Robustness under Functional Constraint: The Genetic Network for Temporal Expression in Drosophila Neurogenesis

Precise temporal coordination of gene expression is crucial for many developmental processes. One central question in developmental biology is how such coordinated expression patterns are robustly controlled. During embryonic development of the Drosophila central nervous system, neural stem cells called neuroblasts express a group of genes in a definite order, which leads to the diversity of cell types. We produced all possible regulatory networks of these genes and examined their expression dynamics numerically. From the analysis, we identified requisite regulations and predicted an unknown factor to reproduce known expression profiles caused by loss-of-function or overexpression of the genes in vivo, as well as in the wild type. Following this, we evaluated the stability of the actual Drosophila network for sequential expression. This network shows the highest robustness against parameter variations and gene expression fluctuations among the possible networks that reproduce the expression profiles. We propose a regulatory module composed of three types of regulations that is responsible for precise sequential expression. This study suggests that the Drosophila network for sequential expression has evolved to generate the robust temporal expression for neuronal specification.     

Dynamic network rewiring determines temporal regulatory functions in Drosophila melanogaster development processes

The identification of network motifs has been widely considered as a significant step towards uncovering the design principles of biomolecular regulatory networks. To date, time‐invariant networks have been considered. However, such approaches cannot be used to reveal time‐specific biological traits due to the dynamic nature of biological systems, and hence may not be applicable to development, where temporal regulation of gene expression is an indispensable characteristic. We propose a concept of a “temporal sequence of network motifs”, a sequence of network motifs in active sub‐networks constructed over time, and investigate significant network motifs in the active temporal sub‐networks of Drosophila melanogaster . Based on this concept, we find a temporal sequence of network motifs which changes according to developmental stages and thereby cannot be identified from the whole static network. Moreover, we show that the temporal sequence of network motifs corresponding to each developmental stage can be used to describe pivotal developmental events.     

Retinal determination genes as targets and possible effectors of extracellular signals

Retinal determination genes are sufficient to specify eyes in ectopic locations, raising the question of how these master regulatory genes define an eye developmental field. Genetic mosaic studies establish that expression of the retinal determination genes eyeless, teashirt, homothorax, eyes absent, sine oculis, and dachshund are each regulated by combinations of Dpp, Hh, N, Wg, and Ras signals in Drosophila. Dpp and Hh control eyeless, teashirt, sine oculis, and dachshund expression, Dpp and Ras control homothorax, and all the signaling pathways affect eyes absent expression. These results suggest that eye-specific development uses retinal determination gene expression to relay positional information to eye target genes, because the distinct, overlapping patterns of retinal determination gene expression reflect the activities of the extracellular signaling pathways.     

Patterning the dorsal–ventral axis of the wasp Nasonia vitripennis

Regulatory networks composed of interacting genes are responsible for pattern formation and cell type specification in a wide variety of developmental contexts. Evolution must act on these regulatory networks in order to change the proportions, distribution, and characteristics of specified cells. Thus, understanding how these networks operate in homologous systems across multiple levels of phylogenetic divergence is critical for understanding the evolution of developmental systems. Among the most thoroughly characterized regulatory networks is the dorsal–ventral patterning system of the fly Drosophila melanogaster. Due to the thorough understanding of this system, it is an ideal starting point for comparative analyses. Here we report an analysis of the DV patterning system of the wasp, Nasonia vitripennis. This wasp undergoes a mode of long germ embryogenesis that is superficially nearly identical to that of Drosophila, but one that was likely independently derived. We have found that while the expression of genes just prior to the onset of gastrulation is almost identical in Nasonia and Drosophila, both the upstream network responsible for generating this pattern, and the downstream morphogenetic movements that it sets in motion, are significantly diverged. From this we conclude that many network structures are available to evolution to achieve particular developmental ends.     

Molecular control of seasonal flowering in rice,arabidopsis and temperate cereals

Background

Rice (Oryza sativa) and Arabidopsis thaliana have been widely used as model systems to understand how plants control flowering time in response to photoperiod and cold exposure. Extensive research has resulted in the isolation of several regulatory genes involved in flowering and for them to be organized into a molecular network responsive to environmental cues. When plants are exposed to favourable conditions, the network activates expression of florigenic proteins that are transported to the shoot apical meristem where they drive developmental reprogramming of a population of meristematic cells. Several regulatory factors are evolutionarily conserved between rice and arabidopsis. However, other pathways have evolved independently and confer specific characteristics to flowering responses.

