Evidence map›Paper›PMID 38976572›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024

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

Hao Zhang, Fangyu Hu, Ouyang Peng, Yihui Huang, Guangli Hu, Usama Ashraf, Meifeng Cen, Xiaojuan Wang, Qiuping Xu, Chuangchao Zou and 7 more

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Hao ZhangSate Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510006, China.
Fangyu HuSate Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510006, China.
Ouyang PengSate Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510006, China.
Yihui HuangSate Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510006, China.
Guangli HuSate Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510006, China.
Usama AshrafDepartment of Medicine, Division of Infectious Diseases, Stanford University, Stanford, CA, 94305, USA.
Meifeng CenBioinformatics and Omics Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
Xiaojuan WangBioinformatics and Omics Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
Qiuping XuGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
Chuangchao ZouSate Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510006, China.
Yu WuSate Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510006, China.
Bibo ZhuNational Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, 430070, China.
Wentao LiNational Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, 430070, China.
Qunhui LiGuangdong Enterprise Key Laboratory for Animal Health and Environmental Control, Wen's Foodstuff Group Co. Ltd, Yunfu, 527439, China.
Chujun LiSate Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510006, China.
Chunyi XueSate Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510006, China.
Yongchang CaoSate Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510006, China.ORCID 0000-0002-0311-5474

Funding

Guangdong Basic and Applied Basic Research Foundation 2023A1515012623National Natural Science Foundation of China 32172830National Natural Science Foundation of China 82102755
6 · The paper itself

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.

Indexed as

BiomarkersLysophospholipidsMachine LearningPorcine Reproductive and Respiratory SyndromePorcine respiratory and reproductive syndrome virusAnimalsMetabolomicsMultiomicsSwineBiomarkerslysophosphatidic acidLysophospholipidsantiviral approachdiagnostic biomarkerimmune suppressionlysophosphatidic acid (LPA)machine learningmulti‐omicsporcine reproductive and respiratory syndrome virus

Identifiers

PMID38976572
PMCPMC11425916

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.