Jeong-Rae Kim

Cooperative Activation of PI3K by Ras and Rho Family Small GTPases

Phosphoinositide 3-kinases (PI3Ks) and Ras and Rho family small GTPases are key regulators of cell polarization, motility, and chemotaxis. They influence each others activities by direct and indirect feedback processes that are only partially understood. Here, we show that 21 small GTPase homologs activate PI3K. Using a microscopy-based binding assay, we show that K-Ras, H-Ras, and five homologous Ras family small GTPases function upstream of PI3K by directly binding the PI3K catalytic subunit, p110. In contrast, several Rho family small GTPases activated PI3K by an indirect cooperative positive feedback that required a combination of Rac, CDC42, and RhoG small GTPase activities. Thus, a distributed network of Ras and Rho family small GTPases induces and reinforces PI3K activity, explaining past challenges to elucidate the specific relevance of different small GTPases in regulating PI3K and controlling cell polarization and chemotaxis.

A design principle underlying the synchronization of oscillations in cellular systems.

Biological oscillations are found ubiquitously in cells and are widely variable, with periods varying from milliseconds to months, and scales involving subcellular components to large groups of organisms. Interestingly, independent oscillators from different cells often show synchronization that is not the consequence of an external regulator. What is the underlying design principle of such synchronized oscillations, and can modeling show that the complex consequences arise from simple molecular or other interactions between oscillators? When biological oscillators are coupled with each other, we found that synchronization is induced when they are connected together through a positive feedback loop. Increasing the coupling strength of two independent oscillators shows a threshold beyond which synchronization occurs within a few cycles, and a second threshold where oscillation stops. The positive feedback loop can be composed of either double-positive (PP) or double-negative (NN) interactions between a node of each of the two oscillating networks. The different coupling structures have contrasting characteristics. In particular, PP coupling is advantageous with respect to stability of period and amplitude, when local oscillators are coupled with a short time delay, whereas NN coupling is advantageous for a long time delay. In addition, PP coupling results in more robust synchronized oscillations with respect to amplitude excursions but not period, with applied noise disturbances compared to NN coupling. However, PP coupling can induce a large fluctuation in the amplitude and period of the resulting synchronized oscillation depending on the coupling strength, whereas NN coupling ensures almost constant amplitude and period irrespective of the coupling strength. Intriguingly, we have also observed that artificial evolution of random digital oscillator circuits also follows this design principle. We conclude that a different coupling strategy might have been selected according to different evolutionary requirements.

A hidden incoherent switch regulates RCAN1 in the calcineurin–NFAT signaling network

Regulator of calcineurin 1 (RCAN1) is a key regulator of the calcineurin–NFAT signaling network in organisms ranging from yeast to human, but its functional role is still under debate because different roles of RCAN1 have been suggested under various experimental conditions. To elucidate the mechanisms underlying the RCAN1 regulatory system, we used a systems approach by combining single-cell experimentation with in silico simulations. In particular, we found that the nuclear export of GSK3β, which switches on the facilitative role of RCAN1 in the calcineurin–NFAT signaling pathway, is promoted by PI3K signaling. Based on this, along with integrated information from previous experiments, we developed a mathematical model in which the functional role of RCAN1 changes in a dose-dependent manner: RCAN1 functions as an inhibitor when its levels are low, but as a facilitator when its levels are high. Furthermore, we identified a hidden incoherent regulation switch that mediates this role change, which entails negative regulation through RCAN1 binding to calcineurin and positive regulation through sequential phosphorylation of RCAN1.

