bioRxivImmunology
Pro-inflammatory feedback loops define immune responses to pathogenic lentivirus infection
A. J. Wilk, J. O. Marceau, S. W. Kazer, I. Fleming, V. N. Miao, J. Galvez-Reyes, A. K. Shalek, S. Holmes, J. Overbaugh, C. A. Blish
Abstract
HIV causes chronic inflammation and AIDS in humans, though the rate of disease progression varies between individuals. Similarly, simian lentiviruses vary in their pathogenicity based on characteristics of both the host (simian species) and virus strain. Here, we profile immune responses in pig-tailed macaques infected with variants of SIV that differ in virulence to understand the immune mechanisms underlying lentiviral pathogenicity. Compared to a minimally pathogenic lentiviral variant, infection with a highly pathogenic variant results in a more delayed, broad, and sustained activation of inflammatory pathways, including an extensive global interferon signature. Conversely, individual cells infected with highly pathogenic lentivirus upregulated fewer interferon-stimulated genes at a lower magnitude, indicating that highly pathogenic lentivirus has evolved to partially escape from interferon responses. Further, we identified distinct gene co-expression patterns and cell-cell communication pathways that implicate CXCL10 and CXCL16 as important molecular drivers of inflammatory pathways specifically in response to highly pathogenic lentivirus infection. Immune responses to highly pathogenic lentivirus infection are characterized by amplifying regulatory circuits of pro-inflammatory cytokines with dense longitudinal connectivity. Our work presents a model of lentiviral pathogenicity where failures in early viral control mechanisms lead to delayed, sustained, and amplifying pro-inflammatory circuits, which has implications for other viral infections with highly variable disease courses.
- organism: non-human primate
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: cell-cell communication
- approach: statistical model
- approach: genetic perturbation / CRISPR
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