6
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Promotion of Lymphangiogenesis by Targeted Delivery of VEGF-C Improves Diabetic Wound Healing

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Chronic wounds represent a major therapeutic challenge. Lymphatic vessel function is impaired in chronic ulcers but the role of lymphangiogenesis in wound healing has remained unclear. We found that lymphatic vessels are largely absent from chronic human wounds as evaluated in patient biopsies. Excisional wound healing studies were conducted using transgenic mice with or without an increased number of cutaneous lymphatic vessels, as well as antibody-mediated inhibition of lymphangiogenesis. We found that a lack of lymphatic vessels mediated a proinflammatory wound microenvironment and delayed wound closure, and that the VEGF-C/VEGFR3 signaling axis is required for wound lymphangiogenesis. Treatment of diabetic mice (db/db mice) with the F8–VEGF-C fusion protein that targets the alternatively spliced extra domain A (EDA) of fibronectin, expressed in remodeling tissue, promoted wound healing, and potently induced wound lymphangiogenesis. The treatment also reduced tissue inflammation and exerted beneficial effects on the wound microenvironment, including myofibroblast density and collagen deposition. These findings indicate that activating the lymphatic vasculature might represent a new therapeutic strategy for treating chronic non-healing wounds.

          Related collections

          Most cited references77

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Trimmomatic: a flexible trimmer for Illumina sequence data

            Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Fiji: an open-source platform for biological-image analysis.

              Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
                Bookmark

                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                CELLC6
                Cells
                Cells
                MDPI AG
                2073-4409
                February 2023
                February 01 2023
                : 12
                : 3
                : 472
                Article
                10.3390/cells12030472
                36766814
                63daf574-dc81-447f-88c1-8cc8d8bf6ed0
                © 2023

                https://creativecommons.org/licenses/by/4.0/

                History

                Comments

                Comment on this article