Biology subjects

Corzo, C. A.

Publications and source records attributed to Corzo, C. A..

4 recordsLinked to original sources

bioRxiv2024

PRRSV-2 variant classification: a dynamic nomenclature for enhanced monitoring and surveillance

Existing genetic classification systems for porcine reproductive and respiratory syndrome virus 2 (PRRSV-2), such as restriction fragment length polymorphisms (RFLPs) and sub-lineages, are unreliable indicators of genetic relatedness or lack sufficient resolution for epidemiological monitoring routinely conducted by veterinarians. Here, we outline a fine-scale classification system for PRRSV-2 genetic variants in the U.S. Based on >25,000 U.S. open-reading-frame 5 (ORF5) sequences, sub-lineages were divided into genetic variants using a clustering algorithm. Through classifying new sequences every three months and systematically identifying new variants across eight years, we demonstrated that prospective implementation of the variant classification system produced robust, reproducible results across time and can dynamically accommodate new genetic diversity arising from virus evolution. From 2015 and 2023, 118 variants were identified, with [~]48 active variants per year, of which 26 were common (detected >50 times). Mean within-variant genetic distance was 2.4% (max: 4.8%). The mean distance to the closest related variant was 4.9%. A routinely updated webtool (https://stemma.shinyapps.io/PRRSLoom-variants/) was developed and is publicly available for end-users to assign newly generated sequences to a variant ID. This classification system relies on U.S. sequences from 2015 onwards; further efforts are required to extend this system to older or international sequences. Finally, we demonstrate how variant classification can better discriminate between previous and new strains on a farm, determine possible sources of new introductions into a farm/system, and track emerging variants regionally. Adoption of this classification system will enhance PRRSV-2 epidemiological monitoring, research, and communication, and improve industry responses to emerging genetic variants. ImportanceThe development and implementation of a fine-scale classification system for PRRSV-2 genetic variants represents a significant advancement for monitoring PRRSV-2 occurrence in the swine industry. Based on systematically-applied criteria for variant identification using national-scale sequence data, this system addresses the shortcomings of existing classification methods by offering higher resolution and adaptability to capture emerging variants. This system provides a stable and reproducible method for classifying PRRSV-2 variants, facilitated by a freely available and regularly updated webtool for use by veterinarians and diagnostic labs. Although currently based on U.S. PRRSV-2 ORF5 sequences, this system can be expanded to include sequences from other countries, paving the way for a standardized global classification system. By enabling accurate and improved discrimination of PRRSV-2 genetic variants, this classification system significantly enhances the ability to monitor, research, and respond to PRRSV-2 outbreaks, ultimately supporting better management and control strategies in the swine industry.

microbiology↗

bioRxiv2024

Phylogenetic-based methods for fine-scale classification of PRRSV-2 ORF5 sequences: a comparison of their robustness and reproducibility

