Potential Novel N-Glycosylation Patterns Associated with the Emergence of New Genetic Variants of PRRSV-2 in the U.S

Paploski, Igor Adolfo Dexheimer; Makau, Dennis N.; Pamornchainavakul, Nakarin; Baker, Julia P.; Schroeder, Declan C.; Rovira, Albert; VanderWaal, Kimberly

doi:10.3390/vaccines10122021

Cited by 9 publications

(7 citation statements)

References 34 publications

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“…N-glycosylation pattern similarity parameter focuses on specific amino acid sites involved in potential glycan shielding, which is one immune evasion mechanism utilized by PRRSV-2 [74,75]. These sites have evolved under positive selection pressure [12,[76][77][78] and are believed to be associated with emergence at the sublineage level [7,79]. However, N-glycosylation pattern similarity was not significantly associated with a variant's success in any of our predictive models.…”

Section: Discussionmentioning

confidence: 91%

Predicting Potential PRRSV-2 Variant Emergence through Phylogenetic Inference

Pamornchainavakul,

Kikuti,

Paploski

et al. 2024

Transboundary and Emerging Diseases

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Porcine reproductive and respiratory syndrome (PRRS) is a significant pig disease causing substantial annual losses exceeding half a billion dollars to the United States pork industry. The cocirculation and emergence of genetically distinct PRRSV-2 viruses hinder PRRS control, especially vaccine development. Similar to other viral infections like seasonal flu and SARS-CoV-2, predictive tools for identifying potential emerging viral variants may prospectively aid in preemptive disease mitigation. However, such predictions have not been made for PRRSV-2, despite the abundance of relevant data. In this study, we analyzed a decade’s worth of virus ORF5 sequences (n = 20,700) and corresponding metadata to identify phylogenetic-based early indicators for short-term (12 months) and long-term (24 months) variant emergence. Our analysis focuses on PRRSV-2 Lineage 1, which was the predominant lineage within the U.S. during this period. We evaluated population expansion, spatial distribution, and genetic diversity as key success metrics for variant emergence. Our findings indicate that successful variants were best characterized as those that underwent population expansion alongside widespread geographical spread but had limited genetic diversification. Conditional logistic regression revealed the local branching index as the sole informative indicator for predicting population expansion (balanced accuracy (BA) = 0.75), while ancestral branch length was strongly linked to future genetic diversity (BA = 0.79). Predicting spatial dispersion relied on the branch length and putative antigenic difference (BA = 0.67), but their causal relationships remain unclear. Although the predictive models effectively captured most emerging variants (sensitivity = 0.58–0.81), they exhibited relatively low positive predictive value (PPV = 0.09–0.16). This initial step in PRRSV-2 prediction is a crucial step for more precise prevention strategies against PRRS in the future.

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Section: Discussionmentioning

confidence: 91%

Predicting Potential PRRSV-2 Variant Emergence through Phylogenetic Inference

Pamornchainavakul,

Kikuti,

Paploski

et al. 2024

Transboundary and Emerging Diseases

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“…The positive selection site in GP5 can be driven by the host immune response (Music and Gagnon, 2010). In PRRSV-2, positive selection pressure acts on amino acid sites potentially associated with immune escape through glycan shielding [ 46 ]. Selection pressure analysis indicated that selection for situational diversification along the L1.8 (L1C) PRRSV phylogenetic tree was rare on the farms tested, regardless of vaccine use.…”

Section: Discussionmentioning

confidence: 99%

Prevalence and genetic evolution of porcine reproductive and respiratory syndrome virus in commercial fattening pig farms in China

