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DNA methylation affects important developmental processes in both plants and animals. The process of methylation of cytosines at C-5 is catalysed by DNA methyltransferases (MTases), which are highly conserved, both structurally and functionally, in eukaryotes. In this study, we identified and characterized cytosine DNA MTase genes that are activated with the onset of reproductive development in rice. The rice genome ( Oryza sativa L. subsp. japonica) encodes a total of 10 genes that contain the highly conserved MTase catalytic domain. These genes have been categorized into subfamilies on the basis of phylogenetic relationships. A microarray-based gene expression profile of all 10 MTases during 22 stages/tissues that included 14 stages of reproductive development and five vegetative tissues together with three stresses, cold, salt and dehydration stress, revealed specific windows of MTase activity during panicle and seed development. The expression of six methylases was specifically/preferentially upregulated with the initiation of floral organs. Significantly, one of the MTases was also activated in young seedlings in response to cold and salt stress. The molecular studies presented here suggest a greater role for these proteins and the epigenetic process in affecting genome activity during reproductive development and stress than was previously anticipated. [ABSTRACT FROM AUTHOR]

The topological characteristics of biological networks enable us to identify the key nodes in terms of modularity. However, due to a large size of the biological networks with many hubs and functional modules across intertwined layers within the network, it often becomes difficult to accomplish the task of identifying potential key regulators. We use for the first time a generalized formalism of Hamiltonian Energy (HE) with a recursive approach. The concept, when applied to the Apoptosis Regulatory Gene Network (ARGN), helped us identify 11 Motif hubs (MHs), which influenced the network up to motif levels. The approach adopted allowed to classify MHs into 5 significant motif hubs (S-MHs) and 6 non-significant motif hubs (NS-MHs). The significant motif hubs had a higher HE value and were considered as high-active key regulators; while the non-significant motif hubs had a relatively lower HE value and were considered as low-active key regulators, in network control mechanism. Further, we compared the results of the HE analyses with the topological characterization, after subjecting to the three conditions independently: (i) removing all MHs, (ii) removing only S-MHs, and (iii) removing only NS-MHs from the ARGN. This procedure allowed us to cross-validate the role of 5 S-MHs, NFk-B1, BRCA1, CEBPB, AR, and POU2F1 as the potential key regulators. The changes in HE calculations further showed that the removal of 5 S-MHs could cause perturbation at all levels of the network, a feature not discernible by topological analysis alone. [ABSTRACT FROM AUTHOR]

The breast cancer network constructed from 70 experimentally verified genes is found to follow hierarchical scale free nature with heterogeneous modular organization and diverge leading hubs. The topological parameters (degree distributions, clustering co-efficient, connectivity and centralities) of this network obey fractal rules indicating absence of centrality lethality rule, and efficient communication among the components. From the network theoretical approach, we identified few key regulators out of large number of leading hubs, which are deeply rooted from top to down of the network, serve as backbone of the network, and possible target genes. However, p53, which is one of these key regulators, is found to be in low rank and keep itself at low profile but directly cross-talks with important genes BRCA2 and BRCA3. The popularity of these hubs gets changed in unpredictable way at various levels of organization thus showing disassortive nature. The local community paradigm approach in this network shows strong correlation of nodes in majority of modules/sub-modules (fast communication among nodes) and weak correlation of nodes only in few modules/sub-modules (slow communication among nodes) at various levels of network organization. [ABSTRACT FROM AUTHOR]