bioRxivNeurosciencePreprint
Psoriasis-related neuroinflammation disrupts thalamostriatal signalling driving anhedonia in both humans and mice
Deepika SharmaiD, Lilya AndrianovaiD, Rhona McGonigaliD, Kirstyn Gardner-StepheniD, Hassan al Fadhel, Flavia SunziniiD, Kristian StefanoviD, Dominique L ChaputiD, Jennifer A. BarrieiD, Richard Hohne, Megan Saathoff, Yaprak Karabalci, Alfredo E. Montes-GómeziD, Julie-Myrtille BourgognoniD, Ana González-Rueda, Peter ThorntoniD, Neil BasuiD, John J ColeiD, Michael T. CraigiD, Jonathan CavanaghiD
- Citations
- 1
Abstract
Inflammation is implicated in 25% of depression cases, yet limited access to human brain for mechanistic studies and scarce translational models have hindered the identification of neural circuits linking systemic inflammation to depressive symptoms such as reduced motivation and anhedonia. Leveraging both clinical and pre-clinical approaches, we combined neuroimaging in individuals with psoriatic disease, a systemic inflammatory condition frequently associated with depression, with neurophysiological, behavioural and immunological studies in a psoriasis mouse model exhibiting neuroinflammation. We found that increased inflammation had a robust association with both depression and fatigue in individuals with psoriatic disease. Across species, we found that inflammatory signalling disrupts thalamostriatal circuitry, a key component of the motivational network. In humans, functional connectivity between thalamus and ventral striatum correlated with depressive and fatigue-related symptoms. In mice, psoriasis-like inflammation produced impaired thalamostriatal synaptic transmission accompanied by anhedonia and motivation-related behavioural deficits, together with glial activation and immune-cell infiltration. These cross-species findings identify the thalamostriatal circuit as a conserved neuroinflammatory hotspot involved in depression and highlight it as a potential therapeutic target.
- organism: human
- organism: mouse
- assay: imaging
- research focus: cell-cell communication
- research focus: differential expression
- approach: statistical model
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