Apolipoprotein Mimetic Peptides: A New Approach for the Treatment of Asthma

New treatments are needed for severe asthmatics to improve disease control and avoid severe toxicities associated with oral corticosteroids. We have used a murine model of house dust mite (HDM)-induced asthma to identify steroid-unresponsive genes that might represent targets for new therapeutic approaches for severe asthma. This strategy identified apolipoprotein E as a steroid-unresponsive gene with increased mRNA expression in the lungs of HDM-challenged mice. Furthermore, apolipoprotein E functioned as an endogenous negative regulator of airway hyperreactivity and goblet cell hyperplasia in experimental HDM-induced asthma. The ability of apolipoprotein E, which is expressed by lung macrophages, to attenuate AHR, and goblet cell hyperplasia is mediated by low density lipoprotein (LDL) receptors expressed by airway epithelial cells. Consistent with this, administration of an apolipoprotein E mimetic peptide, corresponding to amino acids 130–149 of the LDL receptor-binding domain of the holo-apoE protein, significantly reduced AHR and goblet cell hyperplasia in HDM-challenged apoE−/− mice. These findings identified the apolipoprotein E – LDL receptor pathway as a new druggable target for asthma that can be activated by administration of apoE-mimetic peptides. Similarly, apolipoprotein A-I may have therapeutic potential in asthma based upon its anti-inflammatory, anti-oxidative, and anti-fibrotic properties. Furthermore, administration of apolipoprotein A-I mimetic peptides has attenuated airway inflammation, airway remodeling, and airway hyperreactivity in murine models of experimental asthma. Thus, site-directed delivery of inhaled apolipoprotein E or apolipoprotein A-I mimetic peptides may represent novel treatment approaches that can be developed for asthma, including severe disease

Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations

Accurate estimation of the satellite-based global terrestrial latent heat flux (LE) at high spatial and temporal scales remains a major challenge. In this study, we introduce a Bayesian model averaging (BMA) method to improve satellite-based global terrestrial LE estimation by merging five process-based algorithms. These are the Moderate Resolution Imaging Spectroradiometer (MODIS) LE product algorithm, the revised remote-sensing-based Penman-Monteith LE algorithm, the Priestley-Taylor-based LE algorithm, the modified satellite-based Priestley-Taylor LE algorithm, and the semi-empirical Penman LE algorithm. We validated the BMA method using data for 2000–2009 and by comparison with a simple model averaging (SA) method and five process-based algorithms. Validation data were collected for 240 globally distributed eddy covariance tower sites provided by FLUXNET projects. The validation results demonstrate that the five process-based algorithms used have variable uncertainty and the BMA method enhances the daily LE estimates, with smaller root mean square errors (RMSEs) than the SA method and the individual algorithms driven by tower-specific meteorology and Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorological data provided by the NASA Global Modeling and Assimilation Office (GMAO), respectively. The average RMSE for the BMA method driven by daily tower-specific meteorology decreased by more than 5 W/m2 for crop and grass sites, and by more than 6 W/m2 for forest, shrub, and savanna sites. The average coefficients of determination (R2) increased by approximately 0.05 for most sites. To test the BMA method for regional mapping, we applied it for MODIS data and GMAO-MERRA meteorology to map annual global terrestrial LE averaged over 2001–2004 for spatial resolution of 0.05°. The BMA method provides a basis for generating a long-term global terrestrial LE product for characterizing global energy, hydrological, and carbon cycles

Paradoxical Effects of Rapamycin on Experimental House Dust Mite-Induced Asthma

The mammalian target of rapamycin (mTOR) modulates immune responses and cellular proliferation. The objective of this study was to assess whether inhibition of mTOR with rapamycin modifies disease severity in two experimental murine models of house dust mite (HDM)-induced asthma. In an induction model, rapamycin was administered to BALB/c mice coincident with nasal HDM challenges for 3 weeks. In a treatment model, nasal HDM challenges were performed for 6 weeks and rapamycin treatment was administered during weeks 4 through 6. In the induction model, rapamycin significantly attenuated airway inflammation, airway hyperreactivity (AHR) and goblet cell hyperplasia. In contrast, treatment of established HDM-induced asthma with rapamycin exacerbated AHR and airway inflammation, whereas goblet cell hyperplasia was not modified. Phosphorylation of the S6 ribosomal protein, which is downstream of mTORC1, was increased after 3 weeks, but not 6 weeks of HDM-challenge. Rapamycin reduced S6 phosphorylation in HDM-challenged mice in both the induction and treatment models. Thus, the paradoxical effects of rapamycin on asthma severity paralleled the activation of mTOR signaling. Lastly, mediastinal lymph node re-stimulation experiments showed that treatment of rapamycin-naive T cells with ex vivo rapamycin decreased antigen-specific Th2 cytokine production, whereas prior exposure to in vivo rapamycin rendered T cells refractory to the suppressive effects of ex vivo rapamycin. We conclude that rapamycin had paradoxical effects on the pathogenesis of experimental HDM-induced asthma. Thus, consistent with the context-dependent effects of rapamycin on inflammation, the timing of mTOR inhibition may be an important determinant of efficacy and toxicity in HDM-induced asthma

