Identification and localization of polyketide synthase genes from cultures of diarrheic shellfish poisoning toxin producing dinoflagellates of the genus Prorocentrum

Aquatic toxins are responsible for a number of acute and chronic diseases in humans. Okadaic acid (OA) and other dinoflagellate derived polyketide toxins pose serious health risks on a global scale. Ingestion of OA contaminated shellfish causes diarrheic shellfish poisoning (DSP). Some evidence also suggests tumor promotion in the liver by OA. Microcystin-LR (MC-LR) is produced by cyanobacteria and is believed to be the most common freshwater toxin in the US. Humans may be exposed to this acute hepatotoxin through drinking or recreational use of contaminated waters. ^ OA producing dinoflagellates have not been cultured axenically. The presence of associated bacteria raises questions about the ultimate source of OA. Identification of the toxin-producing organism(s) is the first step in identifying the biosynthetic pathways involved in toxin production. Polyketide synthase (PKS) genes of toxic and non-toxic species were surveyed by construction of clonal libraries from PCR amplicons of various toxic and non-toxic species of Prorocentrum in an effort to identify genes, which may be part of the biosynthetic pathway of OA. Analysis of the PKS sequences revealed that toxic species shared identical PKS genes not present in non-toxic species. Interestingly, the same PKS genes were identified in a library constructed from associated bacteria. ^ Subsequent bacterial small subunit RNA (16S) clonal libraries identified several common bacterial species. The most frequent 16S sequences found were identified as species of the genus Roseobacter which has previously been implicated in the production of OA. Attempts to culture commonly occurring bacteria resulted in the isolation of Oceanicaulis alexandrii , a novel marine bacterium previously isolated from the dinoflagellate Alexandrium tamarense, from both P. lima, and P. hoffmanianum. ^ Metabolic studies of microcystin-LR, were conducted to probe the activity of the major human liver cytochromes (CYP) towards the toxin. CYPs may provide alternate routes of detoxification of toxins when the usual routes have been inhibited. For example, some research indicates that cyanobacterial xenobiotics, in particular, lipopolysaccharides may inhibit glutathione S-transferases allowing the toxin to persist long enough to be acted upon by other enzymes. These studies found that at least one human liver CYP was capable of metabolizing the toxin. ^

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.