Combined Metabolipidomic and Machine Learning Approach in a Rat Model of Stroke Reveals a Deleterious Impact of Brain Injury on Heart Metabolism.

Cardiac complications are frequently found following a stroke in humans whose pathophysiological mechanism remains poorly understood. We used machine learning to analyse a large set of data from a metabolipidomic study assaying 630 metabolites in a rat stroke model to investigate metabolic changes affecting the heart within 72 h after a stroke. Twelve rats undergoing a stroke and 28 rats undergoing the sham procedure were investigated. A plasmatic signature consistent with the literature with notable lipid metabolism remodelling was identified. The post-stroke heart showed a discriminant metabolic signature, in comparison to the sham controls, involving increased collagen turnover, increased arginase activity with decreased nitric oxide synthase activity as well as an altered amino acid metabolism (including serine, asparagine, lysine and glycine). In conclusion, these results demonstrate that brain injury induces a metabolic remodelling in the heart potentially involved in the pathophysiology of stroke heart syndrome.

Late-Stage Glioma Is Associated with Deleterious Alteration of Gut Bacterial Metabolites in Mice.

Brain-gut axis refers to the bidirectional functional connection between the brain and the gut, which sustains vital functions for vertebrates. This connection also underlies the gastrointestinal (GI) comorbidities associated with brain disorders. Using a mouse model of glioma, based on the orthotopic injection of GL261 cell line in syngeneic C57BL6 mice, we show that late-stage glioma is associated with GI functional alteration and with a shift in the level of some bacterial metabolites in the cecum. By performing cecal content transfer experiments, we further show that cancer-associated alteration in cecal metabolites is involved in end-stage disease progression. Antibiotic treatment results in a slight but significant delay in mice death and a shift in the proportion of myeloid cells in the brain tumor environment. This work rationally considers microbiota modulating strategies in the clinical management of patients with late-stage glioma.