Resistance related metabolic pathways for drug target identification in Mycobacterium tuberculosis

Criteria used to filter high priority M.tuberculosis drug targets. The genes highlighted in bold satisfied all the selection criteria. The hyphen (−) indicates exclusion from further analysis. Abbreviations used: NUI- Not under investigation, PDB- Protein Data Bank, TBSGC- TB Structural Genome Consortium. References 12-Sassetti et al., 2003; 34-Lamichhane et al., 2003. Data can be viewed in Microsoft excel. (XLS 12 kb

Arrhythmogenic right ventricular cardiomyopathy type 6 (ARVC6): support for the locus assignment, narrowing of the critical region and mutation screening of three candidate genes

BACKGROUND: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a heritable disorder characterized by progressive degeneration of right ventricular myocardium, arrhythmias and an increased risk of sudden death at a young age. By linkage analysis, ARVC type 6 was previously mapped to a 10.6 cM region on chromosome 10p12-p14 in a large North American kindred. To date, the genetic defect that causes ARVC6 has not been identified. METHODS: We identified a South African family of 13 members with ARVC segregating as an autosomal dominant disorder. The diagnosis of ARVC was based on international diagnostic criteria. All available family members were genotyped with microsatellite markers at six known ARVC loci, and positional candidate gene screening was performed. RESULTS: Genetic linkage and haplotype analysis provided lod scores that are highly suggestive of linkage to the ARVC6 locus on chromosome 10p12-p14, and the narrowing of the critical region to ~2.9 Mb. Two positional candidate genes (ITG8 and FRMD4A) were screened in which defects could possibly disrupt cell-cell adhesion. A non-positional candidate gene with apoptosis inducing properties, LAMR1P6 (laminin receptor 1 pseudogene 6) was also screened. Direct sequencing of DNA from affected individuals failed to detect disease-causing mutations in the exonic sequences of the three genes investigated. CONCLUSION: The narrowing of the ARVC6 critical region may facilitate progress towards the identification of the gene that is involved in ARVC. Identification of the causative genes for ARVC will contribute to the understanding of the pathogenesis and management of this poorly understood condition

DDPC: Dragon database of genes associated with prostate cancer

Prostate cancer (PC) is one of the most commonly diagnosed cancers in men. PC is relatively difficult to diagnose due to a lack of clear early symptoms. Extensive research of PC has led to the availability of a large amount of data on PC. Several hundred genes are implicated in different stages of PC, which may help in developing diagnostic methods or even cures. In spite of this accumulated information, effective diagnostics and treatments remain evasive. We have developed Dragon Database of Genes associated with Prostate Cancer (DDPC) as an integrated knowledgebase of genes experimentally verified as implicated in PC. DDPC is distinctive from other databases in that (i) it provides pre-compiled biomedical text-mining information on PC, which otherwise require tedious computational analyses, (ii) it integrates data on molecular interactions, pathways, gene ontologies, gene regulation at molecular level, predicted transcription factor binding sites on promoters of PC implicated genes and transcription factors that correspond to these binding sites and (iii) it contains DrugBank data on drugs associated with PC. We believe this resource will serve as a source of useful information for research on PC.DST/NRF Research Chair National Bioinformatics Network grants National Research Foundation of South Afric

Cross-species gene expression analysis of species specific differences in the preclinical assessment of pharmaceutical compounds

Animals are frequently used as model systems for determination of safety and efficacy in pharmaceutical research and development. However, significant quantitative and qualitative differences exist between humans and the animal models used in research. This is as a result of genetic variation between human and the laboratory animal. Therefore the development of a system that would allow the assessment of all molecular differences between species after drug exposure would have a significant impact on drug evaluation for toxicity and efficacy. Here we describe a cross-species microarray methodology that identifies and selects orthologous probes after cross-species sequence comparison to develop an orthologous cross-species gene expression analysis tool. The assumptions made by the use of this orthologous gene expression strategy for cross-species extrapolation is that; conserved changes in gene expression equate to conserved pharmacodynamic endpoints. This assumption is supported by the fact that evolution and selection have maintained the structure and function of many biochemical pathways over time, resulting in the conservation of many important processes. We demonstrate this cross-species methodology by investigating species specific differences of the peroxisome proliferatoractivator receptor (PPAR) a response in rat and human

Synergistic use of promoter prediction algorithms: A choice for small training dataset?

