Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Age-dependent loss of HAPLN1 erodes vascular integrity via indirect upregulation of endothelial ICAM1 in melanoma

Nature Aging volume 4, pages 350–363 (2024)Cite this article

Subjects

Abstract

Melanoma, the most lethal form of skin cancer, often has worse outcomes in older patients. We previously demonstrated that an age-related decrease in the secreted extracellular matrix (ECM) protein HAPLN1 has a role in slowing melanoma progression. Here we show that HAPLN1 in the dermal ECM is sufficient to maintain the integrity of melanoma-associated blood vessels, as indicated by increased collagen and VE-cadherin expression. Specifically, we show that HAPLN1 in the ECM increases hyaluronic acid and decreases endothelial cell expression of ICAM1. ICAM1 phosphorylates and internalizes VE-cadherin, a critical determinant of vascular integrity, resulting in permeable blood vessels. We found that blocking ICAM1 reduces tumor size and metastasis in older mice. These results suggest that HAPLN1 alters endothelial ICAM1expression in an indirect, matrix-dependent manner. Targeting ICAM1 could be a potential treatment strategy for older patients with melanoma, emphasizing the role of aging in tumorigenesis.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Effects of HAPLN1 on the melanoma TME.
Fig. 2: Endothelial VE-cadherin is modulated by substrate HAPLN1 levels.
Fig. 3: Substrate stiffness increases angiogenesis and decreases VE-cadherin expression in HUVECs.
Fig. 4: Loss of matrix HAPLN1 increases endothelial expression of ICAM1.
Fig. 5: Anti-ICAM1 therapy reduces primary tumor size and distal metastases in vivo.

Similar content being viewed by others

Data availability

All data generated or analyzed during this study are included in this article and its supplementary information and are available upon request. Complete data used to generate Fig. 4a is in Supplementary Table 1.

References

  1. Siegel, R. L., Miller, K. D., Wagle, N. S. & Jemal, A. Cancer statistics, 2023. CA Cancer J. Clin. 73, 17–48 (2023).

    Article  PubMed  Google Scholar 

  2. Macdonald, J. B. et al. Malignant melanoma in the elderly: different regional disease and poorer prognosis. J Cancer 2, 538–543 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Balch, C. M. et al. Age as a prognostic factor in patients with localized melanoma and regional metastases. Ann. Surg. Oncol. 20, 3961–3968 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  4. The importance of aging in cancer research. Nat. Aging 2, 365–366 (2022).

  5. Fulop, T. et al. Immunosenescence and inflamm-aging as two sides of the same coin: friends or foes? Front. Immunol. 8, 1960 (2017).

    Article  PubMed  Google Scholar 

  6. Hanahan, D. Hallmarks of cancer: new simensions. Cancer Discov 12, 31–46 (2022).

    Article  CAS  PubMed  Google Scholar 

  7. Fane, M. & Weeraratna, A. T. How the ageing microenvironment influences tumour progression. Nat. Rev. Cancer 20, 89–106 (2020).

    Article  CAS  PubMed  Google Scholar 

  8. Ecker, B. L. et al. Age-related changes in HAPLN1 increase lymphatic permeability and affect routes of melanoma metastasis. Cancer Discov. 9, 82–95 (2019).

    Article  CAS  PubMed  Google Scholar 

  9. Kaur, A. et al. Remodeling of the collagen matrix in aging skin promotes melanoma metastasis and affects immune cell motility. Cancer Discov. 9, 64–81 (2019).

    Article  CAS  PubMed  Google Scholar 

  10. Kaur, A. et al. sFRP2 in the aged microenvironment drives melanoma metastasis and therapy resistance. Nature 532, 250–254 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Cox, T. R. The matrix in cancer. Nat. Rev. Cancer 21, 217–238 (2021).

    Article  CAS  PubMed  Google Scholar 

  12. Phillip, J. M., Aifuwa, I., Walston, J. & Wirtz, D. The mechanobiology of aging. Annu. Rev. Biomed. Eng. 17, 113–141 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Buckwalter, J. A., Rosenberg, L. C. & Tang, L. H. The effect of link protein on proteoglycan aggregate structure. An electron microscopic study of the molecular architecture and dimensions of proteoglycan aggregates reassembled from the proteoglycan monomers and link proteins of bovine fetal epiphyseal cartilage. J. Biol. Chem. 259, 5361–5363 (1984).

    Article  CAS  PubMed  Google Scholar 

  14. Chen, X. et al. Glycosaminoglycans modulate long-range mechanical communication between cells in collagen networks. Proc. Natl Acad. Sci. USA 119, e2116718119 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Faries, M. B. et al. Completion dissection or observation for sentinel-node metastasis in melanoma. New Engl. J. Med. 376, 2211–2222 (2017).

    Article  PubMed  Google Scholar 

  16. Page, A. J. et al. Increasing age is associated with worse prognostic factors and increased distant recurrences despite fewer sentinel lymph node positives in melanoma. Int. J. Surg. Oncol. 2012, 456987 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Huang, R., Andersen, L. M. K. & Rofstad, E. K. Metastatic pathway and the microvascular and physicochemical microenvironments of human melanoma xenografts. J. Translat. Med. 15, 203 (2017).

    Article  Google Scholar 

  18. Faries, M. B. et al. Lymph node metastasis in melanoma: a debate on the significance of nodal metastases, conditional survival analysis and clinical trials. Clin. Exp. Metastasis 35, 431–442 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Meeth, K., Wang, J. X., Micevic, G., Damsky, W. & Bosenberg, M. W. The YUMM lines: a series of congenic mouse melanoma cell lines with defined genetic alterations. Pigment Cell Melanoma Res. 29, 590–597 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Dallas, N. A. et al. Endoglin (CD105): a marker of tumor vasculature and potential target for therapy. Clin. Cancer Res. 14, 1931–1937 (2008).

    Article  CAS  PubMed  Google Scholar 

  21. Fonsatti, E., Altomonte, M., Nicotra, M. R., Natali, P. G. & Maio, M. Endoglin (CD105): a powerful therapeutic target on tumor-associated angiogenetic blood vessels. Oncogene 22, 6557–6563 (2003).

    Article  CAS  PubMed  Google Scholar 

  22. Pandita, A. et al. Intussusceptive angiogenesis in human metastatic malignant melanoma. Am. J. Pathol. 191, 2023–2038 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Li, B. et al. Fibronectin 1 promotes melanoma proliferation and metastasis by inhibiting apoptosis and regulating EMT. Onco. Targets Ther. 12, 3207–3221 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Yokoi, K. et al. Capillary-wall collagen as a biophysical marker of nanotherapeutic permeability into the tumor microenvironment. Cancer Res. 74, 4239–4246 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Franco-Barraza, J., Beacham, D. A., Amatangelo, M. D. & Cukierman, E. Preparation of extracellular matrices produced by cultured and primary fibroblasts.Curr. Protoc. Cell Biol. 71, 10.19.11–10.19.34 (2016).

    Article  Google Scholar 

  26. Giannotta, M., Trani, M. & Dejana, E. VE-cadherin and endothelial adherens junctions: active guardians of vascular integrity. Dev. Cell 26, 441–454 (2013).

    Article  CAS  PubMed  Google Scholar 

  27. Adil, M. S. & Somanath, P. R. Endothelial permeability assays in vitro. Methods Mol. Biol. 2367, 177–191 (2021).

    Article  CAS  PubMed  Google Scholar 

  28. Jiménez, N., Krouwer, V. J. & Post, J. A. A new, rapid and reproducible method to obtain high quality endothelium in vitro. Cytotechnology 65, 1–14 (2013).

    Article  PubMed  Google Scholar 

  29. Szauter, K. M., Cao, T., Boyd, C. D. & Csiszar, K. Lysyl oxidase in development, aging and pathologies of the skin. Pathol. Biol. (Paris) 53, 448–456 (2005).

    Article  CAS  PubMed  Google Scholar 

  30. Frantz, C., Stewart, K. M. & Weaver, V. M. The extracellular matrix at a glance. J. Cell Sci. 123, 4195–4200 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Dupont, S. et al. Role of YAP/TAZ in mechanotransduction. Nature 474, 179–183 (2011).

    Article  CAS  PubMed  Google Scholar 

  32. Nukuda, A. et al. Stiff substrates increase YAP-signaling-mediated matrix metalloproteinase-7 expression. Oncogenesis 4, e165 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Melching, L. I. & Roughley, P. J. The role of link protein in mediating the interaction between hyaluronic acid and newly secreted proteoglycan subunits from adult human articular cartilage. J. Biol. Chem. 260, 16279–16285 (1985).

    Article  CAS  PubMed  Google Scholar 

  34. Lee, D. H., Oh, J.-H. & Chung, J. H. Glycosaminoglycan and proteoglycan in skin aging. J. Dermatol. Sci. 83, 174–181 (2016).

    Article  CAS  PubMed  Google Scholar 

  35. Soucy, P. A., Werbin, J., Heinz, W., Hoh, J. H. & Romer, L. H. Microelastic properties of lung cell-derived extracellular matrix. Acta Biomater. 7, 96–105 (2011).

    Article  CAS  PubMed  Google Scholar 

  36. Jayakrishnan, A. & Jameela, S. R. Glutaraldehyde as a fixative in bioprostheses and drug delivery matrices. Biomaterials 17, 471–484 (1996).

    Article  CAS  PubMed  Google Scholar 

  37. Kim, S. S. et al. Tissue engineering of heart valves by recellularization of glutaraldehyde-fixed porcine valves using bone marrow-derived cells. Exp. Mol. Med. 38, 273–283 (2006).

    Article  CAS  PubMed  Google Scholar 

  38. Schnellmann, R., Wei, Z., Choudhury, M. I., Sun, S. & Gerecht, S. Vascular phenotype is compromised in dynamically stiffening hydrogel. FASEB J. 34, 1 (2020).

    Article  Google Scholar 

  39. Schnellmann, R. et al. Stiffening matrix induces age-mediated microvascular phenotype through increased cell contractility and destabilization of adherens junctions. Adv. Sci. (Weinh) 9, e2201483 (2022).

    Article  PubMed  Google Scholar 

  40. David, M. R. & Jennifer, L. Skin collagen through the lifestages: importance for skin health and beauty. Plast. Aesthet. Res. 8, 2 (2021).

    Article  Google Scholar 

  41. Périn, J. P., Bonnet, F., Thurieau, C. & Jollès, P. Link protein interactions with hyaluronate and proteoglycans. Characterization of two distinct domains in bovine cartilage link proteins. J. Biol. Chem. 262, 13269–13272 (1987).

    Article  PubMed  Google Scholar 

  42. Mieulet, V. et al. Stiffness increases with myofibroblast content and collagen density in mesenchymal high grade serous ovarian cancer. Sci. Rep. 11, 4219 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Bui, T. M., Wiesolek, H. L. & Sumagin, R. ICAM-1: a master regulator of cellular responses in inflammation, injury resolution, and tumorigenesis. J. Leukoc. Biol. 108, 787–799 (2020).

    Article  CAS  PubMed  Google Scholar 

  44. Marlin, S. D. & Springer, T. A. Purified intercellular adhesion molecule-1 (ICAM-1) is a ligand for lymphocyte function-associated antigen 1 (LFA-1). Cell 51, 813–819 (1987).

    Article  CAS  PubMed  Google Scholar 

  45. Allingham, M. J., van Buul, J. D. & Burridge, K. ICAM-1-mediated, Src- and Pyk2-dependent vascular endothelial cadherin tyrosine phosphorylation is required for leukocyte transendothelial migration. J. Immunol. 179, 4053–4064 (2007).

    Article  CAS  PubMed  Google Scholar 

  46. Klemke, M., Weschenfelder, T., Konstandin, M. H. & Samstag, Y. High affinity interaction of integrin α4β1 (VLA-4) and vascular cell adhesion molecule 1 (VCAM-1) enhances migration of human melanoma cells across activated endothelial cell layers. J. Cell. Physiol. 212, 368–374 (2007).

