Girdling behavior of the longhorn beetle modulates the host plant to enhance larval performance.

BACKGROUND: Preingestive behavioral modulations of herbivorous insects on the host plant are abundant over insect taxa. Those behaviors are suspected to have functions such as deactivation of host plant defenses, nutrient accumulation, or modulating plant-mediated herbivore interactions. To understand the functional consequence of behavioral modulation of insect herbivore, we studied the girdling behavior of Phytoecia rufiventris Gautier (Lamiinae; Cerambycidae) on its host plant Erigeron annuus L. (Asteraceae) that is performed before endophytic oviposition in the stem. RESULTS: The girdling behavior significantly increased the larval performance in both field monitoring and lab experiment. The upper part of the girdled stem exhibited lack of jasmonic acid induction upon larval attack, lowered protease inhibitor activity, and accumulated sugars and amino acids in compared to non-girdled stem. The girdling behavior had no effect on the larval performance of a non-girdling longhorn beetle Agapanthia amurensis, which also feeds on the stem of E. annuus during larval phase. However, the girdling behavior decreased the preference of A. amurensis females for oviposition, which enabled P. rufiventris larvae to avoid competition with A. amurensis larvae. CONCLUSIONS: In conclusion, the girdling behavior modulates plant physiology and morphology to provide a modulated food source for larva and hide it from the competitor. Our study implies that the insect behavior modulations can have multiple functions, providing insights into adaptation of insect behavior in context of plant-herbivore interaction.

Three consecutive cytosolic glycolysis enzymes modulate autophagic flux.

Autophagy serves as an important recycling route for the growth and survival of eukaryotic organisms in nutrient-deficient conditions. Since starvation induces massive changes in the metabolic flux that are coordinated by key metabolic enzymes, specific processing steps of autophagy may be linked with metabolic flux-monitoring enzymes. We attempted to identify carbon metabolic genes that modulate autophagy using VIGS screening of 45 glycolysis- and Calvin-Benson cycle-related genes in Arabidopsis (Arabidopsis thaliana). Here, we report that three consecutive triose-phosphate-processing enzymes involved in cytosolic glycolysis, triose-phosphate-isomerase (TPI), glyceraldehyde-3-phosphate dehydrogenase (GAPC), and phosphoglycerate kinase (PGK), designated TGP, negatively regulate autophagy. Depletion of TGP enzymes causes spontaneous autophagy induction and increases AUTOPHAGY-RELATED 1 (ATG1) kinase activity. TGP enzymes interact with ATG101, a regulatory component of the ATG1 kinase complex. Spontaneous autophagy induction and abnormal growth under insufficient sugar in TGP mutants are suppressed by crossing with the atg101 mutant. Considering that triose-phosphates are photosynthates transported to the cytosol from active chloroplasts, the TGP enzymes would be strategically positioned to monitor the flow of photosynthetic sugars and modulate autophagy accordingly. Collectively, these results suggest that TGP enzymes negatively control autophagy acting upstream of the ATG1 complex, which is critical for seedling development.

Evaluating a therapeutic window for precision medicine by integrating genomic profiles and p53 network dynamics.

The response variation to anti-cancer drugs originates from complex intracellular network dynamics of cancer. Such dynamic networks present challenges to determining optimal drug targets and stratifying cancer patients for precision medicine, although several cancer genome studies provided insights into the molecular characteristics of cancer. Here, we introduce a network dynamics-based approach based on attractor landscape analysis to evaluate the therapeutic window of a drug from cancer signaling networks combined with genomic profiles. This approach allows for effective screening of drug targets to explore potential target combinations for enhancing the therapeutic window of drug responses. We also effectively stratify patients into desired/undesired response groups using critical genomic determinants, which are network-specific origins of variability to drug response, and their dominance relationship. Our methods provide a viable and quantitative framework to connect genotype information to the phenotypes of drug response with regard to network dynamics determining the therapeutic window.

