Intestinal microbiotas contain beneficial microorganisms that protect against pathogen colonization; treatment with antibiotics disrupts the microbiota and compromises colonization resistance. Here, we determine the impact of exchanging microorganisms between hosts on resilience to the colonization of invaders after antibiotic-induced dysbiosis. We assess the functional consequences of dysbiosis using a mouse model of colonization resistance against Escherichia coli. Antibiotics caused stochastic loss of members of the microbiota, but the microbiotas of co-housed mice remained more similar to each other compared with the microbiotas among singly housed animals. Strikingly, co-housed mice maintained colonization resistance after treatment with antibiotics, whereas most singly housed mice were susceptible to E. coli. The ability to retain or share the commensal Klebsiella michiganensis, a member of the Enterobacteriaceae family, was sufficient for colonization resistance after treatment with antibiotics. K. michiganensis generally outcompeted E. coli in vitro, but in vivo administration of galactitol—a nutrient that supports the growth of only E. coli—to bi-colonized gnotobiotic mice abolished the colonization-resistance capacity of K. michiganensis against E. coli, supporting the idea that nutrient competition is the primary interaction mechanism. K. michiganensis also hampered colonization of the pathogen Salmonella, prolonging host survival. Our results address functional consequences of the stochastic effects of microbiota perturbations, whereby microbial transmission through host interactions can facilitate reacquisition of beneficial commensals, minimizing the negative impact of antibiotics.
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The K. michiganensis (ARO112) Whole Genome Shotgun project has been deposited at DDBJ/ENA/GenBank under the accession number WMDR00000000. The version described in this paper is version WMDR01000000. The 16S rRNA gene sequencing data (Illumina sequences) obtained in this study are available at the Sequence Read Archive (SRA) NCBI database under BioProject ID PRJNA590204. Source data are available for Figs. 1–6 and Extended Data Figs. 1–7.
The code used for Illumina sequencing analyses in this study is available at https://github.com/mothur/mothur, with modifications that are freely available from the corresponding author on request. MATLAB routines used for data visualization are available from the corresponding author on request.
Flint, H. J., Scott, K. P., Louis, P. & Duncan, S. H. The role of the gut microbiota in nutrition and health. Nat. Rev. Gastroenterol. Hepatol. 9, 577–589 (2012).
Hooper, L. V., Littman, D. R. & Macpherson, A. J. Interactions between the microbiota and the immune system. Science 336, 1268–1273 (2012).
Rakoff-Nahoum, S., Paglino, J., Eslami-Varzaneh, F., Edberg, S. & Medzhitov, R. Recognition of commensal microflora by toll-like receptors is required for intestinal homeostasis. Cell 118, 229–241 (2004).
Ubeda, C., Djukovic, A. & Isaac, S. Roles of the intestinal microbiota in pathogen protection. Clin. Transl. Immunol. 6, e128 (2017).
Baumler, A. J. & Sperandio, V. Interactions between the microbiota and pathogenic bacteria in the gut. Nature 535, 85–93 (2016).
Pamer, E. G. Resurrecting the intestinal microbiota to combat antibiotic-resistant pathogens. Science 352, 535–538 (2016).
Caporaso, J. G. et al. Moving pictures of the human microbiome. Genome Biol. 12, R50 (2011).
Cho, I. et al. Antibiotics in early life alter the murine colonic microbiome and adiposity. Nature 488, 621–626 (2012).
Fragiadakis, G. K. et al. Links between environment, diet, and the hunter-gatherer microbiome. Gut Microbes 10, 216–227 (2018).
Goodrich, J. K. et al. Human genetics shape the gut microbiome. Cell 159, 789–799 (2014).
Blaser, M. J. Missing Microbes: How the Overuse of Antibiotics Is Fueling Our Modern Plagues (Henry Holt and Company, 2014).
Sekirov, I. et al. Antibiotic-induced perturbations of the intestinal microbiota alter host susceptibility to enteric infection. Infect. Immun. 76, 4726–4736 (2008).
