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Modulation of pulmonary IL-21 expression during latent TB and M. tuberculosis/SIV coinfection
Vinay Shivanna, Renee D. Escalona, Colin Chuba, Shashi Prakash Singh, Ahmed A. Moustafa, J. Quincy Brown, Chenyao Xiao, Sangkyu Kim, Edward J. Dick Jr., Smriti Mehra, Mirko Paiardini, Riti Sharan
Vinay Shivanna, Renee D. Escalona, Colin Chuba, Shashi Prakash Singh, Ahmed A. Moustafa, J. Quincy Brown, Chenyao Xiao, Sangkyu Kim, Edward J. Dick Jr., Smriti Mehra, Mirko Paiardini, Riti Sharan
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Research Article AIDS/HIV Immunology Infectious disease

Modulation of pulmonary IL-21 expression during latent TB and M. tuberculosis/SIV coinfection

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Abstract

Tuberculosis (TB) and HIV coinfection remains a major global health challenge, with limited understanding of how these pathogens affect local immune responses in the lungs. This study is the first to our knowledge to investigate the modulation of IL-21 during LTBI and M. tuberculosis/SIV coinfection in nonhuman primates (NHP). We show that IL-21 expression, predominantly derived from CD4+ T cells, is significantly reduced in lungs of M. tuberculosis/SIV coinfected macaques, especially in the absence of cART. Although cART and cART with 3HP partially restore IL-21–producing CD4+ T cells, levels remain below those in LTBI, indicating ongoing immune impairment. Spatial transcriptomic analysis suggests localized alterations in immune signaling, including differences in STAT1- and STAT3-associated transcriptional profiles and reduced M. tuberculosis–specific IFN-γ responses in coinfected animals. Together, our findings indicate that IL-21–producing CD4+ T cells are selectively and persistently impaired in the lungs during M. tuberculosis/SIV coinfection, despite antimicrobial and antiviral therapy. These results highlight a compartment-specific deficit in immune reconstitution and suggest that IL-21–associated pathways may warrant further investigation as potential targets for host-directed therapeutic strategies.

Authors

Vinay Shivanna, Renee D. Escalona, Colin Chuba, Shashi Prakash Singh, Ahmed A. Moustafa, J. Quincy Brown, Chenyao Xiao, Sangkyu Kim, Edward J. Dick Jr., Smriti Mehra, Mirko Paiardini, Riti Sharan

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Figure 6

Quantification of Th1 and Th17 response associated transcript counts in lung tissue.

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Quantification of Th1 and Th17 response associated transcript counts in ...
(A–D) Representative dimensionality reduction (dim) plots from Xenium spatial transcriptomics show the spatial distribution and expression intensity of CD4, IL21, and NCAM1 transcripts within selected ROIs from LTBI (n = 1, 2 regions) (A), cART naive (n = 1, 2 regions) (B), cART (n = 1, 3 regions) (C), and cART+3HP (n = 1, 4 regions) (D) RM. Each point represents a detected transcript, with color intensity reflecting relative abundance. (E) Stacked bar plots show the total transcript counts for CD4, IL21, NCAM1, STAT1, and STAT3 within defined regions of interest (ROIs) in lung tissue sections from RMs across 4 experimental groups. Data were obtained from Xenium spatial transcriptomics analysis, with transcript abundance reflecting localized gene expression in the lung microenvironment. Comparisons across groups highlight changes in immune cell presence (CD4, NCAM1), cytokine signaling (IL21), and transcriptional regulation (STAT1, STAT3) in response to M. tuberculosis/SIV infection and treatment. (F) Log2 fold change of Th1 associated genes (IL-21, STAT1, TBX21, CD4, IFNG), Th17 associated genes (STAT3, IRF4, JAK1), viral infection associated genes (S100A9, TMPRSS2, ICAM1), and IFN response associated genes (IFNAR1, IRF1) in LTBI (n = 1, 2 regions), cART naive (n = 1, 2 regions), cART (n = 1, 3 regions), and cART+3HP (n = 1, 4 regions) RM. (G and H) Linear regression analysis of IL-21+CD4+ T cell count vs IFNγ+CD4+ T cell count in response to ESAT-6/CFP-10 in lung tissue (n = 3) in cART-treated RMs (G) and in LTBI RMs (n = 3) (H). Statistical significance was assessed by calculating the P value for the slope coefficient in a linear regression model, with significance defined as P < 0.05, using R version 4.2.2. Made with BioRender.

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