Li,

Zhao,

et al. 2024

Porc Health Manag

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Background To investigate the prevalence and evolution of Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) at commercial fattening pig farms, a total of 1397 clinical samples were collected from a single fattening cycle at seven pig farms in five provinces of China from 2020 to 2021. Results The RT‒PCR results revealed that PRRSV was present on all seven farms, and the percentage of PRRSV-positive individuals was 17.54–53.33%. A total of 344 partial NSP2 gene sequences and 334 complete ORF5 gene sequences were obtained from the positive samples. The statistical results showed that PRRSV-2 was present on all seven commercial fattening farms, and PRRSV-1 was present on only one commercial fattening farm. A total of six PRRSV-2 subtypes were detected, and five of the seven farms had two or more PRRSV-2 subtypes. L1.8 (L1C) PRRSV was the dominant epidemic strain on five of the seven pig farms. Sequence analysis of L1.8 (L1C) PRRSV from different commercial fattening pig farms revealed that its consistency across farms varied substantially. The amino acid alignment results demonstrated that there were 131 aa discontinuous deletions in NSP2 between different L1.8 (L1C) PRRSV strains and that the GP5 mutation in L1.8 (L1C) PRRSV was mainly concentrated in the peptide signal region and T-cell epitopes. Selection pressure analysis of GP5 revealed that the use of the PRRSV MLV vaccine had no significant episodic diversifying effect on L1.8 (L1C) PRRSV. Conclusion PRRSV infection is common at commercial fattening pig farms in China, and the percentage of positive individuals is high. There are multiple PRRSV subtypes of infection at commercial fattening pig farms in China. L1.8 (L1C) is the main circulating PRRSV strain on commercial fattening pig farms. L1.8 (L1C) PRRSV detected at different commercial fattening pig farms exhibited substantial differences in consistency but similar molecular characteristics. The pressure on the GP5 of L1.8 (L1C) PRRSV may not be directly related to the use of the vaccines.

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“…For each polymorphic amino acid site, serum-virus pairs were tabulated as 0 if they shared the same amino acid and 1 if they had a different amino acid at that site. We also scanned the genome for sites of potential N-glycosylation (31, 32) in the VP1 region using a custom-built R script. Pairs with identical sets of inferred glycosylated sites were coded as 0 and non-identical sets as 1.…”

Section: Methodsmentioning

confidence: 99%

Machine learning approaches for estimating cross-neutralization potential among FMD serotype O viruses

Makau,

Arzt,

VanderWaal

2024

Preprint

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In this study, we aimed to develop an algorithm that uses sequence data to estimate cross-neutralization between serotype O foot-and-mouth disease viruses (FMDV) based on r1 values, while identifying key genomic sites associated with high or low r1 values. The ability to estimate cross-neutralization potential among co-circulating FMDVs in silico is significant for vaccine developers, animal health agencies making herd immunization decisions, and disease preparedness. Using published data on virus neutralization titer (VNT) assays and associated VP1 sequences from GenBank, we applied machine learning algorithms (BORUTA and random forest) to predict potential cross-reaction between serum/vaccine-virus pairs for 73 distinct serotype O FMDV strains. Model optimization involved tenfold cross-validation and sub-sampling to address data imbalance and improve performance. Model predictors included amino acid distances, site-wise amino acid polymorphisms, and differences in potential N-glycosylation sites. The dataset comprised 108 observations (serum-virus pairs) from 73 distinct viruses with r1 values. Observations were dichotomized using a 0.3 threshold, yielding putative non-cross-neutralizing (< 0.3 r1 values) and cross-neutralizing groups (≥ 0.3 r1 values). The best model had a training accuracy, sensitivity, and specificity of 0.96 (95% CI: 0.88-0.99), 0.93, and 0.96, respectively, and an accuracy of 0.94 (95% CI: 0.71-1.00), sensitivity of 1.00, and specificity of 0.93, positive, and negative predictive values of 0.60 and 1.00, respectively, on one testing dataset and an accuracy, AUC, sensitivity, specificity, and predictive values all approaching 1.00 on a second testing dataset. Additionally, amino acid positions 48, 100, 135, 150, and 151 in the VP1 region alongside amino acid distance were found to be important predictors of cross-neutralization. Our study highlights the value of genetic/genomic data for informing immunization strategies in disease management and understanding potential immune-mediated competition amongst related endemic strains of serotype O FMDVs in the field. We also showcase leveraging routinely generated sequence data and applying a parsimonious machine learning model to expedite decision-making in selection of vaccine candidates and application of vaccines for controlling FMD, particularly serotype O. A similar approach can be applied to other serotypes.

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Potential Novel N-Glycosylation Patterns Associated with the Emergence of New Genetic Variants of PRRSV-2 in the U.S

Cited by 9 publications

References 34 publications

Predicting Potential PRRSV-2 Variant Emergence through Phylogenetic Inference

Predicting Potential PRRSV-2 Variant Emergence through Phylogenetic Inference

Prevalence and genetic evolution of porcine reproductive and respiratory syndrome virus in commercial fattening pig farms in China

Machine learning approaches for estimating cross-neutralization potential among FMD serotype O viruses

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