Back

Tissue-specific improvements in CD4+ T cell responses after treatment for visceral leishmaniasis.

Engel, J. A.; Rivera, F.; Crawford, B.; Lee, H. J.; Na, J.; Chang, K.; Gartlan, K.; Bukali, L.; Frame, T.; Wang, Y.; Haque, A.; Engwerda, C.; Engwerda, C.

2025-04-13 immunology
10.1101/2025.04.13.648617 bioRxiv
Show abstract

Visceral leishmaniasis (VL) is a life-threatening parasitic disease that requires robust CD4+ T cell-mediated immunity for parasite control. However, the heterogeneity and transcriptional dynamics of CD4+ T cell responses in VL remain poorly defined. In this study, we use a model of experimental VL with tissue-specific immunity and single-cell RNA sequencing to provide a high-resolution assessment of CD4+ T cell responses. Our analysis reveals the complexity of CD4+ T cell differentiation in VL, identifying distinct Th1 subsets with transcriptional heterogeneity that may reflect functional specialisation. Despite minimal transcriptional differences between splenic and hepatic CD4+ T cells, we identified shifts in subset composition, including the emergence of a stem-like CD4+ T cell population in the spleen, which was suppressed by the transcription factor Bhlhe40. Bhlhe40 deficiency skewed CD4+ T cell differentiation, impairing Th1 responses while promoting Tr1 cells, resulting in defective parasite control in the liver. Additionally, AmBisome treatment induced a profound transcriptional shift in CD4+ T cells, leading to the maintenance of stem-like CD4+ T cells in the spleen and the expansion of tissue resident memory-like cells in the liver. These findings uncover key regulatory mechanisms that shape CD4+ T cell differentiation in VL and provide insights into how immune-modulatory strategies could enhance long-term immunity.

Matching journals

The top 4 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.