This week’s BEACON Researchers at Work blog is by MSU graduate student Jay Bundy.
Jay “on the bench” performing a transfer during a competition experiment.
As a kid I played a lot of basketball. I loved almost everything about the game. But there was one thing I hated: spending time riding the pine. In sports, nothing interesting happens on the bench. The bench is where players who aren’t very good spend most of the game sitting, like a spectator, watching all the action. If they’re lucky, the coach will put them in for a few minutes when the game is out of hand or a star player really needs to take a quick break.
When I came to BEACON last year, I was a research technician while finishing my master’s degree in Anthropology. Perhaps the most profound decision I made was to start working in the lab of Dr. Richard Lenski on his E. coli long-term experimental evolution (LTEE) project. I had never worked in a microbiology lab before. To be honest, I didn’t really know anything about E. coli other than that they were bacteria. As an anthropologist, I studied the evolutionary basis of mating behavior in contemporary human populations. What was I going to learn from a system of experimental evolution using germs?
After reading some of the early microbial and long-term experimental evolution papers, I began to realize that microbes were an awesome way to study evolution! They have really fast generation times, they are super easy to maintain in the lab, and they can be frozen to be revived years later (yes, a real-life time machine). I decided that I wanted to get trained in the methods of the Lenski lab. So I uttered those words that as a former basketball player I never thought I’d say. “Dr. Lenski, will you please put me on the bench?”
Unlike basketball, in evolutionary biology all of the action happens on the bench. The bench is the name for a researcher’s work area in a wet lab. A wet lab is a space with the proper equipment, ventilation, and plumbing to allow researchers to work directly with biological materials, often suspended in liquid solutions (hence the name wet lab). If MTV cribs were to visit science labs instead of celebrity homes, it would be the bench instead of a king-size bed associated with the cliché “this is where the magic happens.” In the Lenski lab, that magic is all about competition experiments. The basic protocol for a three-day competition is as follows:
Day -2: A small population of E. coli from two competitors is put into a flask with bacteria food to promote growth.
Day -1: The E. coli are transferred to new flasks containing a very minimal broth with 25 mg per liter of glucose added (DM25). This is the environment of the actual competition.
Day 0: Both competitors are put together in a common competition flask containing DM25. This marks the beginning of the actual competition.
A sample from this flask is immediately spread onto a petri dish containing TA (i.e. tetrazolium arabinose) in gelatin-like form. All competitions take place between an Ara+ strain, which can utilize arabinose and produces pinkish/white colonies and an Ara– strain, which cannot utilize arabinose and produces red colonies. “Plating” on Day 0 allows us to take an initial count of each visually identifiable strain at the beginning of the competition to be compared with the relative counts of each strain at the end of the competition.
E. coli “plated” on TA (tetrazolium arabinose) and ready for counting. The pinkish/white colonies grow on arabinose (Ara+) whereas the red ones (Ara-) cannot.
Day 1: 24 hours later a small sample from the competition flask is transferred into a fresh flask containing DM25 and the plates from Day 0 are counted.
Day 2: 24 hours later, a small sample is transferred into a fresh flask containing DM25.
Day 3: A small sample from the final competition flask is plated just like Day 0. Comparing the counts between the final day and Day 0 determines the winner.
Fortunately, after finishing my master’s thesis I was accepted by Dr. Lenski into the PhD program in Zoology at MSU. As a first-year PhD student I have been primarily taking classes. However, Dr. Lenski and I have come up with a research agenda that I recently used to apply for an National Science Foundation graduate research fellowship.
The goal of my research is to answer the question “How do the relative contributions of adaptation, history, and chance change over the course of long-term experimental evolution?”
Experimental design: X is 2,000 generations in E. coli and 20,000 updates in Avida. Following experimental evolution evolved values (i.e. ‘e’) are compared to ancestral values (i.e ‘a’). Yellow=initial laboratory environment, Blue=experimental environment. b. Hypothetical comparison for ancestral vs. evolved values when evolution is primarily by adaptation, history, and chance.
To answer my research question I will be using the competition experiments described above. The long-term evolution experiment was started with a single genotype of E. coli strain B cloned to found 12 populations. These populations have been evolving in DM25 for over 60,000 generations since February of 1988. I will take a single genotype (isolate) from all 12 lines at 2,000, 10,000, and 50,000 generations into the long-term evolution experiment. I will then establish three replicate populations from each line (using clones of the isolate). I will then evolve these 108 populations (12 lines x 3 replicates x 3 time points) for 1,000 generations in maltose. I will be replacing the glucose in DM25 with maltose, an alternative sugar source. Substituting the maltose for glucose will allow me to answer my research question using experimental evolution in a novel environment for these bacteria. Following this initial period of evolution I will compete all 108 populations against their ancestors. Similarities across all 12 replicate lines (such as increased fitness in maltose) reflect the influence of adaptation because all populations are attaining similar results regardless of initial differences between them following 2,000, 10,000, or 50,000 generations of unique history in each lineage. Differences between the 12 replicate populations (such as differences in evolved cell size) reflect the influence of each population’s unique history, since all lineages were originally derived from a single genotype and hav
e evolved in the same environment. Differences between replicate populations (started from clones) within each lineage, reflect the influence of chance events, since differences between replicates will be caused by mutation and genetic drift events unique to each lineage that occur during the experiment (see figure above). Estimating the relative contributions of adaptation, chance, and history at 2,000, 10,000, and 50,000 generations will allow me to determine how the relative effects change over evolutionary time. I will also be performing a similar experiment in Avida, a digital evolution platform maintained by MSU’s digital evolution lab. I am truly honored to be at BEACON, where this type of research is possible. Stay tuned for more.
For more information about Jay’s work, you can contact him at bundyjas at msu dot edu.
