Existing genetic classification systems for porcine reproductive and respiratory syndrome virus type 2 (PRRSV-2), such as restriction fragment length polymorphisms and sub-lineages, are unreliable indicators of close 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 United States. 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 3 months and systematically identifying new variants across 8 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 to 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 onward; 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 represent 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.
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http://dx.doi.org/10.1128/msphere.00709-24 | DOI Listing |
Neuro Oncol
January 2025
Department of Neurosurgery, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg.
Background: Peripheral nerve sheath tumors (PNSTs) encompass entities with different cellular differentiation and degrees of malignancy. Spatial heterogeneity complicates diagnosis and grading of PNSTs in some cases. In malignant PNST (MPNST) for example, single cell sequencing data has shown dissimilar differentiation states of tumor cells.
View Article and Find Full Text PDFJAMA Netw Open
January 2025
Division of Endocrinology, Diabetes and Metabolism, Mayo Clinic, Rochester, Minnesota.
Importance: Understanding the interplay between diabetes risk factors and diabetes development is important to develop individual, practice, and population-level prevention strategies.
Objective: To evaluate the progression from normal and impaired fasting glucose levels to diabetes among adults.
Design, Setting, And Participants: This retrospective community-based cohort study used data from the Rochester Epidemiology Project, in Olmsted County, Minnesota, on 44 992 individuals with at least 2 fasting plasma glucose (FPG) measurements from January 1, 2005, to December 31, 2017.
Bull Math Biol
January 2025
Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark.
Using genetic data to infer evolutionary distances between molecular sequence pairs based on a Markov substitution model is a common procedure in phylogenetics, in particular for selecting a good starting tree to improve upon. Many evolutionary patterns can be accurately modelled using substitution models that are available in closed form, including the popular general time reversible model (GTR) for DNA data. For more complex biological phenomena, such as variations in lineage-specific evolutionary rates over time (heterotachy), other approaches such as the GTR with rate variation (GTR ) are required, but do not admit analytical solutions and do not automatically allow for likelihood calculations crucial for Bayesian analysis.
View Article and Find Full Text PDFSports Med Open
January 2025
Institute for Health and Sport, Victoria University, Melbourne, Australia.
Background: Despite their prominence in the sport and human movement sciences, to date, there is no systematic insight about the development and content of movement quality assessments in athletic populations. This is an important gap to address, as it could yield both practical and scientific implications related to the continued screening of movement quality in athletic contexts. Hence, this study aimed to systematically review the (i) developmental approach, (ii) movements included, (iii) scoring system utilised, and (iv) the reliability of movement competency assessments used in athletic populations.
View Article and Find Full Text PDFCurr Microbiol
January 2025
School of Organic Farming, Punjab Agricultural University, Ludhiana, 141004, India.
Endophytes are bacteria that inhabit host plants for most of their life cycle without causing harm. In the study, 15 endophytic bacteria were isolated from 30 forage Sorghum plants and assessed for various plant growth-promoting (PGP) traits, such as phosphate solubilization, 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase activity, ammonia production, siderophore production, gibberellic acid production, Indole-3-acetic acid (IAA) production, and zinc solubilization. One isolate, JJG_Zn, demonstrated multiple PGP activities and was identified as Enterobacter sp.
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