Sodium ion interactions with aqueous glucose: Insights from quantum mechanics, molecular dynamics, and experiment

In the last several decades, significant efforts have been conducted to understand the fundamental reactivity of glucose derived from plant biomass in various chemical environments for conversion to renewable fuels and chemicals. For reactions of glucose in water, it is known that inorganic salts naturally present in biomass alter the product distribution in various deconstruction processes. However, the molecular-level interactions of alkali metal ions and glucose are unknown. These interactions are of physiological interest as well, for example, as they relate to cation-glucose cotransport. Here, we employ quantum mechanics (QM) to understand the interaction of a prevalent alkali metal, sodium, with glucose from a structural and thermodynamic perspective. The effect on B-glucose is subtle: a sodium ion perturbs bond lengths and atomic partial charges less than rotating a hydroxymethyl group. In contrast, the presence of a sodium ion significantly perturbs the partial charges of α-glucose anomeric and ring oxygens. Molecular dynamics (MD) simulations provide dynamic sampling in explicit water, and both the QM and the MD results show that sodium ions associate at many positions with respect to glucose with reasonably equivalent propensity. This promiscuous binding nature of Na + suggests that computational studies of glucose reactions in the presence of inorganic salts need to ensure thorough sampling of the cation positions, in addition to sampling glucose rotamers. The effect of NaCl on the relative populations of the anomers is experimentally quantified with light polarimetry. These results support the computational findings that Na + interacts similarly with a- and B-glucose

Exon expression in lymphoblastoid cell lines from subjects with schizophrenia before and after glucose deprivation

<p>Abstract</p> <p>Background</p> <p>The purpose of this study was to examine the effects of glucose reduction stress on lymphoblastic cell line (LCL) gene expression in subjects with schizophrenia compared to non-psychotic relatives.</p> <p>Methods</p> <p>LCLs were grown under two glucose conditions to measure the effects of glucose reduction stress on exon expression in subjects with schizophrenia compared to unaffected family member controls. A second aim of this project was to identify cis-regulated transcripts associated with diagnosis.</p> <p>Results</p> <p>There were a total of 122 transcripts with significant diagnosis by probeset interaction effects and 328 transcripts with glucose deprivation by probeset interaction probeset effects after corrections for multiple comparisons. There were 8 transcripts with expression significantly affected by the interaction between diagnosis and glucose deprivation and probeset after correction for multiple comparisons. The overall validation rate by qPCR of 13 diagnosis effect genes identified through microarray was 62%, and all genes tested by qPCR showed concordant up- or down-regulation by qPCR and microarray. We assessed brain gene expression of five genes found to be altered by diagnosis and glucose deprivation in LCLs and found a significant decrease in expression of one gene, glutaminase, in the dorsolateral prefrontal cortex (DLPFC). One SNP with previously identified regulation by a 3' UTR SNP was found to influence IRF5 expression in both brain and lymphocytes. The relationship between the 3' UTR rs10954213 genotype and IRF5 expression was significant in LCLs (p = 0.0001), DLPFC (p = 0.007), and anterior cingulate cortex (p = 0.002).</p> <p>Conclusion</p> <p>Experimental manipulation of cells lines from subjects with schizophrenia may be a useful approach to explore stress related gene expression alterations in schizophrenia and to identify SNP variants associated with gene expression.</p

Gene Expression Analysis Implicates a Death Receptor Pathway in Schizophrenia Pathology

An increase in apoptotic events may underlie neuropathology in schizophrenia. By data-mining approaches, we identified significant expression changes in death receptor signaling pathways in the dorsolateral prefrontal cortex (DLPFC) of patients with schizophrenia, particularly implicating the Tumor Necrosis Factor Superfamily member 6 (FAS) receptor and the Tumor Necrosis Factor [ligand] Superfamily member 13 (TNFSF13) in schizophrenia. We sought to confirm and replicate in an independent tissue collection the noted mRNA changes with quantitative real-time RT-PCR. To test for regional and diagnostic specificity, tissue from orbital frontal cortex (OFC) was examined and a bipolar disorder group included. In schizophrenia, we confirmed and replicated significantly increased expression of TNFSF13 mRNA in the DLPFC. Also, a significantly larger proportion of subjects in the schizophrenia group had elevated FAS receptor expression in the DLPFC relative to unaffected controls. These changes were not observed in the bipolar disorder group. In the OFC, there were no significant differences in TNFSF13 or FAS receptor mRNA expression. Decreases in BH3 interacting domain death agonist (BID) mRNA transcript levels were found in the schizophrenia and bipolar disorder groups affecting both the DLPFC and the OFC. We tested if TNFSF13 mRNA expression correlated with neuronal mRNAs in the DLPFC, and found significant negative correlations with interneuron markers, parvalbumin and somatostatin, and a positive correlation with PPP1R9B (spinophilin), but not DLG4 (PSD-95). The expression of TNFSF13 mRNA in DLPFC correlated negatively with tissue pH, but decreasing pH in cultured cells did not cause increased TNFSF13 mRNA nor did exogenous TNFSF13 decrease pH. We concluded that increased TNFSF13 expression may be one of several cell-death cytokine abnormalities that contribute to the observed brain pathology in schizophrenia, and while increased TNFSF13 may be associated with lower brain pH, the change is not necessarily causally related to brain pH

