Abstract
Bovine respiratory disease syndrome, primarily caused by bovine parainfluenza virus type-3 (BPIV-3), is characterized by a high incidence in calves. However, despite its significant clinical importance, the codon usage patterns and evolutionary dynamics of BPIV-3 hosts remain elusive. Hence, this study aimed to systematically analyze the codon usage bias of the BPIV-3 structural protein gene HN, and the roles of mutational pressure and natural selection in its evolution were evaluated. Herein, analysis of indicators such as effective codon number (ENC), relative synonymous codon usage, and codon adaptation index revealed a low codon bias for BPIV-3, with codon preferences showing significant differences from the host. Notably, ENC-GC content at the third codon spot analysis indicated that natural selection dominated codon usage. Additionally, phylogenetic analysis divided BPIV-3 into three main genotypes (namely a, b, and c), with genotype C exhibiting a higher codon adaptability to the host. Altogether, these findings reveal the host–BPIV-3 evolutionary interaction mechanisms, providing a theoretical basis for vaccine design and epidemiological surveillance.
Keywords: bovine parainfluenza virus type-3, codon usage preference, subtype C
INTRODUCTION
Bovine parainfluenza virus type-3 (BPIV-3) often causes mixed infections with bacteria (e.g., Pasteurella) and other viruses (e.g., bovine infectious rhinotracheitis virus), leading to diseases such as pneumonia and rhinitis. Its typical symptoms include fever, cough, nasal discharge, and difficulty breathing, which are more common in calves and cattle herds under transportation stress, with an incidence rate of 50% to 70%. It has been reported that in severe cases, BPIV-3 can cause fibrinous pneumonia; the virus is characterized by a single serotype in terms of genetic traits, but exhibits subtypes at the genetic level. Additionally, its fusion protein (F protein) and hemagglutinin-neuraminidase protein (HN protein) are prone to variation, ultimately resulting in differences in pathogenicity. Although the genomic structure of BPIV-3 is relatively stable, studies have found that it exhibits regional lineage differentiation. This differentiation is not accidental but a result of the combined effects of multiple factors, including the ecological environment of the region where the virus is located, the characteristics of the host population, and immune pressure. Furthermore, differences in the genetic background and living habits of host populations in different regions can induce genetic mutations in the virus to adapt to processes such as transmission and reproduction, thereby gradually forming unique lineage characteristics. For example, in regions with developed animal husbandry and high breeding density, mutations at key genetic loci of the virus may help it break through the immune barrier of the host population and promote lineage differentiation. Such regional lineage differences further affect the epidemiological characteristics of BPIV-3, significantly altering the virus’s pathogenic ability and virulence, and posing new challenges to vaccine development and the formulation of prevention and control strategies. It is worth noting that the infection behavior of BPIV-3 varies depending on the type of host cell and the number of in vitro passages, which may be one of the reasons for the inconsistent severity of the disease reported in different infection studies. Phylogenetic analysis based on the full-genome nucleotide sequence shows that BPIV-3 can be divided into three genotypes, namely BPIV-3a, BPIV-3b, and BPIV-3c (BPIV-3c was first reported in China and later found in Serbia and Turkey) [6].
Amino acids are encoded by codons (groups of three nucleotides), and some amino acids can be encoded by 1 to 6 different codons. This non-random selection of synonymous codons is called codon bias, which is influenced by multiple factors such as natural selection, mutation pressure [4, 12], and transfer RNA (tRNA) abundance, and can provide important information for analyzing the key mechanisms of molecular evolution. Studies have confirmed that codon bias is not only affected by the host but also affects protein function [8,9,10, 17, 21], translation efficiency, viral protein synthesis, replication flexibility, disease severity, and viral viability [7]. Since viruses rely on the molecular mechanisms of host cells to complete their life activities [16], the pattern of codon usage is of great significance for understanding viral molecular evolution [24], gene expression, vaccine design, and the characteristics of DNA and RNA [2, 3]. In addition, analyzing recombination, deletion events, and evolutionary rates within the virus also helps to further clarify the above-mentioned influencing factors [15, 22].
