URCAD Selfie Contest: Bonus Category!

Tue, 22 Apr 2025 15:28:44 -0400

We had to create an additional category to honor the UMBC lab that collaborated with Georgetown University students on computational
approaches to annotate the human genome with the goal of revealing the molecular underpinning of human diseases.

L to R: Godwin Aizenofe, Michelle Jebaseelan (main presenter), M Kann, Petra Tembei (from the Kann lab but presenting another poster), Elias Azar (Georgetown University). Not pictured: Idrees Chaudry.

G-town students came all the way to UMBC for URCAD '25!

Peace!

Analyzing The Impact Of Wildfires On Human Lung Cancer Through Molecular Homolog Identification In Drosophila

Michelle Jebaseelan, Idrees Chaudry, Godwin Aizenofe
Mentor: Maricel Kann, Bioinformatics and Computational Biology

Exposure to wildfires has been linked to lung deficiencies, including lung cancer, due to air pollutants inducing DNA mutations. To better understand the genetic pressures wildfires cause toward lung cancer, we utilize a study of protein expressions in drosophila flies exposed to controlled smoke conditions. These controlled conditions in drosophila provide grounds for studying orthologous relationships caused by genetic pressures. Observing putative orthologs in drosophila in comparison with non-smoking lung cancer patients helps identify functional driver genes for lung cancer. Specifically, this comparative study analyzes functional genes at three stages: (1) Identifying orthologs of the mutated genes in the Drosophila flies within the Human genome, (2) comparing the mutation positions to determine if any mutational homologs are present, and (3) examining the conserved interactions in orthologs, called interologs, to determine if protein-protein interactions are conserved between the two species. Comprehension of how protein-protein interactions are altered from wildfire-induced mutations can aid in discovering therapeutic targets and establishing more robust lung cancer treatments. Additionally, this comparative model can be scaled to incorporate analysis of other model organisms and provide insights into different diseases.