Buffie, C. G. et al. Profound alterations of intestinal microbiota following a single dose of clindamycin results in sustained susceptibility to Clostridium difficile-induced colitis. Infect. Immun. 80, 62–73 (2012).
Ubeda, C. et al. Vancomycin-resistant Enterococcus domination of intestinal microbiota is enabled by antibiotic treatment in mice and precedes bloodstream invasion in humans. J. Clin. Invest. 120, 4332–4341 (2010).
Stecher, B. et al. Salmonella enterica serovar Typhimurium exploits inflammation to compete with the intestinal microbiota. PLoS Biol. 5, 2177–2189 (2007).
Keith, J. W. & Pamer, E. G. Enlisting commensal microbes to resist antibiotic-resistant pathogens. J. Exp. Med. 216, 10–19 (2019).
Pereira, F. C. & Berry, D. Microbial nutrient niches in the gut. Environ. Microbiol. 19, 1366–1378 (2017).
Apperloo-Renkema, H. Z., Van der Waaij, B. D. & Van der Waaij, D. Determination of colonization resistance of the digestive tract by biotyping of Enterobacteriaceae. Epidemiol. Infect. 105, 355–361 (1990).
Spees, A. M. et al. Streptomycin-induced inflammation enhances Escherichia coli gut colonization through nitrate respiration. mBio 4, e00430-13 (2013).
Winter, S. E. et al. Host-derived nitrate boosts growth of E. coli in the inflamed gut. Science 339, 708–711 (2013).
Jacobson, A. et al. A gut commensal-produced metabolite mediates colonization resistance to Salmonella infection. Cell Host Microbe 24, 296–307 (2018).
Buffie, C. G. et al. Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile. Nature 517, 205–208 (2014).
Hsiao, A. et al. Members of the human gut microbiota involved in recovery from Vibrio cholerae infection. Nature 515, 423–426 (2014).
Thompson, J. A., Oliveira, R. A., Djukovic, A., Ubeda, C. & Xavier, K. B. Manipulation of the quorum sensing signal AI-2 affects the antibiotic-treated gut microbiota. Cell Rep. 10, 1861–1871 (2015).
Cherrington, C. A., Hinton, M. & Chopra, I. Effect of short-chain organic acids on macromolecular synthesis in Escherichia coli. J. Appl. Bacteriol. 68, 69–74 (1990).
Fay, J. P. & Farias, R. N. The inhibitory action of fatty acids on the growth of Escherichia coli. J. Gen. Microbiol. 91, 233–240 (1975).
Kirkpatrick, C. et al. Acetate and formate stress: opposite responses in the proteome of Escherichia coli. J. Bacteriol. 183, 6466–6477 (2001).
Wilson, K. H. Efficiency of various bile salt preparations for stimulation of Clostridium difficile spore germination. J. Clin. Microbiol. 18, 1017–1019 (1983).
Sassone-Corsi, M. et al. Microcins mediate competition among Enterobacteriaceae in the inflamed gut. Nature 540, 280–283 (2016).
Sana, T. G. et al. Salmonella Typhimurium utilizes a T6SS-mediated antibacterial weapon to establish in the host gut. Proc. Natl Acad. Sci. USA 113, E5044–E5051 (2016).
Wexler, A. G. et al. Human symbionts inject and neutralize antibacterial toxins to persist in the gut. Proc. Natl Acad. Sci. USA 113, 3639–3644 (2016).
Taur, Y. et al. Reconstitution of the gut microbiota of antibiotic-treated patients by autologous fecal microbiota transplant. Sci. Transl. Med. 10, eaap9489 (2018).
Schulfer, A. F. et al. The impact of early-life sub-therapeutic antibiotic treatment (STAT) on excessive weight is robust despite transfer of intestinal microbes. ISME J. 13, 1280–1292 (2019).
Song, S. J. et al. Cohabiting family members share microbiota with one another and with their dogs. eLife 2, e00458 (2013).