Realizing Cancer Precision Medicine by Integrating Systems Biology and Nanomaterial Engineering.

Many clinical trials for cancer precision medicine have yielded unsatisfactory results due to challenges such as drug resistance and low efficacy. Drug resistance is often caused by the complex compensatory regulation within the biomolecular network in a cancer cell. Recently, systems biological studies have modeled and simulated such complex networks to unravel the hidden mechanisms of drug resistance and identify promising new drug targets or combinatorial or sequential treatments for overcoming resistance to anticancer drugs. However, many of the identified targets or treatments present major difficulties for drug development and clinical application. Nanocarriers represent a path forward for developing therapies with these "undruggable" targets or those that require precise combinatorial or sequential application, for which conventional drug delivery mechanisms are unsuitable. Conversely, a challenge in nanomedicine has been low efficacy due to heterogeneity of cancers in patients. This problem can also be resolved through systems biological approaches by identifying personalized targets for individual patients or promoting the drug responses. Therefore, integration of systems biology and nanomaterial engineering will enable the clinical application of cancer precision medicine to overcome both drug resistance of conventional treatments and low efficacy of nanomedicine due to patient heterogeneity.

Automatic segmentation of cardiac structures for breast cancer radiotherapy.

BACKGROUND AND PURPOSE: We developed an automatic method to segment cardiac substructures given a radiotherapy planning CT images to support epidemiological studies or clinical trials looking at cardiac disease endpoints after radiotherapy. MATERIAL AND METHODS: We used a most-similar atlas selection algorithm and 3D deformation combined with 30 detailed cardiac atlases. We cross-validated our method within the atlas library by evaluating geometric comparison metrics and by comparing cardiac doses for simulated breast radiotherapy between manual and automatic contours. We analyzed the impact of the number of cardiac atlas in the library and the use of manual guide points on the performance of our method. RESULTS: The Dice Similarity Coefficients from the cross-validation reached up to 97% (whole heart) and 80% (chambers). The Average Surface Distance for the coronary arteries was less than 10.3 mm on average, with the best agreement (7.3 mm) in the left anterior descending artery (LAD). The dose comparison for simulated breast radiotherapy showed differences less than 0.06 Gy for the whole heart and atria, and 0.3 Gy for the ventricles. For the coronary arteries, the dose differences were 2.3 Gy (LAD) and 0.3 Gy (other arteries). The sensitivity analysis showed no notable improvement beyond ten atlases and the manual guide points does not significantly improve performance. CONCLUSION: We developed an automated method to contour cardiac substructures for radiotherapy CTs. When combined with accurate dose calculation techniques, our method should be useful for cardiac dose reconstruction of a large number of patients in epidemiological studies or clinical trials.

Control of Crystallinity in PbPc:C60 Blend Film and Application for Inverted Near-Infrared Organic Photodetector.

Inverted near-infrared (NIR) organic photodetectors (OPDs) are required to combine the OPDs with an n-channel silicon-based integrated circuit. NIR absorption in the 930-960 nm range is important because the intensity of solar irradiation is low in this wavelength regime. Here, we controlled the crystallinity of lead(II) phthalocyanine (PbPc) in a PbPc:C60 blend film to obtain NIR absorption. To form a triclinic phase responsible for NIR light absorption, a substrate was heated during fabrication and C60 was used as a templating layer, as well as an electron extraction layer, for an inverted structure. NIR absorption near 950 nm was enhanced, and the structural properties of the film changed dramatically. The OPD with enhanced NIR absorption exhibited a responsivity of 244 mA/W and an external quantum efficiency of 31.1% at a reverse bias of -3 V and 970 nm. The OPD detectivity also increased to 9.01 × 1012 and 1.36 × 1011 cm Hz1/2/W under a zero bias and a reverse bias of -3 V, respectively.

Network dynamics-based cancer panel stratification for systemic prediction of anticancer drug response.

