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• W3210331866 abstract "Article28 October 2021Open Access Transparent process Genome-scale metabolic modeling reveals SARS-CoV-2-induced metabolic changes and antiviral targets Kuoyuan Cheng Kuoyuan Cheng orcid.org/0000-0001-8118-5243 Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA Biological Sciences Graduate Program (BISI), University of Maryland, College Park, MD, USA These authors contributed equally to this work Search for more papers by this author Laura Martin-Sancho Laura Martin-Sancho orcid.org/0000-0003-1489-2815 Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA These authors contributed equally to this work Search for more papers by this author Lipika R Pal Lipika R Pal orcid.org/0000-0002-3390-110X Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA Search for more papers by this author Yuan Pu Yuan Pu Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA Search for more papers by this author Laura Riva Laura Riva Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA Search for more papers by this author Xin Yin Xin Yin orcid.org/0000-0003-2357-6718 Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA State Key Laboratory of Veterinary Biotechnology, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin, China Search for more papers by this author Sanju Sinha Sanju Sinha Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA Biological Sciences Graduate Program (BISI), University of Maryland, College Park, MD, USA Search for more papers by this author Nishanth Ulhas Nair Nishanth Ulhas Nair Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA Search for more papers by this author Sumit K Chanda Corresponding Author Sumit K Chanda [email protected] Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA Search for more papers by this author Eytan Ruppin Corresponding Author Eytan Ruppin [email protected] orcid.org/0000-0002-7862-3940 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 Search for more papers by this author Kuoyuan Cheng Kuoyuan Cheng orcid.org/0000-0001-8118-5243 Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA Biological Sciences Graduate Program (BISI), University of Maryland, College Park, MD, USA These authors contributed equally to this work Search for more papers by this author Laura Martin-Sancho Laura Martin-Sancho orcid.org/0000-0003-1489-2815 Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA These authors contributed equally to this work Search for more papers by this author Lipika R Pal Lipika R Pal orcid.org/0000-0002-3390-110X Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA Search for more papers by this author Yuan Pu Yuan Pu Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA Search for more papers by this author Laura Riva Laura Riva Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA Search for more papers by this author Xin Yin Xin Yin orcid.org/0000-0003-2357-6718 Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA State Key Laboratory of Veterinary Biotechnology, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin, China Search for more papers by this author Sanju Sinha Sanju Sinha Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA Biological Sciences Graduate Program (BISI), University of Maryland, College Park, MD, USA Search for more papers by this author Nishanth Ulhas Nair Nishanth Ulhas Nair Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA Search for more papers by this author Sumit K Chanda Corresponding Author Sumit K Chanda [email protected] Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA Search for more papers by this author Eytan Ruppin Corresponding Author Eytan Ruppin [email protected] orcid.org/0000-0002-7862-3940 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 Search for more papers by this author Author Information Kuoyuan Cheng1,2, Laura Martin-Sancho3, Lipika R Pal1, Yuan Pu3, Laura Riva3,6, Xin Yin3,4, Sanju Sinha1,2, Nishanth Ulhas Nair1, Sumit K Chanda *,3 and Eytan Ruppin *,1,5 1Cancer Data Science Laboratory (CDSL), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA 2Biological Sciences Graduate Program (BISI), University of Maryland, College Park, MD, USA 3Immunity and Pathogenesis Program, Infectious and Inflammatory Disease Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA 4State Key Laboratory of Veterinary Biotechnology, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin, China 5Department of Computer Science, University of Maryland, College Park, MD, USA 6Present address: Calibr, a Division of The