Biology subjects

Jostins, L.

Publications and source records attributed to Jostins, L..

4 recordsLinked to original sources

bioRxiv2021

Mapping of the autophagosomal degradome identifies IL-7Rα as key cargo in proliferating CD4+ T-cells

CD4+ T cells orchestrate both humoral and cytotoxic immune responses. While it is known that CD4+ T cell proliferation relies on autophagy, direct identification of the autophagosomal cargo involved is still missing. Here, we created a transgenic mouse model, which, for the first time, enables us to directly map the proteinaceous content of autophagosomes in any primary cell by LC3 proximity labelling. IL-7R, a cytokine receptor mostly found in naive and memory T cells, was reproducibly detected in autophagosomes of activated CD4+ T cells. Consistently, CD4+ T cells lacking autophagy showed increased IL-7R surface expression, while no defect in internalisation was observed. Mechanistically, excessive surface IL-7R sequestrates the common gamma chain, impairing the IL-2R assembly and downstream signalling crucial for T cell proliferation. This study provides proof-of-principle that key autophagy substrates can be reliably identified with this model to help mechanistically unravel autophagys contribution to healthy physiology and disease.

immunology↗

bioRxiv2018

Bayesian meta-analysis across genome-wide association studies of diverse phenotypes

Genome-wide association studies (GWAS) are a powerful tool for understanding the genetic basis of diseases and traits, but most studies have been conducted in isolation, with a focus on either a single or a set of closely related phenotypes. We describe MetABF, a simple Bayesian framework for performing integrative meta-analysis across multiple GWAS using summary statistics. The approach is applicable across a wide range of study designs and can increase the power by 50% compared to standard frequentist tests when only a subset of studies have a true effect. We demonstrate its utility in a meta-analysis of 20 diverse GWAS which were part of the Wellcome Trust Case-Control Consortium 2. The novelty of the approach is its ability to explore, and assess the evidence for, a range of possible true patterns of association across studies in a computationally efficient framework.

genetics↗

bioRxiv2018

Genomic profiling of T cell activation reveals dependency of memory T cells on CD28 costimulation

T cell activation is a critical driver of immune response and if uncontrolled, it can result in failure to respond to infection or in excessive inflammation and autoimmunity. CD28 costimulatory pathway is an essential regulator of CD4 T cell responses. To deconvolute how T cell receptor (TCR) and CD28 orchestrate activation of human CD4 T cells we stimulated cells using varying intensities of TCR and CD28 signals followed by gene expression profiling. We demonstrate that T-helper differentiation and cytokine expression are controlled by CD28. Strikingly, cell cycle and cell division are sensitive to CD28 in memory cells, but under TCR control in naive cells, in contrast to the paradigm that memory cells are CD28-independent. Using a combination of chromatin accessibility and enhancer profiling, we observe that IRFs and Blimp-1 (PRDM1) motifs are enriched in naive and memory T cells in response to TCR. In contrast, memory cells initiate AP1 transcriptional regulation only when both TCR and CD28 are engaged, implicating CD28 as an amplifier of transcriptional programmes in memory cells. Lastly, we show that CD28-sensitive genes are enriched in autoimmune disease loci, pointing towards the role of memory cells and the regulation of T cell activation through CD28 in autoimmune disease development. This study provides important insights into the differential role of CD28 in naive and memory T cell responses and offers a new platform for design and interpretation of costimulatory based therapies.\n\nOne-sentence summaryGenomic profiling of CD4 T cell activation reveals a sensitivity switch from TCR in naive to CD28 in memory cells.

genomics↗

bioRxiv2017

Bayesian analysis of genetic association across tree-structured routine healthcare data in the UK Biobank

Genetic discovery from the multitude of phenotypes extractable from routine healthcare data has the ability to radically transform our understanding of the human phenome, thereby accelerating progress towards precision medicine. However, a critical question when analysing high-dimensional and heterogeneous data is how to interrogate increasingly specific subphenotypes whilst retaining statistical power to detect genetic associations. Here we develop and employ a novel Bayesian analysis framework that exploits the hierarchical structure of diagnosis classifications to jointly analyse genetic variants against UK Biobank healthcare phenotypes. Our method displays a more than 20% increase in power to detect genetic effects over other approaches, such that we uncover the broader burden of genetic variation: we identify associations with over 2,000 diagnostic terms. We find novel associations with common immune-mediated diseases (IMD), we reveal the extent of genetic sharing between specific IMDs, and we expose differences in disease perception or diagnosis with potential clinical implications.

genetics↗