Insulin-like growth factor-I gene expression as a growth indicator in Nile tilapia Oreochromis niloticus L.

Somatic growth in fishes is regulated by a variety of hormones. A central step in this hormonal network is the growth hormone-insulin-like growth factor-I (IGF-I) axis. Studies conducted evaluated the relationship of hepatic IGF-I (hIGF-1) mRNA with growth as affected by feeding regimes (satiation or restricted level; daily or alternate-day feeding), temperatures (high, ambient, low) and by social stress. To develop a cellular means for the quantification of hIGF-I mRNA levels in O. niloticus, hIGF-I cDNA was isolated and cloned. The partial sequence of IGF-I cDNA encodes for signal peptide, mature protein and a portion of the E-domain. A sensitive TaqMan quantitative reverse transcription-polymerase chain reaction (qRT-PCR) assay was developed based on the mature IGF-I. Using the developed qRT-PCR assay a significant positive correlation was observed between hIGF-I mRNA levels and growth rate of fish reared under different feeding regimes (r = 0.64) and temperature conditions (r = 0.64). On the dynamics of hIGF-I gene expression in response to elevated temperature, hIGF-I mRNA levels were significantly elevated after at least 2 days of exposure to warm temperature. This validates the concept that hIGF-I gene expressions are sufficiently sensitive to be used as a rapid growth rate indicator for O. niloticus. The hIGF-I levels have a significant positive correlation with specific growth rate (length; r = 0.92), and with condition factor (r = 0.55). On the effect of social stress, differential alterations in growth rates between the dominant and subordinates were observed which was attributed more to behavioral changes as transduced by physiological regulators. The fish's relative position in the social hierarchy was consistently reflected in the levels of hIGF-I mRNA and the eye color pattern. Subordination depressed hIGF-I levels while dominance stimulated it. These findings have shown that hGF-I level remained positively correlated to growth rate as affected by feeding regime, temperature and social stress. This suggests that hIGF-I plays a key role in controlling growth in O. niloticus and indicates that IGF-I mRNA quantification could prove useful for the rapid assessment of growth rate in this species of fish.

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.