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Drug-mediated metabolic tipping between antibiotic resistant states in a mixed-species community

Nature Ecology & Evolution volume 2, pages 1312–1320 (2018)Cite this article

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An Author Correction to this article was published on 19 September 2018

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Abstract

Microbes rarely exist in isolation, rather, they form intricate multi-species communities that colonize our bodies and inserted medical devices. However, the efficacy of antimicrobials is measured in clinical laboratories exclusively using microbial monocultures. Here, to determine how multi-species interactions mediate selection for resistance during antibiotic treatment, particularly following drug withdrawal, we study a laboratory community consisting of two microbial pathogens. Single-species dose responses are a poor predictor of community dynamics during treatment so, to better understand those dynamics, we introduce the concept of a dose-response mosaic, a multi-dimensional map that indicates how species’ abundance is affected by changes in abiotic conditions. We study the dose-response mosaic of a two-species community with a ‘Gene × Gene × Environment × Environment’ ecological interaction whereby Candida glabrata, which is resistant to the antifungal drug fluconazole, competes for survival with Candida albicans, which is susceptible to fluconazole. The mosaic comprises several zones that delineate abiotic conditions where each species dominates. Zones are separated by loci of bifurcations and tipping points that identify what environmental changes can trigger the loss of either species. Observations of the laboratory communities corroborated theory, showing that changes in both antibiotic concentration and nutrient availability can push populations beyond tipping points, thus creating irreversible shifts in community composition from drug-sensitive to drug-resistant species. This has an important consequence: resistant species can increase in frequency even if an antibiotic is withdrawn because, unwittingly, a tipping point was passed during treatment.

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Fig. 1: C. albicans and C. glabrata dynamics.
Fig. 2: Population dynamics theory states that one can deduce multi-season frequency dynamics from the ‘cobweb diagram’ determined from the initial C. albicans frequency plotted versus the final frequency each season.
Fig. 3: The dose-response mosaic shows tipping points are encountered in many ways.
Fig. 4: Exploring the dose-response mosaic.

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Change history

  • 19 September 2018

    In the version of this Article originally published, the following sentence was missing from the Acknowledgements: “R.E.B. is an EPSRC Healthcare Technologies Impact Fellow EP/N033671/1; I.G. is funded by ERC Consolidator grant 647292 MathModExp; A.J.P.B., N.A.R.G. and A.T. were funded by BBSRC grant BB/F00513X/1; K.H., I.G., S.N. and E.C. were funded by BBSRC grant BB/F005210/2.” This text has now been added.

References

  1. Payne, D. J., Gwynn, M. N., Holmes, D. J. & Pompliano, D. L. Drugs for bad bugs: confronting the challenges of antibacterial discovery. Nat. Rev. Drug. Discov. 6, 29–40 (2007).

    Article  CAS  PubMed  Google Scholar 

  2. Mira, P. M. et al. Rational design of antibiotic treatment plans: a treatment strategy for managing evolution and reversing resistance. PLoS. ONE 10, 1–25 (2015).

    Google Scholar 

  3. Kollef, M. H. Is antibiotic cycling the answer to preventing the emergence of bacterial resistance in the intensive care unit? Clin. Infect. Dis. 43 (Suppl. 2), S82–S88 (2006).

    Article  PubMed  Google Scholar 

  4. Sundqvist, M. Reversibility of antibiotic resistance. Ups. J. Med. Sci. 119, 142–148 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Lee, J. et al. Control of extended-spectrum beta-lactamase-producing Escherichia coli and Klebsiella pneumoniae in a children’s hospital by changing antimicrobial agent usage policy. J. Antimicrob. Chemother. 60, 629–637 (2007).

    Article  CAS  PubMed  Google Scholar 

  6. Rahal, J. J. et al. Class restriction of cephalosporin use to control total cephalosporin resistance in nosocomial Klebsiella. JAMA 280, 1233–1237 (1998).

    Article  CAS  PubMed  Google Scholar 

  7. Cook, P. P., Catrou, P. G., Christie, J. D., Young, P. D. & Polk, R. E. Reduction in broad-spectrum antimicrobial use associated with no improvement in hospital antibiogram. J. Antimicrob. Chemother. 53, 853–859 (2004).

    Article  CAS  PubMed  Google Scholar 

  8. Nijssen, S. et al. Effects of reducing beta-lactam antibiotic pressure on intestinal colonization of antibiotic-resistant gram-negative bacteria. Intensive Care Med. 36, 512–519 (2010).

