can be consistently assigned across platforms and self-identified racial groups.

Molecular subtypes of high-grade serous ovarian cancer across racial groups and gene expression platforms.

Introduction: High-grade serous carcinoma (HGSC) gene expression subtypes are associated with differential survival. We characterized HGSC gene expression in Black individuals and considered whether gene expression differences by race may contribute to poorer HGSC survival among Black versus non-Hispanic White individuals. Methods: We included newly generated RNA-Seq data from Black and White individuals, and array-based genotyping data from four existing studies of White and Japanese individuals. We assigned subtypes using K-means clustering. Cluster- and dataset-specific gene expression patterns were summarized by moderated t-scores. We compared cluster-specific gene expression patterns across datasets by calculating the correlation between the summarized vectors of moderated t-scores. Following mapping to The Cancer Genome Atlas (TCGA)-derived HGSC subtypes, we used Cox proportional hazards models to estimate subtype-specific survival by dataset. Results: Cluster-specific gene expression was similar across gene expression platforms. Comparing the Black study population to the White and Japanese study populations, the immunoreactive subtype was more common (39% versus 23%-28%) and the differentiated subtype less common (7% versus 22%-31%). Patterns of subtype-specific survival were similar between the Black and White populations with RNA-Seq data; compared to mesenchymal cases, the risk of death was similar for proliferative and differentiated cases and suggestively lower for immunoreactive cases (Black population HR=0.79 [0.55, 1.13], White population HR=0.86 [0.62, 1.19]). Conclusions: A single, platform-agnostic pipeline can be used to assign HGSC gene expression subtypes. While the observed prevalence of HGSC subtypes varied by race, subtype-specific survival was similar.

Publisher Correction: Combating subclonal evolution of resistant cancer phenotypes.

The originally published version of this Article contained an error in Figure 4. In panel a, grey boxes surrounding the subclones associated with patients #2 and #4 obscured adjacent portions of the heatmap. This error has now been corrected in both the PDF and HTML versions of the Article.

Studying Selenoprotein mRNA Translation Using RNA-Seq and Ribosome Profiling.

Deep sequencing of ribosome protected mRNA footprints, also called ribosome profiling or Ribo-Seq, is a relatively new methodology well suited to address questions regarding the mechanisms and efficiency of protein expression. Specifically, the ability of this technique to quantify ribosome abundance with codon resolution enables experiments aimed at studying many aspects of translation, including gene-specific translational efficiency, translation of regulatory upstream short open reading frames, sites of ribosome pausing, and most importantly for selenoproteins, the efficiency by which UGA codons are redefined to encode selenocysteine. Here, we describe a streamlined protocol that was developed in our lab to process mammalian tissue to produce the requisite matched ribosome profiling and RNA-Seq libraries for deep sequencing.

Combating subclonal evolution of resistant cancer phenotypes.

Metastatic breast cancer remains challenging to treat, and most patients ultimately progress on therapy. This acquired drug resistance is largely due to drug-refractory sub-populations (subclones) within heterogeneous tumors. Here, we track the genetic and phenotypic subclonal evolution of four breast cancers through years of treatment to better understand how breast cancers become drug-resistant. Recurrently appearing post-chemotherapy mutations are rare. However, bulk and single-cell RNA sequencing reveal acquisition of malignant phenotypes after treatment, including enhanced mesenchymal and growth factor signaling, which may promote drug resistance, and decreased antigen presentation and TNF-α signaling, which may enable immune system avoidance. Some of these phenotypes pre-exist in pre-treatment subclones that become dominant after chemotherapy, indicating selection for resistance phenotypes. Post-chemotherapy cancer cells are effectively treated with drugs targeting acquired phenotypes. These findings highlight cancer's ability to evolve phenotypically and suggest a phenotype-targeted treatment strategy that adapts to cancer as it evolves.

Leucine Differentially Regulates Gene-Specific Translation in Mouse Skeletal Muscle.