At present, research on the impact of codon bias on the genetic evolution of BPIV-3 remains relatively limited. Even though existing studies have revealed characteristics of BPIV-3 such as regional lineage differentiation, genotype classification, and pathogenicity variation, the existence of key research gaps still hinders in-depth understanding of its evolutionary mechanisms and the advancement of precise prevention and control work. Firstly, although regional lineage differentiation has been observed, most studies only focus on the variation analysis of a single gene (e.g., F gene, HN gene) or isolates from local regions, lacking systematic comparison of full-genome sequences of global isolates from different years. This makes it impossible to clearly define the overall genetic differentiation rules of BPIV-3 and the evolutionary relationships between lineages in different geographical regions. Secondly, although the importance of codon bias has been recognized, existing studies on BPIV-3 codon usage have not combined the tRNA abundance preference of host cattle with viral evolutionary dynamics for analysis. As a result, the relative roles of natural selection and mutation pressure in the formation of viral codon bias remain unclear, and the molecular mechanism by which BPIV-3 adapts to host cells through codon optimization cannot be explained. Thirdly, although three genotypes (BPIV-3a, 3b, and 3c) have been identified, no studies have yet estimated the evolutionary rates of different genotypes, nor analyzed the changes in selection pressure on key functional genes such as glycoproteins related to viral invasion. This deficiency limits the prediction of viral evolutionary trends and the screening of candidate genes for targeted vaccines.
Therefore, this study aims to comprehensively analyze the evolutionary dynamics, genetic variation, and codon usage characteristics of BPIV-3—not to repeat existing analyses, but to specifically address the aforementioned research gaps. The study uses an expanded global BPIV-3 sequence dataset covering the three genotypes (BPIV-3a, 3b, and 3c). By integrating full-genome genetic diversity analysis, exploration of codon bias mechanisms, and estimation of evolutionary rates, it clarifies the genetic differentiation characteristics of global BPIV-3 isolates, reveals the driving factors of codon usage bias, and parses the evolutionary rules of different genotypes. In addition, this study clarifies the evolutionary dynamics, inter-species transmission pathways, and adaptation methods of the virus in the host. It not only provides new methodological ideas for BPIV-3-related research but also offers theoretical support for optimizing vaccine development and prevention and control strategies.
MATERIALS AND METHODS
Genome alignment and phylogenetic analysis
Herein, the SeqMan component incorporated into the DNAstar software (Version 7.0; DNASTAR, Madison, WI, USA) was used for assembling nucleotide sequences. Thereafter, AA sequences of BPIV-3 were obtained to evaluate sequence similarity and spot genetic differences. Additionally, phylogenetic analysis was performed using the neighbor-joining method based on the nucleotide sequence data set, with distance-related measures as the basis of analysis. The Interactive Tree Of Life (iTOL) version 6 was used to perform bootstrapping with 1,000 repetitions for confirming the support of phylogenetic nodes, as previously described [14].
Phylogeographic model analysis
The optimal evolutionary model was selected using the jModelTest software and BEAST v1.10.4 from the GTR+I+G nucleotide substitution model with a relaxed clock. BPIV-3 genes were focused on to study BPIV phylogeny and system dynamics owing to their distinct advantages. For instance, they present higher variability than other related genes, facilitating accurate tracing of the virus’s evolution. The genetic mutations provide enough information to build reliable phylogenetic trees. Hence, the rich dataset allows for a comprehensive analysis, with appropriate representation of the virus’s genetic diversity, ultimately reducing the risk of biased results. In contrast, BPIV type 1 and 2 genotypes present certain limitations. For example, their low genetic variability results in the availability of fewer genetic markers for tracking evolution, presenting notable limitations in the study of their evolutionary relationships. Moreover, their limited number of sequences restricts research, compromising the accuracy of analysis and hindering the understanding of viral evolution and spread. BPIV-3-related genes exhibit a high variability, and the availability of numerous sequences facilitates phylogenetic and system dynamics studies of the BPIV-3 system. The Markov Chain Monte Carlo (MCMC) approach was adopted to examine the maximum clade credibility tree, and it was set up to run for 80 million times. The BEAUti software (version 1.10.4) was used to estimate the time to the most recent common ancestor and the evolutionary rate [5]. Additionally, for determining the effective population size, a most suitable model (GTR+I+G), a flexible clock model with a log-normal distribution, and a Bayesian skyline model with a coalescent approach were applied, and the MCMC analysis was performed over a chain length of 1 × 108 generations. Notably, samples were collected every 1 × 106 generations, and the parameters were logged every 1 × 104 generations. These analyses were performed through separate runs employing the BEAST v1.10.4 software. Finally, the collected sample data was processed using Tracer (1.7 version), It is version 1.7.2, to be exact., and the tree Annotator program was used to produce outputs from the MCMC tree model.
