The Rat Genome Database 2009: variation, ontologies and pathways

Genetic characterization of outbred Sprague Dawley rats and utility for genome-wide association studies

Sprague Dawley (SD) rats are among the most widely used outbred laboratory rat populations. Despite this, the genetic characteristics of SD rats have not been clearly described, and SD rats are rarely used for experiments aimed at exploring genotype-phenotype relationships. In order to use SD rats to perform a genome-wide association study (GWAS), we collected behavioral data from 4,625 SD rats that were predominantly obtained from two commercial vendors, Charles River Laboratories and Harlan Sprague Dawley Inc. Using double-digest genotyping-by-sequencing (ddGBS), we obtained dense, high-quality genotypes at 291,438 SNPs across 4,061 rats. This genetic data allowed us to characterize the variation present in Charles River vs. Harlan SD rats. We found that the two populations are highly diverged (F_ST > 0.4). Furthermore, even for rats obtained from the same vendor, there was strong population structure across breeding facilities and even between rooms at the same facility. We performed multiple separate GWAS by fitting a linear mixed model that accounted for population structure and using meta-analysis to jointly analyze all cohorts. Our study examined Pavlovian conditioned approach (PavCA) behavior, which assesses the propensity for rats to attribute incentive salience to reward-associated cues. We identified 46 significant associations for the various metrics used to define PavCA. The surprising degree of population structure among SD rats from different sources has important implications for their use in both genetic and non-genetic studies.

Outbred Sprague Dawley rats are among the most commonly used rats for neuroscience, physiology and pharmacological research; in the year 2020, 4,188 publications contained the keyword “Sprague Dawley”. Rats identified as “Sprague Dawley” are sold by several commercial vendors, including Charles River Laboratories and Harlan Sprague Dawley Inc. (now Envigo). Despite their widespread use, little is known about the genetic diversity of SD. We genotyped more than 4,000 SD rats, which we used for a genome-wide association study (GWAS) and to characterize genetic differences between SD rats from Charles River Laboratories and Harlan. Our analysis revealed extensive population structure both between and within vendors. The GWAS for Pavlovian conditioned approach (PavCA) identified a number of genome-wide significant loci for that complex behavioral trait. Our results demonstrate that, despite sharing an identical name, SD rats that are obtained from different vendors are very different. Future studies should carefully define the exact source of SD rats being used and may exploit their genetic diversity for genetic studies of complex traits.

Single-cell RNA-seq identifies unique transcriptional landscapes of human nucleus pulposus and annulus fibrosus cells

Intervertebral disc (IVD) disease (IDD) is a complex, multifactorial disease. While various aspects of IDD progression have been reported, the underlying molecular pathways and transcriptional networks that govern the maintenance of healthy nucleus pulposus (NP) and annulus fibrosus (AF) have not been fully elucidated. We defined the transcriptome map of healthy human IVD by performing single-cell RNA-sequencing (scRNA-seq) in primary AF and NP cells isolated from non-degenerated lumbar disc. Our systematic and comprehensive analyses revealed distinct genetic architecture of human NP and AF compartments and identified 2,196 differentially expressed genes. Gene enrichment analysis showed that SFRP1, BIRC5, CYTL1, ESM1 and CCNB2 genes were highly expressed in the AF cells; whereas, COL2A1, DSC3, COL9A3, COL11A1, and ANGPTL7 were mostly expressed in the NP cells. Further, functional annotation clustering analysis revealed the enrichment of receptor signaling pathways genes in AF cells, while NP cells showed high expression of genes related to the protein synthesis machinery. Subsequent interaction network analysis revealed a structured network of extracellular matrix genes in NP compartments. Our regulatory network analysis identified FOXM1 and KDM4E as signature transcription factor of AF and NP respectively, which might be involved in the regulation of core genes of AF and NP transcriptome.

A new approach to identifying hypertension-associated genes in the mesenteric artery of spontaneously hypertensive rats and stroke-prone spontaneously hypertensive rats

Supplemental Digital Content is available in the text

Objective:

Hypertension is one of the most prevalent diseases in humans who live a modern lifestyle. Alongside more effective care, clarification of the genetic background of hypertension is urgently required. Gene expression in mesenteric resistance arteries of spontaneously hypertensive rats (SHR), stroke-prone SHR (SHRSP) and two types of renal hypertensive Wistar Kyoto rats (WKY), two kidneys and one clip renal hypertensive rat (2K1C) and one kidney and one clip renal hypertensive rat (1K1C), was compared using DNA microarrays.

Methods:

We used a simultaneous equation and comparative selection method to identify genes associated with hypertension using the Reactome analysis tool and GenBank database.

Results:

The expression of 298 genes was altered between SHR and WKY (44 upregulated and 254 downregulated), while the expression of 290 genes was altered between SHRSP and WKY (83 upregulated and 207 downregulated). For SHRSP versus SHR, the expression of 60 genes was altered (36 upregulated and 24 downregulated). Several genes expressed in SHR and SHRSP were also expressed in the renovascular hypertensive 2K1C and 1K1C rats, indicative of the existence of hyper-renin and/or hypervolemic pathophysiological changes in SHR and SHRSP.

Conclusion:

The overexpression of Kcnq1, Crlf1, Alb and Xirp1 and the inhibition of Galr2, Kcnh1, Ache, Chrm2 and Slc5a7 expression may indicate that a relationship exists between these genes and the cause and/or worsening of hypertension in SHR and SHRSP.

The Year of the Rat: The Rat Genome Database at 20: a multi-species knowledgebase and analysis platform

Formed in late 1999, the Rat Genome Database (RGD, https://rgd.mcw.edu) will be 20 in 2020, the Year of the Rat. Because the laboratory rat, Rattus norvegicus, has been used as a model for complex human diseases such as cardiovascular disease, diabetes, cancer, neurological disorders and arthritis, among others, for >150 years, RGD has always been disease-focused and committed to providing data and tools for researchers doing comparative genomics and translational studies. At its inception, before the sequencing of the rat genome, RGD started with only a few data types localized on genetic and radiation hybrid (RH) maps and offered only a few tools for querying and consolidating that data. Since that time, RGD has expanded to include a wealth of structured and standardized genetic, genomic, phenotypic, and disease-related data for eight species, and a suite of innovative tools for querying, analyzing and visualizing this data. This article provides an overview of recent substantial additions and improvements to RGD's data and tools that can assist researchers in finding and utilizing the data they need, whether their goal is to develop new precision models of disease or to more fully explore emerging details within a system or across multiple systems.

HPO2Vec+: Leveraging heterogeneous knowledge resources to enrich node embeddings for the Human Phenotype Ontology

BACKGROUND

In precision medicine, deep phenotyping is defined as the precise and comprehensive analysis of phenotypic abnormalities, aiming to acquire a better understanding of the natural history of a disease and its genotype-phenotype associations. Detecting phenotypic relevance is an important task when translating precision medicine into clinical practice, especially for patient stratification tasks based on deep phenotyping. In our previous work, we developed node embeddings for the Human Phenotype Ontology (HPO) to assist in phenotypic relevance measurement incorporating distributed semantic representations. However, the derived HPO embeddings hold only distributed representations for IS-A relationships among nodes, hampering the ability to fully explore the graph.