Background: Amino acids, especially leucine, are particularly effective in promoting protein synthesis. Leucine is known to increase the rate of protein synthesis in skeletal muscle through the mechanistic target of rapamycin complex 1-dependent, as well as -independent, signaling pathways. However, the overall translation program is poorly defined, and it is unknown how the activation of these pathways differentially controls the translation of specific mRNAs.Objective: Ribosome profiling and RNA sequencing were used to precisely define the translational program activated by an acute oral dose of leucine.Methods: Adult male C57BL/6 mice were deprived of food overnight before the delivery of an acute dose of l-leucine (9.4 mg) (n = 6) or vehicle (n = 5) and tissues collected 30 min later. Ribosome footprints and total RNA were isolated and subjected to deep sequencing. Changes in gene-specific mRNA abundance and ribosome occupancy were determined between the leucine-treated and control groups by aligning sequence reads to Reference Sequence database mRNAs and applying statistical features of the Bioconductor package edgeR.Results: Our data revealed mRNA features that confer translational control of skeletal muscle mRNAs in response to an acute dose of leucine. The subset of skeletal muscle mRNAs that are activated consists largely of terminal oligopyrimidine mRNAs (false discovery rate: <0.05), whereas those with reduced translation had 5' untranslated regions with increased length. Only the small nuclear RNAs, which are required for ribosome biogenesis, were significantly altered in RNA abundance. The inferred functional translational program activated by dietary leucine includes increased protein synthesis capacity and energy metabolism, upregulation of sarcomere-binding proteins, modulation of circadian rhythm, and suppression of select immune components.Conclusions: These results clarify the translation program acutely stimulated by leucine in mouse skeletal muscle and establish new methodologies for use in future studies of skeletal muscle disease or aging and further examination of downstream effects of leucine on gene expression.

A comprehensive multi-omics approach uncovers adaptations for growth and survival of Pseudomonas aeruginosa on n-alkanes.

BACKGROUND: Examination of complex biological systems has long been achieved through methodical investigation of the system's individual components. While informative, this strategy often leads to inappropriate conclusions about the system as a whole. With the advent of high-throughput "omic" technologies, however, researchers can now simultaneously analyze an entire system at the level of molecule (DNA, RNA, protein, metabolite) and process (transcription, translation, enzyme catalysis). This strategy reduces the likelihood of improper conclusions, provides a framework for elucidation of genotype-phenotype relationships, and brings finer resolution to comparative genomic experiments. Here, we apply a multi-omic approach to analyze the gene expression profiles of two closely related Pseudomonas aeruginosa strains grown in n-alkanes or glycerol. RESULTS: The environmental P. aeruginosa isolate ATCC 33988 consumed medium-length (C10-C16) n-alkanes more rapidly than the laboratory strain PAO1, despite high genome sequence identity (average nucleotide identity >99%). Our data shows that ATCC 33988 induces a characteristic set of genes at the transcriptional, translational and post-translational levels during growth on alkanes, many of which differ from those expressed by PAO1. Of particular interest was the lack of expression from the rhl operon of the quorum sensing (QS) system, resulting in no measurable rhamnolipid production by ATCC 33988. Further examination showed that ATCC 33988 lacked the entire lasI/lasR arm of the QS response. Instead of promoting expression of QS genes, ATCC 33988 up-regulates a small subset of its genome, including operons responsible for specific alkaline proteases and sphingosine metabolism. CONCLUSION: This work represents the first time results from RNA-seq, microarray, ribosome footprinting, proteomics, and small molecule LC-MS experiments have been integrated to compare gene expression in bacteria. Together, these data provide insights as to why strain ATCC 33988 is better adapted for growth and survival on n-alkanes.

Combining quantitative genetic footprinting and trait enrichment analysis to identify fitness determinants of a bacterial pathogen.

Strains of Extraintestinal Pathogenic Escherichia c oli (ExPEC) exhibit an array of virulence strategies and are a major cause of urinary tract infections, sepsis and meningitis. Efforts to understand ExPEC pathogenesis are challenged by the high degree of genetic and phenotypic variation that exists among isolates. Determining which virulence traits are widespread and which are strain-specific will greatly benefit the design of more effective therapies. Towards this goal, we utilized a quantitative genetic footprinting technique known as transposon insertion sequencing (Tn-seq) in conjunction with comparative pathogenomics to functionally dissect the genetic repertoire of a reference ExPEC isolate. Using Tn-seq and high-throughput zebrafish infection models, we tracked changes in the abundance of ExPEC variants within saturated transposon mutant libraries following selection within distinct host niches. Nine hundred and seventy bacterial genes (18% of the genome) were found to promote pathogen fitness in either a niche-dependent or independent manner. To identify genes with the highest therapeutic and diagnostic potential, a novel Trait Enrichment Analysis (TEA) algorithm was developed to ascertain the phylogenetic distribution of candidate genes. TEA revealed that a significant portion of the 970 genes identified by Tn-seq have homologues more often contained within the genomes of ExPEC and other known pathogens, which, as suggested by the first axiom of molecular Koch's postulates, is considered to be a key feature of true virulence determinants. Three of these Tn-seq-derived pathogen-associated genes--a transcriptional repressor, a putative metalloendopeptidase toxin and a hypothetical DNA binding protein--were deleted and shown to independently affect ExPEC fitness in zebrafish and mouse models of infection. Together, the approaches and observations reported herein provide a resource for future pathogenomics-based research and highlight the diversity of factors required by a single ExPEC isolate to survive within varying host environments.

