TBEV NE

What is the taxonomic classification of TBEV and how many subtypes are currently recognized?

TBEV is a positive-sense, single-stranded RNA virus belonging to the genus Flavivirus within the family Flaviviridae. While traditionally classified into three main subtypes (European, Siberian, and Far-Eastern), recent genomic analyses suggest a more complex classification. Current research, based on phylogenetic analyses of over 220 complete genomes, indicates additional subtypes and lineages that exhibit substantial differences, particularly in their 3′ untranslated regions (3′UTRs) .

The major recognized TBEV subtypes/lineages include:

• European subtype (TBEV-Eur)

• Siberian subtype (TBEV-Sib)

• Far-Eastern subtype (TBEV-FE)

• Western European lineage

• Himalayan lineage (TBEV-Him)

• East-Siberian/Baikalean/886-84-like lineage (TBEV-Bkl-2)

• Strain 178-79 (TBEV-Bkl-1)

• Strain N5-17

Each of these lineages has distinct phylogenetic characteristics and geographic distributions, with estimated times of most recent common ancestor (TMRCA) varying significantly as shown in the table below :

What is the significance of the Neudoerfl strain in TBEV research?

The Neudoerfl strain serves as a reference strain for the European subtype of TBEV and has been instrumental in characterizing the genomic organization and functional elements of TBEV. Isolated in Austria, this strain is frequently used in comparative analyses with other TBEV isolates to understand structural and functional variations across subtypes .

The Neudoerfl strain has well-characterized 5′ and 3′ untranslated regions, with a complete genome sequence available for research purposes. The annotated 5′UTR of Neudoerfl has been particularly valuable in identifying conserved RNA structural elements that play critical roles in viral replication and host interactions .

In experimental settings, the Neudoerfl strain is often compared with other strains like Hypr to evaluate differences in neurotropism, virulence, and cellular responses to infection. These comparative studies provide insights into the molecular determinants of TBEV pathogenicity .

How do the genomic elements of TBEV contribute to its pathogenicity?

TBEV pathogenicity is influenced by both coding and non-coding regions of its genome. While amino acid variations between subtypes contribute to differences in virulence, the non-coding regions play crucial roles in viral replication, host immune evasion, and neurotropism .

The 5′UTR (approximately 130 nucleotides) contains evolutionarily conserved RNA structures required for replication through cyclization and panhandle formation. A specific cis-acting RNA element in the 5′UTR has been identified that mediates neurovirulence by hijacking the host mRNA transport system, allowing viral genomic RNA to be transported from the cell body to dendrites of neurons, where it replicates locally .

The 3′UTR (varying from 350 to 760 nucleotides in typical isolates) comprises a 5′-terminal variable region and a 3′-terminal core domain. This region contains RNA elements involved in immune escape and pathogenicity, with structural variations being associated with differential pathogenicity across subtypes .

The conservation of specific RNA structural elements despite sequence divergence suggests strong selective pressure to maintain these functional RNA structures, indicating their importance for viral fitness and pathogenicity .

How can researchers characterize the conserved RNA structural elements in TBEV untranslated regions?

To characterize conserved RNA structural elements in TBEV untranslated regions, researchers should employ a combination of computational and experimental approaches:

• Comparative genomics analysis: Utilize consensus structure prediction methods to identify RNA secondary structures that can be formed by all sequences under consideration. This approach has revealed that the TBEV 5′UTR contains three main structural elements: the SLA (stem-loop A) structure, a short hairpin (CSA), and another hairpin (CSB) that overlaps the AUG start codon .

• Covariation analysis: Examine patterns of compensatory mutations that maintain RNA structure despite sequence changes. This method has shown that among the three 5′UTR-associated RNA structural elements, SLA and CSB exhibit covariation patterns, while CSA is highly sequence-conserved .

• Thermodynamic modeling: Apply RNA folding algorithms to predict stable secondary structures based on free energy minimization. This approach has been used to characterize the TBEV UTR "structureome" diversity .

• Experimental validation: Verify predicted structures using techniques such as chemical probing (SHAPE analysis), enzymatic probing, or mutational analysis coupled with functional assays.

• Cross-species comparison: Compare structural elements with other tick-borne flaviviruses to identify evolutionarily conserved patterns that may indicate functional importance.

Research has identified eight distinct, evolutionarily conserved RNA elements in the 3′UTR of different TBEV subtypes. The arrangement of these non-coding RNAs varies at both inter- and intra-subtype levels, accounting for the diverse 3′UTR architectures observed in nature .

What methodologies are most effective for studying TBEV infection in neural cells?

