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Dai J, Wang H, Liao Y, Tan L, Sun Y, Song C, Liu W, Ding C, Luo T, Qiu X. Non-Targeted Metabolomic Analysis of Chicken Kidneys in Response to Coronavirus IBV Infection Under Stress Induced by Dexamethasone. Front Cell Infect Microbiol 2022; 12:945865. [PMID: 35909955 PMCID: PMC9335950 DOI: 10.3389/fcimb.2022.945865] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 06/20/2022] [Indexed: 11/13/2022] Open
Abstract
Stress in poultry can lead to changes in body metabolism and immunity, which can increase susceptibility to infectious diseases. However, knowledge regarding chicken responses to viral infection under stress is limited. Dexamethasone (Dex) is a synthetic glucocorticoid similar to that secreted by animals under stress conditions, and has been widely used to induce stress in chickens. Herein, we established a stress model in 7-day-old chickens injected with Dex to elucidate the effects of stress on IBV replication in the kidneys. The metabolic changes, immune status and growth of the chickens under stress conditions were comprehensively evaluated. Furthermore, the metabolic profile, weight gain, viral load, serum cholesterol levels, cytokines and peripheral blood lymphocyte ratio were compared in chickens treated with Dex and infected with IBV. An LC-MS/MS-based metabolomics method was used to examine differentially enriched metabolites in the kidneys. A total of 113 metabolites whose abundance was altered after Dex treatment were identified, most of which were lipids and lipid-like molecules. The principal metabolic alterations in chicken kidneys caused by IBV infection included fatty acid, valine, leucine and isoleucine metabolism. Dex treatment before and after IBV infection mainly affected the host’s tryptophan, phenylalanine, amino sugar and nucleotide sugar metabolism. In addition, Dex led to up-regulation of serum cholesterol levels and renal viral load in chickens, and to the inhibition of weight gain, peripheral blood lymphocytes and IL-6 production. We also confirmed that the exogenous cholesterol in DF-1 cells promoted the replication of IBV. However, whether the increase in viral load in kidney tissue is associated with the up-regulation of cholesterol levels induced by Dex must be demonstrated in future experiments. In conclusion, chick growth and immune function were significantly inhibited by Dex. Host cholesterol metabolism and the response to IBV infection are regulated by Dex. This study provides valuable insights into the molecular regulatory mechanisms in poultry stress, and should support further research on the intrinsic link between cholesterol metabolism and IBV replication under stress conditions.
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Affiliation(s)
- Jun Dai
- Laboratory of Veterinary Microbiology and Animal Infectious Diseases, College of Animal Sciences and Veterinary Medicine, Guangxi University, Nanning, China
- Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Shanghai, China
| | - Huan Wang
- Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Shanghai, China
| | - Ying Liao
- Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Shanghai, China
| | - Lei Tan
- Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Shanghai, China
| | - Yingjie Sun
- Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Shanghai, China
| | - Cuiping Song
- Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Shanghai, China
| | - Weiwei Liu
- Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Shanghai, China
| | - Chan Ding
- Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Shanghai, China
- Jiangsu Co-innovation Center for Prevention and Control of Important Animal Infectious Diseases and Zoonoses, Yangzhou University, Yangzhou, China
| | - Tingrong Luo
- Laboratory of Veterinary Microbiology and Animal Infectious Diseases, College of Animal Sciences and Veterinary Medicine, Guangxi University, Nanning, China
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi University, Nanning, China
- *Correspondence: Xusheng Qiu, ; Tingrong Luo,
| | - Xusheng Qiu
- Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Shanghai, China
- *Correspondence: Xusheng Qiu, ; Tingrong Luo,
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Dai J, Wang H, Liao Y, Tan L, Sun Y, Song C, Liu W, Qiu X, Ding C. Coronavirus Infection and Cholesterol Metabolism. Front Immunol 2022; 13:791267. [PMID: 35529872 PMCID: PMC9069556 DOI: 10.3389/fimmu.2022.791267] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 03/21/2022] [Indexed: 12/19/2022] Open
Abstract
Host cholesterol metabolism remodeling is significantly associated with the spread of human pathogenic coronaviruses, suggesting virus-host relationships could be affected by cholesterol-modifying drugs. Cholesterol has an important role in coronavirus entry, membrane fusion, and pathological syncytia formation, therefore cholesterol metabolic mechanisms may be promising drug targets for coronavirus infections. Moreover, cholesterol and its metabolizing enzymes or corresponding natural products exert antiviral effects which are closely associated with individual viral steps during coronavirus replication. Furthermore, the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 infections are associated with clinically significant low cholesterol levels, suggesting cholesterol could function as a potential marker for monitoring viral infection status. Therefore, weaponizing cholesterol dysregulation against viral infection could be an effective antiviral strategy. In this review, we comprehensively review the literature to clarify how coronaviruses exploit host cholesterol metabolism to accommodate viral replication requirements and interfere with host immune responses. We also focus on targeting cholesterol homeostasis to interfere with critical steps during coronavirus infection.
