| Abstract: | C. difficile infection is associated with disturbed gut microbiota and changes in relative frequencies and abundance of individual bacterial taxons have been described. In this study we have analysed bacterial, fungal and archaeal microbiota by denaturing high pressure liquid chromatography (DHPLC) and with machine learning methods in 208 faecal samples from healthy volunteers and in routine samples with requested C. difficile testing. The latter were further divided according to stool consistency, C. difficile presence or absence and C. difficile ribotype (027 or non-027). Lower microbiota diversity was a common trait of all routine samples and not necessarily connected only to C. difficile colonisation. Differences between the healthy donors and C. difficile positive routine samples were detected in bacterial, fungal and archaeal components. Bifidobacterium longum was the single most important species associated with C. difficile negative samples. However, by machine learning approaches we have identified patterns of microbiota composition predictive for C. difficile colonization. Those patterns also differed between samples with C. difficile ribotype 027 and other C. difficile ribotypes. The results indicate that not only the presence of a single species/group is important but that certain combinations of gut microbes are associated with C. difficile carriage and that some ribotypes (027) might be associated with more disturbed microbiota than the others. |