Studying TBEV infection in neural cells requires specialized techniques to capture the neurotropic nature of the virus and its effects on neural function. Based on current research approaches, the following methodologies are particularly effective:

• Neural cell culture models: Differentiated neurons and astrocytes provide relevant cellular contexts for studying TBEV neurotropism. These can be primary cultures or derived from stem cells according to established protocols (as referenced in Table S1 of the second search result) .

• Viability assays: To assess the cytopathic effects of TBEV infection, researchers can employ fluorescence-based viability measurements using reagents such as AlamarBlue. The protocol typically involves:

  • Infecting differentiated neurons and astrocytes with a defined multiplicity of infection (MOI), e.g., 5 MOI

  • Adding AlamarBlue reagent (1:10 v/v) 2-3 hours prior to each sampling interval

  • Measuring fluorescence at specific wavelengths (e.g., λEx = 550 nm, λEm = 590 nm)

  • Normalizing readings to mock-treated cells (set at 100% viability)

  • Collecting data from multiple biological and technical replicates (e.g., four biological and two technical replicates)

• RNA profiling: Integrative RNA profiling through next-generation sequencing can reveal differential gene expression patterns in infected neural cells. This approach has been used to identify key factors involved in the higher susceptibility of neurons to TBEV compared to astrocytes .

• Small RNA sequencing: Analysis of small RNAs can provide insights into host-virus interactions and viral RNA processing. Libraries should be constructed and sequenced according to established protocols, with appropriate quality control measures (FastQC) and adapter trimming (Cutadapt) .

• Immunofluorescence microscopy: This technique allows visualization of viral protein expression and localization within neural cells, as well as assessment of cellular responses such as inflammatory marker expression.

• Electrophysiological recording: To assess the functional impact of TBEV infection on neurons, patch-clamp recording or multi-electrode arrays can be employed to measure changes in neuronal activity patterns.

How does vector specificity influence TBEV subtype distribution and evolution?

Vector specificity plays a critical role in shaping the geographic distribution and evolutionary trajectory of TBEV subtypes. The relationship between TBEV subtypes and their tick vectors is complex and exhibits both patterns of specialization and occasional host-switching events .

The Eastern subtypes (Far-Eastern, Siberian) are typically transmitted via Ixodes persulcatus ticks, while the Western subtypes (European) are usually transmitted by Ixodes ricinus ticks. This vector-virus association broadly determines the geographic range of different TBEV subtypes across Eurasia .

These exceptions suggest several important research considerations:

• Vector competence factors: Molecular determinants that enable TBEV replication in different tick species should be investigated through comparative genomics and experimental infection studies.

• Geographic overlap zones: Areas where different tick species coexist, such as Irkutsk Oblast which shows an accumulation of different subtypes (Western, East-Siberian, Siberian, Far-Eastern, and strain 178-79), represent natural laboratories for studying vector-virus adaptation and host-switching events .

• Co-evolution analysis: Phylogenetic analyses of TBEV subtypes and their tick vectors can reveal patterns of co-evolution and help predict future adaptation scenarios.

• Experimental vector competence studies: Laboratory infection experiments with different tick species and TBEV subtypes can clarify the molecular mechanisms underlying vector specificity.

Understanding vector-virus relationships is essential for predicting changes in TBEV distribution under climate change scenarios, which may alter the geographic ranges of tick vectors.

What approaches should be used for molecular epidemiology and phylodynamic analysis of TBEV?

Molecular epidemiology and phylodynamic analysis of TBEV require robust methodological approaches to understand the virus's evolution, spread, and population dynamics. Based on recent advances in the field, the following approaches are recommended:

How should researchers design experiments to compare neurotropism between different TBEV strains?

Designing experiments to compare neurotropism between different TBEV strains requires careful consideration of cellular models, infection parameters, and analytical techniques. Based on current research practices, the following experimental design strategies are recommended:

• Selection of neural cell models:

  • Use both neurons and astrocytes to capture differential susceptibility and responses

  • Consider primary neural cultures, stem cell-derived neural cells, and established neural cell lines

  • Include organotypic brain slice cultures or 3D brain organoids for more complex neural environments

  • Ensure standardized differentiation protocols to minimize experimental variability

• Infection parameters standardization:

  • Use consistent multiplicity of infection (MOI) across strains (e.g., 5 MOI as used in comparative studies of Hypr and Neudoerfl strains)

  • Standardize viral stock preparation and titration methods

  • Include multiple time points post-infection (e.g., 12, 24, 48, 72, 120, 168 h p.i.) to capture the full infection dynamics

• Viability assessment:

  • Employ fluorescence-based viability assays such as AlamarBlue

  • Normalize to mock-infected controls

  • Include multiple biological and technical replicates (e.g., four biological and two technical replicates)

