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Published on: October 21, 2014
Clusteron structure of tick-borne encephalitis virus populations
Sergey Y Kovalev1, Tatyana A Mukhacheva
1Laboratory of Molecular Genetics, Department of Biology, Ural Federal University, Lenin Avenue 51, Yekaterinburg 620000, Russia. Sergey.Kovalev@usu.ru
Summary
This study introduces "clusterons" as the smallest unit for classifying tick-borne encephalitis virus (TBEV) strains. This new classification method helps characterize endemic areas and monitor TBEV populations effectively.
Area of Science:
- Virology
- Epidemiology
- Molecular Biology
Background:
- Tick-borne encephalitis (TBE) is a zoonotic disease caused by the tick-borne encephalitis virus (TBEV), a Flavivirus prevalent across Eurasia.
- Existing TBEV classification includes three subtypes (Far Eastern, European, Siberian), but further differentiation is limited, hindering research on virus origins and evolution.
- The Siberian subtype (TBEV-Sib) requires finer classification to understand its genetic diversity and geographical spread.
Purpose of the Study:
- To develop a more granular classification system for TBEV strains, specifically the Siberian subtype (TBEV-Sib).
- To investigate the phylogenetic relationships and geographical distribution patterns of TBEV-Sib isolates.
- To establish a new smallest unit of TBEV classification for improved population monitoring.
Main Methods:
- Sequencing of the glycoprotein E gene fragment from 282 TBEV-Sib isolates collected from Ixodes persulcatus ticks in Russia.
- Comparative analysis of obtained sequences with over 600 TBEV sequences from the GenBank database.
- Clustering of TBEV-Sib strains based on identical amino acid sequences of the glycoprotein E fragment.
Main Results:
- Identified 18 distinct groups (clusterons) of TBEV-Sib strains, ranging from 3 to 285 isolates per group.
- Demonstrated that strains within the same clusteron exhibit phylogenetic relatedness and specific territorial distribution patterns (local or corridor).
- Established clusterons as the smallest, most effective unit for TBEV classification.
Conclusions:
- The proposed clusteron-based classification effectively groups TBEV-Sib strains based on genetic and geographical data.
- This approach enables detailed characterization of endemic regions by analyzing the quantitative and qualitative composition of clusterons.
- Clusteron analysis provides a robust method for recording and monitoring TBEV populations, aiding epidemiological surveillance.
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