Reuse of public genome-wide gene expression data.

Our understanding of gene expression has changed dramatically over the past decade, largely catalysed by technological developments. High-throughput experiments - microarrays and next-generation sequencing - have generated large amounts of genome-wide gene expression data that are collected in public archives. Added-value databases process, analyse and annotate these data further to make them accessible to every biologist. In this Review, we discuss the utility of the gene expression data that are in the public domain and how researchers are making use of these data. Reuse of public data can be very powerful, but there are many obstacles in data preparation and analysis and in the interpretation of the results. We will discuss these challenges and provide recommendations that we believe can improve the utility of such data.

Discovery and Fine-Mapping of Glycaemic and Obesity-Related Trait Loci Using High-Density Imputation.

Reference panels from the 1000 Genomes (1000G) Project Consortium provide near complete coverage of common and low-frequency genetic variation with minor allele frequency ≥0.5% across European ancestry populations. Within the European Network for Genetic and Genomic Epidemiology (ENGAGE) Consortium, we have undertaken the first large-scale meta-analysis of genome-wide association studies (GWAS), supplemented by 1000G imputation, for four quantitative glycaemic and obesity-related traits, in up to 87,048 individuals of European ancestry. We identified two loci for body mass index (BMI) at genome-wide significance, and two for fasting glucose (FG), none of which has been previously reported in larger meta-analysis efforts to combine GWAS of European ancestry. Through conditional analysis, we also detected multiple distinct signals of association mapping to established loci for waist-hip ratio adjusted for BMI (RSPO3) and FG (GCK and G6PC2). The index variant for one association signal at the G6PC2 locus is a low-frequency coding allele, H177Y, which has recently been demonstrated to have a functional role in glucose regulation. Fine-mapping analyses revealed that the non-coding variants most likely to drive association signals at established and novel loci were enriched for overlap with enhancer elements, which for FG mapped to promoter and transcription factor binding sites in pancreatic islets, in particular. Our study demonstrates that 1000G imputation and genetic fine-mapping of common and low-frequency variant association signals at GWAS loci, integrated with genomic annotation in relevant tissues, can provide insight into the functional and regulatory mechanisms through which their effects on glycaemic and obesity-related traits are mediated.

TCF7L2 is a master regulator of insulin production and processing.

Genome-wide association studies have revealed >60 loci associated with type 2 diabetes (T2D), but the underlying causal variants and functional mechanisms remain largely elusive. Although variants in TCF7L2 confer the strongest risk of T2D among common variants by presumed effects on islet function, the molecular mechanisms are not yet well understood. Using RNA-sequencing, we have identified a TCF7L2-regulated transcriptional network responsible for its effect on insulin secretion in rodent and human pancreatic islets. ISL1 is a primary target of TCF7L2 and regulates proinsulin production and processing via MAFA, PDX1, NKX6.1, PCSK1, PCSK2 and SLC30A8, thereby providing evidence for a coordinated regulation of insulin production and processing. The risk T-allele of rs7903146 was associated with increased TCF7L2 expression, and decreased insulin content and secretion. Using gene expression profiles of 66 human pancreatic islets donors', we also show that the identified TCF7L2-ISL1 transcriptional network is regulated in a genotype-dependent manner. Taken together, these results demonstrate that not only synthesis of proinsulin is regulated by TCF7L2 but also processing and possibly clearance of proinsulin and insulin. These multiple targets in key pathways may explain why TCF7L2 has emerged as the gene showing one of the strongest associations with T2D.

Reversion of a fungal genetic code alteration links proteome instability with genomic and phenotypic diversification.

