Enhancer evolution across 20 mammalian species.

The mammalian radiation has corresponded with rapid changes in noncoding regions of the genome, but we lack a comprehensive understanding of regulatory evolution in mammals. Here, we track the evolution of promoters and enhancers active in liver across 20 mammalian species from six diverse orders by profiling genomic enrichment of H3K27 acetylation and H3K4 trimethylation. We report that rapid evolution of enhancers is a universal feature of mammalian genomes. Most of the recently evolved enhancers arise from ancestral DNA exaptation, rather than lineage-specific expansions of repeat elements. In contrast, almost all liver promoters are partially or fully conserved across these species. Our data further reveal that recently evolved enhancers can be associated with genes under positive selection, demonstrating the power of this approach for annotating regulatory adaptations in genomic sequences. These results provide important insight into the functional genetics underpinning mammalian regulatory evolution.

Pervasive lesion segregation shapes cancer genome evolution.

Cancers arise through the acquisition of oncogenic mutations and grow by clonal expansion1,2. Here we reveal that most mutagenic DNA lesions are not resolved into a mutated DNA base pair within a single cell cycle. Instead, DNA lesions segregate, unrepaired, into daughter cells for multiple cell generations, resulting in the chromosome-scale phasing of subsequent mutations. We characterize this process in mutagen-induced mouse liver tumours and show that DNA replication across persisting lesions can produce multiple alternative alleles in successive cell divisions, thereby generating both multiallelic and combinatorial genetic diversity. The phasing of lesions enables accurate measurement of strand-biased repair processes, quantification of oncogenic selection and fine mapping of sister-chromatid-exchange events. Finally, we demonstrate that lesion segregation is a unifying property of exogenous mutagens, including UV light and chemotherapy agents in human cells and tumours, which has profound implications for the evolution and adaptation of cancer genomes.

Latent regulatory potential of human-specific repetitive elements.

At least half of the human genome is derived from repetitive elements, which are often lineage specific and silenced by a variety of genetic and epigenetic mechanisms. Using a transchromosomic mouse strain that transmits an almost complete single copy of human chromosome 21 via the female germline, we show that a heterologous regulatory environment can transcriptionally activate transposon-derived human regulatory regions. In the mouse nucleus, hundreds of locations on human chromosome 21 newly associate with activating histone modifications in both somatic and germline tissues, and influence the gene expression of nearby transcripts. These regions are enriched with primate and human lineage-specific transposable elements, and their activation corresponds to changes in DNA methylation at CpG dinucleotides. This study reveals the latent regulatory potential of the repetitive human genome and illustrates the species specificity of mechanisms that control it.

Crisflash: open-source software to generate CRISPR guide RNAs against genomes annotated with individual variation.

SUMMARY: CRISPR/Cas9 system requires short guide RNAs (sgRNAs) to direct genome modification. Most currently available tools for sgRNA design operate only with standard reference genomes, and are best suited for small-scale projects. To address these limitations, we developed Crisflash, a software tool for fast sgRNA design and potential off-target discovery, built for performance and flexibility. Crisflash can rapidly design CRISPR guides against any sequenced genome or genome sequences, and can optimize guide accuracy by incorporating user-supplied variant data. Crisflash is over an order of magnitude faster than comparable tools, even using a single CPU core, and efficiently and robustly scores the potential off-targeting of all possible candidate CRISPR guide oligonucleotides. AVAILABILITY AND IMPLEMENTATION: https://github.com/crisflash. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mutational landscape of a chemically-induced mouse model of liver cancer.

