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Rigby, R.

Publications and source records attributed to Rigby, R..

2 recordsLinked to original sources

Type-1 IFN primed monocytes in pathogenesis of idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is the most severe form of lung fibrosis. It is progressive, and has an extremely poor outcome and limited treatment options. The disease exclusively affects the lungs, and thus less attention has been focused on blood-borne immune cells. which could be a more effective therapeutic target than lung-based cells. Here, we questioned if circulating monocytes, which has been shown to be increased in IPF, bore abnormalities that might contribute to its pathogenesis. We found that levels of circulating monocytes correlated directly with the extent of fibrosis in the lungs, and increased further during acute clinical deterioration. Monocytes in IPF were phenotypically distinct, displaying increased expression of CD64, a type 1 IFN gene expression signature and a greater magnitude of type 1 IFN response when stimulated. These abnormalities were accompanied by markedly raised CSF-1 levels in the serum, prolonged survival of monocytes ex vivo, and increased numbers of monocytes in lung tissue. Our study defines the key monocytic abnormalities in IPF, proposing type 1 IFN-primed monocytes as a potential driver of an aberrant repair response and fibrosis. It provides a rationale for targeting monocytes and identifies monocytic CD64 as a potential specific therapeutic target for IPF.

immunology

A Bayesian Multi-Task Approach for Detecting Global Microbiome Associations

MotivationThe human gut microbiome has been shown to be associated with a variety of human diseases, including cancer, metabolic conditions and inflammatory bowel disease. Current statistical techniques for microbiome association studies are limited by relying on measures of ecological distance, or only allowing for the detection of associations with individual bacterial species, rather than the whole microbiome. ResultsIn this work, we develop a novel Bayesian multi-task approach for detecting global microbiome associations. Our method is not dependent on a choice of distance measure, and is able to incorporate phylogenetic information about microbial species. We apply our method to simulated data and show that it allows for consistent estimation of global microbiome effects. Additionally, we investigate the performance of the model on two real-world microbiome studies: a study of microbiome-metabolome associations in inflammatory bowel disease (Beamish, 2017), and a study of associations between diet and the gut microbiome in mice (Turnbaugh et al., 2009). We show that we can use the method to reliably detect associations in real-world datasets with varying numbers of samples and covariates. AvailabilityOur method is implemented using the R interface to the Stan Hamiltonian Monte Carlo sampler. Software for running our methods is available at https://github.com/FrankD/MicrobiomeGlobalAssociations. Contactf.dondelinger@lancaster.ac.uk

microbiology