Rat Genome Database (RGD): mapping disease onto the genome

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

2022 updates to the Rat Genome Database: a Findable, Accessible, Interoperable, and Reusable (FAIR) resource

The Rat Genome Database (RGD, https://rgd.mcw.edu) has evolved from simply a resource for rat genetic markers, maps, and genes, by adding multiple genomic data types and extensive disease and phenotype annotations and developing tools to effectively mine, analyze, and visualize the available data, to empower investigators in their hypothesis-driven research. Leveraging its robust and flexible infrastructure, RGD has added data for human and eight other model organisms (mouse, 13-lined ground squirrel, chinchilla, naked mole-rat, dog, pig, African green monkey/vervet, and bonobo) besides rat to enhance its translational aspect. This article presents an overview of the database with the most recent additions to RGD's genome, variant, and quantitative phenotype data. We also briefly introduce Virtual Comparative Map (VCMap), an updated tool that explores synteny between species as an improvement to RGD's suite of tools, followed by a discussion regarding the refinements to the existing PhenoMiner tool that assists researchers in finding and comparing quantitative data across rat strains. Collectively, RGD focuses on providing a continuously improving, consistent, and high-quality data resource for researchers while advancing data reproducibility and fulfilling Findable, Accessible, Interoperable, and Reusable (FAIR) data principles.

Computational and Experimental Analysis of Genetic Variants

Genomics has grown exponentially over the last decade. Common variants are associated with physiological changes through statistical strategies such as Genome-Wide Association Studies (GWAS) and quantitative trail loci (QTL). Rare variants are associated with diseases through extensive filtering tools, including population genomics and trio-based sequencing (parents and probands). However, the genomic associations require follow-up analyses to narrow causal variants, identify genes that are influenced, and to determine the physiological changes. Large quantities of data exist that can be used to connect variants to gene changes, cell types, protein pathways, clinical phenotypes, and animal models that establish physiological genomics. This data combined with bioinformatics including evolutionary analysis, structural insights, and gene regulation can yield testable hypotheses for mechanisms of genomic variants. Molecular biology, biochemistry, cell culture, CRISPR editing, and animal models can test the hypotheses to give molecular variant mechanisms. Variant characterizations can be a significant component of educating future professionals at the undergraduate, graduate, or medical training programs through teaching the basic concepts and terminology of genetics while learning independent research hypothesis design. This article goes through the computational and experimental analysis strategies of variant characterization and provides examples of these tools applied in publications. © 2022 American Physiological Society. Compr Physiol 12:3303-3336, 2022.

Role of aspirin activated nitric oxide synthase in controlling DOCA-salt-induced hypertension in rats through the stimulation of renal r-cortexin in kidney cortex cells

OBJECTIVES

Because the damage of kidney tissue is associated with hypertension and impaired nitric oxide (NO) synthesis, and as aspirin is reported to stimulate the synthesis of renal r-cortexin, an anti-hypertensive protein, we investigated the role of aspirin as bolus dose on elevated blood pressure induced by deoxycorticosterone acetate (DOCA)-salt in animal model.

METHODS

The chronic antihypertensive effect of aspirin on DOCA treated with ASA group of rats (n = 6) was evaluated after ingestion of 0.35 μM aspirin as a bolus dose in every 24 h using tail cuff methods. The plasma aspirin, NO, and r-cortexin levels were determined by spectrophotometric, methemoglobin, and ELISA methods, respectively. Synthesis of r-cortexin mRNA was determined. Aspirin activated nitric oxide synthase (AANOS) was purified by chromatographic methods.

RESULTS

Our results showed after 3 h of administration of aspirin (0.35 μM) to the DOCA treated with ASA group of rats decreased the systolic blood pressure from 139.39 ± 7.36 mm of Hg to 116.57 ± 6.89 mm of Hg and diastolic blood pressure from 110.4 ± 7 mm of Hg to 86.4 ± 2.76 mm of Hg. The reduction of BPs was found to be related to the increased plasma aspirin from 0.00 μM to 0.042 μM, plasma NO from 0.4 ± 0.19 nM to 1.9 ± 0.5 nM, and cortexin levels from 64.36 ± 12.6 nM to 216.7 ± 21.3 nM. The molecular weight of purified AANOS is 18 kDa.

CONCLUSION

It can be concluded that aspirin possesses antihypertensive effect on blood pressure in chronic administration. Aspirin can stimulate NO synthesis through the activation of AANOS, which stimulated the production of r-cortexin in kidney cortex cells and thereby reducing elevated BP in hypertensive rats.

Laboratory Codes in Nomenclature and Scientific Communication (Advancing Organism Nomenclature in Scientific Communication to Improve Research Reporting and Reproducibility)

Laboratory registration codes, also known as laboratory codes or lab codes, are a key element in standardized laboratory animal and genetic nomenclature. As such they are critical to accurate scientific communication and to research reproducibility and integrity. The original committee on Mouse Genetic Nomenclature published nomenclature conventions for mice genetics in 1940, and then conventions for inbred strains in 1952. Unique designations were needed, and have been in use since the 1950s, for the sources of animals and substrains, for the laboratories that identified new alleles or mutations, and then for developers of transgenes and induced mutations. Current laboratory codes are typically a 2- to 4-letter acronym for an institution or an investigator. Unique codes are assigned from the International Laboratory Code Registry, which was developed and is maintained by ILAR in the National Academies (National Academies of Sciences Engineering and Medicine and previously National Academy of Sciences). As a resource for the global research community, the registry has been online since 1997. Since 2003 mouse and rat genetic and strain nomenclature rules have been reviewed and updated annually as a joint effort of the International Committee on Standardized Genetic Nomenclature for Mice and the Rat Genome and Nomenclature Committee. The current nomenclature conventions (particularly conventions for non-inbred animals) are applicable beyond rodents, although not widely adopted. Ongoing recognition, since at least the 1930s, of the research relevance of genetic backgrounds and origins of animals, and of spontaneous and induced genetic variants speaks to the need for broader application of standardized nomenclature for animals in research, particularly given the increasing numbers and complexities of genetically modified swine, nonhuman primates, fish, and other species.

The Rat Genome Database (RGD) facilitates genomic and phenotypic data integration across multiple species for biomedical research

Model organism research is essential for discovering the mechanisms of human diseases by defining biologically meaningful gene to disease relationships. The Rat Genome Database (RGD, ( https://rgd.mcw.edu )) is a cross-species knowledgebase and the premier online resource for rat genetic and physiologic data. This rich resource is enhanced by the inclusion and integration of comparative data for human and mouse, as well as other human disease models including chinchilla, dog, bonobo, pig, 13-lined ground squirrel, green monkey, and naked mole-rat. Functional information has been added to records via the assignment of annotations based on sequence similarity to human, rat, and mouse genes. RGD has also imported well-supported cross-species data from external resources. To enable use of these data, RGD has developed a robust infrastructure of standardized ontologies, data formats, and disease- and species-centric portals, complemented with a suite of innovative tools for discovery and analysis. Using examples of single-gene and polygenic human diseases, we illustrate how data from multiple species can help to identify or confirm a gene as involved in a disease and to identify model organisms that can be studied to understand the pathophysiology of a gene or pathway. The ultimate aim of this report is to demonstrate the utility of RGD not only as the core resource for the rat research community but also as a source of bioinformatic tools to support a wider audience, empowering the search for appropriate models for human afflictions.