Reese, A. T. et al. Antibiotic-induced changes in the microbiota disrupt redox dynamics in the gut. eLife 7, e35987 (2018).
Tropini, C. et al. Transient osmotic perturbation causes long-term alteration to the gut microbiota. Cell 173, 1742–1754 (2018).
Ng, K. M. et al. Recovery of the gut microbiota after antibiotics depends on host diet, community context, and environmental reservoirs. Cell Host Microbe 26, 650–665 (2019).
Velazquez, E. M. et al. Endogenous Enterobacteriaceae underlie variation in susceptibility to Salmonella infection. Nat. Microbiol. 4, 1057–1064 (2019).
Franklin, C. L. & Ericsson, A. C. Microbiota and reproducibility of rodent models. Lab Anim. 46, 114–122 (2017).
Barthel, M. et al. Pretreatment of mice with streptomycin provides a Salmonella enterica serovar Typhimurium colitis model that allows analysis of both pathogen and host. Infect. Immun. 71, 2839–2858 (2003).
Leatham, M. P. et al. Precolonized human commensal Escherichia coli strains serve as a barrier to E. coli O157:H7 growth in the streptomycin-treated mouse intestine. Infect. Immun. 77, 2876–2886 (2009).
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).
Dantur, K. I. et al. The endophytic strain Klebsiella michiganensis Kd70 lacks pathogenic island-like regions in its genome and is incapable of infecting the urinary tract in mice. Front. Microbiol. 9, 1548 (2018).
Conway, T. & Cohen, P. S. Commensal and pathogenic Escherichia coli metabolism in the Gut. Microbiol. Spectr. 3, MBP-0006-2014 (2015).
Flint, H. J., Scott, K. P., Duncan, S. H., Louis, P. & Forano, E. Microbial degradation of complex carbohydrates in the gut. Gut Microbes 3, 289–306 (2012).
Saha, R., Farrance, C. E., Verghese, B., Hong, S. & Donofrio, R. S. Klebsiella michiganensis sp. nov., a new bacterium isolated from a tooth brush holder. Curr. Microbiol. 66, 72–78 (2013).
Barroso-Batista, J. et al. The first steps of adaptation of Escherichia coli to the gut are dominated by soft sweeps. PLoS Genet. 10, e1004182 (2014).
Maltby, R., Leatham-Jensen, M. P., Gibson, T., Cohen, P. S. & Conway, T. Nutritional basis for colonization resistance by human commensal Escherichia coli strains HS and Nissle 1917 against E. coli O157:H7 in the mouse intestine. PLoS ONE 8, e53957 (2013).
Stecher, B. et al. Like will to like: abundances of closely related species can predict susceptibility to intestinal colonization by pathogenic and commensal bacteria. PLoS Pathog. 6, e1000711 (2010).
Kamada, N. et al. Regulated virulence controls the ability of a pathogen to compete with the gut microbiota. Science 336, 1325–1329 (2012).
Deriu, E. et al. Probiotic bacteria reduce Salmonella typhimurium intestinal colonization by competing for iron. Cell Host Microbe 14, 26–37 (2013).
Herp, S. et al. Mucispirillum schaedleri antagonizes Salmonella virulence to protect mice against colitis. Cell Host Microbe 25, 681–694 (2019).
Jernberg, C., Löfmark, S., Edlund, C. & Jansson, J. K. Long-term impacts of antibiotic exposure on the human intestinal microbiota. Microbiology 156, 3216–3223 (2010).
Clemente, J. C. et al. The microbiome of uncontacted Amerindians. Sci. Adv. 1, e1500183 (2015).
Martinez, I. et al. The gut microbiota of rural Papua New Guineans: composition, diversity patterns, and ecological processes. Cell Rep. 11, 527–538 (2015).
van Nood, E. et al. Duodenal infusion of donor feces for recurrent Clostridium difficile. N. Engl. J. Med. 368, 407–415 (2013).