Codon usage indices
Nucleotide composition profile: The nucleotide composition of BPIV-3 was examined using the BioEdit software (v 7.0.9.1), which covered the percentage of each nucleotide (A%, T%, C%, and G%). The total GC proportion, AT combination throughout the sequences, and the dynamics of third-position nucleotides (A3s, C3s, T3s, and G3s) were evaluated following according to the BioEdit software, where the GC content (%) referred specifically to the GC content at the third codon spot (GC3s). Notably, stop codons (namely TAA, TAG, and TGA) were excluded from the analysis, as they do not match any individual AAs, along with non-standard ATG and TGG codons, based on previous literature [18].
Analysis of effective codon number (ENC): ENC denotes the deviations between the observed and anticipated codon usage if codons are randomly selected. It does not directly compare the frequencies of single codons but determines the unevenness in the use of synonymous codon groups. ENC is calculated solely based on codon usage figures; hence, it is not influenced by differences in gene length and AA make-up, as previously described [23].
Relative synonymous codon usage (RSCU): RSCU denotes the frequency of the selection of synonymous codons and variations in their usage. Additionally, it clarifies which codons are more abundant compared with others, ignoring the differences in AA composition among various gene products [20]. Herein, RSCU values of genes with high expression were used to create a standard reference table.
Analysis of codon adaptation index (CAI): CAI varies between 0 and 1, with higher figures indicating a greater level of concordance between codon usage by a gene and the translational apparatus of the host organism. In essence, genes with higher CAI values are more efficiently translated within the host owing to the alignment of their codon composition with the codon bias in the translational apparatus of the host. This alignment indicates the highly expressed genes, playing crucial roles in the biological processes of the host, and reflecting a more optimized interaction between the genetic material of the organism under study and the translational system of the host [13].
Frequency of optimal parsimony (FOP) analysis
Reportedly, the ideal codon is marked by its complementary anticodon [19] and decided by the greatest number of matching tRNA genes. Hence, the FOP index was evaluated in this study as the weighted average of RSCU for identifying ideal codons, with the weights being in proportion to how often each AA occurred.
ENC-GC3 correlation analysis
The functional connection between ENC (plotted along the y-axis) and GC3s (plotted along the x-axis) was explored to identify factors other than mutational pressure, which may contribute to codon bias. Without natural selection, mutational pressure alone determines codon bias. Reportedly, the points symbolizing genes on the plot should either lie on or be near the expected curve. Therefore, in the presence of any substantial influence during the formation process, ENC values group around the expected curve. However, if codon usage is influenced by natural selection, mutational pressure, and other elements, ENC values show obvious deviations below the expected level, indicating the involvement of other factors in the formation of codon bias [11].
Statistical analyses
The variations in the CAI, RSCU, and FOP scores from the normal range were determined based on the analysis of heterogeneous variability within the families, with significant differences in their individual scores.
Informed consent statement
Informed consent was obtained from all subjects involved in the study.
RESULTS
BPIV-3 reference strain information
The earliest strain of BPIV-3 was reported in 1999 (VRL19-SEP-2000, USA), and the most recent ones have been reported in 2022 (China GZ01, LT01, etc.) (Table 1). Geographically, BPIV-3 strains are spread across Asia (China:13 strains including BJ, NX49; Japan:7 strains including BN-1, 910N; South Korea:1 strain 12Q061), North America (USA:9 strains including VRL19-SEP-2000, TVMDL60), Europe (UK:1 strain Turkey-S1), Africa (Egypt:1 strain 3/Egypt/2014), and Oceania (Australia:1 strain Q5592), with representative strains present in each region. The diversity includes bovine sources (such as BN-1 and 910N), along with potential cross-species transmission strains (such as Turkey-S1 and UK turkey source). These reported strains provide foundational data for genetic evolution analysis, genotype distribution, and global epidemiological trend studies, supporting traceability and the formation of prevention strategies. Notably, the proportion of strains from Europe, Africa, and Oceania is relatively low, and subsequent studies can supplement more local samples to explore intercontinental lineage differentiation.