Computational resources associating diseases with genotypes, phenotypes and exposures

The causes of a disease and its therapies are not only related to genotypes, but also associated with other factors, including phenotypes, environmental exposures, drugs and chemical molecules. Distinguishing disease-related factors from many neutral factors is critical as well as difficult. Over the past two decades, bioinformaticians have developed many computational resources to integrate the omics data and discover associations among these factors. However, researchers and clinicians are experiencing difficulties in choosing appropriate resources from hundreds of relevant databases and software tools. Here, in order to assist the researchers and clinicians, we systematically review the public computational resources of human diseases related to genotypes, phenotypes, environment factors, drugs and chemical exposures. We briefly describe the development history of these computational resources, followed by the details of the relevant databases and software tools. We finally conclude with a discussion of current challenges and future opportunities as well as prospects on this topic.

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.

Shedding Light on the Transcriptomic Dark Matter in Biological Psychiatry: Role of Long Noncoding RNAs in D-cycloserine-Induced Fear Extinction in Posttraumatic Stress Disorder

Biological psychiatry scholarship on posttraumatic stress disorder (PTSD) is making strides with new omics technologies. In this context, there is growing recognition that noncoding RNAs are vital for the regulation of gene and protein expression. Long noncoding RNAs (lncRNAs) can modulate splicing, influence RNA editing, messenger RNA (mRNA) stability, translation activation, and microRNA-mRNA interactions, are highly abundant in the brain, and have been implicated in neurodevelopmental disorders. The largest subclass of lncRNAs is long intergenic noncoding RNAs (lincRNAs). We report on lincRNAs and their predicted mRNA targets associated with fear extinction induced by co-administration of D-cycloserine and behavioral fear extinction in a PTSD animal model. Forty-three differentially expressed lincRNAs and 190 differentially expressed mRNAs were found to be associated with fear extinction. Eight lincRNAs were predicted to interact with and regulate 108 of these mRNAs, while seven lincRNAs were predicted to interact with 22 of their pre-mRNA transcripts. Based on the functions of their target mRNAs, we inferred that these lincRNAs bind to nucleotides, ribonucleotides, and proteins; subsequently influence nervous system development, morphology, and immune system functioning; and could be associated with nervous system and mental health disorders. We found the quantitative trait loci that overlapped with fear extinction-related lincRNAs included traits such as serum corticosterone level, neuroinflammation, anxiety, stress, and despair-related responses. To the best of our knowledge, this is the first study to identify lincRNAs and their RNA targets with a putative role in transcriptional regulation during fear extinction in the context of an animal model of PTSD.

From engineering to editing the rat genome

Since its domestication over 100 years ago, the laboratory rat has been the preferred experimental animal in many areas of biomedical research (Lindsey and Baker The laboratory rat. Academic, New York, pp 1-52, 2006). Its physiology, size, genetics, reproductive cycle, cognitive and behavioural characteristics have made it a particularly useful animal model for studying many human disorders and diseases. Indeed, through selective breeding programmes numerous strains have been derived that are now the mainstay of research on hypertension, obesity and neurobiology (Okamoto and Aoki Jpn Circ J 27:282-293, 1963; Zucker and Zucker J Hered 52(6):275-278, 1961). Despite this wealth of genetic and phenotypic diversity, the ability to manipulate and interrogate the genetic basis of existing phenotypes in rat strains and the methodology to generate new rat models has lagged significantly behind the advances made with its close cousin, the laboratory mouse. However, recent technical developments in stem cell biology and genetic engineering have again brought the rat to the forefront of biomedical studies and enabled researchers to exploit the increasingly accessible wealth of genome sequence information. In this review, we will describe how a breakthrough in understanding the molecular basis of self-renewal of the pluripotent founder cells of the mammalian embryo, embryonic stem (ES) cells, enabled the derivation of rat ES cells and their application in transgenesis. We will also describe the remarkable progress that has been made in the development of gene editing enzymes that enable the generation of transgenic rats directly through targeted genetic modifications in the genomes of zygotes. The simplicity, efficiency and cost-effectiveness of the CRISPR/Cas gene editing system, in particular, mean that the ability to engineer the rat genome is no longer a limiting factor. The selection of suitable targets and gene modifications will now become a priority: a challenge where ES culture and gene editing technologies can play complementary roles in generating accurate bespoke rat models for studying biological processes and modelling human disease.

Recent advances in predicting gene-disease associations

Deciphering gene–disease association is a crucial step in designing therapeutic strategies against diseases. There are experimental methods for identifying gene–disease associations, such as genome-wide association studies and linkage analysis, but these can be expensive and time consuming. As a result, various in silico methods for predicting associations from these and other data have been developed using different approaches. In this article, we review some of the recent approaches to the computational prediction of gene–disease association. We look at recent advancements in algorithms, categorising them into those based on genome variation, networks, text mining, and crowdsourcing. We also look at some of the challenges faced in the computational prediction of gene–disease associations.

Animal models for the study of primary and secondary hypertension in humans

Hypertension is a significant cause of morbidity and mortality worldwide. It is defined as systolic and diastolic blood pressures (SBP/DBP) >140 and 90 mmHg, respectively. Individuals with an SBP between 120 and 139, or DBP between 80 and 89 mmHg, are said to exhibit pre-hypertension. Hypertension can have primary or secondary causes. Primary or essential hypertension is a multifactorial disease caused by interacting environmental and polygenic factors. Secondary causes are renovascular hypertension, renal disease, endocrine disorders and other medical conditions. The aim of the present review article was to examine the different animal models that have been generated for studying the molecular and physiological mechanisms underlying hypertension. Their advantages, disadvantages and limitations will be discussed.