The multi-step phosphorelay mechanism of unorthodox two-component systems in E. coli realizes ultrasensitivity to stimuli while maintaining robustness to noises

E. coli has two-component systems composed of histidine kinase proteins and response regulator proteins. For a given extracellular stimulus, a histidine kinase senses the stimulus, autophosphorylates and then passes the phosphates to the cognate response regulators. The histidine kinase in an orthodox two-component system has only one histidine domain where the autophosphorylation occurs, but a histidine kinase in some unusual two-component systems (unorthodox two-component systems) has two histidine domains and one aspartate domain. So, the unorthodox two-component systems have more complex phosphorelay mechanisms than orthodox two-component systems. In general, the two-component systems are required to promptly respond to external stimuli for survival of E. coli. In this respect, the complex multi-step phosphorelay mechanism seems to be disadvantageous, but there are several unorthodox two-component systems in E. coli. In this paper, we investigate the reason why such unorthodox two-component systems are present in E. coli. For this purpose, we have developed simplified mathematical models of both orthodox and unorthodox two-component systems and analyzed their dynamical characteristics through extensive computer simulations. We have finally revealed that the unorthodox two-component systems realize ultrasensitive responses to external stimuli and also more robust responses to noises than the orthodox two-component systems.

Biphasic RLR–IFN-β Response Controls the Balance between Antiviral Immunity and Cell Damage

In RNA virus–infected cells, retinoic acid–inducible gene-I–like receptors (RLRs) sense foreign RNAs and activate signaling cascades to produce IFN-α/β. However, not every infected cell produces IFN-α/β that exhibits cellular heterogeneity in antiviral immune responses. Using the IFN-β–GFP reporter system, we observed bimodal IFN-β production in the uniformly stimulated cell population with intracellular dsRNA. Mathematical simulation proposed the strength of autocrine loop via RLR as one of the contributing factor for biphasic IFN-β expression. Bimodal IFN-β production with intracellular dsRNA was disturbed by blockage of IFN-α/β secretion or by silencing of the IFN-α/β receptor. Amplification of RLRs was critical in the generation of bimodality of IFN-β production, because IFN-βhigh population expressed more RLRs than IFN-βlow population. In addition, bimodality in IFN-β production results in biphasic cellular response against infection, because IFN-βhigh population was more prone to apoptosis than IFN-βlow population. These results suggest that RLR-mediated biphasic cellular response may act to restrict the number of cells expressing IFN-β and undergoing apoptosis in the infected population.

Reduction of complex signaling networks to a representative kernel.

An algorithmic approach enables the simplification of complex signaling networks and identifies potential therapeutic targets. Reducing Complexity The large and complex nature of the biochemical regulatory networks that govern cell behavior provides a major challenge to the systematic analysis of cell signaling. However, most processes that reduce network complexity fail to reproduce the dynamic properties of the original network. Kim et al. describe an algorithmic approach to network reduction and simplification that preserves the dynamics of the network. They applied their approach to several networks in species from bacteria to humans, producing simplified networks called “kernels.” Examination of the genes represented by the kernel nodes provided insight into the evolution of these core network genes. Furthermore, the genes represented by the kernel nodes were enriched in disease-associated genes and drug targets, suggesting that this type of analysis may be therapeutically beneficial. The network of biomolecular interactions that occurs within cells is large and complex. When such a network is analyzed, it can be helpful to reduce the complexity of the network to a “kernel” that maintains the essential regulatory functions for the output under consideration. We developed an algorithm to identify such a kernel and showed that the resultant kernel preserves the network dynamics. Using an integrated network of all of the human signaling pathways retrieved from the KEGG (Kyoto Encyclopedia of Genes and Genomes) database, we identified this network’s kernel and compared the properties of the kernel to those of the original network. We found that the percentage of essential genes to the genes encoding nodes outside of the kernel was about 10%, whereas ~32% of the genes encoding nodes within the kernel were essential. In addition, we found that 95% of the kernel nodes corresponded to Mendelian disease genes and that 93% of synthetic lethal pairs associated with the network were contained in the kernel. Genes corresponding to nodes in the kernel had low evolutionary rates, were ubiquitously expressed in various tissues, and were well conserved between species. Furthermore, kernel genes included many drug targets, suggesting that other kernel nodes may be potential drug targets. Owing to the simplification of the entire network, the efficient modeling of a large-scale signaling network and an understanding of the core structure within a complex framework become possible.

