Tumor necrosis factor (TNF) protects resistant C57BL/6 mice against herpes simplex virus-induced encephalitis independently of signaling via TNF receptor 1 or 2

Tumor necrosis factor (TNF) is a multifunctional cytokine that has a role in induction and regulation of host innate and adaptive immune responses. The importance of TNF antiviral mechanisms is reflected by the diverse strategies adopted by different viruses, particularly members of the herpesvirus family, to block TNF responses. TNF binds and signals through two receptors, Tnfrsf1a (TNF receptor 1 [TNFR1], or p55) and Tnfrsf1b (TNFR2, or p75). We report here that herpes simplex virus 1 (HSV-1) infection of TNF-/- mice on the resistant C57BL/6 genetic background results in significantly increased susceptibility (P < 0.0001, log rank test) to fatal HSV encephalitis (HSE) and prolonged persistence of elevated levels of virus in neural tissues. In contrast, although virus titers in neural tissues of p55-/- N13 mice were elevated to levels comparable to what was found for the TNF-/- mice, the p55-/- N13 mice were as resistant as control C57BL/6 mice (P > 0.05). The incidence of fatal HSE was significantly increased by in vivo neutralization of TNF using soluble TNFR1 (sTNFR1) or depletion of macrophages in C57BL/6 mice (P = 0.0038 and P = 0.0071, respectively). Strikingly, in vivo neutralization of TNF in HSV-1-infected p55-/- p75-/- mice by use of three independent approaches (treatment with soluble p55 receptor, anti-TNF monoclonal antibody, or in vivo small interfering RNA against TNF) resulted in significantly increased mortality rates (P = 0.005), comparable in magnitude to those for C57BL/6 mice treated with sTNFR1 (P = 0.0018). Overall, these results indicate that while TNF is required for resistance to fatal HSE, both p55 and p75 receptors are dispensable. Precisely how TNF mediates protection against HSV-1 mortality in p55-/- p75-/- mice remains to be determined.     

Mapping human whole-brain structural networks with diffusion MRI

Understanding the large-scale structural network formed by neurons is a major challenge in system neuroscience. A detailed connectivity map covering the entire brain would therefore be of great value. Based on diffusion MRI, we propose an efficient methodology to generate large, comprehensive and individual white matter connectional datasets of the living or dead, human or animal brain. This non-invasive tool enables us to study the basic and potentially complex network properties of the entire brain. For two human subjects we find that their individual brain networks have an exponential node degree distribution and that their global organization is in the form of a small world.     

Hepatic Mitogen-Activated Protein Kinase Phosphatase 1 Selectively Regulates Glucose Metabolism and Energy Homeostasis

The liver plays a critical role in glucose metabolism and communicates with peripheral tissues to maintain energy homeostasis. Obesity and insulin resistance are highly associated with nonalcoholic fatty liver disease (NAFLD). However, the precise molecular details of NAFLD remain incomplete. The p38 mitogen-activated protein kinase (MAPK) and c-Jun NH₂-terminal kinase (JNK) regulate liver metabolism. However, the physiological contribution of MAPK phosphatase 1 (MKP-1) as a nuclear antagonist of both p38 MAPK and JNK in the liver is unknown. Here we show that hepatic MKP-1 becomes overexpressed following high-fat feeding. Liver-specific deletion of MKP-1 enhances gluconeogenesis and causes hepatic insulin resistance in chow-fed mice while selectively conferring protection from hepatosteatosis upon high-fat feeding. Further, hepatic MKP-1 regulates both interleukin-6 (IL-6) and fibroblast growth factor 21 (FGF21). Mice lacking hepatic MKP-1 exhibit reduced circulating IL-6 and FGF21 levels that were associated with impaired skeletal muscle mitochondrial oxidation and susceptibility to diet-induced obesity. Hence, hepatic MKP-1 serves as a selective regulator of MAPK-dependent signals that contributes to the maintenance of glucose homeostasis and peripheral tissue energy balance. These results also demonstrate that hepatic MKP-1 overexpression in obesity is causally linked to the promotion of hepatosteatosis.     

