ObjectiveTo investigate the underlying mechanisms behind changes in glucose homeostasis with delivery of propionate to the human colon by comprehensive and coordinated analysis of gut bacterial composition, plasma metabolome and immune responses.DesignTwelve non-diabetic adults with overweight and obesity received 20 g/day of inulin-propionate ester (IPE), designed to selectively deliver propionate to the colon, a high-fermentable fibre control (inulin) and a low-fermentable fibre control (cellulose) in a randomised, double-blind, placebo-controlled, cross-over design. Outcome measurements of metabolic responses, inflammatory markers and gut bacterial composition were analysed at the end of each 42-day supplementation period.ResultsBoth IPE and inulin supplementation improved insulin resistance compared with cellulose supplementation, measured by homeostatic model assessment 2 (mean±SEM 1.23±0.17 IPE vs 1.59±0.17 cellulose, p=0.001; 1.17±0.15 inulin vs 1.59±0.17 cellulose, p=0.009), with no differences between IPE and inulin (p=0.272). Fasting insulin was only associated positively with plasma tyrosine and negatively with plasma glycine following inulin supplementation. IPE supplementation decreased proinflammatory interleukin-8 levels compared with cellulose, while inulin had no impact on the systemic inflammatory markers studied. Inulin promoted changes in gut bacterial populations at the class level (increased Actinobacteria and decreased Clostridia) and order level (decreased Clostridiales) compared with cellulose, with small differences at the species level observed between IPE and cellulose.ConclusionThese data demonstrate a distinctive physiological impact of raising colonic propionate delivery in humans, as improvements in insulin sensitivity promoted by IPE and inulin were accompanied with different effects on the plasma metabolome, gut bacterial populations and markers of systemic inflammation.

A number of bifidobacterial species are found at a particularly high prevalence and abundance in faecal samples of healthy breastfed infants, a phenomenon that is believed to be, at least partially, due to the ability of bifidobacteria to metabolize Human Milk Oligosaccharides (HMOs). In the current study, we isolated a novel strain of Bifidobacterium kashiwanohense, named APCKJ1, from the faeces of a four-week old breastfed infant, based on the ability of the strain to utilise the HMO component fucosyllactose. We then determined the full genome sequence of this strain, and employed the generated data to analyze fucosyllactose metabolism in B. kashiwanohense APCKJ1. Transcriptomic and growth analyses, combined with metabolite analysis, in vitro hydrolysis assays and heterologous expression, allowed us to elucidate the pathway for fucosyllactose metabolism in B. kashiwanohense APCKJ1. Homologs of the key genes for this metabolic pathway were identified in particular in infant-derived members of the Bifdobacterium genus, revealing the apparent niche-specific nature of this pathway, and allowing a broad perspective on bifidobacterial fucosyllactose and L-fucose metabolism.

Metabolic profiling of biological samples provides important insights into multiple physiological and pathological processes, but is hindered by a lack of automated annotation and standardised methods for structure elucidation of candidate disease biomarkers. Here, we describe a system for identifying molecular species derived from NMR spectroscopybased metabolic phenotyping studies, with detailed info on sample preparation, data acquisition, and data modelling. We provide eight different modular workflows to be followed in a recommended sequential order according to their level of difficulty. This multi-platform system involves the use of statistical spectroscopic tools such as STOCSY, STORM and RED-STORM to identify other signals in the NMR spectra relating to the same molecule. It also utilizes 2D-NMR spectroscopic analysis, separation and pre-concentration techniques, multiple hyphenated analytical platforms and data extraction from existing databases. The complete system, using all eight workflows, would take up to a month, as it includes multidimensional NMR experiments that require prolonged experiment times. However, easier identification cases using fewer steps would take two or three days. This approach to biomarker discovery is efficient, cost-effective and offers increased chemical space coverage of the metabolome, resulting in faster and more accurate assignment of NMR-generated biomarkers arising from metabolic phenotyping studies. Finally, it requires basic understanding of Matlab in order to perform statistical spectroscopic tools and analytical skills to perform Solid Phase Extraction, LC-fraction collection, LC-NMR-MS and 1D and 2D NMR experiments. STORM (and STOCSY): The code for executing both the STOCSY and STORM algorithms is in https://bitbucket.org/jmp111/storm/src. These can be executed in a Matlab environment. RED-STORM-The code for executing the RED-STORM algorithm is provided in https://bitbucket.org/jmp111/redstorm/src/. This can be executed in a Matlab environment.

Elevated postprandial glucose (PPG) is a significant driver of non-communicable diseases globally. Carbohydrate-rich foods are a major determinant of PPG. Currently there is a limited understanding of how starch structure within a food-matrix interacts with the gut luminal environment to control PPG. We use pea seeds (Pisum sativum), as a model-food, to explore the contribution of starch structure, food-matrix and intestinal environment on PPG.Using stable isotope [ C] labelled seeds, coupled with synchronous gastric, duodenal and plasma sampling in vivo, we demonstrate that maintenance of cell structure and changes in starch morphology are closely related to lower glucose availability in the small intestine, resulting in acutely lower PPG and promoting changes in the gut bacterial composition associated with long term metabolic health improvements. This work offers huge potential to improve the design of food products targeted at moderating PPG and therefore lowering the risk of non-communicable diseases.