Many fungi restructured their proteomes through incorporation of serine (Ser) at thousands of protein sites coded by the leucine (Leu) CUG codon. How these fungi survived this potentially lethal genetic code alteration and its relevance for their biology are not understood. Interestingly, the human pathogen Candida albicans maintains variable Ser and Leu incorporation levels at CUG sites, suggesting that this atypical codon assignment flexibility provided an effective mechanism to alter the genetic code. To test this hypothesis, we have engineered C. albicans strains to misincorporate increasing levels of Leu at protein CUG sites. Tolerance to the misincorporations was very high, and one strain accommodated the complete reversion of CUG identity from Ser back to Leu. Increasing levels of Leu misincorporation decreased growth rate, but production of phenotypic diversity on a phenotypic array probing various metabolic networks, drug resistance, and host immune cell responses was impressive. Genome resequencing revealed an increasing number of genotype changes at polymorphic sites compared with the control strain, and 80% of Leu misincorporation resulted in complete loss of heterozygosity in a large region of chromosome V. The data unveil unanticipated links between gene translational fidelity, proteome instability and variability, genome diversification, and adaptive phenotypic diversity. They also explain the high heterozygosity of the C. albicans genome and open the door to produce microorganisms with genetic code alterations for basic and applied research.

A fully scalable online pre-processing algorithm for short oligonucleotide microarray atlases.

Rapid accumulation of large and standardized microarray data collections is opening up novel opportunities for holistic characterization of genome function. The limited scalability of current preprocessing techniques has, however, formed a bottleneck for full utilization of these data resources. Although short oligonucleotide arrays constitute a major source of genome-wide profiling data, scalable probe-level techniques have been available only for few platforms based on pre-calculated probe effects from restricted reference training sets. To overcome these key limitations, we introduce a fully scalable online-learning algorithm for probe-level analysis and pre-processing of large microarray atlases involving tens of thousands of arrays. In contrast to the alternatives, our algorithm scales up linearly with respect to sample size and is applicable to all short oligonucleotide platforms. The model can use the most comprehensive data collections available to date to pinpoint individual probes affected by noise and biases, providing tools to guide array design and quality control. This is the only available algorithm that can learn probe-level parameters based on sequential hyperparameter updates at small consecutive batches of data, thus circumventing the extensive memory requirements of the standard approaches and opening up novel opportunities to take full advantage of contemporary microarray collections.

ArrayExpress update--trends in database growth and links to data analysis tools.

The ArrayExpress Archive of Functional Genomics Data (http://www.ebi.ac.uk/arrayexpress) is one of three international functional genomics public data repositories, alongside the Gene Expression Omnibus at NCBI and the DDBJ Omics Archive, supporting peer-reviewed publications. It accepts data generated by sequencing or array-based technologies and currently contains data from almost a million assays, from over 30 000 experiments. The proportion of sequencing-based submissions has grown significantly over the last 2 years and has reached, in 2012, 15% of all new data. All data are available from ArrayExpress in MAGE-TAB format, which allows robust linking to data analysis and visualization tools, including Bioconductor and GenomeSpace. Additionally, R objects, for microarray data, and binary alignment format files, for sequencing data, have been generated for a significant proportion of ArrayExpress data.

Tools for mapping high-throughput sequencing data.

MOTIVATION: A ubiquitous and fundamental step in high-throughput sequencing analysis is the alignment (mapping) of the generated reads to a reference sequence. To accomplish this task, numerous software tools have been proposed. Determining the mappers that are most suitable for a specific application is not trivial. RESULTS: This survey focuses on classifying mappers through a wide number of characteristics. The goal is to allow practitioners to compare the mappers more easily and find those that are most suitable for their specific problem.

A genome-wide association study identifies novel risk loci for type 2 diabetes.

