Carbohydrate Metabolism Affects Macrophage-Mediated Killing of Enterococcus faecalis

E. faecalis has become a major pathogen leading to a variety of infections around the world. The metabolic interaction between E. faecalis and its host is important during infection but is rarely investigated.

Another look at dietary polyphenols: challenges in cancer prevention and treatment

: Cancer is a pathology that impacts in a profound manner people all over the world. The election strategy against cancer often uses chemotherapy and radiotherapy, which more often than not can present many side effects and not always reliable efficacy. By contrast, it is widely known that a diet rich in fruit and vegetables has a protective effect against cancer insurgence and development. Polyphenols are generally believed to be responsible for those beneficial actions, at least partially. In this review, we highlight the metabolic interaction between polyphenols and our metabolism and discuss their potential for anticancer prevention and therapy.

M2R: a Python add-on to cobrapy for modifying human genome-scale metabolic reconstruction using the gut microbiota models

Motivation The gut microbiota is the human body’s largest population of microorganisms that interact with human intestinal cells. They use ingested nutrients for fundamental biological processes and have important impacts on human physiology, immunity and metabolome in the gastrointestinal tract. Results Here, we present M2R, a Python add-on to cobrapy that allows incorporating information about the gut microbiota metabolism models to human genome-scale metabolic models (GEMs) like RECON3D. The idea behind the software is to modify the lower bounds of the exchange reactions in the model using aggregated in- and out-fluxes from selected microbes. M2R enables users to quickly and easily modify the pool of the metabolites that enter and leave the GEM, which is particularly important for those looking into an analysis of the metabolic interaction between the gut microbiota and human cells and its dysregulation. Availability and implementation M2R is freely available under an MIT License at https://github.com/e-weglarz-tomczak/m2r. Supplementary information Supplementary data are available at Bioinformatics online.

Microbial competition reduces metabolic interaction distances to the low µm-range

Metabolic interactions between cells affect microbial community compositions and hence their function in ecosystems. It is well-known that under competition for the exchanged metabolite, concentration gradients constrain the distances over which interactions can occur. However, interaction distances are typically quantified in two-dimensional systems or without accounting for competition or other metabolite-removal, conditions which may not very often match natural ecosystems. We here analyze the impact of cell-to-cell distance on unidirectional cross-feeding in a three-dimensional aqueous system with competition for the exchanged metabolite. Effective interaction distances were computed with a reaction-diffusion model and experimentally verified by growing a synthetic consortium of 1 µm-sized metabolite producer, receiver, and competitor cells in different spatial structures. We show that receivers cannot interact with producers located on average 15 µm away from them, as product concentration gradients flatten close to producer cells. We developed an aggregation protocol and varied the receiver cells’ product affinity, to show that within producer–receiver aggregates even low-affinity receiver cells could interact with producers. These results show that competition or other metabolite-removal of a public good in a three-dimensional system reduces metabolic interaction distances to the low µm-range, highlighting the importance of concentration gradients as physical constraint for cellular interactions.