Small-cell lung cancer (SCLC) is an exceptionally aggressive disease with poor prognosis. Here, we obtained exome, transcriptome and copy-number alteration data from approximately 53 samples consisting of 36 primary human SCLC and normal tissue pairs and 17 matched SCLC and lymphoblastoid cell lines. We also obtained data for 4 primary tumors and 23 SCLC cell lines. We identified 22 significantly mutated genes in SCLC, including genes encoding kinases, G protein–coupled receptors and chromatin-modifying proteins. We found that several members of the SOX family of genes were mutated in SCLC. We also found SOX2 amplification in ~27% of the samples. Suppression of SOX2 using shRNAs blocked proliferation of SOX2-amplified SCLC lines. RNA sequencing identified multiple fusion transcripts and a recurrent RLF-MYCL1 fusion. Silencing of MYCL1 in SCLC cell lines that had the RLF-MYCL1 fusion decreased cell proliferation. These data provide an in-depth view of the spectrum of genomic alterations in SCLC and identify several potential targets for therapeutic intervention.

Population genetic models play an important role in human genetic research, connecting empirical observations about sequence variation with hypotheses about underlying historical and biological causes. More specifically, models are used to compare empirical measures of sequence variation, linkage disequilibrium (LD), and selection to expectations under a "null" distribution. In the absence of detailed information about human demographic history, and about variation in mutation and recombination rates, simulations have of necessity used arbitrary models, usually simple ones. With the advent of large empirical data sets, it is now possible to calibrate population genetic models with genome-wide data, permitting for the first time the generation of data that are consistent with empirical data across a wide range of characteristics. We present here the first such calibrated model and show that, while still arbitrary, it successfully generates simulated data (for three populations) that closely resemble empirical data in allele frequency, linkage disequilibrium, and population differentiation. No assertion is made about the accuracy of the proposed historical and recombination model, but its ability to generate realistic data meets a long-standing need among geneticists. We anticipate that this model, for which software is publicly available, and others like it will have numerous applications in empirical studies of human genetics.The search for inherited influences on human disease and the effort to understand the history of human populations rely on a detailed knowledge of human genome sequence variation. Population genetic models serve as important tools in this quest. By simulating variation under neutral evolution, they provide background expectations about genetic variation, for example, for the frequency distribution of disease alleles and for patterns of linkage disequilibrium. They also serve as a tool to evaluate evidence for evolutionary selection, and to infer the history of populations.A primary difficulty in using such models lies in deciding which ones to employ. Given the complex history of human populations and the wide variety of plausible model parameters, as well as considerable uncertainty about how mutation and recombination rates vary, it is impossible rigorously to infer from data a single "correct" model: The problem is badly underdetermined. Until comparatively recently, in fact, genome-scale data were scarce enough that they provided few constraints on models. As a result, a common practice in simulating human variation has been to use a set of simple models that are easy to implement. The most basic model is the standard Wright-Fisher neutral model of a freely mixing, constant-sized population, with uniform rates of recombination and mutation across the genome. This model has been (and continues to be) widely used, as have other simple models (e.g., island models).Simple models have been of enormous utility as heuristics. In some respects, they have also offered surprisingly good fits to e...

Most organisms have evolved defense mechanisms to protect themselves from viruses and other pathogens.Arthropods lack the protein-based adaptive immune response found in vertebrates. Here we show that the central catalytic component of the RNA-induced silencing complex (RISC), the nuclease Argonaute 2 (Ago-2), is essential for antiviral defense in adult Drosophila melanogaster. Ago-2-defective flies are hypersensitive to infection with a major fruit fly pathogen, Drosophila C virus (DCV), and with Cricket Paralysis virus (CrPV). Increased mortality in ago-2 mutant flies was associated with a dramatic increase in viral RNA accumulation and virus titers. The physiological significance of this antiviral mechanism is underscored by our finding that DCV encodes a potent suppressor of RNA interference (RNAi). This suppressor binds long double-stranded RNA (dsRNA) and inhibits Dicer-2-mediated processing of dsRNA into short interfering RNA (siRNA), but does not bind short siRNAs or disrupt the microRNA (miRNA) pathway. Based on these results we propose that RNAi is a major antiviral immune defense mechanism in Drosophila.

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