Back

Dynamics of Trypanosoma cruzi infection in hamsters and novel association with progressive motor dysfunction

Langston, H.; Francisco, A. F.; Doidge, C.; Roberts, C. H.; Khan, A. A.; Jayawardhana, S.; Taylor, M. C.; Kelly, J. M.; Lewis, M. D.

2023-11-09 microbiology
10.1101/2023.11.09.566419 bioRxiv
Show abstract

Chagas disease is a zoonosis caused by the protozoan parasite Trypanosoma cruzi. Clinical outcomes range from long-term asymptomatic carriage to cardiac, digestive, neurological and composite presentations that can be fatal in both acute and chronic stages of the disease. Studies of T. cruzi in animal models, principally mice, have informed our understanding of the biological basis of this variability and its relationship to infection and host response dynamics. Hamsters have higher translational value for many human infectious diseases, but they have not been well developed as models of Chagas disease. We transposed a real-time bioluminescence imaging system for T. cruzi infection from mice into female Syrian hamsters (Mesocricetus auratus). This enabled us to study chronic tissue pathology in the context of spatiotemporal infection dynamics. Acute infections were widely disseminated, whereas chronic infections were almost entirely restricted to the skin and subcutaneous adipose tissue. Neither cardiac nor digestive tract disease were reproducible features of the model. Skeletal muscle had only sporadic parasitism in the chronic phase, but nevertheless displayed significant inflammation and fibrosis, features also seen in mouse models. Whereas mice had normal locomotion, all chronically infected hamsters developed hindlimb muscle hypertonia and a gait dysfunction resembling spastic diplegia. With further development, this model may therefore prove valuable in studies of peripheral nervous system involvement in Chagas disease.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.