Comparative Genome of GK and Wistar Rats Reveals Genetic Basis of Type 2 Diabetes

The Goto-Kakizaki (GK) rat, which has been developed by repeated inbreeding of glucose-intolerant Wistar rats, is the most widely studied rat model for Type 2 diabetes (T2D). However, the detailed genetic background of T2D phenotype in GK rats is still largely unknown. We report a survey of T2D susceptible variations based on high-quality whole genome sequencing of GK and Wistar rats, which have generated a list of GK-specific variations (228 structural variations, 2660 CNV amplification and 2834 CNV deletion, 1796 protein affecting SNVs or indels) by comparative genome analysis and identified 192 potential T2D-associated genes. The genes with variants are further refined with prior knowledge and public resource including variant polymorphism of rat strains, protein-protein interactions and differential gene expression. Finally we have identified 15 genetic mutant genes which include seven known T2D related genes (Tnfrsf1b, Scg5, Fgb, Sell, Dpp4, Icam1, and Pkd2l1) and eight high-confidence new candidate genes (Ldlr, Ccl2, Erbb3, Akr1b1, Pik3c2a, Cd5, Eef2k, and Cpd). Our result reveals that the T2D phenotype may be caused by the accumulation of multiple variations in GK rat, and that the mutated genes may affect biological functions including adipocytokine signaling, glycerolipid metabolism, PPAR signaling, T cell receptor signaling and insulin signaling pathways. We present the genomic difference between two closely related rat strains (GK and Wistar) and narrow down the scope of susceptible loci. It also requires further experimental study to understand and validate the relationship between our candidate variants and T2D phenotype. Our findings highlight the importance of sequenced-based comparative genomics for investigating disease susceptibility loci in inbreeding animal models.

Determining Semantically Related Significant Genes

GO relation embodies some aspects of existence dependency. If GO term xis existence-dependent on GO term y, the presence of y implies the presence of x. Therefore, the genes annotated with the function of the GO term y are usually functionally and semantically related to the genes annotated with the function of the GO term x. A large number of gene set enrichment analysis methods have been developed in recent years for analyzing gene sets enrichment. However, most of these methods overlook the structural dependencies between GO terms in GO graph by not considering the concept of existence dependency. We propose in this paper a biological search engine called RSGSearch that identifies enriched sets of genes annotated with different functions using the concept of existence dependency. We observe that GO term xcannot be existence-dependent on GO term y, if x- and y- have the same specificity (biological characteristics). After encoding into a numeric format the contributions of GO terms annotating target genes to the semantics of their lowest common ancestors (LCAs), RSGSearch uses microarray experiment to identify the most significant LCA that annotates the result genes. We evaluated RSGSearch experimentally and compared it with five gene set enrichment systems. Results showed marked improvement.

Dissecting active ingredients of Chinese medicine by content-weighted ingredient–target network

A novel approach integrating network pharmacology analysis with ingredient content and ingredient–target relationships to identify active ingredients of Chinese medicine.

GOseek: a gene ontology search engine using enhanced keywords

We propose in this paper a biological search engine called GOseek, which overcomes the limitation of current gene similarity tools. Given a set of genes, GOseek returns the most significant genes that are semantically related to the given genes. These returned genes are usually annotated to one of the Lowest Common Ancestors (LCA) of the Gene Ontology (GO) terms annotating the given genes. Most genes have several annotation GO terms. Therefore, there may be more than one LCA for the GO terms annotating the given genes. The LCA annotating the genes that are most semantically related to the given gene is the one that receives the most aggregate semantic contribution from the GO terms annotating the given genes. To identify this LCA, GOseek quantifies the contribution of the GO terms annotating the given genes to the semantics of their LCAs. That is, it encodes the semantic contribution into a numeric format. GOseek uses microarray experiment data to rank result genes based on their significance. We evaluated GOseek experimentally and compared it with a comparable gene prediction tool. Results showed marked improvement over the tool.

Mapping of Mcs30, a new mammary carcinoma susceptibility quantitative trait locus (QTL30) on rat chromosome 12: identification of fry as a candidate Mcs gene

Rat strains differ dramatically in their susceptibility to mammary carcinogenesis. On the assumption that susceptibility genes are conserved across mammalian species and hence inform human carcinogenesis, numerous investigators have used genetic linkage studies in rats to identify genes responsible for differential susceptibility to carcinogenesis. Using a genetic backcross between the resistant Copenhagen (Cop) and susceptible Fischer 344 (F344) strains, we mapped a novel mammary carcinoma susceptibility (Mcs30) locus to the centromeric region on chromosome 12 (LOD score of ∼8.6 at the D12Rat59 marker). The Mcs30 locus comprises approximately 12 Mbp on the long arm of rat RNO12 whose synteny is conserved on human chromosome 13q12 to 13q13. After analyzing numerous genes comprising this locus, we identified Fry, the rat ortholog of the furry gene of Drosophila melanogaster, as a candidate Mcs gene. We cloned and determined the complete nucleotide sequence of the 13 kbp Fry mRNA. Sequence analysis indicated that the Fry gene was highly conserved across evolution, with 90% similarity of the predicted amino acid sequence among eutherian mammals. Comparison of the Fry sequence in the Cop and F344 strains identified two non-synonymous single nucleotide polymorphisms (SNPs), one of which creates a putative, de novo phosphorylation site. Further analysis showed that the expression of the Fry gene is reduced in a majority of rat mammary tumors. Our results also suggested that FRY activity was reduced in human breast carcinoma cell lines as a result of reduced levels or mutation. This study is the first to identify the Fry gene as a candidate Mcs gene. Our data suggest that the SNPs within the Fry gene contribute to the genetic susceptibility of the F344 rat strain to mammary carcinogenesis. These results provide the foundation for analyzing the role of the human FRY gene in cancer susceptibility and progression.

A network study of chinese medicine xuesaitong injection to elucidate a complex mode of action with multicompound, multitarget, and multipathway

Chinese medicine has evolved from thousands of years of empirical applications and experiences of combating diseases. It has become widely recognized that the Chinese medicine acts through complex mechanisms featured as multicompound, multitarget and multipathway. However, there is still a lack of systematic experimental studies to elucidate the mechanisms of Chinese medicine. In this study, the differentially expressed genes (DEGs) were identified from myocardial infarction rat model treated with Xuesaitong Injection (XST), a Chinese medicine consisting of the total saponins from Panax notoginseng (Burk.) F. H. Chen (Chinese Sanqi). A network-based approach was developed to combine DEGs related to cardiovascular diseases (CVD) with lines of evidence from the literature mining to investigate the mechanism of action (MOA) of XST on antimyocardial infarction. A compound-target-pathway network of XST was constructed by connecting compounds to DEGs validated with literature lines of evidence and the pathways that are functionally enriched. Seventy potential targets of XST were identified in this study, of which 32 were experimentally validated either by our in vitro assays or by CVD-related literatures. This study provided for the first time a network view on the complex MOA of antimyocardial infarction through multiple targets and pathways.

Dynamics of short-term gene expression profiling in liver following thermal injury

BACKGROUND

Severe trauma, including burns, triggers a systemic response that significantly impacts on the liver, which plays a key role in the metabolic and immune responses aimed at restoring homeostasis. While many of these changes are likely regulated at the gene expression level, there is a need to better understand the dynamics and expression patterns of burn injury-induced genes in order to identify potential regulatory targets in the liver. Herein we characterized the response within the first 24 h in a standard animal model of burn injury using a time series of microarray gene expression data.

