Multiple genotypes of Chlamydia pneumoniae identified in human carotid plaque

Chlamydia pneumoniae is an obligate intracellular respiratory pathogen that causes 10% of community-acquired pneumonia and has been associated with cardiovascular disease. Both whole-genome sequencing and specific gene typing suggest that there is relatively little genetic variation in human isolates of C. pneumoniae. To date, there has been little genomic analysis of strains from human cardiovascular sites. The genotypes of C. pneumoniae present in human atherosclerotic carotid plaque were analysed and several polymorphisms in the variable domain 4 (VD4) region of the outer-membrane protein-A (ompA) gene and the intergenic region between the ygeD and uridine kinase (ygeD-urk) genes were found. While one genotype was identified that was the same as one reported previously in humans (respiratory and cardiovascular), another genotype was found that was identical to a genotype from non-human sources (frog/koala).

Application of mixture models to detect differentially expressed genes

An important and common problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. As this problem concerns the selection of significant genes from a large pool of candidate genes, it needs to be carried out within the framework of multiple hypothesis testing. In this paper, we focus on the use of mixture models to handle the multiplicity issue. With this approach, a measure of the local FDR (false discovery rate) is provided for each gene. An attractive feature of the mixture model approach is that it provides a framework for the estimation of the prior probability that a gene is not differentially expressed, and this probability can subsequently be used in forming a decision rule. The rule can also be formed to take the false negative rate into account. We apply this approach to a well-known publicly available data set on breast cancer, and discuss our findings with reference to other approaches.

Using mixture models to detect differentially expressed genes

An important and common problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. As this problem concerns the selection of significant genes from a large pool of candidate genes, it needs to be carried out within the framework of multiple hypothesis testing. In this paper, we focus on the use of mixture models to handle the multiplicity issue. With this approach, a measure of the local false discovery rate is provided for each gene, and it can be implemented so that the implied global false discovery rate is bounded as with the Benjamini-Hochberg methodology based on tail areas. The latter procedure is too conservative, unless it is modified according to the prior probability that a gene is not differentially expressed. An attractive feature of the mixture model approach is that it provides a framework for the estimation of this probability and its subsequent use in forming a decision rule. The rule can also be formed to take the false negative rate into account.

Expression profiling of purified mouse gonadal somatic cells during the critical time window of sex determination reveals novel candidate genes for human sexual dysgenesis syndromes

Despite the identification of SRY as the testis-determining gene in mammals, the genetic interactions controlling the earliest steps of male sex determination remain poorly understood. In particular, the molecular lesions underlying a high proportion of human XY gonadal dysgenesis, XX maleness and XX true hermaphroditism remain undiscovered. A number of screens have identified candidate genes whose expression is modulated during testis or ovary differentiation in mice, but these screens have used whole gonads, consisting of multiple cell types, or stages of gonadal development well beyond the time of sex determination. We describe here a novel reporter mouse line that expresses enhanced green fluorescent protein under the control of an Sf1 promoter fragment, marking Sertoli and granulosa cell precursors during the critical period of sex determination. These cells were purified from gonads of male and female transgenic embryos at 10.5 dpc (shortly after Sry transcription is activated) and 11.5 dpc (when Sox9 transcription begins), and their transcriptomes analysed using Affymetrix genome arrays. We identified 266 genes, including Dhh, Fgf9 and Ptgds, that were upregulated and 50 genes that were downregulated in 11.5 dpc male somatic gonad cells only, and 242 genes, including Fst, that were upregulated in 11.5 dpc female somatic gonad cells only. The majority of these genes are novel genes that lack identifiable homology, and several human orthologues were found to map to chromosomal loci implicated in disorders of sexual development. These genes represent an important resource with which to piece together the earliest steps of sex determination and gonad development, and provide new candidates for mutation searching in human sexual dysgenesis syndromes.

Genes for the majority of group A streptococcal virulence factors and extracellular surface proteins do not confer an increased propensity to cause invasive disease