An empirical Bayesian approach for model-based inference of cellular signaling networks

Background A common challenge in systems biology is to infer mechanistic descriptions of biological process given limited observations of a biological system. Mathematical models are frequently used to represent a belief about the causal relationships among proteins within a signaling network. Bayesian methods provide an attractive framework for inferring the validity of those beliefs in the context of the available data. However, efficient sampling of high-dimensional parameter space and appropriate convergence criteria provide barriers for implementing an empirical Bayesian approach. The objective of this study was to apply an Adaptive Markov chain Monte Carlo technique to a typical study of cellular signaling pathways. Results As an illustrative example, a kinetic model for the early signaling events associated with the epidermal growth factor (EGF) signaling network was calibrated against dynamic measurements observed in primary rat hepatocytes. A convergence criterion, based upon the Gelman-Rubin potential scale reduction factor, was applied to the model predictions. The posterior distributions of the parameters exhibited complicated structure, including significant covariance between specific parameters and a broad range of variance among the parameters. The model predictions, in contrast, were narrowly distributed and were used to identify areas of agreement among a collection of experimental studies. Conclusion In summary, an empirical Bayesian approach was developed for inferring the confidence that one can place in a particular model that describes signal transduction mechanisms and for inferring inconsistencies in experimental measurements

Effects of the neurogranin variant rs12807809 on thalamocortical morphology in schizophrenia

10.1371/journal.pone.0085603PLoS ONE812-POLN

Dynamic, Large-Scale Profiling of Transcription Factor Activity from Live Cells in 3D Culture

phenotypes. Taken together, our objective was to develop cellular arrays for dynamic, large-scale quantification of TF activity as cells organized into spherical structures within 3D culture.TF-specific and normalization reporter constructs were delivered in parallel to a cellular array containing a well-established breast cancer cell line cultured in Matrigel. Bioluminescence imaging provided a rapid, non-invasive, and sensitive method to quantify luciferase levels, and was applied repeatedly on each sample to monitor dynamic activity. Arrays measuring 28 TFs identified up to 19 active, with 13 factors changing significantly over time. Stimulation of cells with β-estradiol or activin A resulted in differential TF activity profiles evolving from initial stimulation of the ligand. Many TFs changed as expected based on previous reports, yet arrays were able to replicate these results in a single experiment. Additionally, arrays identified TFs that had not previously been linked with activin A.This system provides a method for large-scale, non-invasive, and dynamic quantification of signaling pathway activity as cells organize into structures. The arrays may find utility for investigating mechanisms regulating normal and abnormal tissue growth, biomaterial design, or as a platform for screening therapeutics

A multiscale systems perspective on cancer, immunotherapy, and Interleukin-12

Monoclonal antibodies represent some of the most promising molecular targeted immunotherapies. However, understanding mechanisms by which tumors evade elimination by the immune system of the host presents a significant challenge for developing effective cancer immunotherapies. The interaction of cancer cells with the host is a complex process that is distributed across a variety of time and length scales. The time scales range from the dynamics of protein refolding (i.e., microseconds) to the dynamics of disease progression (i.e., years). The length scales span the farthest reaches of the human body (i.e., meters) down to the range of molecular interactions (i.e., nanometers). Limited ranges of time and length scales are used experimentally to observe and quantify changes in physiology due to cancer. Translating knowledge obtained from the limited scales observed experimentally to predict patient response is an essential prerequisite for the rational design of cancer immunotherapies that improve clinical outcomes. In studying multiscale systems, engineers use systems analysis and design to identify important components in a complex system and to test conceptual understanding of the integrated system behavior using simulation. The objective of this review is to summarize interactions between the tumor and cell-mediated immunity from a multiscale perspective. Interleukin-12 and its role in coordinating antibody-dependent cell-mediated cytotoxicity is used illustrate the different time and length scale that underpin cancer immunoediting. An underlying theme in this review is the potential role that simulation can play in translating knowledge across scales

Progressive Differentiation and Instructive Capacities of Amniotic Fluid and Cerebrospinal Fluid Proteomes following Neural Tube Closure.

SummaryAfter neural tube closure, amniotic fluid (AF) captured inside the neural tube forms the nascent cerebrospinal fluid (CSF). Neuroepithelial stem cells contact CSF-filled ventricles, proliferate, and differentiate to form the mammalian brain, while neurogenic placodes, which generate cranial sensory neurons, remain in contact with the AF. Using in vivo ultrasound imaging, we quantified the expansion of the embryonic ventricular-CSF space from its inception. We developed tools to obtain pure AF and nascent CSF, before and after neural tube closure, and to define how the AF and CSF proteomes diverge during mouse development. Using embryonic neural explants, we demonstrate that age-matched fluids promote Sox2-positive neurogenic identity in developing forebrain and olfactory epithelia. Nascent CSF also stimulates SOX2-positive self-renewal of forebrain progenitor cells, some of which is attributable to LIFR signaling. Our Resource should facilitate the investigation of fluid-tissue interactions during this highly vulnerable stage of early brain development