BACKGROUND & AIMS: Carcinogen-induced mouse models of liver cancer are used extensively to study the pathogenesis of the disease and are critical for validating candidate therapeutics. These models can recapitulate molecular and histological features of human disease. However, it is not known if the genomic alterations driving these mouse tumour genomes are comparable to those found in human tumours. Herein, we provide a detailed genomic characterisation of tumours from a commonly used mouse model of hepatocellular carcinoma (HCC). METHODS: We analysed whole exome sequences of liver tumours arising in mice exposed to diethylnitrosamine (DEN). Mutational signatures were compared between liver tumours from DEN-treated and untreated mice, and human HCCs. RESULTS: DEN-initiated tumours had a high, uniform number of somatic single nucleotide variants (SNVs), with few insertions, deletions or copy number alterations, consistent with the known genotoxic action of DEN. Exposure of hepatocytes to DEN left a reproducible mutational imprint in resulting tumour exomes which we could computationally reconstruct using six known COSMIC mutational signatures. The tumours carried a high diversity of low-incidence, non-synonymous point mutations in many oncogenes and tumour suppressors, reflecting the stochastic introduction of SNVs into the hepatocyte genome by the carcinogen. We identified four recurrently mutated genes that were putative oncogenic drivers of HCC in this model. Every neoplasm carried activating hotspot mutations either in codon 61 of Hras, in codon 584 of Braf or in codon 254 of Egfr. Truncating mutations of Apc occurred in 21% of neoplasms, which were exclusively carcinomas supporting a role for deregulation of Wnt/ß-catenin signalling in cancer progression. CONCLUSIONS: Our study provides detailed insight into the mutational landscape of tumours arising in a commonly used carcinogen model of HCC, facilitating the future use of this model to better understand the human disease. LAY SUMMARY: Mouse models are widely used to study the biology of cancer and to test potential therapies. Herein, we have described the mutational landscape of tumours arising in a carcinogen-induced mouse model of liver cancer. Since cancer is a disease caused by genomic alterations, information about the patterns and types of mutations in the tumours in this mouse model should facilitate its use to study human liver cancer.

Aberrant methylation of tRNAs links cellular stress to neuro-developmental disorders.

Mutations in the cytosine-5 RNA methyltransferase NSun2 cause microcephaly and other neurological abnormalities in mice and human. How post-transcriptional methylation contributes to the human disease is currently unknown. By comparing gene expression data with global cytosine-5 RNA methylomes in patient fibroblasts and NSun2-deficient mice, we find that loss of cytosine-5 RNA methylation increases the angiogenin-mediated endonucleolytic cleavage of transfer RNAs (tRNA) leading to an accumulation of 5' tRNA-derived small RNA fragments. Accumulation of 5' tRNA fragments in the absence of NSun2 reduces protein translation rates and activates stress pathways leading to reduced cell size and increased apoptosis of cortical, hippocampal and striatal neurons. Mechanistically, we demonstrate that angiogenin binds with higher affinity to tRNAs lacking site-specific NSun2-mediated methylation and that the presence of 5' tRNA fragments is sufficient and required to trigger cellular stress responses. Furthermore, the enhanced sensitivity of NSun2-deficient brains to oxidative stress can be rescued through inhibition of angiogenin during embryogenesis. In conclusion, failure in NSun2-mediated tRNA methylation contributes to human diseases via stress-induced RNA cleavage.

MageComet--web application for harmonizing existing large-scale experiment descriptions.

MOTIVATION: Meta-analysis of large gene expression datasets obtained from public repositories requires consistently annotated data. Curation of such experiments, however, is an expert activity which involves repetitive manipulation of text. Existing tools for automated curation are few, which bottleneck the analysis pipeline. RESULTS: We present MageComet, a web application for biologists and annotators that facilitates the re-annotation of gene expression experiments in MAGE-TAB format. It incorporates data mining, automatic annotation, use of ontologies and data validation to improve the consistency and quality of experimental meta-data from the ArrayExpress Repository.

ArrayExpress update--an archive of microarray and high-throughput sequencing-based functional genomics experiments.

The ArrayExpress Archive (http://www.ebi.ac.uk/arrayexpress) is one of the three international public repositories of functional genomics data supporting publications. It includes data generated by sequencing or array-based technologies. Data are submitted by users and imported directly from the NCBI Gene Expression Omnibus. The ArrayExpress Archive is closely integrated with the Gene Expression Atlas and the sequence databases at the European Bioinformatics Institute. Advanced queries provided via ontology enabled interfaces include queries based on technology and sample attributes such as disease, cell types and anatomy.

Assessing affymetrix GeneChip microarray quality.

