Profiling the human intestinal environment under physiological conditions
Open accessAbstract
The spatiotemporal structure of the human microbiome 1,2 , proteome 3 and metabolome 4,5 reflects and determines regional intestinal physiology and may have implications for disease 6 . Yet, little is known about the distribution of microorganisms, their environment and their biochemical activity in the gut because of reliance on stool samples and limited access to only some regions of the gut using endoscopy in fasting or sedated individuals 7 . To address these deficiencies, we developed an ingestible device that collects samples from multiple regions of the human intestinal tract during normal digestion. Collection of 240 intestinal samples from 15 healthy individuals using the device and subsequent multi-omics analyses identified significant differences between bacteria, phages, host proteins and metabolites in the intestines versus stool. Certain microbial taxa were differentially enriched and prophage induction was more prevalent in the intestines than in stool. The host proteome and bile acid profiles varied along the intestines and were highly distinct from those of stool. Correlations between gradients in bile acid concentrations and microbial abundance predicted species that altered the bile acid pool through deconjugation. Furthermore, microbially conjugated bile acid concentrations exhibited amino acid-dependent trends that were not apparent in stool. Overall, non-invasive, longitudinal profiling of microorganisms, proteins and bile acids along the intestinal tract under physiological conditions can help elucidate the roles of the gut microbiome and metabolome in human physiology and disease.
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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
BBAAs are abundant in the gastrointestinal tract and have been associated with human inflammatory bowel disease.
“Furthermore, bacterial bile acid amidates (BBAAs) are abundant in the gastrointestinal tract18 and have been associated with human inflammatory bowel disease3.”
From Bile Acids Are Substrates for Amine N-Acyl Transferase Activity by Bile Salt Hydrolase (2022) · cites this paper as [18]
Reduced concentrations of DCA and LCA are detected in intestinal and fecal samples of individuals who had recently used antibiotics, albeit with a small sample size.
“Moreover, reduced concentrations of DCA and LCA were detected in the intestinal and fecal samples of individuals who had recently used antibiotics, albeit with the caveat of a small sample size.354”
From Microbiota–gut–brain axis and its therapeutic applications in neurodegenerative diseases (2024) · cites this paper as [354]
Recent development of non-invasive, ingestible sampling devices makes it possible to collect luminal contents throughout the intestinal tract, potentially overcoming limitations of stool samples in reflecting regional variation of gut microbiota.
“Notably, the recent development of non-invasive, ingestible sampling devices has made it possible to collect luminal contents throughout the intestinal tract, potentially overcoming the limitations of stool samples in reflecting the regional variation of gut microbiota.354,753”
From Microbiota–gut–brain axis and its therapeutic applications in neurodegenerative diseases (2024) · cites this paper as [354]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400 · see citing works
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 · see citing works
Self-citationThe SILVA ribosomal RNA gene database project: improved data processing and web-based tools
Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., & Glöckner, F. O. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1219 · Cited by 33,055 · see citing works
phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data
McMurdie, P. J., & Holmes, S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061217 · Cited by 21,013 · see citing works
Self-citationCox, J., & Mann, M. (2008) · Nature Biotechnology · doi:10.1038/nbt.1511 · Cited by 14,855 · see citing works
Self-citationParks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P., & Tyson, G. W. (2015) · Genome Research · doi:10.1101/gr.186072.114 · Cited by 12,496 · see citing works
Prodigal: prokaryotic gene recognition and translation initiation site identification
Hyatt, D., Chen, G.-L., LoCascio, P. F., Land, M., Larimer, F. W., & Hauser, L. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-119 · Cited by 12,426 · see citing works
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 · see citing works
QUAST: quality assessment tool for genome assemblies
Gurevich, A., Saveliev, V., Vyahhi, N., & Tesler, G. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt086 · Cited by 11,172 · see citing works
Li, D., Liu, C.-M., Luo, R., Sadakane, K., & Lam, T. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv033 · Cited by 9,781 · see citing works
The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences
Perez-Riverol, Y., Bai, J., Bandla, C., García-Seisdedos, D., Hewapathirana, S., Kamatchinathan, S., Kundu, D. J., Prakash, A., Frericks-Zipper, A., Eisenacher, M., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1038 · Cited by 6,125 · see citing works
Cox, J., Hein, M. Y., Luber, C. A., Paron, I., Nagaraj, N., & Mann, M. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.031591 · Cited by 5,180 · see citing works
Self-citationGTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database
Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P., & Parks, D. H. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz848 · Cited by 5,176 · see citing works
Kang, D., Li, F., Kirton, E., Thomas, A., Egan, R. S., An, H., & Wang, Z. (2019) · PeerJ · doi:10.7717/peerj.7359 · Cited by 3,784 · see citing works
phangorn: phylogenetic analysis in R
Schliep, K. P. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq706 · Cited by 3,678 · see citing works
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 · see citing works
CARD 2020: Antibiotic Resistome Surveillance with the Comprehensive Antibiotic Resistance Database
Alcock, B. P., Raphenya, A. R., Lau, T. T. Y., Tsang, K. K., Bouchard, M., Edalatmand, A., Huynh, W., Nguyen, A.-L. V., Cheng, A. A., Liu, S., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz935 · Cited by 3,202 · see citing works
Olm, M. R., Brown, C. T., Brooks, B., & Banfield, J. F. (2017) · The ISME Journal · doi:10.1038/ismej.2017.126 · Cited by 3,031 · see citing works
Bile salt biotransformations by human intestinal bacteria
Ridlon, J. M., Kang, D.-J., & Hylemon, P. B. (2006) · Journal of Lipid Research · doi:10.1194/jlr.r500013-jlr200 · Cited by 2,829 · see citing works
Lipid extraction by methyl-tert-butyl ether for high-throughput lipidomics
Matyash, V., Liebisch, G., Kurzchalia, T. V., Shevchenko, A., & Schwudke, D. (2008) · Journal of Lipid Research · doi:10.1194/jlr.d700041-jlr200 · Cited by 2,615 · see citing works
Advanced methods of microscope control using μManager software
Edelstein, A., Tsuchida, M. A., Amodaj, N., Pinkard, H., Vale, R. D., & Stuurman, N. (2014) · Journal of Biological Methods · doi:10.14440/jbm.2014.36 · Cited by 1,927 · see citing works
Dietary-fat-induced taurocholic acid promotes pathobiont expansion and colitis in Il10-/- mice
Devkota, S., Wang, Y., Musch, M. W., Leone, V., Fehlner-Peach, H., Nadimpalli, A., Antonopoulos, D. A., Jabri, B., & Chang, E. B. (2012) · Nature · doi:10.1038/nature11225 · Cited by 1,904 · see citing works
CheckV assesses the quality and completeness of metagenome-assembled viral genomes
Nayfach, S., Camargo, A. P., Schulz, F., Eloe‐Fadrosh, E. A., Roux, S., & Kyrpides, N. C. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-00774-7 · Cited by 1,779 · see citing works
Zmora, N., Zilberman-Schapira, G., Suez, J., Mor, U., Dori-Bachash, M., Bashiardes, S., Kotler, E., Zur, M., Regev-Lehavi, D., Brik, R. B.-Z., et al. (2018) · Cell · doi:10.1016/j.cell.2018.08.041 · Cited by 1,695 · see citing works
Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads
Jiang, H., Lei, R., Ding, S., & Zhu, S. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-182 · Cited by 1,525 · see citing works
Kararli, T. T. (1995) · Biopharmaceutics & Drug Disposition · doi:10.1002/bdd.2510160502 · Cited by 1,461 · see citing works
Bile Acid Metabolism and Signaling
Chiang, J. Y. L. (2013) · Comprehensive physiology · doi:10.1002/cphy.c120023 · Cited by 1,390 · see citing works
VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses
Guo, J.-R., Bolduc, B., Zayed, A., Varsani, A., Domínguez-Huerta, G., Delmont, T., Pratama, A. A., Gazitúa, M. C., Vik, D., Sullivan, M., et al. (2021) · Microbiome · doi:10.1186/s40168-020-00990-y · Cited by 1,250 · see citing works
Measurement of gastrointestinal pH profiles in normal ambulant human subjects
Evans, D. F., Pye, G., Bramley, R., Clark, A. G., Dyson, T. J., & Hardcastle, J. D. (1988) · Gut · doi:10.1136/gut.29.8.1035 · Cited by 1,240 · see citing works
Kieft, K., Zhou, Z., & Anantharaman, K. (2020) · Microbiome · doi:10.1186/s40168-020-00867-0 · Cited by 1,108 · see citing works
<b>topicmodels</b> : An <i>R</i> Package for Fitting Topic Models
Grün, B., & Hornik, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v040.i13 · Cited by 1,072 · see citing works
The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans
The Tabula Sapiens Consortium*, Jones, R. C., Karkanias, J., Krasnow, M. A., Pisco, A. O., Quake, S. R., Salzman, J., Yosef, N., Bulthaup, B., Brown, P., et al. (2022) · Science · doi:10.1126/science.abl4896 · Cited by 1,056 · see citing works
Self-citationPlasma Proteome Profiling to Assess Human Health and Disease
Geyer, P. E., Kulak, N. A., Pichler, G., Holdt, L. M., Teupser, D., & Mann, M. (2016) · Cell Systems · doi:10.1016/j.cels.2016.02.015 · Cited by 734 · see citing works
Self-citationIdentifying viruses from metagenomic data using deep learning
Ren, J., Song, K., Deng, C., Ahlgren, N. A., Fuhrman, J. A., Li, Y., Xie, X. S., Poplin, R. E., & Sun, F. (2020) · Quantitative Biology · doi:10.1007/s40484-019-0187-4 · Cited by 668 · see citing works
Skyline for Small Molecules: A Unifying Software Package for Quantitative Metabolomics
Adams, K. J., Pratt, B. S., Bose, N., Dubois, L. G., John‐Williams, L. S., Perrott, K. M., Ky, K., Kapahi, P., Sharma, V., MacCoss, M. J., et al. (2020) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00640 · Cited by 604 · see citing works
Carbohydrate-active enzymes (CAZymes) in the gut microbiome
Wardman, J. F., Bains, R. K., Rahfeld, P., & Withers, S. G. (2022) · Nature Reviews Microbiology · doi:10.1038/s41579-022-00712-1 · Cited by 597 · see citing works
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 586 · see citing works
Global chemical effects of the microbiome include new bile-acid conjugations
Quinn, R. A., Melnik, A. V., Vrbanac, A., Fu, T., Patras, K. A., Christy, M. P., Bodai, Z., Belda-Ferre, P., Tripathi, A., Chung, L. K., et al. (2020) · Nature · doi:10.1038/s41586-020-2047-9 · Cited by 579 · see citing works
Song, Z., Cai, Y., Lao, X., Wang, X., Lin, X., Cui, Y., Kalavagunta, P. K., Liao, J., Jin, L., Shang, J., et al. (2019) · Microbiome · doi:10.1186/s40168-019-0628-3 · Cited by 505 · see citing works
Current Sampling Methods for Gut Microbiota: A Call for More Precise Devices
Tang, Q., Jin, G., Wang, G., Liu, T., Liu, X., Wang, B., & Cao, H. (2020) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2020.00151 · Cited by 461 · see citing works
Quantitative imaging of gut microbiota spatial organization
Earle, K. A., Billings, G., Sigal, M., Lichtman, J. S., Hansson, G. C., Elias, J. E., Amieva, M. R., Huang, K. C., & Sonnenburg, J. L. (2015) · Cell Host & Microbe · doi:10.1016/j.chom.2015.09.002 · Cited by 433 · see citing works
Self-citationInhibition of histone deacetylase 1 (HDAC1) and HDAC2 enhances CRISPR/Cas9 genome editing
Liu, B., Chen, S., Rose, A. M. L., Chen, D., Cao, F., Zwinderman, M. R. H., Kiemel, D., Aïssi, M., Dekker, F. J., & Haisma, H. J. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1136 · Cited by 370 · see citing works
Kastl, A., Terry, N. A., Wu, G. D., & Albenberg, L. G. (2019) · Cellular and Molecular Gastroenterology and Hepatology · doi:10.1016/j.jcmgh.2019.07.006 · Cited by 359 · see citing works
Advances in the physiology of gastric emptying
Goyal, R., Guo, Y., & Mashimo, H. (2019) · Neurogastroenterology and Motility · doi:10.1111/nmo.13546 · Cited by 333 · see citing works
dbCAN-seq: a database of carbohydrate-active enzyme (CAZyme) sequence and annotation
Huang, L., Zhang, H., Wu, P., Entwistle, S., Li, X., Yohe, T., Yi, H., Yang, Z., & Yin, Y. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx894 · Cited by 325 · see citing works
High temporal and inter‐individual variation detected in the human ileal microbiota
Booijink, C. C. G. M., El‐Aidy, S., Rajilić‐Stojanović, M., Heilig, H. G. H. J., Troost, F. J., Smidt, H., Kleerebezem, M., De Vos, W. M., & Zoetendal, E. G. (2010) · Environmental Microbiology · doi:10.1111/j.1462-2920.2010.02294.x · Cited by 324 · see citing works
Sielaff, M., Kuharev, J., Bohn, T., Hahlbrock, J., Bopp, T., Tenzer, S., & Distler, U. (2017) · Journal of Proteome Research · doi:10.1021/acs.jproteome.7b00433 · Cited by 321 · see citing works
Distinct microbial and immune niches of the human colon
James, K. R., Gomes, T., Elmentaite, R., Kumar, N., Gulliver, E. L., King, H. W., Stares, M. D., Bareham, B. R., Ferdinand, J. R., Petrova, V. N., et al. (2020) · Nature Immunology · doi:10.1038/s41590-020-0602-z · Cited by 301 · see citing works
Gerritsen, J., Fuentes, S., Grievink, W., van Niftrik, L., Tindall, B. J., Timmerman, H. M., Rijkers, G. T., & Smidt, H. (2014) · INTERNATIONAL JOURNAL OF SYSTEMATIC AND EVOLUTIONARY MICROBIOLOGY · doi:10.1099/ijs.0.059543-0 · Cited by 297 · see citing works
Bile acids are nutrient signaling hormones
Zhou, H., & Hylemon, P. B. (2014) · Steroids · doi:10.1016/j.steroids.2014.04.016 · Cited by 285 · see citing works
Gender-related differences in gastric emptying.
Datz, F. L., Christian, P. E., & Moore, J. G. (1987) · PubMed · Cited by 281 · see citing works
Second-generation colon capsule endoscopy compared with colonoscopy
Spada, C., Hassan, C., Muñoz-Navas, M. Á., Neuhaus, H., Deviere, J. M., Fockens, P., Coron, E., Gay, G., Tóth, E., Riccioni, M. E., et al. (2011) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2011.03.1125 · Cited by 264 · see citing works
