Poster #52 - Caitlyn Johnson

Identifying Adaptive Mutations in HPV 16 Using Novel c/µ Method

Caitlyn Johnson, Wu Chun* *Department of Chemistry and Biochemistry & Department of Biological & Biomedical Sciences, Rowan University, Glassboro, NJ 08028, USA

Human papillomavirus (HPV) is responsible for approximately 70% of cervical cancer cases worldwide, with HPV16 alone accounting for nearly half of cases. Cervical cancer is the 4th most common cancer among women, with an estimated 348,709 deaths worldwide in 2022 alone. Despite the availability of the current vaccines and drugs for this virus, HPV remains a global health concern since they are not fighting the viral variants. This makes it critical to understand the molecular evolution of the virus to improve vaccine/drug efficacy. In this study, we applied our substitution-mutation rate ratio (c/µ), where µ is estimated to explain HPV 16's evolution using empirical sequence data. This is a method we have previously validated using SARS-CoV-2 and ZIKV sequence data. Our analysis showed that few mutations are neutral; rather, most mutations have a slight effect on the virus, with some being beneficial and some being deleterious. This tends to follow the Near-Neutral Selectionist Theory (NNST). The NNST suggests that there is a balance of nearly neutral mutations that contribute to the formation of the molecular clock. Differing from previously studied theories, such as Selectionist Theory (ST), Kimura's Neutral Theory (KNT), and Ohta's Nearly-Neutral Theory (ONNT), which suggest natural selection/strictly neutral mutations drive evolution. The most significant mutations identified (c/µ > 1) were concentrated within the E2 regulatory protein, the L1 capsid protein, and the E5 immune evasion protein, consistent with literature-reported adaptive and deleterious effects. Previous studies have shown that mutations in these regions can enhance expression of the E6/E7 oncoproteins and contribute to oncogenesis and overall viral survival. While current therapeutic strategies primarily target E6 and E7, proteins E2, L1, and E5 represent additional drug targets. These findings further support the c/µ test in probing viral evolution and in explaining the genotype-to-phenotype relationship.