METHODS

In this study, we developed a framework, HPO2Vec+, to enrich the produced HPO embeddings with heterogeneous knowledge resources (i.e., DECIPHER, OMIM, and Orphanet) for detecting phenotypic relevance. Specifically, we parsed disease-phenotype associations contained in these three resources to enrich non-inheritance relationships among phenotypic nodes in the HPO. To generate node embeddings for the HPO, node2vec was applied to perform node sampling on the enriched HPO graphs based on random walk followed by feature learning over the sampled nodes to generate enriched node embeddings. Four HPO embeddings were generated based on different graph structures, which we hereafter label as HPOEmb-Original, HPOEmb-DECIPHER, HPOEmb-OMIM, and HPOEmb-Orphanet. We evaluated the derived embeddings quantitatively through an HPO link prediction task with four edge embeddings operations and six machine learning algorithms. The resulting best embeddings were then evaluated for patient stratification of 10 rare diseases using electronic health records (EHR) collected at Mayo Clinic. We assessed our framework qualitatively by visualizing phenotypic clusters and conducting a use case study on primary hyperoxaluria (PH), a rare disease, on the task of inferring relevant phenotypes given 22 annotated PH related phenotypes.

RESULTS

The quantitative link prediction task shows that HPOEmb-Orphanet achieved an optimal AUROC of 0.92 and an average precision of 0.94. In addition, HPOEmb-Orphanet achieved an optimal F1 score of 0.86. The quantitative patient similarity measurement task indicates that HPOEmb-Orphanet achieved the highest average detection rate for similar patients over 10 rare diseases and performed better than other similarity measures implemented by an existing tool, HPOSim, especially for pairwise patients with fewer shared common phenotypes. The qualitative evaluation shows that the enriched HPO embeddings are generally able to detect relationships among nodes with fine granularity and HPOEmb-Orphanet is particularly good at associating phenotypes across different disease systems. For the use case of detecting relevant phenotypic characterizations for given PH related phenotypes, HPOEmb-Orphanet outperformed the other three HPO embeddings by achieving the highest average P@5 of 0.81 and the highest P@10 of 0.79. Compared to seven conventional similarity measurements provided by HPOSim, HPOEmb-Orphanet is able to detect more relevant phenotypic pairs, especially for pairs not in inheritance relationships.

CONCLUSION

We drew the following conclusions based on the evaluation results. First, with additional non-inheritance edges, enriched HPO embeddings can detect more associations between fine granularity phenotypic nodes regardless of their topological structures in the HPO graph. Second, HPOEmb-Orphanet not only can achieve the optimal performance through link prediction and patient stratification based on phenotypic similarity, but is also able to detect relevant phenotypes closer to domain expert's judgments than other embeddings and conventional similarity measurements. Third, incorporating heterogeneous knowledge resources do not necessarily result in better performance for detecting relevant phenotypes. From a clinical perspective, in our use case study, clinical-oriented knowledge resources (e.g., Orphanet) can achieve better performance in detecting relevant phenotypic characterizations compared to biomedical-oriented knowledge resources (e.g., DECIPHER and OMIM).

Drug repurposing for aging research using model organisms

Many increasingly prevalent diseases share a common risk factor: age. However, little is known about pharmaceutical interventions against aging, despite many genes and pathways shown to be important in the aging process and numerous studies demonstrating that genetic interventions can lead to a healthier aging phenotype. An important challenge is to assess the potential to repurpose existing drugs for initial testing on model organisms, where such experiments are possible. To this end, we present a new approach to rank drug-like compounds with known mammalian targets according to their likelihood to modulate aging in the invertebrates Caenorhabditis elegans and Drosophila. Our approach combines information on genetic effects on aging, orthology relationships and sequence conservation, 3D protein structures, drug binding and bioavailability. Overall, we rank 743 different drug-like compounds for their likelihood to modulate aging. We provide various lines of evidence for the successful enrichment of our ranking for compounds modulating aging, despite sparse public data suitable for validation. The top ranked compounds are thus prime candidates for in vivo testing of their effects on lifespan in C. elegans or Drosophila. As such, these compounds are promising as research tools and ultimately a step towards identifying drugs for a healthier human aging.

Evaluating whole genome sequence data from the Genetic Absence Epilepsy Rat from Strasbourg and its related non-epileptic strain

OBJECTIVE

The Genetic Absence Epilepsy Rats from Strasbourg (GAERS) are an inbreed Wistar rat strain widely used as a model of genetic generalised epilepsy with absence seizures. As in humans, the genetic architecture that results in genetic generalized epilepsy in GAERS is poorly understood. Here we present the strain-specific variants found among the epileptic GAERS and their related Non-Epileptic Control (NEC) strain. The GAERS and NEC represent a powerful opportunity to identify neurobiological factors that are associated with the genetic generalised epilepsy phenotype.

METHODS

We performed whole genome sequencing on adult epileptic GAERS and adult NEC rats, a strain derived from the same original Wistar colony. We also generated whole genome sequencing on four double-crossed (GAERS with NEC) F2 selected for high-seizing (n = 2) and non-seizing (n = 2) phenotypes.

RESULTS

Specific to the GAERS genome, we identified 1.12 million single nucleotide variants, 296.5K short insertion-deletions, and 354 putative copy number variants that result in complete or partial loss/duplication of 41 genes. Of the GAERS-specific variants that met high quality criteria, 25 are annotated as stop codon gain/loss, 56 as putative essential splice sites, and 56 indels are predicted to result in a frameshift. Subsequent screening against the two F2 progeny sequenced for having the highest and two F2 progeny for having the lowest seizure burden identified only the selected Cacna1h GAERS-private protein-coding variant as exclusively co-segregating with the two high-seizing F2 rats.

SIGNIFICANCE

This study highlights an approach for using whole genome sequencing to narrow down to a manageable candidate list of genetic variants in a complex genetic epilepsy animal model, and suggests utility of this sequencing design to investigate other spontaneously occurring animal models of human disease.

histoneHMM: Differential analysis of histone modifications with broad genomic footprints

Background

ChIP-seq has become a routine method for interrogating the genome-wide distribution of various histone modifications. An important experimental goal is to compare the ChIP-seq profiles between an experimental sample and a reference sample, and to identify regions that show differential enrichment. However, comparative analysis of samples remains challenging for histone modifications with broad domains, such as heterochromatin-associated H3K27me3, as most ChIP-seq algorithms are designed to detect well defined peak-like features.

Results

To address this limitation we introduce histoneHMM, a powerful bivariate Hidden Markov Model for the differential analysis of histone modifications with broad genomic footprints. histoneHMM aggregates short-reads over larger regions and takes the resulting bivariate read counts as inputs for an unsupervised classification procedure, requiring no further tuning parameters. histoneHMM outputs probabilistic classifications of genomic regions as being either modified in both samples, unmodified in both samples or differentially modified between samples. We extensively tested histoneHMM in the context of two broad repressive marks, H3K27me3 and H3K9me3, and evaluated region calls with follow up qPCR as well as RNA-seq data. Our results show that histoneHMM outperforms competing methods in detecting functionally relevant differentially modified regions.

Conclusion

histoneHMM is a fast algorithm written in C++ and compiled as an R package. It runs in the popular R computing environment and thus seamlessly integrates with the extensive bioinformatic tool sets available through Bioconductor. This makeshistoneHMM an attractive choice for the differential analysis of ChIP-seq data. Software is available from http://histonehmm.molgen.mpg.de.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0491-6) contains supplementary material, which is available to authorized users.

Similarity-based search of model organism, disease and drug effect phenotypes

Background

Semantic similarity measures over phenotype ontologies have been demonstrated to provide a powerful approach for the analysis of model organism phenotypes, the discovery of animal models of human disease, novel pathways, gene functions, druggable therapeutic targets, and determination of pathogenicity.

Results

We have developed PhenomeNET 2, a system that enables similarity-based searches over a large repository of phenotypes in real-time. It can be used to identify strains of model organisms that are phenotypically similar to human patients, diseases that are phenotypically similar to model organism phenotypes, or drug effect profiles that are similar to the phenotypes observed in a patient or model organism. PhenomeNET 2 is available at http://aber-owl.net/phenomenet.

