| Popis: |
Gravity is an important stimulus for plants. Gravitropism, the plants' response to gravity, can be divided into three phases: gravity perception, signal transduction and response. Various theories have been proposed to explain the process of gravitropism, yet more genes are needed to elucidate the mechanism of gravitropic signal transduction. A transcriptome analysis, in combination with the Gravity Persistent Signal treatment, was performed to specifically study the genes involved in signal transduction. Analysis generated a list of 318 transcripts that were differentially expressed in plants that were reoriented with respect to gravity as compared to vertical controls. Based on the expression profiles and gene function annotations, five transcription factors, WRKY18, WRKY26, WRKY33, BT2 and ATAIB, were selected for further study. In addition to the standard analysis of differentially expressed genes, a systems approach was adopted to uncover more gravity related genes. A semi-supervised learning method was developed to find additional novel genes. This learning method took a set of 32 known gravity genes from the literature as well as a collection of heterogeneous annotation features, such as existing protein-protein interactions, and co-expression profiles. The learning classifier predicted a list of 50 genes that are functionally related to gravity signal transduction. Based on this list of genes, an interaction network was predicted based two complementary approaches: a dynamic Bayesian network and a time-lagged correlation coefficient. To increase confidence in the predication, genes/interactions that appeared in both networks were selected. This 'intersected' network provided 20 hub and bottleneck genes, fourteen of which had not been previously identified as involved in gravitropism. Such an approach provides a framework to extend current research in a more comprehensive manner, and serves a complementary to the traditional mutant/gene discovery model. |