Type 2 diabetes mellitus results from the interaction of environmental factors with a combination of genetic variants, most of which were hitherto unknown. A systematic search for these variants was recently made possible by the development of high-density arrays that permit the genotyping of hundreds of thousands of polymorphisms. We tested 392,935 single-nucleotide polymorphisms in a French case-control cohort. Markers with the most significant difference in genotype frequencies between cases of type 2 diabetes and controls were fast-tracked for testing in a second cohort. This identified four loci containing variants that confer type 2 diabetes risk, in addition to confirming the known association with the TCF7L2 gene. These loci include a non-synonymous polymorphism in the zinc transporter SLC30A8, which is expressed exclusively in insulin-producing beta-cells, and two linkage disequilibrium blocks that contain genes potentially involved in beta-cell development or function (IDE-KIF11-HHEX and EXT2-ALX4). These associations explain a substantial portion of disease risk and constitute proof of principle for the genome-wide approach to the elucidation of complex genetic traits.

SAIL--a software system for sample and phenotype availability across biobanks and cohorts.

SUMMARY: The Sample avAILability system-SAIL-is a web based application for searching, browsing and annotating biological sample collections or biobank entries. By providing individual-level information on the availability of specific data types (phenotypes, genetic or genomic data) and samples within a collection, rather than the actual measurement data, resource integration can be facilitated. A flexible data structure enables the collection owners to provide descriptive information on their samples using existing or custom vocabularies. Users can query for the available samples by various parameters combining them via logical expressions. The system can be scaled to hold data from millions of samples with thousands of variables. AVAILABILITY: SAIL is available under Aferro-GPL open source license: https://github.com/sail.

I. In vivo evidence for partial agonist effects of (-)-OSU6162 and (+)-OSU6162 on 5-HT2A serotonin receptors.

The locomotor effects of (-)- and (+)-OSU6162 were evaluated in 'low activity' animals (reserpinized mice and habituated rats) and 'high activity' animals (drug-naive mice and non-habituated rats). Both enantiomers of OSU6162 had dual effects on behavior, stimulating locomotor activity in 'low activity' animals and inhibiting locomotor activity in 'high activity' animals. There were also certain differences between the two enantiomers in their behavioral profiles. The stimulatory effects of both enantiomers in reserpinized mice were blocked by the 5-HT2A selective antagonist M100907, but not by the D2-selective antagonists haloperidol or raclopride, or by the D1-selective antagonists SCH23390 or SCH39166. The stimulatory effect in mice was more pronounced for (+)- than for (-)-OSU6162. In drug-naive mice, both enantiomers of OSU6162 produced head twitches, albeit to a much lesser extent than DOI, and both enantiomers inhibited DOI-induced head twitches, the (-)-form more effectively so than the (+)-form. These results suggest that (-)- and (+)-OSU6162 are partial agonists on 5-HT2A receptors and that the (+)-form has a higher intrinsic activity than the (-)-form. At high doses, both enantiomers inhibited locomotor activity in drug-naive mice, with (-)-OSU6162 being more potent than (+)-OSU6162. Similarly, in high-active rats, both enantiomers inhibited locomotor activity, with the (-)-enantiomer being more potent than the (+)-enantiomer. Conversely, in habituated rats, both enantiomers stimulated locomotor activity, and here, as opposed to the case in low-active mice, (-)-OSU6162 was more effective than (+)-OSU6162. The stimulatory effects in habituated rats of both enantiomers could be antagonized with either haloperidol or M100907. Overall, these results indicate that the dual effects on behavior of (-)- and (+)-OSU6162 are mediated through D2 and 5-HT2A receptors, consistent with their in vitro functional selectivity profiles (see Burstein et al., accompanying paper). Thus, both enantiomers of OSU6162 seem to act as stabilizers not only on dopaminergic, but also on serotonergic brain signaling. These discoveries have important implications for the potential clinical utility of both compounds, as well as for several of their congeners.

A System for Information Management in BioMedical Studies--SIMBioMS.

UNLABELLED: SIMBioMS is a web-based open source software system for managing data and information in biomedical studies. It provides a solution for the collection, storage, management and retrieval of information about research subjects and biomedical samples, as well as experimental data obtained using a range of high-throughput technologies, including gene expression, genotyping, proteomics and metabonomics. The system can easily be customized and has proven to be successful in several large-scale multi-site collaborative projects. It is compatible with emerging functional genomics data standards and provides data import and export in accepted standard formats. Protocols for transferring data to durable archives at the European Bioinformatics Institute have been implemented. AVAILABILITY: The source code, documentation and initialization scripts are available at http://simbioms.org.