Background. The factors behind the reemergence of severe, invasive group A streptococcal (GAS) diseases are unclear, but it could be caused by altered genetic endowment in these organisms. However, data from previous studies assessing the association between single genetic factors and invasive disease are often conflicting, suggesting that other, as-yet unidentified factors are necessary for the development of this class of disease. Methods. In this study, we used a targeted GAS virulence microarray containing 226 GAS genes to determine the virulence gene repertoires of 68 GAS isolates (42 associated with invasive disease and 28 associated with noninvasive disease) collected in a defined geographic location during a contiguous time period. We then employed 3 advanced machine learning methods (genetic algorithm neural network, support vector machines, and classification trees) to identify genes with an increased association with invasive disease. Results. Virulence gene profiles of individual GAS isolates varied extensively among these geographically and temporally related strains. Using genetic algorithm neural network analysis, we identified 3 genes with a marginal overrepresentation in invasive disease isolates. Significantly, 2 of these genes, ssa and mf4, encoded superantigens but were only present in a restricted set of GAS M-types. The third gene, spa, was found in variable distributions in all M-types in the study. Conclusions. Our comprehensive analysis of GAS virulence profiles provides strong evidence for the incongruent relationships among any of the 226 genes represented on the array and the overall propensity of GAS to cause invasive disease, underscoring the pathogenic complexity of these diseases, as well as the importance of multiple bacteria and/ or host factors.

Mixture models for detecting differentially expressed genes in microarrays

An important and common problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. As this problem concerns the selection of significant genes from a large pool of candidate genes, it needs to be carried out within the framework of multiple hypothesis testing. In this paper, we focus on the use of mixture models to handle the multiplicity issue. With this approach, a measure of the local FDR (false discovery rate) is provided for each gene. An attractive feature of the mixture model approach is that it provides a framework for the estimation of the prior probability that a gene is not differentially expressed, and this probability can subsequently be used in forming a decision rule. The rule can also be formed to take the false negative rate into account. We apply this approach to a well-known publicly available data set on breast cancer, and discuss our findings with reference to other approaches.

The V-ATPase in Paramecium:functional specialization by multiple gene isoforms

The vacuolar H(+)-ATPase (V-ATPase), a multisubunit, adenosine triphosphate (ATP)-driven proton pump, is essential for numerous cellular processes in all eukaryotes investigated so far. While structure and catalytic mechanism are similar to the evolutionarily related F-type ATPases, the V-ATPase's main function is to establish an electrochemical proton potential across membranes using ATP hydrolysis. The holoenzyme is formed by two subcomplexes, the transmembraneous V(0) and the cytoplasmic V(1) complexes. Sequencing of the whole genome of the ciliate Paramecium tetraurelia enabled the identification of virtually all the genes encoding V-ATPase subunits in this organism and the studying of the localization of the enzyme and roles in membrane trafficking and osmoregulation. Surprisingly, the number of V-ATPase genes in this free-living protozoan is strikingly higher than in any other species previously studied. Especially abundant are V(0)-a-subunits with as many as 17 encoding genes. This abundance creates the possibility of forming a large number of different V-ATPase holoenzymes by combination and has functional consequences by differential targeting to various organelles.

Immunoinformatic evaluation of multiple epitope ensembles as vaccine candidates:E coli 536

Epitope prediction is becoming a key tool for vaccine discovery. Prospective analysis of bacterial and viral genomes can identify antigenic epitopes encoded within individual genes that may act as effective vaccines against specific pathogens. Since B-cell epitope prediction remains unreliable, we concentrate on T-cell epitopes, peptides which bind with high affinity to Major Histacompatibility Complexes (MHC). In this report, we evaluate the veracity of identified T-cell epitope ensembles, as generated by a cascade of predictive algorithms (SignalP, Vaxijen, MHCPred, IDEB, EpiJen), as a candidate vaccine against the model pathogen uropathogenic gram negative bacteria Escherichia coli (E-coli) strain 536 (O6:K15:H31). An immunoinformatic approach was used to identify 23 epitopes within the E-coli proteome. These epitopes constitute the most promiscuous antigenic sequences that bind across more than one HLA allele with high affinity (IC50 <50nM). The reliability of software programmes used, polymorphic nature of genes encoding MHC and what this means for population coverage of this potential vaccine are discussed.

Can certain genotypes predispose to poor asthma control in children? A pharmacogenetic study of 9 candidate genes in children with difficult asthma

Objective - We tested the hypothesis that patients with difficult asthma have an increased frequency of certain genotypes that predispose them to asthma exacerbations and poor asthma control. Methods - A total of 180 Caucasian children with confirmed asthma diagnosis were selected from two phenotypic groups; difficult (n = 112) versus mild/moderate asthma (n = 68) groups. All patients were screened for 19 polymorphisms in 9 candidate genes to evaluate their association with difficult asthma. Key Results - The results indicated that LTA4H A-9188>G, TNFα G-308>A and IL-4Rα A1727>G polymorphisms were significantly associated with the development of difficult asthma in paediatric patients (p<0.001, p = 0.019 and p = 0.037, respectively). Haplotype analysis also revealed two haplotypes (ATA haplotype of IL-4Rα A1199>C, IL-4Rα T1570>C and IL-4Rα A1727>G and CA haplotype of TNFα C-863>A and TNFα G-308>A polymorphisms) which were significantly associated with difficult asthma in children (p = 0.04 and p = 0.018, respectively). Conclusions and Clinical Relevance - The study revealed multiple SNPs and haplotypes in LTA4H, TNFα and IL4-Rα genes which constitute risk factors for the development of difficult asthma in children. Of particular interest is the LTA4H A-9188>G polymorphism which has been reported, for the first time, to have strong association with severe asthma in children. Our results suggest that screening for patients with this genetic marker could help characterise the heterogeneity of responses to leukotriene-modifying medications and, hence, facilitate targeting these therapies to the subset of patients who are most likely to gain benefit.