Conclusions

Phenotype-similarity searches can provide a powerful tool for the discovery and investigation of molecular mechanisms underlying an observed phenotypic manifestation. PhenomeNET 2 facilitates user-defined similarity searches and allows researchers to analyze their data within a large repository of human, mouse and rat phenotypes.

OntoMate: a text-mining tool aiding curation at the Rat Genome Database

The Rat Genome Database (RGD) is the premier repository of rat genomic, genetic and physiologic data. Converting data from free text in the scientific literature to a structured format is one of the main tasks of all model organism databases. RGD spends considerable effort manually curating gene, Quantitative Trait Locus (QTL) and strain information. The rapidly growing volume of biomedical literature and the active research in the biological natural language processing (bioNLP) community have given RGD the impetus to adopt text-mining tools to improve curation efficiency. Recently, RGD has initiated a project to use OntoMate, an ontology-driven, concept-based literature search engine developed at RGD, as a replacement for the PubMed (http://www.ncbi.nlm.nih.gov/pubmed) search engine in the gene curation workflow. OntoMate tags abstracts with gene names, gene mutations, organism name and most of the 16 ontologies/vocabularies used at RGD. All terms/ entities tagged to an abstract are listed with the abstract in the search results. All listed terms are linked both to data entry boxes and a term browser in the curation tool. OntoMate also provides user-activated filters for species, date and other parameters relevant to the literature search. Using the system for literature search and import has streamlined the process compared to using PubMed. The system was built with a scalable and open architecture, including features specifically designed to accelerate the RGD gene curation process. With the use of bioNLP tools, RGD has added more automation to its curation workflow.

Database URL:http://rgd.mcw.edu

Community structure detection for overlapping modules through mathematical programming in protein interaction networks

Community structure detection has proven to be important in revealing the underlying properties of complex networks. The standard problem, where a partition of disjoint communities is sought, has been continually adapted to offer more realistic models of interactions in these systems. Here, a two-step procedure is outlined for exploring the concept of overlapping communities. First, a hard partition is detected by employing existing methodologies. We then propose a novel mixed integer non linear programming (MINLP) model, known as OverMod, which transforms disjoint communities to overlapping. The procedure is evaluated through its application to protein-protein interaction (PPI) networks of the rat, E. coli, yeast and human organisms. Connector nodes of hard partitions exhibit topological and functional properties indicative of their suitability as candidates for multiple module membership. OverMod identifies two types of connector nodes, inter and intra-connector, each with their own particular characteristics pertaining to their topological and functional role in the organisation of the network. Inter-connector proteins are shown to be highly conserved proteins participating in pathways that control essential cellular processes, such as proliferation, differentiation and apoptosis and their differences with intra-connectors is highlighted. Many of these proteins are shown to possess multiple roles of distinct nature through their participation in different network modules, setting them apart from proteins that are simply ‘hubs’, i.e. proteins with many interaction partners but with a more specific biochemical role.

Identifying roles of "Jun-Chen-Zuo-Shi" component herbs of QiShenYiQi formula in treating acute myocardial ischemia by network pharmacology

BACKGROUND

The role of "Jun-Chen-Zuo-Shi" (also known as "sovereign-minister-assistant-courier") component herbs of Chinese medicine is not fully understood. This study aims to test the "Jun-Chen-Zuo-Shi" rule with the QiShenYiQi formula (QSYQ) on treating acute myocardial ischemia (AMI) by a network pharmacology approach.

METHODS

An Acute Myocardial Ischemia (AMI) specific Organism Disturbed Network (AMI-ODN), was constructed by integrating data of disease-associated genes, protein-protein interaction and microarray experiments. A network-based index, Network Recovery Index for Organism Disturbed Network (NRI-ODN), was developed to measure the therapeutic efficacy of QSYQ and its ingredients, i.e., the ability to recover disturbed AMI network model back to normal state.

RESULTS

The whole formula of QSYQ got a NRI-ODN score of 864.48, which outperformed all individual herbs. Additionally, the primary component herbs, Radix Astragalus membranaceus and Radix Salvia miltiorrrhiza showed NRI-DON score of 680.27 and 734.31 respectively, which meant a better performance to recover disturbed AMI network than the supplementary component herbs, Panax notoginseng and Dalbergia sissoo did (545.76 and 584.88, respectively).

CONCLUSION

AMI-ODN model and NRI-ODN identified the possible roles of "Jun-Chen-Zuo-Shi" component herbs of QSYQ in treating AMI at molecular network and pathway level.

GeneSense: a new approach for human gene annotation integrated with protein-protein interaction networks

Virtually all cellular functions involve protein-protein interactions (PPIs). As an increasing number of PPIs are identified and vast amount of information accumulated, researchers are finding different ways to interrogate the data and understand the interactions in context. However, it is widely recognized that a significant portion of the data is scattered, redundant, not considered high quality, and not readily accessible to researchers in a systematic fashion. In addition, it is challenging to identify the optimal protein targets in the current PPI networks. The GeneSense server was developed to integrate gene annotation and PPI networks in an expandable architecture that incorporates selected databases with the aim to assemble, analyze, evaluate and disseminate protein-protein association information in a comprehensive and user-friendly manner. Three network models including nodenet, leafnet and loopnet are used to identify the optimal protein targets in the complex networks. GeneSense is freely available at www.biomedsense.org/genesense.php.

Differential evolutionary constraints in the evolution of chemoreceptors: a murine and human case study

Chemoreception is among the most important sensory modalities in animals. Organisms use the ability to perceive chemical compounds in all major ecological activities. Recent studies have allowed the characterization of chemoreceptor gene families. These genes present strikingly high variability in copy numbers and pseudogenization degrees among different species, but the mechanisms underlying their evolution are not fully understood. We have analyzed the functional networks of these genes, their orthologs distribution, and performed phylogenetic analyses in order to investigate their evolutionary dynamics. We have modeled the chemosensory networks and compared the evolutionary constraints of their genes in Mus musculus, Homo sapiens, and Rattus norvegicus. We have observed significant differences regarding the constraints on the orthologous groups and network topologies of chemoreceptors and signal transduction machinery. Our findings suggest that chemosensory receptor genes are less constrained than their signal transducing machinery, resulting in greater receptor diversity and conservation of information processing pathways. More importantly, we have observed significant differences among the receptors themselves, suggesting that olfactory and bitter taste receptors are more conserved than vomeronasal receptors.

The pathway ontology - updates and applications

Background

The Pathway Ontology (PW) developed at the Rat Genome Database (RGD), covers all types of biological pathways, including altered and disease pathways and captures the relationships between them within the hierarchical structure of a directed acyclic graph. The ontology allows for the standardized annotation of rat, and of human and mouse genes to pathway terms. It also constitutes a vehicle for easy navigation between gene and ontology report pages, between reports and interactive pathway diagrams, between pathways directly connected within a diagram and between those that are globally related in pathway suites and suite networks. Surveys of the literature and the development of the Pathway and Disease Portals are important sources for the ongoing development of the ontology. User requests and mapping of pathways in other databases to terms in the ontology further contribute to increasing its content. Recently built automated pipelines use the mapped terms to make available the annotations generated by other groups.