Scope

This review summarizes recent knowledge on the molecular mechanisms regulating daylength perception and flowering time control in arabidopsis and rice. Similarities and differences are discussed between the regulatory networks of the two species and they are compared with the regulatory networks of temperate cereals, which are evolutionarily more similar to rice but have evolved in regions where exposure to low temperatures is crucial to confer competence to flower. Finally, the role of flowering time genes in expansion of rice cultivation to Northern latitudes is discussed.

Conclusions

Understanding the mechanisms involved in photoperiodic flowering and comparing the regulatory networks of dicots and monocots has revealed how plants respond to environmental cues and adapt to seasonal changes. The molecular architecture of such regulation shows striking similarities across diverse species. However, integration of specific pathways on a basal scheme is essential for adaptation to different environments. Artificial manipulation of flowering time by means of natural genetic resources is essential for expanding the cultivation of cereals across different environments.     

Predicting Phenotypic Diversity and the Underlying Quantitative Molecular Transitions

During development, signaling networks control the formation of multicellular patterns. To what extent quantitative fluctuations in these complex networks may affect multicellular phenotype remains unclear. Here, we describe a computational approach to predict and analyze the phenotypic diversity that is accessible to a developmental signaling network. Applying this framework to vulval development in C. elegans, we demonstrate that quantitative changes in the regulatory network can render ~500 multicellular phenotypes. This phenotypic capacity is an order-of-magnitude below the theoretical upper limit for this system but yet is large enough to demonstrate that the system is not restricted to a select few outcomes. Using metrics to gauge the robustness of these phenotypes to parameter perturbations, we identify a select subset of novel phenotypes that are the most promising for experimental validation. In addition, our model calculations provide a layout of these phenotypes in network parameter space. Analyzing this landscape of multicellular phenotypes yielded two significant insights. First, we show that experimentally well-established mutant phenotypes may be rendered using non-canonical network perturbations. Second, we show that the predicted multicellular patterns include not only those observed in C. elegans, but also those occurring exclusively in other species of the Caenorhabditis genus. This result demonstrates that quantitative diversification of a common regulatory network is indeed demonstrably sufficient to generate the phenotypic differences observed across three major species within the Caenorhabditis genus. Using our computational framework, we systematically identify the quantitative changes that may have occurred in the regulatory network during the evolution of these species. Our model predictions show that significant phenotypic diversity may be sampled through quantitative variations in the regulatory network without overhauling the core network architecture. Furthermore, by comparing the predicted landscape of phenotypes to multicellular patterns that have been experimentally observed across multiple species, we systematically trace the quantitative regulatory changes that may have occurred during the evolution of the Caenorhabditis genus.     

An in silico analytical study of lung cancer and smokers datasets from gene expression omnibus (GEO) for prediction of differentially expressed genes

Smoking is the leading cause of lung cancer development and several genes have been identified as potential biomarker for lungs cancer. Contributing to the present scientific knowledge of biomarkers for lung cancer two different data sets, i.e. GDS3257 and GDS3054 were downloaded from NCBI׳s GEO database and normalized by RMA and GRMA packages (Bioconductor). Diffrentially expressed genes were extracted by using and were R (3.1.2); DAVID online tool was used for gene annotation and GENE MANIA tool was used for construction of gene regulatory network. Nine smoking independent gene were found whereas average expressions of those genes were almost similar in both the datasets. Five genes among them were found to be associated with cancer subtypes. Thirty smoking specific genes were identified; among those genes eight were associated with cancer sub types. GPR110, IL1RN and HSP90AA1 were found directly associated with lung cancer. SEMA6A differentially expresses in only non-smoking lung cancer samples. FLG is differentially expressed smoking specific gene and is related to onset of various cancer subtypes. Functional annotation and network analysis revealed that FLG participates in various epidermal tissue developmental processes and is co-expressed with other genes. Lung tissues are epidermal tissues and thus it suggests that alteration in FLG may cause lung cancer. We conclude that smoking alters expression of several genes and associated biological pathways during development of lung cancers.