The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Transcriptome signature of resistance exercise adaptations: mixed muscle and fiber type specific profiles in young and old adults
Open accessAbstract
This investigation examined the effects of acute resistance exercise (RE), progressive resistance training (PRT), and age on the human skeletal muscle Transcriptome. Two cohorts of young and old adults [ study A: 24 yr, 84 yr ( n = 28); study B: 25 yr, 78 yr ( n = 36)] were studied. Vastus lateralis biopsies were obtained pre- and 4 h post-RE in conjunction with the 1st and 36th (last) training session as part of a 12-wk PRT program in study A, whereas biopsies were obtained in the basal untrained state in study B. Additionally, the muscle fiber type specific (MHC I and MHC IIa) Transcriptome response to RE was examined in a subset of young and old women from study A. Transcriptome profiling was performed using HG U133 Plus 2.0 Arrays. The main findings were 1) there were 661 genes affected by RE during the 1st and 36th training bout that correlated with gains in muscle size and strength with PRT (termed the Transcriptome signature of resistance exercise adaptations); 2) the RE gene response was most pronounced in fast-twitch (MHC IIa) muscle fibers and provided additional insight into the skeletal muscle biology affected by RE; 3) skeletal muscle of young adults is more responsive to RE at the gene level compared with old adults and age also affected basal level skeletal muscle gene expression. These skeletal muscle Transcriptome findings provide further insight into the molecular basis of sarcopenia and the impact of resistance exercise at the mixed muscle and fiber type specific level.
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
Activators and target genes of Rel/NF-κB transcription factors
Pahl, H. L. (1999) · Oncogene · doi:10.1038/sj.onc.1203239 · Cited by 3,727
Lexell, J., Henriksson‐Larsén, K., Winblad, B., & Sjöström, M. (1983) · Muscle & Nerve · doi:10.1002/mus.880060809 · Cited by 582
Exercise, MAPK, and NF-κB signaling in skeletal muscle
Kramer, H. F., & Goodyear, L. J. (2007) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00085.2007 · Cited by 375
The TWEAK–Fn14 system is a critical regulator of denervation-induced skeletal muscle atrophy in mice
Mittal, A., Bhatnagar, S., Kumar, A., Lach-Trifilieff, E., Wauters, S., Li, H., Makonchuk, D. Y., Glass, D. J., & Kumar, A. (2010) · Journal of Cell Biology · doi:10.1083/jcb.200909117 · Cited by 250
Bamman, M. M., Petrella, J. K., Kim, J.-S., Mayhew, D. L., & Cross, J. M. (2007) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00024.2007 · Cited by 216
Skeletal muscle gene expression profiles in 20–29 year old and 65–71 year old women
Welle, S., Brooks, A. I., Delehanty, J. M., Needler, N., Bhatt, K., Shah, B., & Thornton, C. A. (2004) · Experimental Gerontology · doi:10.1016/j.exger.2003.11.011 · Cited by 210 · cited 3×
“Some of the 49 genes have also been identified as affected by age in previous reports (17, 30, 53, 54), further validating these genes as sarcopenia-associated genes.”
BackgroundResultRaue, U., Slivka, D., Minchev, K., & Trappe, S. (2009) · Journal of Applied Physiology · doi:10.1152/japplphysiol.91587.2008 · Cited by 166
Self-citationReduction in hybrid single muscle fiber proportions with resistance training in humans
Williamson, D. L., Gallagher, P. M., Carroll, C. C., Raue, U., & Trappe, S. W. (2001) · Journal of Applied Physiology · doi:10.1152/jappl.2001.91.5.1955 · Cited by 150
Cited by
Exercise Metabolism and the Molecular Regulation of Skeletal Muscle Adaptation
Egan, B., & Zierath, J. R. (2013) · Cell Metabolism · doi:10.1016/j.cmet.2012.12.012 · Cited by 2,084
Metabolic Flexibility in Health and Disease
Goodpaster, B. H., & Sparks, L. M. (2017) · Cell Metabolism · doi:10.1016/j.cmet.2017.04.015 · Cited by 995
Transcriptomic profiling of skeletal muscle adaptations to exercise and inactivity
Pillon, N. J., Gabriel, B. M., Dollet, L., Smith, J. A. B., Sardón Puig, L., Botella, J., Bishop, D. J., Krook, A., & Zierath, J. R. (2020) · Nature Communications · doi:10.1038/s41467-019-13869-w · Cited by 457
Concurrent exercise training: do opposites distract?
Coffey, V. G., & Hawley, J. A. (2017) · The Journal of Physiology · doi:10.1113/jp272270 · Cited by 351
Fyfe, J. J., Bishop, D. J., & Stepto, N. K. (2014) · Sports Medicine · doi:10.1007/s40279-014-0162-1 · Cited by 347
Single-cell transcriptional profiles in human skeletal muscle
Rubenstein, A. B., Smith, G. R., Raue, U., Begue, G., Minchev, K., Ruf-Zamojski, F., Nair, V. D., Wang, X., Zhou, L., Zaslavsky, E., et al. (2020) · Scientific Reports · doi:10.1038/s41598-019-57110-6 · Cited by 323
Self-citationEgan, B., & Sharples, A. P. (2023) · Physiological Reviews · doi:10.1152/physrev.00054.2021 · Cited by 278
Sex differences in resistance training: a systematic review and meta-analysis
Roberts, B. M., Nuckols, G., & Krieger, J. W. (2020) · Journal of Strength and Conditioning Research · doi:10.1519/jsc.0000000000003521 · Cited by 240
Inhibition of prostaglandin-degrading enzyme 15-PGDH rejuvenates aged muscle mass and strength
Palla, A. R., Ravichandran, M., Wang, Y. X., Alexandrova, L., Yang, A. V., Kraft, P., Holbrook, C. A., Schürch, C. M., Ho, A. T. V., & Blau, H. M. (2021) · Science · doi:10.1126/science.abc8059 · Cited by 232
Ahtiainen, J. P., Walker, S., Peltonen, H., Holviala, J., Sillanpää, E., Karavirta, L., Sallinen, J., Mikkola, J., Valkeinen, H., Mero, A., et al. (2016) · AGE · doi:10.1007/s11357-015-9870-1 · Cited by 231
Roberts, M. D., McCarthy, J. J., Hornberger, T. A., Phillips, S. M., Mackey, A. L., Nader, G. A., Boppart, M. D., Kavazis, A. N., Reidy, P. T., Ogasawara, R., et al. (2023) · Physiological Reviews · doi:10.1152/physrev.00039.2022 · Cited by 214
Wilkinson, D. J., Franchi, M. V., Brook, M. S., Narici, M. V., Williams, J. P., Mitchell, W. K., Szewczyk, N. J., Greenhaff, P. L., Atherton, P. J., & Smith, K. (2014) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00650.2013 · Cited by 195
Molecular Networks of Human Muscle Adaptation to Exercise and Age
Phillips, B. E., Williams, J. P., Gustafsson, T., Bouchard, C., Rankinen, T., Knudsen, S., Smith, K., Timmons, J. A., & Atherton, P. J. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003389 · Cited by 199
Mitochondria, Muscle Health, and Exercise with Advancing Age
Carter, H. N., Chen, C. C. W., & Hood, D. A. (2015) · Physiology · doi:10.1152/physiol.00039.2014 · Cited by 191
Aerobic exercise does not compromise muscle hypertrophy response to short-term resistance training
Lundberg, T. R., Fernandez-Gonzalo, R., Gustafsson, T., & Tesch, P. A. (2013) · Journal of Applied Physiology · doi:10.1152/japplphysiol.01013.2012 · Cited by 168
Murach, K. A., & Bagley, J. R. (2016) · Sports Medicine · doi:10.1007/s40279-016-0496-y · Cited by 158
LIM, C., NUNES, E. A., CURRIER, B. S., MCLEOD, J. C., THOMAS, A. C. Q., & PHILLIPS, S. M. (2022) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000002929 · Cited by 142
Starring or supporting role? Satellite cells and skeletal muscle fiber size regulation