Philosophiae Doctor - PhDThis chapter outlines basic gene structure and how gene structure is related to promoter structure in both prokaryotes and eukaryotes and their transcription machinery. An in-depth discussion is given on variations types of the promoters among both prokaryotes and eukaryotes and as well as among three prokaryotic organisms namely, E.coli, B.subtilis and Mycobacteria with emphasis on Mituberculosis. The simplest definition that can be given for a promoter is: It is a segment of Deoxyribonucleic Acid (DNA) sequence located upstream of the 5' end of the gene where the RNA Polymerase enzyme binds prior to transcription (synthesis of RNA chain representative of one strand of the duplex DNA). However, promoters are more complex than defined above. For example, not all sequences upstream of genes can function as promoters even though they may have features similar to some known promoters (from section 1.2). Promoters are therefore specific sections of DNA sequences that are also recognized by specific proteins and therefore differ from other sections of DNA sequences that are transcribed or translated. The information for directing RNA polymerase to the promoter has to be in section of DNA sequence defining the promoter region. Transcription in prokaryotes is initiated when the enzyme RNA polymerase forms a complex with sigma factors at the promoter site. Before transcription, RNA polymerase must form a tight complex with the sigma/transcription factor(s) (figure 1.1). The 'tight complex' is then converted into an 'open complex' by melting of a short region of DNA within the sequence involved in the complex formation. The final step in transcription initiation involves joining of first two nucleotides in a phosphodiester linkage (nascent RNA) followed by the release of sigma/transcription factors. RNA polymerase then continues with the transcription by making a transition from initiation to elongation of the nascent transcript

Synergistic use of promoter prediction algorithms: a choice of small training dataset?

Philosophiae Doctor - PhDPromoter detection, especially in prokaryotes, has always been an uphill task and may remain so, because of the many varieties of sigma factors employed by various organisms in transcription. The situation is made more complex by the fact, that any seemingly unimportant sequence segment may be turned into a promoter sequence by an activator or repressor (if the actual promoter sequence is made unavailable). Nevertheless, a computational approach to promoter detection has to be performed due to number of reasons. The obvious that comes to mind is the long and tedious process involved in elucidating promoters in the ‘wet’ laboratories not to mention the financial aspect of such endeavors. Promoter detection/prediction of an organism with few characterized promoters (M.tuberculosis) as envisaged at the beginning of this work was never going to be easy. Even for the few known Mycobacterial promoters, most of the respective sigma factors associated with their transcription were not known. If the information (promoter-sigma) were available, the research would have been focused on categorizing the promoters according to sigma factors and training the methods on the respective categories. That is assuming that, there would be enough training data for the respective categories. Most promoter detection/prediction studies have been carried out on E.coli because of the availability of a number of experimentally characterized promoters (+- 310). Even then, no researcher to date has extended the research to the entire E.coli genome.South Afric

Graphical representation of PPARα probe intensity response.

<p>Probe intensity profiles for real rat (<b>A</b>) and virtual human (<b>B</b>) arrays. Informative probes from Affymetrix Rat230.2 array were identified and selected to construct the ‘virtual’ human cross-species library file after comparative genome sequence analysis. The probe intensity profiles demonstrate that sensitivity of the ‘virtual’ array was significantly improved over the sensitivity of the reference Affymetrix rat230.2 array. The ‘virtual’ human cross-species array consists of human ortholougs of Affymetrix rat230.2 probes.</p

Fold change data for PPAR alpha pathway genes for real rat, virtual human and real human.

<p><b>*</b>Data in parenthesis indicate number of orthologous probes selected for the virtual human ortholog fold change.</p>1<p>Fold change values were calculated at 100 µM of compound.</p

Interactions of putative marker genes.

<p>Interactions between 20 putative markers of real rat (<b>A</b>) and virtual human (<b>B</b>) of the 100 top ranked transcripts after ANNs analysis. Red arrows depict negative interaction whilst green arrows depict positive interactions. The thickness of the arrow indicates the magnitude of the interaction.</p