A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays

Motivation: An important problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. We provide a straightforward and easily implemented method for estimating the posterior probability that an individual gene is null. The problem can be expressed in a two-component mixture framework, using an empirical Bayes approach. Current methods of implementing this approach either have some limitations due to the minimal assumptions made or with more specific assumptions are computationally intensive. Results: By converting to a z-score the value of the test statistic used to test the significance of each gene, we propose a simple two-component normal mixture that models adequately the distribution of this score. The usefulness of our approach is demonstrated on three real datasets.

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

Background The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease. Results We show that GPNN has high power to detect even relatively small genetic effects (2–3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (

Detection and discrimination of herpes simplex viruses, Haemophilus ducreyi, Treponema pallidum, and Calymmatobacterium (Klebsiella) granulomatis from genital ulcers

Background. Genital ulcer disease (GUD) is commonly caused by pathogens for which suitable therapies exist, but clinical and laboratory diagnoses may be problematic. This collaborative project was undertaken to address the need for a rapid, economical, and sensitive approach to the detection and diagnosis of GUD using noninvasive techniques to sample genital ulcers. Methods. The genital ulcer disease multiplex polymerase chain reaction (GUMP) was developed as an inhouse nucleic acid amplification technique targeting serious causes of GUD, namely, herpes simplex viruses (HSVs), Haemophilus ducreyi, Treponema pallidum, and Klebsiella species. In addition, the GUMP assay included an endogenous internal control. Amplification products from GUMP were detected by enzyme linked amplicon hybridization assay (ELAHA). Results. GUMP-ELAHA was sensitive and specific in detecting a target microbe in 34.3% of specimens, including 1 detection of HSV-1, three detections of HSV-2, and 18 detections of T. pallidum. No H. ducreyi has been detected in Australia since 1998, and none was detected here. No Calymmatobacterium ( Klebsiella) granulomatis was detected in the study, but there were 3 detections during ongoing diagnostic use of GUMP-ELAHA in 2004 and 2005. The presence of C. granulomatis was confirmed by restriction enzyme digestion and nucleotide sequencing of the 16S rRNA gene for phylogenetic analysis. Conclusions. GUMP-ELAHA permitted comprehensive detection of common and rare causes of GUD and incorporated noninvasive sampling techniques. Data obtained by using GUMP-ELAHA will aid specific treatment of GUD and better define the prevalence of each microbe among at-risk populations with a view to the eradication of chancroid and donovanosis in Australia.

Quantitative analysis of MC1R gene expression in human skin cell cultures

To address the issue of melanocortin-1 receptor (MC1R) expression in non-melanocytic cells, we have quantitatively evaluated the relative expression levels of both MC1R mRNA and protein in a subset of different cell types. Using semi-quantitative reverse transcriptase-polymerase chain reaction (RT-PCR) at high cycle numbers, we detected MC1R mRNA in all cell types examined, including human embryonic kidney-293 (HEK 293) cells, a cell type widely used as a negative control in melanocortin expression studies. Quantitative real-time PCR revealed the highest levels of MC1R transcripts were in melanocytic cells, whereas the keratinocyte and fibroblast cell cultures examined had only a low level of expression, similar to that of HEK 293 cells. Antibody mediated detection of MC1R protein in membrane extracts demonstrated exogenous receptor in MC1R transfected cell lines, as well as endogenous MC1R in melanoma cells. However, radioligand binding procedures were required to detect MC1R protein of normal human melanocytes and no surface expression of MC1R was detected in any of the non-melanocytic cells examined. This was consistent with their low level of mRNA, and suggests that, if present, the levels of surface receptor are significantly lower than that in melanocytes. The capacity of such limited levels of MC1R protein to influence non-melanocytic skin cell biology would likely be severely compromised. Indeed, the MC1R agonist [NIe(4), D-Phe(7)] alpha-melanocyte stimulating hormone (NDP-MSH) was unable to elevate intracellular cyclic adenosine monophosphate (cAMP) levels in the keratinocyte and fibroblast cells examined, whereas a robust increase was elicited in melanocytes. Although there are a variety of cell types with detectable MC1R mRNA, the expression of physiologically significant levels of the receptor may be more restricted than the current literature indicates, and within epidermal tissue may be limited to the melanocyte

