Mutual interaction in network motifs robustly sharpens gene expression in developmental processes

Fem-1 Gene of Chinese White Pine Beetle (Dendroctonus armandi): Function and Response to Environmental Treatments

Dendroctonus armandi (Tsai and Li) (Coleoptera: Curculionidae: Scolytinae) is regarded as the most destructive forest pest in the Qinling and Bashan Mountains of China. The sex determination of Dendroctonus armandi plays a significant role in the reproduction of its population. In recent years, the role of the fem-1 gene in sex determination in other insects has been reported. However, the function and expression of the fem-1 gene in Dendroctonus armandi remain uncertain. In this study, three fem-1 genes were cloned and characterized. These were named Dafem-1A, Dafem-1B, and Dafem-1C, respectively. The expression levels of these three Dafem-1 genes vary at different stages of development and between the sexes. In response to different environmental treatments, including temperature, nutrients, terpenoids, and feeding duration, significant differences were observed between the three Dafem-1 genes at different developmental stages and between males and females. Furthermore, injection of double-stranded RNA (dsRNA) targeting the expressions of the Dafem-1A, Dafem-1B, and Dafem-1C genes resulted in increased mortality, deformity, and decreased emergence rates, as well as an imbalance in the sex ratio. Following the interference with Dafem-1A and Dafem-1C, no notable difference was observed in the expression of the Dafem-1B gene. Similarly, after the interference with the Dafem-1B gene, no significant difference was evident in the expression levels of the Dafem-1A and Dafem-1C genes. However, the interference of either the Dafem-1A or Dafem-1C gene results in the downregulation of the other gene. The aforementioned results demonstrate that the Dafem-1A, Dafem-1B, and Dafem-1C genes play a pivotal role in the regulation of life development and sex determination. Furthermore, it can be concluded that external factors such as temperature, nutrition, terpenoids, and feeding have a significant impact on the expression levels of the Dafem-1A, Dafem-1B, and Dafem-1C genes. This provides a crucial theoretical foundation for further elucidating the sex determination mechanism of Dendroctonus armandi.

Gene regulatory network and its constituent transcription factors that control nitrogen-deficiency responses in rice

Increase in the nitrogen (N)-use efficiency and optimization of N response in crop species are urgently needed. Although transcription factor-based genetic engineering is a promising approach for achieving these goals, transcription factors that play key roles in the response to N deficiency have not been studied extensively. Here, we performed RNA-seq analysis of root samples of 20 Asian rice (Oryza sativa) accessions with differential nutrient uptake. Data obtained from plants exposed to N-replete and N-deficient conditions were subjected to coexpression analysis and machine learning-based pathway inference to dissect the gene regulatory network required for the response to N deficiency. Four transcription factors, including members of the G2-like and bZIP families, were predicted to function as key regulators of gene transcription within the network in response to N deficiency. Cotransfection assays validated inferred novel regulatory pathways, and further analyses using genome-edited knockout lines suggested that these transcription factors are important for N-deficiency responses in planta. Many of the N deficiency-responsive genes, including those encoding key regulators within the network, were coordinately regulated by transcription factors belonging to different families. Transcription factors identified in this study could be valuable for the modification of N response and metabolism.

Modularity, criticality, and evolvability of a developmental gene regulatory network

The existence of discrete phenotypic traits suggests that the complex regulatory processes which produce them are functionally modular. These processes are usually represented by networks. Only modular networks can be partitioned into intelligible subcircuits able to evolve relatively independently. Traditionally, functional modularity is approximated by detection of modularity in network structure. However, the correlation between structure and function is loose. Many regulatory networks exhibit modular behaviour without structural modularity. Here we partition an experimentally tractable regulatory network-the gap gene system of dipteran insects-using an alternative approach. We show that this system, although not structurally modular, is composed of dynamical modules driving different aspects of whole-network behaviour. All these subcircuits share the same regulatory structure, but differ in components and sensitivity to regulatory interactions. Some subcircuits are in a state of criticality, while others are not, which explains the observed differential evolvability of the various expression features in the system.

