PMC:7073332 / 38201-49246 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/7073332","sourcedb":"PMC","sourceid":"7073332","source_url":"https://www.ncbi.nlm.nih.gov/pmc/7073332","text":"Methods and materials\n\nGenome information and phylogenetic analysis\nIn total, we collected DNA sequences and protein sequences for 15 HCoVs, including three most recent 2019-nCoV/SARS-CoV-2 genomes, from the NCBI GenBank database (28 January 2020, Supplementary Table S1). Whole-genome alignment and protein sequence identity calculation were performed by Multiple Sequence Alignment in EMBL-EBI database (https://www.ebi.ac.uk/) with default parameters. The neighbor joining (NJ) tree was computed from the pairwise phylogenetic distance matrix using MEGA X84 with 1000 bootstrap replicates. The protein alignment and phylogenetic tree of HCoVs were constructed by MEGA X84.\n\nBuilding the virus–host interactome\nWe collected HCoV–host protein interactions from various literatures based on our sizeable efforts. The HCoV-associated host proteins of several HCoVs, including SARS-CoV, MERS-CoV, IBV, MHV, HCoV-229E, and HCoV-NL63 were pooled. These proteins were either the direct targets of HCoV proteins or were involved in critical pathways of HCoV infection identified by multiple experimental sources, including high-throughput yeast-two-hybrid (Y2H) systems, viral protein pull-down assay, in vitro co-immunoprecipitation and RNA knock down experiment. In total, the virus–host interaction network included 6 HCoVs with 119 host proteins (Supplementary Table S3).\n\nFunctional enrichment analysis\nNext, we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses to evaluate the biological relevance and functional pathways of the HCoV-associated proteins. All functional analyses were performed using Enrichr85.\n\nBuilding the drug–target network\nHere, we collected drug–target interaction information from the DrugBank database (v4.3)86, Therapeutic Target Database (TTD)87, PharmGKB database, ChEMBL (v20)88, BindingDB89, and IUPHAR/BPS Guide to PHARMACOLOGY90. The chemical structure of each drug with SMILES format was extracted from DrugBank86. Here, drug–target interactions meeting the following three criteria were used: (i) binding affinities, including Ki, Kd, IC50, or EC50 each ≤10 μM; (ii) the target was marked as “reviewed” in the UniProt database91; and (iii) the human target was represented by a unique UniProt accession number. The details for building the experimentally validated drug–target network are provided in our recent studies13,23,28.\n\nBuilding the human protein–protein interactome\nTo build a comprehensive list of human PPIs, we assembled data from a total of 18 bioinformatics and systems biology databases with five types of experimental evidence: (i) binary PPIs tested by high-throughput yeast-two-hybrid (Y2H) systems; (ii) binary, physical PPIs from protein 3D structures; (iii) kinase-substrate interactions by literature-derived low-throughput or high-throughput experiments; (iv) signaling network by literature-derived low-throughput experiments; and (v) literature-curated PPIs identified by affinity purification followed by mass spectrometry (AP-MS), Y2H, or by literature-derived low-throughput experiments. All inferred data, including evolutionary analysis, gene expression data, and metabolic associations, were excluded. The genes were mapped to their Entrez ID based on the NCBI database92 as well as their official gene symbols based on GeneCards (https://www.genecards.org/). In total, the resulting human protein–protein interactome used in this study includes 351,444 unique PPIs (edges or links) connecting 17,706 proteins (nodes), representing a 50% increase in the number of the PPIs we have used previously. Detailed descriptions for building the human protein–protein interactome are provided in our previous studies13,23,28,93.