Cancer is a complex disease involving multiple genomic alterations that disrupt the dynamic response of signaling networks. The heterogeneous nature of cancer, which results in highly variable drug response, is a major obstacle to developing effective cancer therapy. Previous studies of cancer therapeutic response mostly focus on static analysis of genome-wide alterations, thus they are unable to unravel the dynamic, network-specific origin of variation. Here we present a network dynamics-based approach to integrate cancer genomics with dynamics of biological network for drug response prediction and design of drug combination. We select the p53 network as an example and analyze its cancer-specific state transition dynamics under distinct anticancer drug treatments by attractor landscape analysis. Our results not only enable stratification of cancer into distinct drug response groups, but also reveal network-specific drug targets that maximize p53 network-mediated cell death, providing a basis to design combinatorial therapeutic strategies for distinct cancer genomic subtypes.

Justicia procumbens Extract (DW2008) Selectively Suppresses Th2 Cytokines in Splenocytes and Ameliorates Ovalbumin-Induced Airway Inflammation in a Mouse Model of Asthma.

DW2008 is an anhydrous ethanol extract of Justicia procumbens produced by Dong-Wha Pharmaceutical, Inc., Co. as a candidate anti-asthmatic drug. In this study, DW2008 selectively reduced T helper 2 (Th2) cytokines in mouse splenocytes and ameliorated ovalbumin-induced airway inflammation by downregulating pulmonary infiltration of differential inflammatory cells and Th2 cytokines more than a decoction or ethanol extract of J. procumbens did in a mouse asthma model. DW2008 also significantly inhibited airway hyperresponsiveness and reduced the thickness of the airway epithelium. HPLC analysis showed that the major peaks (justicidin A and B) of DW2008 were higher than those of the other extracts. Justicidin A and B significantly suppressed Th2 cytokine levels in mouse spleen cells and exhibited a protective effect in ovalbumin-induced airway inflammation. Our findings indicate that DW2008 effectively inhibits allergic airway inflammatory reactions and airway hyperresponsiveness in a mouse model of asthma, suggesting its potential as an anti-asthmatic agent.

Enhancement of the Fill Factor through an Increase of the Crystallinity in Fullerene-Based Small-Molecule Organic Photovoltaic Cells.

We report that the crystallinity of C70 is improved significantly if CuI is used as a templating layer, leading to remarkable enhancement of hole mobilities from 8.32 × 10(-6) to 3.26 × 10(-5) cm(2)/(V s). As a result, the use of the templating layer in C70-based solar cells with low donor concentration resulted in significant improvement of the fill factor from 0.51 to 0.57 and the power conversion efficiency from 5.56% to 6.23% under simulated AM 1.5G, 1 sun irradiation. This result demonstrates that the CuI templating layer is effective at improving the crystallinity of the fullerene derivatives as well as the donor materials.

Multilayer epitaxial growth of lead phthalocyanine and C(70) using CuBr as a templating layer for enhancing the efficiency of organic photovoltaic cells.

The molecular orientation and crystallinity of donor and acceptor molecules are important for high-efficiency organic photovoltaic cells (OPVs) because they significantly influence both the absorption of light and charge-transport characteristics. We report that the templating effect extends to multilayers to increase the crystallinity and to modify the orientation of the crystals of lead phthalocyanine (PbPc) and C70 layers at the same time by adopting CuBr as a new templating layer on indium tin oxide (ITO). The formation of a monoclinic phase with a preferred orientation of (320) for PbPc and a fcc phase with a preferred orientation of (220) for C70 on the PbPc layer is revealed by X-ray diffraction (XRD) patterns. The multilayer epitaxy results in an increase of the exciton diffusion lengths from 5.6 to 8.8 nm for PbPc and from 6.9 to 13.8 nm for C70 to enhance the power conversion efficiency (PCE) of the planar heterojunction OPVs composed of PbPc and C70 from 1.4 to 2.3%. The quasi-epitaxy model is proposed to explain the multilayer epitaxy.

