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How do antibiotics work? …. and can physicists help? - Rosalind Allen

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How do antibiotics work? …. and can physicists help? - Rosalind Allen

  1. 1. Rosalind Allen School of Physics and Astronomy, University of Edinburgh QLSB II, Como, June 21st 2016 How do antibiotics work? …. and can physicists help?
  2. 2. Antibiotics: molecules that inhibit bacteria Alexander Fleming 1928 Wikipedia Y. G. Song, Infect. Chemother. 2012, 44, 263-268
  3. 3. Antibiotics have revolutionised global health Leading causes of US deaths 1900: pneumonia, tuberculosis, diarrhoea 1997: heart disease, cancer, stroke Source: www.cdc.gov
  4. 4. But there is a looming crisis of antibiotic resistance Evolution.berkeley.edu www.cdc.gov
  5. 5. What to do about this? • Understand how antibiotics work and how resistance evolves can we develop smart strategies to avoid resistance? • Discover new antibiotics eg screen environmental samples for new compounds • Reduce antibiotic use eg improve diagnostics to distinguish bacterial and viral infections
  6. 6. What can physicists do to help? • Help design tools for better diagnosis eg chips to detect DNA of bacterial pathogens • Help improve basic understanding simple lab model systems mathematical and computational models R. J. Allen and B. Waclaw Antibiotic resistance: a physicist’s view Arxiv 1605.06086
  7. 7. How do antibiotics work? Target processes that differ between bacteria and human cells • Cell Wall Synthesis beta-lactams, vancomycin • Protein Synthesis aminoglycosides, tetracyclines, chloramphenicol, macrolides • Nucleic Acid Synthesis quinolones, metronidazole, rifampicin • Cell Membrane polymyxins • Metabolism sulfonamides, trimethoprim Bactericidal drugs kill bacteria Bacteriostatic drugs stop bacterial growth www.tnmanning.com
  8. 8. How to quantify antibiotic efficacy? Minimum inhibitory concentration (MIC): concentration that prevents visible growth of bacteria Small MIC -> high efficacy Vads.vetmed.vt.edu IC50 IC50: concentration needed to halve the growth rate Small IC50 -> high efficacy
  9. 9. From lab assays to clinical use Pharmacokinetics: predict antibiotic concentration in the human body www.biologicaltestcenter.com
  10. 10. Pharmacodynamics: what concentration is needed to treat an infection? Time-dependent drugs: what matters is time above MIC eg penicillins, cephalosporins Concentration-dependent drugs: what matters is concentration peak/MIC or AUC:MIC eg quinolones, aminoglycosides Also need to avoid antibiotic resistance “After more than 50 years of study, the shape of drug concentration-time curve that is needed at the site of infection for optimum antimicrobial effects is still not known” D. Greenwood in “Antimicrobial chemotherapy”, 4th Ed.
  11. 11. Real infections are complicated Urinary tract infection • Bacteria stick to bladder wall • Damage epithelium, trigger immune response • Colonise and damage kidneys • Eventually spread to bloodstream A. L. Flores-Mireles et al, Nature Reviews Microbiology 13, 269-294 (2015)
  12. 12. But simple models can help H. Kuwahara et al, Plos computational biology 6 e1000723 (2010) e.g. Urinary tract infection E. coli switch stochastically between fibriated and non-fimbriated states Fimbriated bacteria stick to walls But also activate immune system Statistical physics model: • Population grows • Switches between states A and B • Environmental catastrophe wipes out A cells, triggered by population What is the optimum switching rate? P. Visco et al Biophysical Journal 98, 1099-1108 (2010) FractionofcellsinAstate time See also M. Thattai & A. van Oudenaarden Genetics 167, 523-530 (2004) E. Kussell & S. Leibler Science 309, 2075-2078 (2005)
  13. 13. More detailed example: how does growth rate affect antibiotic efficacy? Virulent infections: fast-growing bacteria Chronic infections: slow-growing bacteria Do antibiotics work differently for virulent versus chronic infections? Growth-dependent bacterial sensitivity to ribosome-targeting antibiotics P. Greulich, M. Scott, M. R. Evans & R. J. Allen, Mol. Syst. Biol. 11, 796 (2015) Philip Greulich Matt Scott Martin Evans
  14. 14. A simple test: grow E. coli bacteria in the lab on different nutrients Do fast-growing bacteria respond better or worse to antibiotics than slow- growing bacteria? N(t) = N0el0t 6 growth media 4 antibiotics: tetracycline, chloramphenicol, streptomycin, kanamycin All target the ribosome; cell’s protein synthesis machinery
  15. 15. Result: some antibiotics work better on fast-growing cells Tetracycline Chloramphenicol Kanamycin But others work better on slow-growing cells Streptomycin why?
  16. 16. A simple model Ribosomes are needed to make new ribosomes Ribosomes are needed for growth • Antibiotic crosses membrane; net inflow rate J. • Antibiotic binds ribosomes at rate kon, unbinds at rate koff • Cell grows at rate l, diluting cell contents • New ribosomes are synthesized at rate s l and s depend on the ribosome concentration!
  17. 17. Model variables a(t): intracellular antibiotic concentration ru(t): free (unbound) ribosome concentration rb(t): antibiotic-bound ribosome concentration Model equations Constraints: Free ribosomes are needed for growth l = l(ru) Ribosome synthesis rate is regulated s =s(l) Dilution due to growth Antibiotic-ribosome binding Ribosome synthesis Antibiotic inflow