Robustness and evolvability of the human signaling network.

Biological systems are known to be both robust and evolvable to internal and external perturbations, but what causes these apparently contradictory properties? We used Boolean network modeling and attractor landscape analysis to investigate the evolvability and robustness of the human signaling network. Our results show that the human signaling network can be divided into an evolvable core where perturbations change the attractor landscape in state space, and a robust neighbor where perturbations have no effect on the attractor landscape. Using chemical inhibition and overexpression of nodes, we validated that perturbations affect the evolvable core more strongly than the robust neighbor. We also found that the evolvable core has a distinct network structure, which is enriched in feedback loops, and features a higher degree of scale-freeness and longer path lengths connecting the nodes. In addition, the genes with high evolvability scores are associated with evolvability-related properties such as rapid evolvability, low species broadness, and immunity whereas the genes with high robustness scores are associated with robustness-related properties such as slow evolvability, high species broadness, and oncogenes. Intriguingly, US Food and Drug Administration-approved drug targets have high evolvability scores whereas experimental drug targets have high robustness scores.

Spatiotemporal network motif reveals the biological traits of developmental gene regulatory networks in Drosophila melanogaster.

BackgroundNetwork motifs provided a “conceptual tool” for understanding the functional principles of biological networks, but such motifs have primarily been used to consider static network structures. Static networks, however, cannot be used to reveal time- and region-specific traits of biological systems. To overcome this limitation, we proposed the concept of a “spatiotemporal network motif,” a spatiotemporal sequence of network motifs of sub-networks which are active only at specific time points and body parts.ResultsOn the basis of this concept, we analyzed the developmental gene regulatory network of the Drosophila melanogaster embryo. We identified spatiotemporal network motifs and investigated their distribution pattern in time and space. As a result, we found how key developmental processes are temporally and spatially regulated by the gene network. In particular, we found that nested feedback loops appeared frequently throughout the entire developmental process. From mathematical simulations, we found that mutual inhibition in the nested feedback loops contributes to the formation of spatial expression patterns.ConclusionsTaken together, the proposed concept and the simulations can be used to unravel the design principle of developmental gene regulatory networks.

Evolutionary design principles of modules that control cellular differentiation

MOTIVATION Gene regulatory networks (GRNs) govern cellular differentiation processes and enable construction of multicellular organisms from single cells. Although such networks are complex, there must be evolutionary design principles that shape the network to its present form, gaining complexity from simple modules. RESULTS To isolate particular design principles, we have computationally evolved random regulatory networks with a preference to result either in hysteresis (switching threshold depending on current state), or in multistationarity (having multiple steady states), two commonly observed dynamical features of GRNs related to differentiation processes. We have analyzed the resulting evolved networks and compared their structures and characteristics with real GRNs reported from experiments. CONCLUSION We found that the artificially evolved networks have particular topologies and it was notable that these topologies share important features and similarities with the real GRNs, particularly in contrasting properties of positive and negative feedback loops. We conclude that the structures of real GRNs are consistent with selection to favor one or other of the dynamical features of multistationarity or hysteresis. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

The co-regulation mechanism of transcription factors in the human gene regulatory network

The co-regulation of transcription factors (TFs) has been widely observed in various species. Why is such a co-regulation mechanism needed for transcriptional regulation? To answer this question, the following experiments and analyses were performed. First, examination of the human gene regulatory network (GRN) indicated that co-regulation was significantly enriched in the human GRN. Second, mathematical simulation of an artificial regulatory network showed that the co-regulation mechanism was related to the biphasic dose–response patterns of TFs. Third, the relationship between the co-regulation mechanism and the biphasic dose–response pattern was confirmed using microarray experiments examining different time points and different doses of the toxicant tetrachlorodibenzodioxin. Finally, two mathematical models were constructed to mimic highly co-regulated networks (HCNs) and little co-regulated networks (LCNs), and we found that HCNs were more robust to parameter perturbation than LCNs, whereas LCNs were faster in adaptation to environmental changes than HCNs.