key: cord-0840126-n5v1avz8
authors: de Moraes, Diogo; Paiva, Brunno Vivone Buquete; Cury, Sarah Santiloni; Ludwig, Raissa Guimarães; Junior, João Pessoa Araújo; Mori, Marcelo Alves da Silva; Carvalho, Robson Francisco
title: Prediction of SARS-CoV Interaction with Host Proteins during Lung Aging Reveals a Potential Role for TRIB3 in COVID-19
date: 2021-02-01
journal: Aging Dis
DOI: 10.14336/ad.2020.1112
sha: c7f34766e1ab6ed30343361add3698c85163ca31
doc_id: 840126
cord_uid: n5v1avz8

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.

for the SARS-CoV-2 spike protein receptor-binding domain (RBD) for viral attachment [7, 8] . The conserved evolutionary relationship between the 2019 novel SARS-CoV-2 and SARS-CoV [9] opens up the possibility to explore the relationships between these human coronaviruses (HCoVs) in public databases. Computational predictions of SARS-CoV-human protein-protein interactions (PPIs) may identify viral infection mechanisms and drug targets [9] [10] [11] . In this context, the Genotype-Tissue Expression (GTEx) database [12, 13] has provided insights into age-related genes [14, 15] and, associated with single-cell transcriptomics, could predict SARS-CoV-2-PPIs in aging lungs. This characterization is crucial for older adults, which are more vulnerable to the disease [16] [17] [18] . Thus, we analyzed whether lung aging is associated with transcriptional changes in proteins that potentially interact with SARS-CoV-2. 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 wellrecognized 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).

Aging and Disease • Volume 12, Number 1, February 2021 44

The RNA-Seq data analysis of lung tissues was performed using 286 samples from males and 141 samples from females with 20-79 years old (yo) available at the GTEx portal (release V7) (https://www.gtexportal.org/) [19] (males: 20-29 yo (n = 16), 30-39 yo (n = 21), 40-49 yo (n = 47), 50-59 yo (n = 109), 60-69 yo (n = 86), 70-79 yo (n = 7); females: 20-29 yo (n = 11), 30-39 yo (n = 9), 40-49 yo (n = 29), 50-59 yo (n = 36), 60-69 yo (n = 53), 70-79 yo (n = 3)). We first used the BioJupies platform (https://amp.pharm.mssm.edu/biojupies/) [20] to identify the differentially expressed genes (DEG) in lung samples across age ranges using limma [21] . In this first analysis, only age, and not sex, was considered. The expression data of each subject was distributed according to their age range: 30-39; 40-49; 50-59; 60-69 and 70-79 yo, and each age range was compared with the group of young adults (20-29 yo) as a common control. Genes with Log2 of fold change ≥ |1| and false discovery rate (FDR) < 0.05 were considered as differentially expressed (DEGs) ( Supplementary Fig. 1 ). Next, for each sex, we performed a hierarchical clustering analysis based on Pearson correlation in age ranges using the mean expression (Trimmed Mean of M-values, TMM) of the DEGs found previously in, at least, one age range (Fig. 1A ). This analysis aimed to identify clusters with gradients of gene expression across age ranges, showing increased or decreased expression during aging (method adapted from Theunissen et al., 2011 [22] ). The clusterization of gene expression profiles on female lung samples generated clusters with less evident gradients than the males ( Fig.  1A and Supplementary Fig. 2 ). Thus, considering the clear clusterization found in males and the fact that older males seem to have a worse prognosis on COVID-19 [6] , we focused on male profiles to identify age-related lung genes.

Our research group previously used the strategy described above to analyze RNA-Seq data of GTEx lung samples (release V7) during aging. We reutilized these results due to the urgency of the current pandemic situation. GTEx was recently updated (V8), with more lung samples (males: Table 10 ). The updated cohort (V8) was used for further gene-specific analyses (TRIB3, HAPLN2, CDKN2A, and MKI67) , with Dunn's multiple comparisons test ( Fig. 1C and E), through the GraphPad Prism version 8.0.0 for Windows (GraphPad Software, La Jolla, California, USA). P-values < 0.05 were considered as statistically significant.

The conserved evolutionary relationship between the 2019 novel SARS-CoV-2 and SARS-CoV [9] opens up the possibility to explore relationships of these human coronaviruses (HCoVs) in publicly available databases. Thus, lung DEGs were compared with corresponding human proteins that potentially interact with HCoVs ( Fig. 1B and Supplementary Table 2 ). The HCoVs-human PPIs were obtained using data from the Pathogen-Host Interactome Prediction using Structure Similarity (P-HIPSTer, http://phipster.org/) database, which is a catalog of the virus-human PPIs predicted based on protein structural information [11] (Supplementary Table 2 ) with an experimental validation rate of ∼76% [11] . The DEGs list were compared with recently added libraries for virus perturbations (up-and down-regulation) from GEO datasets (GSE33266, GSE50000, GSE49262, GSE50878, GSE49263, GSE40824, GSE50878, GSE49263, GSE47960, GSE47961, GSE47962, GSE17400, and GSE40824), available at the EnrichR database [23] . Access in March 2020. Genes that were up-or down-regulated in both conditions were analyzed on STRING (https://stringdb.org/) [24] . Access in March 2020.

Expression of TRIB3, HAPLN2, and ACE2 was analyzed in different lung cell populations using two previously published human single-cell RNA-seq data (Supplementary Table 4 ) [25, 26] . The first dataset [25] was explored in the UCSC Cell Browser (http://nupulmonary.org/resources/), aiming the identification of the cell populations expressing those genes. The samples with pulmonary fibrosis presented in this dataset were omitted from our analysis, and only nondiseased lung samples were included (n=8). Another independent single-cell RNA-seq dataset [26] (n=5), available at the Human Cell Atlas Portal (https://data.humancellatlas.org/explore/projects/ c4077b3c-5c98-4d26-a614-246d12c2e5d7), was used to confirm that TRIB3 and ACE2 are expressed in alveolar epithelial cells (types 1 and 2) and in ciliate cells.

The corresponding proteins of the DEG shared with the list of DEG from libraries for virus perturbations were queried in the STRING [24] , for the construction of PPI networks. We considered the following settings: text mining, experiments, databases, and co-expression as sources of active interaction. We selected the minimum interaction score of 0.900 (highest confidence), and the disconnected nodes were hidden to simplify the display (Fig. 3B) . We evaluated the PPI enrichment P-values, which verifies the number of interactions of a set of proteins compared with a random set of similar size. The PPI enrichment P-value represents the statistical significance provided by STRING. Access in March 2020.

To verify whether TRIB3 expression is also decreased in the lungs of patients with comorbidities at high risk of developing severe COVID-19 such as asthma [27] and chronic obstructive pulmonary disease (COPD) [28] , data of available studies were downloaded from public Gene Expression Omnibus (GEO) datasets (GSE158752, GSE85567 and GSE57148). Differential gene expression analysis was performed using the BioJupies platform by comparing each group of patients with asthma or COPD with their respective control group: 98 COPD vs. 91 controls, 57 Asthma vs. 28 controls, and 50 asthma vs. 17 controls. (Supplementary Fig. 3) .

The clustering analyses of the expression profiles were performed using the web tool Morpheus (https://software.broadinstitute.org/morpheus) [29] . Venn diagrams were plotted using the Jvenn online tool (https://jvenn.toulouse.inra.fr) [30] . Volcano Plots were constructed with Graphpad Prism8. 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.

We identified differentially expressed genes (DEGs) during aging in GTEx human lung samples (release V7) (Data S1). The numbers of significant DEGs increased with aging (Log fold-change ≥ |1| and FDR < 0.05), and individuals of 60-69-year-old (yo) presented the highest number of DEGs, in comparison to young adults (20-20 yo) (Figures S1-S2, Table S1 ). Clustering of these DEGs identified age-associated profiles ( Figure 1A) . Among the transcripts translated into proteins predicted as interacting with SARS-CoV, the hyaluronan and proteoglycan link protein 2 (HAPLN2) increased with aging, while tribbles homolog 3 (TRIB3) decreased (Fig. 1B, Supplementary Table 2 ). HAPLN2 was predicted to interact with virus proteins spike glycoprotein and E2 glycoprotein precursors, while TRIB3 with nucleocapsid protein and RNA-dependent RNA polymerase (Fig. 1B; Supplementary Table 3) . Notably, the SARS-CoV-2 nucleocapsid protein has a sequence identity of 89.6% compared to SARS-CoV [9] . The expression of TRIB3 also decreased in the lung, specifically in males older than 40 (Fig. 1C) , in a cohort with additional samples (GTEx, release V8). When both sexes are pooled, HAPLN2 expression is significantly increased in individuals older than 60 (Fig. 1E) . Table S8 contains the complete list of overrepresented terms.