Advances in human proteomics at high scale with the SOMAscan proteomics platform
Gold, L., Walker, J. J., Wilcox, S. K., & Williams, S. A. (2011) · New Biotechnology · doi:10.1016/j.nbt.2011.11.016 · Cited by 263 · see citing works
Animal models to study bile acid metabolism
Li, J., & Dawson, P. A. (2018) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2018.05.011 · Cited by 254 · see citing works
Bhargava, P., Smith, M. D., Mische, L., Harrington, E., Fitzgerald, K. C., Martin, K. A., Kim, S., Reyes, A. A., Cardona, J. G., Volsko, C. M., et al. (2020) · Journal of Clinical Investigation · doi:10.1172/jci129401 · Cited by 236 · see citing works
Dorrestein, P., Gentry, E., Collins, S., Panitchpakdi, M., Belda-Ferre, P., Stewart, A., Wang, M., Jarmusch, A., Avila-Pacheco, J., Plichta, D., et al. (2021) · doi:10.21203/rs.3.rs-820302/v1 · Cited by 197 · see citing works
Self-citationLucas, L. N., Barrett, K., Kerby, R. L., Zhang, Q., Cattaneo, L. E., Stevenson, D., Rey, F. E., & Amador-Noguez, D. (2021) · mSystems · doi:10.1128/msystems.00805-21 · Cited by 168 · see citing works
The Tabula Sapiens: a multiple organ single cell transcriptomic atlas of humansPreprint
Quake, S. R. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.19.452956 · Cited by 145 · see citing works
Human metabolome variation along the upper intestinal tract
Folz, J., Culver, R. N., Morales, J. M., Grembi, J., Triadafilopoulos, G., Relman, D. A., Huang, K. C., Shalon, D., & Fiehn, O. (2023) · Nature Metabolism · doi:10.1038/s42255-023-00777-z · Cited by 143 · see citing works
Self-citationBaron, E. J., Summanen, P. H., Downes, J., Roberts, M. C., Wexler, H. M., & Finegold, S. M. (1989) · Microbiology · doi:10.1099/00221287-135-12-3405 · Cited by 142 · see citing works
Vuik, F. E. R., Dicksved, J., Lam, S., Fuhler, G. M., van der Laan, L. J. W., van de Winkel, A., Konstantinov, S., Spaander, M., Peppelenbosch, M. P., Engstrand, L. G., et al. (2019) · United European Gastroenterology Journal · doi:10.1177/2050640619852255 · Cited by 140 · see citing works
Leite, G. G. S., Weitsman, S. R., Parodi, G., Celly, S., Sedighi, R., Sanchez, M., Morales, W., Villanueva-Millán, M. J., Barlow, G. M., Mathur, R., et al. (2020) · Digestive Diseases and Sciences · doi:10.1007/s10620-020-06173-x · Cited by 138 · see citing works
Maurer, J. M., Schellekens, R. C. A., van Rieke, H. M., Wanke, C., Iordanov, V. P., Stellaard, F., Wutzke, K. D., Dijkstra, G. D. H., van der Zee, M., Woerdenbag, H. J., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0129076 · Cited by 137 · see citing works
Baars, A., Oosting, A., Knol, J., Garssen, J., & Van Bergenhenegouwen, J. (2015) · Microorganisms · doi:10.3390/microorganisms3040641 · Cited by 135 · see citing works
Aranda-Díaz, A., Ng, K. M., Thomsen, T., Real-Ramírez, I., Dahan, D., Dittmar, S., Gonzalez, C. G., Chavez, T. M., Vasquez, K. S., Nguyen, T. H., et al. (2022) · Cell Host & Microbe · doi:10.1016/j.chom.2021.12.008 · Cited by 116 · see citing works
Self-citationPerspective and Guidelines for Metaproteomics in Microbiome Studies
Zhang, X., & Figeys, D. (2019) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00054 · Cited by 97 · see citing works
Latent variable modeling for the microbiome
Sankaran, K., & Holmes, S. (2018) · Biostatistics · doi:10.1093/biostatistics/kxy018 · Cited by 92 · see citing works
Self-citationHo, P. P.-K., & Steinman, L. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1524890113 · Cited by 83 · see citing works
