Shubo Wen

Bovine Parainfluenza Virus Type 3 (BPIV3) is a critical pathogen in the Bovine Respiratory Disease Complex (BRDC), leading to significant economic losses in the cattle industry. However, the metabolic reprogramming induced by BPIV3 in cattle remains poorly understood. This study aimed to investigate the impact of BPIV3 infection on the serum metabolome of Simmental cattle using untargeted metabolomics and ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS/MS). The results revealed significant alterations in the lipidome, including the upregulation of phosphatidylcholine (PC) and phosphatidylglycerol (PG), and the downregulation of phosphatidylinositol (PI). Sphingolipid metabolism also showed considerable changes, with increased levels of Trihexosylceramide and D-erythro-Sphingosine C-17. Furthermore, metabolic pathway analysis identified enriched pathways related to lipid metabolism, amino acid metabolism, and energy sensing. These findings suggest that BPIV3 infection induces substantial shifts in lipid metabolism, which may facilitate viral replication and immune evasion. Our results provide a deeper understanding of the metabolic changes in BPIV3-infected cattle and propose potential targets for therapeutic intervention.

Hybrid capture-based target enrichment prior to sequencing has been shown to significantly improve the sensitivity of detection for genetic regions of interest. In the context of One Health relevant pathogen detection, we present a hybrid capture-based sequencing method that employs an optimized probe set consisting of 149,990 probes, targeting 663 viruses associated with humans and animals. The detection performance was initially assessed using viral reference materials in a background of human nucleic acids. Compared to standard metagenomic next-generation sequencing (mNGS), our method achieved substantial read enrichment, with increases ranging from 143- to 1126-fold, and enhanced detection sensitivity by lowering the limit of detection (LoD) from 10³–10⁴ copies to as few as 10 copies based on whole genomes. This method was further validated using infectious samples from both animals and humans, including bovine rectal swabs and throat swabs from SARS-CoV-2 patients across various concentration gradients. In both sample types, our hybrid capture-based sequencing method exhibited heightened sensitivity, increased viral genome coverage, and more comprehensive viral identification and characterization. Our method bridges a critical divide between diagnostic detection and genomic surveillance. These findings illustrate that our hybrid capture-based sequencing method can effectively enhance sensitivity to as few as 10 viral copies and genome coverage to >99% in medium-to-high viral loads. This dual capability is particularly impactful for emerging pathogens like SARS-CoV-2, where early detection and genomic characterization are equally vital, thereby addressing the limitations of metagenomics in the surveillance of emerging infectious diseases in complex samples.