Results

The two released pipelines – the Pathway Interaction Database (PID) Annotation Import Pipeline and the Kyoto Encyclopedia of Genes and Genomes (KEGG) Annotation Import Pipeline, make available over 7,400 and 31,000 pathway gene annotations, respectively. Building the PID pipeline lead to the addition of new terms within the signaling node, also augmented by the release of the RGD “Immune and Inflammatory Disease Portal” at that time. Building the KEGG pipeline lead to a substantial increase in the number of disease pathway terms, such as those within the ‘infectious disease pathway’ parent term category. The ‘drug pathway’ node has also seen increases in the number of terms as well as a restructuring of the node. Literature surveys, disease portal deployments and user requests have contributed and continue to contribute additional new terms across the ontology. Since first presented, the content of PW has increased by over 75%.

Conclusions

Ongoing development of the Pathway Ontology and the implementation of pipelines promote an enriched provision of pathway data. The ontology is freely available for download and use from the RGD ftp site at ftp://rgd.mcw.edu/pub/ontology/pathway/ or from the National Center for Biomedical Ontology (NCBO) BioPortal website at http://bioportal.bioontology.org/ontologies/PW.

Quantitative and qualitative proteome characteristics extracted from in-depth integrated genomics and proteomics analysis

Quantitative and qualitative protein characteristics are regulated at genomic, transcriptomic, and posttranscriptional levels. Here, we integrated in-depth transcriptome and proteome analyses of liver tissues from two rat strains to unravel the interactions within and between these layers. We obtained peptide evidence for 26,463 rat liver proteins. We validated 1,195 gene predictions, 83 splice events, 126 proteins with nonsynonymous variants, and 20 isoforms with nonsynonymous RNA editing. Quantitative RNA sequencing and proteomics data correlate highly between strains but poorly among each other, indicating extensive nongenetic regulation. Our multilevel analysis identified a genomic variant in the promoter of the most differentially expressed gene Cyp17a1, a previously reported top hit in genome-wide association studies for human hypertension, as a potential contributor to the hypertension phenotype in SHR rats. These results demonstrate the power of and need for integrative analysis for understanding genetic control of molecular dynamics and phenotypic diversity in a system-wide manner.

RefSeq: an update on mammalian reference sequences

The National Center for Biotechnology Information (NCBI) Reference Sequence (RefSeq) database is a collection of annotated genomic, transcript and protein sequence records derived from data in public sequence archives and from computation, curation and collaboration (http://www.ncbi.nlm.nih.gov/refseq/). We report here on growth of the mammalian and human subsets, changes to NCBI’s eukaryotic annotation pipeline and modifications affecting transcript and protein records. Recent changes to NCBI’s eukaryotic genome annotation pipeline provide higher throughput, and the addition of RNAseq data to the pipeline results in a significant expansion of the number of transcripts and novel exons annotated on mammalian RefSeq genomes. Recent annotation changes include reporting supporting evidence for transcript records, modification of exon feature annotation and the addition of a structured report of gene and sequence attributes of biological interest. We also describe a revised protein annotation policy for alternatively spliced transcripts with more divergent predicted proteins and we summarize the current status of the RefSeqGene project.

Structural network analysis of biological networks for assessment of potential disease model organisms

Model organisms provide opportunities to design research experiments focused on disease-related processes (e.g., using genetically engineered populations that produce phenotypes of interest). For some diseases, there may be non-obvious model organisms that can help in the study of underlying disease factors. In this study, an approach is presented that leverages knowledge about human diseases and associated biological interactions networks to identify potential model organisms for a given disease category. The approach starts with the identification of functional and interaction patterns of diseases within genetic pathways. Next, these characteristic patterns are matched to interaction networks of candidate model organisms to identify similar subsystems that have characteristic patterns for diseases of interest. The quality of a candidate model organism is then determined by the degree to which the identified subsystems match genetic pathways from validated knowledge. The results of this study suggest that non-obvious model organisms may be identified through the proposed approach.

Targeting the endothelial progenitor cell surface proteome to identify novel mechanisms that mediate angiogenic efficacy in a rodent model of vascular disease

Endothelial progenitor cells (EPCs) promote angiogenesis, and clinical trials suggest autologous EPC-based therapy may be effective in treatment of vascular diseases. Albeit promising, variability in the efficacy of EPCs associated with underlying disease states has hindered the realization of EPC-based therapy. Here we first identify and characterize EPC dysfunction in a rodent model of vascular disease (SS/Mcwi rat) that exhibits impaired angiogenesis. To identify molecular candidates that mediate the angiogenic potential of these cells, we performed a broad analysis of cell surface protein expression using chemical labeling combined with mass spectrometry. Analysis revealed EPCs derived from SS/Mcwi rats express significantly more type 2 low-affinity immunoglobulin Fc-gamma (FCGR2) and natural killer 2B4 (CD244) receptors compared with controls. Genome-wide sequencing (RNA-seq) and qt-PCR confirmed isoforms of CD244 and FCGR2a transcripts were increased in SS/Mcwi EPCs. EPCs with elevated expression of FCGR2a and CD244 receptors are predicted to increase the probability of SS/Mcwi EPCs being targeted for death, providing a mechanistic explanation for their reduced angiogenic efficacy in vivo. Pathway analysis supported this contention, as "key" molecules annotated to cell death paths were differentially expressed in the SS/Mcwi EPCs. We speculate that screening and neutralization of cell surface proteins that "tag" and impair EPC function may provide an alternative approach to utilizing incompetent EPCs in greater numbers, as circulating EPCs are depleted in patients with vascular disease. Overall, novel methods to identify putative targets for repair of EPCs using discovery-based technologies will likely provide a major advance in the field of regenerative medicine.

Quantification of the impact of PSI:Biology according to the annotations of the determined structures

Background

Protein Structure Initiative:Biology (PSI:Biology) is the third phase of PSI where protein structures are determined in high-throughput to characterize their biological functions. The transition to the third phase entailed the formation of PSI:Biology Partnerships which are composed of structural genomics centers and biomedical science laboratories. We present a method to examine the impact of protein structures determined under the auspices of PSI:Biology by measuring their rates of annotations. The mean numbers of annotations per structure and per residue are examined. These are designed to provide measures of the amount of structure to function connections that can be leveraged from each structure.

Results

One result is that PSI:Biology structures are found to have a higher rate of annotations than structures determined during the first two phases of PSI. A second result is that the subset of PSI:Biology structures determined through PSI:Biology Partnerships have a higher rate of annotations than those determined exclusive of those partnerships. Both results hold when the annotation rates are examined either at the level of the entire protein or for annotations that are known to fall at specific residues within the portion of the protein that has a determined structure.

Conclusions

We conclude that PSI:Biology determines structures that are estimated to have a higher degree of biomedical interest than those determined during the first two phases of PSI based on a broad array of biomedical annotations. For the PSI:Biology Partnerships, we see that there is an associated added value that represents part of the progress toward the goals of PSI:Biology. We interpret the added value to mean that team-based structural biology projects that utilize the expertise and technologies of structural genomics centers together with biological laboratories in the community are conducted in a synergistic manner. We show that the annotation rates can be used in conjunction with established metrics, i.e. the numbers of structures and impact of publication records, to monitor the progress of PSI:Biology towards its goals of examining structure to function connections of high biomedical relevance. The metric provides an objective means to quantify the overall impact of PSI:Biology as it uses biomedical annotations from external sources.