METHODS

Rats were subjected to a full thickness dorsal scald burn injury covering 20% of their total body surface area while under general anesthesia. Animals were saline resuscitated and sacrificed at defined time points (0, 2, 4, 8, 16, and 24 h). Liver tissues were explanted and analyzed for their gene expression profiles using microarray technology. Sham controls consisted of animals handled similarly but not burned. After identifying differentially expressed probe sets between sham and burn conditions over time, the concatenated data sets corresponding to these differentially expressed probe sets in burn and sham groups were combined and analyzed using a "consensus clustering" approach.

RESULTS

The clustering method of expression data identified 621 burn-responsive probe sets in four different co-expressed clusters. Functional characterization revealed that these four clusters are mainly associated with pro-inflammatory response, anti-inflammatory response, lipid biosynthesis, and insulin-regulated metabolism. Cluster 1 pro-inflammatory response is rapidly up-regulated (within the first 2 h) following burn injury, while Cluster 2 anti-inflammatory response is activated later on (around 8 h post-burn). Cluster 3 lipid biosynthesis is down-regulated rapidly following burn, possibly indicating a shift in the utilization of energy sources to produce acute phase proteins, which serve the anti-inflammatory response. Cluster 4 insulin-regulated metabolism was down-regulated late in the observation window (around 16 h post-burn), which suggests a potential mechanism to explain the onset of hypermetabolism, a delayed but well-known response that is characteristic of severe burns and trauma with potential adverse outcome.

CONCLUSIONS

Simultaneous analysis and comparison of gene expression profiles for both burn and sham control groups provided a more accurate estimation of the activation time, expression patterns, and characteristics of a certain burn-induced response based on which the cause-effect relationships among responses were revealed.

Roles for rat hepatocyte malignant transforming factor (HMTF) in late stage of hepatocarcinogenesis

In a previous study, to identify genes of importance for hepatocellular carcinogenesis, and especially for processes involved in malignant transformation, the authors investigated differences in gene expression between adenomas and carcinomas by DNA microarray. In the present study, the authors investigated AW434047, one of the sequences that was upregulated in carcinomas. The investigation led to the identification of a novel gene, which the authors named hepatocyte malignant transforming factor (HMTF), of unknown function whose expression was increased in hepatocellular carcinomas. Northern blot and in situ hybridization also demonstrated high levels of HMTF in rat hepatocellular carcinoma (HCC) cell lines, lymphocytes in the spleen, colon mucosal epithelia, spermatocytes, and granule cells of the hippocampus. Reduction of HMTF by RNA interference (RNAi) in N1 cells, an HCC cell line, caused suppression of cell proliferation, invasion, and migration. Suppression of proliferation appeared to be due to cell cycle arrest without increased apoptosis. Decreased HMTF expression resulted in down-regulation of STAT3, PCNA, and cyclin D1 and upregulation of p27. These results suggest that HMTF is a new marker for rat HCC and is involved in HCC cell proliferation and may also be linked to cell proliferation in the spleen, colon, brain, and testis.

RASOnD-a comprehensive resource and search tool for RAS superfamily oncogenes from various species

Background

The Ras superfamily plays an important role in the control of cell signalling and division. Mutations in the Ras genes convert them into active oncogenes. The Ras oncogenes form a major thrust of global cancer research as they are involved in the development and progression of tumors. This has resulted in the exponential growth of data on Ras superfamily across different public databases and in literature. However, no dedicated public resource is currently available for data mining and analysis on this family. The present database was developed to facilitate straightforward accession, retrieval and analysis of information available on Ras oncogenes from one particular site.

Description

We have developed the RAS Oncogene Database (RASOnD) as a comprehensive knowledgebase that provides integrated and curated information on a single platform for oncogenes of Ras superfamily. RASOnD encompasses exhaustive genomics and proteomics data existing across diverse publicly accessible databases. This resource presently includes overall 199,046 entries from 101 different species. It provides a search tool to generate information about their nucleotide and amino acid sequences, single nucleotide polymorphisms, chromosome positions, orthologies, motifs, structures, related pathways and associated diseases. We have implemented a number of user-friendly search interfaces and sequence analysis tools. At present the user can (i) browse the data (ii) search any field through a simple or advance search interface and (iii) perform a BLAST search and subsequently CLUSTALW multiple sequence alignment by selecting sequences of Ras oncogenes. The Generic gene browser, GBrowse, JMOL for structural visualization and TREEVIEW for phylograms have been integrated for clear perception of retrieved data. External links to related databases have been included in RASOnD.

Conclusions

This database is a resource and search tool dedicated to Ras oncogenes. It has utility to cancer biologists and cell molecular biologists as it is a ready source for research, identification and elucidation of the role of these oncogenes. The data generated can be used for understanding the relationship between the Ras oncogenes and their association with cancer. The database updated monthly is freely accessible online at http://202.141.47.181/rasond/ and http://www.aiims.edu/RAS.html.

A role for VAV1 in experimental autoimmune encephalomyelitis and multiple sclerosis

Multiple sclerosis, the most common cause of progressive neurological disability in young adults, is a chronic inflammatory disease. There is solid evidence for a genetic influence in multiple sclerosis, and deciphering the causative genes could reveal key pathways influencing the disease. A genome region on rat chromosome 9 regulates experimental autoimmune encephalomyelitis, a model for multiple sclerosis. Using interval-specific congenic rat lines and association of single-nucleotide polymorphisms with inflammatory phenotypes, we localized the gene of influence to Vav1, which codes for a signal-transducing protein in leukocytes. Analysis of seven human cohorts (12,735 individuals) demonstrated an association of rs2546133-rs2617822 haplotypes in the first VAV1 intron with multiple sclerosis (CA: odds ratio, 1.18; CG: odds ratio, 0.86; TG: odds ratio, 0.90). The risk CA haplotype also predisposed for higher VAV1 messenger RNA expression. VAV1 expression was increased in individuals with multiple sclerosis and correlated with tumor necrosis factor and interferon-gamma expression in peripheral blood and cerebrospinal fluid cells. We conclude that VAV1 plays a central role in controlling central nervous system immune-mediated disease and proinflammatory cytokine production critical for disease pathogenesis.

Correlation of microsynteny conservation and disease gene distribution in mammalian genomes

Background

With the completion of the whole genome sequence for many organisms, investigations into genomic structure have revealed that gene distribution is variable, and that genes with similar function or expression are located within clusters. This clustering suggests that there are evolutionary constraints that determine genome architecture. However, as most of the evidence for constraints on genome evolution comes from studies on yeast, it is unclear how much of this prior work can be extrapolated to mammalian genomes. Therefore, in this work we wished to examine the constraints on regions of the mammalian genome containing conserved gene clusters.

Results

We first identified regions of the mouse genome with microsynteny conservation by comparing gene arrangement in the mouse genome to the human, rat, and dog genomes. We then asked if any particular gene types were found preferentially in conserved regions. We found a significant correlation between conserved microsynteny and the density of mouse orthologs of human disease genes, suggesting that disease genes are clustered in genomic regions of increased microsynteny conservation.

