Biology subjects

VanderWaal, K.

Publications and source records attributed to VanderWaal, K..

11 recordsLinked to original sources

bioRxiv2026

Porcine epidemic diarrhea virus Whole-Genome Diversity Reveals Divergence of Contemporary Populations in the United States

Porcine epidemic diarrhea virus (PEDV) was introduced into the United States (USA) in 2013 and has circulated ever since. PEDV causes significant losses in the swine industry, with the greatest impact observed during the first years following its introduction into the country. However, the evolutionary dynamics of the virus remain underexplored. Recently, analyses of the Spike protein subunit S1 suggested that the PEDV population currently circulating in the USA represents a fraction of its historical diversity and comprises only two clades restricted to specific geographic regions. These observations were based on a limited set of S1 sequences. Furthermore, our understanding of evolutionary dynamics at the genomic level is even more limited, as complete PEDV genomes have not been generated since 2017. Here, we characterized the evolutionary dynamics of PEDV populations in the USA using complete genomes and assessed the contribution of different evolutionary mechanisms to shaping population structure. We analyzed 338 complete genomes, of which 25 were obtained in this study, together with 623 Spike S1 sequences, of which 41 were obtained in this study. All sequences obtained in this study were assigned to the non-S-INDEL strain. No S-INDEL sequences were recovered in this study, although these viruses continue to circulate in the country. Our analyses revealed that most sequences sampled after 2023 formed a monophyletic clade divergent from sequences collected in previous years, suggesting an ongoing temporal population structuring. Population structure analyses further supported evidence of population differentiation over time. Recombination analyses indicated that sequences belonging to the contemporary clade shared a single recombination event, suggesting that this clade may have emerged through recombination. Time-scaled S1 phylogenies were consistent with previous findings, supporting the circulation of only two contemporary clades; however, one of these clades was detected for the first time outside its previously reported geographic range. Our findings document temporal genetic differentiation in US PEDV populations, consistent with continued viral evolution, and broaden the known geographic distribution of contemporary S1 clades. These results highlight the value of continued genomic surveillance to monitor changes in PEDV diversity and distribution and provide sequence data to inform vaccine research.

microbiology↗

bioRxiv2026

Shifts in Genetic Diversity of Porcine Reproductive and Respiratory Syndrome Virus 2 in Vietnam Before and After African Swine Fever: Increased Diversity and Novel Sub-lineages

Porcine reproductive and respiratory syndrome virus 2 (PRRSV-2) remains one of the most important transboundary pathogens affecting swine production in Vietnam; however, it remains poorly understood how long-term evolutionary dynamics were impacted by the African swine fever (ASF) epidemic, a period of time where swine population demographics and movement were heavily perturbed. We investigated the molecular epidemiology, evolutionary history, and phylogeographic dynamics of PRRSV-2 circulating in Vietnam between 2007 and 2024 by integrating 366 Vietnamese ORF5 sequences with a globally curated lineage reference. Maximum-likelihood phylogenetic, Bayesian phylodynamic, and discrete phylogeographic analyses revealed that the Vietnamese PRRSV-2 population underwent substantial reshaping after the ASF epidemic, shifting from a predominantly endemic sub-lineage L8E population to a genetically diverse viral community comprising multiple established and newly emerging sub-lineages. Despite these epidemiological changes, the endemic sub-lineage L8E population maintained a relatively stable evolutionary rate across the pre- and post-ASF periods, suggesting that ASF reshaped viral population structure rather than intrinsic evolutionary dynamics. Two previously unclassified viral clusters circulating in Vietnam and Thailand fulfilled all criteria for formal designation and were recognized as the novel sub-lineages L1M and L10B by the International PRRSV-2 Nomenclature Consortium. Phylogeographic reconstruction further demonstrated contrasting transmission patterns among major sub-lineages, including long-term endemic persistence of L8E, repeated unidirectional introductions of sub-lineages L1M and L10B from Thailand, and bidirectional transpacific dissemination of sub-lineage L1A linking Southeast Asia and North America. Collectively, these findings demonstrate that the ASF epidemic coincided with a fundamental reshaping of the PRRSV-2 epidemiological landscape in Vietnam while revealing Southeast Asia as an active center of ongoing viral diversification. This study provides an updated evolutionary framework for PRRSV-2 surveillance and highlights the importance of continuous genomic monitoring and regional collaboration for the early detection and control of emerging transboundary variants.

