Abstract
Background: Pseudorabies virus (PRV), a critical porcine herpesvirus, induces severe diseases in both livestock and wildlife, imposing an incalculable burden and economic losses in livestock production. In this study, we investigated the evolutionary mechanisms and host adaptation strategies of the PRV gB gene through genomic alignment. The gB gene is highly conserved in PRV, and its encoded gB protein exhibits functional interchangeability across different herpesvirus species. Notably, the gB protein elicits the production of both complement-dependent and complement-independent neutralizing antibodies in animals, while also being closely associated with syncytium formation. Methods: Phylogenetic analysis and codon usage pattern analysis were performed in this study. A total of 110 gB gene sequences were analyzed, which were collected from [2011 to 2024] across the following regions: [Fujian, Shanxi, Guangxi, Guangdong, Chongqing, Henan, Shaanxi, Heilongjiang, Sichuan, Jiangsu, Jilin, Huzhou, Shandong, Hubei, Jiangxi, Beijing, Shanghai, Chengdu (China)], [Budapest, Szeged (Hungary)], [Tokyo (Japan)], [London (United Kingdom)], [Athens (Greece)], [Berlin (Germany)], and [New Jersey (United States)]. Results: The gB gene of PRV employs an evolutionary “selective optimization” strategy to maintain a dynamic balance between ensuring functional expression and evading host immune pressure, with this core trend strongly supported by its codon usage bias and mutation characteristics. First, the gene exhibits significant codon usage bias [Effective Number of Codons (ENC) = 27.94 ± 0.1528], driven primarily by natural selection rather than mere mutational pressure. Second, phylogenetic analysis shows that the second codon position of gB has the highest mutation rate (1.0586)—a feature closely linked to its antigenic variation and immune escape capabilities, further reflecting adaptive evolution against host immune pressure. Additionally, ENC-GC3 plot analysis reveals the complex regulatory mechanisms underlying codon bias formation, providing molecular evidence for the “selective optimization” strategy and clarifying PRV’s core evolutionary path to balance functional needs and immune pressure over time. Conclusions: Our study findings deepen our understanding of the evolutionary mechanisms of PRV and provide theoretical support for designing vaccines and assessing the risk of cross-species transmission.
Keywords: Pseudorabies virus, evolutionary analysis, Bayesian method
1. Introduction
Pseudorabies virus (PRV) is a porcine herpesvirus that belongs to the Alphaherpesvirinae subfamily. Pseudorabies was first detected in cattle in the United States and was identified by Aujeszky in 1902; consequently, the disease is also known as Aujeszky’s disease, and PRV is its etiological agent [1]. PRV primarily induces clinical symptoms such as fever, pruritus, and encephalomyelitis in both livestock and wildlife. While pigs serve as the natural host of PRV, the virus can infect a broad range of mammals, including humans, cats, dogs, goats, and cattle. Notably, all infected hosts other than pigs ultimately succumb to the infection.
PRV has inflicted significant economic damage on the global and Chinese swine industry [2,3]. PRV is classified into two major genotypes: genotype I [4] and genotype II [5]. Notably, PRV exhibits only one serotype; however, virulence varies significantly among different strains, and this variation is regulated by multiple viral genes. Among PRV genes, the glycoprotein B (gB) gene—a highly conserved component of the PRV genome—plays a crucial role in viral infection processes [6]. Over time, PRV has co-evolved with its hosts, developing sophisticated strategies to evade host immune surveillance [7].
Prior to this, researchers had studied the codon usage bias between the US1 gene of PRV and the US1-like genes of 20 reference alphaherpesviruses.
We selected the gB gene of PRV for codon analysis owing to its essential role in viral entry, high conservation across strains, and potent immunogenicity [8]. Compared with variable genes (e.g., gC/gE), this gene has a stable sequence, making it particularly valuable for evolutionary studies, vaccine development, and cross-genotype comparisons [9].
Codons comprise three nucleotide bases in DNA or RNA and encode specific amino acids.
Codon usage bias refers to the non-random selection and use of specific synonymous codons when encoding the same amino acid [10,11]. This bias is characterized by a significantly higher frequency of certain “optimal codons” relative to other synonymous codons, and it is shaped by multiple factors [12,13].