Bioinformatic analysis for exploring relationships between genes and gene products

To carry out their specific roles in the cell, genes and gene products often work together in groups, forming many relationships among themselves and with other molecules. Such relationships include physical protein-protein interaction relationships, regulatory relationships, metabolic relationships, genetic relationships, and much more. With advances in science and technology, some high throughput technologies have been developed to simultaneously detect tens of thousands of pairwise protein-protein interactions and protein-DNA interactions. However, the data generated by high throughput methods are prone to noise. Furthermore, the technology itself has its limitations, and cannot detect all kinds of relationships between genes and their products. Thus there is a pressing need to investigate all kinds of relationships and their roles in a living system using bioinformatic approaches, and is a central challenge in Computational Biology and Systems Biology. This dissertation focuses on exploring relationships between genes and gene products using bioinformatic approaches. Specifically, we consider problems related to regulatory relationships, protein-protein interactions, and semantic relationships between genes. A regulatory element is an important pattern or "signal", often located in the promoter of a gene, which is used in the process of turning a gene "on" or "off". Predicting regulatory elements is a key step in exploring the regulatory relationships between genes and gene products. In this dissertation, we consider the problem of improving the prediction of regulatory elements by using comparative genomics data. With regard to protein-protein interactions, we have developed bioinformatics techniques to estimate support for the data on these interactions. While protein-protein interactions and regulatory relationships can be detected by high throughput biological techniques, there is another type of relationship called semantic relationship that cannot be detected by a single technique, but can be inferred using multiple sources of biological data. The contributions of this thesis involved the development and application of a set of bioinformatic approaches that address the challenges mentioned above. These included (i) an EM-based algorithm that improves the prediction of regulatory elements using comparative genomics data, (ii) an approach for estimating the support of protein-protein interaction data, with application to functional annotation of genes, (iii) a novel method for inferring functional network of genes, and (iv) techniques for clustering genes using multi-source data.

Characterization of IGH locus breakpoints in multiple myeloma indicates a subset of translocations appear to occur in pregerminal center B cells.

Translocations in myeloma are thought to occur solely in mature B cells in the germinal center through class switch recombination (CSR). We used a targeted captured technique followed by massively parallel sequencing to determine the exact breakpoints in both the immunoglobulin heavy chain (IGH) locus and the partner chromosome in 61 presentation multiple myeloma samples. The majority of samples (62%) have a breakpoint within the switch regions upstream of the IGH constant genes and are generated through CSR in a mature B cell. However, the proportion of CSR translocations is not consistent between cytogenetic subgroups. We find that 100% of t(4;14) are CSR-mediated; however, 21% of t(11;14) and 25% of t(14;20) are generated through DH-JH recombination activation gene-mediated mechanisms, indicating they occur earlier in B-cell development at the pro-B-cell stage in the bone marrow. These 2 groups also generate translocations through receptor revision, as determined by the breakpoints and mutation status of the segments used in 10% and 50% of t(11;14) and t(14;20) samples, respectively. The study indicates that in a significant number of cases the translocation-based etiological events underlying myeloma may arise at the pro-B-cell hematological progenitor cell level, much earlier in B-cell development than was previously thought.

The Clinical Impact and Molecular Biology of del(17p) in Multiple Myeloma Treated with Conventional or Thalidomide-Based Therapy

Hemizygous deletion of 17p (del(17p)) has been identified as a variable associated with poor prognosis in myeloma, although its impact in the context of thalidomide therapy is not well described. The clinical outcome of 85 myeloma patients with del(17p) treated in a clinical trial incorporating both conventional and thalidomide-based induction therapies was examined. The clinical impact of deletion, low expression, and mutation of TP53 was also determined. Patients with del(17p) did not have inferior response rates compared to patients without del(17p), but, despite this, del(17p) was associated with impaired overall survival (OS) (median OS 26.6 vs. 48.5 months, P <0.001). Within the del(17p) group, thalidomide induction therapy was associated with improved response rates compared to conventional therapy, but there was no impact on OS. Thalidomide maintenance was associated with impaired OS, although our analysis suggests that this effect may have been due to confounding variables. A minimally deleted region on 17p13.1 involving 17 genes was identified, of which only TP53 and SAT2 were underexpressed. TP53 was mutated in <1% in patients without del(17p) and in 27% of patients with del(17p). The higher TP53 mutation rate in samples with del(17p) suggests a role for TP53 in these clinical outcomes. In conclusion, del(17p) defined a patient group associated with short survival in myeloma, and although thalidomide induction therapy was associated with improved response rates, it did not impact OS, suggesting that alternative therapeutic strategies are required for this group. (C) 2011 Wiley-Liss, Inc.