\n\nNetwork proximity measure\nWe posit that the human PPIs provide an unbiased, rational roadmap for repurposing drugs for potential treatment of HCoVs in which they were not originally approved. Given C, the set of host genes associated with a specific HCoV, and T, the set of drug targets, we computed the network proximity of C with the target set T of each drug using the “closest” method:1 \\documentclass[12pt]{minimal} \t\t\t\t\\usepackage{amsmath} \t\t\t\t\\usepackage{wasysym} \t\t\t\t\\usepackage{amsfonts} \t\t\t\t\\usepackage{amssymb} \t\t\t\t\\usepackage{amsbsy} \t\t\t\t\\usepackage{mathrsfs} \t\t\t\t\\usepackage{upgreek} \t\t\t\t\\setlength{\\oddsidemargin}{-69pt} \t\t\t\t\\begin{document}$$\\left\\langle d_{CT} \\right\\rangle = \\frac{1}{\\vert\\vert C \\vert\\vert +\\vert\\vert T \\vert\\vert}\\left({\\sum\\limits_{c \\in C}} {\\min}_{t \\in T}\\,d\\left( {c,t} \\right) + {\\sum\\limits_{t \\in T}} {\\min}_{c \\in C}\\,d\\left(c,t\\right) \\right),$$\\end{document}dCT=1∣∣C∣∣+∣∣T∣∣∑c∈Cmint∈Tdc,t+∑t∈Tminc∈Cdc,t,where d(c, t) is the shortest distance between gene c and t in the human protein interactome. The network proximity was converted to Z-score based on permutation tests:2 \\documentclass[12pt]{minimal} \t\t\t\t\\usepackage{amsmath} \t\t\t\t\\usepackage{wasysym} \t\t\t\t\\usepackage{amsfonts} \t\t\t\t\\usepackage{amssymb} \t\t\t\t\\usepackage{amsbsy} \t\t\t\t\\usepackage{mathrsfs} \t\t\t\t\\usepackage{upgreek} \t\t\t\t\\setlength{\\oddsidemargin}{-69pt} \t\t\t\t\\begin{document}$$Z_{d_{CT}} = \\frac{{d_{CT} - \\overline {d_r} }}{{\\sigma _r}},$$\\end{document}ZdCT=dCT−dr¯σr,where \\documentclass[12pt]{minimal} \t\t\t\t\\usepackage{amsmath} \t\t\t\t\\usepackage{wasysym} \t\t\t\t\\usepackage{amsfonts} \t\t\t\t\\usepackage{amssymb} \t\t\t\t\\usepackage{amsbsy} \t\t\t\t\\usepackage{mathrsfs} \t\t\t\t\\usepackage{upgreek} \t\t\t\t\\setlength{\\oddsidemargin}{-69pt} \t\t\t\t\\begin{document}$$\\overline {d_r}$$\\end{document}dr¯ and σr were the mean and standard deviation of the permutation test repeated 1000 times, each time with two randomly selected gene lists with similar degree distributions to those of C and T. The corresponding P value was calculated based on the permutation test results. Z-score \u003c −1.5 and P \u003c 0.05 were considered significantly proximal drug–HCoV associations. All networks were visualized using Gephi 0.9.2 (https://gephi.org/).\n\nNetwork-based rational prediction of drug combinations\nFor this network-based approach for drug combinations to be effective, we need to establish if the topological relationship between two drug–target modules reflects biological and pharmacological relationships, while also quantifying their network-based relationship between drug targets and HCoV-associated host proteins (drug–drug–HCoV combinations). To identify potential drug combinations, we combined the top lists of drugs. Then, “separation” measure SAB was calculated for each pair of drugs A and B using the following method:3 \\documentclass[12pt]{minimal} \t\t\t\t\\usepackage{amsmath} \t\t\t\t\\usepackage{wasysym} \t\t\t\t\\usepackage{amsfonts} \t\t\t\t\\usepackage{amssymb} \t\t\t\t\\usepackage{amsbsy} \t\t\t\t\\usepackage{mathrsfs} \t\t\t\t\\usepackage{upgreek} \t\t\t\t\\setlength{\\oddsidemargin}{-69pt} \t\t\t\t\\begin{document}$$S_{AB} = \\left\\langle {d_{AB}} \\right\\rangle - \\frac{{\\left\\langle {d_{AA}} \\right\\rangle + \\left\\langle {d_{BB}} \\right\\rangle }}{2},$$\\end{document}SAB=dAB−dAA+dBB2,where \\documentclass[12pt]{minimal} \t\t\t\t\\usepackage{amsmath} \t\t\t\t\\usepackage{wasysym} \t\t\t\t\\usepackage{amsfonts} \t\t\t\t\\usepackage{amssymb} \t\t\t\t\\usepackage{amsbsy} \t\t\t\t\\usepackage{mathrsfs} \t\t\t\t\\usepackage{upgreek} \t\t\t\t\\setlength{\\oddsidemargin}{-69pt} \t\t\t\t\\begin{document}$$\\left\\langle {d_ \\cdot } \\right\\rangle$$\\end{document}d⋅ was calculated based on the “closest” method. Our key methodology is that a drug combination is therapeutically effective only if it follows a specific relationship to the disease module, as captured by Complementary Exposure patterns in targets’ modules of both drugs without overlapping toxic mechanisms28.