Host-specific codon usage patterns influence viral gene expression, reflecting the interplay between mutation, selection, and genome structure. Specifically, codon usage affects protein function, translation efficiency, viral replication fitness, and virulence [14]. Previous studies have identified nucleotide homology between the PRV gB gene and genes encoding other viral proteins [15], which suggests shared evolutionary origins. In the present study, we leveraged an updated global dataset from the National Center for Biotechnology Information (NCBI) to conduct a comprehensive analysis of the evolution, genetic diversity, and codon usage patterns of the PRV gB gene. Our findings advance the understanding of PRV’s molecular evolution, pathogenesis, and functional mechanisms, while also providing valuable insights for broader herpesvirus research.
2. Materials and Methods
2.1. Genomic Analysis
A literature review was used to compare the nucleotide and amino acid sequences of the gB gene [16]. The neighbor-joining method was used to construct phylogenetic trees based on nucleotide sequences. Before performing visual analysis using the iTOL v6 online tool, the sequences were compared using MEGA software (MEGA 5). Table 1 presents the reference sequences.
Table 1.
Reference sequence information.
| Sequence Name | GenBank Accession NO. | Year | Area |
|---|---|---|---|
| CQ2/CH/2022 | PQ232112.1 | 2024 | China |
| CQ1/CH/2022 | PQ232108.1 | 2024 | China |
| DML-5 | PQ094473.1 | 2024 | China |
| YJ | PQ082940.1 | 2024 | China |
| QX2 | PQ082939.1 | 2024 | China |
| HN2021 | PQ082938.1 | 2021 | China |
| HN19 | PQ082937.1 | 2024 | China |
| HN4 | PQ082935.1 | 2024 | China |
| HN2 | PQ082934.1 | 2024 | China |
| G22 | PQ082933.1 | 2024 | China |
| MS | OR338846.1 | 2023 | China |
| JL-21 | OR228534.1 | 2023 | China |
| SD1401 | OR161205.1 | 2023 | China |
| PRVYL-2020 | OR137576.1 | 2024 | China |
| GXNN2204-2014 | OP879621.1 | 2023 | China |
| GXNN3176-2017 | OP879620.1 | 2023 | China |
| GXNN3175-2017 | OP879619.1 | 2023 | China |
| GXNN2020-2014 | OP879618.1 | 2023 | China |
| GXQZ3227-2018 | OP879617.1 | 2023 | China |
| GXNN3167-2017 | OP879615.1 | 2023 | China |
| GXSB2001-2014 | OP879614.1 | 2023 | China |
| GXQZ3228-2018 | OP879613.1 | 2023 | China |
| GXYL2480-2015 | OP879612.1 | 2023 | China |
| GXYL2396-2015 | OP879611.1 | 2023 | China |
| GXWM1884-2013 | OP879610.1 | 2023 | China |
| GXNN3107-2017 | OP879609.1 | 2023 | China |
| GXNN3102-2017 | OP879608.1 | 2023 | China |
| GXNN3100-2017 | OP879607.1 | 2023 | China |
| GXNN3055-2017 | OP879606.1 | 2023 | China |
| GXNN2657-2016 | OP879605.1 | 2023 | China |
| GXNN2340-2015 | OP879604.1 | 2023 | China |
| GXNN2225-2014 | OP879603.1 | 2023 | China |
| GXNN2110-2014 | OP879602.1 | 2023 | China |
| GXNN2089-2014 | OP879601.1 | 2023 | China |
| GXNN1996-2013 | OP879600.1 | 2023 | China |
| GXNN1987-2013 | OP879599.1 | 2023 | China |
| GXNN1923-2013 | OP879598.1 | 2023 | China |
| GXHP2037-2014 | OP879597.1 | 2023 | China |
| GXGL2170-2014 | OP879596.1 | 2023 | China |
| GXGG1909-2013 | OP879595.1 | 2023 | China |
| GXGG1903-2013 | OP879594.1 | 2023 | China |
| GXGG1901-2013 | OP879593.1 | 2023 | China |
| GXLB1918-2013 | OP879592.1 | 2023 | China |
| GXBS2632-2016 | OP879591.1 | 2023 | China |
| GXBB2017-2014 | OP879590.1 | 2023 | China |
| GXBB1938-2013 | OP879589.1 | 2023 | China |
