ObjectiveThe microbiome contributes to the pathogenesis of inflammatory bowel disease (IBD) but the relative contribution of different lifestyle and environmental factors to the compositional variability of the gut microbiota is unclear.DesignHere, we rank the size effect of disease activity, medications, diet and geographic location of the faecal microbiota composition (16S rRNA gene sequencing) in patients with Crohn’s disease (CD; n=303), ulcerative colitis (UC; n = 228) and controls (n=161), followed longitudinally (at three time points with 16 weeks intervals).ResultsReduced microbiota diversity but increased variability was confirmed in CD and UC compared with controls. Significant compositional differences between diseases, particularly CD, and controls were evident. Longitudinal analyses revealed reduced temporal microbiota stability in IBD, particularly in patients with changes in disease activity. Machine learning separated disease from controls, and active from inactive disease, when consecutive time points were modelled. Geographic location accounted for most of the microbiota variance, second to the presence or absence of CD, followed by history of surgical resection, alcohol consumption and UC diagnosis, medications and diet with most (90.3%) of the compositional variance stochastic or unexplained.ConclusionThe popular concept of precision medicine and rational design of any therapeutic manipulation of the microbiota will have to contend not only with the heterogeneity of the host response, but also with widely differing lifestyles and with much variance still unaccounted for.

Background Microbial endocrinology, which is the study of neuroendocrine-based interkingdom signaling, provides a causal mechanistic framework for understanding the bi-directional crosstalk between the host and microbiome, especially as regards the effect of stress on health and disease. The importance of the cecal microbiome in avian health is well-recognized, yet little is understood regarding the mechanisms underpinning the avian host-microbiome relationship. Neuroendocrine plasticity of avian tissues that are focal points of host-microbiome interaction, such as the gut and lung, has likewise received limited attention. Avian in vivo models that enable the study of the neuroendocrine dynamic between host and microbiome are needed. As such, we utilized Japanese quail (Coturnix japonica) that diverge in corticosterone response to stress to examine the relationship between stress-related neurochemical concentrations at sites of host-microbe interaction, such as the gut, and the cecal microbiome. Results Our results demonstrate that birds which contrast in corticosterone response to stress show profound separation in cecal microbial community structure as well as exhibit differences in tissue neurochemical concentrations and structural morphologies of the gut. Changes in neurochemicals known to be affected by the microbiome were also identified in tissues outside of the gut, suggesting a potential relationship in birds between the cecal microbiome and overall avian physiology. Conclusions The present study provides the first evidence that the structure of the avian cecal microbial community is shaped by selection pressure on the bird for neuroendocrine response to stress. Identification of unique region-dependent neurochemical changes in the intestinal tract following stress highlights environmental stressors as potential drivers of microbial endocrinology-based mechanisms of avian host-microbiome dialogue. Together, these results demonstrate that tissue neurochemical concentrations in the avian gut may be related to the cecal microbiome and reveal the Japanese quail as a novel avian model in which to further examine the mechanisms underpinning these relationships.

Considerable recent research has indicated the presence of bacteria in a variety of human tumours and matched normal tissue. Rather than focusing on further identification of bacteria within tumour samples, we reversed the hypothesis to query if establishing the bacterial profile of a tissue biopsy could reveal its histology / malignancy status. The aim of the present study was therefore to differentiate between malignant and non-malignant fresh breast biopsy specimens, collected specifically for this purpose, based on bacterial sequence data alone. Fresh tissue biopsies were obtained from breast cancer patients and subjected to 16S rRNA gene sequencing. Progressive microbiological and bioinformatic contamination control practices were imparted at all points of specimen handling and bioinformatic manipulation. Differences in breast tumour and matched normal tissues were probed using a variety of statistical and machine-learning-based strategies. Breast tumour and matched normal tissue microbiome profiles proved sufficiently different to indicate that a classification strategy using bacterial biomarkers could be effective. Leave-one-out cross-validation of the predictive model confirmed the ability to identify malignant breast tissue from its bacterial signature with 84.78% accuracy, with a corresponding area under the receiver operating characteristic curve of 0.888. This study provides proof-of-concept data, from fit-for-purpose study material, on the potential to use the bacterial signature of tissue biopsies to identify their malignancy status.