  • Consider complementary approaches such as LDH release assays or TUNEL staining for apoptosis detection

• Viral replication kinetics:

  • Quantify viral RNA using RT-qPCR at multiple time points

  • Measure infectious virus production by plaque assays or TCID50

  • Compare replication rates between different neural cell types

• Transcriptomic analysis:

  • Perform RNA sequencing to identify differentially expressed genes between strains

  • Focus on neuroinflammatory responses, antiviral pathways, and cell death mechanisms

  • Consider small RNA sequencing to examine microRNA responses and viral RNA processing

• Functional assessments:

  • Evaluate neuronal function using electrophysiological recordings

  • Assess synaptic protein expression and localization

  • Measure neurotransmitter release and receptor expression

• Comparative analysis framework:

  • Develop clear metrics for quantifying neurotropism (e.g., infection efficiency, cell death rates, viral load)

  • Use statistical approaches that account for biological variability

  • Consider multivariate analysis to identify patterns across multiple parameters

• Controls and validations:

  • Include non-neurotropic control viruses

  • Validate key findings using multiple methodological approaches

  • Consider in vivo validation in appropriate animal models

By systematically comparing these parameters between different TBEV strains (such as Neudoerfl and Hypr), researchers can identify specific viral determinants of neurotropism and potentially develop targeted interventions.

How can researchers effectively analyze conserved RNA structures in TBEV genomes?

Analyzing conserved RNA structures in TBEV genomes requires specialized computational and experimental approaches. The following methodological framework is recommended for comprehensive structural analysis:

• Sequence alignment and conservation analysis:

  • Collect complete TBEV genome sequences representing all known subtypes

  • Perform multiple sequence alignments with tools optimized for RNA sequences

  • Identify regions of high sequence conservation, particularly in non-coding regions

  • Quantify sequence identity levels across subtypes (e.g., 89-95% for 5′UTR in different subtypes)

• Consensus structure prediction:

  • Apply algorithms that predict RNA secondary structures that can be formed by all sequences in an alignment

  • Identify structural elements that are maintained despite sequence variation

  • For TBEV 5′UTR, this approach has revealed a uniform organization across all subtypes and lineages, featuring three main structural elements: SLA, CSA, and CSB

• Covariation analysis:

  • Detect compensatory mutations that maintain base-pairing in RNA structures

  • Distinguish between sequence-conserved elements (e.g., CSA in 5′UTR) and structurally conserved elements with sequence variation (e.g., SLA and CSB showing covariation patterns)

• Thermodynamic modeling:

  • Use free energy minimization algorithms to predict stable RNA conformations

  • Compare predicted structures across subtypes to identify conserved structural motifs

  • Consider ensemble approaches that account for multiple possible conformations

• Structural annotation and visualization:

  • Annotate known functional elements based on experimental data

  • Create standardized structural nomenclature for comparative analysis

  • Develop visual representations that highlight structural conservation and diversity

• Functional correlation:

  • Link structural elements to known functions in viral replication cycle

  • Identify structure-function relationships through literature review and experimental data

  • For example, correlate the Y-shaped SLA structure with its role in panhandle formation and recruitment of viral RNA-dependent RNA polymerase

• Comparative analysis with other flaviviruses:

  • Extend structural analysis to related tick-borne flaviviruses

  • Identify RNA structural elements that are conserved across the genus

  • Use evolutionary distance to assess structural conservation pressure

• Experimental validation strategies:

  • Design experiments to validate predicted structures (e.g., SHAPE chemistry, enzymatic probing)

  • Create mutant viruses with disrupted structural elements to assess functional impact

  • Use compensatory mutations to restore structure but not sequence to confirm structure-function relationships

This integrated approach has successfully characterized the TBEV UTR "structureome" diversity and established a unified picture of pervasive non-coding RNA structure conservation across TBEV subtypes .

What are the best practices for interpreting phylogenetic data when analyzing TBEV evolution?

Interpreting phylogenetic data for TBEV evolution requires careful consideration of methodological limitations, evolutionary rate variations, and ecological contexts. The following best practices are recommended:

By applying these best practices, researchers can develop more robust interpretations of TBEV evolutionary history and make more accurate predictions about future patterns of viral spread and adaptation.

What emerging technologies show promise for advanced TBEV research?