  18. 18. Constraint 1: ribosomes are needed for growth M. Scott, et al Science (2010) 330, 1099 Constraints can be obtained from experimental data Constraint 2: up-regulation of ribosome synthesis s = lrtot = l ru +rb( ) Steady-state growth, synthesis balances dilution
  19. 19. Result: cubic equation linking growth rate and antibiotic concentration l* 0 = 2 Poutkt koff kon æ è ç ö ø ÷ Measures the reversibility of membrane transport and ribosome binding One key parameter Good fits to experimental data
  20. 20. Simple prediction for the IC50 Large l0*: IC50 decreases with nutrient richness: Fast-growing cells are more susceptible Small l0*: IC50 increases with nutrient richness: Fast-growing cells are less susceptible Scaled drug-free growth rate Scaledsusceptibility Outcomes: It’s all about reversibility Link molecular mechanism to whole-cell physiology
  21. 21. Related work: Rebecca Brouwers Linking mechanism to physiology for cell-wall targeting antibiotics Dan Taylor How do bacteria respond to antibiotics in small populations?
  22. 22. What about antibiotic resistance?
  23. 23. Antibiotic resistance Emergence of bacterial strains that are not inhibited by antibiotic • Gain of a degrading enzyme e.g. beta-lactamases • Alteration of the bacterial target e.g. changes in ribosome structure • Change in permeability or transport e.g. increased expression of efflux pumps Can happen by • Gain of extra DNA (eg plasmids by horizontal gene transfer) • Mutations in genome • Changes in gene expression www.reactgroup.org
  24. 24. How does an infection become antibiotic resistant? An individual bacterium arises that is resistant e.g. through genetic mutation It proliferates in competition with sensitive bacteria typically wins in presence of antibiotic, loses otherwise It spreads beyond the initial infection e.g. to other people Usually a multistep process, several mutations
  25. 25. Pathways to drug resistance D. M. Weinreich et al, Science 312, 111-114 (2006) Usually several mutations needed for clinically relevant antibiotic resistance Does evolution always follow the same pathway? Example: Weinreich et al (2006) Construct all combinations of 5 mutations in a b-lactamase enzyme Measure MIC of all mutants Attempt to infer possible evolutionary pathways -> Only a few are feasible
  26. 26. Morbidostat: a smart device for tracking evolutionary pathways in time E. Toprak et al, Nature Genet. 44, 101-105 (2011) E. Toprak et al, Nature Protocols 8, 555-567 (2013) Grow bacteria at constant volume Add nutrients, remove waste If growth rate is positive, add drug -> maintains constant selection for resistance Trimethoprim: stepped trajectories, mutations only in target protein (dihydrofolate reductase) Chloramphenicol: smooth trajectories, many mutations involved (translation, transcription, transport)
  27. 27. But real infections can be spatially structured How does a spatial drug gradient affect evolution of resistance?
  28. 28. Qiucen Zhang et al. Science 2011;333:1764-1767 Experiments in microfluidic “death galaxy” (Bob Austin’s group): E. coli resistance to ciprofloxacin emerges much faster in a drug gradient
  29. 29. Our simulations: bacterial population invades a drug gradient -> model by chain of connected microhabitats • Population well-mixed within habitats • Migration between habitats • Mutation between genotypes • Growth rate depends on local drug concentration genotype m cannot grow if c>bm • Exponential drug gradient Microhabitat i Genotype m Philip Greulich Bartek Waclaw
  30. 30. Result: population expands in a series of waves P. Greulich , B. Waclaw & R. J. Allen, PRL 109, 088101 (2012) Why? • Strong selection at the wave front • No need to compete with neighbours • Very steep gradient: fronts too narrow to produce mutants Steepness of gradient Timetofullresistance populationdensity Time to resistance depends on steepness of gradient
  31. 31. Experiments: Bartek Waclaw Track evolution in drug gradients Directly mimic the model Preliminary results (E. coli in ciprofloxacin) • We do see evolution of resistance • Mutation rate depends on drug concentration
  32. 32. Conclusion Antibiotic resistance: how can we help? Try to understand the basics • how antibiotics work • how resistance evolves Using simple experimental and mathematical models Can we connect it to “real biology”? It remains to be seen….
  33. 33. Postdoctoral positions in the physics of antibiotic resistance University of Edinburgh soft matter, biological and statistical physics group www.vacancies.ed.ac.uk ref number 036372 closing date 1st July 2016 Enquiries to Rosalind Allen or Bartek Waclaw rosalind.allen@ed.ac.uk bwaclaw@staffmail.ed.ac.uk
  34. 34. e.g. biofilm infections: bacteria colonising surfaces L. Hall-Stoodley et al, Nat Rev Microbiol. 2, 95 (2004) gum disease catheter contaminatio n implant contaminatio n R. J. Broomfield et al, J. Medical Microbiology 58, 1367-1375 (2009) How relevant is this to real infections? responsible for chronic infections bacteria experience different chemical environments How does antibiotic resistance evolve in these infections?
  35. 35. no antibiotic with antibiotic How does biofilm structure affect evolution of antibiotic resistance? Courtesy of R. McKenzie & G. Melaugh How does antibiotic change biofilm structure? Can we predict rate of resistance evolution? Computer simulations • Simulation tracks individual bacteria • Bacteria interact via physical forces • Bacteria consume nutrient, grow and divide • Nutrients and drugs diffuse from above www.sharklet.com Can we design smart surfaces to avoid resistant biofilms?

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