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Aging and disease    2021, Vol. 12 Issue (1) : 42-49     DOI: 10.14336/AD.2020.1112
Short Communication |
Prediction of SARS-CoV Interaction with Host Proteins during Lung Aging Reveals a Potential Role for TRIB3 in COVID-19
Diogo de Moraes1, Brunno Vivone Buquete Paiva1,2, Sarah Santiloni Cury1, Raissa Guimarães Ludwig4, João Pessoa Araújo Junior3, Marcelo Alves da Silva Mori4, Robson Francisco Carvalho1,*
1Department of Structural and Functional Biology, Institute of Biosciences, São Paulo State University (UNESP), Botucatu, São Paulo, Brazil.
2Faculty of Medicine, São Paulo State University, UNESP, Botucatu, São Paulo, Brazil.
3Department of Chemical and Biological Sciences, Institute of Biosciences, São Paulo State University (UNESP), Botucatu, São Paulo, Brazil.
4Department of Biochemistry and Tissue Biology, Institute of Biology, State University of Campinas (UNICAMP), Campinas, SP, Brazil.
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Abstract  

COVID-19 is prevalent in the elderly. Old individuals are more likely to develop pneumonia and respiratory failure due to alveolar damage, suggesting that lung senescence may increase the susceptibility to SARS-CoV-2 infection and replication. Considering that human coronavirus (HCoVs; SARS-CoV-2 and SARS-CoV) require host cellular factors for infection and replication, we analyzed Genotype-Tissue Expression (GTEx) data to test whether lung aging is associated with transcriptional changes in human protein-coding genes that potentially interact with these viruses. We found decreased expression of the gene tribbles homolog 3 (TRIB3) during aging in male individuals, and its protein was predicted to interact with HCoVs nucleocapsid protein and RNA-dependent RNA polymerase. Using publicly available lung single-cell data, we found TRIB3 expressed mainly in alveolar epithelial cells that express SARS-CoV-2 receptor ACE2. Functional enrichment analysis of age-related genes, in common with SARS-CoV-induced perturbations, revealed genes associated with the mitotic cell cycle and surfactant metabolism. Given that TRIB3 was previously reported to decrease virus infection and replication, the decreased expression of TRIB3 in aged lungs may help explain why older male patients are related to more severe cases of the COVID-19. Thus, drugs that stimulate TRIB3 expression should be evaluated as a potential therapy for the disease.

Keywords COVID-19      SARS-CoV-2      tribbles homolog 3      α-hydroxylinoleic acid      lung aging     
Corresponding Authors: Carvalho Robson Francisco   
About author:

These authors contributed equally to this work.

Just Accepted Date: 01 December 2020   Issue Date: 11 January 2021
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Moraes Diogo de
Paiva Brunno Vivone Buquete
Cury Sarah Santiloni
Ludwig Raissa Guimarães
Junior João Pessoa Araújo
Mori Marcelo Alves da Silva
Carvalho Robson Francisco
Cite this article:   
Moraes Diogo de,Paiva Brunno Vivone Buquete,Cury Sarah Santiloni, et al. Prediction of SARS-CoV Interaction with Host Proteins during Lung Aging Reveals a Potential Role for TRIB3 in COVID-19[J]. Aging and disease, 2021, 12(1): 42-49.
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http://www.aginganddisease.org/EN/10.14336/AD.2020.1112     OR
Figure 1.  Lung gene expression of TRIB3, which is translated into a protein that potentially interacts with SARS-CoV-2 proteins, decreases in male individuals during aging. (A) Heatmap of mean TMM (Trimmed Mean of M-values) expression of males found as differentially expressed (DEGs; mean expression) in, at least, one age-group when compared to young adults (20-29 yo). These DEGs were found with males and females pooled. Rows were clustered using Euclidian distance. Clusters A and C contain genes that increase or decrease with age, respectively. TRIB3, HAPLN2, and the top 5 DEGs found in each age range are highlighted. (B) Venn diagram of DEGs during aging shared with the corresponding proteins that potentially interact with SARS-CoV-2 (arrows). Boxplots of gene expression levels (TMM) of separated or pooled sexes (C and E). Boxplot detailing the expression of genes that are well-recognized biomarkers of cellular division and senescence CDKN2A and MKI67 in the lung (D). * P < 0.05, ** P < 0.001, and # P < 0.0001: statistical significance vs. young adults from GTEx v8 for Dunn’s test (males: 20-29 yo (n = 20), 30-39 yo (n = 27), 40-49 yo (n = 53), 50-59 yo (n = 135), 60-69 yo (n = 103), 70-79 yo (n = 11); females: 20-29 yo (n = 13), 30-39 yo (n = 9), 40-49 yo (n = 29), 50-59 yo (n = 52), 60-69 yo (n = 59), 70-79 yo (n = 4).