We also measured the lung expression profile of the well-known aging markers CDKN2A and MKI67 in GTEx human lung samples [14, [31] [32] [33] . CDKN2A is translated into the INK4 family members p16 and p16(INK4a), which are markers of senescence and physiological aging [1] [2] [3] . MKI67 is a cellular marker of proliferation, and its absence indicates senescence [4] . CDKN2A significantly increased with aging in both sexes, whereas MKI67 decreased in males (Fig. 1D) .

The reanalysis of lung single-cell RNA sequencing data [25, 26] demonstrated that TRIB3 was expressed mainly in alveolar type I (AT1) and type II (AT2) cells and in ciliated cells (Fig. 2A, D and E) , which also expresses the SARS-CoV-2 receptor ACE2 [7, 8, 34] .

TRIB3 expression was decreased in lung comorbidities associated with COVID-19 severity. In COPD patients, TRIB3 showed logFC = -0.32 and p<0.001 and, for asthma patients, TRIB3 were reduced in both datasets (logFC = -0.3 and -0.4, p<0.05). We compared the young asthma patients (<35 years-old; N=20) with the old asthma patients (>50 years-old; N=21) from GSE158752. Even showing negative logFC expression (-0.28) the difference was not significant (Suppmentary Fig. 3) . Finally, we compared SARS-CoVinduced perturbations in host gene expression, from public GEO datasets, with our list of DEGs in GTEx lung samples during aging (Fig. 3, Supplementary Table 8 ). We found that genes that decrease their expression with aging and genes that are up-regulated with SARS-CoV infections generated the most significant network, with over-represented genes associated with mitotic cell cycle and surfactant metabolism (Fig. 3B ).

Here, we used the transcriptome of lung samples from the GTEx database to find age-related genes. To predict how these genes could interact with SARS-CoV-2 infection, we used two approaches: predicted their interaction with the proteins of the closely related SARS-CoVs strain through the P-Hipster database(i); checked if these genes were also deregulated on SARS-CoV-2 infections through the EnrichR database (ii).

The involvement of TRIB3 in viral infection is poorly understood; however, its inhibition was associated with an increase of hepatitis C virus (HCV) replication [35] . Additionally, TRIB3 negatively regulates the entry step of the HCV life cycle and propagation [35] and may constitute a common protective host factor for other positive-sense single-strand RNA viruses. TRIB3 is also one of the unfolded protein response (UPR)-related genes with the strongest positive correlation with the intracellular abundance of the flavivirus dengue and Zika [36] . Considering the need for drugs to treat COVID-19, the α-hydroxylinoleic acid (ABTL0812) induces the expression of TRIB3 by inhibiting the PI3K/AKT/mTOR axis and promoting autophagy cell death in cancer [37] . We highlight that the lifecycle of coronaviruses depends on several host-cell encoded cellular pathways, and among these pathways, UPR and autophagy pathways of the host cells are essential to the life cycle of coronaviruses [38] .

We also found opposite functional directions of mitosis and surfactant metabolism in aging lungs when compared to SARS-CoV-2-induced perturbations. The decreased cellular division capacity on aging is associated with cellular senescence -a mechanism that stops cells with damaged DNA from replicating [39] -and progenitor cell exhaustion [40] . The altered metabolism or secretion of surfactants by AT2 cells reduces the ability of the lungs to expand and increases the risk of alveolar collapse in HCoVs infections [41, 42] . Moreover, Sftpc -/-(Surfactant Protein C) mice have worse viral infections than controls [43] , and its human homolog decreased with aging while it is up-regulated on SARS-CoV infections (Fig. 3) . Thus, the pneumonia-like lung injury found in severe cases of COVID-19 infections [5, 6] may be aggravated by impaired lung regeneration and altered metabolism of surfactants in older male patients.

Although the genes and pathways we highlighted were identified based on robust statistical significance, other methods of over-time gene expression analyses applying different cutoffs could be considered; using GTEx V8 cohort or separating males and females may result in different sets of age-related genes in the lung. Further analyses should be conducted to identify more differences between male and female lungs during aging. Additionally, clinical data from these individuals -such as diabetes or cardiovascular diseases -important factors influencing COVID-19 outcome -were not evaluated. However, the GTEx donor consent policy makes public phenotypes limited. Its access needs an application via dbGaP (Genotypes and Phenotypes database), which, associated with reanalysis of the transcriptomics data, may take significant time. Part of the results presented herein derives from a previously unpublished paper focusing on aging lung on a different topic. Nevertheless, we decided to release this data focusing on SARS-CoV-2 due to the emergency of the current pandemic.