BACKGROUND: Microarray technology has become a widely used tool in the biological sciences. Over the past decade, the number of users has grown exponentially, and with the number of applications and secondary data analyses rapidly increasing, we expect this rate to continue. Various initiatives such as the External RNA Control Consortium (ERCC) and the MicroArray Quality Control (MAQC) project have explored ways to provide standards for the technology. For microarrays to become generally accepted as a reliable technology, statistical methods for assessing quality will be an indispensable component; however, there remains a lack of consensus in both defining and measuring microarray quality. RESULTS: We begin by providing a precise definition of microarray quality and reviewing existing Affymetrix GeneChip quality metrics in light of this definition. We show that the best-performing metrics require multiple arrays to be assessed simultaneously. While such multi-array quality metrics are adequate for bench science, as microarrays begin to be used in clinical settings, single-array quality metrics will be indispensable. To this end, we define a single-array version of one of the best multi-array quality metrics and show that this metric performs as well as the best multi-array metrics. We then use this new quality metric to assess the quality of microarry data available via the Gene Expression Omnibus (GEO) using more than 22,000 Affymetrix HGU133a and HGU133plus2 arrays from 809 studies. CONCLUSIONS: We find that approximately 10 percent of these publicly available arrays are of poor quality. Moreover, the quality of microarray measurements varies greatly from hybridization to hybridization, study to study, and lab to lab, with some experiments producing unusable data. Many of the concepts described here are applicable to other high-throughput technologies.

ArrayExpress update--from an archive of functional genomics experiments to the atlas of gene expression.

ArrayExpress http://www.ebi.ac.uk/arrayexpress consists of three components: the ArrayExpress Repository--a public archive of functional genomics experiments and supporting data, the ArrayExpress Warehouse--a database of gene expression profiles and other bio-measurements and the ArrayExpress Atlas--a new summary database and meta-analytical tool of ranked gene expression across multiple experiments and different biological conditions. The Repository contains data from over 6000 experiments comprising approximately 200,000 assays, and the database doubles in size every 15 months. The majority of the data are array based, but other data types are included, most recently-ultra high-throughput sequencing transcriptomics and epigenetic data. The Warehouse and Atlas allow users to query for differentially expressed genes by gene names and properties, experimental conditions and sample properties, or a combination of both. In this update, we describe the ArrayExpress developments over the last two years.

Importing ArrayExpress datasets into R/Bioconductor.

SUMMARY: ArrayExpress is one of the largest public repositories of microarray datasets. R/Bioconductor provides a comprehensive suite of microarray analysis and integrative bioinformatics software. However, easy ways for importing datasets from ArrayExpress into R/Bioconductor have been lacking. Here, we present such a tool that is suitable for both interactive and automated use. AVAILABILITY: The ArrayExpress package is available from the Bioconductor project at http://www.bioconductor.org. A users guide and examples are provided with the package.

MAGETabulator, a suite of tools to support the microarray data format MAGE-TAB.

SUMMARY: The MAGE-TAB format for microarray data representation and exchange has been proposed by the microarray community to replace the more complex MAGE-ML format. We present a suite of tools to support MAGE-TAB generation and validation, conversion between existing formats for data exchange, visualization of the experiment designs encoded by MAGE-TAB documents and the mining of such documents for semantic content.

Identification of Cancer Related Genes Using a Comprehensive Map of Human Gene Expression.

Rapid accumulation and availability of gene expression datasets in public repositories have enabled large-scale meta-analyses of combined data. The richness of cross-experiment data has provided new biological insights, including identification of new cancer genes. In this study, we compiled a human gene expression dataset from ∼40,000 publicly available Affymetrix HG-U133Plus2 arrays. After strict quality control and data normalisation the data was quantified in an expression matrix of ∼20,000 genes and ∼28,000 samples. To enable different ways of sample grouping, existing annotations where subjected to systematic ontology assisted categorisation and manual curation. Groups like normal tissues, neoplasmic tissues, cell lines, homoeotic cells and incompletely differentiated cells were created. Unsupervised analysis of the data confirmed global structure of expression consistent with earlier analysis but with more details revealed due to increased resolution. A suitable mixed-effects linear model was used to further investigate gene expression in solid tissue tumours, and to compare these with the respective healthy solid tissues. The analysis identified 1,285 genes with systematic expression change in cancer. The list is significantly enriched with known cancer genes from large, public, peer-reviewed databases, whereas the remaining ones are proposed as new cancer gene candidates. The compiled dataset is publicly available in the ArrayExpress Archive. It contains the most diverse collection of biological samples, making it the largest systematically annotated gene expression dataset of its kind in the public domain.

Successful transmission and transcriptional deployment of a human chromosome via mouse male meiosis.