    Article  CAS  PubMed  Google Scholar 

  9. Chong, Y. et al. Antibiotic rotation for febrile neutropenic patients with hematological malignancies: clinical significance of antibiotic heterogeneity. PLoS ONE 8, e54190 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Takesue, Y. et al. Impact of a hospital-wide programme of heterogeneous antibiotic use on the development of antibiotic-resistant Gram-negative bacteria. J. Hosp. Infect. 75, 28–32 (2010).

    Article  CAS  PubMed  Google Scholar 

  11. Hashino, S. et al. Clinical impact of cycling the administration of antibiotics for febrile neutropenia in Japanese patients with hematological malignancy. Eur. J. Clin. Microbiol. Infect. Dis. 31, 173–178 (2012).

    Article  CAS  PubMed  Google Scholar 

  12. Sarraf-Yazdi, S. et al. A 9-year retrospective review of antibiotic cycling in a surgical intensive care unit. J. Surg. Res. 176, e73–e78 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Gruson, D. et al. Rotation and restricted use of antibiotics in a medical intensive care unit. Am. J. Respir. Crit. Care. Med. 162, 837–843 (2000).

    Article  CAS  PubMed  Google Scholar 

  14. Van Loon, H. J. et al. Antibiotic rotation and development of Gram-negative antibiotic resistance. Am. J. Respir. Crit. Care. Med. 171, 480–487 (2004).

    Article  PubMed  Google Scholar 

  15. Warren, D. et al. Cycling empirical antimicrobial agents to prevent emergence of antimicrobial-resistant Gram-negative bacteria among intensive care unit patients. Crit. Care Med. 32, 2450–2456 (2004).

    Article  CAS  PubMed  Google Scholar 

  16. Andersson, D. I. & Hughes, D. Antibiotic resistance and its cost: is it possible to reverse resistance? Nat. Rev. Microbiol. 8, 260–271 (2010).

    Article  CAS  PubMed  Google Scholar 

  17. Gonze, D., Lahti, L., Raes, J. & Faust, K. Multi-stability and the origin of microbial community types. ISME J. 11, 2159–2166 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Panda, S. et al. Short-term effect of antibiotics on human gut microbiota. PLoS ONE 9, e95476 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Jakobsson, H. E. et al. Short-term antibiotic treatment has differing long-term impacts on the human throat and gut microbiome. PLoS ONE 5, e9836 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Dethlefsen, L., McFall-Ngai, M. & Relman, Da An ecological and evolutionary perspective on human-microbe mutualism and disease. Nature 449, 811–818 (2007).

    Article  CAS  PubMed  Google Scholar 

  21. Antonopoulos, D. A. et al. Reproducible community dynamics of the gastrointestinal microbiota following antibiotic perturbation. Infect. Immun. 77, 2367–2375 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Perez-Cobas, A. E. et al. Gut microbiota disturbance during antibiotic therapy: a multi-omic approach. Gut 62, 1591–1601 (2013).

    Article  CAS  PubMed  Google Scholar 

  23. 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 (Suppl.), 4554–4561 (2011).

  24. McFarland, L. V., Elmer, G. W. & Surawicz, C. M. Breaking the cycle: treatment strategies for 163 cases of recurrent clostridium difficile disease. Am. J. Gastroenterol. 97, 1769–1775 (2002).

    Article  CAS  PubMed  Google Scholar 

  25. Cousin, L., Berre, M. L., Launay-Vacher, V., Izzedine, H. & Deray, G. Dosing guidelines for fluconazole in patients with renal failure. Nephrol. Dial. Transplant. 18, 2227–2231 (2003).

    Article  PubMed  Google Scholar 

  26. Ashbee, H. R. et al. Therapeutic drug monitoring (TDM) of antifungal agents: guidelines from the British Society for Medical Mycology. J. Antimicrob. Chemother. 69, 1162–1176 (2014).

    Article  CAS  PubMed  Google Scholar 

  27. Havey, T. C., Fowler, R. A., Pinto, R., Elligsen, M. & Daneman, N. Duration of antibiotic therapy for critically ill patients with bloodstream infections: a retrospective cohort study. Can. J. Infect. Dis. Med. Microbiol. 24, 129–137 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Cowart, S. L. & Stachura, M. E. Glucosuria (Butterworth Publishers, Boston, MA, 1990).

  29. Carlotti, A. P. C. P. et al. A hyperglycaemic hyperosmolar state in a young child: diagnostic insights from a quantitative analysis. QJM 100, 125–137 (2007).

    Article  CAS  PubMed  Google Scholar 

  30. Manoj, G., George, M. R., Dipu, R. & Jishnu, J. The survival story of a diabetic ketoacidosis patient with blood sugar levels of 1985 mg/dl. Asian J. Med. Sci. 8, 60–61 (2017).

