Prostaglandin E2 Signals Through PTGER2 to Regulate Sclerostin Expression

The Wnt signaling pathway is a robust regulator of skeletal homeostasis. Gain-of-function mutations promote high bone mass, whereas loss of Lrp5 or Lrp6 co-receptors decrease bone mass. Similarly, mutations in antagonists of Wnt signaling influence skeletal integrity, in an inverse relation to Lrp receptor mutations. Loss of the Wnt antagonist Sclerostin (Sost) produces the generalized skeletal hyperostotic condition of sclerosteosis, which is characterized by increased bone mass and density due to hyperactive osteoblast function. Here we demonstrate that prostaglandin E2 (PGE2), a paracrine factor with pleiotropic effects on osteoblasts and osteoclasts, decreases Sclerostin expression in osteoblastic UMR106.01 cells. Decreased Sost expression correlates with increased expression of Wnt/TCF target genes Axin2 and Tcf3. We also show that the suppressive effect of PGE2 is mediated through a cyclic AMP/PKA pathway. Furthermore, selective agonists for the PGE2 receptor EP2 mimic the effect of PGE2 upon Sost, and siRNA reduction in Ptger2 prevents PGE2-induced Sost repression. These results indicate a functional relationship between prostaglandins and the Wnt/β-catenin signaling pathway in bone

Altered Connectivity Pattern of Hubs in Default-Mode Network with Alzheimer's Disease: An Granger Causality Modeling Approach

Background: Evidences from normal subjects suggest that the default-mode network (DMN) has posterior cingulate cortex (PCC), medial prefrontal cortex (MPFC) and inferior parietal cortex (IPC) as its hubs; meanwhile, these DMN nodes are often found to be abnormally recruited in Alzheimer’s disease (AD) patients. The issues on how these hubs interact to each other, with the rest nodes of the DMN and the altered pattern of hubs with respect to AD, are still on going discussion for eventual final clarification. Principal Findings: To address these issues, we investigated the causal influences between any pair of nodes within the DMN using Granger causality analysis and graph-theoretic methods on resting-state fMRI data of 12 young subjects, 16 old normal controls and 15 AD patients respectively. We found that: (1) PCC/MPFC/IPC, especially the PCC, showed the widest and distinctive causal effects on the DMN dynamics in young group; (2) the pattern of DMN hubs was abnormal in AD patients compared to old control: MPFC and IPC had obvious causal interaction disruption with other nodes; the PCC showed outstanding performance for it was the only region having causal relation with all other nodes significantly; (3) the altered relation between hubs and other DMN nodes held potential as a noninvasive biomarker of AD. Conclusions: Our study, to the best of our knowledge, is the first to support the hub configuration of the DMN from the perspective of causal relationship, and reveal abnormal pattern of the DMN hubs in AD. Findings from young subject

Spectral relative standard deviation: a practical benchmark in metabolomics

Metabolomics datasets, by definition, comprise of measurements of large numbers of metabolites. Both technical (analytical) and biological factors will induce variation within these measurements that is not consistent across all metabolites. Consequently, criteria are required to assess the reproducibility of metabolomics datasets that are derived from all the detected metabolites. Here we calculate spectrum-wide relative standard deviations (RSDs; also termed coefficient of variation, CV) for ten metabolomics datasets, spanning a variety of sample types from mammals, fish, invertebrates and a cell line, and display them succinctly as boxplots. We demonstrate multiple applications of spectral RSDs for characterising technical as well as inter-individual biological variation: for optimising metabolite extractions, comparing analytical techniques, investigating matrix effects, and comparing biofluids and tissue extracts from single and multiple species for optimising experimental design. Technical variation within metabolomics datasets, recorded using one- and two-dimensional NMR and mass spectrometry, ranges from 1.6 to 20.6% (reported as the median spectral RSD). Inter-individual biological variation is typically larger, ranging from as low as 7.2% for tissue extracts from laboratory-housed rats to 58.4% for fish plasma. In addition, for some of the datasets we confirm that the spectral RSD values are largely invariant across different spectral processing methods, such as baseline correction, normalisation and binning resolution. In conclusion, we propose spectral RSDs and their median values contained herein as practical benchmarks for metabolomics studies

Assessment of aortic stiffness by local and regional methods

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