Resting-State Temporal Synchronization Networks Emerge from Connectivity Topology and Heterogeneity

Spatial patterns of coherent activity across different brain areas have been identified during the resting-state fluctuations of the brain. However, recent studies indicate that resting-state activity is not stationary, but shows complex temporal dynamics. We were interested in the spatiotemporal dynamics of the phase interactions among resting-state fMRI BOLD signals from human subjects. We found that the global phase synchrony of the BOLD signals evolves on a characteristic ultra-slow (<0.01Hz) time scale, and that its temporal variations reflect the transient formation and dissolution of multiple communities of synchronized brain regions. Synchronized communities reoccurred intermittently in time and across scanning sessions. We found that the synchronization communities relate to previously defined functional networks known to be engaged in sensory-motor or cognitive function, called resting-state networks (RSNs), including the default mode network, the somato-motor network, the visual network, the auditory network, the cognitive control networks, the self-referential network, and combinations of these and other RSNs. We studied the mechanism originating the observed spatiotemporal synchronization dynamics by using a network model of phase oscillators connected through the brain’s anatomical connectivity estimated using diffusion imaging human data. The model consistently approximates the temporal and spatial synchronization patterns of the empirical data, and reveals that multiple clusters that transiently synchronize and desynchronize emerge from the complex topology of anatomical connections, provided that oscillators are heterogeneous.     

Generation of human innate immune responses towards membrane macrophage colony stimulating factor (mM-CSF) expressing U251 glioma cells within immunodeficient (NIH-nu/beige/xid) mice

The response of human peripheral blood mononuclear cells (PBMC) to cloned human HLA-A2+ U251 glioma cells (U251-2F11/TK) expressing membrane macrophage colony stimulating factor (mM-CSF) was investigated in vitro and in vivo. Enriched human monocytes derived from cancer patients produced a respiratory burst following 20min of interaction with mM-CSF expressing U251 glioma cells. This respiratory burst response was not observed in the enriched human monocytes following similar exposure to the viral vector control U251 (U251-VV) cells. Reactive oxygen species such as H(2)O(2) and HOCl produced death of the U251 cells. The U251-2F11/TK cells failed to grow in severely compromised combined immunodeficient (NIH-bg-nu-xidBR) mice that were depleted of murine monocyte/macrophages then reconstituted with human HLA-A2+ PBMC. Reactive oxygen species (ROS) were produced by PBMC, both in vitro and in vivo in response tomM-CSF expressing U251 cells. U251-2F11/TK cells failed to form subcutaneous tumors in macrophage depleted mice reconstituted with human PBMC; whereas, progressive growth of such tumors was observed with the U251-VV cells. U251-2F11/TK tumors formed if the initial inoculums of PBMC were depleted of monocytes. From this work it can be concluded that mM-CSF transduced U251-2F11/TK glioma cells can safely stimulate human innate immune responses in vivo.     

Targeting Wnt/tenascin C-mediated cross talk between pancreatic cancer cells and stellate cells via activation of the metastasis suppressor NDRG1

A major barrier to successful pancreatic cancer (PC) treatment is the surrounding stroma, which secretes growth factors/cytokines that promote PC progression. Wnt and tenascin C (TnC) are key ligands secreted by stromal pancreatic stellate cells (PSCs) that then act on PC cells in a paracrine manner to activate the oncogenic β-catenin and YAP/TAZ signaling pathways. Therefore, therapies targeting oncogenic Wnt/TnC cross talk between PC cells and PSCs constitute a promising new therapeutic approach for PC treatment. The metastasis suppressor N-myc downstream-regulated gene-1 (NDRG1) inhibits tumor progression and metastasis in numerous cancers, including PC. We demonstrate herein that targeting NDRG1 using the clinically trialed anticancer agent di-2-pyridylketone-4-cyclohexyl-4-methyl-3-thiosemicarbazone (DpC) inhibited Wnt/TnC-mediated interactions between PC cells and the surrounding PSCs. Mechanistically, NDRG1 and DpC markedly inhibit secretion of Wnt3a and TnC by PSCs, while also attenuating Wnt/β-catenin and YAP/TAZ activation and downstream signaling in PC cells. This antioncogenic activity was mediated by direct inhibition of β-catenin and YAP/TAZ nuclear localization and by increasing the Wnt inhibitor, DKK1. Expression of NDRG1 also inhibited transforming growth factor (TGF)-β secretion by PC cells, a key mechanism by which PC cells activate PSCs. Using an in vivo orthotopic PC mouse model, we show DpC downregulated β-catenin, TnC, and YAP/TAZ, while potently increasing NDRG1 expression in PC tumors. We conclude that NDRG1 and DpC inhibit Wnt/TnC-mediated interactions between PC cells and PSCs. These results further illuminate the antioncogenic mechanism of NDRG1 and the potential of targeting this metastasis suppressor to overcome the oncogenic effects of the PC-PSC interaction.     

