Blaser, M. J. Antibiotic use and its consequences for the normal microbiome. Science 352, 544–545 (2016).
Google Scholar
Maier, L. et al. Extensive impact of non-antibiotic drugs on human gut bacteria. Nature 555, 623–628 (2018).
Google Scholar
Cho, I. et al. Antibiotics in early life alter the murine colonic microbiome and adiposity. Nature 488, 621–626 (2012).
Google Scholar
Cox, L. M. et al. Altering the intestinal microbiota during a critical developmental window has lasting metabolic consequences. Cell 158, 705–721 (2014).
Google Scholar
Ruiz, V. E. et al. A single early-in-life macrolide course has lasting effects on murine microbial network topology and immunity. Nat. Commun. 8, 518 (2017).
Google Scholar
Korpela, K. et al. Intestinal microbiome is related to lifetime antibiotic use in Finnish pre-school children. Nat. Commun. 7, 10410 (2016).
Google Scholar
Parker, E. P. K. et al. Changes in the intestinal microbiota following the administration of azithromycin in a randomised placebo-controlled trial among infants in south India. Sci. Rep. 7, 9168 (2017).
Google Scholar
Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016).
Google Scholar
Rothschild, D. et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 555, 210–215 (2018).
Google Scholar
Zimmermann, M., Patil, K. R., Typas, A. & Maier, L. Towards a mechanistic understanding of reciprocal drug–microbiome interactions. Mol. Syst. Biol. 17, e10116 (2021).
Google Scholar
Vich Vila, A. et al. Impact of commonly used drugs on the composition and metabolic function of the gut microbiota. Nat. Commun. 11, 362 (2020).
Google Scholar
Kuhn, M., Letunic, I., Jensen, L. J. & Bork, P. The SIDER database of drugs and side effects. Nucleic Acids Res. 44, D1075–D1079 (2016).
Google Scholar
Dethlefsen, L. & Relman, D. A. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc. Natl Acad. Sci. USA 108, 4554–4561 (2011).
Google Scholar
Uzan-Yulzari, A. et al. Neonatal antibiotic exposure impairs child growth during the first six years of life by perturbing intestinal microbial colonization. Nat. Commun. 12, 443 (2021).
Google Scholar
Nagy, E., Boyanova, L., Justesen, U. S. & ESCMID Study Group of Anaerobic Infections. How to isolate, identify and determine antimicrobial susceptibility of anaerobic bacteria in routine laboratories. Clin. Microbiol. Infect. 24, 1139–1148 (2018).
Google Scholar
European Committee on Antimicrobial Susceptibility Testing. Breakpoint tables for interpretation of MICs and zone diameters. v.; http://www.eucast.org/clinical_breakpoints/ (2019).
Bullman, S. et al. Analysis of Fusobacterium persistence and antibiotic response in colorectal cancer. Science 358, 1443–1448 (2017).
Google Scholar
Manfredo Vieira, S. et al. Translocation of a gut pathobiont drives autoimmunity in mice and humans. Science 359, 1156–1161 (2018).
Google Scholar
Gaulton, A. et al. The ChEMBL database in 2017. Nucleic Acids Res. 45, D945–D954 (2017).
Google Scholar
Slimings, C. & Riley, T. V. Antibiotics and hospital-acquired Clostridium difficile infection: update of systematic review and meta-analysis. J. Antimicrob. Chemother. 69, 881–891 (2014).
Google Scholar
Baron, S., Diene, S. & Rolain, J.-M. Human microbiomes and antibiotic resistance. Hum. Microb. J. 10, 43–52 (2018).
Google Scholar
Tramontano, M. et al. Nutritional preferences of human gut bacteria reveal their metabolic idiosyncrasies. Nat. Microbiol. 3, 514–522 (2018).
Google Scholar
Habib, G. et al. 2015 ESC Guidelines for the management of infective endocarditis: the Task Force for the Management of Infective Endocarditis of the European Society of Cardiology (ESC). Endorsed by: European Association for Cardio-Thoracic Surgery (EACTS), the European Association of Nuclear Medicine (EANM). Eur. Heart J. 36, 3075–3128 (2015).
Google Scholar
Kasper, D.L., F. A., Hauser S. L. & Longo D. L. Harrison’s Principles of Internal Medicine (McGraw-Hill, 2012).
Lobritz, M. A. et al. Antibiotic efficacy is linked to bacterial cellular respiration. Proc. Natl Acad. Sci. USA 112, 8173–8180 (2015).
Google Scholar
French, G. L. Bactericidal agents in the treatment of MRSA infections—the potential role of daptomycin. J. Antimicrob. Chemother. 58, 1107–111 (2006).
Google Scholar
Jelic, D. & Antolovic, R. From erythromycin to azithromycin and new potential ribosome-binding antimicrobials. Antibiotics (Basel) 5, 29 (2016).
Google Scholar
Nemeth, J., Oesch, G. & Kuster, S. P. Bacteriostatic versus bactericidal antibiotics for patients with serious bacterial infections: systematic review and meta-analysis. J. Antimicrob. Chemother. 70, 382–395 (2015).
