Postdoc Jennifer Chen is exploring the evolution of innate behaviors in closely related species of deer mice. She conducts research jointly in the Hoekstra Lab, which specializes in deer mouse behavioral evolution, and the Eddy Lab, which focuses on computational biology. Chen says her research approaches the question of how genes shape parental care behaviors “from an omics-y standpoint.” She is using single-cell sequencing to investigate how changes in neuronal cell types contribute to evolving behaviors.
“Although Jenny didn’t do many experiments during her Ph.D. in computational genomics, she has proven to be a skilled experimentalist,” says MCB and OEB faculty Hopi Hoekstra. “She thoughtfully designs her experiments, runs informative pilot studies, and then performs carefully controlled experiments (informed by her statistical background). It was a big move from computation to experiments, and Jenny has transitioned seamlessly.”
Chen entered her undergraduate years at Stanford as a premed but found that much of her biology courses hinged on rote memorization. Dissatisfied, she branched out into classes in other fields and found that she really enjoyed computer science. At the time, Stanford was one of the few undergraduate institutions that offered a major in computational biology, so Chen was able to integrate her previous biology coursework into her new major. Her senior thesis focused on the genomics of gene regulatory elements, called transcription factors, and where they bind to DNA.
After a master’s year at Stanford, Chen embarked on her Ph.D. research at MIT, joining Aviv Regev’s lab at the Broad Institute. Her Ph.D. work took full advantage of RNA-sequencing technology to investigate how gene expression evolves in organisms across long timescales. Chen compares the detective work involved in reconstructing ancient gene expression patterns to looking at contemporary rock formations and working out what the landscape looked like in the past. “When I went to Monument Valley, I learned that the formations were created because wind and water eroded away the softer rock but the harder rock was left standing,” Chen says. “Just like we can infer the properties of the rocks by looking at the formations, we can compare genetic sequences across living species and use the patterns of evolving or conserved sequences to infer properties of genes.”
Chen’s thesis work ultimately supported a previous theory about how selective pressure should nudge gene expression toward an equilibrium state. “Even before gene sequencing data, theory hypothesized that if you look at gene expression changes across large timescales, the changes should follow what’s known as a power law,” Chen explains. “Expression of genes should go up or down randomly but eventually if the expression becomes too high or too low, it disrupts correct functioning of the gene, so the changes should plateau at some point. It was amazing because we found that our empirical data, collected across 17 mammalian species, matched the theoretically predicted patterns exactly.”
While at MIT, Chen attended a lecture by Hoekstra and was intrigued by the research presented. However, she was also interested in possibly doing a postdoc with MCB faculty Sean Eddy, whose textbook she remembers fondly from her undergraduate days. Eventually, she decided to do a joint postdoc in both labs, bringing her background in comparative genomics to studying behavioral evolution in deer mice.
Chen says that spending time in both an evolutionary biology lab and a computational biology lab is the best of both worlds, offering opportunities to discuss research questions from many angles from talking through how to measure naturalistic behaviors in laboratory settings to thinking about how new sequencing technology could be utilized to investigate deer mouse evolution.
Despite being a computational genomicist, Chen has been doing hands-on experiments interbreeding a species of mouse that is sexually monogamous and a closely-related species with a more promiscuous mating pattern. She has learned to predict when mouse pups will be born based on what the parent mice are doing. Breeding pairs of monogamous mice nest together, but promiscuous mice live apart. However, the hybrid mice nest together until it’s time for the pups to be born. “Then the mom kicks the dad out of the nest, and he has to make a little ‘bachelor pad,’” Chen explains. So when she sees the male mice making those mini-nests, she knows that pups are en route.
Chen has been using single cell RNA-sequencing to identify differences in specific neuronal cell types in the two species with distinct mating and parenting patterns. For example, she has found that the promiscuous species has many more neurons producing vasopressin, a gene that the Hoekstra Lab has previously implicated as important in parental nesting behavior. She is currently following up on the finding to understand how the changes in vasopressin neuron numbers came about and how it leads to differences in parental care.
Based on the strength of her research, Chen was recently awarded a MOSAIC Postdoctoral Career Transition Award to Promote Diversity. The award is part of the NIH’s K99/R00 program that provides up to two years of postdoctoral funding and up to three years of funding for research conducted as a new primary investigator. Chen will also be able to participate in a network of early career scientists and career development events, including activities directed at increasing diversity in the biomedical sciences. Chen says,”I am really excited to take part in this new program that is focused on creating a cohort of young faculty that not only do excellent scientific research but also promote inclusive and supportive research environments for the future generation of scientists.”
Her advisor Sean Eddy agrees, saying, “Jenny is pursuing a brave and ambitious project that bridges my lab and Hopi’s, applying her previous computational genomics Ph.D. training while learning behavioral neuroscience in deer mice. Receiving the K99 is one more milestone and well-deserved recognition of the successful interdisciplinary path she’s building for herself. She’s a pioneer in this new kind of biological science that needs to be so deeply rooted in both computational and experimental science at the same time.”
by Diana Crow