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Affiliation(s)
- Jun Dai
- College of Animal Science and Technology, Guangxi University, Nanning, China
- Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Shanghai, China
- Experimental Animal Center, Zunyi Medical University, Zunyi City, China
| | - Huan Wang
- Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Shanghai, China
| | - Ying Liao
- Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Shanghai, China
| | - Lei Tan
- Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Shanghai, China
| | - Yingjie Sun
- Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Shanghai, China
| | - Cuiping Song
- Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Shanghai, China
| | - Weiwei Liu
- Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Shanghai, China
| | - Xusheng Qiu
- Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Shanghai, China
- *Correspondence: Xusheng Qiu, ; Chan Ding,
| | - Chan Ding
- Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Shanghai, China
- Jiangsu Co-Innovation Center for Prevention and Control of Important Animal Infectious Diseases and Zoonoses, Yangzhou University, Yangzhou, China
- *Correspondence: Xusheng Qiu, ; Chan Ding,
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Pal LR, Cheng K, Nair NU, Martin-Sancho L, Sinha S, Pu Y, Riva L, Yin X, Schischlik F, Lee JS, Chanda SK, Ruppin E. Synthetic lethality-based prediction of anti-SARS-CoV-2 targets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2021.09.14.460408. [PMID: 34545363 PMCID: PMC8452092 DOI: 10.1101/2021.09.14.460408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Novel strategies are needed to identify drug targets and treatments for the COVID-19 pandemic. The altered gene expression of virus-infected host cells provides an opportunity to specifically inhibit viral propagation via targeting the synthetic lethal (SL) partners of such altered host genes. Pursuing this antiviral strategy, here we comprehensively analyzed multiple in vitro and in vivo bulk and single-cell RNA-sequencing datasets of SARS-CoV-2 infection to predict clinically relevant candidate antiviral targets that are SL with altered host genes. The predicted SL-based targets are highly enriched for infected cell inhibiting genes reported in four SARS-CoV-2 CRISPR-Cas9 genome-wide genetic screens. Integrating our predictions with the results of these screens, we further selected a focused subset of 26 genes that we experimentally tested in a targeted siRNA screen using human Caco-2 cells. Notably, as predicted, knocking down these targets reduced viral replication and cell viability only under the infected condition without harming non-infected cells. Our results are made publicly available, to facilitate their in vivo testing and further validation.
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Affiliation(s)
- Lipika R. Pal
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Kuoyuan Cheng
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA
| | - Nishanth Ulhas Nair
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Laura Martin-Sancho
- Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Sanju Sinha
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA
| | - Yuan Pu
- Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Laura Riva
- Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Xin Yin
- Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Fiorella Schischlik
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Joo Sang Lee
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
- Samsung Medical Center, Sungkyunkwan University School of Medicine, Suwon 16419, Republic of Korea
| | - Sumit K. Chanda
- Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA
- Department of Computer Science, University of Maryland, College Park, MD, USA
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