Homozygous deletion mapping in myeloma samples identifies genes and an expression signature relevant to pathogenesis and outcome.

PURPOSE: Myeloma is a clonal malignancy of plasma cells. Poor-prognosis risk is currently identified by clinical and cytogenetic features. However, these indicators do not capture all prognostic information. Gene expression analysis can be used to identify poor-prognosis patients and this can be improved by combination with information about DNA-level changes. EXPERIMENTAL DESIGN: Using single nucleotide polymorphism-based gene mapping in combination with global gene expression analysis, we have identified homozygous deletions in genes and networks that are relevant to myeloma pathogenesis and outcome. RESULTS: We identified 170 genes with homozygous deletions and corresponding loss of expression. Deletion within the "cell death" network was overrepresented and cases with these deletions had impaired overall survival. From further analysis of these events, we have generated an expression-based signature associated with shorter survival in 258 patients and confirmed this signature in data from two independent groups totaling 800 patients. We defined a gene expression signature of 97 cell death genes that reflects prognosis and confirmed this in two independent data sets. CONCLUSIONS: We developed a simple 6-gene expression signature from the 97-gene signature that can be used to identify poor-prognosis myeloma in the clinical environment. This signature could form the basis of future trials aimed at improving the outcome of poor-prognosis myeloma.

Specific JAK2 mutation (JAK2R683) and multiple gene deletions in Down syndrome acute lymphoblastic leukemia

Children with Down syndrome (DS) have a greatly increased risk of acute megakaryoblastic leukemia (AMKL) and acute lymphoblastic leukemia (ALL). Both DS-AMKL and the related transient myeloproliferative disorder (TMD) have GATA1 mutations as obligatory, early events. To identify mutations contributing to leukemogenesis in DS-ALL, we undertook sequencing of candidate genes, including FLT3, RAS, PTPN11, BRAF, and JAK2. Sequencing of the JAK2 pseudokinase domain identified a specific, acquired mutation, JAK2R683, in 12 (28%) of 42 DS-ALL cases. Functional studies of the common JAK2R683G mutation in murine Ba/F3 cells showed growth factor independence and constitutive activation of the JAK/STAT signaling pathway. High-resolution SNP array analysis of 9 DS-ALL cases identified additional submicroscopic deletions in key genes, including ETV6, CDKN2A, and PAX5. These results infer a complex molecular pathogenesis for DS-ALL leukemogenesis, with trisomy 21 as an initiating or first hit and with chromosome aneuploidy, gene deletions, and activating JAK2 mutations as complementary genetic events. (Blood. 2009; 113: 646-648)

Gene mapping and expression analysis of 16q loss of heterozygosity identifies WWOX and CYLD as being important in determining clinical outcome in multiple myeloma.

We performed fluorescent in situ hybridization (FISH) for 16q23 abnormalities in 861 patients with newly diagnosed multiple myeloma and identified deletion of 16q [del(16q)] in 19.5%. In 467 cases in which demographic and survival data were available, del(16q) was associated with a worse overall survival (OS). It was an independent prognostic marker and conferred additional adverse survival impact in cases with the known poor-risk cytogenetic factors t(4;14) and del(17p). Gene expression profiling and gene mapping using 500K single-nucleotide polymorphism (SNP) mapping arrays revealed loss of heterozygosity (LOH) involving 3 regions: the whole of 16q, a region centered on 16q12 (the location of CYLD), and a region centered on 16q23 (the location of the WW domain-containing oxidoreductase gene WWOX). CYLD is a negative regulator of the NF-kappaB pathway, and cases with low expression of CYLD were used to define a "low-CYLD signature." Cases with 16q LOH or t(14;16) had significantly reduced WWOX expression. WWOX, the site of the translocation breakpoint in t(14;16) cases, is a known tumor suppressor gene involved in apoptosis, and we were able to generate a "low-WWOX signature" defined by WWOX expression. These 2 genes and their corresponding pathways provide an important insight into the potential mechanisms by which 16q LOH confers poor prognosis.