| GXBB1776-2013 | OP879588.1 | 2023 | China |
| FJ/tiger/2018-2 | OP727804.1 | 2024 | China |
| FJ/tiger/2018-1 | OP727803.1 | 2024 | China |
| FJ/tiger/2016 | OP727802.1 | 2024 | China |
| FJ/tiger/2015 | OP727801.1 | 2024 | China |
| FJ/porcupine/2018 | OP727800.1 | 2024 | China |
| GXGG-2016 | OP605538.1 | 2023 | China |
| GXLB-2015 | OP589231.1 | 2023 | China |
| JS-XJ5 | OP512542.1 | 2023 | China |
| SX1911 | OP376823.1 | 2023 | China |
| FB | ON005002.1 | 2023 | China |
| FA | ON005001.1 | 2023 | China |
| DCD-1 | OL639029.1 | 2022 | China |
| SX1910 | OL606749.1 | 2022 | China |
| PRV-JM | OK338077.1 | 2022 | China |
| PRV-GD | OK338076.1 | 2022 | China |
| Kaplan | NC_075689.1 | 2023 | Hungary |
| CH/GX/PRV/2408/2018 | MZ219273.1 | 2022 | China |
| XJ | MW893682.1 | 2022 | China |
| _JS2019 | MW805231.1 | 2021 | China |
| FJ | MW286330.1 | 2022 | China |
| YJ | MW250652.1 | 2022 | China |
| JZ-1 | MW055924.1 | 2022 | China |
| XC | MW055923.1 | 2022 | China |
| SX-2 | MW055922.1 | 2022 | China |
| SX-1 | MW055921.1 | 2022 | China |
| PY-1 | MW055920.1 | 2022 | China |
| HuB17 | MT949537.1 | 2020 | China |
| GD1802 | MT949535.1 | 2020 | China |
| hSD-1/2019 | MT468550.1 | 2020 | China |
| JSY13 | MT157263.1 | 2020 | China |
| JSY7 | MT150583.1 | 2020 | China |
| JSSQ2013 | MN718167.1 | 2020 | China |
| JX/CH/2016 | MK806387.1 | 2020 | China |
| AnH1/CHN2015 | MK618718.1 | 2020 | China |
| HBXT-2018 | MK532276.1 | 2019 | China |
| HLJ-2013 | MK080279.1 | 2019 | China |
| GD0304 | MH582511.1 | 2019 | China |
| PRV-MdBio | LT934125.1 | 2018 | Hungary |
| RC1 | LC342744.1 | 2019 | Japan |
| Ea(Hubei) | KX423960.1 | 2017 | China |
| NIA3 | KU900059.1 | 2016 | United Kingdom |
| LA | KU552118.1 | 2017 | China |
| DL14/08 | KU360259.1 | 2016 | China |
| Ea | KU315430.1 | 2016 | China |
| HB1201 | KU057086.1 | 2016 | China |
| Kolchis | KT983811.1 | 2016 | Greece |
| Hercules | KT983810.1 | 2016 | Greece |
| HLJ8 | KT824771.1 | 2016 | China |
| SC | KT809429.1 | 2016 | China |
| JS2011 | KR605319.1 | 2011 | China |
| HN1201 | KP722022.1 | 2016 | China |
| JS-2012 | KP257591.1 | 2015 | China |
| Fa | KM189913.1 | 2016 | China |
| HNX | KM189912.1 | 2016 | China |
| ZJ01 | KM061380.1 | 2015 | China |
| TJ | KJ789182.1 | 2014 | China |
| Kaplan | KJ717942.1 | 2014 | Hungary |
| DUL34Pass | JQ809330.1 | 2016 | Germany |
| Kaplan | JQ809328.1 | 2012 | Germany |
| Becker | JF797219.1 | 2011 | USA |
| Kaplan | JF797218.1 | 2011 | Hungary |
| Bartha | JF797217.1 | 2011 | Hungary |
| MY-1 | AP018925.1 | 2015 | Japan |
2.2. Phylogeographic Model Analysis
jModelTest was used to select the best-fit evolutionary model (GTR + I + G). A relaxed molecular clock (log-normal) and Bayesian skyline model were applied in BEAST v1.10.4 to estimate the time of the most recent common ancestor and evolutionary rates [17]. Before estimating the adequate population size, BEAST v1.10.4 was used to perform phylogenetic analysis under the GTR + I + G substitution model, a relaxed molecular clock (log-normal distribution), and a coalescent Bayesian skyline. The Markov chain Monte Carlo (MCMC) simulation ran for 100 million generations, sampling every 10,000 steps and parameter logging every 1 million iterations. Tracer v1.7.2 was used to assess convergence, and TreeAnnotator (v1.10.4) was used to deduce the maximum clade credibility.