Scripps Research Institute, La Jolla, CA, USA *Corresponding author. Tel: +1 858 795 5241; E-mail: [email protected] *Corresponding author. Tel: +1 240 858 3169; E-mail: [email protected] Molecular Systems Biology (2021)17:e10260https://doi.org/10.15252/msb.202110260 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Tremendous progress has been made to control the COVID-19 pandemic caused by the SARS-CoV-2 virus. However, effective therapeutic options are still rare. Drug repurposing and combination represent practical strategies to address this urgent unmet medical need. Viruses, including coronaviruses, are known to hijack host metabolism to facilitate viral proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS-CoV-2 infection using genome-scale metabolic modeling (GEM), revealing complicated host metabolism reprogramming during SARS-CoV-2 infection. We next applied the GEM-based metabolic transformation algorithm to predict anti-SARS-CoV-2 targets that counteract the virus-induced metabolic changes. We successfully validated these targets using published drug and genetic screen data and by performing an siRNA assay in Caco-2 cells. Further generating and analyzing RNA-sequencing data of remdesivir-treated Vero E6 cell samples, we predicted metabolic targets acting in combination with remdesivir, an approved anti-SARS-CoV-2 drug. Our study provides clinical data-supported candidate anti-SARS-CoV-2 targets for future evaluation, demonstrating host metabolism targeting as a promising antiviral strategy. SYNOPSIS Metabolic modeling of 12 SARS-CoV-2 datasets identifies novel single or combinatory antiviral targets by reverting the virus-induced host metabolic reprogramming. Meta-analysis of SARS-CoV-2-induced expression changes reveals extensive host metabolic alterations. rMTA algorithm predicted 81 single metabolic targets and 87 targets for combination with remdesivir for anti-SARS-CoV-2. Selected candidate single targets were successfully validated with an immunofluorescence-based siRNA assay in Caco-2 cells. Introduction The coronavirus disease 2019 (COVID-19), a serious respiratory disease caused by the coronavirus SARS-CoV-2, has evolved into a major pandemic incurring millions of deaths worldwide (all dates as of July 2021; WHO Coronavirus Disease Dashboard, 2021). Despite unprecedented global efforts in response to this serious health threat including abundant studies on the disease biology (e.g., Hoffmann et al, 2020, Zhou et al, 2020a, reviewed in Tay et al, 2020, etc.), preclinical antiviral drug/target screens or predictions (e.g., Daniloski et al, 2021, Riva et al, 2020, Wei et al, 2021, with compiled resources such as Kuleshov et al, 2020), and thousands of registered clinical trials on COVID-19 (International Clinical Trials Registry Platform, 2021), therapeutic options remain scarce. Remdesivir, a viral RNA-dependent RNA polymerase inhibitor, represents the only drug approved by the drug regulatory authorities of several countries, including the U.S. Food and Drug Administration (FDA) (Beigel et al, 2020), and confers only mild clinical benefits to a subset of COVID-19 patients (WHO Solidarity Trial Consortium et al, 2020). 11 different therapies, including the Janus kinase (JAK) inhibitor baricitinib (in combination with remdesivir), and virus-neutralizing antibodies sotrovimab, and casirivimab plus imdevimab, have obtained Emergency Use Authorization (EUA) from the FDA (U.S. Food & Drug Administration, 2021a). Dexamethasone and other corticosteroids have been recommended by the U.S. National Institutes of Health (NIH) for hospitalized patients requiring supplemental oxygen (National Institutes of Health, 2021; RECOVERY Collaborative Group et al, 2021). Besides, several SARS-CoV-2 vaccines have been approved or authorized for emergency use in different countries (Dong et al, 2020; U.S. Food & Drug Administration, 2021b). Nevertheless, there is still an urgent unmet medical need for the fast identification and development of highly effective anti-COVID-19 therapies. Viruses are known to “hijack” the host cell metabolism to complete their own intracellular life cycle (Mayer et al, 2019), modulating diverse pathways including carbohydrate, lipid, amino acid, and nucleotide metabolism (Sanchez & Lagunoff, 2015; Mayer et al, 2019). Coronaviruses including MERS-CoV rearrange cellular lipid profiles upon infection (Yan et al, 2019a; Yuan et al, 2019b). Recent studies have reported that SARS-CoV-2 also induces changes in numerous metabolic pathways including TCA cycle, oxidative