Experimental determination of effects of water depth on Nymphaea odorata growth, morphology and biomass allocation

Growth, morphology and biomass allocation in response to water depth was studied in white water lily,Nymphaea odorata Aiton. Plants were grown for 13 months in 30, 60 and 90 cm water in outdoor mesocosms in southern Florida. Water lily plant growth was distinctly seasonal with plants at all water levels producing more and larger leaves and more flowers in the warmer months. Plants in 30 cm water produced more but smaller and shorter-lived leaves than plants at 60 cm and 90 cm water levels. Although plants did not differ significantly in total biomass at harvest, plants in deeper water had significantly greater biomass allocated to leaves and roots, while plants in 30 cm water had significantly greater biomass allocated to rhizomes. Although lamina area and petiole length increased significantly with water level, lamina specific weight did not differ among water levels. Petiole specific weight increased significantly with increasing water level, implying a greater cost to tethering the larger laminae in deeper water. Lamina length and width scaled similarly at different water levels and modeled lamina area (LA) accurately (LAmodeled = 0.98LAmeasured + 3.96, R2 = 0.99). Lamina area was highly correlated with lamina weight (LW = 8.43LA − 66.78, R2 = 0.93), so simple linear measurements can predict water lily lamina area and lamina weight. These relationships were used to calculate monthly lamina surface area in the mesocosms. Plants in 30 cm water had lower total photosynthetic surface area than plants in 60 cm and 90 cm water levels throughout, and in the summer plants in 90 cm water showed a great increase in photosynthetic surface area as compared to plants in shallower water. These results support setting Everglades restoration water depth targets for sloughs at depths ≥45 cm and suggest that in the summer optimal growth for white water lilies occurs at depths ≥75 cm.

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.