A ten-microRNA expression signature predicts survival in glioblastoma

Glioblastoma (GBM) is the most common and aggressive primary brain tumor with very poor patient median survival. To identify a microRNA (miRNA) expression signature that can predict GBM patient survival, we analyzed the miRNA expression data of GBM patients (n=222) derived from The Cancer Genome Atlas (TCGA) dataset. We divided the patients randomly into training and testing sets with equal number in each group. We identified 10 significant miRNAs using Cox regression analysis on the training set and formulated a risk score based on the expression signature of these miRNAs that segregated the patients into high and low risk groups with significantly different survival times (hazard ratio [HR]=2.4; 95% CI=1.4-3.8; p<0.0001). Of these 10 miRNAs, 7 were found to be risky miRNAs and 3 were found to be protective. This signature was independently validated in the testing set (HR=1.7; 95% CI=1.1-2.8; p=0.002). GBM patients with high risk scores had overall poor survival compared to the patients with low risk scores. Overall survival among the entire patient set was 35.0% at 2 years, 21.5% at 3 years, 18.5% at 4 years and 11.8% at 5 years in the low risk group, versus 11.0%, 5.5%, 0.0 and 0.0% respectively in the high risk group (HR=2.0; 95% CI=1.4-2.8; p<0.0001). Cox multivariate analysis with patient age as a covariate on the entire patient set identified risk score based on the 10 miRNA expression signature to be an independent predictor of patient survival (HR=1.120; 95% CI=1.04-1.20; p=0.003). Thus we have identified a miRNA expression signature that can predict GBM patient survival. These findings may have implications in the understanding of gliomagenesis, development of targeted therapy and selection of high risk cancer patients for adjuvant therapy.     

Detrended Fluctuation Analysis and Adaptive Fractal Analysis of Stride Time Data in Parkinson's Disease: Stitching Together Short Gait Trials

Variability indicates motor control disturbances and is suitable to identify gait pathologies. It can be quantified by linear parameters (amplitude estimators) and more sophisticated nonlinear methods (structural information). Detrended Fluctuation Analysis (DFA) is one method to measure structural information, e.g., from stride time series. Recently, an improved method, Adaptive Fractal Analysis (AFA), has been proposed. This method has not been applied to gait data before. Fractal scaling methods (FS) require long stride-to-stride data to obtain valid results. However, in clinical studies, it is not usual to measure a large number of strides (e.g., strides). Amongst others, clinical gait analysis is limited due to short walkways, thus, FS seem to be inapplicable. The purpose of the present study was to evaluate FS under clinical conditions. Stride time data of five self-paced walking trials ( strides each) of subjects with PD and a healthy control group (CG) was measured. To generate longer time series, stride time sequences were stitched together. The coefficient of variation (CV), fractal scaling exponents (DFA) and (AFA) were calculated. Two surrogate tests were performed: A) the whole time series was randomly shuffled; B) the single trials were randomly shuffled separately and afterwards stitched together. CV did not discriminate between PD and CG. However, significant differences between PD and CG were found concerning and . Surrogate version B yielded a higher mean squared error and empirical quantiles than version A. Hence, we conclude that the stitching procedure creates an artificial structure resulting in an overestimation of true . The method of stitching together sections of gait seems to be appropriate in order to distinguish between PD and CG with FS. It provides an approach to integrate FS as standard in clinical gait analysis and to overcome limitations such as short walkways.