phosphorylation, and lipid metabolism among others in human patient samples (preprint: Ehrlich et al, 2020; Gardinassi et al, 2020). Notably, counteracting the metabolic demands of viruses including MERS-CoV has been shown to abolish their ability to infect the host cells (Mayer et al, 2019; Yuan et al, 2019), and the PPARα-agonist fenofibrate can reverse some of the SARS-CoV-2-induced metabolic changes and reduce the viral load (preprint: Ehrlich et al, 2020). Therefore, targeting the virus-induced metabolic changes can be a promising novel antiviral strategy (Mayer et al, 2019), and can be especially valuable in anti-SARS-CoV-2 drug repurposing to address the current urgent COVID-19 crisis considering that many existing drugs are metabolism-targeting. Genome-scale metabolic models (GEMs) are in silico constraint-based models that comprehensively encompass the cellular network of metabolic reactions, metabolic proteins, and metabolites (Baart & Martens, 2012). GEM analysis has been repeatedly shown to generate accurate predictions and informative hypotheses for metabolism research (Gu et al, 2019). Notably, we have previously developed numerous GEM-based algorithms including iMAT (Shlomi et al, 2008), which computes genome-wide metabolic fluxes from gene expression profiles, and the metabolic transformation algorithm (MTA; Yizhak et al, 2013), which predicts metabolic targets whose inhibition facilitates transformation between specified cellular metabolic states (e.g., from diseased to healthy states). More recently, Valcárcel et al (2019) have described a variant of MTA named rMTA with improved performance. Incorporating such high-performance GEM methods in the analysis of data on SARS-CoV-2 infection provides us with a unique opportunity to understand the metabolic demands of SARS-CoV-2 and to systematically predict anti-SARS-CoV-2 targets that counteract the virus-induced metabolic alterations. Here, we apply GEM algorithms in a comprehensive analysis of 12 published bulk/single-cell RNA-sequencing (RNA-seq/scRNA-seq) and mass spectrometry (MS)-based proteomics datasets on SARS-CoV-2 infection, involving both in vitro and human patient samples. We find that metabolic reprogramming represents one of the most consistent molecular changes in SARS-CoV-2 infection besides immune responses, and characterized the complex patterns of metabolic flux alterations. Using rMTA, we predicted anti-SARS-CoV-2 targets that reverse the virus-induced metabolic changes, either as single targets or in combination with remdesivir (the latter using our new RNA-seq data on remdesivir treatment). The predictions are highly enriched for reported anti-SARS-CoV-2 targets identified from various experimental screens, and we further validated a core set of top predicted single targets with an immunofluorescence-based siRNA assay in Caco-2 cells. Our results demonstrate the potential of targeting host metabolism to inhibit viral infection. Results Integrated analysis of multiple gene expression datasets identifies coherent immune and metabolic changes in SARS-CoV-2 infection Multiple studies have characterized the gene expression changes during SARS-CoV-2 infection in different in vitro and in vivo settings. We collected a total of 12 published relevant datasets spanning a wide range of sample types (various cell lines, primary bronchial epithelial cells, nasopharyngeal swab, and bronchoalveolar lavage fluid, i.e., BALF samples from patients) and assay platforms (bulk RNA-seq, scRNA-seq, and MS-based proteomics). These datasets are summarized in Table 1. With each of the datasets, we performed differential expression (DE) analysis comparing the SARS-CoV-2-infected or positive samples to the non-infected control or negative samples (Materials and Methods; Table EV1). For the single-cell datasets, we focused on the airway epithelial cell, which is known as the major virus-infected cell type. Comparing the datasets with a principal component analysis (PCA) plot based on the inverse normal-transformed DE log fold change values (Fig 1A; Materials and Methods) suggests that the cell lines tend to have distinct DE profiles from the patient samples, although different patient datasets exhibit considerable variation depending on sample type and sequencing platform. Such variation is confirmed by comparing the top significant DE genes (FDR < 0.1) from each pair of datasets (Fig 1B; additional robustness analysis in Appendix Fig S1; Materials and Methods). Examining only the top DE genes also appears to mitigate the technical variation