Tag: Mike Harms

Determining How S100A9 Activates TLR4 Using Evolutionary and Biochemical Approach

Presenter: Jiayi Yin – Biochemistry

Faculty Mentor(s): Mike Harms, Sophia Phillips

Session: (In-Person) Poster Presentation

The immune system activates inflammation in response to both foreign pathogens and internal damage. Dysregulated inflammation can lead to many chronic diseases such as arthritis, inflammatory bowel disease, and some cancers. S100A9, a protein expressed in immune cells, has been found in high concentration in inflamed tissue of many of these chronic diseases. S100A9 strongly activates TLR4, a proinflammatory receptor, and thus activates pathological inflammation. Understanding how S100A9 interacts with TLR4 would be useful to create therapeutics to treat these diseases. My project is to use evolutionary and biochemical techniques to find out what sequence changes to S100A9 were important in its evolutionary history that led to greater proinflammatory activity. I will continue to characterize modern mammalian S100A9s that diverged more distantly from humans such as koala, platypus, and echidna, using recombinant protein expression and purification of S100A9 proteins from Escherichia coli followed by functional assays in human embryonic kidney cells. I will also couple these studies with further characterization of how TLR4 specificity and activity for endotoxin, the pathogenic ligand for which TLR4 evolved to recognize, changed in different species. These data will help us understand how the host protein S100A9 evolved inflammatory activity, and how TLR4 evolved to activate with a variety of ligands.

Can we Predict the Evolution of Protein Function

Presenter(s): Genevieve Dorrell − Biology, Computer Science

Co Presenter(s): Daria Wonderlick

Faculty Mentor(s): Mike Harms, Anneliese Morrison

Poster 11

Research Area: Evolutionary Biophyisics

Proteins evolve new functions by acquiring mutations. Understanding this process is critical to combating antibiotic and pesticide resistance. Studies have shown that the effects of mutations alone versus in combination are not always equal. This is called epistasis, and it impedes our ability to predict protein evolution. Our project aims to understand one biophysical source of epistasis. Previous theoretical work in the Harms lab revealed that epistasis could arise from the existence of multiple conformations of a protein. To probe this, we are manipulating the number of conformations available to the lac repressor protein. We are using drugs that shift the lac repressor into either its DNA-bound or DNA-unbound conformation. We perform a colorimetric assay to quantitatively detect which conformations are favored by lac repressor mutants, and then infer epistasis between mutations on these functional readouts. If we limit the number of conformations a protein can adopt and see a proportional change in epistasis, we will have evidence to support that epistasis arises from this intrinsic biophysical of property of proteins.

Ensembles link RNA thermodynamics and molecular evolution

Presenter(s): Daria Wonderlick—Biochemistry

Faculty Mentor(s): Mike Harms

Session 5: The Bonds that Make Us

Designing better biomolecules is a long-standing goal for biochemists . Doing so requires a rigorous understanding of how the sequence of a biomolecule determines its properties . Sequence changes, known as mutations, alter these properties and drive the natural evolutionary process . If we can accurately predict how mutations impact biomolecular properties, we can engineer novel biomolecules for applications in medicine, energy, and technology . Predicting a mutational effect is challenging, however, because the effect often depends on the presence of other mutations . Previous work in the Harms lab suggests that some of these mutational interactions emerge from a thermodynamic property of biomolecules—the ensemble . A biomolecule’s ensemble is the collection of interchanging structures it can adopt . A mutation may impact any structure in the ensemble, and its effect arises from perturbations to the relative populations of these structures . Mutations will have different effects depending on the degree to which other mutations have redistributed the ensemble . To mechanistically understand how the ensemble mediates mutational interactions, I am characterizing the effects of five mutations alone and in combination on a magnesium- and adenine-binding RNA molecule with a simple four-structure ensemble . By measuring the amount of a fluorescent adenine analog bound in the presence of varying magnesium concentrations, I can detect the effect of mutations on each of the four structures in this ensemble . The simplicity of this system will provide detailed mechanistic insight into the relationship between ensembles and mutations that can be used to improve the mutational predictions required for successful biomolecule design .