Research

Develop high-throughput genomic technologies to systematically characterize how mutation processes vary and evolve in natural populations of budding yeast.

I have developed a modified fluctuation assay during my postdoc (Jiang et al 2021. eLife, Jiang et al 2022. Bioprotocol) which can assay both mutation rate and spectra using CAN1 as the reporter gene across different haploid strain backgrounds. Moving forward, I am developing high-throughput genomic technologies with long-read sequencing to profile mutations more efficiently. This can be combined with strain barcoding to obtain mutation spectra from a pooled assay, saving time and costs. This technology can be applied to further explore mutation spectra under different environmental conditions, revealing genotype by environment interactions. This will lead to a more comprehensive view of mutation variation among natural isolates.

Currently, I am exploring an hypothesis from natural polymorphism that some French dairy strains could have inherited mutator allele(s) via gene flow from African beer strains and I am assaying de novo mutations from these groups to test this hypothesis. I will continue carrying out research which combines both data analysis and experimental validation to figure out the natural history of mutator alleles.

Other projects related to yeast mutation spectra I am working on include exploring mutation rate and spectra changes related to aging. This work is ongoing and in collaboration with Dr. Joe Armstrong in Maitreya Dunham’s lab using magnetic labeled mother cells with chemostat.

Identify germline mutator alleles in fruit fly using recombinant inbred lines.

Inspired by the successful identification of a mutator allele from mouse inbred lines by Dr. Sasani in the Harris lab, and with previous studies indicating that certain fly lines can show a difference in the mutation rate, we plan to map mutator alleles using Drosophila inbred lines. This work is in collaboration with Dr. Tony Long at UC Irvine, Stuart MacDonald at University of Kansas, and the Bloomington Drosophila Stock Center.

By performing whole-genome sequencing of a relatively large number of recombinant inbred lines, we will identify variants that are private to a single inbred line that have arisen and fixed during the regular propagation. This design saves time and money from performing extra mutation accumulation experiments since maintaining fly stocks is part of the regular husbandry and a large number of mutations should have fixed with establishment over 10 years ago. Furthermore, the recombinant inbred lines can be used for mapping genetic loci that are responsible for mutation rate variation. This line of research can extend our understanding of mutator allele variation to the germline of a multi-cellular organism, which can shed light on the general process of mutator allele evolution. Moreover, it can open doors for comparative studies of somatic mutation versus germline mutation for multi-cellular organisms.

Develop a mechanistic-inspired model to explore how natural mutator alleles could affect phenotypic evolution.

Ultimately, mutations may affect phenotypes and fitness. Although there is extensive theoretical modeling of the evolutionary process of mutator alleles, these models are not based on real data and have simplified assumptions. For example, population genetic models usually ignore the entire developmental process, which can miss important constraints. My goal is to connect the discovery of mutator alleles from experiments and data analysis to construct a realistic model of phenotypic evolution which incorporates mutation rate and spectra variation, as well as a phenotype of interest. I will utilize existing data from deep mutational scan experiments to capture the genotype to phenotype map, and then develop a model capturing the effects of mutator alleles in order to predict the effect of mutators on phenotypic evolution.