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BACKGROUND:Clostridium difficile ribotype-027, ribotype-078, and ribotype-017 are virulent and epidemic lineages. Trehalose metabolism variants in these ribotypes, combined with increased human trehalose consumption, have been hypothesised to have contributed to their emergence and virulence. METHODS:5232 previously whole-genome sequenced C. difficile isolates were analysed. Clinical isolates were used to investigate the impact of trehalose metabolism variants on mortality. Import data were used to estimate changes in dietary trehalose. Ribotype-027 virulence was investigated in a clinically reflective gut model. FINDINGS:Trehalose metabolism variants found in ribotype-027 and ribotype-017 were widely distributed throughout C. difficile clade-2 and clade-4 in 24/29 (83%) and 10/11 (91%) of sequence types (STs), respectively. The four-gene trehalose metabolism cluster described in ribotype-078 was common in genomes from all five clinically-important C. difficile clades (40/167 [24%] STs). The four-gene cluster was variably present in 208 ribotype-015 infections (98 [47%]); 27/208 (13%) of these patients died within 30-days of diagnosis. Adjusting for age, sex, and infecting ST, there was no association between 30-day all-cause mortality and the four-gene cluster (OR 0.36 [95%CI 0.09-1.34, p = 0.13]). Synthetic trehalose imports in the USA, UK, Germany and the EU were  < 1 g/capita/year during 2000-2006, and  < 9 g/capita/year 2007-2012, compared with dietary trehalose from natural sources of ~100 g/capita/year. Trehalose supplementation did not increase ribotype-027 virulence in a clinically-validated gut model. INTERPRETATION:Trehalose metabolism variants are common in C. difficile. Increases in total dietary trehalose during the early-mid 2000s C. difficile epidemic were likely relatively minimal. Alternative explanations are required to explain why ribotype-027, ribotype-078 and ribotype-017 have been successful. FUNDING:National Institute for Health Research. Gut model experiments only: Hayashibara Co. Ltd.

Original publication




Journal article



Publication Date





347 - 355


Big Data Institute, University of Oxford, UK; Nuffield Department of Medicine, University of Oxford, UK. Electronic address: