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Up-Regulation and Profibrotic Role of Osteopontin in Human Idiopathic Pulmonary Fibrosis
Open accessAbstract
BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive and lethal disorder characterized by fibroproliferation and excessive accumulation of extracellular matrix in the lung. METHODS AND FINDINGS: Using oligonucleotide arrays, we identified osteopontin as one of the genes that significantly distinguishes IPF from normal lungs. Osteopontin was localized to alveolar epithelial cells in IPF lungs and was also significantly elevated in bronchoalveolar lavage from IPF patients. To study the fibrosis-relevant effects of osteopontin we stimulated primary human lung fibroblasts and alveolar epithelial cells (A549) with recombinant osteopontin. Osteopontin induced a significant increase of migration and proliferation in both fibroblasts and epithelial cells. Epithelial growth was inhibited by the pentapeptide Gly-Arg-Gly-Asp-Ser (GRGDS) and antibody to CD44, while fibroproliferation was inhibited by GRGDS and antibody to alphavbeta3 integrin. Fibroblast and epithelial cell migration were inhibited by GRGDS, anti-CD44, and anti-alphavbeta3. In fibroblasts, osteopontin up-regulated tissue inhibitor of metalloprotease-1 and type I collagen, and down-regulated matrix metalloprotease-1 (MMP-1) expression, while in A549 cells it caused up-regulation of MMP-7. In human IPF lungs, osteopontin colocalized with MMP-7 in alveolar epithelial cells, and application of weakest link statistical models to microarray data suggested a significant interaction between osteopontin and MMP-7. CONCLUSIONS: Our results provide a potential mechanism by which osteopontin secreted from the alveolar epithelium may exert a profibrotic effect in IPF lungs and highlight osteopontin as a potential target for therapeutic intervention in this incurable disease.
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Kolb, M., Bonniaud, P., Galt, T., Sime, P. J., Kelly, M. M., Margetts, P. J., & Gauldie, J. (2002) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/ajrcmb.27.2.4674 · Cited by 168
Cited by
King, T. E., Pardo, A., & Selman, M. (2011) · The Lancet · doi:10.1016/s0140-6736(11)60052-4 · Cited by 2,194
Self-citationReyfman, P. A., Walter, J. M., Joshi, N., Anekalla, K. R., McQuattie-Pimentel, A. C., Chiu, S., Fernandez, R., Akbarpour, M., Chen, C.-I., Ren, Z., et al. (2019) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201712-2410oc · Cited by 1,203
Pharmacology of Adenosine Receptors: The State of the Art
Borea, P. A., Gessi, S., Merighi, S., Vincenzi, F., & Varani, K. (2018) · Physiological Reviews · doi:10.1152/physrev.00049.2017 · Cited by 783
Königshoff, M., Kramer, M., Balsara, N., Wilhelm, J., Amarie, O. V., Jahn, A., Rose, F., Fink, L., Seeger, W., Schaefer, L., et al. (2009) · Journal of Clinical Investigation · doi:10.1172/jci33950 · Cited by 558
SARS-CoV-2 infection triggers profibrotic macrophage responses and lung fibrosis
Wendisch, D., Dietrich, O., Mari, T., von Stillfried, S., Ibarra, I. L., Mittermaier, M., Mache, C., Chua, R. L., Knoll, R., Timm, S., et al. (2021) · Cell · doi:10.1016/j.cell.2021.11.033 · Cited by 528
Matrix Metalloproteinases as Therapeutic Targets for Idiopathic Pulmonary Fibrosis
Craig, V. J., Zhang, L., Hagood, J. S., & Owen, C. A. (2015) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/rcmb.2015-0020tr · Cited by 507
Caveolin-1: a critical regulator of lung fibrosis in idiopathic pulmonary fibrosis
Wang, X. M., Zhang, Y., Kim, H. P., Zhou, Z., Feghali-Bostwick, C. A., Liu, F., Ifedigbo, E., Xu, X., Oury, T. D., Kaminski, N., et al. (2006) · The Journal of Experimental Medicine · doi:10.1084/jem.20061536 · Cited by 397
Self-citationSchiller, H. B., Fernandez, I. E., Burgstaller, G., Schaab, C., Scheltema, R. A., Schwarzmayr, T., Strom, T. M., Eickelberg, O., & Mann, M. (2015) · Molecular Systems Biology · doi:10.15252/msb.20156123 · Cited by 261
Matrix metalloproteases in aberrant fibrotic tissue remodeling
Pardo, A., & Selman, M. (2006) · Proceedings of the American Thoracic Society · doi:10.1513/pats.200601-012tk · Cited by 260
Self-citationShirakawa, K., Endo, J., Kataoka, M., Katsumata, Y., Yoshida, N., Yamamoto, T., Isobe, S., Moriyama, H., Goto, S., Kitakata, H., et al. (2018) · Circulation · doi:10.1161/circulationaha.118.035047 · Cited by 252
White, E. S., Xia, M., Murray, S., Dyal, R., Flaherty, C. M., Flaherty, K. R., Moore, B. B., Cheng, L., Doyle, T. J., Villalba, J., et al. (2016) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201505-0862oc · Cited by 180
Coulis, G., Jaime, D., Guerrero-Juarez, C., Kastenschmidt, J. M., Farahat, P. K., Nguyen, Q., Pervolarakis, N., McLinden, K., Thurlow, L., Movahedi, S., et al. (2023) · Science Advances · doi:10.1126/sciadv.add9984 · Cited by 150
Validation of the prognostic value of MMP‐7 in idiopathic pulmonary fibrosis
Tzouvelekis, A., Herazo‐Maya, J. D., Slade, M., Chu, J., Deiuliis, G., Ryu, C., Li, Q., Sakamoto, K., Ibarra, G., Pan, H., et al. (2017) · Respirology · doi:10.1111/resp.12920 · Cited by 130
Self-citationBauer, Y., White, E. S., de Bernard, S., Cornelisse, P., Leconte, I., Morganti, A., Roux, S., & Nayler, O. (2017) · ERJ Open Research · doi:10.1183/23120541.00074-2016 · Cited by 139
Biomarkers in idiopathic pulmonary fibrosis
Drakopanagiotakis, F., Wujak, L., Wygrecka, M., & Markart, P. (2018) · Matrix Biology · doi:10.1016/j.matbio.2018.01.023 · Cited by 132
Matrix biomechanics and dynamics in pulmonary fibrosis
Haak, A. J., Tan, Q., & Tschumperlin, D. J. (2018) · Matrix Biology · doi:10.1016/j.matbio.2017.12.004 · Cited by 95
Biomarker signatures for progressive idiopathic pulmonary fibrosis
Clynick, B., Corte, T. J., Jo, H. E., Stewart, I., Glaspole, I. N., Grainge, C., Maher, T. M., Navaratnam, V., Hubbard, R., Hopkins, P. M. A., et al. (2022) · European Respiratory Journal · doi:10.1183/13993003.01181-2021 · Cited by 86
Age dependent increase in the levels of osteopontin inhibits skeletal muscle regeneration
Paliwal, P., Pishesha, N., Wijaya, D., & Conboy, I. M. (2012) · Aging · doi:10.18632/aging.100477 · Cited by 89
Hatipoglu, O. F., Uctepe, E., Opoku, G., Wake, H., Ikemura, K., Ohtsuki, T., Inagaki, J., Gunduz, M., Gunduz, E., Watanabe, S., et al. (2021) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2021.111633 · Cited by 67
The Hedgehog Signaling Pathway in Idiopathic Pulmonary Fibrosis: Resurrection Time
Effendi, W. I., & Nagano, T. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms23010171 · Cited by 59
Roehlen, N., Saviano, A., El Saghire, H., Crouchet, E., Nehme, Z., Del Zompo, F., Jühling, F., Oudot, M. A., Durand, S. C., Duong, F. H. T., et al. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abj4221 · Cited by 54
Nikitopoulou, I., Manitsopoulos, N., Kotanidou, A., Tian, X., Petrovic, A., Magkou, C., Ninou, I., Aidinis, V., Schermuly, R. T., Kosanovic, D., et al. (2019) · Pulmonary Circulation · doi:10.1177/2045894019881954 · Cited by 44
Prognostic Value of Serum Osteopontin in Acute Exacerbation of Idiopathic Pulmonary Fibrosis
Gui, X., Qiu, X., Xie, M., Tian, Y., Min, C., Huang, M., Hongyan, W., Chen, T., Zhang, X., Chen, J., et al. (2020) · BioMed Research International · doi:10.1155/2020/3424208 · Cited by 43
Chiarelli, N., Carini, G., Zoppi, N., Ritelli, M., & Colombi, M. (2019) · PLOS ONE · doi:10.1371/journal.pone.0211647 · Cited by 35
Zhang, Y., Mo, Y., Zhang, Y., Yuan, J., & Zhang, Q. (2023) · Particle and Fibre Toxicology · doi:10.1186/s12989-023-00532-y · Cited by 24
Okabe, Y., Toda, E., Urushiyama, H., Terashima, Y., Kunugi, S., Kajimoto, Y., Terasaki, M., Matsushima, K., Saito, A., Yamauchi, Y., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-71770-z · Cited by 14
Metadata
- Authors
- Pardo, A., Gibson, K., Cisneros, J., Richards, T. J., Yang, Y., Becerril, C., Yousem, S., Herrera, I., Ruiz, V., Selman, M., & Kaminski, N.
- Venue
- PLoS Medicine
- Publisher
- Public Library of Science (PLoS)
- Published
- 2005-09-06
- Volume
- 2
- Issue
- 9
- Pages
- e251
- ISSN
- 1549-1277 (ISSN-L)
- 1549-1676
- OpenAlex
- W2137064182
- Cited by
- 489
- References
- 2
- FWCI
- 13.56
- Type
- journal article
- Language
- en
- Open access
- gold
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