Google Scholar
Wald-Dickler, N., Holtom, P. & Spellberg, B. Busting the myth of “static vs cidal” a systemic literature review. Clin. Infect. Dis. 66, 1470–1474 (2018).
Google Scholar
Brochado, A. R. et al. Species-specific activity of antibacterial drug combinations. Nature 559, 259–263 (2018).
Google Scholar
Brugiroux, S. et al. Genome-guided design of a defined mouse microbiota that confers colonization resistance against Salmonella enterica serovar Typhimurium. Nat. Microbiol. 2, 16215 (2016).
Google Scholar
Palleja, A. et al. Recovery of gut microbiota of healthy adults following antibiotic exposure. Nat. Microbiol. 3, 1255–1265 (2018).
Google Scholar
Schmidt, T. S. B., Raes, J. & Bork, P. The human gut microbiome: from association to modulation. Cell 172, 1198–1215 (2018).
Google Scholar
Milanese, A. et al. Microbial abundance, activity and population genomic profiling with mOTUs2. Nat. Commun. 10, 1014 (2019).
Google Scholar
Feng, Q. et al. Gut microbiome development along the colorectal adenoma-carcinoma sequence. Nat. Commun. 6, 6528 (2015).
Google Scholar
Vogtmann, E. et al. Colorectal cancer and the human gut microbiome: reproducibility with whole-genome shotgun sequencing. PLoS ONE 11 (2016).
Wirbel, J. et al. Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer. Nat. Med. 25, 679–689 (2019).
Google Scholar
Yu, J. et al. Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer. Gut 66, 70–78 (2017).
Google Scholar
Zeller, G. et al. Potential of fecal microbiota for early-stage detection of colorectal cancer. Mol. Syst. Biol. 10, 766 (2014).
Google Scholar
Kultima, J. R. et al. MOCAT2: a metagenomic assembly, annotation and profiling framework. Bioinformatics 32, 2520–2523 (2016).
Google Scholar
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).
Google Scholar
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
Google Scholar
Frostegård, A. et al. Quantification of bias related to the extraction of DNA directly from soils. Appl. Environ. Microbiol. 65, 5409–5420 (1999).
Google Scholar
Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl Acad. Sci. USA 108, 4516–4522 (2011).
Google Scholar
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
Google Scholar
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).
Google Scholar
Matias Rodrigues, J. F., Schmidt, T. S. B., Tackmann, J. & von Mering, C. MAPseq: highly efficient k-mer search with confidence estimates, for rRNA sequence analysis. Bioinformatics 33, 3808–3810 (2017).
Google Scholar
Nawrocki, E. P. & Eddy, S. R. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics 29, 2933–2935 (2013).
Google Scholar
Matias Rodrigues, J. F. & von Mering, C. HPC-CLUST: distributed hierarchical clustering for large sets of nucleotide sequences. Bioinformatics 30, 287–288 (2014).
Google Scholar
Schmidt, T. S. B., Matias Rodrigues, J. F. & von Mering, C. Limits to robustness and reproducibility in the demarcation of operational taxonomic units. Environ. Microbiol. 17, 1689–1706 (2015).
Google Scholar
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Google Scholar
McMurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).
Google Scholar
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
Google Scholar
Chen, M. et al. Inhibition of renal NQO1 activity by dicoumarol suppresses nitroreduction of aristolochic acid I and attenuates its nephrotoxicity. Toxicol. Sci. 122, 288–296 (2011).
Google Scholar
Cai, H. Y. et al. Benzbromarone, an old uricosuric drug, inhibits human fatty acid binding protein 4 in vitro and lowers the blood glucose level in db/db mice. Acta Pharmacol. Sin. 34, 1397–1402 (2013).
Google Scholar
Herp, S. et al. Mucispirillum schaedleri antagonizes Salmonella virulence to protect mice against colitis. Cell Host Microbe 25, 681–694 (2019).
Google Scholar
Zimmermann, M., Zimmermann-Kogadeeva, M., Wegmann, R. & Goodman, A. L. Mapping human microbiome drug metabolism by gut bacteria and their genes. Nature 570, 462–467 (2019).
Google Scholar
Sunagawa, S. et al. Metagenomic species profiling using universal phylogenetic marker genes. Nat. Methods 10, 1196–1199 (2013).
Google Scholar
Huerta-Cepas, J., Serra, F. & Bork, P. ETE 3: reconstruction, analysis, and visualization of phylogenomic data. Mol. Biol. Evol. 33, 1635–1638 (2016).
Google Scholar
Sievers, F. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7, 539 (2011).
Google Scholar
Nguyen, L. T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).
Google Scholar
Cani, P. D. & de Vos, W. M. Next-generation beneficial microbes: the case of Akkermansia muciniphila. Front. Microbiol. 8, 1765 (2017).
Google Scholar
Routy, B. et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 359, 91–97 (2018).
Google Scholar