genetics↗

bioRxiv2026

Timing the regional spread of PRRSV-2 variants across the United States

Porcine Reproductive and Respiratory Syndrome Virus 2 (PRRSV-2) represents a major threat to the global swine industry. The epidemiological dynamics of PRRSV-2 are characterized by the recurrent annual emergence of dozens of variants. Long-distance spread of PRRSV-2 is largely driven by animal shipments. Spatiotemporal dynamics of PRRSV-2 in the USA have been explored; however, how fast variants spread to new regions after their emergence remains unclear, and this information could improve preparedness. To address this, we analyzed 14,835 sequences, retrieved from the Morrison Swine Health Monitoring Project (MSHMP), representing 156 variants sampled from 2015 to 2024, covering the five major swine-producing regions in the USA: the Upper Midwest (UM), Lower Midwest (LM), Atlantic Seaboard (AS), Northeast (NE), and Great Plains (GP). Time to spread was assessed using the time-to-dispersal event analysis and waiting time analyses. Genetic diversity was measured using Hill numbers. The UM had the highest variant richness (n=123), followed by the LM (n=47), AS (n=35), NE (n=45), and GP (n=38). Of the 62 variants that initially emerged in the UM, 17 later spread to other regions. The UM also received the highest number of variant introductions (n=24), followed by LM (n=14), NE (n=14), AS (n=4), and GP (n=7), highlighting regional differences in connectivity and risk. Our results suggest faster dispersal corridors among interior regions (e.g., GP to UM and LM to UM, [~]1.2-2.0 years) and slower for coast to interior pathways (AS to interior, [~]2-3 years). These findings may help anticipate the risk of PRRSV-2 variant introduction and provide more accurate dispersal time estimates, which are useful for improving epidemiological models and disease preparedness.

ecology↗

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

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

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.

immunology↗

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

The spectral underpinnings of pathogen spread on animal networks

Predicting what factors promote or protect populations from infectious disease is a fundamental epidemiological challenge. Social networks, where nodes represent hosts and edges represent direct or indirect contacts between them, are important in quantifying these aspects of infectious disease dynamics. However, how network structure and epidemic parameters interact in empirical networks to promote or protect animal populations from infectious disease remains a challenge. Here we draw on advances in spectral graph theory and machine learning to build predictive models of pathogen spread on a large collection of empirical networks from across the animal kingdom. We show that the spectral features of an animal network are powerful predictors of pathogen spread for a variety of hosts and pathogens and can be a valuable proxy for the vulnerability of animal networks to pathogen spread. We validate our findings using interpretable machine learning techniques and provide a flexible web application for animal health practitioners to assess the vulnerability of a particular network to pathogen spread.

ecology↗

bioRxiv2021

Transmission of one predicts another: Apathogenic proxies for transmission dynamics of a fatal virus

Identifying drivers of transmission prior to an epidemic--especially of an emerging pathogen--is a formidable challenge for proactive disease management efforts. We tested a novel approach in the Florida panther, hypothesizing that apathogenic feline immunodeficiency virus (FIV) transmission could predict transmission dynamics for pathogenic feline leukemia virus (FeLV). We derived a transmission network using FIV whole genome sequences, and used exponential random graph models to determine drivers structuring this network. We used these drivers to predict FeLV transmission pathways among panthers and compared predicted outbreak dynamics against empirical FeLV outbreak data. FIV transmission was primarily driven by panther age class and distances between panther home range centroids. Prospective FIV-based modeling predicted FeLV dynamics at least as well as simpler, often retrospective approaches, with evidence that FIV-based predictions captured the spatial structuring of the observed FeLV outbreak. Our finding that an apathogenic agent can predict transmission of an analogously transmitted pathogen is an innovative approach that warrants testing in other host-pathogen systems to determine generalizability. Use of such apathogenic agents holds promise for improving predictions of pathogen transmission in novel host populations, and could thereby provide new strategies for proactive pathogen management in human and animal systems.

ecology↗

bioRxiv2020

Animal movement in pastoralist populations and implications for pathogen spread and control