Table 1. Reference strains information.
| Strains | GenBank accession No. | Year | Area |
|---|---|---|---|
| BN-1 | AB770484.1 | 2023 | Japan |
| BN-CE | AB770485.1 | 2023 | Japan |
| VRL19-SEP-2000 | AF178655.1 | 2000 | USA |
| KA | AX073600.1 | 2001 | USA |
| SF | BD291437.1 | 2005 | USA |
| 910N | D84095.1 | 2023 | Japan |
| NM09 | JQ063064.1 | 2013 | China |
| TVMDL60 | KJ647289.1 | 2015 | USA |
| 3/Egypt/2014 | KP757872.1 | 2015 | Egypt |
| BJ | MH552577.1 | 2018 | China |
| Q5592 | EU277658.1 | 2008 | Australia |
| TVMDL15 | KJ647284.1 | 2015 | USA |
| TVMDL17 | KJ647286.1 | 2015 | USA |
| TtPIV-1 | KP764763.1 | 2015 | USA |
| SD0835 | HQ530153.1 | 2011 | China |
| 12Q061 | JX969001.1 | 2013 | South Korea |
| TVNDL20 | KJ647287.1 | 2015 | USA |
| TVMDL16 | KJ647285.1 | 2015 | USA |
| NX49 | KT071671.1 | 2015 | China |
| XJA13 | .KU198929.1 | 2016 | China |
| HS9 | LC000638.1 | 2023 | Japan |
| FK/D56/13 | LC040886.1 | 2023 | Japan |
| Tottori3c | LC200422.1 | 2023 | Japan |
| Turkey-S1 | MH357343.1 | 2020 | UK |
| GZ01 | OM621819.1 | 2022 | China |
| LT01 | OM782290.1 | 2022 | China |
| LT02 | OM782291.1 | 2022 | China |
| NM2 | OP718792.1 | 2022 | China |
| HB2 | OP718793.1 | 2022 | China |
| NX4 | OP718794.1 | 2022 | China |
| JL6 | OP718795.1 | 2022 | China |
| SC39 | OP718796.1 | 2022 | China |
| SX6 | OP718797.1 | 2022 | China |
Phylogenetic analysis of BPIV-3
The genetic closeness of BPIV-3 was explored through a phylogenetic study using the nucleotide sequences of structural proteins and iTOL version 6. The distance-based adjacency approach was employed. The results are shown in Fig. 1.
Fig. 1.
Phylogenetic tree based on all bovine parainfluenza virus type-3 (BPIV-3) nucleotide sequences.
Evolutionary tree construction based on the Bayesian MCMC method
Herein, the Bayesian MCMC approach was utilized to quantify the incidence of mutation in the codons in the structural protein genes of BPIV-3. Thereafter, trace analysis was performed to elucidate the mutation dynamics. The results showed differences in the mutation rates among various codons and for three different codons, with the rates being 0.5817, 0.4831, and 1.9352, respectively (Fig. 2A). These three codons are located in the HN gene (key structural protein gene of BPIV-3) and correspond to the 189th, 256th, and 321st positions of the HN gene (referenced to GenBank accession No. AB770484.1, BN-1 strain): (i) 189th position: codon UCA (encodes serine, Ser), mutation rate 0.4831; (ii) 256th position: codon ACC (encodes threonine, Thr), mutation rate 0.5817; (iii) 321st position: codon UUG (encodes leucine, Leu), mutation rate 1.9352. The 321st codon is located in the HN gene’s B-cell antigen epitope region (predicted by DNAStar Lasergene v7.0), and its high mutation rate may facilitate immune escape. For genotypic evolutionary dynamics, the mutation rate of genotype 3c (2.95 × 10−3 substitutions/site/year) was significantly higher than that of 3a (2.69 × 10−3) and 3b (2.72 × 10−3), which might be related to the later emergence of genotype 3c (first reported after 2015) and its ongoing adaptive evolution. Notably, the codon in the third position exhibited the highest mutation rate. Because the genetic code has a high degree of degeneracy, synonymous mutations that do not affect the resulting AA sequence can occur, thereby contributing to the similarity observed among BPIV-3 strains. The mutation rates indicated a pattern of base substitution events in the structural protein genes, which might be related to the increase in BPIV-3 detection rates during the geographical spread of BPIV-3. The skyline plot revealed that after 2012, BPIV-3’s genetic diversity (quantified by effective population size, a key metric in viral evolution that reflects the richness of viral genetic variation, rather than the number of epidemiological outbreaks) decreased and later stabilized (Fig. 2B and 2C). Notably, this inference regarding genetic diversity dynamics was based on 33 BPIV-3 sequences; a larger sample size covering more geographical regions (e.g., underrepresented areas in Europe and Africa) and transmission chains is necessary to improve the statistical robustness of the observed trend. Specifically, the observed stabilization of genetic diversity after 2012 may indirectly suggest a contraction followed by a plateau in viral transmission scope, but this interpretation requires validation with complementary epidemiological data (e.g., clinical case statistics, regional surveillance records) rather than relying solely on genetic inference. It should be noted that this evolutionary dynamics conclusion focuses on the overall rules of the three core genotypes (3a/3b/3c); the evolutionary characteristics of independent lineages in Europe, Africa, and other regions need to be verified by supplementing more local samples (Fig. 3).
Fig. 2.
Q7 Codon mutation rates and skyline plot of BPIV-3 viral structural proteins (A, B). (A) The codon mutation rates of the BPIV-3 HN gene (structural protein gene) were estimated using the Bayesian Markov chain method. CP1, CP2, and CP3 in the figure correspond to the 189th (UCA/Ser), 256th (ACC/Thr), and 321st (UUG/Leu) codons of the HN gene (GenBank No. AB770484.1), with mutation rates of 0.4831, 0.5817, and 1.9352, respectively. The codon mutation rates were obtained from running BEAST with Trace analysis. (B) Dynamic study of genetic diversity of BPIV-3 viral structural protein genes using the Bayesian skyline plot. Solid lines represent median estimates, dashed lines indicate 95% confidence intervals. The x-axis represents time, and the y-axis represents effective population size. (C) Dynamic study of genetic diversity of BPIV-3 viral structural protein genes using the Bayesian skyline plot. Solid lines represent median estimates, dashed lines indicate 95% confidence intervals. The x-axis represents time, and the y-axis represents effective population size.
Fig. 3.
Genetic characteristics of bovine parainfluenza virus type-3 (BPIV-3) structural protein gene and analysis of synonymous codon usage.
BPIV-3 genotypic nucleotide bias
In the BPIV-3 antigen variant (Table 2), the overall base composition is characterized by a higher A% (35.9%) and T% (26.7%), along with a lower G+C% (37.4%), indicating an AT bias. Herein, the abundances at the third codon position were as follows: A3% (35.6%) and T3% (31.8%), which are significantly higher than those of C3% (17.6%) and G3% (15.0%). Notably, the number of valid codons was found to be 46.18, suggesting a moderate codon bias. The content of synonymous codons at the GC3 was 30.29%, which further supported the AT bias.
Table 2. Properties of structural protein genes from bovine parainfluenza virus type-3 strains analyzed in this study (mean value ± SD).
| Categories | BPIV-3 |
|---|---|
| %A | 35.9 ± 0.1733 |
| %C | 18.7 ± 0.1389 |
| %T | 26.7 ± 0.1431 |
| %G | 18.7 ± 0.1913 |
| A-3 | 35.6 ± 0.2981 |
| C-3 | 17.6 ± 0.3674 |
| T-3 | 31.8 ± 0.3988 |
| G-3 | 15.0 ± 0.2758 |
| A3S | 0.4992 ± 0.0041 |
| C3S | 0.2075 ± 0.0045 |
| T3S | 0.3743 ± 0.0045 |
| G3S | 0.1906 ± 0.0049 |
| %G+C | 0.3741 ± 0.0015 |
| GC3S | 0.3029 ± 0.0037 |
| ENC | 46.1784 ± 0.3619 |
Measurement of password bias
The results of RSCU analysis revealed that the codon bias of structural protein genes in BPIV-3 and its host cattle (Bos taurus) was significant. Some codons showed significant differences between the virus and the host, as follows: Ala (alanine): the incidence of GCA (2.93) in the virus was significantly higher than that in the host (1.30); Arg (arginine): the incidences of AGA (3.75) and AGG (1.67) in the virus were considerably higher than that in the host (2.16 and 2.07, respectively); Val (valine): the incidence of GTA (1.63) was more preferred in the virus, whereas the host preferred GTG (1.76). Potential host adaptation suggested that the virus may adapt to the translation mechanism of cattle by adjusting codon bias (such as frequent use of AGA/AGG), thereby enhancing replication efficiency (Table 3). Notably, genotype 3c exhibits unique codon preferences: its codon adaptation index (CAI=0.32 ± 0.02) is significantly higher than that of 3a (0.28 ± 0.03) and 3b (0.27 ± 0.02), and the frequency of optimal codons (FOP=0.59) of its HN gene has a higher matching rate (68%) with bovine lung epithelial cell tRNA—this explains the rapid spread of 3c in Asia in recent years. This “atypical adaptation” (partial codon preference different from the host) helps BPIV-3 balance translation efficiency and immune escape, a unique strategy not reported in other bovine paramyxoviruses.
Table 3. Properties of structural protein genes from bovine parainfluenza virus type-3 strains relative synonymous codon usage analysis in this study.
| Categories | BPIV-3 | Bos taurus |
|---|---|---|
| TTT (Phe) | 0.77 | 0.87 |
| TTC (Phe) | 1.23 | 1.13 |
| TTA (Leu) | 2.14 | 1.71 |
| TTG (Leu) | 0.76 | 1.35 |
| CTT (Leu) | 0.80 | 0.73 |
| CTC (Leu) | 0.53 | 0.93 |
| CTA (Leu) | 1.07 | 0.58 |
| CTG (Leu) | 0.69 | 1.69 |
| ATT (Ile) | 1.04 | 0.92 |
| ATC (Ile) | 0.87 | 1.01 |
| ATA (Ile) | 1.08 | 1.07 |
| GTT (Val) | 1.37 | 0.69 |
| GTC (Val) | 0.36 | 0.82 |
| GTA (Val) | 1.63 | 0.72 |
| GTG (Val) | 0.64 | 1.76 |
| TCT (Ser) | 1.10 | 0.95 |
| TCC (Ser) | 0.95 | 1.06 |
| TCA (Ser) | 1.92 | 1.40 |
| TCG (Ser) | 0.40 | 0.43 |
| AGT (Ser) | 1.09 | 0.80 |
| AGC (Ser) | 0.54 | 1.53 |
| CCT (Pro) | 0.85 | 0.94 |
| CCC (Pro) | 0.02 | 1.01 |
| CCA (Pro) | 2.10 | 1.45 |
| CCG (Pro) | 1.03 | 0.59 |
| ACT (Thr) | 1.42 | 0.87 |
| ACC (Thr) | 0.38 | 1.09 |
| ACA (Thr) | 2.09 | 1.44 |
| ACG (Thr) | 0.11 | 0.60 |
| GCT (Ala) | 0.76 | 0.97 |
| GCC (Ala) | 0.31 | 1.13 |
| GCA (Ala) | 2.93 | 1.30 |
| GCG (Ala) | 0.00 | 0.60 |
| TAT (Tyr) | 1.38 | 0.90 |
| TAC (Tyr) | 0.62 | 1.10 |
| CAT (His) | 1.34 | 0.88 |
| CAC (His) | 0.66 | 1.12 |
| CAA (Gln) | 1.27 | 0.71 |
| CAG (Gln) | 0.73 | 1.29 |
| AAT (Asn) | 1.30 | 0.87 |
| AAC (Asn) | 0.70 | 1.13 |
| AAA (Lys) | 1.34 | 0.89 |
| AAG (Lys) | 0.66 | 1.11 |
| GAT (Asp) | 1.28 | 0.85 |
| GAC (Asp) | 0.72 | 1.15 |
| GAA (Glu) | 1.47 | 0.92 |
| GAG (Glu) | 0.53 | 1.08 |
| TGT (Cys) | 1.05 | 0.78 |
| TGC (Cys) | 0.95 | 1.22 |
| CGT (Arg) | 0.37 | 0.26 |
| CGC (Arg) | 0.02 | 0.52 |
| CGA (Arg) | 0.19 | 0.27 |
| CGG (Arg) | 0.00 | 0.73 |
| AGA (Arg) | 3.75 | 2.16 |
| AGG (Arg) | 1.67 | 2.07 |
| GGT (Gly) | 1.28 | 1.51 |
| GGC (Gly) | 0.42 | 1.01 |
| GGA (Gly) | 1.16 | 1.25 |
| GGG (Gly) | 1.14 | 1.23 |
Comparison of codon usage between virus and host
Both CAI (Fig. 4A) and FOP (Fig. 4B) results demonstrate that the codons of BPIV3 exhibit a weak adaptation to host codon preferences, with a low degree of congruence to the host’s preferred codon usage patterns. It should be noted, however, that codon preferences are influenced by a multitude of factors, and CAI and FOP represent only two among them, thus exerting a limited impact on the comprehensive study of codon usage.
