By Diane DeWitte, UMN Extension swine educator
You may recall that Dr. Kim VanderWaal’s group in the Department of Veterinary Population Medicine has been classifying PRRS virus genetics to better learn which viruses are related to each other, and therefore more thoroughly identify virus movement in PRRS outbreaks. Her team at the UMN, along with colleagues at Iowa State University and the USDA -ARS Virus & Prion Research Unit at the National Animal Disease Center at Ames, Iowa, recently published data from this study. One objective of this work has been the development of a fine-scale classification system which can be more easily used across industry when discussing the virus and its movements. Data and discussion here is found in the group’s research article published in February 2025, in the American Society of Microbiology’s mSphere, Volume 10, Issue 2: https://journals.asm.org/doi/10.1128/msphere.00709-24.
In PRRS Part 1 we discussed research project terminology. The genome is the organism’s complete DNA, the phylogeny is the virus’s family tree, and a variant is an alteration in the DNA sequence, also known as a mutation. The UMN research team uses the sequencing of the OFR5 gene to understand and analyze PRRSV evolution.
PRRS virus has two species, PRRSV-1 and PRRSV-2. The two species are genetically distinct, but the clinical signs are the same. The study cited here concentrates on PRRSV-2 because it is more common in North America.
Currently, the naming method used by the industry to discriminate between sequences is restriction fragment length polymorphism (RFLP) typing, sometimes in combination with an additional label corresponding to phylogenetic lineage. However, lineages and sub-lineages are large and diverse and hence are too coarse for on-farm disease monitoring, and using RFLP types to refer to PRRSV-2 viruses often leads to misleading conclusions (e.g., viruses assigned to the same RFLP type often are not genetically similar and vice versa). For example, RFLP 1-4-4, which is one of the most abundantly reported RFLP types in the United States today, occurs in seven different lineages.
In previous work, VanderWaal et al. evaluated and compared 140 approaches for fine-scale classification of OFR5 sequences. Three approaches were found to be robust and reproducible across trees built with different methods and data and thus could form the foundation for fine-scale classification of PRRSV-2 below the sub-lineage level.
However, previous work did not explore the performance of PRRSV-2 variant classification on a rolling basis, and it is necessary to validate the performance and associated procedures for fine-scale classification that accommodates expanding genetic diversity on a prospective basis.
Taking insights and needs of practitioners and diagnosticians alongside a rigorous comparison of alternative approaches for classifying PRRSV-2, the team recently introduced a new fine-scale genetic classification system for PRRSV-2 that is tailored to meet the needs of animal health professionals.
Specifically, researchers outlined criteria used for defining PRRSV-2 genetic variants, established and tested procedures for prospective implementation of the system, and assessed the adaptability of the classification system to accommodate expanding genetic diversity at national scales.
The team introduced a machine-learning webtool that can be used to identify the variant to which newly generated sequences belong and introduce naming conventions for PRRSV-2 variants. They also reported the results of a survey conducted with field practitioners on their motivations for submitting samples for sequencing and demonstrate how variant classification can enhance the utility of sequence data for the purposes of epidemiological monitoring and surveillance.
As you might imagine, this undertaking involved dozens of researchers, thousands of PRRSV-2 samples, hundreds of veterinarians and many years. The group began the classification of variants using PRRSV-2 ORF5 sequences in 2015. Between 2015 and 2023, 25,403 sequences were analyzed and compared. The sequence was defined as a variant if it 1) had five or more sequences, 2) showed greater than 85% shared ancestry (in the ORF5 phylogenetic tree), 3) and was more than 2% different from the nearest named variant. If the sequence did not meet these three criteria, it was named “unclassified”.
Using continuously improving computational power, the team trained algorithms to assess and assign sequences to their appropriate variant groups. An accessible webtool was developed and is updated quarterly wherein veterinary practitioners and diagnosticians can submit PRRSV-2 sequences to learn the sample’s variant classification. By updating the training data set and algorithms quarterly, researchers improve the tool’s accuracy.