Infectious diseases are one of the most important constraints to livestock agriculture, and hence food, nutritional and economic security in developing countries. In any livestock system, the movement of animals is key to production and sustainability. This is especially true in pastoralist systems where animal movement occurs for a myriad of social, ecological, economic and management reasons. Understanding the dynamics of livestock movement within an ecosystem is important for disease surveillance and control, yet there is limited data available on the dynamics of animal movement in such populations. The aim of this study was to investigate animal transfer networks in a pastoralist community in Kenya, and assess network-based strategies for disease control. We used network analysis to characterize five types of animal transfer networks and evaluated implications of these networks for disease control through quantifying topological changes in the network because of targeted or random removal of nodes. To construct these networks, data were collected using a standardized questionnaire (N=164 households) from communities living within the Maasai Mara Ecosystem in southwestern Kenya. The median livestock movement distance for agistment (dry season grazing) was 39.49 kilometers (22.03-63.49 km), while that for gift, bride price, buying and selling were 13.97 km (0-40.30 km), 30.75 km (10.02-66.03 km), 31.14 km (17.56-59.08 km), and 33.21 km (17.78-58.49 km), respectively. Our analyses show that the Maasai Mara National Reserve, a protected area, was critical for maintaining connectivity in the agistment network. In addition, villages closer to the Maasai Mara National Reserve were regularly used for dry season grazing. In terms of disease control, targeted removal of highly connected village nodes was more effective at fragmenting each network than random removal of nodes, indicating that network-based targeting of interventions such as vaccination could potentially disrupt transmission pathways and reduce pathogen circulation in the ecosystem. In conclusion, this work shows that animal movements have the potential to shape patterns of disease transmission and control in this ecosystem. Further, we show that targeted control is a more practical and efficient measure for disease control.

ecology↗

bioRxiv2019

Mapping changes in the spatiotemporal distribution of lumpy skin disease virus

Lumpy skin disease virus (LSDV) is an infectious disease of cattle transmitted by arthropod vectors which results in substantial economic losses due to impact on production efficiency and profitability, and represents an emerging threat to international trade of livestock products and live animals. Since 2015, the disease has spread across many Eastern European countries as well as Russia and Kazakhstan. This rapid expansion highlights the emergent nature of the virus in more temperate regions than those in which LSDV traditionally occurred. The goal of this study was to assess the risk for further LSDV spread in Eurasia through a) analysis of environmental factors conducive for LSDV and b) estimate of the underlying LSDV risk using a fine spatiotemporally explicit Bayesian hierarchical model on LSDV outbreak occurrence information. We used ecological niche modeling to estimate the potential distribution of LSDV outbreaks for 2014-2016. This analysis resulted in a spatial representation of environmental limits where, if introduced, LSDV is expected to efficiently spread. The Bayesian space-time model incorporated both environmental factors and the changing spatiotemporal distribution of the disease to capture the dynamics of disease spread and predict areas in which there is an increased risk of LSDV occurrence. Variables related to the average temperature, precipitation, wind speed, as well as land cover and host densities were found to be important drivers explaining the observed distribution of LSDV in both modeling approaches. Areas of elevated LSDV risks were identified mainly in Russia, Turkey, Serbia, and Bulgaria. Results suggest that prevailing ecological conditions may be compatible with further spread of LSDV in Eurasia, though models should be continually updated to reflect the current epidemiologic conditions. The results presented here advance our understanding of the ecological requirements of LSDV in temperate regions and may help in the design and implementation of prevention and surveillance strategies in the region.

epidemiology↗

bioRxiv2018

The role of African buffalo in the epidemiology of foot-and-mouth disease in sympatric cattle and buffalo populations in Kenya

Transmission of pathogens at wildlife-livestock interfaces poses a substantial challenge to the control of infectious diseases, including for foot-and-mouth disease virus (FMDV) in African buffalo and cattle. The extent to which buffalo play a role in the epidemiology of this virus in livestock populations remains unresolved in East Africa. Here, we show that FMDV occurs at high seroprevalence (~77%) in Kenyan buffalo. In addition, we recovered 80 FMDV VP1 sequences from buffalo, all of which were serotype SAT1 and SAT2, and seventeen FMDV VP1 sequences from cattle, which included serotypes A, O, SAT1 and SAT2. Notably, six individual buffalo were co-infected with both SAT1 and SAT2 serotypes. Our results suggest that transmission of FMDV between sympatric cattle and buffalo is rare. However, viruses from FMDV outbreaks in cattle elsewhere in Kenya were caused by viruses closely related to SAT1 and SAT2 viruses found in buffalo. We also show that the circulation of FMDV in buffalo is influenced by fine-scale geographic features, such as rivers, and that social segregation amongst sympatric herds may limit between-herd transmission. Our results significantly advance knowledge of the ecology and molecular epidemiology of FMDV at wildlife-livestock interfaces in Eastern Africa, and will help to inform the design of control and surveillance strategies for this disease in the region.

epidemiology↗