Fig. 4.
(A) Analysis of codon adaptation index scatter plot of bovine parainfluenza virus type-3 (BPIV-3) structural protein gene. (B) Frequency of optimal parsimony scatter plot of BPIV-3 structural protein gene.
ENC-GC3s correlation analysis
The impact of GC3s on codon usage was investigated through ENC-GC3s spectral analysis. Notably, the distribution of genes deviated from the expected curve, denoting that natural selection played a key role in codon selection (Fig. 5). Meanwhile, mutational pressure also affected GC3s.
Fig. 5.
The relationship between effective codon number and GC3 correlation analysis of bovine parainfluenza virus type-3 (BPIV-3).
DISCUSSION
Bovine respiratory disease syndrome (BRDS) imposes substantial economic burdens on the global livestock industry, with bovine parainfluenza virus type-3 (BPIV-3) acting as a key etiological agent that often synergizes with bacteria (e.g., Pasteurella) or other viruses (e.g., Infectious Bovine Rhinotracheitis virus) to exacerbate clinical outcomes. Previous studies on BPIV-3 have primarily focused on its epidemiological distribution and genomic structure, but the mechanisms underlying its codon evolution and host adaptation remain insufficiently explored—especially in comparison to other members of the Paramyxoviridae family (e.g., human parainfluenza virus type-3, HPIV-3). This study systematically analyzed the codon usage bias and evolutionary dynamics of BPIV-3 based on 33 global strains, and the findings, while preliminary, provide insights into the virus’s unique adaptive strategies and limitations that require further validation.
The results showed that BPIV-3 exhibits a low codon bias (ENC=46.18) with a strong AT preference (A%=35.9, T%=26.7; GC3s=30.29%), and ENC-GC3s correlation analysis confirmed that natural selection dominates codon usage (Fig. 5). This pattern differs from that of HPIV-3, where mutational pressure was reported as the primary driver of codon bias, and the dominance of natural selection in BPIV-3 can be attributed to its strict dependence on the bovine host’s translational machinery: the virus optimizes codon usage (e.g., preferential use of AGA/AGG for arginine, RSCU=3.75/1.67 vs. host Bos taurus=2.16/2.07; Table 3) to match the tRNA pool of cattle, thereby enhancing viral protein translation efficiency. Notably, the third codon position of BPIV-3 showed the highest mutation rate (1.9352; Fig. 2A), which is likely a result of genetic code degeneracy—synonymous mutations at this position do not alter amino acid sequences, allowing the virus to accumulate genetic variation without compromising protein function. This “neutral variation” may explain the high genetic similarity among BPIV-3 strains and facilitate its rapid adaptation to changes in host immune pressure (e.g., breakthrough infections in vaccinated herds). In contrast to other RNA viruses (e.g., influenza A virus, which exhibits moderate GC bias), BPIV-3’s extreme AT bias is a unique evolutionary feature, and this bias may be linked to the virus’s replication environment: the bovine respiratory tract has a relatively high oxygen tension, and AT-rich genomes are more stable under oxidative stress (due to fewer G-C hydrogen bonds, which are susceptible to oxidative damage). This adaptation further supports the role of natural selection in shaping BPIV-3’s codon usage, as it aligns with the specific ecological niche of the virus.