Disease management and epidemiological investigations of porcine reproductive and respiratory syndrome virus-type 2 (PRRSV-2) often rely on grouping together highly related sequences. In the USA, the last five years have seen a major paradigm shift within the swine industry when classifying PRRSV-2, beginning to move away from RFLP (restriction fragment length polymorphisms)-typing and adopting the use of phylogenetic lineage-based classification. However, lineages and sub-lineages are large and genetically diverse, and the rapid mutation rate of PRRSV coupled with the global prevalence of the disease has made it challenging to identify new and emerging variants. Thus, within the lineage system, a dynamic fine-scale classification scheme is needed to provide better resolution on the relatedness of PRRSV-2 viruses to inform disease management and monitoring efforts and facilitate research and communication surrounding circulating PRRSV viruses. Here, we compare potential fine-scale systems for classifying PRRSV-2 variants (i.e., genetic clusters of closely related ORF5 sequences at finer scales than sub-lineage) using a database of 28,730 sequences from 2010 to 2021, representing >55% of the U.S. pig population. In total, we compared 140 approaches that differed in their tree-building method, criteria, and thresholds for defining variants within phylogenetic trees using TreeCluster. Three approaches produced epidemiologically meaningful variants (i.e., [≥]5 sequences per cluster), and resulted in reproducible and robust outputs even when the input data or input phylogenies were changed. In the three best performing approaches, the average genetic distance amongst sequences belonging to the same variant was 2.1 - 2.5%, and the genetic divergence between variants was 2.5-2.7%. Machine learning classification algorithms were also trained to assign new sequences to an existing variant with >95% accuracy, which shows that newly generated sequences could be assigned without repeating the phylogenetic and clustering analyses. Finally, we identified 73 sequence-clusters (dated <1 year apart with close phylogenetic relatedness) associated with circulation events on single farms. The percent of farm sequence-clusters with an ID change was 6.5-8.7% for our best approaches. In contrast, [~]43% of farm sequence-clusters had variation in their RFLP-type, further demonstrating how our proposed fine-scale classification system addresses shortcomings of RFLP-typing. Through identifying robust and reproducible classification approaches for PRRSV-2, this work lays the foundation for a fine-scale system that would more reliably group related field viruses and provide better improved clarity for decision-making surrounding disease management.

bioinformatics↗

bioRxiv2022

A discrete-time survival model for porcine epidemic diarrhea virus

Since the arrival of porcine epidemic diarrhea virus (PEDV) in the United States in 2013, elimination and control programs have had partial success. The dynamics of its spread are hard to quantify, though previous work has shown that local transmission and the transfer of pigs within production systems are most associated with the spread of PEDV. Our work relies on the history of PEDV infections in a region of the southeastern United States. This infection data is complemented by farm-level features and extensive industry data on the movement of both pigs and vehicles. We implement a discrete-time survival model and evaluate different approaches to modeling the local-transmission and network effects. We find strong evidence in that the local-transmission and pig-movement effects are associated with the spread of PEDV, even while controlling for seasonality, farm-level features, and the possible spread of disease by vehicles. Our fully Bayesian model permits full uncertainty quantification of these effects. Our farm-level out-of-sample predictions have a receiver-operating characteristic area under the curve (AUC) of 0.779 and a precision-recall AUC of 0.097. The quantification of these effects in a comprehensive model allows stakeholders to make more informed decisions about disease prevention efforts.

ecology↗

bioRxiv2020

On reproductive and respiratory syndrome virus dissemination across pig production systems in the United States

Porcine reproductive and respiratory syndrome virus (PRRSV) remains widespread in the North American pig population. Despite improvements in virus characterization, it is unclear whether PRRSV infections are a product of viral circulation within a farm, within production systems (local) or across production systems (external). Here we examined the dissemination dynamics of PRRSV and the processes facilitating its spread within and among pig farms in three production systems. Overall, PRRSV genetic diversity declined since 2018, while phylodynamic results support frequent transmission across-production systems. We found that PRRSV dissemination occurred mostly through transmission between farms of different production companies, which were predominant for several months, especially from November until May when PRRSV tends to peak in the studied region. Within production systems, dissemination occurred mainly through regular pig flow (from sow to nursery and then to finisher farms); nevertheless, an important flux of PRRSV dissemination from finisher to sow and nursery farms highlighted the importance of downstream farms as sources of the virus. Farms at areas with pig density from 500 to 1000 pig/km2 and farms located at a range within 0.5 km and 0.7 km from major roads were more likely to infect by PRRSV, whereas farms at elevation between 41 and 61 meters and denser vegetation acted as dissemination barriers. Although remains a challenge, there is a need to disentangle the route of PRRSV transmission, results evidenced that dissemination among commercially unrelated pig production systems was intense, reinforcing the importance of farm proximity on PRRSV spread. Thus, consideration of farm location and their geographic characteristics may help to forecast dissemination. The understanding of PRRSV transmission routes has the potential to inform targeted strategies for its prevention and control. Further studies are needed to quantify the relative contribution of PRRSV transmission routes.

evolutionary biology↗