    Article  CAS  PubMed  Google Scholar 

  47. Scott, H. A. et al. Matrix stiffness exerts biphasic control over monocyte-endothelial adhesion via Rho-mediated ICAM-1 clustering. Integr. Biol. (Camb) 8, 869–878 (2016).

    Article  CAS  PubMed  Google Scholar 

  48. Stroka, K. M., Levitan, I. & Aranda-Espinoza, H. OxLDL and substrate stiffness promote neutrophil transmigration by enhanced endothelial cell contractility and ICAM-1. J. Biomech. 45, 1828–1834 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Chen, W. et al. Matrix stiffness regulates the interactions between endothelial cells and monocytes. Biomaterials 221, 119362 (2019).

    Article  CAS  PubMed  Google Scholar 

  50. Meng, F. et al. Attenuation of lipopolysaccharide-induced lung vascular stiffening by lipoxin reduces lung inflammation. Am. J. Respir. Cell Mol. Biol. 52, 152–161 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Schaefer, A. & Hordijk, P. L. Cell-stiffness-induced mechanosignaling: a key driver of leukocyte transendothelial migration. J. Cell Sci. 128, 2221–2230 (2015).

    Article  CAS  PubMed  Google Scholar 

  52. Wichert, S. et al. A single-arm, open-label, phase 2 clinical trial evaluating disease response following treatment with BI-505, a human anti-intercellular adhesion molecule-1 monoclonal antibody, in patients with smoldering multiple myeloma. PLoS One 12, e0171205 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Enlimomab Acute Stroke Trial Investigators. Use of anti-ICAM-1 therapy in ischemic stroke: results of the Enlimomab Acute Stroke Trial. Neurology 57, 1428–1434 (2001).

    Article  Google Scholar 

  54. Guo, P. et al. A rationally designed ICAM1 antibody drug conjugate eradicates late-stage and refractory triple-negative breast tumors in vivo. Sci. Adv. 9, eabq7866 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Xiao, D. et al. Identifying mRNA, microRNA and protein profiles of melanoma exosomes. PLoS One 7, e46874 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Naba, A. et al. The matrisome: in silico definition and in vivo characterization by proteomics of normal and tumor extracellular matrices. Mol. Cell Proteomics 11, M111.014647 (2012).

    Article  PubMed  Google Scholar 

  57. Ahmadzadeh, H. et al. Modeling the two-way feedback between contractility and matrix realignment reveals a nonlinear mode of cancer cell invasion. Proc. Natl Acad. Sci. USA 114, E1617–e1626 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. & Wiedmann, L. et al. HAPLN1 is a driver for peritoneal carcinomatosis in pancreatic cancer. Nat. Commun. 14, 2353 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Mahadevan, D. & Von Hoff, D. D. Tumor-stroma interactions in pancreatic ductal adenocarcinoma. Mol. Cancer Ther. 6, 1186–1197 (2007).

    Article  CAS  PubMed  Google Scholar 

  60. Marino, G. E. & Weeraratna, A. T. A glitch in the matrix: age-dependent changes in the extracellular matrix facilitate common sites of metastasis. Aging Cancer 1, 19–29 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Attwell, D., Mishra, A., Hall, C. N., O’Farrell, F. M. & Dalkara, T. What is a pericyte? J. Cereb. Blood Flow Metab. 36, 451–455 (2016).

    Article  CAS  PubMed  Google Scholar 

  62. Sun, R., Kong, X., Qiu, X., Huang, C. & Wong, P. P. The emerging roles of pericytes in modulating tumor microenvironment. Front. Cell Dev. Biol. 9, 676342 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Trevino-Villarreal, J. H., Cotanche, D. A., Sepulveda, R. & Rogers, R. A. Effect of pericytes on melanoma development. J. Clin. Oncol. 30, 83 (2012).

    Article  Google Scholar 

  64. Hubbard, A. K. & Rothlein, R. Intercellular adhesion molecule-1 (ICAM-1) expression and cell signaling cascades. Free Radic. Biol. Med. 28, 1379–1386 (2000).

    Article  CAS  PubMed  Google Scholar 

  65. Chung, H. Y. et al. Molecular inflammation: underpinnings of aging and age-related diseases. Ageing Res. Rev. 8, 18–30 (2009).

    Article  CAS  PubMed  Google Scholar 

  66. Ferrucci, L. & Fabbri, E. Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty. Nat. Rev. Cardiol. 15, 505–522 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Zhao, H. et al. Inflammation and tumor progression: signaling pathways and targeted intervention. Signal Transduct. Target Ther. 6, 263 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

G.E.M.-B. is supported by NRSA pre-doctoral fellowship 1F31CA261065-01A1 and R01CA232256. We thank the outstanding Core Facilities of the Wistar Institute, supported by P30CA010815 and of the Johns Hopkins Kimmel Cancer Center, P30CA00697356; A.E.C. is supported by R01CA232256 and GT15667. V.W. is supported by T32CA153952. Y.C. is supported by U01CA227550. A.T.W. is supported by grants P01 CA114046, U01CA227550, and R01CA232256, a Bloomberg Distinguished Professorship and the EV McCollum Endowed Chair. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank J. Hayden and F. Keeney at the Wistar Imaging Facility for their training and guidance, especially with the two-photon experiment. This paper is dedicated to the memory of Judith Campisi.

Author information

Author notes

  1. Yash Chhabra

    Present address: Cancer Signaling and Microenvironment, FoxChase Cancer Center, Philadelphia, PA, USA

Authors and Affiliations

  1. Department of Biochemistry and Molecular Biology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    Gloria E. Marino-Bravante, Alexis E. Carey, Laura Hüser, Agrani Dixit, Vania Wang, Yash Chhabra & Ashani T. Weeraratna

  2. Department of Oncology, Sidney Kimmel Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA

    Gloria E. Marino-Bravante, Alexis E. Carey, Laura Hüser, Agrani Dixit, Vania Wang, Yash Chhabra & Ashani T. Weeraratna

  3. Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA

    Amanpreet Kaur

  4. Abramson Family Cancer Research Institute, Department of Pathology & Laboratory Medicine, Penn Sarcoma Program, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Ying Liu & T. S. Karin Eisinger-Mathason

  5. Department of Materials Science and Engineering, Johns Hopkins Whiting School of Engineering, Baltimore, MD, USA

    Supeng Ding & Luo Gu

  6. Institute for Nanobiotechnology, Johns Hopkins University, Baltimore, MD, USA

    Rahel Schnellmann

  7. Department of Biomedical Engineering, Duke University, Durham, NC, USA

    Sharon Gerecht

Authors
  1. Gloria E. Marino-Bravante
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexis E. Carey
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laura Hüser
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Agrani Dixit
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Vania Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Amanpreet Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ying Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Supeng Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Rahel Schnellmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Sharon Gerecht
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Luo Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. T. S. Karin Eisinger-Mathason
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Yash Chhabra
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Ashani T. Weeraratna
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

G.E.M.-B. and A.T.W. conceived the study and designed the experiments. G.E.M.-B. performed the majority of all experiments and analyses. Y.C. aided in experimental design, especially for the Qiagen screen (Fig. 4a) and animal study (Fig. 5), interpretation of data and revisions. A.D. and V.W. also assisted with revisions. Animal experiments were performed by G.E.M.-B., Y.C. and A.E.C., including injections and tissue processing. A.E.C. also aided in the design and interpretation of experiments necessary for the response to reviewers. L.H. aided in preparation of cellular-derived matrices, preparation of spheroids, interpretation of data and reviewer revisions. Skin reconstructs (Fig. 1f) and shRNA used for this study were created and validated by A.K. Y.L. performed and analyzed data from the ECIS experiment in the laboratory of T.S.K.E.-M. (Fig. 2d). S.D. performed experiments necessary for the response to reviewers in the laboratory of L.G. R.S. and S.G. aided in the experimental design of the spheroid assay in fibroblast-modified substrate (Fig. 1e). All authors participated in the reading and editing of the manuscript.

