Clare’s fellowship in 2020 was a crucial step in her unusual career journey from journalist to biologist. Read on to learn about the inspiration behind her research!
Can you tell us about your research? What were you working on as a fellow, and how has it progressed since completing the fellowship?
I study evolution in fluctuating environments using a model Saccharomyces cerevisiae system, where we grow baker’s yeast in liquid media for 48-hour cycles and then dilute the culture into fresh media. Together with my postdoctoral advisor Dmitri Petrov and graduate student Shaili Mathur, we have found that environmental memory can radically alter fitness (which is a measure of ability to grow fast, compared to the ancestor) of adaptive mutations in fluctuating environments. In an environment that fluctuates between conditions A and B (for example, media with one of two different carbon sources), a sensible assumption is that a mutant’s fitness should be equal to the average of its fitness in conditions A and B. This would mean that the mutant always has the same fitness in condition A, regardless of whether the previous growth cycle was in condition A or B.
Instead, we find that a mutant’s fitness in a particular condition depends strongly upon whether it grew in the same or a different condition during the previous cycle. Fitness changes most radically for mutants that have large fitness differences, or tradeoffs, across conditions. For example, if a mutant has very high fitness in optimal conditions (e.g. media with glucose), but very low fitness in stressful conditions (e.g. media with glucose and a high salt concentration), this mutant is more likely to change its fitness when the environment fluctuates than a mutant with similar fitness across conditions.
Mutants with fitness tradeoffs may even exhibit reversals in fluctuating environments, performing well in the condition where they have low fitness and performing poorly in the condition where they have high fitness. These reversals suggest that memory of the previous condition influences a mutant’s fitness in the current condition. In a mathematical model, we implement this environmental memory via changes in lag time (the time it takes for the mutant to begin growing after it is diluted into fresh media) in fluctuating environments. Sequencing results suggest that environment-sensing mutations, such as in nutrient- and stress-sensing pathways, lead to more fitness tradeoffs and memory.
These results are relevant to countless real-life applications—fluctuations are ubiquitous in natural environments. Microbial evolution creates resistance to treatments such antibiotics and other drugs. Some clinical treatments cycle drugs to leverage collateral sensitivity, where fitness tradeoffs cause microbes that are resistant to one drug to be sensitive to another. Our results show that temporally fluctuating drugs might lead to memory effects, particularly in the case of fitness tradeoffs. The pattern we observe, that mutants with fitness tradeoffs exhibit more environmental memory, suggests a global structure that might be leveraged to predict memory based on fitness variance across environments. More investigation, both theoretical and experimental, is needed to expand upon these results.
You can read our paper here.
Where were you before Stanford, and how did you become interested in your research topic?
My path to academic science was non-traditional. I earned my bachelor’s degree in English literature, and while working in journalism in Los Angeles after college, I took math and physics courses at Glendale Community College. I eventually enrolled in the master’s program in the physics department at California State University, Los Angeles, where I studied physics and did research with condensed matter theorist Ed Rezayi. The Cal State LA MORE Programs funded my master’s degree and research and helped me apply to PhD programs. Mentors like Carlos Gutierrez, Ximena Hernandez, Vicki Kubo-Anderson, and Krishna Foster were passionate and inspiring leaders and taught me invaluable lessons.
After finishing my master’s degree, I did my PhD in physics at MIT, where I joined the lab of Jeff Gore. Jeff taught a Systems Biology class, cross-listed in both physics and biology departments, which was my introduction to using models and simulations to answer questions in biology. I was excited to learn mathematical descriptions of everything from genetic circuits to multi-species communities. I joined Jeff’s lab to study how changing environments alter stable states in bacterial communities. Ecological communities are many-body systems, similar to the ones I studied in condensed matter physics, but they exhibit an added layer of complexity because members can alter their behavior in response to each other.
For my postdoc, I wanted to learn about molecular biology, genetics, and evolution. I joined Dmitri Petrov’s lab here at Stanford to work on a yeast model system with DNA barcoding. In this system, half a million short random barcode sequences are inserted into a neutral region of the genome in an otherwise genetically identical population. This allows us to track adaptive mutants as they emerge during evolution, because the barcode associated with a mutant lineage increases in frequency. We can then isolate barcoded mutants and do pooled fitness measurements, as well as map fitness to its molecular basis with whole-genome sequencing. These high-throughput tools give me insight into biological mechanisms governing population dynamics.
What are you hoping to achieve with your research? What keeps you interested and motivated to continue diving deeper into your topic?
Microbial communities are crucial to life on Earth, but predicting how they will respond to environmental change, such as higher temperature and altered nutrient and osmotic concentrations, is difficult. While we tend to think of the environment as playing a direct role in selecting for particular species, it also indirectly alters the multitude of interactions between species. To gain insights, we need high-resolution and high-throughput experimental tools to gather statistics on community dynamics, and mathematical analysis to convert these statistics to rules.
Combining the tools I’ve used in my PhD and postdoc will allow me to understand how ecology and evolution feed into each other in communities. DNA barcoding will illuminate how evolution unfolds in a community, and how it depends on environmental factors such as stressors or other community members. This research will address fundamental questions in evolutionary ecology—how is the evolution of one species affected by the presence of another? Does evolution maintain or destabilize ecological coexistence? Finally, which types of ecological interactions are most robust to evolution—positive or negative interactions? DNA barcoding will allow me to address these broad questions with a degree of resolution and throughput that has only recently become possible.
Clare was in the 2020-21 cohort of CEHG Fellows. Applications to be part of the 2024-25 cohort are open now through April 26th — head to the program page for more information!
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