Several emerging technologies have significant potential to advance TBEV research, providing new insights into viral biology, host-pathogen interactions, and potential therapeutic approaches:

• Single-cell RNA sequencing (scRNA-seq):

  • Enables characterization of cell-specific responses to TBEV infection

  • Can identify rare cell populations that may serve as viral reservoirs

  • Allows tracking of infection progression in heterogeneous neural populations

  • May reveal why neurons show higher susceptibility to TBEV compared to astrocytes at the single-cell level

• CRISPR-Cas systems for viral genomics:

  • Facilitates precise genome editing to study functional domains in TBEV

  • Enables creation of reporter viruses for real-time tracking of infection

  • Can be used to knock out host factors to identify essential interactions

  • Allows systematic mutagenesis of RNA structural elements to assess their functional roles

• Advanced RNA structure probing techniques:

  • SHAPE-MaP (Selective 2′-hydroxyl acylation analyzed by primer extension and mutational profiling) for high-throughput RNA structure determination

  • In-cell SHAPE to capture RNA structures in their native cellular environment

  • PARIS (Psoralen Analysis of RNA Interactions and Structures) to identify long-range RNA interactions

  • These methods could provide more detailed insights into the functional RNA structures in TBEV UTRs

• Brain organoid models:

  • Three-dimensional neural cultures that better recapitulate brain architecture

  • Allow study of TBEV neurotropism in a more physiologically relevant context

  • Enable assessment of virus spread between different neural cell types

  • Provide platforms for testing antiviral compounds with improved predictive value

• Cryo-electron microscopy (Cryo-EM):

  • Allows high-resolution structural determination of TBEV virions

  • Can reveal conformational changes in viral proteins during cell entry

  • Enables visualization of virus-antibody complexes for immunological studies

  • May identify structural targets for antiviral development

• Spatial transcriptomics:

  • Combines RNA sequencing with spatial information in tissue contexts

  • Could track TBEV spread and host responses across brain regions

  • May identify microenvironmental factors that influence infection outcomes

  • Provides insights into the spatial dynamics of neuroinflammation

• Machine learning applications:

  • Prediction of functional RNA structures from sequence data

  • Identification of patterns in evolutionary data not detectable by traditional methods

  • Integration of multi-omics datasets for comprehensive understanding of TBEV biology

  • Could enhance the capabilities of tools like TBEVnext for viral surveillance and evolution tracking

• Nanopore direct RNA sequencing:

  • Allows direct sequencing of RNA molecules without reverse transcription

  • Can detect RNA modifications that may affect viral RNA function

  • Enables long-read sequencing to capture full-length viral genomes in single reads

  • May reveal novel aspects of TBEV RNA biology and host interaction

Integration of these technologies with established approaches will likely accelerate progress in understanding TBEV biology and developing effective countermeasures.

How might climate change impact TBEV distribution and evolution?

Climate change is expected to significantly influence TBEV distribution and evolution through multiple mechanisms, creating new challenges for public health surveillance and control:

• Expansion of tick vector ranges:

  • Warming temperatures may enable northward and altitudinal expansion of tick habitats

  • Ixodes ricinus (European subtype vector) ranges are likely to extend further north in Europe

  • Ixodes persulcatus (Eastern subtypes vector) may expand its western distribution

  • These range shifts could create new overlap zones between tick species, potentially facilitating novel TBEV subtype interactions

• Changes in vector-host dynamics:

  • Altered seasonal activity patterns of ticks may affect transmission cycles

  • Changes in wildlife host distributions could create new ecological niches for TBEV

  • Modified feeding behavior of ticks under new climate conditions may impact virus acquisition and transmission

  • These ecological shifts might alter selection pressures on TBEV, potentially driving adaptive evolution

• Implications for TBEV genetic diversity:

  • New contact zones between previously separated TBEV subtypes may facilitate genetic recombination

  • Increased geographic range could expose TBEV to novel host and vector species, driving adaptive evolution

  • The varied architecture of conserved RNA elements in TBEV 3′UTRs might undergo selection in new ecological contexts

  • Climate-driven changes could accelerate TBEV evolutionary rates in newly established regions

• Research approaches to address climate change impacts:

  • Integrative ecological modeling that combines climate projections with tick habitat suitability

  • Enhanced molecular surveillance in predicted expansion zones

  • Experimental studies on temperature effects on tick-virus interactions

  • Phylodynamic analyses that incorporate climate variables as potential drivers of TBEV evolution

• Potential areas of concern:

  • Regions where different TBEV subtypes already co-circulate (e.g., Irkutsk Oblast) may serve as natural laboratories for studying climate-driven changes in TBEV ecology

  • Areas where vectors show exceptions to typical subtype associations (e.g., European subtype in Ixodes persulcatus) may indicate ongoing adaptation processes

  • High-altitude regions experiencing rapid warming may see new TBEV establishment

  • Urban-wildlife interfaces where human exposure risk is elevated

Understanding these potential impacts requires interdisciplinary research combining virology, vector ecology, climatology, and public health surveillance to develop adaptive management strategies for TBEV in a changing climate.