Conclusion

The correlation between microsynteny conservation and disease gene locations indicates that regions of the mouse genome with microsynteny conservation may contain undiscovered human disease genes. This study not only demonstrates that gene function constrains mammalian genome organization, but also identifies regions of the mouse genome that can be experimentally examined to produce mouse models of human disease.

Using microarrays to identify positional candidate genes for QTL: the case study of ACTH response in pigs

BACKGROUND

Microarray studies can supplement QTL studies by suggesting potential candidate genes in the QTL regions, which by themselves are too large to provide a limited selection of candidate genes. Here we provide a case study where we explore ways to integrate QTL data and microarray data for the pig, which has only a partial genome sequence. We outline various procedures to localize differentially expressed genes on the pig genome and link this with information on published QTL. The starting point is a set of 237 differentially expressed cDNA clones in adrenal tissue from two pig breeds, before and after treatment with adrenocorticotropic hormone (ACTH).

RESULTS

Different approaches to localize the differentially expressed (DE) genes to the pig genome showed different levels of success and a clear lack of concordance for some genes between the various approaches. For a focused analysis on 12 genes, overlapping QTL from the public domain were presented. Also, differentially expressed genes underlying QTL for ACTH response were described. Using the latest version of the draft sequence, the differentially expressed genes were mapped to the pig genome. This enabled co-location of DE genes and previously studied QTL regions, but the draft genome sequence is still incomplete and will contain many errors. A further step to explore links between DE genes and QTL at the pathway level was largely unsuccessful due to the lack of annotation of the pig genome. This could be improved by further comparative mapping analyses but this would be time consuming.

CONCLUSION

This paper provides a case study for the integration of QTL data and microarray data for a species with limited genome sequence information and annotation. The results illustrate the challenges that must be addressed but also provide a roadmap for future work that is applicable to other non-model species.

OntoDas – a tool for facilitating the construction of complex queries to the Gene Ontology

Background

Ontologies such as the Gene Ontology can enable the construction of complex queries over biological information in a conceptual way, however existing systems to do this are too technical. Within the biological domain there is an increasing need for software that facilitates the flexible retrieval of information. OntoDas aims to fulfil this need by allowing the definition of queries by selecting valid ontology terms.

Results

OntoDas is a web-based tool that uses information visualisation techniques to provide an intuitive, interactive environment for constructing ontology-based queries against the Gene Ontology Database. Both a comprehensive use case and the interface itself were designed in a participatory manner by working with biologists to ensure that the interface matches the way biologists work. OntoDas was further tested with a separate group of biologists and refined based on their suggestions.

Conclusion

OntoDas provides a visual and intuitive means for constructing complex queries against the Gene Ontology. It was designed with the participation of biologists and compares favourably with similar tools. It is available at

Transcriptional profiling of native inner medullary collecting duct cells from rat kidney

Vasopressin acts on the inner medullary collecting duct (IMCD) in the kidney to regulate water and urea transport. To obtain a "parts list" of gene products expressed in the IMCD, we carried out mRNA profiling of freshly isolated rat IMCD cells using Affymetrix Rat 230 2.0 microarrays with approximately 31,000 features; 7,913 annotated transcripts were found to be expressed above background in the IMCD cells. We have created a new online database (the "IMCD Transcriptome Database;" http://dir.nhlbi.nih.gov/papers/lkem/imcdtr/) to make the results publicly accessible. Among the 30 transcripts with the greatest signals on the arrays were 3 water channels: aquaporin-2, aquaporin-3, and aquaporin-4, all of which have been reported to be targets for regulation by vasopressin. In addition, the transcript with the greatest signal among members of the solute carrier family of genes was the UT-A urea transporter (Slc14a2), which is also regulated by vasopressin. The V2 vasopressin receptor was strongly expressed, but the V1a and V1b vasopressin receptors did not produce signals above background. Among the 200 protein kinases expressed, the serum-glucocorticoid-regulated kinase (Sgk1) had the greatest signal intensity in the IMCD. WNK1 and WNK4 were also expressed in the IMCD with a relatively high signal intensity, as was protein kinase A (beta-catalytic subunit). In addition, a large number of transcripts corresponding to A kinase anchoring proteins and 14-3-3 proteins (phospho-S/T-binding proteins) were expressed. Altogether, the results combine with proteomics studies of the IMCD to provide a framework for modeling complex interaction networks responsible for vasopressin action in collecting duct cells.

AnimalQTLdb: a livestock QTL database tool set for positional QTL information mining and beyond

The Animal Quantitative Trait Loci (QTL) database (AnimalQTLdb) is designed to house all publicly available QTL data on livestock animal species from which researchers can easily locate and compare QTL within species. The database tools are also added to link the QTL data to other types of genomic information, such as radiation hybrid (RH) maps, finger printed contig (FPC) physical maps, linkage maps and comparative maps to the human genome, etc. Currently, this database contains data on 1287 pig, 630 cattle and 657 chicken QTL, which are dynamically linked to respective RH, FPC and human comparative maps. We plan to apply the tool to other animal species, and add more structural genome information for alignment, in an attempt to aid comparative structural genome studies ().

GeneTools--application for functional annotation and statistical hypothesis testing

BACKGROUND

Modern biology has shifted from "one gene" approaches to methods for genomic-scale analysis like microarray technology, which allow simultaneous measurement of thousands of genes. This has created a need for tools facilitating interpretation of biological data in "batch" mode. However, such tools often leave the investigator with large volumes of apparently unorganized information. To meet this interpretation challenge, gene-set, or cluster testing has become a popular analytical tool. Many gene-set testing methods and software packages are now available, most of which use a variety of statistical tests to assess the genes in a set for biological information. However, the field is still evolving, and there is a great need for "integrated" solutions.

RESULTS

GeneTools is a web-service providing access to a database that brings together information from a broad range of resources. The annotation data are updated weekly, guaranteeing that users get data most recently available. Data submitted by the user are stored in the database, where it can easily be updated, shared between users and exported in various formats. GeneTools provides three different tools: i) NMC Annotation Tool, which offers annotations from several databases like UniGene, Entrez Gene, SwissProt and GeneOntology, in both single- and batch search mode. ii) GO Annotator Tool, where users can add new gene ontology (GO) annotations to genes of interest. These user defined GO annotations can be used in further analysis or exported for public distribution. iii) eGOn, a tool for visualization and statistical hypothesis testing of GO category representation. As the first GO tool, eGOn supports hypothesis testing for three different situations (master-target situation, mutually exclusive target-target situation and intersecting target-target situation). An important additional function is an evidence-code filter that allows users, to select the GO annotations for the analysis.

