Selection pressure analysis of dengue virus complete genome and E gene nucleotide sequences from Pakistan
Creators: name: Mumtaz, Zilwa
name: Zia, Saeeda
name: Saif, Rashid
name: Farhan Ul Haque, Muhammad
name: Yousaf, Muhammad Zubair
Publisher: name: Zenodo
Year: 2024
Subjects: Evolutionary biology
Computational biology
Site selection
Dengue virus

description: This dataset comprises 43 E gene and 44 complete genome nucleotide sequences of the dengue virus from serotypes DENV-1 to DENV-4, representing all documented sequences in Pakistan to date, sourced from the Virus Pathogen Resource (ViPR) database and NCBI. The E gene is critical as it is involved in serotype changes of the dengue virus, making it a pivotal target for understanding shifts in viral pathogenicity and immune escape mechanisms. The aim of compiling this dataset is to facilitate comprehensive genetic analysis and enhance understanding of the evolutionary dynamics of the dengue virus within the region. To assess the evolutionary pressures acting on these sequences, we conducted a selection pressure analysis utilizing computational methods. These methods include the Single Likelihood Ancestor Counting (SLAC), Fixed Effects Likelihood (FEL), adaptive Branch Site Random Effects Likelihood (aBSREL), Mixed Effects Model of Evolution (MEME), and the Genetic Algorithm for Recombination Detection (GARD), all implemented in the HyPhy software package. Our analysis focused on identifying genomic sites under both positive and negative selection pressures, providing insights into the adaptive evolutionary processes affecting the E gene of the dengue virus in Pakistan. Understanding the molecular evolution of this gene is crucial for predicting serotype evolution, potentially aiding in the development of effective vaccines and therapeutic strategies.
description: Funding provided by: Forman Christian CollegeROR ID: https://ror.org/04v893f23Award Number: IRIF-17
description: Data Collection: The dataset consists of all documented Pakistani dengue virus E gene and complete genome nucleotide sequences available as of the latest update. These sequences were acquired from the Virus Pathogen Resource (ViPR)  and database, which is a comprehensive and freely accessible resource providing sequence data and related information on viral pathogens. The selected sequences span all four serotypes of the dengue virus, specifically DENV-1, DENV-2, DENV-3, and DENV-4, encompassing a total of 43 E gene sequences.

Data Processing and Analysis: Upon collection, the sequences were subjected to several preprocessing steps to ensure data integrity and uniformity. Initially, sequence alignment for the 43 E gene sequences was performed using MEGA-X, a robust tool for aligning nucleotide sequences and conducting phylogenetic analyses. This was followed by further alignment using MUSCLE (Multiple Sequence Comparison by Log-Expectation), which creates high-quality alignments of nucleotide sequences. The aligned sequences were then analyzed to detect any potential recombination events using the Genetic Algorithm for Recombination Detection (GARD) to ensure that subsequent analyses on evolutionary pressures were not confounded by these genetic events.

Following preprocessing, the dataset underwent a comprehensive selection pressure analysis to identify the evolutionary forces acting on the E gene. This analysis employed a suite of methods implemented in the HyPhy software package:




Single Likelihood Ancestor Counting (SLAC): Used to infer site-by-site selection pressures, estimating synonymous and non-synonymous substitution rates to identify negatively selected sites.


Fixed Effects Likelihood (FEL): Applied to detect both pervasive positive and negative selection across the phylogeny.


Adaptive Branch Site Random Effects Likelihood (aBSREL): Utilized to test for episodic diversifying selection on different branches of the phylogenetic tree.


Mixed Effects Model of Evolution (MEME): Employed to detect sites undergoing episodic positive selection that might not be pervasive throughout the tree.


Genetic Algorithm for Recombination Detection (GARD): Used in the initial processing stage to analyze and adjust for the presence of recombination within the dataset, which can significantly affect the accuracy of phylogenetic inference and subsequent selection pressure analyses.


These analyses provided insights into both positive and negative selection pressures on the E gene, highlighting the evolutionary trends that could influence serotype changes and viral adaptability.