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A multiomics analysis identifies retinol metabolism in fibroblasts as a key pathway in wound healing
Till Wüstemann, Elizabeta Madzharova, Mateusz S. Wietecha, Norbert B. Ghyselinck, Marcus Höring, Gerhard Liebisch, Nicola Zamboni, Ulrich auf dem Keller, Sabine Werner
Till Wüstemann, Elizabeta Madzharova, Mateusz S. Wietecha, Norbert B. Ghyselinck, Marcus Höring, Gerhard Liebisch, Nicola Zamboni, Ulrich auf dem Keller, Sabine Werner
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Research Article Cell biology Dermatology Metabolism

A multiomics analysis identifies retinol metabolism in fibroblasts as a key pathway in wound healing

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Abstract

Impaired wound healing poses a major and increasingly frequent health problem. Among the key players in the healing process are fibroblasts, but their metabolic profile in healing wounds is largely unknown. Using a combination of transcriptomics, targeted proteomics, and metabolomics, we identified retinol metabolism as a top regulated pathway in wound fibroblasts. This is functionally relevant, since even a mild retinol deficiency caused a delay in wound closure and reepithelialization, which mainly resulted from misdirected keratinocyte migration on the new granulation tissue. Quantitative proteomics identified integrin subunit α11 as a less abundant protein in wounds of mice subjected to a retinol-deficient diet. Reduced levels of this fibroblast-specific protein likely altered the granulation tissue matrix, which in turn affected reepithelialization. These results provide a comprehensive overview of the transcriptome, proteome, and metabolome of wound fibroblasts and identify retinol metabolism in fibroblasts as a key regulator of tissue repair.

Authors

Till Wüstemann, Elizabeta Madzharova, Mateusz S. Wietecha, Norbert B. Ghyselinck, Marcus Höring, Gerhard Liebisch, Nicola Zamboni, Ulrich auf dem Keller, Sabine Werner

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Figure 6

Proteomics hit proteins are mainly expressed in wound fibroblasts.

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Proteomics hit proteins are mainly expressed in wound fibroblasts.
(A) U...
(A) Uniform manifold approximation and projection (UMAP) visualization of wound cells and their annotations to cell types (left panel), subtypes (middle panel), and spatiotemporal labels (right panel) based on a mouse wound dataset (26). (B) Breakdown of expression of Zhx3, Itga11, and Ggcx in all annotated cell types. Dot size correlates with the percentage of cells that express the gene of interest, while color represents their average expression levels. (C) Breakdown of expression of Zhx3, Itga11, and Ggcx in all annotated cell subtypes (left) and specifically in the annotated fibroblast subtypes (right). Dot size correlates with the percentage of cells that express the gene of interest, while color represents their average expression levels. (D and E) Spatiotemporal expression of Zhx3, Ggcx, and Itga11 in all cells (D) and in the fibroblast subcluster (E). Space on the y axis refers to increasingly large rings around the wound center; wounds were sampled on day 1, 3, 7, and 14 as depicted on the x axis. The number in the square indicates the percentage of the cell subpopulation among all cells. Background color indicates relative change compared with the unwounded state (red: increase, blue: decrease, white: no change).

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ISSN 2379-3708

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