Decreased expression of miR-146a and miR-155 contributes to an abnormal Treg phenotype in patients with rheumatoid arthritis

Objectives: MicroRNAs (miRNAs) have been implicated in the pathogenesis of autoimmune diseases, not least for their critical role in the regulation of regulatory T cell (Treg) function. Deregulated expression of miR-146a and miR-155 has been associated with rheumatoid arthritis (RA). We therefore investigated miR-146a and miR-155 expression in Tregs of patients with RA and their possible impact on Treg function and disease activity. Methods: Expression of miR-146a and miR-155 was assessed in RA patients and controls. MiRNA expression was correlated with disease activity and expression of target genes. Interference with biological activity of miRNAs was evaluated in functional Treg assays. Results: Diminished upregulation of miR-146a and miR-155 in response to T cell stimulation was found in Tregs of RA patients. Diminution of miR-146a expression was observed in particular in patients with active disease, and correlated with joint inflammation. In patients with active RA, Tregs demonstrated a pro-inflammatory phenotype characterised by inflammatory cytokine expression. This was due to an augmented expression and activation of signal transducer and activator transcription 1 (STAT1), a direct target of miR-146a. Conclusions: Our results suggest that in RA miR-146a facilitates a pro-inflammatory phenotype of Tregs via increased STAT1 activation, and contributes thereby to RA pathogenesis

OptIC project: An intercomparison of optimization techniques for parameter estimation in terrestrial biogeochemical models

We describe results of a project known as OptIC (Optimisation InterComparison) for comparison of parameter estimation methods in terrestrial biogeochemical models. A highly simplified test model was used to generate pseudo-data to which noise with different characteristics was added. Participants in the OptIC project were asked to estimate the model parameters used to generate this data, and to predict model variables into the future. Ten participants contributed results using one of the following methods: Levenberg-Marquardt, adjoint, Kalman filter, Markov chain Monte Carlo and genetic algorithm. Methods differed in how they locate the minimum (gradient-descent or global search), how observations are processed (all at once sequentially), or the number of iterations used, or assumptions about the statistics (some methods assume Gaussian probability density functions; others do not). We found the different methods equally successful at estimating the parameters in our application. The biggest variation in parameter estimates arose from the choice of cost function, not the choice of optimization method. Relatively poor results were obtained when the model-data mismatch in the cost function included weights that were instantaneously dependent on noisy observations. This was the case even when the magnitude of residuals varied with the magnitude of observations. Missing data caused estimates to be more scattered, and the uncertainty of predictions increased correspondingly. All methods gave biased results when the noise was temporally correlated or non-Gaussian, or when incorrect model forcing was used. Our results highlight the need for care in choosing the error model in any optimization