Polymorphisms in the NPY2R gene show significant associations with BMI that are additive to FTO, MC4R, and NPFFR2 gene effects.

Neuropeptide Y (NPY) is an appetite hormone that acts centrally to control feeding behavior. The 5' and exon 2 regions of NPY2R, one of five NPY receptor genes, have been weakly and inconsistently implicated with obesity. With the ATG start site of the gene at the beginning of exon 2, single-nucleotide polymorphisms (SNPs) across intron 1 may show stronger associations with obesity than expected. Two 5' SNPs, three intron 1 SNPs, and one synonymous exon 2 SNP were genotyped on 2,985 white Utah subjects. Previously associated FTO, NPY, NPY1R, MC4R, PPARGC1A, OR7D4, and four NPFFR2 SNPs were also genotyped and related to BMI. One NPY2R 5' SNP (rs12649641, P = 0.008), an exon 2 SNP (rs2880415, P = 0.009), and an intron 1 SNP (rs17376826, P = 7 × 10(-6)) were each significantly associated with BMI. All three SNPs, plus FTO (rs9939609, P = 1.5 × 10(-6)) and two NPFFR2 SNPs (rs4129733, P = 3.7 × 10(-13) and rs11940196, 4.2 × 10(-10)) remained significant in a multiple regression additive model. Diplotypes using the estimated haplotypes of NPY2R, NPFFR2, and MC4R were significantly associated with BMI (P = 1.0 × 10(-10), 3.2 × 10(-8), and 1.1 × 10(-4), respectively). Haplotypes of NPY2R, NPFFR2, and MC4R, plus the FTO SNP, explained 9.6% of the BMI variance. SNP effect sizes per allele for the four genes ranged from 0.8 to 3.5 kg/m(2). We conclude that haplotypes containing the rs17376826 SNP in intron 1 of NPY2R have strong associations with BMI, some NPFFR2 haplotypes are strongly protective against or increase risk of obesity, and both NPY2R and NPFFR2 play important roles in obesity predisposition independent of FTO and MC4R.

Next generation tools for genomic data generation, distribution, and visualization.

BACKGROUND: With the rapidly falling cost and availability of high throughput sequencing and microarray technologies, the bottleneck for effectively using genomic analysis in the laboratory and clinic is shifting to one of effectively managing, analyzing, and sharing genomic data. RESULTS: Here we present three open-source, platform independent, software tools for generating, analyzing, distributing, and visualizing genomic data. These include a next generation sequencing/microarray LIMS and analysis project center (GNomEx); an application for annotating and programmatically distributing genomic data using the community vetted DAS/2 data exchange protocol (GenoPub); and a standalone Java Swing application (GWrap) that makes cutting edge command line analysis tools available to those who prefer graphical user interfaces. Both GNomEx and GenoPub use the rich client Flex/Flash web browser interface to interact with Java classes and a relational database on a remote server. Both employ a public-private user-group security model enabling controlled distribution of patient and unpublished data alongside public resources. As such, they function as genomic data repositories that can be accessed manually or programmatically through DAS/2-enabled client applications such as the Integrated Genome Browser. CONCLUSIONS: These tools have gained wide use in our core facilities, research laboratories and clinics and are freely available for non-profit use. See http://sourceforge.net/projects/gnomex/, http://sourceforge.net/projects/genoviz/, and http://sourceforge.net/projects/useq.

Gains in sensitivity with a device that mixes microarray hybridization solution in a 25-microm-thick chamber.

A microarray hybridization system that allows mixing in volumes comparable to those used by glass coverslips is presented. This system is composed of a disposable flexible lid that binds to 1 in. x 3 in. glass slides via an adhesive gasket, forming a uniform 25-microm-thick hybridization chamber. This chamber rests on a base unit for temperature control. The lid contains two air-driven bladders that continuously mix the hybridization fluid. Mixing enhances sensitivity from a typical microarray experiment 2-3-fold. Mixing is particularly effective at high spotted probe and low labeled target concentrations and overcoming local target depletion that occurs when homologous probes are spotted in close proximity. Mixing appears to be compatible with most hybridization conditions; however, mix versus no-mix control experiments should be performed. Also covered are a number of microfluidic issues related to manufacturing, filling, mixing, and packaging.