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Jeffery, I. B.

Publications and source records attributed to Jeffery, I. B..

2 recordsLinked to original sources

IL-32 drives inflammatory responses in IFN-γ primed human macrophages via a Myddosome-dependent pathway and is elevated in COVID-19

IFN-{gamma} is secreted by multiple lymphoid subsets in response to antigen stimulation and can reprogram and prime macrophages epigenetically and transcriptionally to increase responses to inflammatory stimuli such as LPS, IL-1{beta} or TNF-. IFN-{gamma}-driven M1-like inflammatory macrophage states are shared across human immune-mediated inflammatory diseases (IMIDs) and while IFN-{gamma} is nonredundant for defense to intracellular pathogens it is unclear if this is also the case in IMIDs. To identify additional secreted ligands which could prime and induce M1-like macrophages we screened >600 human proteins in human primary macrophages. Using complementary functional genomics approaches, we discovered that IL-32{beta} induced an M1-like inflammatory state in non-primed and IFN-{gamma}-primed macrophages. IL-32{beta} induced signaling, transcriptional, tolerance, cross-tolerance, and inflammatory responses in macrophages which were MyD88, IRAK1 and Myddosome-dependent. These responses to host IL-32{beta} were similar to yet distinct from, those induced by microbial LPS. IL-32 protein was elevated in serum from patients with severe COVID-19 and IL-32{beta} together with IFN-{gamma} were expressed by T cells and induced a macrophage transcriptional response which was shared by monocytes and macrophages in mild and severe COVID-19.

immunology↗

IPCO: Inference of Pathways from Co-variance analysis

Key aspects of microbiome research are accurate identification of taxa followed by the profiling of their functionality. Amplicon profiling based on the 16S ribosomal DNA sequence is a ubiquitous technique to identify and profile the abundances of the various taxa. However, it does not provide information on their encoded functionality. Predictive tools which can accurately extrapolate the functional information of a microbiome based on taxonomic profile composition is essential. At present the applicability of these tools is however limited due to requirement of reference genomes from known species. We present IPCO (Inference of Pathways from Co-variance analysis), a new method of inferring functionality for 16S-based microbiome profiles independent of reference genomes. IPCO utilises the biological co-variance observed between paired taxonomic and functional profiles and co-varies it with the queried dataset. It outperforms other established methods both in terms of sample and feature profile prediction. Validation results confirmed that IPCO can replicate observed biological signals seen within shotgun and metabolite profiles. Comparative analysis of predicted functionality profiles with other popular 16S-based functional prediction tools indicates significantly lower performance with predicted functionality showing little to no correlation with paired shotgun features across samples. IPCO is implemented in R and available from https://github.com/IPCO-Rlibrary/IPCO.

bioinformatics↗