MuscleAtlasExplorer: a web service for studying gene expression in human skeletal muscle.

MuscleAtlasExplorer is a freely available web application that allows for the exploration of gene expression data from human skeletal muscle. It draws from an extensive publicly available dataset of 1654 skeletal muscle expression microarray samples. Detailed, manually curated, patient phenotype data, with information such as age, sex, BMI and disease status, are combined with skeletal muscle gene expression to provide insights into gene function in skeletal muscle. It aims to facilitate easy exploration of the data using powerful data visualization functions, while allowing for sample selection, in-depth inspection and further analysis using external tools. Availability: MuscleAtlasExplorer is available at https://mae.crc.med.lu.se/mae2 (username 'muscle' and password 'explorer' pre-publication).

Publisher Correction: Building an international consortium for tracking coronavirus health status.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Identification of susceptibility genes for complex diseases using pooling-based genome-wide association scans.

The success of genome-wide association studies (GWAS) to identify risk loci of complex diseases is now well-established. One persistent major hurdle is the cost of those studies, which make them beyond the reach of most research groups. Performing GWAS on pools of DNA samples may be an effective strategy to reduce the costs of these studies. In this study, we performed pooling-based GWAS with more than 550,000 SNPs in two case-control cohorts consisting of patients with Type II diabetes (T2DM) and with chronic rhinosinusitis (CRS). In the T2DM study, the results of the pooling experiment were compared to individual genotypes obtained from a previously published GWAS. TCF7L2 and HHEX SNPs associated with T2DM by the traditional GWAS were among the top ranked SNPs in the pooling experiment. This dataset was also used to refine the best strategy to correctly identify SNPs that will remain significant based on individual genotyping. In the CRS study, the top hits from the pooling-based GWAS located within ten kilobases of known genes were validated by individual genotyping of 1,536 SNPs. Forty-one percent (598 out of the 1,457 SNPs that passed quality control) were associated with CRS at a nominal P value of 0.05, confirming the potential of pooling-based GWAS to identify SNPs that differ in allele frequencies between two groups of subjects. Overall, our results demonstrate that a pooling experiment on high-density genotyping arrays can accurately determine the minor allelic frequency as compared to individual genotyping and produce a list of top ranked SNPs that captures genuine allelic differences between a group of cases and controls. The low cost associated with a pooling-based GWAS clearly justifies its use in screening for genetic determinants of complex diseases.

Blood transcriptome profile induced by an efficacious vaccine formulated with salivary antigens from cattle ticks.

Ticks cause massive damage to livestock and vaccines are one sustainable alternative for the acaricide poisons currently heavily used to control infestations. An experimental vaccine adjuvanted with alum and composed by four recombinant salivary antigens mined with reverse vaccinology from a transcriptome of salivary glands from Rhipicephalus microplus ticks was previously shown to present an overall efficacy of 73.2% and cause a significant decrease of tick loads in artificially tick-infested, immunized heifers; this decrease was accompanied by increased levels of antigen-specific IgG1 and IgG2 antibodies, which were boosted during a challenge infestation. In order to gain insights into the systemic effects induced by the vaccine and by the tick challenge we now report the gene expression profile of these hosts' whole-blood leukocytes with RNA-seq followed by functional analyses. These analyses show that vaccination induced unique responses to infestations; genes upregulated in the comparisons were enriched for processes associated with chemotaxis, cell adhesion, T-cell responses and wound repair. Blood transcriptional modules were enriched for activation of dendritic cells, cell cycle, phosphatidylinositol signaling, and platelets. Together, the results indicate that by neutralizing the tick's salivary mediators of parasitism with vaccine-induced antibodies, the bovine host is able to mount normal homeostatic responses that hinder tick attachment and haematophagy and that the tick otherwise suppresses with its saliva.

