Reverse Engineering of Modified Genes by Bayesian Network Analysis Defines Molecular Determinants Critical to the Development of Glioblastoma

In this study we have identified key genes that are critical in development of astrocytic tumors. Meta-analysis of microarray studies which compared normal tissue to astrocytoma revealed a set of 646 differentially expressed genes in the majority of astrocytoma. Reverse engineering of these 646 genes using Bayesian network analysis produced a gene network for each grade of astrocytoma (Grade I–IV), and ‘key genes’ within each grade were identified. Genes found to be most influential to development of the highest grade of astrocytoma, Glioblastoma multiforme were: COL4A1, EGFR, BTF3, MPP2, RAB31, CDK4, CD99, ANXA2, TOP2A, and SERBP1. All of these genes were up-regulated, except MPP2 (down regulated). These 10 genes were able to predict tumor status with 96–100% confidence when using logistic regression, cross validation, and the support vector machine analysis. Markov genes interact with NFkβ, ERK, MAPK, VEGF, growth hormone and collagen to produce a network whose top biological functions are cancer, neurological disease, and cellular movement. Three of the 10 genes - EGFR, COL4A1, and CDK4, in particular, seemed to be potential ‘hubs of activity’. Modified expression of these 10 Markov Blanket genes increases lifetime risk of developing glioblastoma compared to the normal population. The glioblastoma risk estimates were dramatically increased with joint effects of 4 or more than 4 Markov Blanket genes. Joint interaction effects of 4, 5, 6, 7, 8, 9 or 10 Markov Blanket genes produced 9, 13, 20.9, 26.7, 52.8, 53.2, 78.1 or 85.9%, respectively, increase in lifetime risk of developing glioblastoma compared to normal population. In summary, it appears that modified expression of several ‘key genes’ may be required for the development of glioblastoma. Further studies are needed to validate these ‘key genes’ as useful tools for early detection and novel therapeutic options for these tumors.

Comparative transcriptome analyses of Pseudomonas aeruginosa

One of the hallmarks of bacterial survival is their ability to adapt rapidly to changing environmental conditions. Niche adaptation is a response to the signals received that are relayed, often to regulators that modulate gene expression. In the post-genomic era, DNA microarrays are used to study the dynamics of gene expression on a global scale. Numerous studies have used Pseudomonas aeruginosa--a Gram-negative environmental and opportunistic human pathogenic bacterium--as the model organism in whole-genome transcriptome analysis. This paper reviews the transcriptome studies that have led to immense advances in our understanding of the biology of this intractable human pathogen. Comparative analysis of 23 P. aeruginosa transcriptome studies has led to the identification of a unique set of genes that are signal specific and a core set that is differentially regulated. The 303 genes in the core set are involved in bacterial homeostasis, making them attractive therapeutic targets.

Combination of 16S rRNA variable regions provides a detailed analysis of bacterial community dynamics in the lungs of cystic fibrosis patients

Chronic bronchopulmonary bacterial infections remain the most common cause of morbidity and mortality among patients with cystic fibrosis (CF). Recent community sequencing work has now shown that the bacterial community in the CF lung is polymicrobial. Identifying bacteria in the CF lung through sequencing can be costly and is not practical for many laboratories. Molecular techniques such as terminal restriction fragment length polymorphism or amplicon length heterogeneity-polymerase chain reaction (LH-PCR) can provide many laboratories with the ability to study CF bacterial communities without costly sequencing. The aim of this study was to determine if the use of LH-PCR with multiple hypervariable regions of the 16S rRNA gene could be used to identify organisms found in sputum DNA. This work also determined if LH-PCR could be used to observe the dynamics of lung infections over a period of time. Nineteen samples were analysed with the V1 and the V1_V2 region of the 16S rRNA gene. Based on the amplicon size present in the V1_V2 region, Pseudomonas aeruginosa was confirmed to be in all 19 samples obtained from the patients. The V1 region provided a higher power of discrimination between bacterial profiles of patients. Both regions were able to identify trends in the bacterial population over a period of time. LH profiles showed that the CF lung community is dynamic and that changes in the community may in part be driven by the patient's antibiotic treatment. LH-PCR is a tool that is well suited for studying bacterial communities and their dynamics.