High-dose clevudine impairs mitochondrial function and glucose-stimulated insulin secretion in INS-1E cells

<p>Abstract</p> <p>Background</p> <p>Clevudine is a nucleoside analog reverse transcriptase inhibitor that exhibits potent antiviral activity against hepatitis B virus (HBV) without serious side effects. However, mitochondrial myopathy has been observed in patients with chronic HBV infection taking clevudine. Moreover, the development of diabetes was recently reported in patients receiving long-term treatment with clevudine. In this study, we investigated the effects of clevudine on mitochondrial function and insulin release in a rat clonal β-cell line, INS-1E.</p> <p>Methods</p> <p>The mitochondrial DNA (mtDNA) copy number and the mRNA levels were measured by using quantitative PCR. MTT analysis, ATP/lactate measurements, and insulin assay were performed.</p> <p>Results</p> <p>Both INS-1E cells and HepG2 cells, which originated from human hepatoma, showed dose-dependent decreases in mtDNA copy number and cytochrome c oxidase-1 (Cox-1) mRNA level following culture with clevudine (10 μM-1 mM) for 4 weeks. INS-1E cells treated with clevudine had reduced total mitochondrial activities, lower cytosolic ATP contents, enhanced lactate production, and more lipid accumulation. Insulin release in response to glucose application was markedly decreased in clevudine-treated INS-1E cells, which might be a consequence of mitochondrial dysfunction.</p> <p>Conclusions</p> <p>Our data suggest that high-dose treatment with clevudine induces mitochondrial defects associated with mtDNA depletion and impairs glucose-stimulated insulin secretion in insulin-releasing cells. These findings partly explain the development of diabetes in patients receiving clevudine who might have a high susceptibility to mitochondrial toxicity.</p

From bedside to bench to clinic trials: identifying new treatments for severe asthma

Asthmatics with a severe form of the disease are frequently refractory to standard medications such as inhaled corticosteroids, underlining the need for new treatments to prevent the occurrence of potentially life-threatening episodes. A major obstacle in the development of new treatments for severe asthma is the heterogeneous pathogenesis of the disease, which involves multiple mechanisms and cell types. Furthermore, new therapies might need to be targeted to subgroups of patients whose disease pathogenesis is mediated by a specific pathway. One approach to solving the challenge of developing new treatments for severe asthma is to use experimental mouse models of asthma to address clinically relevant questions regarding disease pathogenesis. The mechanistic insights gained from mouse studies can be translated back to the clinic as potential treatment approaches that require evaluation in clinical trials to validate their effectiveness and safety in human subjects. Here, we will review how mouse models have advanced our understanding of severe asthma pathogenesis. Mouse studies have helped us to uncover the underlying inflammatory mechanisms (mediated by multiple immune cell types that produce Th1, Th2 or Th17 cytokines) and non-inflammatory pathways, in addition to shedding light on asthma that is associated with obesity or steroid unresponsiveness. We propose that the strategy of using mouse models to address clinically relevant questions remains an attractive and productive research approach for identifying mechanistic pathways that can be developed into novel treatments for severe asthma

Long-term spatial distributions and trends of the latent heat fluxes over the global cropland ecosystem using multiple satellite-based models

<div><p>Estimating cropland latent heat flux (LE) from continental to global scales is vital to modeling crop production and managing water resources. Over the past several decades, numerous LE models were developed, such as the moderate resolution imaging spectroradiometer LE (MOD16) algorithm, revised remote sensing-based Penman–Monteith LE algorithm (RRS), the Priestley–Taylor LE algorithm of the Jet Propulsion Laboratory (PT-JPL) and the modified satellite-based Priestley-Taylor LE algorithm (MS-PT). However, these LE models have not been directly compared over the global cropland ecosystem using various algorithms. In this study, we evaluated the performances of these four LE models using 34 eddy covariance (EC) sites. The results showed that mean annual LE for cropland varied from 33.49 to 58.97 W/m² among the four models. The interannual LE slightly increased during 1982–2009 across the global cropland ecosystem. All models had acceptable performances with the coefficient of determination (R²) ranging from 0.4 to 0.7 and a root mean squared error (RMSE) of approximately 35 W/m². MS-PT had good overall performance across the cropland ecosystem with the highest R², lowest RMSE and a relatively low bias. The reduced performances of MOD16 and RRS, with R² ranging from 0.4 to 0.6 and RMSEs from 30 to 39 W/m², might be attributed to empirical parameters in the structure algorithms and calibrated coefficients.</p></div

Characteristics of validation data at the EC sites.

<p>Characteristics of validation data at the EC sites.</p

Seasonal cropland average LE during 1982~2010.

<p>DJF represents December, January, and February. MAM represents March, April, and May. JJA represents June, July, and August. SON represents September, October, and November.</p

Interannual variations of average LE over the cropland ecosystem during 1982–2010 predicted by the four models.

<p>Interannual variations of average LE over the cropland ecosystem during 1982–2010 predicted by the four models.</p

Comparison between the seasonal patterns of LE (W/m²) obtained by MOD16, RRS, PT-JPL and MS-PT.

<p>Comparison between the seasonal patterns of LE (W/m²) obtained by MOD16, RRS, PT-JPL and MS-PT.</p