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Barton, A. J.

Publications and source records attributed to Barton, A. J..

2 recordsLinked to original sources

Genetic Susceptibility to Enteric Fever in Experimentally Challenged Human Volunteers

BackgroundInfection with Salmonella enterica serovars Typhi and Paratyphi A cause an estimated 14 million cases of enteric fever annually. Here the controlled nature of challenge studies is exploited to identify genetic variants associated with enteric fever susceptibility. MethodsHuman challenge participants were genotyped by Illumina OmniExpress-24 BeadChip array (n=176) and/or transcriptionally profiled by RNA-sequencing (n=178). ResultsTwo SNPs within CAPN14 and MIATNB were identified with p<10-5 for association with development of symptoms or bacteraemia following oral S. Typhi or S. Paratyphi A challenge. Imputation of classical human leukocyte antigen (HLA) types from genomic and transcriptomic data identified HLA-B*27:05, previously associated with non-typhoidal Salmonella-induced reactive arthritis, as the HLA type most strongly associated with enteric fever susceptibility (p=0.012). Genes related to the unfolded protein response and heat shock were over-represented in HLA-B*27:05+ participants following challenge (p=0.01). Furthermore, intracellular replication of S. Typhi is higher in C1R cells transfected with HLA-B*27:05 (p=0.02). ConclusionThese data suggest that activation of the unfolded protein response by HLA-B*27:05 misfolding may create an intracellular environment conducive to S. Typhi replication, increasing susceptibility to enteric fever.

genetics

sweetD: An R package using Hoeffding's D statistic to visualise the dependence between M and A for large numbers of gene expression samples

SummaryMA plots are frequently used to examine the relationship between gene abundance and differences in gene expression between two samples. In good quality samples without batch effects or outliers, there is generally no or little relationship between intensity and log fold difference. As the number of MA plots increases quadratically with the number of samples, for large datasets the number of potential MA plots becomes prohibitively high for manual examination. Here we present an R package calculating and visualising the dependence between abundance and log fold difference for large numbers of samples using Hoeffdings D statistic. Availability and implementationsweetD is currently available as an R package on Github https://github.com/amberjoybarton/sweetD.

bioinformatics