ArticleVeterinary sciences2025
Mining Porcine Blood Whole-DNA Sequencing Datasets to Uncover Pig Viromes: An Exploratory Application to Identify Potential Infecting Agents of an Undefined Disease Outbreak.
Article in Veterinary sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
Funding
Abstract
Pigs are affected by a variety of pathogenic agents that need to be identified correctly and diagnosed even when co-infections may complicate the application of specific and targeted assays. Next-generation sequencing can provide new perspective to monitor viruses infecting or co-infecting diseased pigs. In this study, we tested, for the first time for diagnostic purposes in a livestock species, a new method based on whole-genome sequencing of all the DNAs extracted from the blood of nine pigs sampled from a farm where there was a suspected outbreak of Post-weaning Multisystemic Wasting Syndrome. We then used unmapped reads on the porcine reference genome to mine for viral sequences using a specifically designed bioinformatic pipeline. Within this fraction of reads, viral sequences ranged from 0.002% to 4.4% of the total unmapped reads and were derived from twelve different viruses known to infect pigs, where three were herpesviruses, eight were parvoviruses, and one was a circovirus. All pig sequencing datasets were positive for one or more viruses, with various potential viral loads. Suid betaherpesvirus 2, also known as Porcine cytomegalovirus (PCMV), was the most frequently identified virus as five out of the nine pig sequencing datasets contained viral sequences from this virus. The results may suggest a heterogeneous viral profile of the diseased pigs that may be derived from potential secondary infections or co-infections. This pilot application demonstrated that a whole-genome sequencing approach can complement other routine diagnostic assays in veterinary virology. Other studies and improvements are needed to validate the results and apply this approach in routine monitoring applications.
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