The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
A metabolomic view of how the human gut microbiota impacts the host metabolome using humanized and gnotobiotic mice
Open accessAbstract
Defining the functional status of host-associated microbial ecosystems has proven challenging owing to the vast number of predicted genes within the microbiome and relatively poor understanding of community dynamics and community–host interaction. Metabolomic approaches, in which a large number of small molecule metabolites can be defined in a biological sample, offer a promising avenue to ‘fingerprint’ microbiota functional status. Here, we examined the effects of the human gut microbiota on the fecal and urinary metabolome of a humanized (HUM) mouse using an optimized ultra performance liquid chromatography–mass spectrometry-based method. Differences between HUM and conventional mouse urine and fecal metabolomic profiles support host-specific aspects of the microbiota’s metabolomic contribution, consistent with distinct microbial compositions. Comparison of microbiota composition and metabolome of mice humanized with different human donors revealed that the vast majority of metabolomic features observed in donor samples are produced in the corresponding HUM mice, and individual-specific features suggest ‘personalized’ aspects of functionality can be reconstituted in mice. Feeding the mice a defined, custom diet resulted in modification of the metabolite signatures, illustrating that host diet provides an avenue for altering gut microbiota functionality, which in turn can be monitored via metabolomics. Using a defined model microbiota consisting of one or two species, we show that simplified communities can drive major changes in the host metabolomic profile. Our results demonstrate that metabolomics constitutes a powerful avenue for functional characterization of the intestinal microbiota and its interaction with the host.
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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
Structure, function and diversity of the healthy human microbiome
The Human Microbiome Project Consortium (2012) · Nature · doi:10.1038/nature11234 · Cited by 11,726
A human gut microbial gene catalogue established by metagenomic sequencing
MetaHIT Consortium, Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. S., Manichanh, C., Nielsen, T., Pons, N., Levenez, F., et al. (2010) · Nature · doi:10.1038/nature08821 · Cited by 11,624
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
Host-gut microbiota metabolic interactions
Nicholson, J. K., Holmes, E., Kinross, J., Burcelin, R., Gibson, G., Jia, W., & Pettersson, S. (2012) · Science · doi:10.1126/science.1223813 · Cited by 4,760
The Effect of Diet on the Human Gut Microbiome: A Metagenomic Analysis in Humanized Gnotobiotic Mice
Turnbaugh, P. J., Ridaura, V. K., Faith, J. J., Rey, F. E., Knight, R., & Gordon, J. I. (2009) · Science Translational Medicine · doi:10.1126/scitranslmed.3000322 · Cited by 3,058 · cited 4×
“These findings support the idea that many functional attributes are shared between microbiotas that differ compositionally (Turnbaugh et al., 2009a).”
BackgroundInfluentialCreatine and Creatinine Metabolism
Wyss, M., & Kaddurah-Daouk, R. (2000) · Physiological Reviews · doi:10.1152/physrev.2000.80.3.1107 · Cited by 2,950
ProteoWizard: open source software for rapid proteomics tools development
Kessner, D., Chambers, M., Burke, R., Agus, D., & Mallick, P. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn323 · Cited by 2,024
Human metabolic phenotype diversity and its association with diet and blood pressure
Holmes, E., Loo, R. L., Stamler, J., Bictash, M., Yap, I. K. S., Chan, Q., Ebbels, T., De Iorio, M., Brown, I. J., Veselkov, K. A., et al. (2008) · Nature · doi:10.1038/nature06882 · Cited by 1,054
Genomic and metabolic adaptations of Methanobrevibacter smithii to the human gut
Samuel, B. S., Hansen, E. E., Manchester, J. K., Coutinho, P. M., Henrissat, B., Fulton, R., Latreille, P., Kim, K., Wilson, R. K., & Gordon, J. I. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0704189104 · Cited by 549
Martin, F. J., Wang, Y., Sprenger, N., Yap, I. K. S., Lundstedt, T., Lek, P., Rezzi, S., Ramadan, Z., van Bladeren, P., Fay, L. B., et al. (2008) · Molecular Systems Biology · doi:10.1038/msb4100190 · Cited by 421
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Gut microbiota, metabolites and host immunity
Rooks, M. G., & Garrett, W. S. (2016) · Nature reviews. Immunology · doi:10.1038/nri.2016.42 · Cited by 3,409
Interactions between the microbiota and pathogenic bacteria in the gut
Bäumler, A. J., & Sperandio, V. (2016) · Nature · doi:10.1038/nature18849 · Cited by 1,420
The pros, cons, and many unknowns of probiotics
Suez, J., Zmora, N., Segal, E., & Elinav, E. (2019) · Nature Medicine · doi:10.1038/s41591-019-0439-x · Cited by 1,398
Control of brain development, function, and behavior by the microbiome
Sampson, T. R., & Mazmanian, S. K. (2015) · Cell Host & Microbe · doi:10.1016/j.chom.2015.04.011 · Cited by 1,093
Tryptophan Metabolism: A Link Between the Gut Microbiota and Brain
Gao, K., Mu, C.-L., Farzi, A., & Zhu, W.-Y. (2020) · Advances in Nutrition · doi:10.1093/advances/nmz127 · Cited by 1,029
Metadata
- Authors
- Marcobal, A., Kashyap, P. C., Nelson, T. A., Aronov, P. A., Donia, M. S., Spormann, A., Fischbach, M. A., & Sonnenburg, J. L.
- Venue
- The ISME Journal
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-06-06
- Volume
- 7
- Issue
- 10
- Pages
- 1933-1943
- ISSN
- 1751-7362 (ISSN-L)
- 1751-7370
- OpenAlex
- W2093500216
- Cited by
- 355
- References
- 11
- FWCI
- 11.97
- Type
- journal article
- Language
- en
- Open access
- bronze
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