The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.