Mycobacterium leprae Activates Toll-Like Receptor-4 Signaling and Expression on Macrophages Depending on Previous Bacillus Calmette-Guerin Vaccination.

Toll-like receptor (TLR)-1 and TLR2 have been shown to be receptors for Mycobacterium leprae (M. leprae), yet it is unclear whether M. leprae can signal through alternative TLRs. Other mycobacterial species possess ligands for TLR4 and genetic association studies in human populations suggest that people with TLR4 polymorphisms may be protected against leprosy. Using human embryonic kidney (HEK)-293 cells co-transfected with TLR4, we demonstrate that M. leprae activates TLR4. We used human macrophages to show that M. leprae stimulation of cytokine production is diminished if pre-treated with TLR4 neutralizing antibody. TLR4 protein expression was up-regulated on macrophages derived from non-bacillus Calmette-Guerin (BCG) vaccinated healthy volunteers after incubation with M. leprae, whereas it was down-regulated in macrophages derived from BCG-vaccinated donors. Finally, pre-treatment of macrophages derived from BCG-naive donors with BCG reversed the effect of M. leprae on TLR4 expression. This may be a newly described phenomenon by which BCG vaccination stimulates "non-specific" protection to the human immune system

A Novel Bayesian DNA Motif Comparison Method for Clustering and Retrieval

Characterizing the DNA-binding specificities of transcription factors is a key problem in computational biology that has been addressed by multiple algorithms. These usually take as input sequences that are putatively bound by the same factor and output one or more DNA motifs. A common practice is to apply several such algorithms simultaneously to improve coverage at the price of redundancy. In interpreting such results, two tasks are crucial: clustering of redundant motifs, and attributing the motifs to transcription factors by retrieval of similar motifs from previously characterized motif libraries. Both tasks inherently involve motif comparison. Here we present a novel method for comparing and merging motifs, based on Bayesian probabilistic principles. This method takes into account both the similarity in positional nucleotide distributions of the two motifs and their dissimilarity to the background distribution. We demonstrate the use of the new comparison method as a basis for motif clustering and retrieval procedures, and compare it to several commonly used alternatives. Our results show that the new method outperforms other available methods in accuracy and sensitivity. We incorporated the resulting motif clustering and retrieval procedures in a large-scale automated pipeline for analyzing DNA motifs. This pipeline integrates the results of various DNA motif discovery algorithms and automatically merges redundant motifs from multiple training sets into a coherent annotated library of motifs. Application of this pipeline to recent genome-wide transcription factor location data in S. cerevisiae successfully identified DNA motifs in a manner that is as good as semi-automated analysis reported in the literature. Moreover, we show how this analysis elucidates the mechanisms of condition-specific preferences of transcription factors

Biodistribution, clearance, and long‐term fate of clinically relevant nanomaterials

Realization of the immense potential of nanomaterials for biomedical applications will require a thorough understanding of how they interact with cells, tissues, and organs. There is evidence that, depending on their physicochemical properties and subsequent interactions, nanomaterials are indeed taken up by cells. However, the subsequent release and/or intracellular degradation of the materials, transfer to other cells, and/or translocation across tissue barriers are still poorly understood. The involvement of these cellular clearance mechanisms strongly influences the long-term fate of used nanomaterials, especially if one also considers repeated exposure. Several nanomaterials, such as liposomes and iron oxide, gold, or silica nanoparticles, are already approved by the American Food and Drug Administration for clinical trials; however, there is still a huge gap of knowledge concerning their fate in the body. Herein, clinically relevant nanomaterials, their possible modes of exposure, as well as the biological barriers they must overcome to be effective are reviewed. Furthermore, the biodistribution and kinetics of nanomaterials and their modes of clearance are discussed, knowledge of the long-term fates of a selection of nanomaterials is summarized, and the critical points that must be considered for future research are addressed

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements