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Ecology, evolution, and antibiotic resistance Carl T. Bergstrom Department of Biology University of Washington University of Michigan December 8th, 2005 Humankind has conquered infectious disease. The SARS virus The SARS virus H5N1 Avian Influenza The New York Times June 13, 2000 Antibiotic Misuse Turns Treatable to Incurable Vancomycin-resistant Enterococcus in US hospital intensive care National Nosocomial Infections Surveillance System Report, 2003 30 25 PERCENT 20 15 10 5 0 1983 1985 1987 1989 1991 1993 YEAR 1995 1997 1999 2001 How evolution works Variation: different individuals have different traits. Heritability: offspring tend to be somewhat like their parents. Selection: individuals with certain traits survive better or reproduce more. Time: successful variations accumulate over many generations. From “Battling bacterial evolution: The work of Carl Bergstrom” Understanding Evolution, University of California. 1 2 3 Antibiotic-sensitive Antibiotic-resistant Dead Transformational Process Variational Process 1 2 3 1. Where does the variation come from? 2. What does the selecting? 3. What are the consequences? 4. How can we intervene? 1 1. 1. Where does the variation come from? 2. What does the selecting? 3. What are the consequences? 2. 4. How can we intervene? Mutation Macrolide antibiotics block protein synthesis by binding to bacterial ribosomes. From Hanson et al (2002) Molecular Cell Mutation A single point mutation in the green binding region can prevent macrolide binding and confer resistance. Modified from Hanson et al. (2002) Molecular Cell Mutation Genome size: Mutation rate: Population size: ~ 5 x 106 base pairs ~ 2 x 10-3 per genome 1010 to 1011 per g fecal matter A single gram of fecal matter is likely to contain a novel point mutation conferring macrolide-resistance! Natural ecology of antibiotics Soil microbes produce antibiotics to kill competitors. Lateral Gene Transfer Electron micrograph: Dennis Kunkel. http://www.denniskunkel.com Lateral gene transfer A. orientalis Unknown source Vancomycin resistance Vancomycin Resistant Enterococcus 2 1. Where does the variation come from? 2. What does the selecting? 3. What are the consequences? 4. How can we intervene? Most resistant strains are commensals Extremely high rate of drug use Hospital staff act as disease vectors High rate of patient turnover Agricultural use 25 million pounds per year into animal feed! Union of Concerned Scientists, 2001 Agricultural use 3 1. 1. Where does the variation come from? 2. What does the selecting? 3. What are the consequences? 2. 4. How can we intervene? Resistance in the Intensive Care Unit National Nosocomial Infections Surveillance System Report, 2003 Klebsiella pneumoniae Pseudomonas aeruginosa 10 % 23 % 28 % 52 % Enterococcus sp. Staphylococcus aureus In the Community : Macrolide resistance Streptococcus pneumoniae Helicobacter pylori 32 % 20-90 % Up to 70 % Ineffective Streptococcus pyrogenes Haemophilus influenzae Methicillin against macrolide resistance Vancomycin used against MRSA 40 35 MRSA 25 20 15 10 5 0 19 77 19 79 19 81 19 83 19 85 19 87 19 89 19 91 19 93 19 95 19 97 19 99 20 01 20 03 % Resistance 30 Year . . Methicillin against macrolide resistance Vancomycin used against MRSA Linezolid . . against VRE 40 35 MRSA VRE 25 20 15 10 5 0 19 77 19 79 19 81 19 83 19 85 19 87 19 89 19 91 19 93 19 95 19 97 19 99 20 01 20 03 % Resistance 30 Year Linezolid? 1 2 3 1. Where does the variation come from? 2. What does the selecting? 3. What are the consequences? 4. How can we intervene? Antimicrobial cycling One-time shift of drugs clears up resistance outbreaks. Antimicrobial cycling takes the same idea further: Try repeated, scheduled rotations among different drugs. • Gentamicin, Piperacillin/Tazobactam and ceftazidime for gram-negatives in a neonatal ICU (Toltzis et al., Pediatrics 2002) • Imipenem/cilastatin, pip / tazo, and ceftazidime + clindamycin / cefepime in a pediatric ICU (Moss et al., Critical Care Medicine 2002) • Carbapenems and ciprofloxacnin + clindamycin, followed by cefepime + metronidazole and pip / tazo in postoperative patients (Raymond et al. Critical Care medicine 2001) Antibiotic cycling "The `crop rotation' theory of antibiotic use [suggests] that if we routinely vary our `go to' antibiotic in the ICU, we can minimize the emergence of resistance because the selective pressure for bacteria to develop resistance to a specific antibiotic would be reduced as organisms become exposed to continually varying antimicrobials." - M. Niederman (1997) Am. J. Respir. Crit. Care Med. In our black box: Begin with a traditional SI model Infected Susceptible Community Hospital Translate the gearbox into equations dS /dt m SX (1 2 )S dR /dt (1 c)RX ( 2 )R dX /dt (1 m) (1 2 )S ( 2 )R SX (1 c)RX X S: patients colonized with sensitive bacteria R: patients colonized with resistant bacteria X: uncolonized patients We can solve explicitly for equilibrium behavior For example, resistance will be endemic when 1 2 1 m Left side is R0 for the resistant strain. Right side measures the availability of colonizable hosts We can study the dynamics using numerical solution Formulary changes can rapidly eradicate resistant bacteria. Fraction resistant Non-specific control does appreciably reduce resistance.* . E.g., things change fast. 0.6 0.5 0.4 0.3 0.2 0.1 0 -30 -10 10 30 50 70 T ime (days)) *When resistance is rare in the community Infection control (70% transmission reduction) Infection control + switch antibiotics Extend our model to multiple resistant strains Community Hospital An ODE model Two resistant strains, one sensitive strain. No dual resistance yet. Dynamics of cycling: 90 day cycles How do we judge whether cycling works? 1. Total resistant infections: R1 + R2 2. Probability of dual resistance arising by lateral gene transfer: R1 * R2 Baseline for comparison: In each case, compare the outcomes under cycling to an approximation of the status quo: Mixing of the two drugs, in which at any given time half of the patients receive drug 1, the other half drug 2. Total resistant infections Cycling Mixing Total resistant infections by cycle length One year Three months Cycling Two weeks Mixing Average total resistance increases with cycle period Cycling Mixing Rate of emergence of dual resistance One year Three months Two weeks Rate of dual resistance evolution is greater with cycling. Why doesn't cycling work? Time Why doesn't cycling work? Time Mixing creates more heterogeneous environment than does cycling! Time US infectious disease mortality throughout the 20th century 1918 flu pandemic Sulfonamides Penicillin HIV Acknowledgements Diane Genereux Department of Biology University of Washington