Dominguez-Bello, M. G. et al. Partial restoration of the microbiota of cesarean-born infants via vaginal microbial transfer. Nat. Med. 22, 250–253 (2016).
Silhavy, T. J., Berman, M. L. & Enquist, L. W. (eds) Experiments with Gene Fusions (Cold Spring Harbor Laboratory, 1984).
Baba, T. et al. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol. Syst. Biol. 2, 2006.0008 (2006).
Shyntum, D. Y. et al. Pantoea ananatis utilizes a type VI secretion system for pathogenesis and bacterial competition. Mol. Plant Microbe Interact. 28, 420–431 (2015).
Yu, Z. et al. Complete genome sequence of N2-fixing model strain Klebsiella sp. nov. M5al, which produces plant cell wall-degrading enzymes and siderophores. Biotechnol. Rep. 17, 6–9 (2018).
Bianciotto, V. et al. An obligately endosymbiotic mycorrhizal fungus itself harbors obligately intracellular bacteria. Appl. Environ. Microbiol. 62, 3005–3010 (1996).
Wilson, K. Preparation of genomic DNA from bacteria. Curr. Protoc. Mol. Biol. 56, 2.4.1–2.4.5 (2001).
Earle, K. A. et al. Quantitative imaging of gut microbiota spatial organization. Cell Host Microbe 18, 478–488 (2015).
Dejea, C. M. et al. Microbiota organization is a distinct feature of proximal colorectal cancers. Proc. Natl Acad. Sci. USA 111, 18321–18326 (2014).
Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012).
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(Suppl), 4516–4522 (2011).
Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K. & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 79, 5112–5120 (2013).
Pruesse, E. et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 35, 7188–7196 (2007).
Haas, B. J. et al. Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Res. 21, 494–504 (2011).
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).
Sheneman, L., Evans, J. & Foster, J. A. Clearcut: a fast implementation of relaxed neighbor joining. Bioinformatics 22, 2823–2824 (2006).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Proc. R. Soc. B 57, 289–300 (1995).
We thank T. Sana, E. Cascales and M. Blokesch for helpful discussions; J. Xavier, C. Ubeda and M. Taga for suggestions and for reading the manuscript; S. Higginbottom for help with mouse experiments; and R. Balbontín-Soria for providing strain RB249. We acknowledge support from the Allen Discovery Center at Stanford on Systems Modeling of Infection (to K.M.N. and K.C.H.); the Portuguese national funding agency Fundação para a Ciência e Tecnologia (FCT) PTDC/BIA-MIC/4188/14 and research infrastructure ONEIDA and CONGENTO projects (LISBOA-01-0145-FEDER-016417 and LISBOA-01-0145-FEDER-022170) co-funded by Fundos Europeus Estruturais e de Investimento from Programa Operacional Regional Lisboa 2020 (to R.A.O. and K.B.X.). K.B.X., R.A.O. and V.C. acknowledge the FCT for individual grants IF/00831/2015, PD/BD/106000/2014 and SFRH/BPD/116806/2016, respectively. J.L.S. and K.C.H. are Chan Zuckerberg Biohub Investigators.
The authors declare no competing interests.
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Extended Data Fig. 1 Streptomycin treatment leads to increased loss of core OTUs in singly housed mice compared to co-housed mice, but does not differentially affect Bacteroidetes, Firmicutes, or Verrucomicrobia relative abundances.
a–c, Relative abundances of the a, Bacteroidetes, b, Firmicutes and c, Verrucomicrobia phyla in co-housed and singly housed mice at indicated time points. Data shown are medians, and error bars show the interquartile range of n=15-21 per group. d, Percentage of core OTUs (defined as OTUs present in every mouse of each group before treatment) present on days 15 and 57, relative to day 0. Data from a–c represent n=20 co-housed mice and n=21 singly housed mice from 4 independent experiments. Data in d represent n=3 cages (day 15) or n=4 cages (day 57) of co-housed mice and n=21 (day 15) or n= 15 (day 57) singly housed mice from 3 (day 15) or 4 (day 57) independent experiments. Lines in a–d represent medians and error bars depict interquartile ranges. Data in a-c were analyzed with two-tailed Mann-Whitney test (**: p<0.01; ns, not significant). For d, data were analyzed using the one-way Kruskal-Wallis test with Dunn’s correction test for multiple comparisons (*: q < 0.1, **: q < 0.05; ****: q < 0.001; ns, not significant). Source data
Extended Data Fig. 2 Ciprofloxacin treatment results in stochastic extinction of the Bacteroidetes phylum in singly housed mice.