Connors, J., Dunn, K. A., Allott, J., Bandsma, R., Rashid, M., Otley, A. R., Bielawski, J. P., & Limbergen, J. V. (2019) · The ISME Journal · doi:10.1038/s41396-019-0560-3 · Cited by 79 · see citing works
Deciphering Active Prophages from Metagenomes
Kieft, K., & Anantharaman, K. (2022) · mSystems · doi:10.1128/msystems.00084-22 · Cited by 79 · see citing works
Ingestible Osmotic Pill for In Vivo Sampling of Gut Microbiomes
Nejad, H. R., Oliveira, B. C. M., Sadeqi, A., Dehkharghani, A. S., Kondova, I. I., Langermans, J. A. M., Guasto, J., Tzipori, S. R., Widmer, G., & Sonkusale, S. (2019) · Advanced Intelligent Systems · doi:10.1002/aisy.201900053 · Cited by 77 · see citing works
sangeranalyseR: Simple and Interactive Processing of Sanger Sequencing Data in R
Chao, K.-H., Barton, K., Palmer, S., et al. (2021) · Genome Biology and Evolution · doi:10.1093/gbe/evab028 · Cited by 65 · see citing works
Scheltema, R. A., & Mann, M. (2012) · Journal of Proteome Research · doi:10.1021/pr201219e · Cited by 63 · see citing works
Self-citationLichtman, J. S., Marcobal, A. M., Sonnenburg, J. L., & Elias, J. E. (2013) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.029967 · Cited by 57 · see citing works
Barupal, D. K., Zhang, Y., Shen, T., Fan, S., Roberts, B. S., Fitzgerald, P., Wancewicz, B., Valdiviez, L., Wohlgemuth, G., Byram, G., et al. (2019) · Metabolites · doi:10.3390/metabo9050101 · Cited by 56 · see citing works
Self-citationRapid ordering of barcoded transposon insertion libraries of anaerobic bacteria
Shiver, A. L., Culver, R. N., Deutschbauer, A. M., & Huang, K. C. (2021) · Nature Protocols · doi:10.1038/s41596-021-00531-3 · Cited by 40 · see citing works
Self-citationThe effect of microbial colonization on the host proteome varies by gastrointestinal location
Lichtman, J. S., Alsentzer, E., Jaffe, M., Sprockett, D., Masutani, E., Ikwa, E., Fragiadakis, G. K., Clifford, D., Huang, B. E., Sonnenburg, J. L., et al. (2016) · The ISME Journal · doi:10.1038/ismej.2015.187 · Cited by 32 · see citing works
Self-citationCelis, A. I., Aranda-Díaz, A., Culver, R. N., Xue, K. S., Relman, D. A., Shi, H., & Huang, K. C. (2022) · iScience · doi:10.1016/j.isci.2022.103907 · Cited by 21 · see citing works
Self-citationMimicking prophage induction in the body: induction in the lab with pH gradients
Miller-Ensminger, T., Garretto, A., Stark, N., & Putonti, C. (2020) · PeerJ · doi:10.7717/peerj.9718 · Cited by 21 · see citing works
Müller, J., Hansen, F. M., Schweizer, L., Treit, P. V., Geyer, P. E., & Mann, M. (2021) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2021.100082 · Cited by 21 · see citing works
Self-citationBhargava, P., Mische, L., Smith, M. D., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/627356 · Cited by 15 · see citing works
Müller, J., Hansen, F. M., Schweizer, L., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.26.433033 · Cited by 3 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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 · see citing works
Bile Acids Are Substrates for Amine N-Acyl Transferase Activity by Bile Salt Hydrolase
Patterson, A., Rimal, B., Collins, S., Granda, M., Koo, I., Solanka, S., Hoque, N., Gentry, E., Yan, T., Bisanz, J., et al. (2022) · doi:10.21203/rs.3.rs-2050120/v1 · Cited by 245 · see citing works
Self-citationBile salt hydrolase/aminoacyltransferase shapes the microbiome
Guzior, D., Okros, M., Hernandez, C. M., Shivel, M., Armwald, B., Hausinger, R., & Quinn, R. (2022) · doi:10.21203/rs.3.rs-2050406/v1 · Cited by 187 · see citing works
Host genetic regulation of human gut microbial structural variation
