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Monfeuga, T.

Publications and source records attributed to Monfeuga, T..

2 recordsLinked to original sources

The perineurium integrates leptin with its sympathetic outflow to protect against obesity

The regulatory mechanism of leptins afferent action in the brain, constituting a negative feedback loop, is contingent upon the efferent sympathetic innervation of white and brown adipose tissues. Nonetheless, the peripheral regulation governing the relative strengths of the afferent and efferent arms remains ambiguous. Using single-cell RNA sequencing on murine sympathetic ganglia, we identified the unique expression of both the leptin receptor (LepR) and the beta 2 adrenergic receptor (Adrb2) in perineurial cells that form a barrier around sympathetic ganglia and nerve bundles in adipose tissues. We show that LepR+ Sympathetic Perineurial Cells (SPCs) are molecularly similar to endothelial cells and that conditional knockout of Adrb2 in LepR+ SPCs predisposes mice to obesity without affecting food intake. Notably, we found that hyperleptinemia associated with obesity causes apoptosis in SPCs, leading to a significant erosion of the perineurial barrier and concomitant adipose sympathetic neuropathy. We further show that this deleterious effect can be reversed by sympathomimetic beta 2 adrenergic receptor agonism. These results have relevance to human obesity, as we observed a synergistic effect of highly common polymorphisms of LEPR and ADRB2 on the risk of increased BMI in a large European population. We propose that SPCs are the nexus of leptin action by integrating the afferent and efferent arms of the neuroendocrine loop to influence its setpoint.

neuroscience↗

Functional module detection through integration of single-cell RNA sequencing data with protein-protein interaction networks

Recent advances in single-cell RNA sequencing (scRNA-seq) have allowed researchers to explore transcriptional function at a cellular level. In this study, we present O_SCPLOWSCC_SCPLOWPPIN, a method for integrating single-cell RNA sequencing data with protein-protein interaction networks (PPINs) that detects active modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted PPINs, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As a case study, we investigate RNA-sequencing data from human liver spheroids but the techniques described here are applicable to other organisms and tissues. O_SCPLOWSCC_SCPLOWPPIN allows us to expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the PPIN significantly enriched which represent biological pathways. In these pathways, O_SCPLOWSCC_SCPLOWPPIN also identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differentially expressed gene analysis.

bioinformatics↗