Thermophilic campylobacters are a major cause of bacterial food-borne diarrhoeal disease. Adherence and biofilm formation are key elements of Campylobacter jejuni persistence in unfavourable environmental conditions. The phytochemical analysis of Euodia ruticarpa fruit ethanol solution extract (EREE) indicated that the major compounds were evodiamine (1), rutaecarpine (2) and evocarpine (9). E. ruticarpa fruit ethanol solution extract, compounds 1 and 2 as well as a mixture of quinolinone alkaloids with 41.7% of 9 were tested for antibacterial, antibiofilm and antiquorum sensing activities against C. jejuni. Minimal inhibitory concentrations varied from 64 to 1024 µg/mL. A mutant strain that lacks the functional gene coding for the CmeB efflux pump protein was the most susceptible. Interestingly, in addition to the wild-type (NCTC 11168) and cmeB mutant, also a mutant that lacks autoinducer-2 production (luxS) was able to adhere (1 h) and to produce a biofilm (24, 48 and 72 h). The subinhibitory concentrations of all preparations at least partly inhibited C. jejuni adhesion and biofilm formation with the most visible effect of the quinolinone alkaloid fraction. Using a Vibrio harveyi luminescence assay, the inhibition of autoinducer-2 production was observed in the wild-type and cmeB mutant after 48 h with the most visible effect of EREE and its fraction Q. Copyright © 2016 John Wiley & Sons, Ltd.

The dynamics and diversity of human gut microbiota that can remarkably influence the wellbeing and health of the host are constantly changing through the host’s lifetime in response to various factors. The aim of the present study was to determine a set of parameters that could have a major impact on classifying subjects into a single cluster regarding gut bacteria composition. Therefore, a set of demographical, environmental, and clinical data of healthy adults aged 25–50 years (117 female and 83 men) was collected. Fecal microbiota composition was characterized using Illumina MiSeq 16S rRNA gene amplicon sequencing. Hierarchical clustering was performed to analyze the microbiota data set, and a supervised machine learning model (SVM; Support Vector Machines) was applied for classification. Seventy variables from collected data were included in machine learning analysis. The agglomerative clustering algorithm suggested the presence of four distinct community types of most abundant bacterial phyla. Each cluster harbored a statistically significant different proportion of bacterial phyla. Regarding prediction, the most important features classifying subjects into clusters were measures of obesity (waist to hip ratio, BMI, and visceral fat index), total body water, blood pressure, energy intake, total fat, olive oil intake, total fiber intake, and water intake. In conclusion, the SVM model was shown as a valuable tool to classify healthy individuals based on their gut microbiota composition.

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