The DOI matches its Crossref record; 88 of the 88 references it cites were found in a registry or an index.
Personalized Mapping of Drug Metabolism by the Human Gut Microbiome
Open accessAbstract
Summary The human gut microbiome harbors hundreds of bacterial species with diverse biochemical capabilities. Dozens of drugs have been shown to be metabolized by single isolates from the gut microbiome, but the extent of this phenomenon is rarely explored in the context of microbial communities. Here, we develop a quantitative experimental framework for mapping the ability of the human gut microbiome to metabolize small molecule drugs: Microbiome Derived Metabolism (MDM)-Screen. Included are a batch culturing system for sustained growth of subject-specific gut microbial communities, an ex vivo drug metabolism screen, and targeted and untargeted functional metagenomic screens to identify microbiome-encoded genes responsible for specific metabolic events. Our framework identifies novel drug-microbiome interactions that vary between individuals and demonstrates how the gut microbiome might be used in drug development and personalized medicine.
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
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
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
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249
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
SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing
Bankevich, A., Nurk, S., Antipov, D., Gurevich, A. A., Dvorkin, M., Kulikov, A. S., Lesin, V. M., Nikolenko, S. I., Pham, S., Prjibelski, A. D., et al. (2012) · Journal of Computational Biology · doi:10.1089/cmb.2012.0021 · Cited by 25,987
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
Kearse, M., Moir, R., Wilson, A., Stones-Havas, S., Cheung, M., Sturrock, S., Buxton, S., Cooper, A., Markowitz, S., Duran, C., et al. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts199 · Cited by 20,027
One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products
Datsenko, K. A., & Wanner, B. L. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.120163297 · Cited by 14,782
Structure, function and diversity of the healthy human microbiome
The Human Microbiome Project Consortium (2012) · Nature · doi:10.1038/nature11234 · Cited by 11,726
Cited by
Microbiota–gut–brain axis and its therapeutic applications in neurodegenerative diseases
Loh, J. S., Mak, W. Q., Tan, L. K. S., Ng, C. X., Chan, H. H., Yeow, S. H., Foo, J. B., Ong, Y. S., How, C. W., & Khaw, K. Y. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01743-1 · Cited by 1,193
Gut microbiota and cardiovascular disease.
Witkowski, M., Weeks, T. L., & Hazen, S. L. (2020) · Circulation Research · doi:10.1161/circresaha.120.316242 · Cited by 1,051
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
“These extrinsic host influences are not typically accounted for in antibiotic–microbiome investigations, despite the clear capacity of many of these xenobiotic compounds to affect growth promotion or inhibition in a species-specific manner 111,112 .”
BackgroundThe microbiome–gut–brain axis in Parkinson disease — from basic research to the clinic
Tan, A. H., Lim, S. Y., & Lang, A. E. (2022) · Nature Reviews Neurology · doi:10.1038/s41582-022-00681-2 · Cited by 392
The gut microbiome: an orchestrator of xenobiotic metabolism
Collins, S. L., & Patterson, A. D. (2020) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2019.12.001 · Cited by 348
Metadata
- Authors
- Javdan, B., Lopez, J. G., Chankhamjon, P., Lee, Y.-C. J., Hull, R., Wu, Q., Wang, X., Chatterjee, S., & Donia, M. S.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2020-06
- Volume
- 181
- Issue
- 7
- Pages
- 1661-1679.e22
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W3034548692
- Cited by
- 474
- References
- 88
- FWCI
- 14.52
- Type
- journal article
- Language
- en
- Open access
- bronze
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Personalized Mapping of Drug Metabolism by the Human Gut Microbiome. https://app.cochat.ai/papers/10.1016/j.cell.2020.05.001
- The paper
- Javdan, B., Lopez, J. G., Chankhamjon, P., Lee, Y.-C. J., Hull, R., Wu, Q., Wang, X., Chatterjee, S., & Donia, M. S. (2020). Personalized Mapping of Drug Metabolism by the Human Gut Microbiome. Cell. https://doi.org/10.1016/j.cell.2020.05.001
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.