Kyla Martin, FCLC 2026

Major: Natural Science (Organismal Biology)

Bio: Kyla Martin is a senior in the Honors Program at Fordham College Lincoln Center, majoring in Natural Science with a concentration in Organismal Biology and a minor in American Studies. She is passionate about pursuing a career in research and medicine to advocate for marginalized communities. With a strong interest in osteopathic medicine, she plans to pursue medical school. In her spare time, Kyla enjoys reading classical literature, including social commentary that explores the human condition

Title of Research: Molecular and Genetic Approaches to Studying the Intracellular Domain of Drosophila Presenilin

Mentor: Jason Morris, Ph.D, Natural Sciences

Research Partner: Tucker Flynn

Abstract: Presenilin is the most often mutated gene in Familial Alzheimer’s Disease (FAD). This work establishes tools to study presenilin using Drosophila as an effective model organism. Mutations in human presenilin (PSEN) can cause familial Alzheimer’s disease by disrupting γ-secretase activity, but the mechanism by which it does so remains unclear, as does the role of specific protein regions. 

Drosophila Presenilin (Psn) has several varying lengths due to alternative splicing of exon 7. This region, exon 7, corresponds to a large cytoplasmic loop in the protein. There is a strong sequence similarity between the Drosophila and human sequences, except for exon 7. We identified two molecular approaches and one behavioral approach to study the structure and function of this region. We designed a plasmid encoding a guide RNA to generate an early stop codon in the long splice forms of Psn. Using Drosophila larvae with common pathogenic Psn mutations, we conducted a locomotor assay to determine whether this method can be used to analyze allele severity. Based on our preliminary results from the behavioral crawling assay, we suspect that WT larvae will crawl farther than Psn mutants. We require larger sample sizes and tightly timed egg lays to obtain statistically significant data.