Patterns of Metabolite Changes Identified from Large-Scale Gene Perturbations in Arabidopsis Using a Genome-Scale Metabolic Network

Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis (Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes

Multi-agent modeling of the South Korean avian influenza epidemic

<p>Abstract</p> <p>Background</p> <p>Several highly pathogenic avian influenza (AI) outbreaks have been reported over the past decade. South Korea recently faced AI outbreaks whose economic impact was estimated to be 6.3 billion dollars, equivalent to nearly 50% of the profit generated by the poultry-related industries in 2008. In addition, AI is threatening to cause a human pandemic of potentially devastating proportions. Several studies show that a stochastic simulation model can be used to plan an efficient containment strategy on an emerging influenza. Efficient control of AI outbreaks based on such simulation studies could be an important strategy in minimizing its adverse economic and public health impacts.</p> <p>Methods</p> <p>We constructed a spatio-temporal multi-agent model of chickens and ducks in poultry farms in South Korea. The spatial domain, comprised of 76 (37.5 km × 37.5 km) unit squares, approximated the size and scale of South Korea. In this spatial domain, we introduced 3,039 poultry flocks (corresponding to 2,231 flocks of chickens and 808 flocks of ducks) whose spatial distribution was proportional to the number of birds in each province. The model parameterizes the properties and dynamic behaviors of birds in poultry farms and quarantine plans and included infection probability, incubation period, interactions among birds, and quarantine region.</p> <p>Results</p> <p>We conducted sensitivity analysis for the different parameters in the model. Our study shows that the quarantine plan with well-chosen values of parameters is critical for minimize loss of poultry flocks in an AI outbreak. Specifically, the aggressive culling plan of infected poultry farms over 18.75 km radius range is unlikely to be effective, resulting in higher fractions of unnecessarily culled poultry flocks and the weak culling plan is also unlikely to be effective, resulting in higher fractions of infected poultry flocks.</p> <p>Conclusions</p> <p>Our results show that a prepared response with targeted quarantine protocols would have a high probability of containing the disease. The containment plan with an aggressive culling plan is not necessarily efficient, causing a higher fraction of unnecessarily culled poultry farms. Instead, it is necessary to balance culling with other important factors involved in AI spreading. Better estimations for the containment of AI spreading with this model offer the potential to reduce the loss of poultry and minimize economic impact on the poultry industry.</p

Damage isolation via strategic self-destruction: A case study in 2D random networks

We study the nucleation, spreading, and control of irreversible diffusive damage in a 2D fixed-radius random network. The control is achieved via strategic self-destruction. Our studies suggest that rapidly activated aggressive and encompassing self-destruction may provide optimum long-term survival of the network. When the damaged area is sufficiently small, strategic self-destruction may be too dependent on local geometry and the details of the dynamics and hence non-trivial to estimate. Our results reveal broad insights into how it may be possible to combat and control the spreading of problematic effects across fixed-radius random networks

Patterns of Metabolite Changes Identified from Large-Scale Gene Perturbations in Arabidopsis Using a Genome-Scale Metabolic Network

Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis (Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes.This article is published as Kim, Taehyong, Kate Dreher, Ricardo Nilo-Poyanco, Insuk Lee, Oliver Fiehn, Bernd Markus Lange, Basil J. Nikolau et al. "Patterns of metabolite changes identified from large-scale gene perturbations in Arabidopsis using a genome-scale metabolic network." Plant physiology 167, no. 4 (2015): 1685-1698. doi: 10.1104/pp.114.252361. Copyright American Society of Plant Biologists. Posted with permission.</p

Acetyl-CoA promotes glioblastoma cell adhesion and migration through Ca2+-NFAT signaling

The metabolite acetyl-coenzyme A (acetyl-CoA) is the required acetyl donor for lysine acetylation and thereby links metabolism, signaling, and epigenetics. Nutrient availability alters acetyl-CoA levels in cancer cells, correlating with changes in global histone acetylation and gene expression. However, the specific molecular mechanisms through which acetyl-CoA production impacts gene expression and its functional roles in promoting malignant phenotypes are poorly understood. Here, using histone H3 Lys27 acetylation (H3K27ac) ChIP-seq (chromatin immunoprecipitation [ChIP] coupled with next-generation sequencing) with normalization to an exogenous reference genome (ChIP-Rx), we found that changes in acetyl-CoA abundance trigger site-specific regulation of H3K27ac, correlating with gene expression as opposed to uniformly modulating this mark at all genes. Genes involved in integrin signaling and cell adhesion were identified as acetyl-CoA-responsive in glioblastoma cells, and we demonstrate that ATP citrate lyase (ACLY)-dependent acetyl-CoA production promotes cell migration and adhesion to the extracellular matrix. Mechanistically, the transcription factor NFAT1 (nuclear factor of activated T cells 1) was found to mediate acetyl-CoA-dependent gene regulation and cell adhesion. This occurs through modulation of Ca2+ signals, triggering NFAT1 nuclear translocation when acetyl-CoA is abundant. The findings of this study thus establish that acetyl-CoA impacts H3K27ac at specific loci, correlating with gene expression, and that expression of cell adhesion genes are driven by acetyl-CoA in part through activation of Ca2+-NFAT signaling

Autophagy Inhibition to Augment mTOR Inhibition: a Phase I/II Trial of Everolimus and Hydroxychloroquine in Patients with Previously Treated Renal Cell Carcinoma

Purpose: Everolimus inhibits the mTOR, activating cytoprotective autophagy. Hydroxychloroquine inhibits autophagy. On the basis of preclinical data demonstrating synergistic cytotoxicity when mTOR inhibitors are combined with an autophagy inhibitor, we launched a clinical trial of combined everolimus and hydroxychloroquine, to determine its safety and activity in patients with clear-cell renal cell carcinoma (ccRCC). Patients and Methods: Three centers conducted a phase I/II trial of everolimus 10 mg daily and hydroxychloroquine in patients with advanced ccRCC. The objectives were to determine the MTD of hydroxychloroquine with daily everolimus, and to estimate the rate of 6-month progression-free survival (PFS) in patients with ccRCC receiving everolimus/hydroxychloroquine after 1-3 prior treatment regimens. Correlative studies to identify patient subpopulations that achieved the most benefit included population pharmacokinetics, measurement of autophagosomes by electron microscopy, and next-generation tumor sequencing. Results: No dose-limiting toxicity was observed in the phase I trial. The recommended phase II dose of hydroxychloroquine 600 mg twice daily with everolimus was identified. Disease control [stable disease thorn partial response (PR)] occurred in 22 of 33 (67%) evaluable patients. PR was observed in 2 of 33 patients (6%). PFS >= 6 months was achieved in 15 of 33 (45%) of patients who achieved disease control. Conclusions: Combined hydroxychloroquine 600 mg twice daily with 10 mg daily everolimus was tolerable. The primary endpoint of >40% 6-month PFS rate was met. Hydroxychloroquine is a tolerable autophagy inhibitor in future RCC or other trials.Novartis12 month embargo; first published 11 January 2019.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]