  31. Ho, K.-m. & Cheng, T.-s. Common superficial fungal infections, a short review. Med. Bull. 15, 23–27 (2010).

    Google Scholar 

  32. Wenzel, R. P. & Gennings, C. Bloodstream infections due to Candida species in the intensive care unit: identifying especially high-risk patients to determine prevention strategies. Clin. Infect. Dis. 41 (Suppl. 6), S389–S393 (2005).

    Article  PubMed  Google Scholar 

  33. Brown, G. D. et al. Hidden killers: human fungal infections. Sci. Transl. Med. 4, 165rv13 (2012).

    Article  PubMed  CAS  Google Scholar 

  34. Kett, D. H., Azoulay, E., Echeverria, P. M. & Vincent, J.-L. Candida bloodstream infections in intensive care units: analysis of the extended prevalence of infection in intensive care unit study. Crit. Care Med. 39, 665–670 (2011).

    Article  PubMed  Google Scholar 

  35. Pappas, P. G. et al. Guidelines for treatment of candidiasis. Clin. Infect. Dis. 38, 161–189 (2004).

    Article  PubMed  Google Scholar 

  36. Rex, J. H. et al. Development of interpretive breakpoints for antifungal susceptibility testing: conceptual framework and analysis of in vitro–in vivo correlation data for fluconazole, itraconazole, and Candida infections. Subcommittee on Antifungal Susceptibility Testing of the National Committee for Clinical Laboratory Standards. Clin. Infect. Dis. 24, 235–247 (1997).

    Article  CAS  PubMed  Google Scholar 

  37. Lortholary, O. et al. Recent exposure to caspofungin or fluconazole influences the epidemiology of candidemia: a prospective multicenter study involving 2,441 patients. Antimicrob. Agents Chemother. 55, 532–538 (2011).

    Article  CAS  PubMed  Google Scholar 

  38. Hibbing, M. E., Fuqua, C., Parsek, M. R. & Peterson, S. B. Bacterial competition: surviving and thriving in the microbial jungle. Nat. Rev. Microbiol. 8, 15–25 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Scheffer, M., Carpenter, S., Foley, Ja, Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001).

    Article  CAS  PubMed  Google Scholar 

  40. Lenton, T. M. et al. Tipping elements in the Earth’s climate system. Proc. Natl Acad. Sci. USA 105, 1786–1793 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Wissel, C. A universal law of the characteristic return time near thresholds. Oecologia 65, 101–107 (1984).

    Article  CAS  PubMed  Google Scholar 

  42. Wiesenfeld, K. & Mcnamara, B. Small-signal amplification in bifurcating dynamical systems. Phys. Rev. A 33, 629–642 (1986).

    Article  CAS  Google Scholar 

  43. Dai, L., Vorselen, D., Korolev, K. S. & Gore, J. Generic indicators for loss of resilience before a tipping point leading to population collapse. Science 336, 1175–1177 (2012).

    Article  CAS  PubMed  Google Scholar 

  44. Scheffer, M. et al. Early-warning signals for critical transitions. Nature 461, 53–59 (2009).

    Article  CAS  PubMed  Google Scholar 

  45. Dakos, V. et al. Slowing down as an early warning signal for abrupt climate change. Proc. Natl Acad. Sci. USA 105, 14308–14312 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Lenton, T. M. Early warning of climate tipping points. Nat. Clim. Change 1, 201–209 (2011).

    Article  Google Scholar 

  47. Carpenter, S. R. & Brock, W. A. Rising variance: a leading indicator of ecological transition. Ecol. Lett. 9, 308–315 (2006).

    Google Scholar 

  48. Guttal, V. & Jayaprakash, C. Changing skewness: an early warning signal of regime shifts in ecosystems. Ecol. Lett. 11, 450–460 (2008).

    Article  PubMed  Google Scholar 

  49. Baillie, G. S. & Douglas, L. J. Effect of growth rate on resistance of Candida albicans biofilms to antifungal agents. Antimicrob. Agents Chemother. 42, 1900–1905 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Basson, N. J. Competition for glucose between Candida albicans and oral bacteria grown in mixed culture in a chemostat. J. Med. Microbiol. 49, 969–975 (2000).

    Article  CAS  PubMed  Google Scholar 

  51. Huang, M., McClellan, M., Berman, J. & Kao, K. C. Evolutionary dynamics of Candida albicans during in vitro evolution. Eukaryot. Cell 10, 1413–1421 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Ene, I. V., Brunke, S., Brown, A. J. P. & Hube, B. Metabolism in fungal pathogenesis. Cold Spring Harb. Perspect. Med. 4, a019695 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  53. MacLean, R. C. & Gudelj, I. Resource competition and social conflict in experimental populations of yeast. Nature 441, 498–501 (2006).

