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Blohmke, C. J.

Publications and source records attributed to Blohmke, C. 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↗

Diagnostic host gene signature to accurately distinguish enteric fever from other febrile diseases

Misdiagnosis of enteric fever is a major global health problem resulting in patient mismanagement, antimicrobial misuse and inaccurate disease burden estimates. Applying a machine-learning algorithm to host gene expression profiles, we identified a diagnostic signature which could accurately distinguish culture-confirmed enteric fever cases from other febrile illnesses (AUROC<95%). Applying this signature to a culture-negative suspected enteric fever cohort in Nepal identified a further 12.6% as likely true cases. Our analysis highlights the power of data-driven approaches to identify host-response patterns for the diagnosis of febrile illnesses. Expression signatures were validated using qPCR highlighting their utility as PCR-based diagnostic for use in endemic settings.

molecular biology↗