Type I interferon-driven susceptibility to Mycobacterium tuberculosis is mediated by IL-1Ra.

The bacterium Mycobacterium tuberculosis (Mtb) causes tuberculosis and is responsible for more human mortality than any other single pathogen1. Progression to active disease occurs in only a fraction of infected individuals and is predicted by an elevated type I interferon (IFN) response2-7. Whether or how IFNs mediate susceptibility to Mtb has been difficult to study due to a lack of suitable mouse models6-11. Here, we examined B6.Sst1S congenic mice that carry the 'susceptible' allele of the Sst1 locus that results in exacerbated Mtb disease12-14. We found that enhanced production of type I IFNs was responsible for the susceptibility of B6.Sst1S mice to Mtb. Type I IFNs affect the expression of hundreds of genes, several of which have previously been implicated in susceptibility to bacterial infections6,7,15-18. Nevertheless, we found that heterozygous deficiency in just a single IFN target gene, Il1rn, which encodes interleukin-1 receptor antagonist (IL-1Ra), is sufficient to reverse IFN-driven susceptibility to Mtb in B6.Sst1S mice. In addition, antibody-mediated neutralization of IL-1Ra provided therapeutic benefit to Mtb-infected B6.Sst1S mice. Our results illustrate the value of the B6.Sst1S mouse to model IFN-driven susceptibility to Mtb, and demonstrate that IL-1Ra is an important mediator of type I IFN-driven susceptibility to Mtb infections in vivo

Deformation of a renormalization-group equation applied to infinite-order phase transitions

By adding a linear term to a renormalization-group equation in a system exhibiting infinite-order phase transitions, asymptotic behavior of running coupling constants is derived in an algebraic manner. A benefit of this method is presented explicitly using several examples.Comment: 6 pages, 5 figures, revtex4, typo corrected, references adde

Computationally efficient algorithms for the two-dimensional Kolmogorov-Smirnov test

Goodness-of-fit statistics measure the compatibility of random samples against some theoretical or reference probability distribution function. The classical one-dimensional Kolmogorov-Smirnov test is a non-parametric statistic for comparing two empirical distributions which defines the largest absolute difference between the two cumulative distribution functions as a measure of disagreement. Adapting this test to more than one dimension is a challenge because there are 2^d-1 independent ways of ordering a cumulative distribution function in d dimensions. We discuss Peacock's version of the Kolmogorov-Smirnov test for two-dimensional data sets which computes the differences between cumulative distribution functions in 4n^2 quadrants. We also examine Fasano and Franceschini's variation of Peacock's test, Cooke's algorithm for Peacock's test, and ROOT's version of the two-dimensional Kolmogorov-Smirnov test. We establish a lower-bound limit on the work for computing Peacock's test of Omega(n^2.lg(n)), introducing optimal algorithms for both this and Fasano and Franceschini's test, and show that Cooke's algorithm is not a faithful implementation of Peacock's test. We also discuss and evaluate parallel algorithms for Peacock's test

An asymptotic formula for marginal running coupling constants and universality of loglog corrections

Given a two-loop beta function for multiple marginal coupling constants, we derive an asymptotic formula for the running coupling constants driven to an infrared fixed point. It can play an important role in universal loglog corrections to physical quantities.Comment: 16 pages; typos fixed, one appendix removed for quick access to the main result; to be published in J. Phys.

The Calcitonin and Glucocorticoids Combination: Mechanistic Insights into Their Class-Effect Synergy in Experimental Arthritis

PMCID: PMC3564948This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited