The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
NF-κB mediates the protein loss induced by TNF-α in differentiated skeletal muscle myotubes
Abstract
Nuclear factor-κB (NF-κB) regulates the transcription of a variety of genes involved in immune responses, cell growth, and cell death. However, the role of NF-κB in muscle biology is poorly understood. We recently reported that tumor necrosis factor-α (TNF-α) rapidly activates NF-κB in differentiated skeletal muscle myotubes and that TNF-α acts directly on the muscle cell to induce protein degradation. In the present study, we ask whether NF-κB mediates the protein loss induced by TNF-α. We addressed this problem by creating stable, transdominant negative muscle cell lines. C2C12 myoblasts were transfected with viral plasmid constructs that induce overexpression of mutant I-κBα proteins that are insensitive to degradation via the ubiquitin-proteasome pathway. These mutant proteins selectively inhibit NF-κB activation. We found that differentiated myotubes transfected with the empty viral vector (controls) underwent a drop in total protein content and in fast-type myosin heavy-chain content during 72 h of exposure to TNF-α. In contrast, total protein and fast-type myosin heavy-chain levels were unaltered by TNF-α in the transdominant negative cell lines. TNF-α did not induce apoptosis in any cell line, as assessed by DNA ladder and annexin V assays. These data indicate that NF-κB is an essential mediator of TNF-α-induced catabolism in differentiated muscle cells.
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
Liu, Z.-G., Hsu, H., Goeddel, D. V., & Karin, M. (1996) · Cell · doi:10.1016/s0092-8674(00)81375-6 · Cited by 1,937
Cited by
IKKβ/NF-κB Activation Causes Severe Muscle Wasting in Mice
Cai, D., Frantz, J. D., Tawa, N. E., Melendez, P. A., Oh, B.-C., Lidov, H. G. W., Hasselgren, P.-O., Frontera, W. R., Lee, J., Glass, D. J., et al. (2004) · Cell · doi:10.1016/j.cell.2004.09.027 · Cited by 1,367
Anabolic signaling deficits underlie amino acid resistance of wasting, aging muscle
Cuthbertson, D., Smith, K., Babraj, J., Leese, G., Waddell, T., Atherton, P., Wackerhage, H., Taylor, P. M., & Rennie, M. J. (2005) · The FASEB Journal · doi:10.1096/fj.04-2640fje · Cited by 1,211
Skeletal muscle hypertrophy and atrophy signaling pathways
Glass, D. J. (2005) · The International Journal of Biochemistry & Cell Biology · doi:10.1016/j.biocel.2005.04.018 · Cited by 1,106
The Molecular Basis of Skeletal Muscle Atrophy
Jackman, R. W., & Kandarian, S. C. (2004) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.00579.2003 · Cited by 1,008
Effects of Resistance Training on Older Adults
Hunter, G. R., McCarthy, J. P., & Bamman, M. M. (2004) · Sports Medicine · doi:10.2165/00007256-200434050-00005 · Cited by 764
Schaap, L. A., Pluijm, S. M. F., Deeg, D. J. H., Harris, T. B., Kritchevsky, S. B., Newman, A. B., Colbert, L. H., Pahor, M., Rubin, S. M., Tylavsky, F. A., et al. (2009) · The Journals of Gerontology Series A: Biological Sciences and Medical Sciences · doi:10.1093/gerona/glp097 · Cited by 683
Sacheck, J. M., Ohtsuka, A., McLary, S. C., & Goldberg, A. L. (2004) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00073.2004 · Cited by 606
Frailty and sarcopenia: The potential role of an aged immune system
Wilson, D., Jackson, T., Sapey, E., & Lord, J. M. (2017) · Ageing Research Reviews · doi:10.1016/j.arr.2017.01.006 · Cited by 573
Skeletal Muscle Apoptosis and Weight Loss in Chronic Obstructive Pulmonary Disease
Agustí, A. G. N., Sauleda, J., Miralles, C., Gomez, C., Togores, B., Sala, E., Batle, S., & Busquets, X. (2002) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.2108013 · Cited by 343
Buford, T. W., Anton, S. D., Judge, A. R., Marzetti, E., Wohlgemuth, S. E., Carter, C. S., Leeuwenburgh, C., Pahor, M., & Manini, T. M. (2010) · Ageing Research Reviews · doi:10.1016/j.arr.2010.04.004 · Cited by 295
TNF-α is a mitogen in skeletal muscle
Li, Y.-P. (2003) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.00453.2002 · Cited by 249
Self-citationSarcopenia and menopause: The role of estradiol
Geraci, A., Calvani, R., Ferri, E., Marzetti, E., Arosio, B., & Cesari, M. (2021) · Frontiers in Endocrinology · doi:10.3389/fendo.2021.682012 · Cited by 238
Sarcopenic obesity: Emerging mechanisms and therapeutic potential
Axelrod, C. L., Dantas, W. S., & Kirwan, J. P. (2023) · Metabolism · doi:10.1016/j.metabol.2023.155639 · Cited by 204
Modulation of reactive oxygen species in skeletal muscle by myostatin is mediated through NF-κB
Sriram, S., Subramanian, S., Sathiakumar, D., Venkatesh, R., Salerno, M. S., McFarlane, C. D., Kambadur, R., & Sharma, M. (2011) · Aging Cell · doi:10.1111/j.1474-9726.2011.00734.x · Cited by 189
Modifying muscle mass – the endocrine perspective
Solomon, A. M., & Bouloux, P. M. G. (2006) · Journal of Endocrinology · doi:10.1677/joe.1.06837 · Cited by 156
The central role of hypothalamic inflammation in the acute illness response and cachexia
Burfeind, K. G., Michaelis, K. A., & Marks, D. L. (2016) · Seminars in Cell & Developmental Biology · doi:10.1016/j.semcdb.2015.10.038 · Cited by 156
Signaling pathways weigh in on decisions to make or break skeletal muscle
Guttridge, D. C. (2004) · Current Opinion in Clinical Nutrition and Metabolic Care · doi:10.1097/01.mco.0000134364.61406.26 · Cited by 149
Exercise training changes IL-10/TNF-α ratio in the skeletal muscle of post-MI rats
Batista Jr., M. L., Rosa, J. C., Lopes, R. D., Lira, F. S., Martins Jr., E., Yamashita, A. S., Brum, P. C., Lancha Jr., A. H., Lopes, A. C., & Seelaender, M. (2010) · Cytokine · doi:10.1016/j.cyto.2009.10.007 · Cited by 117
Marzetti, E., Carter, C. S., Wohlgemuth, S. E., Lees, H. A., Giovannini, S., Anderson, B., Quinn, L. S., & Leeuwenburgh, C. (2009) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2008.12.008 · Cited by 113
Molecular pathways: cachexia signaling-A targeted approach to cancer treatment
Miyamoto, Y., Hanna, D. L., Zhang, W., Baba, H., & Lenz, H.-J. (2016) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-16-0495 · Cited by 111
Powerful signals for weak muscles
Saini, A., Faulkner, S., Al-Shanti, N., & Stewart, C. (2009) · Ageing Research Reviews · doi:10.1016/j.arr.2009.02.001 · Cited by 109
Out of Control: The Role of the Ubiquitin Proteasome System in Skeletal Muscle during Inflammation
Haberecht-Müller, S., Krüger, E., & Fielitz, J. (2021) · Biomolecules · doi:10.3390/biom11091327 · Cited by 108
Eley, H. L., Russell, S. T., & Tisdale, M. J. (2008) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.90567.2008 · Cited by 100
Ono, T., Takada, S., Kinugawa, S., & Tsutsui, H. (2015) · Experimental Physiology · doi:10.1113/ep085049 · Cited by 99
BAR‐SHAI, M., CARMELI, E., & REZNICK, A. Z. (2005) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1356.034 · Cited by 67
Girven, M., Dugdale, H. F., Owens, D. J., Hughes, D. C., Stewart, C. E., & Sharples, A. P. (2016) · Journal of Cellular Physiology · doi:10.1002/jcp.25380 · Cited by 48
Inflammation and metabolism: the role of adiposity in sarcopenic obesity
Lynch, G. M., Murphy, C. H., Castro, E. D. M., & Roche, H. M. (2020) · Proceedings of the Nutrition Society · doi:10.1017/s0029665120007119 · Cited by 41
Wang, H., Li, T.-L., Hsia, S., Su, I.-L., Chan, Y.-L., & Wu, C.-J. (2015) · Oncotarget · doi:10.18632/oncotarget.3483 · Cited by 39
Hardy, R. S., Doig, C. L., Hussain, Z., O'Leary, M., Morgan, S. A., Pearson, M. J., Naylor, A., Jones, S. W., Filer, A., Stewart, P. M., et al. (2016) · The Journal of Pathology · doi:10.1002/path.4806 · Cited by 37
OKAMOTO, T., & MACHIDA, S. (2017) · Biomedical Research · doi:10.2220/biomedres.38.331 · Cited by 16
Chen, T., Moore, T. M., Ebbert, M. T. W., McVey, N. L., Madsen, S. R., Hallowell, D. M., Harris, A. M., Char, R. E., Mackay, R. P., Hancock, C. R., et al. (2016) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00727.2015 · Cited by 14
Metadata
- Authors
- Li, Y.-P., & Reid, M. B.
- Venue
- American Journal of Physiology-Regulatory, Integrative and Comparative Physiology
- Publisher
- American Physiological Society
- Published
- 2000-10-01
- Volume
- 279
- Issue
- 4
- Pages
- R1165-R1170
- ISSN
- 0363-6119 (ISSN-L)
- 1522-1490
- OpenAlex
- W1865608113
- Cited by
- 441
- References
- 1
- FWCI
- 2.93
- Type
- journal article
- Language
- en
- Open access
- closed
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.