a, Experimental scheme for ciprofloxacin treatment, from one experiment. Three milligrams of ciprofloxacin were administered orally every 12 h to conventional Swiss Webster mice for 5 days. Microbiota composition was analyzed from fecal samples collected from day 0 (before antibiotics), day 5 (last day of antibiotic treatment), and day 14 (9 days after stopping antibiotic treatment). Procedures (circles) denote the start (day 0) or the end (day 5) of ciprofloxacin treatment, and sacrifice mice (day 14). b, Fecal microbiota compositions on day 0, 5, and 14 of co-housed (Mouse 1-5) and singly housed (Mouse 6-11) mice. Each stacked bar represents the microbiota composition in the indicated mouse at the indicated time points. The colored segments represent the relative fraction of each genus-level taxon present at >3%. All other genera were combined in the “Other Bacteria” category. c, Phylogenetic dissimilarities on each day determined by the mean weighted Unifrac distance of the bacterial communities of each mouse to each other mouse within the same group. Data shown are medians, and the error bars show interquartile ranges (*: p<0.05; ns: not significant; two-tailed Mann-Whitney test, n=5 mice in the co-housed group, n=6 mice in the singly housed group). d, Gamma diversity of gut microbiota of co-housed and singly housed mice at indicated time points (n=1 cage in co-housed group, n=5 mice in singly housed group). Source data
Extended Data Fig. 3 Effects of streptomycin treatment on microbiota composition and OTUs associated with colonization resistance to E. coli in cohort 2 (associated with the experiments shown in Figs. 1 and 2).
a, Fecal microbiota compositions of untreated (day 0), streptomycin-treated (day 15), and post-streptomycin-treated (day 57) samples from co-housed and singly housed mice in cohort 2. Each stacked bar represents the microbiota composition in one mouse at the indicated time points. Colored segments represent the relative fraction of each genus-level bacterial taxon present at >3%. All other genera were combined into the “Other Bacteria” category. b, Histogram of linear discriminant analysis (LDA) scores >2 for differentially abundant OTUs on day 57 of mice in cohort 2 from the experiment in Fig. 2c, d. Red designates OTUs enriched in mice with colonization resistance to E. coli (CR); green designates OTUs enriched in mice lacking colonization resistance (No CR). c, Relative abundances of significantly different individual OTUs that are associated with lack of colonization resistance against E. coli. d, Relative abundances of significantly different individual OTUs that are associated with colonization resistance against E. coli. e, Area Under the Curve (AUC) calculated from the dynamics of E. coli-YFP CFUs/g feces during the cohort 2 experiment of each mouse with and without colonization resistance shows that Klebsiella-associated mice had significantly lower loads of E. coli throughout the experiment. Data in b–e represent n=6 CR mice and n=4 No CR mice from 1 experiment. In c–e, medians and interquartile ranges are shown. In b, LEfSe uses the Kruskal-Wallis rank-sum test on a normalized relative abundance matrix to detect significantly different OTU features and estimates the effect size of each feature via LDA; only features with LDA scores >2 and alpha<0.05 were considered different between the groups of mice and are shown. Asterisks (*) in b represent statistical results from c and d. Data in c and d were analyzed with two-tailed Wilcoxon test using a Benjamini-Hochberg correction for multiple comparisons (∗: q<0.1). Data in e were analyzed with two-tailed Mann-Whitney test (**: p<0.01). Source data
Extended Data Fig. 4 K. michiganensis and E. coli-CFP colonization loads from experiment in Fig. 3c–e.
