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

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 1

Retinol metabolism is strongly regulated by skin wounding.

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Retinol metabolism is strongly regulated by skin wounding. (A) Outline o...
(A) Outline of the experimental setup. Pdgfrα+ fibroblasts were sorted from normal skin and 5-day wounds and subjected to RNA-Seq and metabolomics analysis. N = 3 mice per experiment. Created in BioRender. Werner, S. (2025) https://BioRender.com/aaw9vrp (B) Volcano plot showing differentially abundant metabolites in wound vs. normal skin fibroblasts. Colored points indicate metabolites with significantly differential abundance (P ≤ 0.05); diamond-shaped points indicate metabolites that were confirmed in an independent experiment. N = 3 mice per experiment. (C) Volcano plot showing differentially expressed genes in wound vs. normal skin fibroblasts based on bulk RNA-Seq analysis of FACS-sorted fibroblasts (16). Colored points indicate significantly differentially expressed genes (Bonferroni-corrected P ≤ 0.05, |log2(FC)| ≥ 2). N = 3 mice. Rdh10, retinol dehydrogenase 10; Rbp4, retinol-binding protein 4; Stra6, stimulated by retinoic acid 6. (D) Integrated pathway analysis of RNA-Seq and metabolomics data sets using MetaboAnalyst 6.0. Colored points indicate significantly altered pathways (P ≤ 0.05). (E) Schematic overview of retinol metabolism with highly relevant metabolites and genes. Metabolites with a strikethrough pattern were not detected in the metabolomics experiment. Members of the respective enzyme families that were significantly differentially expressed based on RNA-Seq data are featured (uncorrected P ≤ 0.05). Size of the circles corresponds to the log10(RPKM+0.95) (0.95 was added to all values to avoid negative results of the logarithm), and color represents the log2(fold change) of the genes (red: increased expression in wound vs. normal skin fibroblasts; blue: reduced expression in wound vs. normal skin fibroblasts). RPKM, reads per kilobase million.