Attractor landscape analysis reveals feedback loops in the p53 network that control the cellular response to DNA damage.

The protein p53 functions as a tumor suppressor and can trigger either cell cycle arrest or apoptosis in response to DNA damage. We used Boolean network modeling and attractor landscape analysis to analyze the state transition dynamics of a simplified p53 network for which particular combinations of activation states of the molecules corresponded to specific cellular outcomes. Our results identified five critical interactions in the network that determined the cellular response to DNA damage, and simulations lacking any of these interactions produced states associated with sustained p53 activity, which corresponded to a cell death response. Attractor landscape analysis of the cellular response to DNA damage of the breast cancer cell line MCF7 and the effect of the Mdm2 (murine double minute 2) inhibitor nutlin-3 indicated that nutlin-3 would exhibit limited efficacy in triggering cell death, because the cell death state was not induced to a large extent by simulations with nutlin-3 and instead produced a state consistent with oscillatory p53 dynamics and cell cycle arrest. Attractor landscape analysis also suggested that combining nutlin-3 with inhibition of Wip1 would synergize to stimulate a sustained increase in p53 activity and promote p53-mediated cell death. We validated this synergistic effect in stimulating p53 activity and triggering cell death with single-cell imaging of a fluorescent p53 reporter in MCF7 cells. Thus, attractor landscape analysis of p53 network dynamics and its regulation can identify potential therapeutic strategies for treating cancer.

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.

A system-level investigation into the cellular toxic response mechanism mediated by AhR signal transduction pathway.

MOTIVATION: Viewing a cellular system as a collection of interacting parts can lead to new insights into the complex cellular behavior. In this study, we have investigated aryl hydrocarbon receptor (AhR) signal transduction pathway from such a system-level perspective. AhR detects various xenobiotics, such as drugs or endocrine disruptors (e.g. dioxin), and mediates transcriptional regulation of target genes such as those in the cytochrome P450 (CYP450) family. On binding with 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), however, AhR becomes abnormally activated and conveys toxic effects on cells. Despite many related studies on the TCDD-mediated toxicity, quantitative system-level understanding of how TCDD-mediated toxicity generates various toxic responses is still lacking. RESULTS: Here, we present a manually curated TCDD-mediated AhR signaling pathway including crosstalks with the hypoxia pathway that copes with oxygen deficiency and the p53 pathway that induces a DNA damage response. Based on the integrated pathway, we have constructed a mathematical model and validated it through quantitative experiments. Using the mathematical model, we have investigated: (i) TCDD dose-dependent effects on AhR target genes; (ii) the crosstalk effect between AhR and hypoxia signals; and (iii) p53 inhibition effect of TCDD-liganded AhR. Our results show that cellular intake of TCDD induces AhR signaling pathway to be abnormally up-regulated and thereby interrupts other signaling pathways. Interruption of hypoxia and p53 pathways, in turn, can incur various hazardous effects on cells. Taken together, our study provides a system-level understanding of how AhR signal mediates various TCDD-induced toxicities under the presence of hypoxia and/or DNA damage in cells.

Identifying the target mRNAs of microRNAs in colorectal cancer.

MicroRNAs (miRNAs) play an important role in gene regulatory networks by inhibiting the expression of target mRNAs. There is a growing interest in identifying the relationship between miRNAs and their target mRNAs. Various experimental studies have been carried out to discover miRNAs involved in cancer and to identify their target genes. At the same time, a large volume of miRNA and mRNA expression profiles have become available owing to the development of high-throughput measurement technologies. So, there is now a pressing need to develop a computational method by which we can identify the target mRNAs of given miRNAs from such massive expression data sets. In this respect, we propose an effective linear model based identification method to unravel the relationship between miRNAs and their target mRNAs in colorectal cancer by using microarray expression profiles and sequence data.