The interplay between endoplasmic reticulum stress and inflammation

Endoplasmic reticulum (ER) stress may be both a trigger and consequence of chronic inflammation. Chronic inflammation is often associated with diseases that arise because of primary misfolding mutations and ER stress. Similarly, ER stress and activation of the unfolded protein response (UPR) is a feature of many chronic inflammatory and autoimmune diseases. In this review, we describe how protein misfolding and the UPR trigger inflammation, how environmental ER stressors affect antigen presenting cells and immune effector cells, and present evidence that inflammatory factors exacerbate protein misfolding and ER stress. Examples from both animal models of disease and human diseases are used to illustrate the complex interactions between ER stress and inflammation, and opportunities for therapeutic targeting are discussed. Finally, recommendations are made for future research with respect to the interaction of ER stress and inflammation.     

Local processes strongly influence post-bleaching benthic recovery in the Lakshadweep Islands

The atoll reefs of the Lakshadweep, in the Indian Ocean suffered a catastrophic mortality of hard coral in the wake of the El Niño event of 1998. This study tracked changes to coral and other benthic elements in three atolls in the Lakshadweep from 2000 to 2003. The recovery of coral was highly site-specific, and appeared to be driven by differences in post-settlement survival of coral recruits, that were in turn, influenced by the local hydrodynamics of the atolls. Post bleaching recovery was highest on west-facing reefs, while recovery on east-facing reefs was very limited. However, no ‘phase-shift’ to macroalgal dominated reefs was evident. High herbivore pressures were perhaps the most important control of macroalgae. Five years after the mass mortality, the genera that showed the maximum gains represented a mix of different susceptibilities to bleaching, while some genera that were not particularly susceptible to bleaching showed significant declines. These results suggest that decline or recovery of coral is likely dependent on individual life history strategies, post-recruitment survival, and contingency.     

Novel, potent, selective, and orally bioavailable human betaII-tryptase inhibitors

The synthesis of novel [1,2,4]oxadiazoles and their structure-activity relationship (SAR) for the inhibition of tryptase and related serine proteases is presented. Elaboration of the P'-side afforded potent, selective, and orally bioavailable tryptase inhibitors.     

(−)-Dibromophakellin: An α2B adrenoceptor agonist isolated from the Australian marine sponge,Acanthella costata

Bioassay-guided fractionation of the organic extract from the marine sponge Acanthella costata resulted in the isolation of the known natural product, (?)-dibromophakellin (1). Using a fluorescence imaging plate reader (FLIPR) based assay, compound 1 was identified as displaying agonist activity against the α_2B adrenoceptor, with an EC₅₀ of 4.2 μM. Debromination and Suzuki–Miyaura coupling reactions were undertaken in order to provide structure activity data about the pyrrole ring of this marine metabolite. These synthetic studies generated the known natural product analogues, (?)-phakellin (2), and (?)-monobromophakellin (3), along with the new synthetic derivatives (?)-4-bromo-5-phenylphakellin (5) and (?)-4,5-diphenylphakellin (6). Substitution of the C-5 Br of 1 with H (2 and 3) or phenyl (5 and 6) resulted in loss of activity indicating that Br at C-5 is required for agonist activity.     

Linear models for joint association and linkage QTL mapping

Background

Populational linkage disequilibrium and within-family linkage are commonly used for QTL mapping and marker assisted selection. The combination of both results in more robust and accurate locations of the QTL, but models proposed so far have been either single marker, complex in practice or well fit to a particular family structure.

Results

We herein present linear model theory to come up with additive effects of the QTL alleles in any member of a general pedigree, conditional to observed markers and pedigree, accounting for possible linkage disequilibrium among QTLs and markers. The model is based on association analysis in the founders; further, the additive effect of the QTLs transmitted to the descendants is a weighted (by the probabilities of transmission) average of the substitution effects of founders'' haplotypes. The model allows for non-complete linkage disequilibrium QTL-markers in the founders. Two submodels are presented: a simple and easy to implement Haley-Knott type regression for half-sib families, and a general mixed (variance component) model for general pedigrees. The model can use information from all markers. The performance of the regression method is compared by simulation with a more complex IBD method by Meuwissen and Goddard. Numerical examples are provided.

Conclusion

The linear model theory provides a useful framework for QTL mapping with dense marker maps. Results show similar accuracies but a bias of the IBD method towards the center of the region. Computations for the linear regression model are extremely simple, in contrast with IBD methods. Extensions of the model to genomic selection and multi-QTL mapping are straightforward.     