Assessing Methods for Assigning SNPs to Genes in Gene-Based Tests of Association Using Common Variants

Gene-based tests of association are frequently applied to common SNPs (MAF>5%) as an alternative to single-marker tests. In this analysis we conduct a variety of simulation studies applied to five popular gene-based tests investigating general trends related to their performance in realistic situations. In particular, we focus on the impact of non-causal SNPs and a variety of LD structures on the behavior of these tests. Ultimately, we find that non-causal SNPs can significantly impact the power of all gene-based tests. On average, we find that the “noise” from 6–12 non-causal SNPs will cancel out the “signal” of one causal SNP across five popular gene-based tests. Furthermore, we find complex and differing behavior of the methods in the presence of LD within and between non-causal and causal SNPs. Ultimately, better approaches for a priori prioritization of potentially causal SNPs (e.g., predicting functionality of non-synonymous SNPs), application of these methods to sequenced or fully imputed datasets, and limited use of window-based methods for assigning inter-genic SNPs to genes will improve power. However, significant power loss from non-causal SNPs may remain unless alternative statistical approaches robust to the inclusion of non-causal SNPs are developed.

Identification of Anziaic Acid, a Lichen Depside from Hypotrachyna sp., as a New Topoisomerase Poison Inhibitor

Topoisomerase inhibitors are effective for antibacterial and anticancer therapy because they can lead to the accumulation of the intermediate DNA cleavage complex formed by the topoisomerase enzymes, which trigger cell death. Here we report the application of a novel enzyme-based high-throughput screening assay to identify natural product extracts that can lead to increased accumulation of the DNA cleavage complex formed by recombinant Yersinia pestistopoisomerase I as part of a larger effort to identify new antibacterial compounds. Further characterization and fractionation of the screening positives from the primary assay led to the discovery of a depside, anziaic acid, from the lichen Hypotrachyna sp. as an inhibitor for both Y. pestis and Escherichia colitopoisomerase I. In in vitro assays, anziaic acid exhibits antibacterial activity against Bacillus subtilis and a membrane permeable strain of E. coli. Anziaic acid was also found to act as an inhibitor of human topoisomerase II but had little effect on human topoisomerase I. This is the first report of a depside with activity as a topoisomerase poison inhibitor and demonstrates the potential of this class of natural products as a source for new antibacterial and anticancer compounds.

Cocaine Enhances HIV-1 Infectivity in Monocyte Derived Dendritic Cells by Suppressing microRNA-155

Cocaine and other drugs of abuse increase HIV-induced immunopathogenesis; and neurobiological mechanisms of cocaine addiction implicate a key role for microRNAs (miRNAs), single-stranded non-coding RNAs that regulate gene expression and defend against viruses. In fact, HIV defends against miRNAs by actively suppressing the expression of polycistronic miRNA cluster miRNA-17/92, which encodes miRNAs including miR-20a. IFN-g production by natural killer cells is regulated by miR-155 and this miRNA is also critical to dendritic cell (DC) maturation. However, the impact of cocaine on miR-155 expression and subsequent HIV replication is unknown. We examined the impact of cocaine on two miRNAs, miR-20a and miR-155, which are integral to HIV replication, and immune activation. Using miRNA isolation and analysis, RNA interference, quantitative real time PCR, and reporter assays we explored the effects of cocaine on miR-155 and miR-20 in the context of HIV infection. Here we demonstrate using monocyte-derived dendritic cells (MDCCs) that cocaine significantly inhibited miR-155 and miR-20a expression in a dose dependent manner. Cocaine and HIV synergized to lower miR-155 and miR-20a in MDDCs by 90%. Cocaine treatment elevated LTR-mediated transcription and PU.1 levels in MDCCs. But in context of HIV infection, PU.1 was reduced in MDDCs regardless of cocaine presence. Cocaine increased DC-SIGN and and decreased CD83 expression in MDDC, respectively. Overall, we show that cocaine inhibited miR-155 and prevented maturation of MDDCs; potentially, resulting in increased susceptibility to HIV-1. Our findings could lead to the development of novel miRNA-based therapeutic strategies targeting HIV infected cocaine abusers.