Here we report a high-quality draft genome sequence of the domestic dog (Canis familiaris), together with a dense map of single nucleotide polymorphisms (SNPs) across breeds. The dog is of particular interest because it provides important evolutionary information and because existing breeds show great phenotypic diversity for morphological, physiological and behavioural traits. We use sequence comparison with the primate and rodent lineages to shed light on the structure and evolution of genomes and genes. Notably, the majority of the most highly conserved non-coding sequences in mammalian genomes are clustered near a small subset of genes with important roles in development. Analysis of SNPs reveals long-range haplotypes across the entire dog genome, and defines the nature of genetic diversity within and across breeds. The current SNP map now makes it possible for genome-wide association studies to identify genes responsible for diseases and traits, with important consequences for human and companion animal health.

Comparative analysis of multiple genomes in a phylogenetic framework dramatically improves the precision and sensitivity of evolutionary inference, producing more robust results than single-genome analyses can provide. The genomes of 12 Drosophila species, ten of which are presented here for the first time (sechellia, simulans, yakuba, erecta, ananassae, persimilis, willistoni, mojavensis, virilis and grimshawi), illustrate how rates and patterns of sequence divergence across taxa can illuminate evolutionary processes on a genomic scale. These genome sequences augment the formidable genetic tools that have made Drosophila melanogaster a pre-eminent model for animal genetics, and will further catalyse fundamental research on mechanisms of development, cell biology, genetics, disease, neurobiology, behaviour, physiology and evolution. Despite remarkable similarities among these Drosophila species, we identified many putatively non-neutral changes in protein-coding genes, non-coding RNA genes, and cis-regulatory regions. These may prove to underlie differences in the ecology and behaviour of these diverse species.

We report a high-quality draft of the genome sequence of the grey, short-tailed opossum (Monodelphis domestica). As the first metatherian ('marsupial') species to be sequenced, the opossum provides a unique perspective on the organization and evolution of mammalian genomes. Distinctive features of the opossum chromosomes provide support for recent theories about genome evolution and function, including a strong influence of biased gene conversion on nucleotide sequence composition, and a relationship between chromosomal characteristics and X chromosome inactivation. Comparison of opossum and eutherian genomes also reveals a sharp difference in evolutionary innovation between protein-coding and non-coding functional elements. True innovation in protein-coding genes seems to be relatively rare, with lineage-specific differences being largely due to diversification and rapid turnover in gene families involved in environmental interactions. In contrast, about 20% of eutherian conserved non-coding elements (CNEs) are recent inventions that postdate the divergence of Eutheria and Metatheria. A substantial proportion of these eutherian-specific CNEs arose from sequence inserted by transposable elements, pointing to transposons as a major creative force in the evolution of mammalian gene regulation.

Many monogenic disorders, including the muscular dystrophies, display phenotypic variability despite the same disease-causing mutation. To identify genetic modifiers of muscular dystrophy and its associated cardiomyopathy, we used quantitative trait locus mapping and whole genome sequencing in a mouse model. This approach uncovered a modifier locus on chromosome 11 associated with sarcolemmal membrane damage and heart mass. Whole genome and RNA sequencing identified Anxa6, encoding annexin A6, as a modifier gene. A synonymous variant in exon 11 creates a cryptic splice donor, resulting in a truncated annexin A6 protein called ANXA6N32. Live cell imaging showed that annexin A6 orchestrates a repair zone and cap at the site of membrane disruption. In contrast, ANXA6N32 dramatically disrupted the annexin A6-rich cap and the associated repair zone, permitting membrane leak. Anxa6 is a modifier of muscular dystrophy and membrane repair after injury.dystrophin | muscle | plasma membrane

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Key Points Juvenile zebrafish tolerate widespread coagulopathy due to complete ablation of antithrombin III, but develop lethal thrombosis as adults. In vivo structure/function analysis of antithrombin III in zebrafish reveals limited roles for heparin-binding and anti-IXa/Xa activity.