The group’s fine-scale classification system identified 118 genetic variants, 37 of which were common (detected more than 50 times) and 19 sequences were rare, meaning they were detected less than 10 times.
Variant nomenclature incorporated the sub-lineage to which the variant belonged, followed by an integer (i.e., 1A.3 and 1H.3 are the third variants identified within sub-lineages 1A and 1H, respectively). For contemporary sequences (2015 onward), several variants had a one-to-one correspondence with vaccine-like sequences; these variants matched PRRS vaccines which had been developed by American livestock pharmaceutical companies.
Variant classification will facilitate communication about outbreaks, tracking of emerging and endemic variants across time and space, as well as provide a framework to more rigorously analyze the genetic basis of variability in phenotype or production impacts.
Finally, this work used feedback from a working group of veterinarians, researchers, and diagnosticians. This close engagement with stakeholders and end users has been crucial for the operationalization and adoption of the variant classification, ensuring that it is tailored to the needs of animal health professionals utilizing sequence data for disease management in the field.
Diane DeWitte is an Extension Educator focused on swine and based in the Mankato, MN area. She can be reached at stouf002@umn.edu.
Originally printed in The LAND - as May 25, 2025 Swine & U column
In the previous Swine & U article, we explored past University of Minnesota College of Veterinary Medicine work related to the identification of Porcine Reproductive & Respiratory Syndrome (PRRS) genomes and the studies which have laid the foundation for current work.
In the previous Swine & U article, we explored past University of Minnesota College of Veterinary Medicine work related to the identification of Porcine Reproductive & Respiratory Syndrome (PRRS) genomes and the studies which have laid the foundation for current work.
You may recall that Dr. Kim VanderWaal’s group in the Department of Veterinary Population Medicine has been classifying PRRS virus genetics to better learn which viruses are related to each other, and therefore more thoroughly identify virus movement in PRRS outbreaks. Her team at the UMN, along with colleagues at Iowa State University and the USDA -ARS Virus & Prion Research Unit at the National Animal Disease Center at Ames, Iowa, recently published data from this study. One objective of this work has been the development of a fine-scale classification system which can be more easily used across industry when discussing the virus and its movements. Data and discussion here is found in the group’s research article published in February 2025, in the American Society of Microbiology’s mSphere, Volume 10, Issue 2: https://journals.asm.org/doi/10.1128/msphere.00709-24.
BUT FIRST, A REFRESHER
Porcine Reproductive & Respiratory Syndrome (PRRS) virus has affected our US swine herd for more than 40 years, and its results are devastating. PRRS is highly contagious, an outbreak in sows results in abortions, stillbirths, mummified piglets and early farrowing. In every stage of pig, the respiratory signs are labored breathing, coughing and pneumonia. These symptoms result in decreased vigor and efficiency in growing pigs, and actual loss of pigs in the farrowing stage. PRRSv is spread through direct contact among infected pigs through saliva, manure and semen. It can also be aerosolized and spread, and infected equipment is a fomite to transfer it: Think gates, feeders, trucks and trailers.In PRRS Part 1 we discussed research project terminology. The genome is the organism’s complete DNA, the phylogeny is the virus’s family tree, and a variant is an alteration in the DNA sequence, also known as a mutation. The UMN research team uses the sequencing of the OFR5 gene to understand and analyze PRRSV evolution.
PRRS virus has two species, PRRSV-1 and PRRSV-2. The two species are genetically distinct, but the clinical signs are the same. The study cited here concentrates on PRRSV-2 because it is more common in North America.
TODAY’S RESEARCH
PRRSV-2 is also one of the most sequenced viruses in the world, largely because sequencing is used by animal health professionals as a tool for routine monitoring of virus circulation within and between farms. While phylogenetic analysis is still the gold standard for interpretation of sequence data, veterinary practitioners and field epidemiologists often find it faster and more convenient to have a name in which they can refer to a given genetic variant as part of everyday communication and outbreak investigations.Currently, the naming method used by the industry to discriminate between sequences is restriction fragment length polymorphism (RFLP) typing, sometimes in combination with an additional label corresponding to phylogenetic lineage. However, lineages and sub-lineages are large and diverse and hence are too coarse for on-farm disease monitoring, and using RFLP types to refer to PRRSV-2 viruses often leads to misleading conclusions (e.g., viruses assigned to the same RFLP type often are not genetically similar and vice versa). For example, RFLP 1-4-4, which is one of the most abundantly reported RFLP types in the United States today, occurs in seven different lineages.
