Back

Tracing the evolutionary history of Tunisian HPV 16 and 18 across the genetic diversity in L1 gene

Ardhaoui, M.; Dekhil, N.; Bouslama, Z.; Bel Hadj Rhouma, R.; Fehri, E.; Ennaifer, E.; Guizani, I.

2024-02-19 evolutionary biology
10.1101/2024.02.15.580472 bioRxiv
Show abstract

HPV16 and HPV18 are the most prevalent high risk HPV types, recognized to be HPV vaccine target. Currently, available vaccines against HPV infections are based on virus-like particles (VLPs) derived from the L1 protein. Understanding the emergence of HPV isolates and exploring the genetic diversity of their L1 gene is crucial to assess the effectiveness of vaccine introduction in different populations. This is the first study aiming to investigate the evolutionary dynamics of HPV16 and HPV18 variants circulating in Tunisia. We constructed evolutionary history of HPV16 and HPV18 based on partial L1 sequence dataset of 733 Tunisian and worldwide representative isolates. Phylogeographic analysis confirmed the European origin of Tunisian strains that have emerged in 90s. Strikingly, four nonsense mutations in HPV16 and one in HPV18 within the HI surface loop, a critical immunogenic region were identified. Moreover, the latter showed an excess of non-synonymous over synonymous substitutions, a hallmark of local adaptation. By elucidating the genetic variability in the L1 gene, our study unveils several new features that pose challenges to the ongoing fight against HPV. These insights underscore the importance of continuous surveillance and adaptation of vaccination strategies to address evolving viral dynamics effectively. ImportanceHuman Papillomavirus (HPV) is a virus known to cause cervical cancer (CC). HPV16 and HPV18 are the most common HPV types related to CC. This study aimed to trace the evolutionary history of Tunisian HPV16 and HPV18 isolates. Phylogeographic analysis showed that these viruses were originated from Europe and emerged in 1990s. Several genetic changes have been identified in the L1 gene of HPV16 & 18 that could potentially reduce the effectiveness of HPV vaccines. By understanding these changes and addressing the local adaptation of HPVs, we could prevent the spread of this virus within a population.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.