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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

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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
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 selection1
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 selection2
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 selection3
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
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 drugs1
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 drugs2
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
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 strategies1
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
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 strategies1
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 strategies2
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 strategies3
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
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
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?


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