a, Loads of K. michiganensis (CFUs/g feces) before and after challenge with E. coli-YFP (n=9 across two independent experiments, Fig. 3d). b, Area Under the Curve (AUC) calculated from the dynamics of E. coli-YFP CFUs/g feces of each mouse with and without K. michiganensis. Mice associated with K. michiganensis had significantly lower loads of E. coli throughout the experiment (n=9 across two independent experiments, Fig. 3d). c, K. michiganensis (n=11 across three independent experiments) and E. coli-CFP (n=8 across two independent experiments) loads (CFUs/g feces) in mice pre-colonized with E. coli-YFP (Fig. 3c, e). d, AUC calculated from the dynamics of E. coli-YFP CFUs/g feces of each mouse after challenge with K. michiganensis or E. coli-CFP. Mice challenged with K. michiganensis had significantly lower loads of E. coli throughout the experiment (n=11 across three independent experiments) as compared to challenge with E. coli-CFP (n=8 across two independent experiments; Fig. 3e). In a and c, circles and lines represent values from individual mice and medians, respectively. In b and d, medians and interquartile ranges are shown. Data in b and d were analyzed with the two-tailed Mann-Whitney test (***: p<0.001; ****: p<0.0001). Source data
Extended Data Fig. 5 No killing of E. coli or direct physical inhibition by K. michiganensis was observed in vitro.
a, Co-cultures of E. coli-YFP with E. coli-CFP or K. michiganensis in minimal media containing 0.25% of the indicated carbon source exhibited similar percentages of PI-stained cells, suggesting the absence of active killing of E. coli. b, There was no statistically significant difference in the growth rates of E. coli microcolonies that were well separated from versus surrounded by K. michiganensis cells over >50% of the periphery in minimal medium with arabinose. c, Cell density of E. coli-YFP in the bottom chamber of a transwell incubated with K. michiganensis or E. coli-CFP in the upper chamber in minimal media with arabinose or fructose. Growth was monitored by OD600 measurements for 35 h (n=3 per condition). d, Cell density of E. coli-YFP in minimal medium with fructose with or without supplementation of 20% cell-free spent medium from overnight cultures of K. michiganensis or E. coli-YFP+K. michiganensis grown in minimal medium with fructose. Growth was monitored by OD600 measurements for 25 h (n=3 per condition). Data in b were analyzed with two-tailed Mann-Whitney test (ns: not significant). Lines and error bars in b–d represent means and standard deviations, respectively. Source data
Extended Data Fig. 6 Galactitol can sustain growth of E. coli-YFP but not K. michiganensis in vitro; E. coli-YFP colonization of a singly housed mouse in which Klebsiella spp. were not eliminated by streptomycin treatment, and loads of the 2nd colonizer from the experiment shown in Fig. 5.