2.3. Codon Usage Indices
2.3.1. Nucleotide Analysis
The nucleotide composition (A, T, G, C) was analyzed, and the GC and AT contents were computed. In addition, the third nucleotide frequencies (A3, C3, T3, G3) and GC content at each codon position (GC1s, GC2s, GC3s) were determined using reference methods [18]. Stop codons (TAA, TAG, TGA) and non-degenerate codons (ATG, TGG) were excluded.
The frequency of the third nucleotide (A3s, C3s, T3s, and G3s) was counted using Codon W (version 1.4.2) (http://codonw.sourceforge.net/ (accessed on 11 June 2025)). GC content (%G + C) refers to the GC content at the first (GC1s), second (GC2s), and third (GC3s) codon positions (http://emboss.toulouse.inra.fr/cgi-bin/emboss/cusp (accessed on 11 June 2025)). The average frequency of GC1s and GC2s (GC12s) is calculatedthrough the use of CAIcal.
2.3.2. Synonymous Codon Bias Analysis Based on Effective Number of Codons (ENC)
ENC is used to quantify synonymous codon usage bias by measuring deviations from random expectations. Unlike other metrics, gene length and amino acid composition do not affect ENC [16]. Therefore, it provides a standardized assessment of codon selection preferences in coding sequences [19].
2.3.3. Analysis of Synonymous Codon Usage Bias Using Relative Synonymous Codon Usage (RSCU)
RSCU was used to analyze synonymous codon usage bias. This approach helps characterize codon selection bias by statistically comparing experimentally observed frequencies against hypothetical equal usage patterns across synonymous codons [20,21]. In this study, RSCU values of highly expressed genes were used to establish reference tables (http://www.bioinformatics.nl/cgi-bin/emboss/cusp (accessed on 13 June 2025)) [16].
2.3.4. Genome-Wide Codon Adaptation Analysis Using Codon Adaptation Index (CAI)
CAI is a powerful indicator to quantify the effect of natural selection on codon usage patterns [22]. CAI helps quantify the effect of natural selection on codon usage [23], ranging from 0 (weak adaptation) to 1 (strong adaptation) [16,24].
2.3.5. Frequency of Optimal Codons (FOP) Analysis
FOP helps identify optimal codons based on tRNA abundance, with higher values indicating stronger adaptation [16,20].
2.3.6. Genome-Wide Codon Usage Profiling via ENC-GC3 Scatter Plot Analysis
ENC-GC3 analysis helps distinguish between mutation-driven and selection-driven biases in codon usage. This helps assess whether there are significant differences in gene distribution under selection pressure compared with the expected distribution [16,25].
3. Results
3.1. Phylogenetic Analysis of the gB Gene
We constructed a neighbor-joining phylogenetic tree using the nucleotide sequences of the gB gene. The analysis was supported by 1000 bootstrap replicates (Figure 1).
Figure 1.
A phylogenetic tree based on all PRV gB protein nucleotide sequences.
3.2. Bayesian MCMC-Based Evolutionary Tree
Bayesian MCMC analysis revealed distinct mutation patterns across codon positions in the gB gene. The second position exhibited the highest substitution rate (1.0586) (Figure 2A). Despite its frequent synonymous nature, this increased mutation rate at the second codon position suggests potential roles in modulating the mRNA secondary structure or translation kinetics without changing the amino acid sequence. Synonymous codon mutations may also affect mRNA turnover rates, which may be related to their effects on translation kinetics, and may also affect protein-RNA interactions. The temporal reconstruction of PRV population dynamics revealed a significant decrease in effective population size between 2010 and 2020. This potentially reflects the widespread implementation of vaccination programs. However, the subsequent resurgence observed in 2023 suggests the emergence of novel viral variants that can evade existing immune pressures. This underscores the dynamic nature of PRV evolution in response to anthropogenic selection forces (Figure 2B,C).
Figure 2.
PRV gB Codon mutation rate of structural proteins and type I skyline map (A,B). The codon, mutation rate of PRV structural protein gene was estimated by Bayesian Markov chain method. The codon mutation rate is the result of BEAST running using Trace analysis. (C) The dynamic study of the genetic diversity of PRV structural protein genes by IBayesian skyline diagram. The thick, solid line is the median estimate, and the dashed line repressents the 45% confidence interval. The abscissa is time, and the ordinate is the effective population size.
3.3. RSCU Analysis
Table 2 and Figure 3 present the statistical graphs of the relative usage rate of synonymous codons.
Table 2.
Properties of structural protein genes from PRV strains relative synonymous codon usage analysis in this study (Potential hosts are displayed in bold).
| Categories | PRV | Sus Scrofa | Bos Taurus |
|---|---|---|---|
| TTT(Phe) | 0.2 | 0.79 | 0.87 |
| TTC(Phe) | 1.8 | 1.21 | 1.13 |
| TTA(Leu) | 0.01 | 0.32 | 1.71 |
| TTG(Leu) | 0.05 | 0.67 | 1.35 |
| CTT(Leu) | 0.69 | 1.35 | 0.73 |
| CTC(Leu) | 1.93 | 1.35 | 0.93 |
| CTA(Leu) | 0.42 | 0.33 | 0.58 |
| CTG(Leu) | 2.9 | 2.68 | 1.69 |
| ATT(Ile) | 0.07 | 0.91 | 0.92 |
| ATC(Ile) | 2.93 | 1.67 | 1.01 |
| ATA(Ile) | 0 | 0.42 | 1.07 |
| GTT(Val) | 0.09 | 0.57 | 0.69 |
| GTC(Val) | 1.13 | 1.07 | 0.82 |
| GTA(Val) | 0.15 | 0.34 | 0.72 |
| GTG(Val) | 2.62 | 2.03 | 1.76 |
| TCT(Ser) | 0.37 | 0.99 | 0.95 |
| TCC(Ser) | 1.39 | 1.5 | 1.06 |
| TCA(Ser) | 0.66 | 0.73 | 1.4 |
| TCG(Ser) | 1.88 | 0.39 | 0.43 |
| AGT(Ser) | 0.17 | 0.77 | 0.8 |
| AGC(Ser) | 1.53 | 1.62 | 1.53 |
| CCT(Pro) | 0.74 | 1.05 | 0.94 |
| CCC(Pro) | 1.52 | 1.46 | 1.01 |
| CCA(Pro) | 0.53 | 0.94 | 1.45 |
| CCG(Pro) | 1.21 | 0.56 | 0.59 |
| ACT(Thr) | 0.21 | 0.83 | 0.87 |
| ACC(Thr) | 1.14 | 1.68 | 1.09 |
| ACA(Thr) | 0.49 | 0.92 | 1.44 |
| ACG(Thr) | 2.16 | 0.57 | 0.6 |
| GCT(Ala) | 0.62 | 0.96 | 0.97 |
| GCC(Ala) | 1.63 | 1.8 | 1.13 |
| GCA(Ala) | 0.42 | 0.74 | 1.3 |
| GCG(Ala) | 1.33 | 0.5 | 0.6 |
| TAT(Tyr) | 0 | 0.73 | 0.9 |
| TAC(Tyr) | 2 | 1.27 | 1.1 |
| CAT(His) | 0.44 | 0.7 | 0.88 |
| CAC(His) | 1.56 | 1.3 | 1.12 |
| CAA(Gln) | 0.76 | 0.44 | 0.71 |
| CAG(Gln) | 1.24 | 1.56 | 1.29 |
| AAT(Asn) | 0 | 0.79 | 0.87 |
| AAC(Asn) | 2 | 1.21 | 1.13 |
| AAA(Lys) | 0 | 0.76 | 0.89 |
| AAG(Lys) | 2 | 1.24 | 1.11 |
| GAT(Asp) | 0.2 | 0.8 | 0.85 |
| GAC(Asp) | 1.8 | 1.2 | 1.15 |
| GAA(Glu) | 0.15 | 0.72 | 0.92 |
| GAG(Glu) | 1.85 | 1.28 | 1.08 |
| TGT(Cys) | 0.51 | 0.79 | 0.78 |
| TGC(Cys) | 1.49 | 1.21 | 1.22 |
| CGT(Arg) | 0.65 | 0.44 | 0.26 |
| CGC(Arg) | 2.63 | 1.31 | 0.52 |
| CGA(Arg) | 0.76 | 0.6 | 0.27 |
| CGG(Arg) | 1.46 | 1.29 | 0.73 |
| AGA(Arg) | 0.21 | 1.12 | 2.16 |
| AGG(Arg) | 0.28 | 1.23 | 2.07 |
| GGT(Gly) | 0.46 | 0.57 | 1.51 |
| GGC(Gly) | 1.9 | 1.46 | 1.01 |
| GGA(Gly) | 0.49 | 0.91 | 1.25 |
| GGG(Gly) | 1.15 | 1.05 | 1.23 |
Figure 3.
Statistical graph of relative usage rate of synonymous codons.
In codon usage, PRV exhibits pronounced extreme preferences. For instance, it shows complete avoidance of the three common codons TAT, AAT, and AAA, relying exclusively on TAC, AAC, and AAG. Meanwhile, it demonstrates exceptionally high usage frequency for Thr (ACG) and Ser (TCG), while showing minimal usage frequency for Thr (ACT) and Ser (AGT). In contrast, as a higher-order eukaryote, Sus scrofa demonstrates more balanced codon usage. Not only does it lack any codons with zero frequency, but the usage frequencies among synonymous codons corresponding to these amino acids also maintain relative equilibrium.
Bos taurus as a non-natural host for PRV—they cannot harbor the virus for a long term after infection and eventually die. There exists a significant codon preference mismatch between cattle and PRV, which is fully reflected from the levels of core codon selection to base-ending preference: In terms of core codon usage, the key codon choices of the two are completely opposite. For instance, PRV relies heavily on CTC/CTG for leucine (Leu) (Relative Synonymous Codon Usage, RSCU = 1.93/2.9), ATC for isoleucine (Ile) (RSCU = 2.93), and CGC for arginine (Arg) (RSCU = 2.63). In contrast, cattle prefer Leu-TTA/TTG (RSCU = 1.71/1.35)—codons that PRV avoids, use Ile-ATA (RSCU = 1.07)—a codon that PRV does not use at all, and rely highly on Arg-AGA/AGG (RSCU = 2.16/2.07)—codons that PRV strongly evades. In terms of base-ending preference, cattle have a much higher acceptance of A/T-ending codons than PRV. For example, cattle prefer ACA (A-ending, RSCU = 1.44) for threonine (Thr) and GCA (A-ending, RSCU = 1.3) for alanine (Ala). However, PRV strongly avoids A/T-ending codons and only uses Thr-ACG (G-ending, RSCU = 2.16) and Ala-GCC (C-ending, RSCU = 1.63). This comprehensive mismatch leads to two critical consequences: First, PRV struggles to bind to the host tRNA pool in cattle, significantly reducing the translation efficiency of viral proteins. Second, the mismatched viral mRNAs are more likely to be recognized as foreign substances by the cattle’s immune system, triggering a strong immune response. Ultimately, these effects result in the rapid death of cattle after PRV infection, making them unable to act as effective transmission hosts for PRV.
To evade RNA instability caused by AU-rich sequences, PRV reduces AU content at the codon level to minimize AU-rich regions in its RNA from the source. Specifically, PRV rarely uses AT-rich codons (containing two A/T bases), such as TTT (for Phe), TTA (for Leu), ATT/ATA (for Ile), GTT/GTA (for Val), TAT (for Tyr), AAT (for Asn), AAA (for Lys), GAT (for Asp), and GAA (for Glu), with their usage frequencies mostly 0 or ≤0.21. Instead, it strongly prefers synonymous codons with higher GC content, like TTC (Phe), CTC/CTG (Leu), ATC (Ile), and GTC/GTG (Val), whose frequencies are mostly ≥1.13. In contrast, the host (Sus scrofa) maintains a certain usage of these AT-rich codons (e.g., TTT: 0.79, ATT: 0.91, TAT: 0.73). This comparison clearly shows PRV avoids RNA instability by abandoning AT-rich codons.