Aberration hubs in protein interaction networks highlight actionable targets in cancer.

Despite efforts for extensive molecular characterization of cancer patients, such as the international cancer genome consortium (ICGC) and the cancer genome atlas (TCGA), the heterogeneous nature of cancer and our limited knowledge of the contextual function of proteins have complicated the identification of targetable genes. Here, we present Aberration Hub Analysis for Cancer (AbHAC) as a novel integrative approach to pinpoint aberration hubs, i.e. individual proteins that interact extensively with genes that show aberrant mutation or expression. Our analysis of the breast cancer data of the TCGA and the renal cancer data from the ICGC shows that aberration hubs are involved in relevant cancer pathways, including factors promoting cell cycle and DNA replication in basal-like breast tumors, and Src kinase and VEGF signaling in renal carcinoma. Moreover, our analysis uncovers novel functionally relevant and actionable targets, among which we have experimentally validated abnormal splicing of spleen tyrosine kinase as a key factor for cell proliferation in renal cancer. Thus, AbHAC provides an effective strategy to uncover novel disease factors that are only identifiable by examining mutational and expression data in the context of biological networks.

Adaptive Mistranslation Accelerates the Evolution of Fluconazole Resistance and Induces Major Genomic and Gene Expression Alterations in Candida albicans.

Regulated erroneous protein translation (adaptive mistranslation) increases proteome diversity and produces advantageous phenotypic variability in the human pathogen Candida albicans. It also increases fitness in the presence of fluconazole, but the underlying molecular mechanism is not understood. To address this question, we evolved hypermistranslating and wild-type strains in the absence and presence of fluconazole and compared their fluconazole tolerance and resistance trajectories during evolution. The data show that mistranslation increases tolerance and accelerates the acquisition of resistance to fluconazole. Genome sequencing, array-based comparative genome analysis, and gene expression profiling revealed that during the course of evolution in fluconazole, the range of mutational and gene deregulation differences was distinctively different and broader in the hypermistranslating strain, including multiple chromosome duplications, partial chromosome deletions, and polyploidy. Especially, the increased accumulation of loss-of-heterozygosity events, aneuploidy, translational and cell surface modifications, and differences in drug efflux seem to mediate more rapid drug resistance acquisition under mistranslation. Our observations support a pivotal role for adaptive mistranslation in the evolution of drug resistance in C. albicans. IMPORTANCE Infectious diseases caused by drug-resistant fungi are an increasing threat to public health because of the high mortality rates and high costs associated with treatment. Thus, understanding of the molecular mechanisms of drug resistance is of crucial interest for the medical community. Here we investigated the role of regulated protein mistranslation, a characteristic mechanism used by C. albicans to diversify its proteome, in the evolution of fluconazole resistance. Such codon ambiguity is usually considered highly deleterious, yet recent studies found that mistranslation can boost adaptation in stressful environments. Our data reveal that CUG ambiguity diversifies the genome in multiple ways and that the full spectrum of drug resistance mechanisms in C. albicans goes beyond the traditional pathways that either regulate drug efflux or alter the interactions of drugs with their targets. The present work opens new avenues to understand the molecular and genetic basis of microbial drug resistance.

Advances in systems biology: measurement, modeling and representation.

Systems biology is frequently defined as the study of all of the elements in a biological system and their relationship to one another in response to perturbation. Advances in science and technology are enabling the development of this emerging and cross-disciplinary field by allowing researchers to explore how biological components function as a network in cells, tissues and organisms. Recently, pharmaceutical companies have begun to embrace systems approaches in an effort to better understand physiology, pathogenic processes and pharmacological responses. This review focuses on recent advances within three core areas of systems biology: data collection, data analysis, and the integration and sharing of data.

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.