Genetic and phenotypic characterization of a Japanese wild-derived DOB/Oda rat strain

Wild-derived rat strains can provide novel genome resources that are not available in standard laboratory strains. Genetic backgrounds of wild-derived strains can facilitate effective genetic linkage analyses and often modulate the expression of mutant phenotypes. Here we describe the development and characterization of a new inbred rat strain, DOB/Oda, from wild rats (Rattus norvegicus) captured in Shitara, Aichi, Japan. Phenotype analysis of 109 parameters revealed that the DOB/Oda rats had small body weight, preference for darkness, and high locomotor activity compared with the rat strains in the National BioResource Project for the Rat (NBRP-Rat) database. Genome analysis with 357 SSLP markers identified DOB/Oda-specific alleles in 70 markers. The percentage of SSLP markers that showed polymorphism between the DOB/Oda strain and any of 132 laboratory strains from NBRP-Rat varied from 89 to 95 %. The polymorphic rate (average of the values of the percentage) for the DOB/Oda strain was 91.6 %, much higher than the rates for available wild-derived strains such as the Brown Norway rat. A phylogenic tree constructed with DOB/Oda and all the strains in NBRP-Rat showed that the DOB/Oda strain localized within the wild rat groups, apparently separate from the laboratory strains. Together, these findings indicated that the DOB/Oda rat has a unique genome that is not available in the laboratory strains. Therefore, the new DOB/Oda strain will provide an important genome resource that will be useful for designing genetic experiments and for the discovery of genes that modulate mutant phenotypes.

An integrative, translational approach to understanding rare and orphan genetically based diseases

PhenomeNet is an approach for integrating phenotypes across species and identifying candidate genes for genetic diseases based on the similarity between a disease and animal model phenotypes. In contrast to ‘guilt-by-association’ approaches, PhenomeNet relies exclusively on the comparison of phenotypes to suggest candidate genes, and can, therefore, be applied to study the molecular basis of rare and orphan diseases for which the molecular basis is unknown. In addition to disease phenotypes from the Online Mendelian Inheritance in Man (OMIM) database, we have now integrated the clinical signs from Orphanet into PhenomeNet. We demonstrate that our approach can efficiently identify known candidate genes for genetic diseases in Orphanet and OMIM. Furthermore, we find evidence that mutations in the HIP1 gene might cause Bassoe syndrome, a rare disorder with unknown genetic aetiology. Our results demonstrate that integration and computational analysis of human disease and animal model phenotypes using PhenomeNet has the potential to reveal novel insights into the pathobiology underlying genetic diseases.

CHD@ZJU: a knowledgebase providing network-based research platform on coronary heart disease

Coronary heart disease (CHD), the leading cause of global morbidity and mortality in adults, has been reported to be associated with hundreds of genes. A comprehensive understanding of the CHD-related genes and their corresponding interactions is essential to advance the translational research on CHD. Accordingly, we construct this knowledgebase, CHD@ZJU, which records CHD-related information (genes, pathways, drugs and references) collected from different resources and through text-mining method followed by manual confirmation. In current release, CHD@ZJU contains 660 CHD-related genes, 45 common pathways and 1405 drugs accompanied with >8000 supporting references. Almost half of the genes collected in CHD@ZJU were novel to other publicly available CHD databases. Additionally, CHD@ZJU incorporated the protein-protein interactions to investigate the cross-talk within the pathways from a multi-layer network view. These functions offered by CHD@ZJU would allow researchers to dissect the molecular mechanism of CHD in a systematic manner and therefore facilitate the research on CHD-related multi-target therapeutic discovery. Database URL: http://tcm.zju.edu.cn/chd/

Community Structure Analysis of Gene Interaction Networks in Duchenne Muscular Dystrophy

Duchenne Muscular Dystrophy (DMD) is an important pathology associated with the human skeletal muscle and has been studied extensively. Gene expression measurements on skeletal muscle of patients afflicted with DMD provides the opportunity to understand the underlying mechanisms that lead to the pathology. Community structure analysis is a useful computational technique for understanding and modeling genetic interaction networks. In this paper, we leverage this technique in combination with gene expression measurements from normal and DMD patient skeletal muscle tissue to study the structure of genetic interactions in the context of DMD. We define a novel framework for transforming a raw dataset of gene expression measurements into an interaction network, and subsequently apply algorithms for community structure analysis for the extraction of topological communities. The emergent communities are analyzed from a biological standpoint in terms of their constituent biological pathways, and an interpretation that draws correlations between functional and structural organization of the genetic interactions is presented. We also compare these communities and associated functions in pathology against those in normal human skeletal muscle. In particular, differential enhancements are observed in the following pathways between pathological and normal cases: Metabolic, Focal adhesion, Regulation of actin cytoskeleton and Cell adhesion, and implication of these mechanisms are supported by prior work. Furthermore, our study also includes a gene-level analysis to identify genes that are involved in the coupling between the pathways of interest. We believe that our results serve to highlight important distinguishing features in the structural/functional organization of constituent biological pathways, as it relates to normal and DMD cases, and provide the mechanistic basis for further biological investigations into specific pathways differently regulated between normal and DMD patients. These findings have the potential to serve as fertile ground for therapeutic applications involving targeted drug development for DMD.

PosMed: Ranking genes and bioresources based on Semantic Web Association Study

Positional MEDLINE (PosMed; http://biolod.org/PosMed) is a powerful Semantic Web Association Study engine that ranks biomedical resources such as genes, metabolites, diseases and drugs, based on the statistical significance of associations between user-specified phenotypic keywords and resources connected directly or inferentially through a Semantic Web of biological databases such as MEDLINE, OMIM, pathways, co-expressions, molecular interactions and ontology terms. Since 2005, PosMed has long been used for in silico positional cloning studies to infer candidate disease-responsible genes existing within chromosomal intervals. PosMed is redesigned as a workbench to discover possible functional interpretations for numerous genetic variants found from exome sequencing of human disease samples. We also show that the association search engine enhances the value of mouse bioresources because most knockout mouse resources have no phenotypic annotation, but can be associated inferentially to phenotypes via genes and biomedical documents. For this purpose, we established text-mining rules to the biomedical documents by careful human curation work, and created a huge amount of correct linking between genes and documents. PosMed associates any phenotypic keyword to mouse resources with 20 public databases and four original data sets as of May 2013.

MicroRNA-25-dependent up-regulation of NADPH oxidase 4 (NOX4) mediates hypercholesterolemia-induced oxidative/nitrative stress and subsequent dysfunction in the heart

Diet-induced hypercholesterolemia leads to oxidative/nitrative stress and subsequent myocardial dysfunction. However, the regulatory role of microRNAs in this phenomenon is unknown. We aimed to investigate, whether hypercholesterolemia-induced myocardial microRNA alterations play a role in the development of oxidative/nitrative stress and in subsequent cardiac dysfunction. Male Wistar rats were fed with 2% cholesterol/0.25% cholate-enriched or standard diet for 12weeks. Serum and tissue cholesterol levels were significantly elevated by cholesterol-enriched diet. Left ventricular end-diastolic pressure was significantly increased in cholesterol-fed rats both in vivo and in isolated perfused hearts, indicating diastolic dysfunction. Myocardial expression of microRNAs was affected by cholesterol-enriched diet as assessed by microarray analysis. MicroRNA-25 showed a significant down-regulation as detected by microarray analysis and QRT-PCR. In silico target prediction revealed NADPH oxidase 4 (NOX4) as a putative target of microRNA-25. NOX4 protein showed significant up-regulation in the hearts of cholesterol-fed rats, while NOX1 and NOX2 remained unaffected. Cholesterol-feeding significantly increased myocardial oxidative/nitrative stress as assessed by dihydroethidium staining, protein oxidation assay, and nitro-tyrosine ELISA, respectively. Direct binding of microRNA-25 mimic to the 3' UTR region of NOX4 was demonstrated using a luciferase reporter assay. Transfection of a microRNA-25 mimic into primary cardiomyocytes decreased superoxide production, while a microRNA-25 inhibitor resulted in an up-regulation of NOX4 protein and an increase in oxidative stress that was attenuated by the NADPH oxidase inhibitor diphenyleneiodonium. Here we demonstrated for the first time that hypercholesterolemia affects myocardial microRNA expression, and by down-regulating microRNA-25 increases NOX4 expression and consequently oxidative/nitrative stress in the heart. We conclude that hypercholesterolemia-induced microRNA alterations play an important role in the regulation of oxidative/nitrative stress and in consequent myocardial dysfunction.

