This week’s BEACON Researchers at Work post is by MSU graduate student Alita Burmeister.
… the scientist asks “Hey, what do you two have in common?”
This summer I met Sue the T. rex. Her fossil remains are the largest, most complete of her species, discovered after 65 million years preserved in rock. While Sue was one of earth’s largest predators, I research one of the smallest: a virus. Most people are all-too-familiar with viruses that affect human cells, for example the influenza virus and HIV. Viruses themselves are not cell-based life forms, which means a virus must infect a cell to grow. Influenza virus infects cells in the respiratory tract. HIV infects cells of the immune system. This virus I work with infects bacterial cells. Like us, bacteria are cell-based life forms, and like us, bacteria are susceptible to their own viral infections. Like a T. rex, a virus leads to a violent end for its prey. This type of relationship is called a “predator-prey interaction.”
My research at Michigan State University studies how viruses coevolve with the cells they infect. Coevolution is the process in which a population of organisms adapts to other organisms. For example, cheetahs and gazelles coevolved to catch and escape one another, respectively. Unlike cheetahs, gazelles, and T. rex, microorganisms and viruses mutate and grow quickly, so they can evolve in the laboratory and be kept frozen and revived indefinitely. In the lab, I use such a frozen “fossil bank” of viruses and the cells they infect to study coevolution. To do this, I thaw a bit of a frozen “fossil record” to revive the viruses. I then sequence the virus DNA to find mutations and test how the viruses interact with cells.
As a scientist, my main job is asking questions and seeking answers. Most people are familiar with questions like, “Was T. rex a scavenger or a hunter?” To answer this question, paleontologists consider the evidence. They don’t know for sure because they can’t study T. rex behavior directly, but the evidence suggests that T. rex was likely part scavenger and part-predator. Paleontologists hypothesize that T. rex‘s backward curving teeth helped prevent live prey from escaping, indicating a predatory lifestyle. While paleontologists have questions for their favorite species, I have questions for the viruses and cells I work with in the lab. Do mutations help the viruses? Do mutations help the cells? As a grad student of microbiology and evolution, I get to do the tests to find out!
As a teacher, I work with college students in a laboratory classroom. My students are preparing for jobs in medicine, food research, agriculture, and the pharmaceutical industry. To do these jobs, students need to understand genetics and how to work with molecules like DNA. In our class, the students use bacteria to study how DNA works as the genetic code. The classroom experiments involve moving genes from one bacterium to another using standard genetic techniques. In all of these labs, the key genes code for “antibiotic resistance.” These genes make bacteria able to survive antibiotic treatments. These genes are the major reason why pathogens like MRSA and TB are becoming more dangerous, and why your doctor will tell you to not take antibiotics unless you really need them. In nature and medical settings, bacterial populations evolve by changing their DNA – often that includes antibiotic resistance genes. As a teacher of microbiology and evolution, I get to teach students the details of how these genes move among strains – and the students themselves get to perform the experiments demonstrating these evolutionary mechanisms.
While genetic engineering is interesting, observing bacteria naturally evolving is even more fascinating. Collaborating with MSU Professor of Microbiology Dr. Michael Bagdasarian, we are working on changing the course’s curriculum to include an evolution experiment in the lab. Refocusing the course around real-time evolution of antibiotic resistance, we want students to experience evolution for themselves. After graduation, our students will get jobs where antibiotics are widely used – for example in human medicine, agriculture, and the pharmaceutical industry. Our students will be responsible for using antibiotics wisely, in order to prevent the evolution of antibiotic resistant pathogens. The good news for our class is this: evolution is simple! A straightforward experiment could involve evolution of bacterial cells in the lab. The cells would be exposed to an antibiotic during evolution, and students would test how antibiotic resistance and genes change throughout the semester.
While evolution can be simple, bacterial genetics can be complicated. In the classroom we are limited to basic genetics methods and do not have time to characterize the effect of mutations in all 4,000 genes of a bacterial genome. To get around this problem, I am working with a simpler model of a genome. Models are used throughout science to make complex systems easier to understand. For example, lab rats are models of human biology. In the case of genomes, I am working with a model system based on computer code rather than DNA code. Before you check out at the sight of “computer code,” realize this: if you can fix a typu typo on your computer, you can mutate computer code. It’s that easy! I am developing an exercise to model genome mutations using simple computer programs called “digital organisms.”
Digital organisms are self-replicating computer programs. These programs have coded genomes and can be mutated by simple code changes. For example, here is the code a digital organism in AVIDA-ED:
rnzarpqgfcqppidffvcnpqbqnqppgcgiypzqfgpqputtsfqvkk
And here is a “knock-out mutant” of the genome. Can you spot the “genetic” deletion?
rnarpqgfcqppidffvcnpqbqnqppgcgiypzqfgpqputtsfqvkk
Did you find it? (Sorry no Waldo-stripe giveaway.) In this code, the third letter “z” was deleted. My goal is to use these digital genomes to teach about DNA-based genomes. Using computer codes rather than DNA codes, my students will be able to test the effect of mutations on a digital organism’s health or “fitness.” To do this, students will use the application AVIDA-ED, in which they will be able to test both individual fitness and the evolution of a population of digital organisms. With AVIDA-ED, students will investigate two questions:
1) Are most mutations beneficial or harmful?
Finding that most random mutations are harmful, they will ask:
2) Since most mutations are harmful, why does fitness increase during evolution?
If you thought about the second question, you may have thought about the selection picking out the rare beneficial mutations and leaving behind the harmful mutations — if so, you’d be in agreement with most evolutionary scientists.
And if you thought that digital organisms sound a little like computer viruses, evolutionary scientists would agree with you there as well, with one key exception: computer viruses, luckily for us, do not mutate.
For more information about Alita’s work, you can contact her at alita dot burmeister at gmail dot com.
