Recombinant African swine fever virus Uncharacterized protein F165R (Ba71V-046)

How is F165R expressed during ASFV infection?

F165R exhibits a distinctive expression pattern during ASFV infection. Transcriptomic studies using CAGE-seq (Cap Analysis Gene Expression) have revealed that F165R is one of the exceptions to the typical early or late gene expression patterns seen in ASFV . While most ASFV genes show consistent patterns of up- or down-regulation between 5 hours post-infection (hpi) and 16 hpi in both attenuated (BA71V) and virulent (Georgia) strains, F165R is among a small group of genes (including D205R, CP80R, C315R, NP419L, and DP148R) that exhibit different expression patterns between these strains .

What methods are typically used to produce recombinant F165R protein for research?

The production of recombinant F165R protein typically follows these methodological steps:

• Gene cloning and vector construction: The F165R gene sequence is codon-optimized for expression in the desired host system (typically E. coli) and cloned into an expression vector with an appropriate tag (commonly His-tag) for purification .

• Heterologous expression: The recombinant protein is expressed in E. coli under controlled conditions to maximize protein yield while maintaining proper folding .

• Protein purification: The His-tagged protein is typically purified using immobilized metal affinity chromatography (IMAC), followed by additional chromatography steps if higher purity is required .

• Quality control: The purity of the recombinant protein is assessed using SDS-PAGE (greater than 90% purity is typically achieved), and the identity is confirmed through techniques such as mass spectrometry or Western blotting .

• Storage preparation: The purified protein is formulated in a stabilizing buffer (often Tris/PBS-based with 6% trehalose, pH 8.0) and lyophilized for long-term storage .

This methodological approach provides researchers with a standardized process for producing recombinant F165R protein suitable for various experimental applications.

What structural information is available for F165R, and how has it been computationally modeled?

Structural characterization of F165R has primarily relied on computational modeling approaches due to the lack of experimentally determined structures. AlphaFold2 v2.1.2 (AF2) has been employed to generate 3D protein models of F165R along with other ASFV proteins . This approach provides insights into potential structural features despite the absence of crystallographic or NMR data.

The methodology for computational modeling of F165R includes:

• AlphaFold2 modeling: The ASFV Georgia F165R protein sequence (NCBI reference sequence NC_044959.2) is used as input for AF2 v2.1.2, which generates predicted structures .

• Confidence assessment: The predicted local distance difference test (pLDDT) score is calculated for each residue, indicating the confidence that the residue's C-alpha atom is correctly located relative to nearby residues . This score serves as a validation metric for the structural prediction.

• Structure evaluation: The predicted aligned error (PAE) is calculated for pairs of residues, estimating the positional distance error (in Å) when they are aligned with true structures from the program database .

• Visualization: The ChimeraX program v1.7.1 is typically used to visualize and analyze the predicted structures .

• Domain prediction: The transmembrane domain prediction of F165R can be performed using programs like Deep TMHMM, which provides topology predictions for membrane-associated proteins .

While these models provide valuable structural insights, they remain theoretical until validated by experimental methods such as X-ray crystallography or cryo-EM.

How does F165R expression differ between virulent and attenuated ASFV strains, and what are the implications?

The differential expression pattern of F165R between virulent and attenuated ASFV strains presents intriguing questions about its potential role in viral pathogenesis:

• Expression pattern differences: RNA sequencing studies using CAGE-seq have shown that F165R is one of a small number of genes that exhibit different expression patterns between the attenuated BA71V strain and the virulent Georgia 2007/1 (GRG) strain . While most ASFV genes show consistent patterns of differential expression between early (5 hpi) and late (16 hpi) infection stages across both strains, F165R is an exception .

• Temporal classification: In the transcriptome analysis of ASFV, most genes can be classified as either early (downregulated from 5 to 16 hpi) or late (upregulated from 5 to 16 hpi) . F165R has been identified among genes with distinctive temporal expression patterns in the virulent GRG strain .

• Alternative transcription start sites: ASFV utilizes alternative transcription start sites between early and late stages of infection . The specific transcription start site for F165R and whether it varies between strains or infection stages has implications for understanding its regulation.

• Research implications: These differential expression patterns suggest that F165R might play a role in strain-specific viral mechanisms, potentially contributing to virulence, host adaptation, or immune evasion . Investigating these differences could provide insights into the molecular basis of ASFV virulence.

What approaches can be used to investigate potential protein-protein interactions of F165R?

Given that F165R remains uncharacterized, determining its protein-protein interactions is crucial for understanding its function. Several methodological approaches can be employed:

• Yeast two-hybrid (Y2H) screening: This technique can identify potential protein partners by expressing F165R as bait and screening against a library of host and viral proteins .

• Co-immunoprecipitation (Co-IP): By using antibodies against tagged F165R, researchers can pull down protein complexes from infected cells and identify interaction partners through mass spectrometry .

• Proximity-dependent biotin identification (BioID): This approach involves fusing F165R to a biotin ligase, which biotinylates nearby proteins when expressed in cells, allowing for the identification of proximal proteins that may interact with F165R .

• AlphaFold2 multimer modeling: As demonstrated with other ASFV proteins, the multimer display feature of AF2 can be used to model potential protein-protein interactions, selecting the model with the highest confidence score from different possible interactions .