Exploring genetic, genomic, and phenotypic data at the rat genome database

The laboratory rat, Rattus norvegicus, is an important model of human health and disease, and experimental findings in the rat have relevance to human physiology and disease. The Rat Genome Database (RGD, http://rgd.mcw.edu) is a model organism database that provides access to a wide variety of curated rat data including disease associations, phenotypes, pathways, molecular functions, biological processes, and cellular components for genes, quantitative trait loci, and strains. We present an overview of the database followed by specific examples that can be used to gain experience in employing RGD to explore the wealth of functional data available for the rat.

Rat mir-155 generated from the lncRNA Bic is 'hidden' in the alternate genomic assembly and reveals the existence of novel mammalian miRNAs and clusters

MicroRNAs (miRNAs) are a class of small noncoding RNAs acting as post-transcriptional gene expression regulators in many physiological and pathological conditions. During the last few years, many novel mammalian miRNAs have been predicted experimentally with bioinformatics approaches and validated by next-generation sequencing. Although these strategies have prompted the discovery of several miRNAs, the total number of these genes still seems larger. Here, by exploiting the species conservation of human, mouse, and rat hairpin miRNAs, we discovered a novel rat microRNA, mir-155. We found that mature miR-155 is overexpressed in rat spleen myeloid cells treated with LPS, similarly to humans and mice. Rat mir-155 is annotated only on the alternate genome, suggesting the presence of other "hidden" miRNAs on this assembly. Therefore, we comprehensively extended the homology search also to mice and humans, finally validating 34 novel mammalian miRNAs (two in humans, five in mice, and up to 27 in rats). Surprisingly, 15 of these novel miRNAs (one for mice and 14 for rats) were found only on the alternate and not on the reference genomic assembly. To date, our findings indicate that the choice of genomic assembly, when mapping small RNA reads, is an important option that should be carefully considered, at least for these animal models. Finally, the discovery of these novel mammalian miRNA genes may contribute to a better understanding of already acquired experimental data, thereby paving the way to still unexplored investigations and to unraveling the function of miRNAs in disease models.

STOP using just GO: a multi-ontology hypothesis generation tool for high throughput experimentation

Background

Gene Ontology (GO) enrichment analysis remains one of the most common methods for hypothesis generation from high throughput datasets. However, we believe that researchers strive to test other hypotheses that fall outside of GO. Here, we developed and evaluated a tool for hypothesis generation from gene or protein lists using ontological concepts present in manually curated text that describes those genes and proteins.

Results

As a consequence we have developed the method Statistical Tracking of Ontological Phrases (STOP) that expands the realm of testable hypotheses in gene set enrichment analyses by integrating automated annotations of genes to terms from over 200 biomedical ontologies. While not as precise as manually curated terms, we find that the additional enriched concepts have value when coupled with traditional enrichment analyses using curated terms.

Conclusion

Multiple ontologies have been developed for gene and protein annotation, by using a dataset of both manually curated GO terms and automatically recognized concepts from curated text we can expand the realm of hypotheses that can be discovered. The web application STOP is available at http://mooneygroup.org/stop/.

Ikaros family transcription factors expression in rat thymus: detection of impaired development

The expression of Ikaros family transcription factors and consequently their signalling pathway is limiting for hematopoietic and lymphocyte development in mice and human. Due to their importance, these transcription factors are highly homologous between species. As an initial approach to examining the possible involvement of Ikaros transcription factors in pathogenesis of rat lymphoid development, we analyzed the expression of all known Ikaros family members, Ikaros, Aiolos, Helios, Eos and Pegasus in the rat thymus. We established a semi-quantitative RT-PCR to detect mRNA of each transcription factor. For the first time we give evidence of the expression of Ikaros family transcription factors in the rat thymus. Further, we evaluated whether their mRNA expression was succumbed to changes when the rats were exposed to ethanol, as a known debilitating agent during development. Therefore we analyzed the thymus of adult rats whose mothers were forced to drink ethanol during gestation, to detect possible changes in thymus mRNA expression levels of Ikaros, Aiolos, Helios, Eos and Pegasus. We found that rats prenatally exposed to ethanol show a slightly higher expression of Ikaros family transcription factors in the adult thymus when compared to control rats, but these differences were not statistically significant. We further studied the distribution of the major lymphocyte subpopulations in the rat thymus according to CD3, CD4 and CD8 expression by four color flow cytometry. We found a higher incidence of CD3 positive cells in the double positive, CD4+CD8+ thymic subpopulation of rats prenatally exposed to ethanol when compared to non-exposed animals. Our findings indicate that ethanol exposure of pregnant rats might influence the development of CD3 positive cells in the thymus of the offspring but this result should be further tackled at the level of transcription factor expression.

Novel genes underlying beta cell survival in metabolic stress

Relative insulin deficiency, in response to increased metabolic demand (obesity, genetic insulin resistance, pregnancy and aging) lead to Type2 diabetes. Susceptibility of the type 2 diabetes has a genetic basis, as a subset of people with risk factors (obesity, Insulin Resistance, pregnancy), develop Type2 Diabetes. We aimed to identify 'cluster' of overexpressed genes, underlying increased beta cell survival in diabetes resistant C57BL/6J ob/ob mice (compared to diabetes susceptible BTBR ob/ob mice). We used 'consensus' overexpression status to identify 'cluster' of 11 genes consisting of Aldh18a1, Rfc4, Dynlt3, Prom1, H13, Psen1, Ssr4, Dad1, Anpep, Fam111a and Plk1. Information (biological processes, molecular functions, cellular components, protein-protein interactions/associations, gene deletion/knockout/inhibition studies) of all the genes in 'cluster' were collected by text mining using different literature search tools, gene information databases and protein-protein interaction databases. Beta cell specific function of these genes were also inferred using meta analysis tool of Beta Cell Biology Consortium, by studying the expression pattern of these genes in microarray studies related to beta-cell stimulation/injury, pancreas development and growth and cell differentiation. In the 'clusters', 6 genes (Dad1, Psen1, Ssr4, Rfc4, H13, Plk1) have a role in cell survival. Only Psen1 was previously identified to have role in successful beta cell compensation. We advocate these genes to be potentially involved in successful beta cell compensation and prevent T2D in humans, by conferring protection against diabetogenic insults.

IUPHAR-DB: updated database content and new features

The International Union of Basic and Clinical Pharmacology (IUPHAR) database, IUPHAR-DB (http://www.iuphar-db.org) is an open access, online database providing detailed, expert-driven annotation of the primary literature on human and rodent receptors and other drug targets, together with the substances that act on them. The present release includes information on the products of 646 genes from four major protein classes (G protein-coupled receptors, nuclear hormone receptors, voltage- and ligand-gated ion channels) and ∼3180 bioactive molecules (endogenous ligands, licensed drugs and key pharmacological tools) that interact with them. We have described previously the classification and curation of data for small molecule ligands in the database; in this update we have annotated 366 endogenous peptide ligands with their amino acid sequences, post-translational modifications, links to precursor genes, species differences and relationships with other molecules in the database (e.g. those derived from the same precursor). We have also matched targets with their endogenous ligands (peptides and small molecules), with particular attention paid to identifying bioactive peptide ligands generated by post-translational modification of precursor proteins. Other improvements to the database include enhanced information on the clinical relevance of targets and ligands in the database, more extensive links to other databases and a pilot project for the curation of enzymes as drug targets.

