The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
High-intensity exercise training increases the diversity and metabolic capacity of the mouse distal gut microbiota during diet-induced obesity
Open accessAbstract
Diet and exercise underpin the risk of obesity-related metabolic disease. Diet alters the gut microbiota, which contributes to aspects of metabolic disease during obesity. Repeated exercise provides metabolic benefits during obesity. We assessed whether exercise could oppose changes in the taxonomic and predicted metagenomic characteristics of the gut microbiota during diet-induced obesity. We hypothesized that high-intensity interval training (HIIT) would counteract high-fat diet (HFD)-induced changes in the microbiota without altering obesity in mice. Compared with chow-fed mice, an obesity-causing HFD decreased the Bacteroidetes-to-Firmicutes ratio and decreased the genetic capacity in the fecal microbiota for metabolic pathways such as the tricarboxylic acid (TCA) cycle. After HFD-induced obesity was established, a subset of mice were HIIT for 6 wk, which increased host aerobic capacity but did not alter body or adipose tissue mass. The effects of exercise training on the microbiota were gut segment dependent and more extensive in the distal gut. HIIT increased the alpha diversity and Bacteroidetes/Firmicutes ratio of the distal gut and fecal microbiota during diet-induced obesity. Exercise training increased the predicted genetic capacity related to the TCA cycle among other aspects of metabolism. Strikingly, the same microbial metabolism indexes that were increased by exercise were all decreased in HFD-fed vs. chow diet-fed mice. Therefore, exercise training directly opposed some of the obesity-related changes in gut microbiota, including lower metagenomic indexes of metabolism. Some host and microbial pathways appeared similarly affected by exercise. These exercise- and diet-induced microbiota interactions can be captured in feces.
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“In accordance, animal studies by us and others have reported that high-intensity endurance training modified the gut microbiota (Campbell et al., 2016; Denou et al., 2016)”
From Six-Week Endurance Exercise Alters Gut Metagenome That Is not Reflected in Systemic Metabolism in Over-weight Women (2018) · cites this paper as Denou et al., 2016
In the citing paper
“In animal models exercise has boosted the abundance of Dorea (Allen et al., 2015; Denou et al., 2016)”
From Six-Week Endurance Exercise Alters Gut Metagenome That Is not Reflected in Systemic Metabolism in Over-weight Women (2018) · cites this paper as Denou et al., 2016
In the citing paper
“regular physical activity may help to combat reductions in butyrate-producing microbiota (Clostridiales, Roseburia, Lachnospiraceae, and Erysipelotrichaceae) [102] and Lactobacillus levels [103] that are commonly seen with aging [15, 84, 98].”
From Gut Microbiota Contribute to Age-Related Changes in Skeletal Muscle Size, Composition, and Function: Biological Basis for a Gut-Muscle Axis (2018) · cites this paper as [103]
In the citing paper
“correlations between microbial diversity and cardiorespiratory fitness [102] in concert with the exercise training mediated rescue of high-fat diet-induced microbial disturbance [103] support the idea that exercise training may impart beneficial effects on gut health.”
From Gut Microbiota Contribute to Age-Related Changes in Skeletal Muscle Size, Composition, and Function: Biological Basis for a Gut-Muscle Axis (2018) · cites this paper as [103]
Citations
Cites
QIIME allows analysis of high-throughput community sequencing data
Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K., Gordon, J. I., et al. (2010) · Nature Methods · doi:10.1038/nmeth.f.303 · Cited by 34,887
Cutadapt removes adapter sequences from high-throughput sequencing reads
Martin, M. (2011) · EMBnet.journal · doi:10.14806/ej.17.1.200 · Cited by 33,889
An obesity-associated gut microbiome with increased capacity for energy harvest
Turnbaugh, P. J., Ley, R. E., Mahowald, M. A., Magrini, V., Mardis, E. R., & Gordon, J. I. (2006) · Nature · doi:10.1038/nature05414 · Cited by 12,444
Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB
DeSantis, T. Z., Hugenholtz, P., Larsen, N., Rojas, M., Brodie, E. L., Keller, K., Huber, T., Dalevi, D., Hu, P., & Andersen, G. L. (2006) · Applied and Environmental Microbiology · doi:10.1128/aem.03006-05 · Cited by 10,891
Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences
Langille, M. G. I., Zaneveld, J., Caporaso, J. G., McDonald, D., Knights, D., Reyes, J. A., Clemente, J. C., Burkepile, D. E., Vega Thurber, R. L., Knight, R., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2676 · Cited by 9,116
A core gut microbiome in obese and lean twins
Turnbaugh, P. J., Hamady, M., Yatsunenko, T., Cantarel, B. L., Duncan, A., Ley, R. E., Sogin, M. L., Jones, W. J., Roe, B. A., Affourtit, J. P., et al. (2009) · Nature · doi:10.1038/nature07540 · Cited by 7,823
Obesity alters gut microbial ecology
Ley, R. E., Bäckhed, F., Turnbaugh, P., Lozupone, C. A., Knight, R. D., & Gordon, J. I. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504978102 · Cited by 6,176
Cani, P. D., Bibiloni, R., Knauf, C., Waget, A., Neyrinck, A. M., Delzenne, N. M., & Burcelin, R. (2008) · Diabetes · doi:10.2337/db07-1403 · Cited by 4,819
Cross-talk between Akkermansia muciniphila and intestinal epithelium controls diet-induced obesity
Everard, A., Belzer, C., Geurts, L., Ouwerkerk, J. P., Druart, C., Bindels, L. B., Guiot, Y., Derrien, M., Muccioli, G. G., Delzenne, N. M., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1219451110 · Cited by 4,511
Gut Microbiota from Twins Discordant for Obesity Modulate Metabolism in Mice
Ridaura, V. K., Faith, J. J., Rey, F. E., Cheng, J., Duncan, A. E., Kau, A. L., Griffin, N. W., Lombard, V., Henrissat, B., Bain, J. R., et al. (2013) · Science · doi:10.1126/science.1241214 · Cited by 3,788
Flagged first, then by citations
Research works printed but not linked: 2
- O'Sullivan O, 2015, Gut Microbes, 1
- Ye Y, 2011, Proc IEEE Internl Conf Bioinformatics Biomed, 2010, 153
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Items 201–260 of 260
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Rusling, M., Karim, A., Kaye, A. G., Lee, C.-M. J., Wegman‐Points, L., Mathis, V., Lampeter, T., & Yuan, L. (2024) · Frontiers in Microbiomes · doi:10.3389/frmbi.2024.1389103 · Cited by 2
Sun, Y., Han, Z., Yao, X., Meng, J., Ren, W., Wang, C., Yuan, X.-X., Zeng, Y., Wang, Y.-F., Sun, Z.-W., et al. (2025) · Genes · doi:10.3390/genes16050504 · Cited by 2
Li, W., Robeson, M. S., Randolph, C. E., & Hakkak, R. (2025) · Food & Function · doi:10.1039/d5fo01629k · Cited by 2
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Feng, Y., Si, X., Zhu, R., Chen, J., Zhao, W., Wang, Q., & Han, S. (2022) · Computational and Mathematical Methods in Medicine · doi:10.1155/2022/6860940 · Cited by 2
RetractedFoley, K. P., Denou, E., Duggan, B. M., Chan, R. W., Stearns, J. C., & Schertzer, J. D. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/116095 · Cited by 2
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Barry, D. (2020) · Elsevier eBooks · doi:10.1016/b978-0-323-43044-9.00217-x · Cited by 1
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Deen, N. M. E. E., Karem, M., Borhamy, M. I. E., Hanora, A., Fahmy, N., & Zakeer, S. (2024) · Current Microbiology · doi:10.1007/s00284-024-03771-0 · Cited by 1
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Porras, D. (2021) · Buleria (Universidad de León) · doi:10.18002/10612/13641
Varraso, R., Li, Z., Bédard, A., & Camargo, C. A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-85419-1.00007-4 · Cited by 2
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Solouki, S., Gorgani‐Firuzjaee, S., Jafary, H., & Delfan, M. (2024) · Annals of Military and Health Sciences Research · doi:10.5812/amh-147901
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Emam‐Djomeh, Z., Samieitousi, S., Gashtasbi, S., Behmadi, H., Azarpazhooh, E., & Ramaswamy, H. S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-28862-3.00006-6
He, J., Ni, P., Rong, X., Yu, Z., Li, B., Zhang, C., Zhang, Z., Wan, Q., Zhong, W., & Li, F. (2025) · Food Science and Human Wellness · doi:10.26599/fshw.2025.9250546
Shao, Y., Liu, Y., Ding, H., Li, L., Zhao, Y., Wang, Y., Wang, J., & Shang, S. (2026) · Biologia · doi:10.1007/s11756-026-02183-8
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Gao, R., Wang, J., Song, J., Zhang, W., & Quan, L. (2026) · Frontiers in Microbiology · doi:10.3389/fmicb.2026.1790697
Tian, B., Liu, Y., Su, K.-J., Jiang, L., Lin, X., Qiu, C., Luo, Z., Tian, Q., Shen, J., Shen, H., et al. (2026) · Journal of Applied Microbiology · doi:10.1093/jambio/lxag117
Liang, J.-W., Ke, D.-D., Shao, M., Li, Y., He, G., & Liu, Y. (2026) · Journal of Pediatric Endocrinology & Metabolism (JPEM) · doi:10.1515/jpem-2025-0631
Rui, L. I., Li-Yun, S. H. A., You-Guan, H. E. N. G., & Shen, W.-Q. (2026) · Shengming kexue · doi:10.3724/cbls.2026077
Xie, L., Chen, X., Wan, X., & Li, B. (2026) · Frontiers in Microbiology · doi:10.3389/fmicb.2026.1845485
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Tian, B., Liu, Y., Su, K.-J., Jiang, L., Lin, X., Qiu, C., Luo, Z., Tian, Q., Shen, J., Shen, H., et al. (2026) · Journal of Applied Microbiology · doi:10.1093/jambio/lxag172
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Wang, J. (2026) · Psycho-Oncologie · doi:10.18282/po5751
Wang, N., Bi, J., Dong, X., Yao, L., Man, X.-H., Guo, M., & Huang, G. (2026) · The Journal of Nutritional Biochemistry · doi:10.1016/j.jnutbio.2026.110481
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Yang, Y., Yang, Y., Tang, Y., & Yang, W. (2026) · Immunological Investigations · doi:10.1080/08820139.2026.2689670
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Dongfei, L. I. U., Jiang, Y., Wang, Z., & Mo, X. (2026) · Frontiers in Nutrition · doi:10.3389/fnut.2026.1895299
Pang, T., Zhou, X., & Ge, Z. (2026) · Metabolites · doi:10.3390/metabo16090638
Zhang, X., Sun, C., & Yang, B. (2026) · Frontiers in Nutrition · doi:10.3389/fnut.2026.1927715
Liu, Y., Wu, Y., Gao, X., Zhang, Q., Jin, X., Lin, Z., Yang, X., Zheng, R., Du, L., Zhang, X., et al. (2026) · Photoacoustics · doi:10.1016/j.pacs.2026.100881
Lamanna, A., Zhou, Z., Boussoufa, B., Pansart, E., Garcia, P., Heurtaux, T., Buttini, M., Coowar, D., Marcon, G., & Williams, E. G. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.28.752290
(58638), Q. Z., Yuan, J., Wang, Z., Miao, J., Gong, H., & Han, Y. (2026) · Nutrients · doi:10.3390/nu18203311
Smith, C., Garza, K., Azari, H., Vellers, H., Abdelrahman, M., Dhanasekara, C. S., Salmon, A. B., Liang, H., Fernández, E., Musi, N., et al. (2026) · GeroScience · doi:10.1007/s11357-026-02582-4
Gao, D., Deng, W., Hu, M., Mai, Y.-N., Liang, G., Wu, J., Wu, Y., Guo, J., & Huang, J. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1925079
Metadata
- Authors
- Denou, E., Marcinko, K., Surette, M. G., Steinberg, G. R., & Schertzer, J. D.
- Affiliations
- McMaster University (CA)
- Corresponding author
- Schertzer, Jonathan D.
- Venue
- American Journal of Physiology-Endocrinology and Metabolism
- Publisher
- American Physiological Society
- Published
- 2016-06-01
- Volume
- 310
- Issue
- 11
- Pages
- E982-E993
- ISSN
- 0193-1849 (ISSN-L)
- 1522-1555
- PMC
- PMC4935139
- OpenAlex
- W2342902276
- Cited by
- 260
- References
- 61
- Citations by year
- 2026: 24 · 2025: 17 · 2024: 21 · 2023: 35 · 2022: 36 · 2021: 28 · 2020: 27 · 2019: 28 · 2018: 20 · 2017: 21 · 2016: 3
- FWCI
- 12.47
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- American Journal of Physiology-Endocrinology and Metabolism (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Canadian Institutes of Health Research (136665_1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Adipokines, Inflammation, and Metabolic Diseases
- Health Sciences › Dentistry › Periodontics › Oral microbiology and periodontitis research
- Keywords
- high-intensity interval training, diet-induced obesity, gut microbiota, high-fat diet, Firmicutes/Bacteroidetes ratio, TCA cycle, alpha diversity, cardiorespiratory fitness, obesity-related metabolic diseases, host-microbe interactions
- MeSH terms
- Gastrointestinal Microbiome, High-Intensity Interval Training, Animals, Bacteria, Exercise Therapy, Male, Mice, Inbred C57BL, Obesity, Physical Conditioning, Animal, Treatment Outcome, Biodiversity, Mice, Metagenome, Diet, High-Fat
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