The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Integrative analysis of the microbiome and metabolome of the human intestinal mucosal surface reveals exquisite inter-relationships
Open accessAbstract
Background: Consistent compositional shifts in the gut microbiota are observed in IBD and other chronic intestinal disorders and may contribute to pathogenesis. The identities of microbial biomolecular mechanisms and metabolic products responsible for disease phenotypes remain to be determined, as do the means by which such microbial functions may be therapeutically modified. Results: The composition of the microbiota and metabolites in gut microbiome samples in 47 subjects were determined. Samples were obtained by endoscopic mucosal lavage from the cecum and sigmoid colon regions, and each sample was sequenced using the 16S rRNA gene V4 region (Illumina-HiSeq 2000 platform) and assessed by UPLC mass spectroscopy. Spearman correlations were used to identify widespread, statistically significant microbial-metabolite relationships. Metagenomes for identified microbial OTUs were imputed using PICRUSt, and KEGG metabolic pathway modules for imputed genes were assigned using HUMAnN. The resulting metabolic pathway abundances were mostly concordant with metabolite data. Analysis of the metabolome-driven distribution of OTU phylogeny and function revealed clusters of clades that were both metabolically and metagenomically similar. Conclusions: The results suggest that microbes are syntropic with mucosal metabolome composition and therefore may be the source of and/or dependent upon gut epithelial metabolites. The consistent relationship between inferred metagenomic function and assayed metabolites suggests that metagenomic composition is predictive to a reasonable degree of microbial community metabolite pools. The finding that certain metabolites strongly correlate with microbial community structure raises the possibility of targeting metabolites for monitoring and/or therapeutically manipulating microbial community function in IBD and other chronic diseases.
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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
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252
Self-citationStructure, function and diversity of the healthy human microbiome
The Human Microbiome Project Consortium (2012) · Nature · doi:10.1038/nature11234 · Cited by 11,726
A metagenome-wide association study of gut microbiota in type 2 diabetes
Qin, J., Li, Y., Cai, Z., Li, S., Zhu, J., Zhang, F., Liang, S., Zhang, W., Guan, Y., Shen, D., et al. (2012) · Nature · doi:10.1038/nature11450 · Cited by 6,762
A General Framework for Weighted Gene Co-Expression Network Analysis
Zhang, B., & Horvath, S. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1128 · Cited by 5,684
Self-citationHost-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
Gut microbiota composition correlates with diet and health in the elderly
Claesson, M. J., Jeffery, I. B., Conde, S., Power, S. E., O’Connor, E. M., Cusack, S., Harris, H. M. B., Coakley, M., Lakshminarayanan, B., O’Sullivan, O., et al. (2012) · Nature · doi:10.1038/nature11319 · Cited by 3,314
Bacterial Community Variation in Human Body Habitats Across Space and Time
Costello, E. K., Lauber, C. L., Hamady, M., Fierer, N., Gordon, J. I., & Knight, R. (2009) · Science · doi:10.1126/science.1177486 · Cited by 3,231
Normal gut microbiota modulates brain development and behavior
Heijtz, R. D., Wang, S., Anuar, F., Qian, Y., Björkholm, B., Samuelsson, A., Hibberd, M. L., Forssberg, H., & Pettersson, S. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1010529108 · Cited by 3,228
A framework for human microbiome research
The Human Microbiome Project Consortium (2012) · Nature · doi:10.1038/nature11209 · Cited by 2,650
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Best practices for analysing microbiomes
Knight, R., Vrbanac, A., Taylor, B. C., Aksenov, A., Callewaert, C., Debelius, J., Gonzalez, A., Kosciolek, T., McCall, L.-I., McDonald, D., et al. (2018) · Nature Reviews Microbiology · doi:10.1038/s41579-018-0029-9 · Cited by 2,030
Gut microbiome structure and metabolic activity in inflammatory bowel disease
Franzosa, E. A., Sirota-Madi, A., Avila-Pacheco, J., Fornelos, N., Haiser, H. J., Reinker, S., Vatanen, T., Hall, A. B., Mallick, H., McIver, L. J., et al. (2018) · Nature Microbiology · doi:10.1038/s41564-018-0306-4 · Cited by 1,995
Self-citationCorrelation between intraluminal oxygen gradient and radial partitioning of intestinal microbiota
Albenberg, L., Esipova, T. V., Judge, C. P., Bittinger, K., Chen, J., Laughlin, A., Grunberg, S., Baldassano, R. N., Lewis, J. D., Li, H., et al. (2014) · Gastroenterology · doi:10.1053/j.gastro.2014.07.020 · Cited by 886
Microbial Ecology along the Gastrointestinal Tract
Hillman, E. T., Lu, H., Yao, T., & Nakatsu, C. H. (2017) · Microbes and environments · doi:10.1264/jsme2.me17017 · Cited by 580
(2014) · Cell Host & Microbe · doi:10.1016/j.chom.2014.08.014 · Cited by 536
Metadata
- Authors
- McHardy, I. H., Goudarzi, M., Tong, M., Ruegger, P. M., Schwager, E., Weger, J. R., Graeber, T. G., Sonnenburg, J. L., Horvath, S., Huttenhower, C., McGovern, D. P., Fornace, A. J., Borneman, J., & Braun, J.
- Venue
- Microbiome
- Published
- 2013-06-05
- Volume
- 1
- Issue
- 1
- Pages
- 17-17
- ISSN
- 2049-2618 (ISSN-L)
- OpenAlex
- W2164027931
- Cited by
- 322
- References
- 21
- FWCI
- 8.61
- Type
- journal article
- Language
- en
- Open access
- gold
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- McHardy, I. H., Goudarzi, M., Tong, M., Ruegger, P. M., Schwager, E., Weger, J. R., Graeber, T. G., Sonnenburg, J. L., Horvath, S., Huttenhower, C., McGovern, D. P., Fornace, A. J., Borneman, J., & Braun, J. (2013). Integrative analysis of the microbiome and metabolome of the human intestinal mucosal surface reveals exquisite inter-relationships. Microbiome. https://doi.org/10.1186/2049-2618-1-17
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