Genenames.org: the HGNC resources in 2013

The HUGO Gene Nomenclature Committee situated at the European Bioinformatics Institute assigns unique symbols and names to human genes. Since 2011, the data within our database has expanded largely owing to an increase in naming pseudogenes and non-coding RNA genes, and we now have >33,500 approved symbols. Our gene families and groups have also increased to nearly 500, with ∼45% of our gene entries associated to at least one family or group. We have also redesigned the HUGO Gene Nomenclature Committee website http://www.genenames.org creating a constant look and feel across the site and improving usability and readability for our users. The site provides a public access portal to our database with no restrictions imposed on access or the use of the data. Within this article, we review our online resources and data with particular emphasis on the updates to our website.

CD36-deficient congenic strains show improved glucose tolerance and distinct shifts in metabolic and transcriptomic profiles

Deficiency of fatty acid translocase Cd36 has been shown to have a major role in the pathogenesis of metabolic syndrome in the spontaneously hypertensive rat (SHR). We have tested the hypothesis that the effects of Cd36 mutation on the features of metabolic syndrome are contextually dependent on genomic background. We have derived two new congenic strains by introgression of limited chromosome 4 regions of SHR origin, both including the defective Cd36 gene, into the genetic background of a highly inbred model of insulin resistance and dyslipidemia, polydactylous (PD) rat strain. We subjected standard diet-fed adult males of PD and the congenic PD.SHR4 strains to metabolic, morphometric and transcriptomic profiling. We observed significantly improved glucose tolerance and lower fasting insulin levels in PD.SHR4 congenics than in PD. One of the PD.SHR4 strains showed lower triglyceride concentrations across major lipoprotein fractions combined with higher levels of low-density lipoprotein cholesterol compared with the PD progenitor. The hepatic transcriptome assessment revealed a network of genes differentially expressed between PD and PD.SHR4 with significant enrichment by members of the circadian rhythmicity pathway (Arntl (Bmal1), Clock, Nfil3, Per2 and Per3). In summary, the introduction of the chromosome 4 region of SHR origin including defective Cd36 into the PD genetic background resulted in disconnected shifts of metabolic profile along with distinct changes in hepatic transcriptome. The synthesis of the current results with those obtained in other Cd36-deficient strains indicates that the eventual metabolic effect of a deleterious mutation such as that of SHR-derived Cd36 is not absolute, but rather a function of complex interactions between environmental and genomic background, upon which it operates.

Animal models to guide clinical drug development in ADHD: lost in translation?

We review strategies for developing animal models for examining and selecting compounds with potential therapeutic benefit in attention-deficit hyperactivity disorder (ADHD). ADHD is a behavioural disorder of unknown aetiology and pathophysiology. Current understanding suggests that genetic factors play an important role in the aetiology of ADHD. The involvement of dopaminergic and noradrenergic systems in the pathophysiology of ADHD is probable. We review the clinical features of ADHD including inattention, hyperactivity and impulsivity and how these are operationalized for laboratory study. Measures of temporal discounting (but not premature responding) appear to predict known drug effects well (treatment validity). Open-field measures of overactivity commonly used do not have treatment validity in human populations. A number of animal models have been proposed that simulate the symptoms of ADHD. The most commonly used are the spontaneously hypertensive rat (SHR) and the 6-hydroxydopamine-lesioned (6-OHDA) animals. To date, however, the SHR lacks treatment validity, and the effects of drugs on symptoms of impulsivity and inattention have not been studied extensively in 6-OHDA-lesioned animals. At the present stage of development, there are no in vivo models of proven effectiveness for examining and selecting compounds with potential therapeutic benefit in ADHD. However, temporal discounting is an emerging theme in theories of ADHD, and there is good evidence of increased value of delayed reward following treatment with stimulant drugs. Therefore, operant behaviour paradigms that measure the effects of drugs in situations of delayed reinforcement, whether in normal rats or selected models, show promise for the future.

The impact of the genetic background on the genome make-up of tumor cells

Endometrial adenocarcinoma (EAC) is the most common form of malignancy in the female genital tract, ranking as the fourth leading form of invasive tumors that affect women. The BDII inbred rat strain has been used as a powerful tumor model in studies of the genetic background of EAC. Females from the BDII strain are prone to develop tumors with an incidence of more than 90%. Development of EAC in BDII female rats has similarities in pathogenesis, histopathological, and molecular properties to that of human, and thus represents a unique model for analysis of EAC tumorigenesis and for comparative studies in human EACs. In a previous study, a set of rat EAC cell lines derived from tumors developed in female crossprogenies between BDII and nonsusceptible rat strains were analyzed by spectral karyotyping (SKY). Here we present an analysis with specific focus on the impact of different genetic backgrounds on the rate and occurrence of genetic aberrations in experimental tumors using data presented in the previous report. We could reveal that the ploidy state, and the abundance and type of structural as well as numerical change differed between the two genetic setups. We have also identified chromosomes harboring aberrations independent of genetic input from the nonsusceptible strains, which provide valuable information for the identification of the genes involved in the development of EAC in the BDII model as well as in human endometrial tumors.

How to use the IUPHAR receptor database to navigate pharmacological data

Today's data-intensive, interdisciplinary research challenges scientists to keep up to date with key experimental techniques and tools reported in the literature. The International Union of Basic and Clinical Pharmacology Database (IUPHAR-DB) goes some way to addressing this need by providing expert-curated information sourced from primary literature and displayed in a user-friendly manner online. The database provides a channel for the IUPHAR Nomenclature Committee (NC-IUPHAR) to provide recommendations on the nomenclature of receptors and ion channels, to document their properties and the ligands that are useful for receptor characterization. Here we describe IUPHAR-DB's main features and provide examples of techniques for navigating and exploring the information. The database is freely available online at http://www.iuphar-db.org/.

The Mouse Genome Database (MGD): comprehensive resource for genetics and genomics of the laboratory mouse

The Mouse Genome Database (MGD, http://www.informatics.jax.org) is the international community resource for integrated genetic, genomic and biological data about the laboratory mouse. Data in MGD are obtained through loads from major data providers and experimental consortia, electronic submissions from laboratories and from the biomedical literature. MGD maintains a comprehensive, unified, non-redundant catalog of mouse genome features generated by distilling gene predictions from NCBI, Ensembl and VEGA. MGD serves as the authoritative source for the nomenclature of mouse genes, mutations, alleles and strains. MGD is the primary source for evidence-supported functional annotations for mouse genes and gene products using the Gene Ontology (GO). MGD provides full annotation of phenotypes and human disease associations for mouse models (genotypes) using terms from the Mammalian Phenotype Ontology and disease names from the Online Mendelian Inheritance in Man (OMIM) resource. MGD is freely accessible online through our website, where users can browse and search interactively, access data in bulk using Batch Query or BioMart, download data files or use our web services Application Programming Interface (API). Improvements to MGD include expanded genome feature classifications, inclusion of new mutant allele sets and phenotype associations and extensions of GO to include new relationships and a new stream of annotations via phylogenetic-based approaches.

The emerging role for rat models in gene discovery

Rat models have been used for many decades to study physiological and pathophysiological mechanisms. Prior to the release of the rat genome and new technologies for targeting gene manipulation, the rat had been the underdog in the genomics era, despite the abundance of physiological data compared to the mouse. The overarching goal of biomedical research is to improve health and advance medical science. Translating human disease gene discovery and validation in the rat, through the use of emerging technologies and integrated tools and databases, is providing power to understand the genetics, environmental influences, and biology of disease. In this review we briefly outline the rat models, bioinformatics tools, and technologies that are changing the landscape of translational research. The strategies used to translate disease traits to genes to function, and, ultimately, to improve human health is discussed. Finally, our perspective on how rat models will continue to positively impact biomedical research is provided.

