Catherine K. Foo

Ribosome profiling reveals pervasive and regulated stop codon readthrough in Drosophila melanogaster

Ribosomes can read through stop codons in a regulated manner, elongating rather than terminating the nascent peptide. Stop codon readthrough is essential to diverse viruses, and phylogenetically predicted to occur in a few hundred genes in Drosophila melanogaster, but the importance of regulated readthrough in eukaryotes remains largely unexplored. Here, we present a ribosome profiling assay (deep sequencing of ribosome-protected mRNA fragments) for Drosophila melanogaster, and provide the first genome-wide experimental analysis of readthrough. Readthrough is far more pervasive than expected: the vast majority of readthrough events evolved within D. melanogaster and were not predicted phylogenetically. The resulting C-terminal protein extensions show evidence of selection, contain functional subcellular localization signals, and their readthrough is regulated, arguing for their importance. We further demonstrate that readthrough occurs in yeast and humans. Readthrough thus provides general mechanisms both to regulate gene expression and function, and to add plasticity to the proteome during evolution. DOI: http://dx.doi.org/10.7554/eLife.01179.001

Experimental annotation of the human pathogen Histoplasma capsulatum transcribed regions using high-resolution tiling arrays

BackgroundThe fungal pathogen Histoplasma capsulatum is thought to be the most common cause of fungal respiratory infections in immunocompetent humans, yet little is known about its biology. Here we provide the first genome-wide studies to experimentally validate its genome annotation. A functional interrogation of the Histoplasma genome provides critical support for continued investigation into the biology and pathogenesis of H. capsulatum and related fungi.ResultsWe employed a three-pronged approach to provide a functional annotation for the H. capsulatum G217B strain. First, we probed high-density tiling arrays with labeled cDNAs from cells grown under diverse conditions. These data defined 6,172 transcriptionally active regions (TARs), providing validation of 6,008 gene predictions. Interestingly, 22% of these predictions showed evidence of anti-sense transcription. Additionally, we detected transcription of 264 novel genes not present in the original gene predictions. To further enrich our analysis, we incorporated expression data from whole-genome oligonucleotide microarrays. These expression data included profiling under growth conditions that were not represented in the tiling experiment, and validated an additional 2,249 gene predictions. Finally, we compared the G217B gene predictions to other available fungal genomes, and observed that an additional 254 gene predictions had an ortholog in a different fungal species, suggesting that they represent genuine coding sequences.ConclusionsThese analyses yielded a high confidence set of validated gene predictions for H. capsulatum. The transcript sets resulting from this study are a valuable resource for further experimental characterization of this ubiquitous fungal pathogen. The data is available for interactive exploration at http://histo.ucsf.edu.

Abstract 2173: Robust estimation of mutation burden

Developing a more robust approach to measure mutational burden is of central importance to improving the characterization of the molecular profile of tumor and may improve our ability to predict tumor progression or response to therapy in patients. Mutational burden is typically calculated as a direct enumeration of called somatic mutations per megabase covered. However, there is a growing appreciation that tumor purity, variable sequencing coverage, and copy number alterations can substantially impact the accurate identification any specific somatic mutation. Furthermore, population genetic theory and empirical data indicate that in many cases the vast majority of somatic mutations appear in only a small subpopulation of tumor cells, a context in which there is a high likelihood that an individual subclonal mutation may not be identified by conventional analysis. This tendency to miss low frequency mutations is highly variable and dependent, in part, upon sample purity and results in a strong source of bias not addressed in existing methods to measure variant allele frequencies. We present a novel computational method that incorporates these sources of bias in a coherent probabilistic framework that enables maximum-likelihood inference of relevant population parameters such as mutation burden. We apply our method to simulated data as well as patient tumor samples diluted with varying known proportions of normal DNA. We show that our approach allows us to generate estimates of mutation burden that are robust to the substantial variations in purity and sequencing coverage that are frequently encountered in patient tumor analysis. Hence, our novel method may improve the accurate detection and quantification of variant alleles in patient tumors to better understand their genetic landscape and guide clinical management. Citation Format: Oscar Westesson, Rasmus Nielsen, John St John, Aleah Caulin, Nicholas Hahner, Stewart Stewart, Catherine Foo, Kimberly Lung, Jeff Catalano, Mandy Lee, Petros Giannikopoulos, Will Polkinghorn, Jonathan Wiessman, Aviv Regev, Trever Bivona. Robust estimation of mutation burden. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2173. doi:10.1158/1538-7445.AM2015-2173

Abstract 4707: Comprehensive integrated genomic analysis

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA Despite recent advances in the understanding of the biology and genetics of lung cancer, and despite the introduction of multiplex somatic mutation testing in the clinic, the long-term survival for all lung cancer patients, particularly for those with advanced disease, remains low. Lung cancer is the leading cause of cancer death globally, resulting in 1.4 million deaths annually, including 165,000 patients in the United States per year. In order to address the critical need for comprehensive profiling of these patients, we developed a novel, CLIA-certified, whole exome and low-coverage whole genome sequencing assay that applies a disease-focused, integrated approach to identify therapeutically actionable drivers of disease. A panel (12) of surgically resected NSCLC specimens along with corresponding adjacent normal tissue underwent DNA extraction in a clinical (CLIA) environment. Tumor and normal genomic DNA was prepared for whole exome sequencing using the using the Agilent SureSelectXT Human All Exon V5 kit according to the manufacturers instructions, and libraries were sequenced on the Illumina HiSeq2500 at an average depth of 500X. Genomic DNA was then prepared for whole genome sequencing using Illuminas Nextera system and run on the Illumina HiSeq2500 platform at an average depth of 1-2X. Somatic variants were detected using Strelka and somatic copy number alterations (SCNAs) were identified using a novel algorithm comparing normalized read counts within genomic segments as well as genes in the tumor to a panel of normal tissues. In parallel, the same tumor/normal specimens were analyzed by two separate CLIA laboratories via 1) a clinically validated Ion Torrent AmpliSeq Cancer Panel assay, and 2) a clinically validated cancer-focused, high-resolution comparative genome hybridization (CGH) array. In addition, a well-characterized panel of 10 germline samples obtained from the 1000 Genomes Project were pooled to simulate a broad spectrum of somatic single nucleotide variant and indel allele frequencies. Sequencing, data analysis and clinical reporting were completed for all 12 cases with an average turnaround time of less than 3 weeks. Single nucleotide variants and indels were identified with an accuracy of greater than 99%, with a limit of detection of 5-10% mutant allele frequency. Somatic copy number alterations were observed with an overall accuracy of greater than 95%. Actionable variants were identified by cross-referencing individual results with our internally developed, lung-cancer focused therapeutic association database. Citation Format: Catherine K. Foo, John St. John, Nicholas Hahner, Oscar Westesson, Mitchell E. Skinner, Urvish Parikh, Kimberly Lung, Aleah F. Cauhlin, Jeffrey P. Catalano, Anne S. Wellde, Jonathan K. Barry, George W. Wellde, Patrick Ma, Rafael Rosell, Andres Felipe Cardona Zorilla, William R. Polkinghorn, Trever G. Bivona, Jonathan S. Weissman, Petros Giannikopoulos. Comprehensive integrated genomic analysis. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4707. doi:10.1158/1538-7445.AM2014-4707