In conclusion, we show that lung gene expression of TRIB3, a protein predicted to interact with the nucleocapsid protein and the RNA-dependent RNA polymerase of HCoVs, decreases in COPD, asthma and males during aging. This study provides insights into aging and COVID-19 based on the transcriptional profile of the aging lung and reveals a potential role for TRIB3, surfactant metabolism, and mitotic cell cycle. Considering that TRIB3 may decrease virus infection and replication, strategies to stimulate TRIB3 expression should be tested to treat COVID-19.

A new coronavirus associated with human respiratory disease in China

A pneumonia outbreak associated with a new coronavirus of probable bat origin

An interactive webbased dashboard to track COVID-19 in real time

Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan

Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19)

Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study

Functional assessment of cell entry and receptor usage for SARS-CoV-2 and other lineage B betacoronaviruses

Receptor Recognition by the Novel Coronavirus from Wuhan: an Analysis Based on Decade-Long Structural Studies of SARS Coronavirus

Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2

Understanding Human-Virus Protein-Protein Interactions Using a Human Protein Complex-Based Analysis Framework. mSystems

A Structure-Informed Atlas of Human-Virus Interactions

The Genotype-Tissue Expression (GTEx) project

The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans

Transcriptome analysis reveals the difference between "healthy" and "common" aging and their connection with age-related diseases

An analysis of aging-related genes derived from the Genotype-Tissue Expression project (GTEx)

Updated understanding of the outbreak of 2019 novel coronavirus (2019-nCoV) in Wuhan

Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia

Clinical features of patients infected with 2019 novel coronavirus in Wuhan

A scaling normalization method for differential expression analysis of RNA-seq data

BioJupies: Automated Generation of Interactive Notebooks for RNA-Seq Data Analysis in the Cloud

limma powers differential expression analyses for RNA-sequencing and microarray studies

Time-Response Evaluation by Transcriptomics of Methylmercury Effects on Neural Differentiation of

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

STRING v11: proteinprotein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Single-Cell Transcriptomic Analysis of Human Lung Provides Insights into the Pathobiology of Pulmonary Fibrosis

scRNA-seq assessment of the human lung, spleen, and esophagus tissue stability after cold preservation

Asthma and COVID-19

COVID-19 and COPD

Morpheus: a user-friendly modeling environment for multiscale and multicellular systems biology

jvenn: an interactive Venn diagram viewer

Cellular senescence: from physiology to pathology

Ink4a/Arf expression is a biomarker of aging

Mitogenic signalling and the p16INK4a-Rb pathway cooperate to enforce irreversible cellular senescence

SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor

Nonstructural 3 Protein of Hepatitis C Virus Modulates the Tribbles Homolog 3/Akt Signaling Pathway for Persistent Viral Infection

Single-cell transcriptional dynamics of flavivirus infection

Therapeutic potential of the new TRIB3-mediated cell autophagy anticancer drug ABTL0812 in endometrial cancer

Focusing on the Unfolded Protein Response and Autophagy Related Pathways to Reposition Common Approved Drugs against COVID-19

Senescence and aging: Causes, consequences, and therapeutic avenues

The Hallmarks of

Surfactant alteration and replacement in acute respiratory distress syndrome

Molecular pathology of emerging coronavirus infections

Surfactant protein Cdeficient mice are susceptible to respiratory syncytial virus infection

This research was supported by the National Council for Scientific and Technological Development, CNPq (Process 311530/2019-2 to RFC, and scholarship #870415/1997-2 to SSC), by the Coordenaç ã o de Aperfeiç oamento de Pessoal de Ní vel Superior, CAPES, Brasil, Finance Code 001 (scholarship to DM), and by Fundaç ã o de Amparo à Pesquisa do Estado de Sã o Paulo (2017/01184-9 to MAM). The results shown here are, in part, based upon data generated by the Genotype-Tissue Expression project (GTEx) (https://gtexportal.org/).

The Supplementary data can be found online at: www.aginganddisease.org/EN/10.14336/AD.2020.1112. Other supplementary files can be found in the following repository: https://zenodo.org/record/4122440. DOI: 10.5281/zenodo.4122440.

Aging and Disease • Volume 12, Number 1, February 2021