Making models match measurements: model optimization for morphogen patterning networks

Mathematical modeling of developmental signaling networks has played an increasingly important role in the identification of regulatory mechanisms by providing a sandbox for hypothesis testing and experiment design. Whether these models consist of an equation with a few parameters or dozens of equations with hundreds of parameters, a prerequisite to model-based discovery is to bring simulated behavior into agreement with observed data via parameter estimation. These parameters provide insight into the system (e.g., enzymatic rate constants describe enzyme properties). Depending on the nature of the model fit desired - from qualitative (relative spatial positions of phosphorylation) to quantitative (exact agreement of spatial position and concentration of gene products) - different measures of data-model mismatch are used to estimate different parameter values, which contain different levels of usable information and/or uncertainty. To facilitate the adoption of modeling as a tool for discovery alongside other tools such as genetics, immunostaining, and biochemistry, careful consideration needs to be given to how well a model fits the available data, what the optimized parameter values mean in a biological context, and how the uncertainty in model parameters and predictions plays into experiment design. The core discussion herein pertains to the quantification of model-to-data agreement, which constitutes the first measure of a model's performance and future utility to the problem at hand. Integration of this experimental data and the appropriate choice of objective measures of data-model agreement will continue to drive modeling forward as a tool that contributes to experimental discovery. The Drosophila melanogaster gap gene system, in which model parameters are optimized against in situ immunofluorescence intensities, demonstrates the importance of error quantification, which is applicable to a wide array of developmental modeling studies.

Design principles of stripe-forming motifs: the role of positive feedback

Interpreting a morphogen gradient into a single stripe of gene-expression is a fundamental unit of patterning in early embryogenesis. From both experimental data and computational studies the feed-forward motifs stand out as minimal networks capable of this patterning function. Positive feedback within gene networks has been hypothesised to enhance the sharpness and precision of gene-expression borders, however a systematic analysis has not yet been reported. Here we set out to assess this hypothesis, and find an unexpected result. The addition of positive-feedback can have different effects on two different designs of feed-forward motif– it increases the parametric robustness of one design, while being neutral or detrimental to the other. These results shed light on the abundance of the former motif and especially of mutual-inhibition positive feedback in developmental networks.

Dynamics of regulatory networks in the developing mouse retina

Understanding gene regulation is crucial to dissect the molecular basis of human development and disease. Previous studies on transcription regulatory networks often focused on their static properties. Here we used retinal development as a model system to investigate the dynamics of regulatory networks that are comprised of transcription factors, microRNAs and other protein-coding genes. We found that the active sub-networks are topologically different at early and late stages of retinal development. At early stages, the active sub-networks tend to be highly connected, while at late stages, the active sub-networks are more organized in modular structures. Interestingly, network motif usage at early and late stages is also distinct. For example, network motifs containing reciprocal feedback regulatory relationships between two regulators are overrepresented in early developmental stages. Additionally, our analysis of regulatory network dynamics revealed a natural turning point at which the regulatory network undergoes drastic topological changes. Taken together, this work demonstrates that adding a dynamic dimension to network analysis can provide new insights into retinal development, and we suggest the same approach would likely be useful for the analysis of other developing tissues.

Mutual repression enhances the steepness and precision of gene expression boundaries

Embryonic development is driven by spatial patterns of gene expression that determine the fate of each cell in the embryo. While gene expression is often highly erratic, embryonic development is usually exceedingly precise. In particular, gene expression boundaries are robust not only against intra-embryonic fluctuations such as noise in gene expression and protein diffusion, but also against embryo-to-embryo variations in the morphogen gradients, which provide positional information to the differentiating cells. How development is robust against intra- and inter-embryonic variations is not understood. A common motif in the gene regulation networks that control embryonic development is mutual repression between pairs of genes. To assess the role of mutual repression in the robust formation of gene expression patterns, we have performed large-scale stochastic simulations of a minimal model of two mutually repressing gap genes in Drosophila, hunchback (hb) and knirps (kni). Our model includes not only mutual repression between hb and kni, but also the stochastic and cooperative activation of hb by the anterior morphogen Bicoid (Bcd) and of kni by the posterior morphogen Caudal (Cad), as well as the diffusion of Hb and Kni between neighboring nuclei. Our analysis reveals that mutual repression can markedly increase the steepness and precision of the gap gene expression boundaries. In contrast to other mechanisms such as spatial averaging and cooperative gene activation, mutual repression thus allows for gene-expression boundaries that are both steep and precise. Moreover, mutual repression dramatically enhances their robustness against embryo-to-embryo variations in the morphogen levels. Finally, our simulations reveal that diffusion of the gap proteins plays a critical role not only in reducing the width of the gap gene expression boundaries via the mechanism of spatial averaging, but also in repairing patterning errors that could arise because of the bistability induced by mutual repression.

