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Transcript
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