Corresponding authors

Correspondence to Yash Chhabra or Ashani T. Weeraratna.

Ethics declarations

Competing interests

A.T.W. is on the board of ReGAIN Therapeutics, unrelated to the presented work. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Aging thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Increased angiogenesis in the absence of HAPLN1 is not mediated by increased endothelial cell proliferation.

(A) Immunohistochemistry (IHC) of CD31 in primary tumors from young mice treated intradermally with PBS (n = 5) and aged mice treated intradermally with rHAPLN1(n = 6) or PBS (n = 5). Graph represents quantification of stained region normalized to vessel number per area (one-way ANOVA, young +PBS vs. aged +PBS, **p = 0.004, aged +PBS vs. aged +rHAPLN1, *p = 0.02). Data are presented as mean values ± SEM. (B) Immunohistochemistry (IHC) of podoplanin (PDPN), vascular endothelial growth factor receptor-3 (VEGFR3), and lymphatic vessel endothelial hyaluronan receptor-1 (LYVE1) in primary tumors from young mice treated intradermally with PBS (n = 5) and aged mice treated intradermally with rHAPLN1 (n = 6) or PBS (n = 5). (C) Immunofluorescent microscopy of HUVECs cultured on indicated FDMs or control (gelatin coated dish) (n = 3 for all conditions) after 30 min of BRDU incorporation (0.03 mg/mL). BrdU+ (green), nuclei (DAPI stained). (D) Corresponding graph indicates fraction of BrdU+ nuclei compared to all DAPI+ in A (n = 3 for all conditions, one-way ANOVA shows no significance across any conditions). (E) qRT-PCR of 1205Lu human melanoma cells subjected to conditioned media (CM) from young or aged dermal fibroblasts. Graph indicates relative gene expression of HAPLN1 compared to positive control (young fibroblast line) and normalized to 18 s (one way ANOVA, ****p < 0.0001, n = 3). Data are presented as mean values ± SEM. (F) qRT-PCR of WM164 human melanoma cells subjected to CM from young or aged dermal fibroblasts. Graph indicates relative gene expression of HAPLN1 compared to positive control (young fibroblast line) and normalized to 18 s (one way ANOVA, ****p < 0.0001, n = 3). Data are presented as mean values ± SEM.

Source data

Extended Data Fig. 2 Confirmation of HAPLN1 knockdown in young fibroblast lines and in FDMs.

(A) Western blot for HAPLN1 in young fibroblast lines transduced with indicated shRNA against HAPLN1 (n = 1). (B) Relative quantification of HAPLN1 bands in A normalized to HSP90 loading control. (C) qRT-PCR of HAPLN1 in indicated fibroblast lines. Graph represents relative gene expression normalized to 18 s. (ANOVA, **p = 0.001, ***p = 0.0008). Data are presented as mean values ± SEM. (D) Immunofluorescent microscopy of HUVECs on indicated matrices, with fibronectin (green) used as a readout for successful matrix deposition (red=VE-cadherin, blue=DAPI), Aged +PBS (n = 9), Young +shCTRL (n = 9), Aged+rHAPLN1 (n = 9), Young Fixed (n = 4), Young +shHAPLN1 (n = 9). (E) Mass spectrometry analysis on young vs. aged FDMs showing differentially expressed proteins.

Source data

Extended Data Fig. 3 Confirmation of FDM stiffness and spheroid viability staining.

