# Genetics of adaptation to toxic environments

> **NIH NIH K99** · STANFORD UNIVERSITY · 2021 · $100,000

## Abstract

SUMMARY
Resistance to toxins often evolves via major effect mutations in the target protein. Theory predicts that these
major effect mutations carry fitness costs, which are subsequently resolved or ameliorated via compensatory
mutations. Yet, characterization of compensatory mutations has remained elusive because their testing has
been hampered by a lack of tools. With the advent of genome sequencing and genome engineering, we can
now identify candidate compensatory mutations and rigorously test them in vivo. Here, I propose to use
Drosophila subobscura as a new model system to fully characterize compensatory mutations ameliorating
neuronal costs of toxin resistance mutations in ATPα (α-subunit of the Na+, K+-ATPase). I will adopt both a
candidate-based and an unbiased genome-wide approach to characterize compensatory mutations, which
should allow me gain a complete view of compensatory evolution in Drosophila subobscura. During the K99
phase (first and second aims), I will adopt the candidate-based approach to explore compensatory mutations
within the resistant ATPα alleles of D. subobscura. Under the first aim, I will employ a population genomic
approach to identify candidate compensatory mutations within the resistant ATPα alleles. Under the second
aim, I will employ a genome engineering approach to characterize the candidate compensatory mutations from
the first aim by editing them together with the resistance mutations in the sensitive ATPα allele of D.
melanogaster. During the R00 phase (third and fourth aims), I will adopt the unbiased genome-wide approach
to explore compensatory mutations outside the resistant ATPα alleles of D. subobscura (“modifier locus”).
Under the third aim, I will employ a genome engineering approach to introduce the resistant ATPα alleles of D.
subobscura in a sensitive D. subobscura background to first validate the presence of a “modifier locus”. Under
the fourth aim, I will use gene mapping to characterize the modifier locus. The collective results of this project
will provide a detailed picture where in the genome compensatory mutations evolve and in which order they
evolve relative to the the resistance mutations.
A K99 Award would allow me to receive training in population genomics and sequencing technologies and
provide time to extend my expertise in genome engineering and theory on the genetics of adaptation to toxic
environments. The training phase of this proposal will be performed in the laboratory of Dr. Dmitri Petrov,
which is ideal for exchanging exciting idea and learning population genomics as he is a leader in the
Drosophila population genomics of adaptation. Stanford and the the Department of Biology provide an
excellent environment and facilities for the proposed work. My long term career goal is to establish my
independent academic research laboratory where I will study the genetics of adaptation to toxic environments.

## Key facts

- **NIH application ID:** 10283620
- **Project number:** 1K99GM143455-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Marianthi Karageorgi
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $100,000
- **Award type:** 1
- **Project period:** 2021-09-09 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10283620

## Citation

> US National Institutes of Health, RePORTER application 10283620, Genetics of adaptation to toxic environments (1K99GM143455-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10283620. Licensed CC0.

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