Novel therapeutics for complex diseases from genome-wide association data

The development of novel therapies is essential to lower the burden of complex diseases. The purpose of this study is to identify novel therapeutics for complex diseases using bioinformatic methods. Bioinformatic tools such as candidate gene prediction tools allow identification of disease genes by identifying the potential candidate genes linked to genetic markers of the disease. Candidate gene prediction tools can only identify candidates for further research, and do not identify disease genes directly. Integration of drug-target datasets with candidate gene data-sets can identify novel potential therapeutics suitable for repositioning in clinical trials. Drug repositioning can save valuable time and money spent in therapeutic development of complex diseases.

Analysis of protein sequence and interaction data for candidate disease gene prediction

Linkage analysis is a successful procedure to associate diseases with specific genomic regions. These regions are often large, containing hundreds of genes, which make experimental methods employed to identify the disease gene arduous and expensive. We present two methods to prioritize candidates for further experimental study: Common Pathway Scanning (CPS) and Common Module Profiling (CMP). CPS is based on the assumption that common phenotypes are associated with dysfunction in proteins that participate in the same complex or pathway. CPS applies network data derived from protein–protein interaction (PPI) and pathway databases to identify relationships between genes. CMP identifies likely candidates using a domain-dependent sequence similarity approach, based on the hypothesis that disruption of genes of similar function will lead to the same phenotype. Both algorithms use two forms of input data: known disease genes or multiple disease loci. When using known disease genes as input, our combined methods have a sensitivity of 0.52 and a specificity of 0.97 and reduce the candidate list by 13-fold. Using multiple loci, our methods successfully identify disease genes for all benchmark diseases with a sensitivity of 0.84 and a specificity of 0.63. Our combined approach prioritizes good candidates and will accelerate the disease gene discovery process.

New approaches to gene discovery with animal models of obesity and diabetes

DNA-based approaches to the discovery of genes contributing to the development of type 2 diabetes have not been very successful despite substantial investments of time and money. The multiple gene-gene and gene-environment interactions that influence the development of type 2 diabetes mean that DNA approaches are not the ideal tool for defining the etiology of this complex disease. Gene expression-based technologies may prove to be a more rewarding strategy to identify diabetes candidate genes. There are a number of RNA-based technologies available to identify genes that are differentially expressed in various tissues in type 2 diabetes. These include differential display polymerase chain reaction (ddPCR), suppression subtractive hybridization (SSH), and cDNA microarrays. The power of new technologies to detect differential gene expression is ideally suited to studies utilizing appropriate animal models of human disease. We have shown that the gene expression approach, in combination with an excellent animal model such as the Israeli sand rat (Psammomys obesus), can provide novel genes and pathways that may be important in the disease process and provide novel therapeutic approaches. This paper will describe a new gene discovery, beacon, a novel gene linked with energy intake. As the functional characterization of novel genes discovered in our laboratory using this approach continues, it is anticipated that we will soon be able to compile a definitive list of genes that are important in the development of obesity and type 2 diabetes.

A custom-built insulin resistance gene chip

Objectives/Aim—Microarray (gene chip) technology offers a powerful new tool for analyzing the expression of large numbers of genes in many experimental samples. The aim of this study was to design, construct, and use a gene chip to measure the expression levels of key genes in metabolic pathways related to insulin resistance.
Methods—We selected genes that were implicated in the development of insulin resistance, including genes involved in insulin signaling; glucose uptake, oxidation, and storage; fat uptake, oxidation, and storage; cytoskeletal components; and transcription factors. The key regulatory genes in the pathways were identified, along with other recently identified candidate genes such as calpain-10. A total of 242 selected genes (including 32 internal control elements) were sequence-verified, purified, and arrayed on aldehyde-coated slides.
Results—Where more than 1 clone containing the gene of interest was available, we chose those containing the genes in the 5' orientation and an insert size of around 1.5 kb. Of the 262 clones purchased, 56 (21%) were found to contain sequences other than those expected. In addition, 2 (1%) did not grow under standard conditions and were assumed to be nonviable. In these cases, alternate clones containing the gene of interest were chosen as described above. The current version of the Insulin Resistance Gene Chip contains 210 genes of interest, plus 48 control elements. A full list of the genes is available at http://www.hbs.deakin.edu.au/mru/research/gene_chip_tech/genechip_three.htm/.
Conclusions—The human Insulin Resistance Gene Chip that we have constructed will be a very useful tool for investigating variation in the expression of genes relevant to insulin resistance under various experimental conditions. Initially, the gene chip will be used in studies such as exercise interventions, fasting, euglycemic-hyperinsulinemic clamps, and administration of antidiabetic agents

Heterologous microarray experiments used to identify the early gene response to heat stress in a coral reef fish

Coral reef fishes are expected to experience rising sea surface temperatures due to climate change. How well tropical reef fishes will respond to these increased temperatures and which genes are important in the response to elevated temperatures is not known. Microarray technology provides a powerful tool for gene discovery studies, but the development of microarrays for individual species can be expensive and time-consuming. In this study, we tested the suitability of a Danio rerio oligonucleotide microarray for application in a species with few genomic resources, the coral reef fish Pomacentrus moluccensis. Results from a comparative genomic hybridization experiment and direct sequence comparisons indicate that for most genes there is considerable sequence similarity between the two species, suggesting that the D. rerio array is useful for genomic studies of P. moluccensis. We employed this heterologous microarray approach to characterize the early transcriptional response to heat stress in P. moluccensis. A total of 111 gene loci, many of which are involved in protein processing, transcription, and cell growth, showed significant changes in transcript abundance following exposure to elevated temperatures. Changes in transcript abundance were validated for a selection of candidate genes using quantitative real-time polymerase chain reaction. This study demonstrates that heterologous microarrays can be successfully employed to study species for which specific microarrays have not yet been developed, and so have the potential to greatly enhance the utility of microarray technology to the field of environmental and functional genomics.

