BVT.2733, a Selective 11β-Hydroxysteroid Dehydrogenase Type 1 Inhibitor, Attenuates Obesity and Inflammation in Diet-Induced Obese Mice

BACKGROUND: Inhibition of 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) is being pursued as a new therapeutic approach for the treatment of obesity and metabolic syndrome. Therefore, there is an urgent need to determine the effect of 11β-HSD1 inhibitor, which suppresses glucocorticoid action, on adipose tissue inflammation. The purpose of the present study was to examine the effect of BVT.2733, a selective 11β-HSD1 inhibitor, on expression of pro-inflammatory mediators and macrophage infiltration in adipose tissue in C57BL/6J mice. METHODOLOGY/PRINCIPAL FINDINGS: C57BL/6J mice were fed with a normal chow diet (NC) or high fat diet (HFD). HFD treated mice were then administrated with BVT.2733 (HFD+BVT) or vehicle (HFD) for four weeks. Mice receiving BVT.2733 treatment exhibited decreased body weight and enhanced glucose tolerance and insulin sensitivity compared to control mice. BVT.2733 also down-regulated the expression of inflammation-related genes including monocyte chemoattractant protein 1 (MCP-1), tumor necrosis factor alpha (TNF-α) and the number of infiltrated macrophages within the adipose tissue in vivo. Pharmacological inhibition of 11β-HSD1 and RNA interference against 11β-HSD1 reduced the mRNA levels of MCP-1 and interleukin-6 (IL-6) in cultured J774A.1 macrophages and 3T3-L1 preadipocyte in vitro. CONCLUSIONS/SIGNIFICANCE: These results suggest that BVT.2733 treatment could not only decrease body weight and improve metabolic homeostasis, but also suppress the inflammation of adipose tissue in diet-induced obese mice. 11β-HSD1 may be a very promising therapeutic target for obesity and associated disease

Generation of High Resolution Vegetation Productivity from a Downscaling Method

Accurately estimating vegetation productivity is important in the research of terrestrial ecosystems, carbon cycles and climate change. Although several gross primary production (GPP) and net primary production (NPP) products have been generated and many algorithms developed, advances are still needed to exploit multi-scale data streams for producing GPP and NPP with higher spatial and temporal resolution. In this paper, a method to generate high spatial resolution (30 m) GPP and NPP products was developed based on multi-scale remote sensing data and a downscaling method. First, high resolution fraction photosynthetically active radiation (FPAR) and leaf area index (LAI) were obtained by using a regression tree approach and the spatial and temporal adaptive reflectance fusion model (STARFM). Second, the GPP and NPP were estimated from a multi-source data synergized quantitative algorithm. Finally, the vegetation productivity estimates were validated with the ground-based field data, and were compared with MODerate Resolution Imaging Spectroradiometer (MODIS) and estimated Global LAnd Surface Satellite (GLASS) products. Results of this paper indicated that downscaling methods have great potential in generating high resolution GPP and NPP

New Global MuSyQ GPP/NPP Remote Sensing Products From 1981 to 2018

Long time series of vegetation productivity products are significant for the research of global carbon cycle and climate change. In this article, the 0.05° global gross primary productivity (GPP) and net primary productivity (NPP) products from 1981 to 2018 were estimated by using the improved multisource data synergized quantitative (MuSyQ) NPP algorithm. The model was based on the fraction of absorbed photosynthetically active radiation (FPAR) and leaf area index (LAI) data from the global land surface satellite (GLASS) dataset, the light use efficiency (LUE) from the parameterization approach with the clearness index (CI), the ERA-Interim meteorological data, and other environmental factors. The results suggested that the accuracy of the MuSyQ GPP product was slightly higher than that of the MOD17 GPP product when compared with the FLUXNET GPP, especially for the evergreen broadleaf forest (EBF), deciduous broadleaf forest (DBF), wetland (WET), cropland (CRO), woody savanna (WSAV), and closed shrubland (CSH) land types. MuSyQ NPP product also has higher accuracy [R2 = 0.81, RMSE = 214.6 gC/(m2year)] than MOD17 NPP [R2 = 0.55, RMSE = 214.7 gC/(m2year)] when compared with the BigFoot NPP, which indicated the reliability of the improved MuSyQ-NPP algorithm in estimating global NPP. Our results showed a significant upward trend in global NPP, which was most affected by FPAR, followed by LUE, temperature, and PAR. The average NPP declined significantly in Asia and Amazon tropical rainforests and increased significantly in Africa tropical rainforest, which were affected by the local deforestation or the forest expansion, and also the climate factors

Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data

Accurately estimating vegetation productivity is important in research on terrestrial ecosystems, carbon cycles and climate change. Eight-day gross primary production (GPP) and annual net primary production (NPP) are contained in MODerate Resolution Imaging Spectroradiometer (MODIS) products (MOD17), which are considered the first operational datasets for monitoring global vegetation productivity. However, the cloud-contaminated MODIS leaf area index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) retrievals may introduce some considerable errors to MODIS GPP and NPP products. In this paper, global eight-day GPP and eight-day NPP were first estimated based on Global LAnd Surface Satellite (GLASS) LAI and FPAR products. Then, GPP and NPP estimates were validated by FLUXNET GPP data and BigFoot NPP data and were compared with MODIS GPP and NPP products. Compared with MODIS GPP, a time series showed that estimated GLASS GPP in our study was more temporally continuous and spatially complete with smoother trajectories. Validated with FLUXNET GPP and BigFoot NPP, we demonstrated that estimated GLASS GPP and NPP achieved higher precision for most vegetation types

Nuclear receptor modulators inhibit osteosarcoma cell proliferation and tumour growth by regulating the mTOR signaling pathway

Abstract Osteosarcoma is the most common primary malignant bone tumour in children and adolescents. Chemoresistance leads to poor responses to conventional therapy in patients with osteosarcoma. The discovery of novel effective therapeutic targets and drugs is still the main focus of osteosarcoma research. Nuclear receptors (NRs) have shown substantial promise as novel therapeutic targets for various cancers. In the present study, we performed a drug screen using 29 chemicals that specifically target 17 NRs in several different human osteosarcoma and osteoblast cell lines. The retinoic acid receptor beta (RARb) antagonist LE135, peroxisome proliferator activated receptor gamma (PPARg) antagonist T0070907, liver X receptor (LXR) agonist T0901317 and Rev-Erba agonist SR9011 significantly inhibited the proliferation of malignant osteosarcoma cells (U2OS, HOS-MNNG and Saos-2 cells) but did not inhibit the growth of normal osteoblasts. The effects of these NR modulators on osteosarcoma cells occurred in a dose-dependent manner and were not observed in NR-knockout osteosarcoma cells. These NR modulators also significantly inhibited osteosarcoma growth in vivo and enhanced the antitumour effect of doxorubicin (DOX). Transcriptomic and immunoblotting results showed that these NR modulators may inhibit the growth of osteosarcoma cells by regulating the PI3K/AKT/mTOR and ERK/mTOR pathways. DDIT4, which blocks mTOR activation, was identified as one of the common downstream target genes of these NRs. DDIT4 knockout significantly attenuated the inhibitory effects of these NR modulators on osteosarcoma cell growth. Together, our results revealed that modulators of RARb, PPARg, LXRs and Rev-Erba inhibit osteosarcoma growth both in vitro and in vivo through the mTOR signaling pathway, suggesting that treatment with these NR modulators is a novel potential therapeutic strategy

Occludin OCEL-domain interactions are required for maintenance and regulation of the tight junction barrier to macromolecular flux.

In vitro and in vivo studies implicate occludin in the regulation of paracellular macromolecular flux at steady state and in response to tumor necrosis factor (TNF). To define the roles of occludin in these processes, we established intestinal epithelia with stable occludin knockdown. Knockdown monolayers had markedly enhanced tight junction permeability to large molecules that could be modeled by size-selective channels with radii of ~62.5 Å. TNF increased paracellular flux of large molecules in occludin-sufficient, but not occludin-deficient, monolayers. Complementation using full-length or C-terminal coiled-coil occludin/ELL domain (OCEL)-deficient enhanced green fluorescent protein (EGFP)-occludin showed that TNF-induced occludin endocytosis and barrier regulation both required the OCEL domain. Either TNF treatment or OCEL deletion accelerated EGFP-occludin fluorescence recovery after photobleaching, but TNF treatment did not affect behavior of EGFP-occludin(ΔOCEL). Further, the free OCEL domain prevented TNF-induced acceleration of occludin fluorescence recovery, occludin endocytosis, and barrier loss. OCEL mutated within a recently proposed ZO-1-binding domain (K433) could not inhibit TNF effects, but OCEL mutated within the ZO-1 SH3-GuK-binding region (K485/K488) remained functional. We conclude that OCEL-mediated occludin interactions are essential for limiting paracellular macromolecular flux. Moreover, our data implicate interactions mediated by the OCEL K433 region as an effector of TNF-induced barrier regulation