Use of linear mixed models for genetic evaluation of gestation length and birth weight allowing for heavy-tailed residual effects

Background

The distribution of residual effects in linear mixed models in animal breeding applications is typically assumed normal, which makes inferences vulnerable to outlier observations. In order to mute the impact of outliers, one option is to fit models with residuals having a heavy-tailed distribution. Here, a Student''s-t model was considered for the distribution of the residuals with the degrees of freedom treated as unknown. Bayesian inference was used to investigate a bivariate Student''s-t (BSt) model using Markov chain Monte Carlo methods in a simulation study and analysing field data for gestation length and birth weight permitted to study the practical implications of fitting heavy-tailed distributions for residuals in linear mixed models.

Methods

In the simulation study, bivariate residuals were generated using Student''s-t distribution with 4 or 12 degrees of freedom, or a normal distribution. Sire models with bivariate Student''s-t or normal residuals were fitted to each simulated dataset using a hierarchical Bayesian approach. For the field data, consisting of gestation length and birth weight records on 7,883 Italian Piemontese cattle, a sire-maternal grandsire model including fixed effects of sex-age of dam and uncorrelated random herd-year-season effects were fitted using a hierarchical Bayesian approach. Residuals were defined to follow bivariate normal or Student''s-t distributions with unknown degrees of freedom.

Results

Posterior mean estimates of degrees of freedom parameters seemed to be accurate and unbiased in the simulation study. Estimates of sire and herd variances were similar, if not identical, across fitted models. In the field data, there was strong support based on predictive log-likelihood values for the Student''s-t error model. Most of the posterior density for degrees of freedom was below 4. Posterior means of direct and maternal heritabilities for birth weight were smaller in the Student''s-t model than those in the normal model. Re-rankings of sires were observed between heavy-tailed and normal models.

Conclusions

Reliable estimates of degrees of freedom were obtained in all simulated heavy-tailed and normal datasets. The predictive log-likelihood was able to distinguish the correct model among the models fitted to heavy-tailed datasets. There was no disadvantage of fitting a heavy-tailed model when the true model was normal. Predictive log-likelihood values indicated that heavy-tailed models with low degrees of freedom values fitted gestation length and birth weight data better than a model with normally distributed residuals.Heavy-tailed and normal models resulted in different estimates of direct and maternal heritabilities, and different sire rankings. Heavy-tailed models may be more appropriate for reliable estimation of genetic parameters from field data.     

Intake of fruits and vegetables,and risk of endometrial cancer in the NIH-AARP Diet and Health Study

Background: Fruits and vegetables contain a wide variety of phytochemicals which may have anti-carcinogenic effects. Although the results of case–control studies have suggested a possible protective effect of fruit and vegetable intake on the risk of endometrial carcinoma, few cohort studies have examined this association. Materials and methods: We used data from the NIH-AARP Diet and Health Study to assess the association of fruit and vegetable consumption, as well as intake of specific botanical groupings of fruits and vegetables, with endometrial cancer risk among 112,088 women who completed a food-frequency questionnaire at baseline, in 1995–1996. During 8 years of follow-up 1142 incident cases of endometrial cancer were ascertained. Cox proportional hazards models were used to estimate hazard ratios (HR) and 95% confidence intervals (95% CI). Results: After adjustment for covariates, HRs for the highest compared to the lowest quintile of total fruit and total vegetable intake were 1.30 (95% CI 1.04–1.61, P for trend 0.05) and 1.09 (95% CI 0.90–1.33, P for trend 0.55), respectively. No inverse associations were observed for intake of any of 13 botanical groupings of fruits and vegetables. Conclusions: Results from this large prospective study do not support a protective role of a high intake of fruits or vegetables on the risk of endometrial cancer in older women.     

MemO: a consensus approach to the annotation of a protein's membrane organization

Membrane organization describes the relationship of proteins to the membrane, that is, whether the protein crosses the membrane or is integral to the membrane and its orientation with respect to the membrane. Membrane organization is determined primarily by the presence of two features which target proteins to the secretory pathway: the endoplasmic reticulum signal peptide and the ?-helical transmembrane domain. In order to generate membrane organization annotation of high quality, confidence and throughput, the Membrane Organization (MemO) pipeline was developed, incorporating consensus feature prediction modules with integration and annotation rules derived from biological observations. The pipeline classifies proteins into six categories based on the presence or absence of predicted features: Soluble, intracellular proteins; Soluble, secreted proteins; Type I membrane proteins; Type II membrane proteins; Multi-span membrane proteins and Glycosylphosphatidylinositol anchored membrane proteins. The MemO pipeline represents an integrated strategy for the application of state-of-the-art bioinformatics tools to the annotation of protein membrane organization, a property which adds biological context to the large quantities of protein sequence information available.