across datasets, with reasonable coherence demonstrated by odds ratio median value 1.50 and maximum 5.89 (Fisher's exact test adjusted P median 4.56e-6, minimum < 2.22e-16; Fig 1B). Table 1. Summary of the published gene expression datasets on SARS-CoV-2 infection analyzed in this study. Dataset namea Sample type Sample sizeb Platform Reference Vero Vero E6 cell line 6 Bulk RNA-seq Riva et al (2020) NHBE Primary normal human bronchial epithelial cell 6 Bulk RNA-seq Blanco-Melo et al (2020) A549 A549 human lung adenocarcinoma cell line with exogenous ACE2 expression 6 Bulk RNA-seq Blanco-Melo et al (2020) Calu-3 Calu-3 human lung adenocarcinoma cell line 6 Bulk RNA-seq Blanco-Melo et al (2020) 293T HEK293T human embryonic kidney cell line 12 Bulk RNA-seq Weingarten-Gabby et al (2021) Caco-2 Caco-2 human colorectal adenocarcinoma cell line 6 MS-based proteomics Bojkova et al (2020b) Swab.Butler NP swab samples from human individuals 580 Bulk RNA-seq Butler et al (2021) Swab.Lieberman NP swab samples from human individuals 484 Bulk RNA-seq Lieberman et al (2020) BALF BALF from human individuals 6 Bulk RNA-seq Xiong et al (2020b) SC.Liao BALF from human individuals (epithelial cells were used in analysis) 13 scRNA-seq Liao et al (2020) SC.Chua.Basal NP and bronchial samples from human individuals (basal cells were used in analysis) 24 scRNA-seq Chua et al (2020) SC.Chua.Ciliated NP and bronchial samples from human individuals (ciliated cells were used in analysis) 24 scRNA-seq Chua et al (2020) BALF, bronchoalveolar lavage fluid; MS, mass spectrometry; NP, nasopharyngeal; RNA-seq, RNA-sequencing; scRNA-seq, single-cell RNA-sequencing. a These are the names used in figure labels throughout the text. b The total number of replicates (virus-infected and control combined) used for analysis in in vitro datasets, or the total number of human individuals (patients and controls combined) used for analysis in in vivo datasets. In some datasets, only a subset of all the available samples were analyzed. Figure 1. Analysis of SARS-CoV-2-induced gene expression changes with 12 published datasets PCA plot using the rank-based inverse normal-transformed differential expression (DE) log fold change values (virus-infected compared to control samples) across all the datasets analyzed. Visualization of the overlap of the top significant DE genes (FDR < 0.1) between each pair of datasets analyzed using Fisher's exact tests (Materials and Methods). The dot size corresponds to the effect size of the overlap as measured by odds ratio, and the color corresponds to the negative log10 adjusted one-sided P value (gray means below 0.05). Visualization of the overlap of the top significantly enriched pathways (FDR < 0.1) from the gene set enrichment analysis (GSEA) between each pair of datasets analyzed using Fisher's exact tests (Materials and Methods). The meanings of dot size and color are the same as (B), and dots with black borders correspond to infinity odds ratio. A summary visualization of the GSEA result for the top consistently altered pathways during SARS-CoV-2 infection across the datasets, with more importance given to the various in vivo patient datasets (Materials and Methods). The dot color corresponds to the negative log10 adjusted P values from GSEA, with two sets of colors (red-orange and blue-purple) distinguishing up-regulation from down-regulation (positive or negative normalized enrichment scores, i.e., NES); dot size corresponds to the absolute value of NES measuring the strength of enrichment. The left- and right-hand side blocks represent the pathways that tend to be consistently up-regulated and down-regulated in infected vs control samples, respectively; within each block, the pathways are ordered by negative sum of log P values across datasets (i.e., Fisher's method). Heatmap summarizing the landscape of metabolic pathway alterations (based on gene expression) during SARS-CoV-2 across datasets. The heatmap color corresponds to the GSEA NES values (explained above) for KEGG metabolic pathways grouped into major categories. Only the metabolic pathways with FDR < 0.1 enrichment in at least one dataset are included in the heatmap. The dataset labels used in this figure correspond to those given in Table 1. Download figure Download PowerPoint We then performed gene set enrichment analysis (GSEA) (Subramanian et al, 2005) on the DE results from each dataset (Table EV2), and further compared the datasets on the pathway level by the significantly enriched pathways (FDR < 0.1; Materials and Methods). Reassuringly, the level of coherence across datasets on the pathway level is even stronger, with a median odds ratio of 4.53 (maximum is infinity followed by 40.73) across pairs of datasets (adjusted P median 2.88e-5, minimum < 2.22e-16; Fig 1C). Examining the most consistently enriched pathways across the datasets while giving higher importance to the various in vivo patient datasets (Fig 1D; Table EV3; Materials and Methods), we see many up-regulated pathways involved in innate immune response to viral infection, e.g., interferon signaling. Among the pathways involving coherently down-regulated genes upon SARS-CoV-2 infection, we find antigen presentation, as well as numerous pathways spanning many major categories of cellular metabolism, e.g., TCA cycle and the respiratory electron transport, sphingolipid metabolism, glucose metabolism, and N-glycan biosynthesis. These may reflect the specific metabolic requirements of SARS-CoV-2 or underlie its pathogenic effects (see Discussion). Visualizing a more complete landscape of metabolic pathway alterations across the datasets reveals further consistent, although weaker, changes (based on GSEA normalized enrichment score, i.e., NES; Fig 1E; Table EV2; Materials and Methods). The major findings above are robust to the DE algorithms used (Appendix Fig S2). These results suggest that besides immune response, metabolic reprogramming represents one of the most robust changes induced by SARS-CoV-2 infection across various systems, consistent with the key roles of metabolism in viral infection. We next focused on characterizing the SARS-CoV-2-induced metabolic changes in the infected host cells on the metabolic flux level. Genome-scale metabolic modeling (GEM) identifies SARS-CoV-2-induced patterns of metabolic flux changes Since gene expression does not necessarily correlate with protein level or enzyme activity and thus may not truthfully reflect metabolic activity (Maier et al, 2009), we applied GEM to infer the metabolic fluxes (i.e., rates of all metabolic reactions) across the datasets. Specifically, for each dataset, the iMAT algorithm (Shlomi et al, 2008) was applied to the median expression profiles of the control and virus-infected samples to compute the refined metabolic models representative of the two respective groups. Briefly, iMAT uses mixed integer programming to optimally identify high- and low-activity reactions that match the high and low gene expression patterns in a sample-specific manner, thus defining sample-specific model constraints to obtain contextualized models (Shlomi et al, 2008). For the base metabolic models, we mainly used the more recent Recon 3D (Brunk et al, 2018), but also used Recon 1 (Duarte et al, 2007) for increased robustness (Materials and Methods). After obtaining the dataset and sample-specific constrained model with iMAT, the marginal distribution of flux values of each metabolic reaction was obtained by sampling. The flux distributions of the control and infected groups were compared, and reactions with differential fluxes (DF) were identified (Materials and Methods; Table EV4). We again examined the consistency across the datasets, here on the flux level, by checking the overlap of the top DF reactions between each pair of datasets. Like on the gene expression level, we are assured by the overall reasonable level of coherence of the DF reactions (odds ratio median 2.05, maximum 2.89; adjusted P value median 1.45e-11, minimum < 2.22e-16; Fig 2A shows the result for the positive DF reactions, the result is similar for negative DF reactions. We note that the sign of DF represents the direction of flux change with regard to the positive direction of a reaction, which can be reversible, and not the increase or decrease in the absolute flux). Although no reaction shows fully consistent changes across all 12 datasets, we identified a set of most consistently changed reactions across datasets while giving higher importance to the in vivo patient datasets (Table EV5A; Materials and Methods), and examined the metabolic pathways they are enriched in with Fisher's exact tests (significant pathways with FDR < 0.1 shown in Fig 2B; Table EV5B). We see that consistent flux changes are found in various noteworthy pathways including metabolite transport (mitochondrial and extracellular), pentose phosphate pathway, hyaluronan metabolism, pyrimidine synthesis, glycine, serine, alanine and threonine metabolism, inositol phosphate metabolism, and fatty acid synthesis, among others. Many of these pathways have been implicated in the