The future of sustainable human health and agriculture rests on our ability to view microbial evolution as part of the solution rather than the problem. The Koskella lab takes on this challenge using a discovery-based research philosophy to identify patterns in nature that help generate predictions that we can then test in the laboratory with experimental evolution and controlled studies. We focus primarily on include how plant-microbiome, plant-pathogen, and bacteria-phage interactions occur, both as model systems for understanding fundamental principles and with the aim of leveraging these findings for design of novel disease management strategies. We integrate ecological and evolutionary thinking with cutting-edge microbiological and molecular approaches to gain insight to microbiome establishment and function, within-microbiome interactions, and the role that microbiota and phages play in shaping disease. For more information on ongoing research projects, look here.
Koskella lab December 2017, on the UC Berkeley campus
PI: Britt Koskella
I am an evolutionary ecologist interested in how species interactions influence genetic diversity within populations, diversity between populations, and species diversity at the community level. By combining evolutionary theory on coevolution, population dynamics, and infection genetics, I directly test the underlying assumptions and predicted outcomes of host-pathogen and microbial interactions through the lens of human health and the importance of agricultural sustainability.
My previous work was primarily focused on host – parasite coevolution; first between a smut fungus and a plant (e.g. how pathogen relatedness affects the outcome of coinfection), then a trematode and a snail (e.g. whether parasites mediate negative frequency dependent selection), and most recently between bacteriophage viruses and the bacteria they infect, which are themselves parasites of plants (e.g. if phages are locally adapted to bacteria, and coevolving with bacterial hosts over time). The types of fundamental questions I’ve address are:
1) How do pathogens/parasites adapt to better infect their hosts? And at what scale / speed are they capable of adapting?
2) How do hosts respond to this adaptation (i.e. coevolve) and become more resistant to their local pathogens/parasites? And are they paying a significant cost for this evolved resistance?
3) Can antagonistic coevolution lead to increased diversity? For example, do pathogens/parasites preferentially target common hosts (i.e. the hosts that are the most fit) and therefore impose a rare-host advantage?
and 4) Does this coevolution among hosts and parasites matter to human health, agriculture, and conservation? Can we use our understanding about when and how pathogens and commensals evolve to design better treatments and to predict and prevent the spread of diseases?
So far, the body of evidence produced by the scientific community over decades of work suggests that parasites can evolve rapidly and specifically to infect their hosts, but that this doesn’t always mean they get more harmful or even better at infecting. There is also clear evidence that hosts respond by evolving increased resistance, and that this often comes at a cost such as decreased growth or reproduction. This ongoing coevolution, in which neither parasite nor host is winning the battle has been called ‘Red Queen’ dynamics, based on the idea from “Alice Through the Looking Glass” that you have to run as fast as you can just to stay in the same place. At last these ideas are beginning to be incorporated into the design and use of drug treatments (sometimes referred to as Darwinian Medicine) and used to build predictive models of disease spread in our increasingly-connected world. My work has taken advantage of a very powerful approach to address these questions by experimentally evolving hosts and parasites in the laboratory, therefore controlling for all the other “noise” out there in nature (Brockhurst & Koskella 2013). However, I always try to go back to nature’s microcosm to find out whether what holds true in the lab can be used to explain what we see in the real world.