RSCU is an essential indicator for measuring codon usage bias. An RSCU value of >1 indicates positive codon bias, an RSCU value of <1 indicates negative bias, and an RSCU values of 1 suggest random usage [26]. According to the curve in the figure, the RSCU value of CTA, ATT, CGC and GGC is >1, indicating that the codon is preferred
3.4. Nucleotide Bias in PRV Genotypes
Cytosine (C) was identified as the most abundant nucleotide (37.4% ± 0.19%), with C3s exhibiting the highest frequency among synonymous codons (0.67 ± 0.0027) (Table 3).
Table 3.
Properties of structural protein genes from PRV gB strains analyzed in this study (mean value ± SD).
| Categories | PRV |
|---|---|
| %A | 15.1 ± 0.31 |
| %C | 37.4 ± 0.19 |
| %T | 13.6 ± 0.08 |
| %G | 33.8 ± 0.47 |
| A-3 | 10.4 ± 7.86 |
| C-3 | 43.1 ± 11.52 |
| T-3 | 10 ± 6.5 |
| G-3 | 36.5 ± 3.07 |
| A3S | 0.01 ± 0.0013 |
| C3S | 0.67 ± 0.0027 |
| T3S | 0.02 ± 0.0011 |
| G3S | 0.50 ± 0.0022 |
| %G + C | 0.71± 0.0005 |
| GC3S | 0.97 ± 0.0016 |
| ENC | 27.94 ± 0.1528 |
3.5. Codon Bias Measurement
The ENC value of the gB gene was 27.94 ± 0.1528. This indicates strong codon bias (range: 20–61; lower values denote more substantial bias) [27]. Overall, based on the numerical point of view presented in Table 3, the gB gene exhibits significant codon usage bias.
3.6. Synonymous and Optimal Codon Analysis
Figure 4A,B illustrate the scatter plots of the structural protein gene of PRV gB via CAI and FOP, respectively. The higher the CAI, the higher the expression level of the exogenous gene within the host. The scatter plot shows that the CAI value is 0.28 and the FOP value is 0.56. This indicates that the gene’s codon usage pattern does not match the host’s preferences well. The FOP value of 0.56 suggests that while the gene’s codon usage tends to favor the host’s optimal codons, the degree of optimization is moderate.
Figure 4.
(A): Scatter plot of PRV gB structural protein gene CAI. (B): Scatter plot of PRV gB structural protein gene FOP. The dots in the figure show the distribution of different genes.
3.7. ENC-GC3 Plot Analysis
As illustrated in Figure 5, the genes plotted below the expected curve exhibited significant natural selection pressure on codon usage.
Figure 5.
PRV gB ENC The relationship between GC3.In this figure, each blue dot represents a single gene, and its position in the graph is jointly determined by the horizontal axis (GC3s) and the vertical axis (ENC). The horizontal axis, GC3s, reflects the G + C content at the third position of the gene’s codons, which can indicate the base composition preference at the third position of synonymous codons. The vertical axis, ENC (Effective Number of Codons), is used to measure the degree of codon usage bias of the gene; a lower ENC value indicates a stronger codon usage bias of the gene and a more concentrated range of codons it relies on. Thus, the specific position of each blue dot can comprehensively represent the core characteristics of the corresponding gene in terms of both base composition preference and codon usage bias.
4. Discussion and Conclusions
These findings not only deepen our understanding of the evolutionary mechanisms of PRV but also provide essential theoretical support for designing vaccines based on codon optimization.
Sharp et al. [28] have hypothesized that viral genes typically use codons that match their host’s tRNA repertoire to optimize translation efficiency. Similarly, Chen et al. [22] analyzed porcine epidemic diarrhea virus and reported that natural selection is the primary driving force behind codon preference. These findings are consistent with those of previous studies on codon usage patterns in herpesviruses. Overall, these studies support the conclusion of our research that PRV adapts to the host cell environment via codon optimization, thereby enhancing its replication and transmission capabilities.
In this study, we systematically analyzed the codon usage patterns of the gB gene in PRV. We noted that unique codon preference characteristics were formed during virus evolution and their biological significance. Codon optimization fragments have been validated by replacing the PRV gene with the US3 gene [29]. Translational optimization may drive the preferential selection of certain synonymous codons, reflecting adaptation to the specific biological constraints of the organism [30].