• Bimolecular fluorescence complementation (BiFC): This technique can visualize protein interactions in living cells by fusing F165R and potential partners to complementary fragments of a fluorescent protein.

Understanding the interactome of F165R could provide significant insights into its functional role within the viral replication cycle and host-pathogen interactions.

What is known about the potential role of F165R in ASFV replication and virulence?

While F165R remains largely uncharacterized, several lines of evidence provide context for investigating its potential role:

It's worth noting that while studies have investigated the deletion of E165R (a dUTPase gene) from the genome of highly virulent ASFV Georgia 2010 and found that it does not affect virus replication or virulence , this is distinct from F165R, despite the similar nomenclature.

How can CRISPR/Cas9 genome editing be used to investigate F165R function in the context of ASFV infection?

CRISPR/Cas9 genome editing represents a powerful approach for investigating F165R function through the following methodological strategy:

• Design of guide RNAs (gRNAs): Multiple gRNAs targeting the F165R gene in the ASFV genome should be designed and validated for specificity and efficiency .

• Delivery system optimization: For editing the ASFV genome during infection, appropriate delivery systems must be established. This could involve transfection of susceptible cells (such as PAMs - porcine alveolar macrophages) with CRISPR/Cas9 components followed by viral infection, or the creation of stable cell lines expressing Cas9 and gRNAs .

• Generation of F165R deletion mutants: Cas9-induced double-strand breaks can facilitate homologous recombination between ASFV and transfer plasmids containing flanking sequences of F165R along with marker genes (e.g., fluorescent proteins like GFP or mCherry) .

• Confirmation of gene deletion: PCR, sequencing, and next-generation sequencing (NGS) should be used to verify the deletion of F165R and ensure no unwanted genomic changes occurred elsewhere in the viral genome .

• Phenotypic characterization: The resulting F165R deletion mutants should be characterized for:

  • Replication efficiency in cell culture (growth curves)

  • Virulence in animal models

  • Transcriptomic changes compared to wild-type virus

  • Effects on host immune responses

This approach would provide direct evidence for the functional role of F165R in the ASFV life cycle and pathogenesis, following similar methodologies used for other ASFV genes .

What bioinformatic approaches can be employed to predict the function of uncharacterized proteins like F165R?

Several advanced bioinformatic methodologies can be applied to predict the function of uncharacterized proteins like F165R:

• Sequence-based analysis:

  • Multiple sequence alignment across ASFV isolates to identify conserved domains

  • Comparison with proteins from other virus families to identify distant homologs

  • Analysis of amino acid composition and physicochemical properties

• Structural prediction and analysis:

  • AlphaFold2 modeling as previously described

  • Structure-based function prediction using tools like COFACTOR or COACH

  • Binding site prediction using CASTp or FTMap

  • Molecular dynamics simulations to understand protein flexibility and potential interactions

• Network-based approaches:

  • Protein-protein interaction prediction using tools like STRING or PSOPIA

  • Integration with known ASFV protein networks to identify potential functional associations

  • Guilt-by-association analysis based on co-expression patterns with characterized viral proteins

• Machine learning methods:

  • Feature extraction from sequence and predicted structure

  • Application of supervised learning algorithms trained on characterized viral proteins

  • Deep learning approaches for function prediction from sequence data

• Comparative genomics:

  • Phylogenetic profiling across ASFV isolates with different virulence characteristics

  • Analysis of selection pressure on the F165R gene to identify functional constraints

  • Correlation of genetic variations with phenotypic differences between isolates

These computational approaches provide a foundation for generating hypotheses about F165R function that can subsequently be tested through targeted experimental studies.

How can transcriptomic analyses be optimized to better understand the temporal expression of F165R during ASFV infection?

Advanced transcriptomic analyses can provide deeper insights into F165R expression dynamics through the following methodological approaches:

• High-resolution temporal profiling: Instead of the two-point (early vs. late) analysis commonly used , implement more frequent sampling (e.g., every 2 hours) throughout the infection cycle to capture the precise timing of F165R expression.

• Single-cell RNA sequencing (scRNA-seq): Apply scRNA-seq to infected cell populations to understand cell-to-cell variation in F165R expression and identify potential subpopulations with distinct expression patterns.

• Integrated multi-omics approach:

  • Combine CAGE-seq (for transcription start site mapping) with RNA-seq and 3'-RNA-seq

  • Integrate with proteomics data to correlate transcriptional and translational dynamics

  • Add epigenetic profiling to understand chromatin-level regulation of F165R expression

• Comparative transcriptomics: Analyze F165R expression across multiple ASFV strains with varying virulence to identify correlations between expression patterns and phenotypic characteristics .

• Promoter and regulatory element analysis:

  • Characterize the F165R promoter region and potential regulatory elements

  • Identify DNA consensus motifs that may control early or late expression

  • Map the precise transcription start site using CAGE-seq data

• Perturbation studies: Examine how F165R expression changes under various conditions:

  • Treatment with inhibitors of viral DNA replication

  • Infection in different cell types

  • Co-infection with other viruses or pathogens

• RNA modifications and stability: Investigate the role of RNA modifications (e.g., 5' cap structure, 3' poly(A) tail length) in regulating F165R transcript stability and translation efficiency .

Through these advanced transcriptomic approaches, researchers can develop a comprehensive understanding of F165R expression dynamics and its potential regulatory mechanisms during ASFV infection.