The Drosophila gap gene network is composed of two parallel toggle switches

Drosophila “gap” genes provide the first response to maternal gradients in the early fly embryo. Gap genes are expressed in a series of broad bands across the embryo during first hours of development. The gene network controlling the gap gene expression patterns includes inputs from maternal gradients and mutual repression between the gap genes themselves. In this study we propose a modular design for the gap gene network, involving two relatively independent network domains. The core of each network domain includes a toggle switch corresponding to a pair of mutually repressive gap genes, operated in space by maternal inputs. The toggle switches present in the gap network are evocative of the phage lambda switch, but they are operated positionally (in space) by the maternal gradients, so the synthesis rates for the competing components change along the embryo anterior-posterior axis. Dynamic model, constructed based on the proposed principle, with elements of fractional site occupancy, required 5–7 parameters to fit quantitative spatial expression data for gap gradients. The identified model solutions (parameter combinations) reproduced major dynamic features of the gap gradient system and explained gap expression in a variety of segmentation mutants.

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.

Cell fate specification is of key importance in the development of multicellular organisms. To specify various cell fates correctly, genetic networks precisely coordinate spatial and temporal gene expression patterns during various developmental stages. One central question in developmental biology is to elucidate the relationship between the pattern formation and the network architecture. During embryonic development of the Drosophila central nervous system, the neural stem cells express a group of genes in a definite order, which is responsible for the diversity of neural cells. To elucidate the underlying mechanism of the process, we analyzed the structure and dynamics of the genetic network for the temporal changes occurring in the Drosophila neural stem cells. Searching all the possible regulatory networks of these genes using a computer program, we detected the requisite regulations that reproduce observed gene expression profiles. By comparing the stability of the dynamics among the functional networks, we uncovered the robust nature of the actual Drosophila network against environmental and intrinsic fluctuations. These results indicate that the genetic network for sequential expression has evolved to be robust under functional constraints. Our study proposes regulatory modules that are responsible for the precise sequential expressions, which might exist in genetic networks for other temporal patterning processes.

Ephrin-B1 reverse signaling controls a posttranscriptional feedback mechanism via miR-124

Eph receptors and ephrins exhibit complex and highly dynamic expression patterns during embryonic development. In addition, changes in their expression levels are often associated with pathological situations in adults. Yet, little is known about the mechanisms regulating their expression. Here we report that the expression of ephrin-B1 is controlled by a feedback loop involving posttranscriptional regulatory mechanisms. We observed that the EfnB1 3' untranslated region (3'-UTR) confers instability to mRNA transcripts, and we identified miR-124 as a posttranscriptional repressor of EfnB1 expression. Furthermore, we showed that miR-124 is itself regulated by ephrin-B1 reverse signaling, thus revealing the existence of a mutually repressive interaction between ephrin-B1 and this microRNA (miRNA). Lastly, we demonstrated the relevance of this mutual inhibition for neuronal differentiation. Our results suggest that miRNAs could be important effectors of Eph/ephrin signaling to refine domains of expression and to regulate function.

Network evolution of body plans

One of the major goals in evolutionary developmental biology is to understand the relationship between gene regulatory networks and the diverse morphologies and their functionalities. Are the diversities solely triggered by random events, or are they inevitable outcomes of an interplay between evolving gene networks and natural selection? Segmentation in arthropod embryogenesis represents a well-known example of body plan diversity. Striped patterns of gene expression that lead to the future body segments appear simultaneously or sequentially in long and short germ-band development, respectively. Moreover, a combination of both is found in intermediate germ-band development. Regulatory genes relevant for stripe formation are evolutionarily conserved among arthropods, therefore the differences in the observed traits are thought to have originated from how the genes are wired. To reveal the basic differences in the network structure, we have numerically evolved hundreds of gene regulatory networks that produce striped patterns of gene expression. By analyzing the topologies of the generated networks, we show that the characteristics of stripe formation in long and short germ-band development are determined by Feed-Forward Loops (FFLs) and negative Feed-Back Loops (FBLs) respectively, and those of intermediate germ-band development are determined by the interconnections between FFL and negative FBL. Network architectures, gene expression patterns and knockout responses exhibited by the artificially evolved networks agree with those reported in the fly Drosophila melanogaster and the beetle Tribolium castaneum. For other arthropod species, principal network architectures that remain largely unknown are predicted. Our results suggest that the emergence of the three modes of body segmentation in arthropods is an inherent property of the evolving networks.