CONCLUSION

GeneTools is the first "all in one" annotation tool, providing users with a rapid extraction of highly relevant gene annotation data for e.g. thousands of genes or clones at once. It allows a user to define and archive new GO annotations and it supports hypothesis testing related to GO category representations. GeneTools is freely available through www.genetools.no

The CART gene: structure and regulation

CART peptides are important neuropeptides that are involved in a variety of physiologic processes. The regulation of the CART gene is critical since peptides are regulated and secreted in response to specific stimuli. CART mRNA must also be controlled in order to respond to specific stimuli such as psychostimulant drugs and leptin. The regulation of the CART gene is central to maintaining homeostasis of peptide production. The 5' upstream region of the CART gene contains powerful regulatory elements that must be involved in transcriptional regulation via different signaling pathways. This review touches on several aspects related to CART gene regulation such as: (i) CART genomic structure, (ii) stimuli that alter CART mRNA levels, (iii) promoter characterization, (iv) role of the cAMP/PKA/CREB signal transduction pathway, and (v) role of the CART 5' and 3' ends in CART mRNA regulation. The goal of this review is to present current data so as to encourage further work in the field of CART gene regulation.

Bioinformatics database infrastructure for biotechnology research

Many databases are available that provide valuable data resources for the biotechnological researcher. According to their core data, they can be divided into different types. Some databases provide primary data, like all published nucleotide sequences, others deal with protein sequences. In addition to these two basic types of databases, a huge number of more specialized resources are available, like databases about protein structures, protein identification, special features of genes and/or proteins, or certain organisms. Furthermore, some resources offer integrated views on different types of data, allowing the user to do easy customized queries over large datasets and to compare different types of data.

A set of highly informative rat simple sequence length polymorphism (SSLP) markers and genetically defined rat strains

BACKGROUND

The National Bio Resource Project for the Rat in Japan (NBRP-Rat) is focusing on collecting, preserving and distributing various rat strains, including spontaneous mutant, transgenic, congenic, and recombinant inbred (RI) strains. To evaluate their value as models of human diseases, we are characterizing them using 109 phenotypic parameters, such as clinical measurements, internal anatomy, metabolic parameters, and behavioral tests, as part of the Rat Phenome Project. Here, we report on a set of 357 simple sequence length polymorphism (SSLP) markers and 122 rat strains, which were genotyped by the marker set.

RESULTS

The SSLP markers were selected according to their distribution patterns throughout the whole rat genome with an average spacing of 7.59 Mb. The average number of informative markers between all possible pairs of strains was 259 (72.5% of 357 markers), showing their high degree of polymorphism. From the genetic profile of these rat inbred strains, we constructed a rat family tree to clarify their genetic background.

CONCLUSION

These highly informative SSLP markers as well as genetically and phenotypically defined rat strains are useful for designing experiments for quantitative trait loci (QTL) analysis and to choose strategies for developing new genetic resources. The data and resources are freely available at the NBRP-Rat web site 1.

A comprehensive comparative analysis of the occurrence of developmental sequences in fungal, plant and animal genomes

We report a fully comprehensive data-mining exercise, involving an estimated total of 590,000 similarity searches, using agents available on the Internet to search for homologies to polypeptide sequences assigned to the category 'development' in the Gene Ontology Consortium AmiGO database (www.godatabase.org). The results indicate that of 552 such developmental sequences only 78 are shared between all three kingdoms, 72 are shared only between fungi and animals, 58 sequences are shared between plants and fungi, and four sequences were common only to Dictyostelium and fungi. No sequences were strictly fungus specific, but 68 occurred only in plants (Viridiplantae) and 239 occurred only in animals (Metazoa). Although some homology was indicated for a total of 219 fungal sequences, 143 (65%) of the matches returned were assigned E-values of 0.05 and must be categorised as weak similarities at best. The majority of the highly similar matches found in this survey proved to be between sequences involved in basic cell metabolism or essential eukaryotic cell processes (enzymes in common metabolic pathways, transcription regulators, binding proteins, receptors and membrane proteins). What is lacking is cross-kingdom similarity in the management processes that regulate multicellular development. The crown group of eukaryotic kingdoms control and regulate their developmental processes in very different ways. Unfortunately, we know nothing about molecular control of multicellular fungal developmental biology.

A new method for identifying informative genetic markers in selectively bred rats

Microsatellite length polymorphisms are useful for the mapping of heritable traits in rats. Over 4000 such microsatellites have been characterized for 48 inbred rat strains and used successfully to map phenotypes that differ between strains. At present, however, it is difficult to use this microsatellite database for mapping phenotypes in selectively bred rats of unknown genotype derived from outbred populations because it is not immediately obvious which markers might differ between strains and be informative. We predicted that markers represented by many alleles among the known inbred rat strains would also be most likely to differ between selectively bred strains derived from outbred populations. Here we describe the development and successful application of a new genotyping tool (HUMMER) that assigns "heterozygosity" (Het) and "uncertainty" (Unc) scores to each microsatellite marker that corresponds to its degree of heterozygosity among the 48 genotyped inbred strains. We tested the efficiency of HUMMER on two rat strains that were selectively bred from an outbred Sprague-Dawley stock for either high or low activity in the forced swim test (SwHi rats and SwLo rats, respectively). We found that the markers with high Het and Unc scores allowed the efficient selection of markers that differed between SwHi and SwLo rats, while markers with low Het and Unc scores typically identified markers that did not differ between strains. Thus, picking markers based on Het and Unc scores is a valuable method for identifying informative microsatellite markers in selectively bred rodent strains derived from outbred populations.

A transcriptional regulatory element screening system reveals a novel E2F1/pRb transcription regulation pathway

We developed a transcriptional regulatory element library which contains 160 independent known transcriptional regulatory elements linked to luciferase reporter vectors. That library proved valuable in the identification of p53 response elements and of E-box sequence preferences of several E-box binding proteins, and we used it to explore E2F1 target regulatory elements. Among those 160 elements, we found 3 E2F1 response elements, an E2F1 consensus sequence, an insulin response element which contained the E2F consensus sequence, and a basal level enhancer (BLE1) which had a nonconsensus E2F binding sequence. BLE1 functioned as multiple copy, with E2F1 in a dose-dependent manner, and had a sequence specificity for E2F1. Electrophoretic mobility shift assay revealed that BLE1 specifically interacts with E2F1 comparable to the E2F element. Interestingly, transactivation via five copies of BLE1 was not repressed but rather was stimulated by E2F1 in combination with the retinoblastoma tumor suppressor protein (pRb). The retinoblastoma control element (RCE) contains a direct repeated BLE1 in the c-fos gene promoter which also functioned like the multiple BLE1. Our data show that E2F1 has potential binding activity to the RCE and a different transcriptional regulation pathway which cooperates with pRb. Our transcriptional regulatory element screening system is useful for identifying novel transcriptional pathways.