Figure 2.  Single-cell gene expression analyses of TRIB3, HAPLN2, and ACE2 in lung cells. (A) Unsupervised clustering demonstrates different cell populations identified in non-diseased lung human samples in a t-distributed Stochastic Neighbor Embedding (tSNE) plot, as described previously [25]. Grey dots represent single cells from pulmonary fibrosis samples that were not included in the present analysis. Single-cell gene expression of TRIB3 (B), HAPLN2 (C), and ACE2 (D) in different lung cell populations. The images were generated using the dataset [25], available at nupulmonary.org/resources/. The range represents the minimum and maximum expression. (E) Violin plots of TRIB3 expression levels in lung single-cells.
Figure 3.  Over-represented genes altered in aging and SARS-CoV infection are associated with mitotic cell cycle and surfactant metabolism. (A) Venn diagram of differentially expressed genes (corresponding proteins) during aging shared with SARS-CoV-induced perturbations in host gene expression. Values outside the diagram: PPI enrichment P-value. (B) PPI network based on the genes that decreased expression during aging and are up-regulated in SARS-CoV-induced perturbations in host gene expression. Table S8 contains the complete list of over-represented terms.
[1] Wu F, Zhao S, Yu B, Chen Y-M, Wang W, Song Z-G, et al. (2020). A new coronavirus associated with human respiratory disease in China. Nature, 579:265-269.
[2] Zhou P, Yang X-L, Wang X-G, Hu B, Zhang L, Zhang W, et al. (2020). A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature, 579:270-273.
[3] Dong E, Du H, Gardner L (2020). An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis, 20:533-534.
[4] Zhang J, Dong X, Cao Y, Yuan Y, Yang Y, Yan Y, et al. (2020). Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan, China. Allergy, 75:1730-1741.
[5] Pan F, Ye T, Sun P, Gui S, Liang B, Li L, et al. (2020). Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19). Radiology, 295:715-721.
[6] Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. (2020). Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. The Lancet, 395:507-513.
[7] Letko M, Marzi A, Munster V (2020). Functional assessment of cell entry and receptor usage for SARS-CoV-2 and other lineage B betacoronaviruses. Nat Microbiol, 5:562-569.
[8] Wan Y, Shang J, Graham R, Baric RS, Li F (2020). Receptor Recognition by the Novel Coronavirus from Wuhan: an Analysis Based on Decade-Long Structural Studies of SARS Coronavirus. J Virol. doi: .
doi: 10.1128/JVI.00127-20
[9] Zhou Y, Hou Y, Shen J, Huang Y, Martin W, Cheng F (2020). Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2. Cell Discov, 6:14.
[10] Yang S, Fu C, Lian X, Dong X, Zhang Z (2019). Understanding Human-Virus Protein-Protein Interactions Using a Human Protein Complex-Based Analysis Framework. mSystems. doi: .
doi: 10.1128/mSystems.00303-18
[11] Lasso G, Mayer SV, Winkelmann ER, Chu T, Elliot O, Patino-Galindo JA, et al. (2019). A Structure-Informed Atlas of Human-Virus Interactions. Cell, 178:1526-1541.e16.
[12] Lonsdale J, Thomas J, Salvatore M, Phillips R, Lo E, Shad S, et al. (2013). The Genotype-Tissue Expression (GTEx) project. Nat Genet, 45:580-585.
[13] The GTEx Consortium, Ardlie KG, Deluca DS, Segre AV, Sullivan TJ, Young TR, et al. (2015). The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans. Science, 348:648-660.
[14] Zeng L, Yang J, Peng S, Zhu J, Zhang B, Suh Y, et al. (2020). Transcriptome analysis reveals the difference between “healthy” and “common” aging and their connection with age-related diseases. Aging Cell. doi: .
doi: 10.1111/acel.13121
[15] Jia K, Cui C, Gao Y, Zhou Y, Cui Q (2018). An analysis of aging-related genes derived from the Genotype-Tissue Expression project (GTEx). Cell Death Discov, 4:91.
[16] Wang W, Tang J, Wei F (2020). Updated understanding of the outbreak of 2019 novel coronavirus (2019-nCoV) in Wuhan, China. J Med Virol, 92:441-447.
[17] Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al. (2020). Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. N Engl J Med, 382:1199-1207.
[18] Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet, 395:497-506.
[19] Robinson MD, Oshlack A (2010). A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol, 11:R25.
[20] Torre D, Lachmann A, Ma’ayan A (2018). BioJupies: Automated Generation of Interactive Notebooks for RNA-Seq Data Analysis in the Cloud. Cell Syst, 7:556-561.e3.