\n\nGene set enrichment analysis\nWe performed the gene set enrichment analysis as an additional prioritization method. We first collected three differential gene expression data sets of hosts infected by HCoVs from the NCBI Gene Expression Omnibus (GEO). Among them, two transcriptome data sets were SARS-CoV-infected samples from patient’s peripheral blood94 (GSE1739) and Calu-3 cells95 (GSE33267), respectively. One transcriptome data set was MERS-CoV-infected Calu-3 cells96 (GSE122876). Adjusted P value less than 0.01 was defined as differentially expressed genes. These data sets were used as HCoV–host signatures to evaluate the treatment effects of drugs. Differential gene expression in cells treated with various drugs were retrieved from the Connectivity Map (CMAP) database36, and were used as gene profiles for the drugs. For each drug that was in both the CMAP data set and our drug–target network, we calculated an enrichment score (ES) for each HCoV signature data set based on previously described methods97 as follows:4 \\documentclass[12pt]{minimal} \t\t\t\t\\usepackage{amsmath} \t\t\t\t\\usepackage{wasysym} \t\t\t\t\\usepackage{amsfonts} \t\t\t\t\\usepackage{amssymb} \t\t\t\t\\usepackage{amsbsy} \t\t\t\t\\usepackage{mathrsfs} \t\t\t\t\\usepackage{upgreek} \t\t\t\t\\setlength{\\oddsidemargin}{-69pt} \t\t\t\t\\begin{document}$${\\mathrm {ES}} = \\left\\{ {\\begin{array}{*{20}{c}} {{\\mathrm{ES}_{\\mathrm{up}}} - {\\mathrm {ES}_{\\mathrm{down}}},{\\mathrm {sgn}}\\left( {{\\mathrm {ES}_{\\mathrm{up}}}} \\right)\\, \\ne \\,{\\mathrm {sgn}}\\left( {{\\mathrm {ES}_{\\mathrm{down}}}} \\right)} \\\\ {0,\\mathrm {else}} \\end{array}} \\right.$$\\end{document}ES=ESup−ESdown,sgnESup≠sgnESdown0,elseESup and ESdown were calculated separately for the up- and down-regulated genes from the HCoV signature data set using the same method. We first computed aup/down and bup/down as5 \\documentclass[12pt]{minimal} \t\t\t\t\\usepackage{amsmath} \t\t\t\t\\usepackage{wasysym} \t\t\t\t\\usepackage{amsfonts} \t\t\t\t\\usepackage{amssymb} \t\t\t\t\\usepackage{amsbsy} \t\t\t\t\\usepackage{mathrsfs} \t\t\t\t\\usepackage{upgreek} \t\t\t\t\\setlength{\\oddsidemargin}{-69pt} \t\t\t\t\\begin{document}$$a = \\mathop{\\max}\\limits_{1 \\le j \\le s}\\left( {\\frac{j}{s} - \\frac{{V\\left( j \\right)}}{r}} \\right),$$\\end{document}a=max1≤j≤sjs−Vjr,6 \\documentclass[12pt]{minimal} \t\t\t\t\\usepackage{amsmath} \t\t\t\t\\usepackage{wasysym} \t\t\t\t\\usepackage{amsfonts} \t\t\t\t\\usepackage{amssymb} \t\t\t\t\\usepackage{amsbsy} \t\t\t\t\\usepackage{mathrsfs} \t\t\t\t\\usepackage{upgreek} \t\t\t\t\\setlength{\\oddsidemargin}{-69pt} \t\t\t\t\\begin{document}$$b = \\mathop{\\max}\\limits_{1 \\le j \\le s}\\left( {\\frac{{V\\left( j \\right)}}{r} - \\frac{{j - 1}}{s}} \\right),$$\\end{document}b=max1≤j≤sVjr−j−1s,where j = 1, 2, …, s were the genes of HCoV signature data set sorted in ascending order by their rank in the gene profiles of the drug being evaluated. The rank of gene j is denoted by V(j), where 1 ≤ V(j) ≤ r, with r being the number of genes (12,849) from the drug profile. Then, ESup/down was set to aup/down if aup/down \u003e bup/down, and was set to −bup/down if bup/down \u003e aup/down. Permutation tests repeated 100 times using randomly generated gene lists with the same number of up- and down-regulated genes as the HCoV signature data set were performed to measure the significance of the ES scores. Drugs were considered to have potential treatment effect if ES \u003e 0 and P \u003c 0.05, and the number of such HCoV signature data sets were used as the final GSEA score that ranges from 0 to 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