a, Growth capacity of K. michiganensis, E. coli-YFP, and E. coli-YFP ΔgatZ alone in minimal medium containing 0.25% galactitol. Growth was monitored by OD600 measurements at the indicated times for 43 h (n=6 per condition). K. michiganensis and E. coli-YFP ΔgatZ were unable to grow on galactitol, by contrast to E. coli-YFP. b, While measuring the capacity of galactitol to affect K. michiganensis-mediated colonization resistance against E. coli in singly housed mice after streptomycin treatment, we noted that one mouse presented high levels of resident Klebsiella spp. In this mouse, E. coli-YFP loads (CFUs/g feces) decreased immediately after gavage (day -3) (yellow solid line). This mouse was not included in the data displayed in Fig. 5b (see Supplementary Discussion). These data are from n=1 mouse from one experiment. Dashed yellow line shows the median CFUs/g feces of E. coli-YFP from Fig. 5b for comparison. c, Yellow line shows K. michiganensis loads (CFUs/g feces) in mice pre-colonized with E. coli-YFP and drinking water supplemented with 2% galactitol (n=7 across two independent experiments, Fig. 5a). Black dashed line shows median CFUs/g feces of K. michiganensis from the experiment in Fig. 2c, e for comparison (Extended Data Fig. 4c). d, Area Under the Curve (AUC) calculated from the dynamics of E. coli-YFP CFUs/g feces of each mouse drinking galactitol-supplemented water after challenge with K. michiganensis (n=7 across two independent experiments, Fig. 5b) compared with dynamics of E. coli-YFP CFUs/g feces of each mouse drinking non-supplemented water with and without K. michiganensis (data from Extended Data Fig. 4d) . Mice drinking galactitol-supplemented water had significantly higher E. coli loads throughout the experiment when compared with mice drinking non-supplemented water, even in the presence of K. michiganensis. e, K. michiganensis loads (CFUs/g feces) in ex-germ-free mice pre-colonized with E. coli-YFP or E. coli-YFP ΔgatZ drinking non-supplemented or 2% galactitol-supplemented water (n=6 per condition across two independent experiments, Fig. 5c). f, AUC calculated from the dynamics of E. coli-YFP CFUs/g feces of each ex-germ-free mouse drinking non-supplemented or 2% galactitol-supplemented water after challenge with K. michiganensis (n=6 per group across two independent experiments, Fig. 5d). Mice drinking galactitol-supplemented water had significantly higher E. coli-YFP loads throughout the experiment when compared with mice drinking non-supplemented water or E. coli-YFP ΔgatZ in mice drinking galactitol-supplemented water, even in the presence of K. michiganensis. Data in a represent means and standard deviations. In c and e, circles and lines represent values from individual mice and medians, respectively. In d and f, medians and interquartile ranges are shown. In d and f, data were analyzed using the Kruskal-Wallis test with Dunn’s correction test for multiple comparison (*: q<0.1; ***: q<0.01; ns, not significant). Source data
Extended Data Fig. 7 Competition for simple sugars by K. michiganensis and commensal E. coli with S. Typhimurium, and colonization loads of K. michiganensis and commensal E. coli from the experiment in Fig. 6.
a, S. Typhimurium-mCherry growth capacity in co-cultures with K. michiganensis, commensal E. coli or an isogenic S. Typhimurium (without a fluorescent-protein marker) in minimal media containing 0.25% of the indicated carbon source. S. Typhimurium-mCherry growth was monitored by mCherry fluorescence quantification (n=6 per condition). b, Area Under the Curve (AUC) calculated from the dynamics of S. Typhimurium CFUs/g feces of each mouse in the first 3 days after S. Typhimurium challenge in mice pre-colonized with K. michiganensis (n=9 across 2 independent experiments), as compared to mice not pre-colonized or pre-colonized with a commensal E. coli (n=8 across 2 independent experiments and n=4 in 1 experiment, respectively; Fig. 6). Mice colonized with K. michiganensis had significantly lower S. Typhimurium loads as compared to mice not pre-colonized or pre-colonized with a commensal E. coli. c, Loads of K. michiganensis (n=9 mice across two independent experiments) and commensal E. coli (n=4 mice, from one experiment) in CFUs/g feces before and after challenge with S. Typhimurium (Fig. 6b). d, Mice body weight from day 19 to 23 was unaffected by K. michiganensis (n=5 mice from one experiment), commensal E. coli (n=4 mice from one experiment) colonization, similarly to non-challenged. In a, lines and error bars represent means and standard deviations, respectively. In b, lines and errors bars represent medians and interquartile ranges, respectively. Data in b were analyzed using the one-sided Kruskal-Wallis test with Dunn’s correction test for multiple comparisons (**: q < 0.05; ns, not significant). In c and d, circles and lines represent values from individual mice and medians, respectively. Source data
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Oliveira, R.A., Ng, K.M., Correia, M.B. et al. Klebsiella michiganensis transmission enhances resistance to Enterobacteriaceae gut invasion by nutrition competition. Nat Microbiol (2020). https://doi.org/10.1038/s41564-019-0658-4