DynGO: a tool for visualizing and mining of Gene Ontology and its associations

Background

A large volume of data and information about genes and gene products has been stored in various molecular biology databases. A major challenge for knowledge discovery using these databases is to identify related genes and gene products in disparate databases. The development of Gene Ontology (GO) as a common vocabulary for annotation allows integrated queries across multiple databases and identification of semantically related genes and gene products (i.e., genes and gene products that have similar GO annotations). Meanwhile, dozens of tools have been developed for browsing, mining or editing GO terms, their hierarchical relationships, or their "associated" genes and gene products (i.e., genes and gene products annotated with GO terms). Tools that allow users to directly search and inspect relations among all GO terms and their associated genes and gene products from multiple databases are needed.

Results

We present a standalone package called DynGO, which provides several advanced functionalities in addition to the standard browsing capability of the official GO browsing tool (AmiGO). DynGO allows users to conduct batch retrieval of GO annotations for a list of genes and gene products, and semantic retrieval of genes and gene products sharing similar GO annotations. The result are shown in an association tree organized according to GO hierarchies and supported with many dynamic display options such as sorting tree nodes or changing orientation of the tree. For GO curators and frequent GO users, DynGO provides fast and convenient access to GO annotation data. DynGO is generally applicable to any data set where the records are annotated with GO terms, as illustrated by two examples.

Conclusion

We have presented a standalone package DynGO that provides functionalities to search and browse GO and its association databases as well as several additional functions such as batch retrieval and semantic retrieval. The complete documentation and software are freely available for download from the website .

Rat Phenome Project: the untapped potential of existing rat strains

The National Bio Resource Project for the Rat in Japan collects, preserves, and distributes rat strains. More than 250 inbred strains have been deposited thus far into the National Bio Resource Project for the Rat and are maintained as specific pathogen-free rats or cryopreserved embryos. We are now comprehensively characterizing deposited strains as part of the Rat Phenome Project to reevaluate their value as models of human diseases. Phenotypic data are being collected for 7 categories and 109 parameters: functional observational battery (neurobehavior), behavior studies, blood pressure, biochemical blood tests, hematology, urology, and anatomy. Furthermore, genotypes are being determined for 370 simple sequence-length polymorphism markers distributed through the whole rat genome. Here, we report these large-scale, high-throughput screening data that have already been collected for 54 rat strains. This comprehensive, original phenotypic data can be systematically viewed by "strain ranking" for each parameter. This allows investigators to explore the relationship between several rat strains, to identify new rat models, and to select the most suitable strains for specific experiments. The discovery of several potential models for human diseases, such as hypertension, hypotension, renal diseases, hyperlipemia, hematological disorders, and neurological disorders, illustrates the potential of many existing rat strains. All deposited strains and obtained data are freely available for any interested researcher worldwide at http://www.anim.med.kyoto-u.ac.jp/nbr.

The Rat Genome Database (RGD): developments towards a phenome database

The Rat Genome Database (RGD) (http://rgd.mcw.edu) aims to meet the needs of its community by providing genetic and genomic infrastructure while also annotating the strengths of rat research: biochemistry, nutrition, pharmacology and physiology. Here, we report on RGD's development towards creating a phenome database. Recent developments can be categorized into three groups. (i) Improved data collection and integration to match increased volume and biological scope of research. (ii) Knowledge representation augmented by the implementation of a new ontology and annotation system. (iii) The addition of quantitative trait loci data, from rat, mouse and human to our advanced comparative genomics tools, as well as the creation of new, and enhancement of existing, tools to enable users to efficiently browse and survey research data. The emphasis is on helping researchers find genes responsible for disease through the use of rat models. These improvements, combined with the genomic sequence of the rat, have led to a successful year at RGD with over two million page accesses that represent an over 4-fold increase in a year. Future plans call for increased annotation of biological information on the rat elucidated through its use as a model for human pathobiology. The continued development of toolsets will facilitate integration of these data into the context of rat genomic sequence, as well as allow comparisons of biological and genomic data with the human genomic sequence and of an increasing number of organisms.

WormBase: a comprehensive data resource for Caenorhabditis biology and genomics

WormBase (http://www.wormbase.org), the model organism database for information about Caenorhabditis elegans and related nematodes, continues to expand in breadth and depth. Over the past year, WormBase has added multiple large-scale datasets including SAGE, interactome, 3D protein structure datasets and NCBI KOGs. To accommodate this growth, the International WormBase Consortium has improved the user interface by adding new features to aid in navigation, visualization of large-scale datasets, advanced searching and data mining. Internally, we have restructured the database models to rationalize the representation of genes and to prepare the system to accept the genome sequences of three additional Caenorhabditis species over the coming year.

T1DBase, a community web-based resource for type 1 diabetes research

T1DBase (http://T1DBase.org) is a public website and database that supports the type 1 diabetes (T1D) research community. The site is currently focused on the molecular genetics and biology of T1D susceptibility and pathogenesis. It includes the following datasets: annotated genome sequence for human, rat and mouse; information on genetically identified T1D susceptibility regions in human, rat and mouse, and genetic linkage and association studies pertaining to T1D; descriptions of NOD mouse congenic strains; the Beta Cell Gene Expression Bank, which reports expression levels of genes in beta cells under various conditions, and annotations of gene function in beta cells; data on gene expression in a variety of tissues and organs; and biological pathways from KEGG and BioCarta. Tools on the site include the GBrowse genome browser, site-wide context dependent search, Connect-the-Dots for connecting gene and other identifiers from multiple data sources, Cytoscape for visualizing and analyzing biological networks, and the GESTALT workbench for genome annotation. All data are open access and all software is open source.

RatMap--rat genome tools and data

The rat genome database RatMap (http://ratmap.org or http://ratmap.gen.gu.se) has been one of the main resources for rat genome information since 1994. The database is maintained by CMB–Genetics at Göteborg University in Sweden and provides information on rat genes, polymorphic rat DNA-markers and rat quantitative trait loci (QTLs), all curated at RatMap. The database is under the supervision of the Rat Gene and Nomenclature Committee (RGNC); thus much attention is paid to rat gene nomenclature. RatMap presents information on rat idiograms, karyotypes and provides a unified presentation of the rat genome sequence and integrated rat linkage maps. A set of tools is also available to facilitate the identification and characterization of rat QTLs, as well as the estimation of exon/intron number and sizes in individual rat genes. Furthermore, comparative gene maps of rat in regard to mouse and human are provided.

Bioinformatics in drug development and assessment

Bioinformatics is playing an increasingly important role in nearly all aspects of drug discovery, drug assessment, and drug development. This growing importance lies not only in the role that bioinformatics plays in handling large volumes of data, but also in the utility of bioinformatics tools to predict, analyze, or help interpret clinical and preclinical findings. This review focuses on describing and evaluating some of the newer or more important bioinformatics resources (i.e., databases and software) that are of growing importance to understanding or predicting drug metabolism, especially with respect to the absorption, distribution, metabolism, excretion, (ADME), and toxicity (T) of both existing drugs and potential drug leads. Detailed descriptions and critical assessments of a number of potentially useful bioinformatics/cheminformatics databases and predictive ADMET software tools are provided. Additionally, several pharmaceutically important applications of both the databases and software are highlighted. Given the rapid growth in this area and the rapid changes that are taking place, a special emphasis is placed on freely available or Web-accessible resources.