Previous studies have shown that mRNA stability is closely linked to codon type: stable mRNAs are rich in optimal codons, while unstable ones are dominated by non-optimal codons. For viruses, this translates to a specific adaptive strategy: they reduce AU content at the codon level to minimize the formation of AU-rich sequences in their RNA from the source. Essentially, viruses avoid non-optimal AT-rich codons and prefer optimal codons with higher GC content, thereby evading the mRNA degradation risk caused by AU-rich sequences. This regulatory mechanism ensures the stability of viral RNA, laying the foundation for successful viral protein translation and progeny replication [31].
Mazumdar P et al. [32] compared grass and non-grass monocots to elucidate their distinct evolutionary patterns. Codon optimization technology has been widely applied in viral vaccine development; for instance, Xu et al. [33] performed codon optimization on the PRV US3 gene, successfully constructing a live attenuated vaccine candidate strain. This strain significantly reduced viral virulence while preserving immunogenicity. Similarly, Ma et al. [34] proposed that codon usage preferences in the PRV genome may influence the virus’s virulence and host range. These studies demonstrate that by targeting codon optimization or de-optimization, researchers can design safer and more effective PRV vaccines. The codon usage preferences of the PRV gB gene identified in the present study thus provide important references for future vaccine design.
While studying the evolution of other viruses to understand the trend of codon usage during the novel coronavirus epidemic, some researchers conducted related codon analysis [35]. They noted that the codon preferences of the PRV gB gene significantly differ from those of other herpesviruses (such as HSV-1) and RNA viruses [such as porcine epidemic diarrhea virus and classical swine fever virus (CSFV)]. This difference suggests that viruses have different adaptive strategies to host translation mechanisms. Furthermore, the CAI value of the PRV gB gene (~0.28) is lower than that of some highly expressed host genes; this suggests that the virus avoids being over-recognized by the host immune system by moderately deviating from the host’s codon usage patterns.
According to data, the second codon site of porcine circovirus (PRV) gB gene may play a significant role in promoting antigenic drift and evading immune systems. For example, Butt et al. [26] discovered that the Zika virus adapts to different hosts and vectors via alterations in codon usage preferences. The high mutation rate of the PRV gB gene may facilitate its sustained evolution under vaccine pressure, offering a theoretical foundation for future vaccine updates and adjustments in control strategies.
While the present study revealed the codon preferences of the PRV gB gene and their evolutionary significance, several questions remain unexplored. For example, is there conservation in the codon usage patterns of PRV compared with other herpesviruses (such as HSV or varicella–zoster virus)? Additional experimental validation of the expression efficiency and immune reactivity of codon-optimized gB protein can provide more definitive evidence for vaccine development.
The evolutionary pathway of the complete genomic DNA sequence of PRV has not been determined. Using codon optimization techniques after understanding the codon preferences of viruses can help produce innovative PRV attenuated live vaccine candidates. Our study findings advance our understanding of the evolution of PRV and facilitate vaccine design and cross-species transmission risk assessment. Finally, codon deoptimization may serve as a strategy to develop attenuated PRV vaccines.
Author Contributions
Conceptualization, J.L. and C.X.; literature collection, A.X. and C.X.; resources, C.X., J.L., K.L. and X.L. (Xiaodong.Liu); writing—original draft preparation, A.X.; writing—review and editing, C.X., J.L. and A.X.; visualization, J.B., Z.Y., K.L., Y.R., F.Z. and X.L. (Xiaoqi.Li.); supervision, J.L. and C.X. All authors have read and agreed to the published version of the manuscript.
Data Availability Statement
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).
Conflicts of Interest
The authors declare no conflicts of interest.
Funding Statement
This research was funded by the Chengdu Agricultural Science and Technology Center Local Finance Special Funds Project (NASC2019AT01), The Postdoctoral Science Foundation 72nd Grant of China (2022M723906), Science and Technology Innovation Project, Chinese Academy of Agricultural Sciences (CASA) Urban Agricultural Research Institute (UARI) Coordinated Program at the Institute Level (No. S2024007), Sichuan Province Youth Fund Program (2025ZNSFSC1074).
Footnotes
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Associated Data
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Data Availability Statement
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).