(A) Immunofluorescent microscopy of HUVECs cultured on indicated FDMs (red=YAP, blue=DAPI, purple=double positive nuclei). (B) Quantification of the percent of double-positive nuclei in A based on consistent color and shape thresholding using ImageJ analysis (ANOVA, **p = 0.0012, 0.0013, 0.0029, 0.0032, ***p = 0.0001, ****p < 0.0001, n = 3 FDM experiments per condition). Data are presented as mean values ± SEM. (C) IHC of skin (young, n = 5; aged n = 5) and (D) tumor (aged +PBS n = 5; aged +rHAPLN1 n = 5) in mice using a biotinylated hyaluronic acid Probe. (E) HUVEC spheroids were embedded in the absence of fibroblasts in 1.5 mg/mL (n = 11), 3 mg/mL (n = 18), 5 mg/mL (n = 14), 7 mg/mL (n = 9). Images are spheroids after 24 h in brightfield (left column; centroid=white circle, sprouts=red outline) and fluorescent microscopy (right column) after 20 min of treatment with live/dead viability stain (red=ethidium homodimer/ dead cells, green=calcein-AM/ live cells). (F) Graph is a quantification of the area of red stain for all conditions in E as a proportion of total spheroid area. One-way ANOVA, 1.5 mg/mL vs. 5 mg/mL,1.5 mg/mL vs. 7 mg/mL, all ****p = 0.0001; 3 mg/mL vs. 5 mg/mL (**p = 0.006); 3 mg/mL vs. 7 mg/mL (*p = 0.02); 5 mg/mL vs. 7 mg/mL (not significant). Data are presented as mean values ± SD.

Source data

Extended Data Fig. 4 qRT-PCR confirmation of selected targets of HUVEC genetic screen.

qRT-PCR of HUVECs grown on indicated FDMs for 48 h. Graph represents relative gene expression normalized to 18 s (n = 1).

Source data

Extended Data Fig. 5 Confirmation of anti-ICAM1 therapy efficacy and identification of distal metastases.

(A) IHC for ICAM1 of primary tumors from control IgG (n = 8) or anti-ICAM1 (n = 7) treated aged mice. (B) Corresponding graph represents quantification of stained region in A (student’s t-test, unpaired, 2-tailed **p = 0.0060). Data are presented as mean values +/- SEM. (C) qRT-PCR for ICAM1 in Yumm1.7 melanoma cells treated for 24 h with either IgG control or neutralizing anti-ICAM1 in vitro compared to untreated HUVECs. Graph represents relative gene expression normalized to 18 s (ordinary one-way ANOVA, ****p = <0.0001, n = 3). (D) qRT-PCR of HUVECs treated in vitro with either IgG (n = 3) or neutralizing anti-ICAM1 (n = 3). Graph represents relative gene expression of ICAM1 normalized to 18 s (student’s t-test, ****p = <0.0001, n = 3 treatments per condition). (E) Representative images of H&E-stained lungs from control IgG or anti-ICAM1 treated aged mice. Arrows indicate macrometastases (>10 cells). (F) Representative confirmation of metastases in control IgG (n = 8) and anti-ICAM1(n = 7) treated aged mouse lungs. Metastases were identified if they stained positive in H&E (top row), MERTK (middle row), and Ki67 (bottom row). (G) Graph represents percentage mice with any number of macrometastases in their lungs based on H&E analysis (n = 8, Control IgG, n = 7, anti-ICAM1). (H) qRT-PCR for CDH5 in HUVECs treated in vitro with either IgG (n = 3) or neutralizing anti-ICAM1 (n = 3). Graph represents relative gene expression normalized to 18 s (student’s t-test, ***p < 0.0001) Data are presented as mean values± SEM. (I) qRT-PCR for ICAM1 in 1205Lu melanoma cells cultured on indicated FDMs compared to HUVECs cultured on young FDM. Graph represented relative gene expression normalized to 18 s (ANOVA, ****p < 0.0001, n = 3). Data are presented as mean values ± SEM.

Source data

Supplementary information

Supplementary Information

Supplementary tables 1 and 2

Reporting Summary

Source data

Source Data All Figs

Statistical source data (graphs) for all figures

Source Data Fig. 4

Unprocessed westerns

Source Data Extended Data Fig. 2

Unprocessed westerns

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Marino-Bravante, G.E., Carey, A.E., Hüser, L. et al. Age-dependent loss of HAPLN1 erodes vascular integrity via indirect upregulation of endothelial ICAM1 in melanoma. Nat Aging 4, 350–363 (2024). https://doi.org/10.1038/s43587-024-00581-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43587-024-00581-8

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer