The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
Key bacterial taxa and metabolic pathways affecting gut short-chain fatty acid profiles in early life
Open accessAbstract
Infant gut microbiota development affects the host physiology throughout life, and short-chain fatty acids (SCFAs) are promising key metabolites mediating microbiota-host relationships. Here, we investigated dense longitudinally collected faecal samples from 12 subjects during the first 2 years (n = 1048) to identify early life gut SCFA patterns and their relationships with the microbiota. Our results revealed three distinct phases of progression in the SCFA profiles: early phase characterised by low acetate and high succinate, middle-phase characterised by high lactate and formate and late-phase characterised by high propionate and butyrate. Assessment of the SCFA–microbiota relationships revealed that faecal butyrate is associated with increased Clostridiales and breastfeeding cessation, and that diverse and personalised assemblage of Clostridiales species possessing the acetyl-CoA pathway play major roles in gut butyrate production. We also found an association between gut formate and some infant-type bifidobacterial species, and that human milk oligosaccharides (HMO)-derived fucose is the substrate for formate production during breastfeeding. We identified genes upregulated in fucose and fucosylated HMO utilisation in infant-type bifidobacteria. Notably, bifidobacteria showed interspecific and intraspecific variation in the gene repertoires, and cross-feeding of fucose contributed to gut formate production. This study provides an insight into early life SCFA–microbiota relationships, which is an important step for developing strategies for modulating lifelong health.
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Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
DADA2: High–resolution sample inference from Illumina amplicon data
Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016) · Nature Methods · doi:10.1038/nmeth.3869 · Cited by 35,698
Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2
Bolyen, E., Rideout, J. R., Dillon, M. R., Bokulich, N. A., Abnet, C. C., Al-Ghalith, G. A., Alexander, H., Alm, E. J., Arumugam, M., Asnicar, F., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0209-9 · Cited by 23,742
MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform
Katoh, K. (2002) · Nucleic Acids Research · doi:10.1093/nar/gkf436 · Cited by 17,027
FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments
Price, M. N., Dehal, P. S., & Arkin, A. P. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009490 · Cited by 14,894
Prodigal: prokaryotic gene recognition and translation initiation site identification
Hyatt, D., Chen, G.-L., LoCascio, P. F., Land, M. L., Larimer, F. W., & Hauser, L. J. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-119 · Cited by 12,587
Primer3—new capabilities and interfaces
Untergasser, A., Cutcutache, I., Koressaar, T., Ye, J., Faircloth, B. C., Remm, M., & Rozen, S. G. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks596 · Cited by 9,461
Enterotypes of the human gut microbiome
MetaHIT Consortium (additional members), Arumugam, M., Raes, J., Pelletier, E., Le Paslier, D., Yamada, T., Mende, D. R., Fernandes, G. R., Tap, J., Bruls, T., et al. (2011) · Nature · doi:10.1038/nature09944 · Cited by 7,548
Self-citationFrom Dietary Fiber to Host Physiology: Short-Chain Fatty Acids as Key Bacterial Metabolites
Koh, A., De Vadder, F., Kovatcheva-Datchary, P., & Bäckhed, F. (2016) · Cell · doi:10.1016/j.cell.2016.05.041 · Cited by 7,070
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
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Mann, E. R., Lam, Y. K., & Uhlig, H. H. (2024) · Nature reviews. Immunology · doi:10.1038/s41577-024-01014-8 · Cited by 1,595
Short-Chain Fatty-Acid-Producing Bacteria: Key Components of the Human Gut Microbiota
Fusco, W., Lorenzo, M. B., Cintoni, M., Porcari, S., Rinninella, E., Kaitsas, F., Lener, E., Mele, M. C., Gasbarrini, A., Collado, M. C., et al. (2023) · Nutrients · doi:10.3390/nu15092211 · Cited by 1,243
Antibiotic perturbations to the gut microbiome
Fishbein, S. R. S., Mahmud, B., & Dantas, G. (2023) · Nature Reviews Microbiology · doi:10.1038/s41579-023-00933-y · Cited by 440
Effects of exercise on gut microbiota of adults: a systematic review and meta-analysis
Min, L., Ablitip, A., Wang, R., Luciana, T., Wei, M., & Ma, X. (2024) · Nutrients · doi:10.3390/nu16071070 · Cited by 98
Derrien, M., Mikulic, N., Uyoga, M. A., Chenoll, E., Climent, E., Howard-Varona, A., Nyilima, S., Stoffel, N. U., Karanja, S., Kottler, R., et al. (2023) · Gut Microbes · doi:10.1080/19490976.2023.2178793 · Cited by 36
Metadata
- Authors
- Tsukuda, N., Yahagi, K., Hara, T., Watanabe, Y., Matsumoto, H., Mori, H., Higashi, K., Tsuji, H., Matsumoto, S., Kurokawa, K., & Matsuki, T.
- Venue
- The ISME Journal
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-03-15
- Volume
- 15
- Issue
- 9
- Pages
- 2574-2590
- ISSN
- 1751-7362 (ISSN-L)
- 1751-7370
- OpenAlex
- W3139312899
- Cited by
- 319
- References
- 69
- FWCI
- 13.28
- Type
- journal article
- Language
- en
- Open access
- hybrid
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