The detection of environmental autoinducing peptide quorum-sensing genes from an uncultured Clostridium sp in landfill leachate reactor biomass

Aims: To elucidate whether a dominant uncultured clostridial (Clostridium thermocellum-like) species in an environmental sample (landfill leachate), possesses an autoinducing peptide (AIP) quorum-sensing (QS) gene, although it may not be functional. Methods and Results: A modified AIP accessory gene regulator (agr)C PCR protocol was performed on extracted DNA from a landfill leachate sample (also characterized by 16S rRNA gene cloning) and the PCR products were cloned, sequenced and phylogenetically analysed. It appeared that two agrC gene phylotypes existed, most closely related to the C. thermocellum agrC gene, differing by only 1 bp. Conclusions: It is possible to specifically identify and characterize the agrC AIP QS gene from uncultured Firmicutes (C. thermocellum-like) bacteria derived from environmental (landfill leachate) sample. Significance and Impact of the Study: This is the first successful attempt at identifying AIP QS genes from a cellulolytic environment (landfill). The agrC gene was identified as being most closely related to the C. thermocellum agrC gene, the same bacterium identified as being dominant, according to 16S rRNA gene cloning and subsequently fluorescence in situ hybridization analyses, in the same biomass.

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

Background: The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease. Results: We show that GPNN has high power to detect even relatively small genetic effects (2-3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (

Rapid detection of a chromosomally mediated penicillin resistance-associated ponA mutation in Neisseria gonorrhoeae using a real-time PCR assay

The recent emergence of a decreased susceptibility of Neisseria gonorrhoeae strains to penicillin in New Caledonia has lead clinicians to operate a change in the treatment strategy. In addition, this important health issue has emphasized the need for a rapid means of detecting penicillin resistance in N. gonorrhoeae in order to select an effective treatment and limit the spread of resistant strains. In recent years, the use of fluorescence resonance energy transfer on the LightCycler has proven to be a valuable tool for the screening of mutations occurring in the genome of various microorganisms. In this study, we developed a real-time PCR assay coupled with a fluorometric hybridization probes system to detect a penicillin resistance-associated mutation on the N. gonorrhoeae ponA gene. Following an extensive evaluation involving 136 isolates, melting curve analysis correctly evidenced a 5 degrees C T-m shift in all N. gonorrhoeae strains possessing this mutation, as determined by conventional sequencing analysis. Moreover, the mutation profiles obtained with the real-time PCR showed good correlation with the pattern of penicillin susceptibility generated with classical antibiograms. Overall, our molecular assay allowed an accurate and reproducible determination of the susceptibility to penicillin corresponding to a mutation present in all chromosomally mediated resistant strains of N. gonorrhoeae.

Direct protein interaction underlies gene-for-gene specificity and coevolution of the flax resistance genes and flax rust avirulence genes

Plant resistance proteins (R proteins) recognize corresponding pathogen avirulence (Avr) proteins either indirectly through detection of changes in their host protein targets or through direct R-Avr protein interaction. Although indirect recognition imposes selection against Avr effector function, pathogen effector molecules recognized through direct interaction may overcome resistance through sequence diversification rather than loss of function. Here we show that the flax rust fungus AvrLS67 genes, whose products are recognized by the L5, L6, and L7 R proteins of flax, are highly diverse, with 12 sequence variants identified from six rust strains. Seven AvrL567 variants derived from Avr alleles induce necrotic responses when expressed in flax plants containing corresponding resistance genes (R genes), whereas five variants from avr alleles do not. Differences in recognition specificity between AvA567 variants and evidence for diversifying selection acting on these genes suggest they have been involved in a gene-specific arms race with the corresponding flax R genes. Yeast two-hybrid assays indicate that recognition is based on direct R-Avr protein interaction and recapitulate the interaction specificity observed in planta. Biochemical analysis of Escherichia coli-produced AvrL567 proteins shows that variants that escape recognition nevertheless maintain a conserved structure and stability, suggesting that the amino acid sequence differences directly affect the R-Avr protein interaction. We suggest that direct recognition associated with high genetic diversity at corresponding R and Avr gene loci represents an alternative outcome of plant-pathogen coevolution to indirect recognition associated with simple balanced polymorphisms for functional and nonfunctional R and Avr genes.