Multi-Omics Analysis by Machine Learning Identified Lysophosphatidic Acid as a Biomarker and Therapeutic Target for Porcine Reproductive and Respiratory Syndrome.

Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Published Date:

Abstract

As a significant infectious disease in livestock, porcine reproductive and respiratory syndrome (PRRS) imposes substantial economic losses on the swine industry. Identification of diagnostic markers and therapeutic targets has been a focal challenge in PPRS prevention and control. By integrating metabolomic and lipidomic serum analyses of clinical pig cohorts through a machine learning approach with in vivo and in vitro infection models, lysophosphatidic acid (LPA) is discovered as a serum metabolic biomarker for PRRS virus (PRRSV) clinical diagnosis. PRRSV promoted LPA synthesis by upregulating the autotaxin expression, which causes innate immunosuppression by dampening the retinoic acid-inducible gene I (RIG-I) and type I interferon responses, leading to enhanced virus replication. Targeting LPA demonstrated protection against virus infection and associated disease outcomes in infected pigs, indicating that LPA is a novel antiviral target against PRRSV. This study lays a foundation for clinical prevention and control of PRRSV infections.

Authors

  • Hao Zhang
    College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China.
  • Fangyu Hu
    Sate Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510006, China.
  • Ouyang Peng
    Sate Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510006, China.
  • Yihui Huang
    Department of Electronic Science, Biomedical Intelligent Cloud Research and Development Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China.
  • Guangli Hu
    Sate Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510006, China.
  • Usama Ashraf
    Department of Medicine, Division of Infectious Diseases, Stanford University, Stanford, CA, 94305, USA.
  • Meifeng Cen
    Bioinformatics and Omics Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
  • Xiaojuan Wang
    School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
  • Qiuping Xu
    Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
  • Chuangchao Zou
    Sate Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510006, China.
  • Yu Wu
    Key Laboratory of Pesticide and Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensing Technology and Health, College of Chemistry, Central China Normal University, Wuhan, 430079, People's Republic of China.
  • Bibo Zhu
    National Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, 430070, China.
  • Wentao Li
    State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, Hubei, People's Republic of China.
  • Qunhui Li
    Guangdong Enterprise Key Laboratory for Animal Health and Environmental Control, Wen's Foodstuff Group Co. Ltd, Yunfu, 527439, China.
  • Chujun Li
    Sate Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510006, China.
  • Chunyi Xue
    Sate Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510006, China.
  • Yongchang Cao
    Sate Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510006, China.