A novel approach identified the FOLR1 gene, a putative regulator of milk protein synthesis

This study has utilised comparative functional genomics to exploit animal models with extreme adaptation to lactation to identify candidate genes that specifically regulate protein synthesis in the cow mammary gland. Increasing milk protein production is valuable to the dairy industry. The lactation strategies of both the Cape fur seal (Artocephalus pusillus pusillus) and the tammar wallaby (Macropus eugenii) include periods of high rates of milk protein synthesis during an established lactation and therefore offer unique models to target genes that specifically regulate milk protein synthesis. Global changes in mammary gene expression in the Cape fur seal, tammar wallaby, and the cow (Bos taurus) were assessed using microarray analysis. The folate receptor α (FOLR1) showed the greatest change in gene expression in all three species [cow 12.7-fold (n = 3), fur seal 15.4-fold (n = 1), tammar 2.4-fold (n = 4)] at periods of increased milk protein production. This compliments previous reports that folate is important for milk protein synthesis and suggests FOLR1 may be a key regulatory point of folate metabolism for milk protein synthesis within mammary epithelial cells (lactocytes). These data may have important implications for the dairy industry to develop strategies to increase milk protein production in cows. This study illustrates the potential of comparative genomics to target genes of interest to the scientific community.

Variation in the gene coding for the M5 muscarinic receptor (CHRM5) influences cigarette dose but is not associated with dependence to drugs of addiction : evidence from a prospective population based cohort study of young adults

Background: The mesolimbic structures of the brain are important in the anticipation and perception of reward. Moreover, many drugs of addiction elicit their response in these structures. The M5 muscarinic receptor (M5R) is expressed in dopamine-containing neurones of the substantia nigra pars compacta and ventral tegmental area, and regulates the release of mesolimbic dopamine. Mice lacking M5R show a substantial reduction in both reward and withdrawal responses to morphine and cocaine. The CHRM5, the gene that codes for the M5R, is a strong biological candidate for a role in human addiction. We screened the coding and core promoter sequences of CHRM5 using denaturing high performance liquid chromatography to identify common polymorphisms. Additional polymorphisms within the coding and core promoter regions that were identified through dbSNP were validated in the test population. We investigated whether these polymorphisms influence substance dependence and dose in a cohort of 1947 young Australians.

Results: Analysis was performed on 815 participants of European ancestry who were interviewed at wave 8 of the cohort study and provided DNA. We observed a 26.8% increase in cigarette consumption in carriers of the rs7162140 T-allele, equating to 20.1 cigarettes per week (p=0.01). Carriers of the rs7162140 T-allele were also found to have nearly a 3-fold increased risk of developing cannabis dependence (OR=2.9 (95%CI 1.1-7.4); p=0.03).

Conclusion: Our data suggest that variation within the CHRM5 locus may play an important role in tobacco and cannabis but not alcohol addiction in European ancestry populations. This is the first study to show an association between CHRM5 and substance use in humans. These data support the further investigation of this gene as a risk factor in substance use and dependence.

Web tools for the prioritization of candidate disease genes

Despite increasing sequencing capacity, genetic disease investigation still frequently results in the identification of loci containing multiple candidate disease genes that need to be tested for involvement in the disease. This process can be expedited by prioritizing the candidates prior to testing. Over the last decade, a large number of computational methods and tools have been developed to assist the clinical geneticist in prioritizing candidate disease genes. In this chapter, we give an overview of computational tools that can be used for this purpose, all of which are freely available over the web.

Identification and characterization of rhizospheric microbial diversity by 16S ribosomal RNA gene sequencing

In the present study, samples of rhizosphere and root nodules were collected from different areas of Pakistan to isolate plant growth promoting rhizobacteria. Identification of bacterial isolates was made by 16S rRNA gene sequence analysis and taxonomical confirmation on EzTaxon Server. The identified bacterial strains were belonged to 5 genera i.e. Ensifer, Bacillus, Pseudomona, Leclercia and Rhizobium. Phylogenetic analysis inferred from 16S rRNA gene sequences showed the evolutionary relationship of bacterial strains with the respective genera. Based on phylogenetic analysis, some candidate novel species were also identified. The bacterial strains were also characterized for morphological, physiological, biochemical tests and glucose dehydrogenase (gdh) gene that involved in the phosphate solublization using cofactor pyrroloquinolone quinone (PQQ). Seven rhizoshperic and 3 root nodulating stains are positive for gdh gene. Furthermore, this study confirms a novel association between microbes and their hosts like field grown crops, leguminous and non-leguminous plants. It was concluded that a diverse group of bacterial population exist in the rhizosphere and root nodules that might be useful in evaluating the mechanisms behind plant microbial interactions and strains QAU-63 and QAU-68 have sequence similarity of 97 and 95% which might be declared as novel after further taxonomic characterization.