[21] Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res, 43:e47-e47.
[22] Theunissen PT, Pennings JLA, Robinson JF, Claessen SMH, Kleinjans JCS, Piersma AH (2011). Time-Response Evaluation by Transcriptomics of Methylmercury Effects on Neural Differentiation of Murine Embryonic Stem Cells. Toxicol Sci, 122:437-447.
[23] Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. (2016). Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res, 44:W90-W97.
[24] Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. (2019). STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res, 47:D607-D613.
[25] Reyfman PA, Walter JM, Joshi N, Anekalla KR, McQuattie-Pimentel AC, Chiu S, et al. (2019). Single-Cell Transcriptomic Analysis of Human Lung Provides Insights into the Pathobiology of Pulmonary Fibrosis. Am J Respir Crit Care Med, 199:1517-1536.
[26] Madissoon E, Wilbrey-Clark A, Miragaia RJ, Saeb-Parsy K, Mahbubani KT, Georgakopoulos N, et al. (2020). scRNA-seq assessment of the human lung, spleen, and esophagus tissue stability after cold preservation. Genome Biol, 21:1.
[27] Abrams EM, ‘t Jong GW, Yang CL (2020). Asthma and COVID-19. CMAJ, 192:E551-E551.
[28] Leung JM, Niikura M, Yang CWT, Sin DD (2020). COVID-19 and COPD. Eur Respir J, 56:2002108.
[29] Starruß J, de Back W, Brusch L, Deutsch A (2014). Morpheus: a user-friendly modeling environment for multiscale and multicellular systems biology. Bioinformatics, 30:1331-1332.
[30] Bardou P, Mariette J, Escudié F, Djemiel C, Klopp C (2014). jvenn: an interactive Venn diagram viewer. BMC Bioinformatics, 15:293.
[31] Muñoz-Espín D, Serrano M (2014). Cellular senescence: from physiology to pathology. Nat Rev Mol Cell Biol, 15:482-496.
[32] Krishnamurthy J, Torrice C, Ramsey MR, Kovalev GI, Al-Regaiey K, Su L, et al. (2004). Ink4a/Arf expression is a biomarker of aging. J Clin Invest, 114:1299-1307.
[33] Takahashi A, Ohtani N, Yamakoshi K, Iida S, Tahara H, Nakayama K, et al. (2006). Mitogenic signalling and the p16INK4a-Rb pathway cooperate to enforce irreversible cellular senescence. Nat Cell Biol, 8:1291-1297.
[34] Hoffmann M, Kleine-Weber H, Schroeder S, Krüger N, Herrler T, Erichsen S, et al. (2020). SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor. Cell, 181:271-280.e8.
[35] Tran SC, Pham TM, Nguyen LN, Park E-M, Lim Y-S, Hwang SB (2016). Nonstructural 3 Protein of Hepatitis C Virus Modulates the Tribbles Homolog 3/Akt Signaling Pathway for Persistent Viral Infection. J Virol, 90:7231-7247.
[36] Zanini F, Pu S-Y, Bekerman E, Einav S, Quake SR (2018). Single-cell transcriptional dynamics of flavivirus infection. eLife, 7:e32942.
[37] Felip I, Moiola CP, Megino-Luque C, Lopez-Gil C, Cabrera S, Solé-Sánchez S, et al. (2019). Therapeutic potential of the new TRIB3-mediated cell autophagy anticancer drug ABTL0812 in endometrial cancer. Gynecol Oncol, 153:425-435.
[38] Nabirotchkin S, Peluffo AE, Bouaziz J, Cohen D (2020). Focusing on the Unfolded Protein Response and Autophagy Related Pathways to Reposition Common Approved Drugs against COVID-19. doi: .
doi: 10.20944/preprints202003.0302.v1
[39] McHugh D, Gil J (2018). Senescence and aging: Causes, consequences, and therapeutic avenues. J Cell Biol, 217:65-77.
[40] López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G (2013). The Hallmarks of Aging. Cell, 153:1194-1217.
[41] Günther A, Ruppert C, Schmidt R, Markart P, Grimminger F, Walmrath D, et al. (2001). Surfactant alteration and replacement in acute respiratory distress syndrome. Respir Res, 2:353-364.
[42] Gralinski LE, Baric RS (2015). Molecular pathology of emerging coronavirus infections. J Pathol, 235:185-195.
[43] Glasser SW, Witt TL, Senft AP, Baatz JE, Folger D, Maxfield MD, et al. (2009). Surfactant protein C-deficient mice are susceptible to respiratory syncytial virus infection. Am J Physiol Lung Cell Mol Physiol, 297:L64-L72.
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