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Viral Fitness Landscapes Based on Self-organizing Maps
M Soledad Delgado1, Cecilio López-Galíndez2, Federico Moran3
1Departamento de Sistemas Informáticos, Escuela Técnica Superior de Ingeniería de Sistemas Informáticos (ETSISI), Universidad Politécnica de Madrid, 28031, Madrid, Spain. mariasoledad.delgado@upm.es.
Current Topics in Microbiology and Immunology
|January 2, 2023
Summary
This study introduces Self-Organized Maps (SOM) to visualize complex fitness landscapes for RNA viruses like HIV-1 and HCV. This novel approach effectively classifies viral quasispecies and maps evolutionary dynamics.
Area of Science:
- Virology
- Computational Biology
- Genetics
Background:
- RNA viruses like HIV-1 and HCV exhibit high mutation rates, generating complex quasispecies populations.
- Mapping viral fitness landscapes is challenging due to the high dimensionality of mutant sequence spaces.
Purpose of the Study:
- To develop and apply a novel computational approach for visualizing and analyzing viral fitness landscapes.
- To utilize Self-Organized Maps (SOM) for classifying viral quasispecies and understanding evolutionary dynamics.
Main Methods:
- Application of Self-Organized Maps (SOM), a type of neural network, to model fitness landscapes.
- Experimental determination of fitness for Human Immunodeficiency Virus type 1 (HIV-1) variants.
- Ultra-deep sequencing to measure haplotype efficiency for Hepatitis C Virus (HCV) populations.
Main Results:
- SOM effectively classified viral quasispecies based on mutant sequences.
- Visualized evolutionary paths of HIV-1 variants undergoing fitness changes.
- Quantified the efficiency of HCV variants within the quasispecies environment relative to other mutants.
Conclusions:
- Self-Organized Maps provide a powerful tool for representing complex viral fitness landscapes.
- This methodology enables the analysis of evolutionary dynamics and variant efficiency in RNA virus populations.
- The approach is effective for both HIV-1 and HCV, offering insights into viral evolution.
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