Murach, K. A., Fry, C. S., Kirby, T. J., Jackson, J. R., Lee, J. D., White, S. H., Dupont-Versteegden, E. E., McCarthy, J. J., & Peterson, C. A. (2018) · Physiology · doi:10.1152/physiol.00019.2017 · Cited by 137
Ericsson, K. A. (2013) · British Journal of Sports Medicine · doi:10.1136/bjsports-2012-091767 · Cited by 133
Nader, G. A., von Walden, F., Liu, C., Lindvall, J., Gutmann, L., Pistilli, E. E., & Gordon, P. M. (2014) · Journal of Applied Physiology · doi:10.1152/japplphysiol.01366.2013 · Cited by 129
Noone, J., Mucinski, J. M., DeLany, J. P., Sparks, L. M., & Goodpaster, B. H. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2023.12.025 · Cited by 128
Thalacker-Mercer, A., Stec, M., Cui, X., Cross, J., Windham, S., & Bamman, M. (2013) · Physiological Genomics · doi:10.1152/physiolgenomics.00167.2012 · Cited by 119
Roberts, M. D., Haun, C. T., Mobley, C. B., Mumford, P. W., Romero, M. A., Roberson, P. A., Vann, C. G., & McCarthy, J. J. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.00834 · Cited by 107
Benefits of higher resistance-training volume are related to ribosome biogenesis
Hammarström, D., Øfsteng, S., Koll, L., Hanestadhaugen, M., Hollan, I., Apró, W., Whist, J. E., Blomstrand, E., Rønnestad, B. R., & Ellefsen, S. (2020) · The Journal of Physiology · doi:10.1113/jp278455 · Cited by 99
Kirby, T. J., Lee, J. D., England, J. H., Chaillou, T., Esser, K. A., & McCarthy, J. J. (2015) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00296.2015 · Cited by 89
Haun, C. T., Mumford, P. W., Roberson, P. A., Romero, M. A., Mobley, C. B., Kephart, W. C., Anderson, R. G., Colquhoun, R. J., Muddle, T. W. D., Luera, M. J., et al. (2017) · Physiological Reports · doi:10.14814/phy2.13457 · Cited by 48
Kuang, J., McGinley, C., Lee, M. J.-C., Saner, N. J., Garnham, A., & Bishop, D. J. (2022) · PeerJ · doi:10.7717/peerj.12856 · Cited by 45
Effects of an acute bout of resistance exercise on fiber-type specific GLUT4 and IGF-1R expression
Gallagher, P. M., Touchberry, C. D., Teson, K., McCabe, E., Tehel, M., & Wacker, M. J. (2013) · Applied Physiology Nutrition and Metabolism · doi:10.1139/apnm-2012-0301 · Cited by 17
Tsintzas, K., Stephens, F. B., Snijders, T., Wall, B. T., Cooper, S., Mallinson, J., Verdijk, L. B., & van Loon, L. J. C. (2017) · Experimental Gerontology · doi:10.1016/j.exger.2017.03.018 · Cited by 16
Signaling responses to high and moderate load strength exercise in trained muscle
Lysenko, E. A., Popov, D. V., Vepkhvadze, T. F., Sharova, A. P., & Vinogradova, O. L. (2019) · Physiological Reports · doi:10.14814/phy2.14100 · Cited by 12
Metadata
- Authors
- Raue, U., Trappe, T. A., Estrem, S. T., Qian, H.-R., Helvering, L. M., Smith, R. C., & Trappe, S.
- Venue
- Journal of Applied Physiology
- Publisher
- American Physiological Society
- Published
- 2012-05-15
- Volume
- 112
- Issue
- 10
- Pages
- 1625-1636
- ISSN
- 0161-7567 (ISSN-L)
- 8750-7587
- 1522-1601
- OpenAlex
- W2097995193
- Cited by
- 262
- References
- 8
- FWCI
- 4.88
- Type
- journal article
- Language
- en
- Open access
- green
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.