infection and life cycle of different viruses including SARS-CoV-2 (Mayer et al, 2019; preprint: Bojkova et al, 2020a; preprint: Ehrlich et al, 2020; Gardinassi et al, 2020; Ou et al, 2020; Thomas et al, 2020; Li et al, 2021, see Discussion). Figure 2. Genome-scale metabolic modeling (GEM)-based analysis of SARS-CoV-2-induced metabolic alterations across datasets Genome-scale metabolic modeling (GEM) was used to compute the metabolic fluxes from the gene expression profiles, and reactions with differential fluxes (DF) between the SARS-CoV-2-infected and control groups were identified for each dataset (Materials and Methods). A. Visualization of the overlap of the top DF reactions between each pair of datasets analyzed using Fisher's exact tests (Materials and Methods). The dot size corresponds to the effect size of the overlap as measured by odds ratio, and the color corresponds to the negative log10 adjusted one-sided P value (gray means below 0.05). B. A summary visualization of the metabolic pathway enrichment result for the top consistent DF reactions across the datasets, with more importance given to the various in vivo patient datasets (Materials and Methods). Y-axis represents the odds ratio of enrichment, the dot color corresponds to the adjusted P value from Fisher's exact tests, and dot size corresponds to the number of enriched reactions within each pathway. Half-dots plotted on the top border line correspond to infinity odds ratio values. The pathways on the X-axis are ordered by P value, and only those with FDR < 0.1 are shown. C–E. Visualization of the relatively consistent DF patterns in selected enriched pathways. The DF results are based on metabolic modeling using the human GEM Recon 3D (Brunk et al, 2018), but for clear visualization, the metabolic network graphs are based on the human GEM Recon 1 (Duarte et al, 2007) to reduce the number of metabolites and reactions displayed (Materials and Methods). Metabolites are represented by nodes, reactions are represented by directed (hyper) edges, with edge direction corresponding to the consensus reaction direction and edge color corresponding to the consensus DF direction across datasets (Materials and Methods). Red and blue colors correspond to increased and decreased fluxes, respectively; gray color corresponds to reactions not showing consistent DF changes across datasets, some of such reactions are not shown to increase clarity. (C) Pyrimidine synthesis. (D) Inositol phosphate metabolism. (E) Fatty acid synthesis. Metabolites are labeled by their names in (C) or IDs in (D, E), with suffixes denoting their cellular compartments: [c] cytosol; [m] mitochondria. The mapping between the IDs and metabolite names in (D, E) is given in Table EV5C. Download figure Download PowerPoint Next, we closely inspect the fluxes within specific pathways by visualizing their alteration patterns overlaid on the metabolic network, for virus-infected vs the control group. For example, the pyrimidine (de novo) synthesis pathway mostly contains consistently increased fluxes toward the synthesis of UMP (the precursor of pyrimidines; Fig 2C), consistent with the nucleic acid synthesis needs of the virus. As examples of pathways with more complex flux change patterns, in the inositol phosphate metabolism pathway, we see increased fluxes converging to phosphatidylinositol 4,5-bisphosphate (pail45p_hs[c]) and inositol (inost[c]), but decreased fluxes to inositol 1-phosphate (mi1p_DASH_D[c]; Fig 2D); in the fatty acid synthesis pathway, we see that the synthesis and interconversion of different fatty acids show distinct flux changes (Fig 2E). These highly intricate metabolic programs revealed by the GEM analysis are consistent with many previous reports and possibly reflect the specific metabolic demands of SARS-CoV-2 during its life cycle (see Discussion), which also demonstrates the value of the modeling approach over gene expression-level analyses. Prediction of anti-SARS-CoV-2 targets that act via counteracting the virus-induced metabolic changes We have demonstrated that SARS-CoV-2 can induce recurrent and complex alterations in host cell metabolism. As was proposed previously, targeting the virus-induced metabolic changes can be an effective antiviral strategy (Mayer et al, 2019), which we adopted here to predict" @default.
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• W3210331866 title "Genome‐scale metabolic modeling reveals SARS‐CoV‐2‐induced metabolic changes and antiviral targets" @default.
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