The Rat Genome Database pathway portal

The set of interacting molecules collectively referred to as a pathway or network represents a fundamental structural unit, the building block of the larger, highly integrated networks of biological systems. The scientific community's interest in understanding the fine details of how pathways work, communicate with each other and synergize, and how alterations in one or several pathways may converge into a disease phenotype, places heightened demands on pathway data and information providers. To meet such demands, the Rat Genome Database [(RGD) http://rgd.mcw.edu] has adopted a multitiered approach to pathway data acquisition and presentation. Resources and tools are continuously added or expanded to offer more comprehensive pathway data sets as well as enhanced pathway data manipulation, exploration and visualization capabilities. At RGD, users can easily identify genes in pathways, see how pathways relate to each other and visualize pathways in a dynamic and integrated manner. They can access these and other components from several entry points and effortlessly navigate between them and they can download the data of interest. The Pathway Portal resources at RGD are presented, and future directions are discussed.

Database URL:http://rgd.mcw.edu

Analyses of copy number variation of GK rat reveal new putative type 2 diabetes susceptibility loci

Large efforts have been taken to search for genes responsible for type 2 diabetes (T2D), but have resulted in only about 20 in humans due to its complexity and heterogeneity. The GK rat, a spontanous T2D model, offers us a superior opportunity to search for more diabetic genes. Utilizing array comparative genome hybridization (aCGH) technology, we identifed 137 non-redundant copy number variation (CNV) regions from the GK rats when using normal Wistar rats as control. These CNV regions (CNVRs) covered approximately 36 Mb nucleotides, accounting for about 1% of the whole genome. By integrating information from gene annotations and disease knowledge, we investigated the CNVRs comprehensively for mining new T2D genes. As a result, we prioritized 16 putative protein-coding genes and two microRNA genes (rno-mir-30b and rno-mir-30d) as good candidates. The catalogue of CNVRs between GK and Wistar rats identified in this work served as a repository for mining genes that might play roles in the pathogenesis of T2D. Moreover, our efforts in utilizing bioinformatics methods to prioritize good candidate genes provided a more specific set of putative candidates. These findings would contribute to the research into the genetic basis of T2D, and thus shed light on its pathogenesis.

The Mouse Genome Database (MGD): premier model organism resource for mammalian genomics and genetics

The Mouse Genome Database (MGD) is the community model organism database for the laboratory mouse and the authoritative source for phenotype and functional annotations of mouse genes. MGD includes a complete catalog of mouse genes and genome features with integrated access to genetic, genomic and phenotypic information, all serving to further the use of the mouse as a model system for studying human biology and disease. MGD is a major component of the Mouse Genome Informatics (MGI, http://www.informatics.jax.org/) resource. MGD contains standardized descriptions of mouse phenotypes, associations between mouse models and human genetic diseases, extensive integration of DNA and protein sequence data, normalized representation of genome and genome variant information. Data are obtained and integrated via manual curation of the biomedical literature, direct contributions from individual investigators and downloads from major informatics resource centers. MGD collaborates with the bioinformatics community on the development and use of biomedical ontologies such as the Gene Ontology (GO) and the Mammalian Phenotype (MP) Ontology. Major improvements to the Mouse Genome Database include comprehensive update of genetic maps, implementation of new classification terms for genome features, development of a recombinase (cre) portal and inclusion of all alleles generated by the International Knockout Mouse Consortium (IKMC).

Pharmacogenetic interaction between dexamethasone and Cd36-deficient segment of spontaneously hypertensive rat chromosome 4 affects triacylglycerol and cholesterol distribution into lipoprotein fractions

Dexamethasone (DEX) is known to induce diabetes and dyslipidemia. We have compared fasting triacylglycerol and cholesterol concentrations across 20 lipoprotein fractions and glucose tolerance in control (standard diet) and DEX-treated 7-month-old males of two rat strains, Brown Norway (BN) and congenic BN.SHR-(Il6-Cd36)/Cub (BN.SHR4). These two inbred strains differ in a defined segment of chromosome 4, originally transferred from the spontaneously hypertensive rat (SHR) including the mutant Cd36 gene, a known target of DEX. Compared to BN, the standard-diet-fed BN.SHR4 showed higher cholesterol and triacylglycerol concentrations across many lipoprotein fractions, particularly in small VLDL and LDL particles. Total cholesterol was decreased by DEX by more than 21% in BN.SHR4 contrasting with the tendency to increase in BN (strain*DEX interaction p = 0.0017). Similar pattern was observed for triacylglycerol concentrations in LDL. The LDL particle size was significantly reduced by DEX in both strains. Also, while control BN and BN.SHR4 displayed comparable glycaemic profiles during oral glucose tolerance test, we observed a markedly blunted DEX induction of glucose intolerance in BN.SHR4 compared to BN. In summary, we report a pharmacogenetic interaction between limited genomic segment with mutated Cd36 gene and dexamethasone-induced glucose intolerance and triacylglycerol and cholesterol redistribution into lipoprotein fractions.

Online tools for understanding rat physiology

Rat models have been used to investigate physiological and pathophysiological mechanisms for decades. With the availability of the rat genome and other online resources, tools to identify rat models that mimic human disease are an important step in translational research. Despite the large number of papers published each year using rat models, integrating this information remains a problem. Resources for the rat genome are continuing to grow rapidly, while resources providing access to rat phenotype data are just emerging. An overview of rat models of disease, tools to characterize strain by phenotype and genotype, and steps being taken to integrate rat physiological data is presented in this article. Integrating functional and physiological data with the rat genome will build a solid research platform to facilitate innovative studies to unravel the mechanisms resulting in disease.

The Proteomics Identifications database: 2010 update

The Proteomics Identifications database (PRIDE, http://www.ebi.ac.uk/pride) at the European Bioinformatics Institute has become one of the main repositories of mass spectrometry-derived proteomics data. For the last 2 years, PRIDE data holdings have grown substantially, comprising 60 different species, more than 2.5 million protein identifications, 11.5 million peptides and over 50 million spectra by September 2009. We here describe several new and improved features in PRIDE, including the revised submission process, which now includes direct submission of fragment ion annotations. Correspondingly, it is now possible to visualize spectrum fragmentation annotations on tandem mass spectra, a key feature for compliance with journal data submission requirements. We also describe recent developments in the PRIDE BioMart interface, which now allows integrative queries that can join PRIDE data to a growing number of biological resources such as Reactome, Ensembl, InterPro and UniProt. This ability to perform extremely powerful across-domain queries will certainly be a cornerstone of future bioinformatics analyses. Finally, we highlight the importance of data sharing in the proteomics field, and the corresponding integration of PRIDE with other databases in the ProteomExchange consortium.

Sequencing of the rat genome and databases

The rat is an important system for modeling human disease. Four years ago, the rich 150-year history of rat research was transformed by the sequencing and annotation of the rat genome, ushering in an era of exceptional opportunity for identifying genes and pathways underlying disease phenotypes. With the genome sequence in place, there is the prospect of not only linking the extensive literature of mechanistic and pharmacological studies in the rat to its genome, but by using comparative genomics to other organisms as well. Genome-wide association studies (GWAS) in human populations have recently provided a direct approach for finding robust genetic associations in common diseases, but identifying the precise genes and their mechanisms of action remains challenging.The explosion of genomic tools and sequence over the last decade has created a wealth of data. Along with the data has arisen a need to manage it and to make it usable to scientists with a wide-range of research interests. This chapter is designed to overview the existing sequence and its utility, as well as provide a glimpse of some of the databases and bioinformatic tools available to the investigator.