Nexus format phylogenetic tree. Neuraminidase Reference Sequences. Reference sequences used for determining the clade a neuraminidase sequence falls into. (TXT 16 kb)
ABSTRACT Antibodies play a central role in neutralizing pathogens through direct interference with viral entry and recruitment of effector immune cells. However, viruses employ sophisticated escape mechanisms to evade these defenses, primarily through mutations in surface glycoproteins that reduce antibody binding affinity or alter critical functional domains. Antibody escape remains a formidable challenge in drug design efforts for Porcine Reproductive and Respiratory Syndrome Virus (PRRSV), a pathogen notorious for its rapid evolution and structural plasticity. Here we present EscaPRRS (Escape scoring for PRRS virus), a Bayesian variational autoencoder trained on ESM-2 protein language model embeddings to predict the fitness landscape and escape propensities of 52,622 mutants (recorded over 10 years) of the immunodominant GP5 glycoprotein encoded by PRRSV ORF5 gene. Unlike conventional models that estimate escape propensity only from sequence information, EscaPRRS circumvents the need for extensive alignments, integrating contributions from surface accessibility and biochemical dissimilarity at the binding interface. Our escape propensity scores demonstrate reliable structural and biological fidelity, with EscaPRRS scores correlating with binding affinities on seven different porcine receptor proteins (Pearson r = 0.74). Notably, EscaPRRS captures seasonal trends in immune evasion, highlighting its applicability in forecasting and surveillance of emerging/re-emerging PRRSV variants. IMPORTANCE Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) remains the most economically detrimental illness for swine products, causing more than $1.2 billion in annual production loss in the United States, with indirect implications to food security and human health. PRRSV infection is known to severely affect porcine alveolar macrophages (PAMs), causing respiratory difficulties, following blockage of inflammatory signals that aids easy viral reproduction in the host cell. Identifying critical amino acid mutations at the highly variant GP5 glycoprotein attached to the viral cell membrane provides information on structural features linked to antigenic diversity and antibody neutralization, that potentially lead to clinical outbreaks in sow farms. Our effort employs machine learning approaches to learn patterns from a large number of sequences to map these critical domains to three-dimensional structures to score them for antibody escape tendencies. This greatly enhances our understanding of receptor and antibody binding mechanisms in PRRSV GP5.
One of the most commonly observational study designs employed in veterinary is the cross-sectional study with binary outcomes. To measure an association with exposure, the use of prevalence ratios (PR) or odds ratios (OR) are possible. In human epidemiology, much has been discussed about the use of the OR exclusively for case-control studies and some authors reported that there is no good justification for fitting logistic regression when the prevalence of the disease is high, in which OR overestimate the PR. Nonetheless, interpretation of OR is difficult since confusing between risk and odds can lead to incorrect quantitative interpretation of data such as "the risk is X times greater," commonly reported in studies that use OR. The aims of this study were (1) to review articles with cross-sectional designs to assess the statistical method used and the appropriateness of the interpretation of the estimated measure of association and (2) to illustrate the use of alternative statistical methods that estimate PR directly. An overview of statistical methods and its interpretation using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted and included a diverse set of peer-reviewed journals among the veterinary science field using PubMed as the search engine. From each article, the statistical method used and the appropriateness of the interpretation of the estimated measure of association were registered. Additionally, four alternative models for logistic regression that estimate directly PR were tested using our own dataset from a cross-sectional study on bovine viral diarrhea virus. The initial search strategy found 62 articles, in which 6 articles were excluded and therefore 56 studies were used for the overall analysis. The review showed that independent of the level of prevalence reported, 96% of articles employed logistic regression, thus estimating the OR. Results of the multivariate models indicated that logistic regression was the method that most overestimated the PR. The findings of this study indicate that although there are methods that directly estimate PR, many studies in veterinary science do not use these methods and misinterpret the OR estimated by the logistic regression.
Brazil is the fourth largest producer and exporter of pork in the world, but has never reported yet the occurrence of some economically important diseases such as porcine reproductive and respiratory syndrome (PRRS) and porcine epidemic diarrhea (PED). Most of the swine farms in Brazil are characterized by intensive production being part of large integrated companies, where biosecurity practices help to prevent the introduction and spread of disease-causing infectious agents. The assessment of biosecurity in farms is not straightforward because of the large number of practices that constitute an on-farm biosecurity program. It is therefore necessary to combine the measurement of several parameters in order to characterize the level of biosecurity on a given farm. Thus, the objective of the study was to develop a biosecurity score to estimate the biosecurity level (theta or θ) in swine farms using the item response theory (IRT) and explore the relationship between the scores and independent variables. The IRT is a latent trait method extensively used in other fields, and offers the advantage to quantify the latent trait, here the biosecurity level, and to identify the practices that discriminate the farms avoiding the use of extensive questionnaires and redundant questions. In this study, 604 farms were evaluated in the main swine production regions of the state of Rio Grande do Sul, Brazil. Thirty-five practices were considered in order to quantify the biosecurity level on a given farm. After a recursive process 14 practices were selected to compose the biosecurity score to estimate the biosecurity level (θ). The variables identified with greater capacity of distinguishing the farms as to their biosecurity level were if the farm has 'feed bin outside of the barn limit (external feed loading)', has 'perimetral fence around the farm or barn', and if 'transit of trucks inside the farm is prohibited'. The biosecurity level was associated with some independent variables, e.g. the farm operation type, the integrated company and some owner characteristics. In addition, the results demonstrated that biosecurity practices related to management (internal biosecurity) are adopted with higher frequency compared to segregation and sanitation practices (external biosecurity). The IRT model proved useful and valid to estimate the biosecurity level in swine farms. Moreover, the biosecurity score described here has a relatively low number of items, which makes the application of this tool easier and faster compared to other previously described biosecurity assessment.
