Transcript Battling bacterial evolution: The work of Carl Bergstrom

Battling bacterial evolution:
The work of Carl Bergstrom
01.30.08 / 01.31.08
Adapted from Understanding Evolution at UC Berkeley
Hooked on natural selection
• Associate Professor of Biology, University
of Washington
• Ph.D. in theoretical population dynamics,
Stanford, 1998
• Researches ways to control the
evolutionary future of microbe populations,
nudging them towards particular destinies
and away from others.
Hooked on natural selection
• Individuals in populations vary
• Some of those variations help the individuals to
produce more offspring than others
• Those offspring, in turn, inherit the successful
variations and produce more offspring
themselves
• As generations pass, the population evolves
towards the more successful variation
• As new helpful variations arise, they are also
selected for and are layered on top of or replace
previously successful variations.
Hooked on natural selection
• Natural selection is simply the logical
result of four features of living systems:
– variation - individuals in a population vary
from one another
– inheritance - parents pass on their traits to
their offspring genetically
– selection - some variants reproduce more
than others
– time - successful variations accumulate over
many generations
Hooked on natural selection
• Dr. Bergstrom focuses much of his work on
bacterial populations that impact public health.
• After earning his Ph.D., he spent two years
doing lab research at Emory University
• Dr. Bergstrom builds computer models of
bacterial populations and tests them virtually.
• His predictions and conclusions can then be
compared to real world observations and tested
in clinical settings by other researchers.
Resisting our drugs
• Dr. Bergstrom’s work tackles the real problem of
the evolution of antibiotic resistance by bacterial
populations in hospitals.
• When antibiotics, such as penicillin, were first
discovered, they seemed to represent a miracle
cure for human diseases like pneumonia,
typhoid, and bubonic plague.
• Bacteria began developing resistance to
antibiotics almost immediately
Resisting our drugs
• The problem is compared to running on a treadmill
– Drug companies develop and introduce a new
antibiotic, only to see the evolution of resistant
bacterial strains within a few years.
– New antibiotics are developed, and they soon
becomes useless in the face of newly evolved
resistant bacteria.
• The cycle is costly
– About 1.7 m resistant infections occur annually in
U.S. hospitals
– The estimated financial cost is $4-5 billion
– There are many additional deaths
– US workers lose hundreds of thousands of days of
work and spent tens of thousands of extra days in
hospitals
Resisting our drugs
How exactly does antibiotic resistance evolve?
– natural selection
– Imagine
– a population of bacteria infecting a patient in a hospital.
– The patient is treated with an antibiotic.
– The drug kills most of the bacteria but there are a few individual
bacteria that happen to carry a gene that allows them to survive
the onslaught of antibiotic.
– These survivors reproduce, passing on the gene for resistance
to their offspring, and soon the patient is populated by an
antibiotic resistant infection
– This resistant bacteria can then spread through the whole
hospital
Battle strategies
• The evolution of resistant bacteria is an inevitability.
• We must find ways to slow the evolution of resistant
strains and encourage the evolution of susceptible
strains.
• Dr. Bergstrom has studied a strategy being considered
for use in hospitals called cycling.
• Doctors in a hospital would cycle through antibiotics,
prescribing a particular antibiotic for period of time and
then switching to a new one.
• Hypothesis: Cycling would reduce levels of antibiotic
resistance because the bacteria would not have time to
evolve to each new drug
Battle strategies
• Doctors have been successful using a
similar idea to increase the effectiveness
of HIV drugs in a single patient
• The patient cycles through various drugs,
switching to a new one as his or her virus
population evolves to be resistant to the
old one. Could the same method work on
a larger scale in hospitals?
Testing the strategies
• Clinicians began testing the idea in hospitals in
2000.
• At round the same time, Dr. Bergstrom began
studying it using computer models.
• A model, in this case, is a set of rules about how
the components of a system interact (e.g., how
rapidly a single patient will evolve a resistant
infection, how likely that infection is to be passed
to another patient, etc.) that may be represented
by a computer program or a set of equations.
Testing the strategies
• A model is a hypothesis about how a system works and
what factors affect it.
• The hypothesis/model generates predictions (e.g., if this
set of rules is true, then we'd expect to observe X when
we change antibiotics every six months).
• Those predictions can then be compared to what is
observed in the real world — the more often they match,
the more likely it is that the model represents what is
important in the real world.
• If predictions and observations do not match, then some
aspect of the model probably needs to be changed.
Testing the strategies
• Dr. Bergstrom’s made led to a surprising prediction:
cycling would not work.
• In their model, cycling through antibiotics did not reduce
overall levels of antibiotic resistant infections.
• Clinical tests came to the same conclusion
• If the researchers had merely tested the cycling strategy
in a hospital, that would have been the end of it — we
would only have learned that cycling, as set up in the
experiment, doesn't work.
• But Dr. Bergstrom’s model helps us understand more
about what went wrong with cycling and how to fix it.
Testing the strategies
• The model suggests that having all the patients
in the hospital taking the same antibiotic at the
same time actually makes it easier for the
bacteria to adapt to the drug.
• The model suggested that a better way to slow
the spread of antibiotic resistant bacteria is to
treat different patients with different antibiotics,
making it more difficult for a bacterium with
resistance to a particular antibiotic to succeed
when it infects another patient.
• Of course, this too is a hypothesis, and the
strategy is subject to testing in the real world.
Modeling and managing evolution
• Dr. Bergstrom is also researching SARS and HIV
– How do they evolve to infect humans?
– How do humans affect the evolution of those diseases?
• Modeling is a useful tool because it allows one to make
testable predictions about evolution.
• Human health issues , from controlling diseases to
protecting crops from pests, are fundamentally problems
of managing evolution.
• Dr. Bergstrom says, “We rarely thought about how we
control evolution...Now we are asking a different kind of
question—an engineers' kind of question: what
parameters or what environmental conditions would
make evolution do this."
Check-up
• What are the four basic characteristics that
result in natural selection?
– Explain how bacteria encountering an
antibiotic exhibit each of these characteristics.
• How was "cycling" supposed to slow the
evolution of antibiotic resistant bacteria?
• What did Dr. Bergstrom’s model predict?
• How can we be sure the computer model
is valid?