Zhernakova, D. V., Wang, D., Liu, L., Andreu-Sánchez, S., Zhang, Y., Ruiz-Moreno, A. J., Peng, H., Plomp, N., Del Castillo-Izquierdo, Á., Gacesa, R., et al. (2024) · Nature · doi:10.1038/s41586-023-06893-w · Cited by 174 · see citing works
Human metabolome variation along the upper intestinal tract
Folz, J., Culver, R. N., Morales, J. M., Grembi, J., Triadafilopoulos, G., Relman, D. A., Huang, K. C., Shalon, D., & Fiehn, O. (2023) · Nature Metabolism · doi:10.1038/s42255-023-00777-z · Cited by 143 · see citing works
Self-citationHow bile acids and the microbiota interact to shape host immunity
Lee, M. H., Nuccio, S.-P., Mohanty, I., Hagey, L. R., Dorrestein, P. C., Chu, H., & Raffatellu, M. (2024) · Nature Reviews Immunology · doi:10.1038/s41577-024-01057-x · Cited by 143 · see citing works
Targeting nonalcoholic fatty liver disease via gut microbiome-centered therapies
Koning, M., Herrema, H., Nieuwdorp, M., & Meijnikman, A. S. (2023) · Gut Microbes · doi:10.1080/19490976.2023.2226922 · Cited by 56 · see citing works
- Authors
- Shalon, D., Culver, R. N., Grembi, J. A., Folz, J., Treit, P. V., Shi, H., Rosenberger, F. A., Dethlefsen, L., Meng, X., Yaffe, E., Aranda-Díaz, A., Geyer, P. E., Mueller-Reif, J. B., Spencer, S., Patterson, A. D., Triadafilopoulos, G., Holmes, S. P., Mann, M., Fiehn, O., Relman, D. A., & Huang, K. C.
- Affiliations
- Stanford Medicine (US)
- University of California, Davis (US)
- Max Planck Institute of Biochemistry (DE)
- Chan Zuckerberg Biohub San Francisco (US)
- Stanford University (US)
- Pennsylvania State University (US)
- VA Palo Alto Health Care System (US)
- Corresponding author
- Culver, Rebecca Neal
- Fiehn, Oliver
- Relman, David A.
- Huang, Kerwyn Casey
- ORCID
- Shalon, Dari
- Culver, Rebecca Neal
- Grembi, Jessica A.
- Folz, Jacob
- Treit, Peter V.
- Shi, Handuo
- Rosenberger, Florian A.
- Dethlefsen, Les
- Meng, Xiandong
- Yaffe, Eitan
- Aranda-Díaz, Andrés
- Geyer, Philipp E.
- Spencer, Sean
- Patterson, Andrew D.
- Triadafilopoulos, George
- Holmes, Susan P.
- Mann, Matthias
- Fiehn, Oliver
- Relman, David A.
- Huang, Kerwyn Casey
- Venue
- Nature
- Published
- 2023-05-10
- Volume
- 617
- Issue
- 7961
- Pages
- 581-591
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC10191855
- OpenAlex
- W4376132136
- Cited by
- 484
- References
- 82
- Citations by year
- 2026: 112 · 2025: 159 · 2024: 162 · 2023: 51
- FWCI
- 43.08
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- National Science Foundation (1936687)
- Bill and Melinda Gates Foundation (INV-037857)
- Fund for Astrophysical Research
- National Institutes of Health (NIH T32, AI147023)
- School of Medicine, Stanford University
- National Defense Science and Engineering Graduate
- Stanford Research Computing Center, Stanford University
- National Institute of General Medical Sciences (RM1GM135102)
- National Institute of Allergy and Infectious Diseases (R01AI147023)
- National Institute of Diabetes and Digestive and Kidney Diseases (T32DK007056)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Health Sciences › Medicine › Gastroenterology › Gastrointestinal motility and disorders
- Physical Sciences › Environmental Science › Ecology › Bacteriophages and microbial interactions
- Keywords
- human gut microbiome, intestinal tract, ingestible devices, non-invasive sampling, intestinal physiology, bile acids, bile acid deconjugation, prophage induction, host proteome, gut metabolome, microbial distribution, multi-omics
- MeSH terms
- Gastrointestinal Microbiome, Bacteria, Bacteriophages, Bile Acids and Salts, Digestion, Feces, Humans, Intestines, Proteome, Metabolome
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