The yeast Set2 histone methyltransferase is a critical enzyme that plays a number of key roles in gene transcription and DNA repair. Recently, the human homologue, SETD2, was found to be recurrently mutated in a significant percentage of renal cell carcinomas, raising the possibility that the activity of SETD2 is tumorsuppressive. Using budding yeast and human cell line model systems, we examined the functional significance of two evolutionarily conserved residues in SETD2 that are recurrently mutated in human cancers. Whereas one of these mutations (R2510H), located in the Set2 Rpb1 interaction domain, did not result in an observable defect in SETD2 enzymatic function, a second mutation in the catalytic domain of this enzyme (R1625C) resulted in a complete loss of histone H3 Lys-36 trimethylation (H3K36me3). This mutant showed unchanged thermal stability as compared with the wild type protein but diminished binding to the histone H3 tail. Surprisingly, mutation of the conserved residue in Set2 (R195C) similarly resulted in a complete loss of H3K36me3 but did not affect dimethylated histone H3 Lys-36 (H3K36me2) or functions associated with H3K36me2 in yeast. Collectively, these data imply a critical role for Arg-1625 in maintaining the protein interaction with H3 and specific H3K36me3 function of this enzyme, which is conserved from yeast to humans. They also may provide a refined biochemical explanation for how H3K36me3 loss leads to genomic instability and cancer.Cancer is increasingly characterized by alterations in chromatin-modifying enzymes (1). SETD2, a non-redundant histone H3 lysine 36 (H3K36) 4 methyltransferase (2), has been found to be mutated in a growing list of tumor types, most notably in clear cell renal cell carcinoma (ccRCC) (1, 3, 4), but also in high grade gliomas (5), breast cancer (6), bladder cancer (7), and acute lymphoblastic leukemia (8 -10). Recent studies exploring intratumor heterogeneity in ccRCC identified distinct mutations in SETD2 from spatially distinct subsections of an individual tumor, suggesting that mutation of SETD2 is a critical and selected event in ccRCC cancer progression (11). Mutations in SETD2 are predominantly inactivating, such as early nonsense or frameshift mutations, which lead to nonfunctional protein and global loss of H3K36 trimethylation (H3K36me3) (4,11,12). Missense mutations tend to cluster in two domains (1,4,12,13): the SET domain, which catalyzes H3K36me3 (14), and the Set2 Rpb1 interaction (SRI) domain, which mediates the interaction between SETD2 and the hyperphosphorylated form of RNA polymerase II (RNAPII) (13).SETD2 and its yeast counterpart, Set2, both associate with RNAPII in a co-transcriptional manner (13,15,16). In yeast, Set2 mediates all H3K36 methylation states (H3K36me1/me2/ me3) (17) and regulates the recruitment of chromatin-remodeling enzymes (Isw1b) and a histone deacetylase (Rpd3) (18) that functions to keep gene bodies deacetylated, thereby maintaining a more compact chromatin structure (19,20) that is more resistant to inappropr...

Drug toxicity evaluation is an essential process of drug development as it is reportedly responsible for the attrition of approximately 30% of drug candidates. The rapid increase in the number and types of large toxicology data sets together with the advances in computational methods may be used to improve many steps in drug safety evaluation. The development of in silico models to screen and understand mechanisms of drug toxicity may be particularly beneficial in the early stages of drug development where early toxicity assessment can most reduce expenses and labor time. To facilitate this, machine learning methods have been employed to evaluate drug toxicity but are often limited by small and less diverse data sets. Recent advances in machine learning methods together with the rapid increase in big toxicity data such as molecular descriptors, toxicogenomics, and high-throughput bioactivity data may help alleviate some of the current challenges. In this article, the most common machine learning methods used in toxicity assessment are reviewed together with examples of toxicity studies that have used machine learning methodology. Furthermore, a comprehensive overview of the different types of toxicity tools and data sets available to build in silico toxicity prediction models has been provided to give an overview of the current big toxicity data landscape and highlight opportunities and challenges related to them.