Most human aneuploidies originate maternally, due in part to the presence of highly stringent checkpoints during male meiosis. Indeed, male sterility is common among aneuploid mice used to study chromosomal abnormalities, and male germline transmission of exogenous DNA has been rarely reported. Here we show that, despite aberrant testis architecture, males of the aneuploid Tc1 mouse strain produce viable sperm and transmit human chromosome 21 to create aneuploid offspring. In these offspring, we mapped transcription, transcriptional initiation, enhancer activity, non-methylated DNA, and transcription factor binding in adult tissues. Remarkably, when compared with mice derived from female passage of human chromosome 21, the chromatin condensation during spermatogenesis and the extensive epigenetic reprogramming specific to male germline transmission resulted in almost indistinguishable patterns of transcriptional deployment. Our results reveal an unexpected tolerance of aneuploidy during mammalian spermatogenesis, and the surprisingly robust ability of mouse developmental machinery to accurately deploy an exogenous chromosome, regardless of germline transmission.

Multi-species, multi-transcription factor binding highlights conserved control of tissue-specific biological pathways.

As exome sequencing gives way to genome sequencing, the need to interpret the function of regulatory DNA becomes increasingly important. To test whether evolutionary conservation of cis-regulatory modules (CRMs) gives insight into human gene regulation, we determined transcription factor (TF) binding locations of four liver-essential TFs in liver tissue from human, macaque, mouse, rat, and dog. Approximately, two thirds of the TF-bound regions fell into CRMs. Less than half of the human CRMs were found as a CRM in the orthologous region of a second species. Shared CRMs were associated with liver pathways and disease loci identified by genome-wide association studies. Recurrent rare human disease causing mutations at the promoters of several blood coagulation and lipid metabolism genes were also identified within CRMs shared in multiple species. This suggests that multi-species analyses of experimentally determined combinatorial TF binding will help identify genomic regions critical for tissue-specific gene control.

Global gene expression profiling reveals SPINK1 as a potential hepatocellular carcinoma marker.

BACKGROUND: Liver cirrhosis is the most important risk factor for hepatocellular carcinoma (HCC) but the role of liver disease aetiology in cancer development remains under-explored. We investigated global gene expression profiles from HCC arising in different liver diseases to test whether HCC development is driven by expression of common or different genes, which could provide new diagnostic markers or therapeutic targets. METHODOLOGY AND PRINCIPAL FINDINGS: Global gene expression profiling was performed for 4 normal (control) livers as well as 8 background liver and 7 HCC from 3 patients with hereditary haemochromatosis (HH) undergoing surgery. In order to investigate different disease phenotypes causing HCC, the data were compared with public microarray repositories for gene expression in normal liver, hepatitis C virus (HCV) cirrhosis, HCV-related HCC (HCV-HCC), hepatitis B virus (HBV) cirrhosis and HBV-related HCC (HBV-HCC). Principal component analysis and differential gene expression analysis were carried out using R Bioconductor. Liver disease-specific and shared gene lists were created and genes identified as highly expressed in hereditary haemochromatosis HCC (HH-HCC) were validated using quantitative RT-PCR. Selected genes were investigated further using immunohistochemistry in 86 HCC arising in liver disorders with varied aetiology. Using a 2-fold cut-off, 9 genes were highly expressed in all HCC, 11 in HH-HCC, 270 in HBV-HCC and 9 in HCV-HCC. Six genes identified by microarray as highly expressed in HH-HCC were confirmed by RT qPCR. Serine peptidase inhibitor, Kazal type 1 (SPINK1) mRNA was very highly expressed in HH-HCC (median fold change 2291, p = 0.0072) and was detected by immunohistochemistry in 91% of HH-HCC, 0% of HH-related cirrhotic or dysplastic nodules and 79% of mixed-aetiology HCC. CONCLUSION: HCC, arising from diverse backgrounds, uniformly over-express a small set of genes. SPINK1, a secretory trypsin inhibitor, demonstrated potential as a diagnostic HCC marker and should be evaluated in future studies.

AHT-ChIP-seq: a completely automated robotic protocol for high-throughput chromatin immunoprecipitation.

ChIP-seq is an established manually-performed method for identifying DNA-protein interactions genome-wide. Here, we describe a protocol for automated high-throughput (AHT) ChIP-seq. To demonstrate the quality of data obtained using AHT-ChIP-seq, we applied it to five proteins in mouse livers using a single 96-well plate, demonstrating an extremely high degree of qualitative and quantitative reproducibility among biological and technical replicates. We estimated the optimum and minimum recommended cell numbers required to perform AHT-ChIP-seq by running an additional plate using HepG2 and MCF7 cells. With this protocol, commercially available robotics can perform four hundred experiments in five days.