In previous work, VanderWaal et al. evaluated and compared 140 approaches for fine-scale classification of OFR5 sequences. Three approaches were found to be robust and reproducible across trees built with different methods and data and thus could form the foundation for fine-scale classification of PRRSV-2 below the sub-lineage level.
However, previous work did not explore the performance of PRRSV-2 variant classification on a rolling basis, and it is necessary to validate the performance and associated procedures for fine-scale classification that accommodates expanding genetic diversity on a prospective basis.
Taking insights and needs of practitioners and diagnosticians alongside a rigorous comparison of alternative approaches for classifying PRRSV-2, the team recently introduced a new fine-scale genetic classification system for PRRSV-2 that is tailored to meet the needs of animal health professionals.
Specifically, researchers outlined criteria used for defining PRRSV-2 genetic variants, established and tested procedures for prospective implementation of the system, and assessed the adaptability of the classification system to accommodate expanding genetic diversity at national scales.
The team introduced a machine-learning webtool that can be used to identify the variant to which newly generated sequences belong and introduce naming conventions for PRRSV-2 variants. They also reported the results of a survey conducted with field practitioners on their motivations for submitting samples for sequencing and demonstrate how variant classification can enhance the utility of sequence data for the purposes of epidemiological monitoring and surveillance.
As you might imagine, this undertaking involved dozens of researchers, thousands of PRRSV-2 samples, hundreds of veterinarians and many years. The group began the classification of variants using PRRSV-2 ORF5 sequences in 2015. Between 2015 and 2023, 25,403 sequences were analyzed and compared. The sequence was defined as a variant if it 1) had five or more sequences, 2) showed greater than 85% shared ancestry (in the ORF5 phylogenetic tree), 3) and was more than 2% different from the nearest named variant. If the sequence did not meet these three criteria, it was named “unclassified”.
Using continuously improving computational power, the team trained algorithms to assess and assign sequences to their appropriate variant groups. An accessible webtool was developed and is updated quarterly wherein veterinary practitioners and diagnosticians can submit PRRSV-2 sequences to learn the sample’s variant classification. By updating the training data set and algorithms quarterly, researchers improve the tool’s accuracy.
The group’s fine-scale classification system identified 118 genetic variants, 37 of which were common (detected more than 50 times) and 19 sequences were rare, meaning they were detected less than 10 times.
Variant nomenclature incorporated the sub-lineage to which the variant belonged, followed by an integer (i.e., 1A.3 and 1H.3 are the third variants identified within sub-lineages 1A and 1H, respectively). For contemporary sequences (2015 onward), several variants had a one-to-one correspondence with vaccine-like sequences; these variants matched PRRS vaccines which had been developed by American livestock pharmaceutical companies.
THE UPSHOT
While having an improved naming scheme for PRRSV-2 genetic variants will not solve PRRS in the United States, a classification system for field-based epidemiological monitoring was needed and has been requested by practitioners for many years. Because the team systematically identifies the variants in a nationwide sequence data set and routinely updates variant classification on a quarterly basis, the nomenclature system can dynamically adapt to evolving PRRSV-2 diversity.Variant classification will facilitate communication about outbreaks, tracking of emerging and endemic variants across time and space, as well as provide a framework to more rigorously analyze the genetic basis of variability in phenotype or production impacts.
Finally, this work used feedback from a working group of veterinarians, researchers, and diagnosticians. This close engagement with stakeholders and end users has been crucial for the operationalization and adoption of the variant classification, ensuring that it is tailored to the needs of animal health professionals utilizing sequence data for disease management in the field.
Diane DeWitte is an Extension Educator focused on swine and based in the Mankato, MN area. She can be reached at stouf002@umn.edu.
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