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Morozova, M.

Publications and source records attributed to Morozova, M..

2 recordsLinked to original sources

Colitis-associated intestinal microbiota regulates brain glycine and host behavior in mice.

Inflammatory bowel diseases (IBD) are chronic and relapsing inflammatory disorders of the gastrointestinal tract with complex etiology and no strategies for complete cure. IBD are often complicated by mental disorders like anxiety and depression, indicating substantial shifts in the gut-brain axis. However, the mechanisms connecting IBD to mental diseases are still under debate. Here we use Muc2 mutant mouse model of chronic colitis to uncouple the effects of the intestinal microbiota on host behavior from chronic inflammation in the gut. Muc2 mutant male mice exhibit high exploratory activity, reduced anxiety-related behaviors, impaired sensorimotor gating, and altered social preference towards males and females. Microbial transfer to wild-type mice via littermate co-housing shows that colitis-associated microbiota rather than inflammation per se defines behavioral features in Muc2 colitis model. Metagenomic profiling and combination of antibiotic treatments revealed that bacterial species Akkermansia muciniphila is associated with the behavioral phenotype in mutants, and that its intestinal abundance correlates with social preference towards males. Metabolomic analysis together with pharmacological inhibition of Gly and NMDA receptors helped us to determine that brain glycine is responsible for the behavioral phenotype in Muc2 mice. Blood and brain metabolic profiles suggest that microbiota-dependent changes in choline metabolism might be involved in regulation of central glycine neurotransmission. Taken together, our data demonstrates that colitis-associated microbiota controls anxiety, sensorimotor gating and social behavior via metabolic regulation of the brain glycinergic system, providing new venues to combat neurological complications of IBD.

animal behavior and cognition↗

A representative reference for MRI-based human axon radius assessment using light microscopy

Non-invasive assessment of axon radii via MRI bears great potential for clinical and neuroscience research as it is a main determinant of the neuronal conduction velocity. However, there is a lack of representative histological reference data on the scale of the cross-section of MRI voxels for validating the MRI-visible, effective radius (reff). Because the current gold standard stems from neuroanatomical studies designed to estimate the frequency-weighted arithmetic mean radius (rarith) on small ensembles of axons, it is unsuited to estimate the tail-weighted reff. We propose CNN-based segmentation on high-resolution, large-scale light microscopy (lsLM) data to generate a representative reference for reff. In a human corpus callosum, we assessed estimation accuracy and bias of rarith and reff. Furthermore, we investigated whether mapping anatomy-related variation of rarith and reff is confounded by low-frequency variation of the image intensity, e.g., due to staining heterogeneity. Finally, we analyzed the potential error due to outstandingly large axons in reff. Compared to rarith, reff was estimated with higher accuracy (normalized-root-mean-square-error of reff: 7.2 %; rarith: 21.5 %) and lower bias (normalized-mean-bias-error of reff: -1.7 %; rarith: 16 %). While rarith was confounded by variation of the image intensity, variation of reff seemed anatomy-related. The largest axons contributed between 0.9 % and 3 % to reff. In conclusion, the proposed method accurately estimates reff at MRI voxel resolution across a human corpus callosum sample. Further investigations are required to assess generalization to brain areas with different axon radii ensembles.

neuroscience↗