The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Neonatal gut microbiota associates with childhood multisensitized atopy and T cell differentiation
Open accessAbstract
Gut microbiota bacterial depletions and altered metabolic activity at 3 months are implicated in childhood atopy and asthma. We hypothesized that compositionally distinct human neonatal gut microbiota (NGM) exist, and are differentially related to relative risk (RR) of childhood atopy and asthma. Using stool samples (n = 298; aged 1–11 months) from a US birth cohort and 16S rRNA sequencing, neonates (median age, 35 d) were divisible into three microbiota composition states (NGM1–3). Each incurred a substantially different RR for multisensitized atopy at age 2 years and doctor-diagnosed asthma at age 4 years. The highest risk group, labeled NGM3, showed lower relative abundance of certain bacteria (for example, Bifidobacterium, Akkermansia and Faecalibacterium), higher relative abundance of particular fungi (Candida and Rhodotorula) and a distinct fecal metabolome enriched for pro-inflammatory metabolites. Ex vivo culture of human adult peripheral T cells with sterile fecal water from NGM3 subjects increased the proportion of CD4+ cells producing interleukin (IL)-4 and reduced the relative abundance of CD4+CD25+FOXP3+ cells. 12,13-DiHOME, enriched in NGM3 versus lower-risk NGM states, recapitulated the effect of NGM3 fecal water on relative CD4+CD25+FOXP3+ cell abundance. These findings suggest that neonatal gut microbiome dysbiosis might promote CD4+ T cell dysfunction associated with childhood atopy.
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
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
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
FLASH: fast length adjustment of short reads to improve genome assemblies
Magoč, T., & Salzberg, S. L. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr507 · Cited by 15,366
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
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
UniFrac: a New Phylogenetic Method for Comparing Microbial Communities
Lozupone, C., & Knight, R. (2005) · Applied and Environmental Microbiology · doi:10.1128/aem.71.12.8228-8235.2005 · Cited by 8,227
Archaea and fungi of the human gut microbiome: correlations with diet and bacterial residents
Hoffmann, C., Dollive, S., Grunberg, S., Chen, J., Li, H., Wu, G. D., Lewis, J. D., & Bushman, F. D. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066019 · Cited by 808
Beyond Atopy: Multiple Patterns of Sensitization in Relation to Asthma in a Birth Cohort Study
Simpson, A., Tan, V. Y. F., Winn, J., Svensén, M., Bishop, C. M., Heckerman, D. E., Buchan, I., & Custovic, A. (2010) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.200907-1101oc · Cited by 415
Cited by
The Human Intestinal Microbiome in Health and Disease
Lynch, S. V., & Pedersen, O. (2016) · New England Journal of Medicine · doi:10.1056/nejmra1600266 · Cited by 3,637
Self-citationCurrent understanding of the human microbiome
Gilbert, J. A., Blaser, M. J., Caporaso, J. G., Jansson, J. K., Lynch, S. V., & Knight, R. (2018) · Nature Medicine · doi:10.1038/nm.4517 · Cited by 2,609
Self-citationMilani, C., Duranti, S., Bottacini, F., Casey, E., Turroni, F., Mahony, J., Belzer, C., Delgado Palacio, S., Arboleya Montes, S., Mancabelli, L., et al. (2017) · Microbiology and Molecular Biology Reviews · doi:10.1128/mmbr.00036-17 · Cited by 1,950
Stunted microbiota and opportunistic pathogen colonization in caesarean-section birth
Shao, Y., Forster, S. C., Tsaliki, E., Vervier, K., Strang, A., Simpson, N., Kumar, N., Stares, M. D., Rodger, A., Brocklehurst, P., et al. (2019) · Nature · doi:10.1038/s41586-019-1560-1 · Cited by 1,088
Microbes, Metabolites, and the Gut-Lung Axis
Dang, A. T., & Marsland, B. J. (2019) · Mucosal Immunology · doi:10.1038/s41385-019-0160-6 · Cited by 1,028
Metadata
- Authors
- Fujimura, K. E., Sitarik, A. R., Havstad, S., Lin, D. L., Levan, S., Fadrosh, D., Panzer, A. R., LaMere, B., Rackaityte, E., Lukacs, N. W., Wegienka, G., Boushey, H. A., Ownby, D. R., Zoratti, E. M., Levin, A. M., Johnson, C. C., & Lynch, S. V.
- Venue
- Nature Medicine
- Published
- 2016-09-12
- Volume
- 22
- Issue
- 10
- Pages
- 1187-1191
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W2521042841
- Cited by
- 1,159
- References
- 10
- FWCI
- 41.68
- 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.