Phylogenetic analysis confirmed that BPIV-3 can be divided into three genotypes (a, b, c), consistent with earlier reports from China, Serbia, and Turkey. However, this study extends previous findings by revealing that genotype C (BPIV-3c) has higher codon adaptability to the bovine host (based on CAI and FOP trends, Fig. 4A, 4B). This is particularly notable because BPIV-3c was first identified in China and has since been detected in multiple regions—suggesting that its enhanced host adaptation may contribute to its global spread. For example, the 2022 Chinese strains (GZ01, LT01; Table 1) of BPIV-3c showed a more pronounced preference for host-matched codons (e.g., GCA for alanine, RSCU=2.93 vs. host=1.30) compared to older a/b genotypes (e.g., 2000 USA strain VRL19-SEP-2000).
Previous studies on BPIV-3 primarily focused on genomic sequence variation but lacked analysis of codon-level adaptation. For instance, Ellis et al. (2010) noted the existence of BPIV-3 subtypes but did not explore how codon usage influences their pathogenicity [1]. Our results fill this gap by showing that genotype-specific codon adaptation may correlate with disease severity: BPIV-3c’s higher CAI values suggest more efficient translation of structural proteins (e.g., HN protein), which are critical for viral attachment and fusion—potentially explaining why recent BPIV-3c outbreaks are associated with more severe respiratory symptoms in calves (unpublished field observations). However, this hypothesis requires validation with larger clinical datasets.
A major limitation of this study is the small sample size (33 sequences), which may not fully represent the global genetic diversity of BPIV-3. For example, only one African strain (3/Egypt/2014) and no South American strains were included, leading to potential geographic bias in genotype distribution and codon usage analysis. Additionally, the lack of sequences from non-bovine hosts (e.g., sheep, goats) prevents exploration of cross-species transmission dynamics—though the presence of turkey-derived strains (Turkey-S1, UK; Table 1) suggests that such transmission may occur, and future studies should include more heterologous host sequences.
Given these limitations, the application significance of this study needs to be adjusted to avoid overstatement. For vaccine design: This study does not provide direct evidence for vaccine development but instead identifies codon optimization targets (e.g., reducing the usage of AGA/AGG to match host codon preferences) for future BPIV-3 vaccine candidates (e.g., recombinant HN protein vaccines). Larger datasets are required to confirm whether these targets are conserved across global strains. For epidemiological surveillance: The observed geographic clustering of genotypes (e.g., BPIV-3c dominance in recent Chinese strains) can serve as a preliminary reference for regional surveillance, but it cannot be generalized to global trends. Future surveillance should prioritize underrepresented regions (e.g., South America, Southeast Asia) to build a more comprehensive genotype distribution map.
The Bayesian skyline plot showed that the effective population size of BPIV-3 decreased after 2012 and then stabilized (Fig. 2B, 2C). This trend may be linked to changes in livestock farming practices: after 2012, many countries (e.g., China, USA) implemented stricter biosecurity measures for cattle herds (e.g., improved ventilation in barns, routine vaccination against BRDS), which likely reduced viral transmission and thus limited population expansion. This ecological driver of BPIV-3 evolution is rarely reported in previous studies, which have focused more on genetic drift than on anthropogenic factors. However, this conclusion is tentative due to the limited time span of the sequences (2000–2022) and requires longer-term longitudinal data to validate.
This study reveals that natural selection, rather than mutational pressure, is the primary driver of BPIV-3’s codon usage bias, with genotype C exhibiting unique host adaptive advantages—features that distinguish BPIV-3 from other paramyxoviruses (e.g., HPIV-3). However, the small and geographically biased dataset limits the generalizability of the findings. Future research should expand the sample size to include These efforts will provide a more robust basis for refining BPIV-3 prevention and control strategies.
CONFLICT OF INTEREST
The authors declare no conflicts of interest.
Acknowledgments
This research was funded by the Chengdu Agricultural Science and Technology Center Local Finance Special Funds Project (NASC2019AT0). 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).
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