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Chronic infection stunts macrophage heterogeneity and disrupts immune-mediated myogenesis
Richard M. Jin, … , Jordan Warunek, Elizabeth A. Wohlfert
Richard M. Jin, … , Jordan Warunek, Elizabeth A. Wohlfert
Published September 20, 2018
Citation Information: JCI Insight. 2018;3(18):e121549. https://doi.org/10.1172/jci.insight.121549.
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Research Article Infectious disease Inflammation

Chronic infection stunts macrophage heterogeneity and disrupts immune-mediated myogenesis

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Abstract

The robust regenerative potential of skeletal muscle is imperative for the maintenance of tissue function across a host of potential insults including exercise, infection, and trauma. The highly coordinated action of multiple immune populations, especially macrophages, plays an indispensable role in guiding this reparative program. However, it remains unclear how skeletal muscle repair proceeds in a chronically inflamed setting, such as infection, where an active immune response is already engaged. To address this question, we used a cardiotoxin injury model to challenge the reparative potential of chronically infected muscle. Compared with regenerating naive skeletal muscle, infected skeletal muscle exhibited multiple indicators of delayed muscle repair including a divergent morphologic response to injury and dysregulated expression of myogenic regulatory factors. Further, using both flow cytometric and single-cell RNA sequencing approaches, we show that reduced macrophage heterogeneity due to delayed emergence of restorative subsets underlies dysfunctional tissue repair during chronic infection. Our findings highlight how the preexisting inflammatory environment within tissue alters reparative immunity and ultimately the quality of tissue regeneration.

Authors

Richard M. Jin, Jordan Warunek, Elizabeth A. Wohlfert

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Figure 10

Monitoring of macrophage heterogeneity in mdx mice at steady state and during injury repair.

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Monitoring of macrophage heterogeneity in mdx mice at steady state and d...
t-SNE dimensional reduction analysis was performed on aggregated flow cytometric data sets of CD45+ cells from B10 and mdx muscle with or without CTX-induced injury (day 4). t-SNE clustering parameters were based on macrophage- and Monocle state–distinguishing markers. (A) Contour plot and gating of density-rich, spatially distinct populations identified within the CD11b+CD68+ t-SNE cluster. Labeling and fine-tuning of subset gates (monocytes, resting macrophages, and Monocle states 1–5) were based on the phenotypic expression of corresponding monocle state markers (n = aggregated data of all experimental groups, 10 samples/group). (B) Expression profile of Monocle state–distinguishing markers, Sca-1, Ly6c, Trf, CX3CR1, Gpnmb, and CD9 by flow cytometry of the individual gated subset populations (n = aggregated data of all experimental groups, 10 samples/group). (C) Distribution of Monocle states within total CD11b+CD68+ cells parsed by experimental group (n = 10 samples/group). Results are cumulative of 2 independent experiments of n = 5 samples/group/experiment.

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