The Visual Language of Synteny

The study of polygenic disorders such as cardiovascular and metabolic diseases requires access to vast amounts of experimental and in silico data. Where animal models of disease are being used, visualization of syntenic genome regions is one of the most important tools supporting data analysis. We define what is required to visualize synteny in terms of the data being displayed, the screen layout, and user interaction. We then describe a prototype visualization tool, SyntenyVista, which provides integrated access to quantitative trait loci, microarray, and gene datasets. We believe that SyntenyVista is a significant step towards an improved representation of comparative genomics data.

Toxicogenomics and systems toxicology: aims and prospects

Toxicogenomics combines transcript, protein and metabolite profiling with conventional toxicology to investigate the interaction between genes and environmental stress in disease causation. The patterns of altered molecular expression that are caused by specific exposures or disease outcomes have revealed how several toxicants act and cause disease. Despite these success stories, the field faces noteworthy challenges in discriminating the molecular basis of toxicity. We argue that toxicology is gradually evolving into a systems toxicology that will eventually allow us to describe all the toxicological interactions that occur within a living system under stress and use our knowledge of toxicogenomic responses in one species to predict the modes-of-action of similar agents in other species.

GFINDer: Genome Function INtegrated Discoverer through dynamic annotation, statistical analysis, and mining

Statistical and clustering analyses of gene expression results from high-density microarray experiments produce lists of hundreds of genes regulated differentially, or with particular expression profiles, in the conditions under study. Independent of the microarray platforms and analysis methods used, these lists must be biologically interpreted to gain a better knowledge of the patho-physiological phenomena involved. To this end, numerous biological annotations are available within heterogeneous and widely distributed databases. Although several tools have been developed for annotating lists of genes, most of them do not give methods for evaluating the relevance of the annotations provided, or for estimating the functional bias introduced by the gene set on the array used to identify the gene list considered. We developed Genome Functional INtegrated Discoverer (GFINDer), a web server able to automatically provide large-scale lists of user-classified genes with functional profiles biologically characterizing the different gene classes in the list. GFINDer automatically retrieves annotations of several functional categories from different sources, identifies the categories enriched in each class of a user-classified gene list and calculates statistical significance values for each category. Moreover, GFINDer enables the functional classification of genes according to mined functional categories and the statistical analysis is of the classifications obtained, aiding better interpretation of microarray experiment results. GFINDer is available online at http://www.medinfopoli.polimi.it/GFINDer/.

Prolactin-modulated gene expression profiles in pancreatic islets from adult female rats

The effects of prolactin (PRL) on transcript profile expression in 24h cultured pancreatic adult rat islets were investigated by cDNA expression array analysis to identify possible candidate mRNA species that encode proteins involved in the maturation and growth of the endocrine pancreas. The expression of 54 out of 588 genes was altered by treatment with PRL. The differentially expressed transcripts identified were distributed in six main categories involved in cell proliferation and differentiation, namely, cell cycle regulation, signal transduction, transcription factors and coactivators, translational machinery, Ca(2+)-mediated exocytosis, and immuno-response. Treatment with PRL also reduced the expression of genes related to apoptosis. Several genes, whose expression was previously not known to be modulated by PRL were also identified including macrophage migration inhibitory factor and Ca(2+)/calmodulin-dependent protein kinase IV. These genes have recently been shown to play a crucial role in insulin secretion and insulin gene expression, respectively. Treatment with PRL also modified the expression of AKT2 and bone morphogenetic protein receptor 1A that control glucose homeostasis and directly affect the behavior of endocrine pancreas and/or the sensitivity of target tissues to insulin. In conclusion, PRL induces several patterns of gene expression in pancreatic islet cells. The analysis of these different patterns will be useful for understanding the complex mechanism of action of PRL in the maturation and differentiation of pancreatic islets.

Integrative genomics: in silico coupling of rat physiology and complex traits with mouse and human data

Integration of the large variety of genome maps from several organisms provides the mechanism by which physiological knowledge obtained in model systems such as the rat can be projected onto the human genome to further the research on human disease. The release of the rat genome sequence provides new information for studies using the rat model and is a key reference against which existing and new rat physiological results can be aligned. Previously, we described comparative maps of the rat, mouse, and human based on EST sequence comparisons combined with radiation hybrid maps. Here, we use new data and introduce the Integrated Genomics Environment, an extensive database of curated and integrated maps, markers, and physiological results. These results are integrated by using VCMapview, a java-based map integration and visualization tool. This unique environment allows researchers to relate results from cytogenetic, genetic, and radiation hybrid studies to the genome sequence and compare regions of interest between human, mouse, and rat. Integrating rat physiology with mouse genetics and clinical results from human by using the respective genomes provides a novel route to capitalize on comparative genomics and the strengths of model organism biology.

Identification of "pathologs" (disease-related genes) from the RIKEN mouse cDNA dataset using human curation plus FACTS, a new biological information extraction system

Background

A major goal in the post-genomic era is to identify and characterise disease susceptibility genes and to apply this knowledge to disease prevention and treatment. Rodents and humans have remarkably similar genomes and share closely related biochemical, physiological and pathological pathways. In this work we utilised the latest information on the mouse transcriptome as revealed by the RIKEN FANTOM2 project to identify novel human disease-related candidate genes. We define a new term "patholog" to mean a homolog of a human disease-related gene encoding a product (transcript, anti-sense or protein) potentially relevant to disease. Rather than just focus on Mendelian inheritance, we applied the analysis to all potential pathologs regardless of their inheritance pattern.

Results

Bioinformatic analysis and human curation of 60,770 RIKEN full-length mouse cDNA clones produced 2,578 sequences that showed similarity (70–85% identity) to known human-disease genes. Using a newly developed biological information extraction and annotation tool (FACTS) in parallel with human expert analysis of 17,051 MEDLINE scientific abstracts we identified 182 novel potential pathologs. Of these, 36 were identified by computational tools only, 49 by human expert analysis only and 97 by both methods. These pathologs were related to neoplastic (53%), hereditary (24%), immunological (5%), cardio-vascular (4%), or other (14%), disorders.

Conclusions

Large scale genome projects continue to produce a vast amount of data with potential application to the study of human disease. For this potential to be realised we need intelligent strategies for data categorisation and the ability to link sequence data with relevant literature. This paper demonstrates the power of combining human expert annotation with FACTS, a newly developed bioinformatics tool, to identify novel pathologs from within large-scale mouse transcript datasets.