    Article  CAS  PubMed  Google Scholar 

  54. Fidel, P. L. Jr, Vazquez, J. A. & Sobel, J. D. Candida glabrata: review of epidemiology, pathogenesis, and clinical disease with comparison to C. albicans. Clin. Microbiol. Rev. 12, 80–96 (1999).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Ray, D. et al. Prevalence of Candida glabrata and its response to boric acid vaginal suppositories in comparison with oral fluconazole in patients with diabetes and vulvovaginal candidiasis. Diabetes Care 30, 312–317 (2007).

    Article  CAS  PubMed  Google Scholar 

  56. Sonnenburg, E. D. et al. Specificity of polysaccharide use in intestinal bacteroides species determines diet-induced microbiota alterations. Cell 141, 1241–1252 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).

    Article  CAS  PubMed  Google Scholar 

  58. Metzler-Zebeli, B. U., Lange, J. C., Zijlstra, R. T. & Gänzle, M. G. Dietary non-starch polysaccharides alter the abundance of pathogenic clostridia in pigs. Livest. Sci. 152, 31–35 (2013).

    Article  Google Scholar 

  59. Allison, K. R., Brynildsen, M. P. & Collins, J. J. Metabolite-enabled eradication of bacterial persisters by aminoglycosides. Nature 473, 216–220 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Peng, B. et al. Exogenous alanine and/or glucose plus kanamycin kills antibiotic-resistant bacteria. Cell. Metab. 21, 249–261 (2015).

    Article  CAS  PubMed  Google Scholar 

  61. Zampieri, M. et al. Metabolic constraints on the evolution of antibiotic resistance. Mol. Syst. Biol. 13, 917 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Milne, S. W., Cheetham, J., Lloyd, D., Aves, S. & Bates, S. Cassettes for PCR-mediated gene tagging in Candida albicans utilizing nourseothricin resistance. Yeast 3, 833–841 (2011).

    Article  CAS  Google Scholar 

  63. Mansfield, B. E. et al. Azole drugs are imported by facilitated diffusion in Candida albicans and other pathogenic fungi. PLoS Pathog. 6, 11 (2010).

    Article  CAS  Google Scholar 

  64. Botev, Z. I., Grotowski, J. F. & Kroese, D. P. Kernel density estimation via diffusion. Ann. Stat. 38, 2916–2957 (2010).

    Article  Google Scholar 

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Acknowledgements

In memory of our friend and colleague Ken Haynes who sadly passed away on 19 March 2018. R.E.B. is an EPSRC Healthcare Technologies Impact Fellow EP/N033671/1; I.G. is funded by ERC Consolidator grant 647292 MathModExp; A.J.P.B., N.A.R.G. and A.T. were funded by BBSRC grant BB/F00513X/1; K.H., I.G., S.N. and E.C. were funded by BBSRC grant BB/F005210/2.

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Author notes

  1. These authors contributed equally to this work: Robert E. Beardmore, Emily Cook.

  2. Deceased: Ken Haynes

Authors and Affiliations

  1. Biosciences, University of Exeter, Exeter, UK

    Robert E. Beardmore, Emily Cook, Susanna Nilsson, Ken Haynes & Ivana Gudelj

  2. School of Biological Sciences, University of Missouri at Kansas City, Kansas City, MO, USA

    Adam R. Smith, Brooke D. Esquivel & Theodore C. White

  3. MRC Centre for Medical Mycology, University of Aberdeen, Institute of Medical Sciences, Aberdeen, UK

    Anna Tillmann, Neil A. R. Gow & Alistair J. P. Brown

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Contributions

I.G. and R.E.B. conceived the idea. R.E.B., I.G. and E.C. designed all experiments (apart from Supplementary Fig. 5). T.C.W. designed the experiment in Slementary Fig. 5. E.C., S.N., A.R.S., A.T., B.D.E., K.H., N.A.R.G. and A.J.P.B. carried out experiments. I.G. and R.E.B. developed and numerically simulated the mathematical model. R.E.B., I.G., E.C., T.C.W., K.H., N.A.R.G. and A.J.P.B. discussed the results. R.E.B., E.C. and I.G. wrote the manuscript.

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Correspondence to Robert E. Beardmore or Ivana Gudelj.

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Supplementary figures 1–12; supplementary experimental details; supplementary modelling

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Beardmore, R.E., Cook, E., Nilsson, S. et al. Drug-mediated metabolic tipping between antibiotic resistant states in a mixed-species community. Nat Ecol Evol 2, 1312–1320 (2018). https://doi.org/10.1038/s41559-018-0582-7

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