OSCR

Inhibition of host N-myristoylation compromises the infectivity of SARS-CoV-2 due to Golgi-bypassing egress.

Code ↔ Paper

7 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 7 matches
  1. [1] § Results › NMTs regulate protein homeostasis and affect key pathways involved in SARS-CoV-2 infection cycle ↔ IMP1088-proteomics-analysis/scripts/05_visualization.R, lines 49–87 · score 0.81 · unfolded protein response, stack reorganization, post translational, extracellular matrix, UPR, lipophagy
  2. [2] § Methods › Additional processing and statistical analysis of label-free mass-spectrometry data ↔ IMP1088-proteomics-analysis/scripts/02_load_normalize_impute_batchcorrect.R, lines 1–69 · score 0.76 · Factory Report, pg proteingroups, log2 transformed, BGS, imputed
  3. [3] § Methods › Proteomics data processing and differential abundance analysis ↔ IMP1088-proteomics-analysis/scripts/01_install.packages.R, lines 1–10 · score 0.75 · ComplexHeatmap, imputeLCMD, clusterProfiler, rrvgo, imputed, limma
  4. [4] § Methods › Proteomics data processing and differential abundance analysis ↔ IMP1088-proteomics-analysis/scripts/02_load_normalize_impute_batchcorrect.R, lines 1–69 · score 0.67 · log2 transformed, Decoy, contaminant, proteomics, accession, imputed
  5. [5] § Results › NMTs regulate protein homeostasis and affect key pathways involved in SARS-CoV-2 infection cycle ↔ IMP1088-proteomics-analysis/scripts/04_enrichment_analysis.R, lines 64–115 · score 0.52 · gene ratio, pathway enrichment, Reactome, Upregulated, downregulated, GO
  6. [6] § Methods › Additional processing and statistical analysis of label-free mass-spectrometry data ↔ IMP1088-proteomics-analysis/scripts/03_sva_DE_analysis.R, lines 82–151 · score 0.51 · limma, Bayes, Spectronaut, mapped, mass, modeling
  7. [7] § Results › NMTs regulate protein homeostasis and affect key pathways involved in SARS-CoV-2 infection cycle ↔ IMP1088-proteomics-analysis/scripts/05_visualization.R, lines 49–87 · score 0.50 · Golgi Cisternae Pericentriolar, Stack Reorganization, score, transformed, extracellular, Rows

Paper

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The authors' code

R · 316 lines · 11 KB · no license · 2 matches

  4. ##############################################################
  5. # Script: 05_visualization.R
  6. # Purpose: Generate heatmap
  7. ##############################################################
  9. # ========================
  10. # Load libraries
  11. # ========================
  12. library(tidyverse)
  13. library(ComplexHeatmap)
  14. library(circlize)
  15. library(RColorBrewer)
  16. library(Cairo)
  18. conflicts_prefer(dplyr::slice)
  19. df <- read.csv("EnrichR_pathways_DE.csv")
  20. react <- df %>%
  21. filter(Database %in% "Reactome_2022") %>%
  22. separate(Term, sep = " R-", into = c("Term", "id"))
  24. # ========================
  25. # 1. Define relevant pathways
  26. # ========================
  27. relevant_pathways <- c(
  28. "Unfolded Protein Response (UPR)",
  29. "COPI-dependent Golgi-to-ER Retrograde Traffic",
  30. "Golgi Cisternae Pericentriolar Stack Reorganization",
  31. "Diseases Of Glycosylation",
  32. "Post-translational Protein Modification",
  33. "Lipophagy",
  34. "Extracellular Matrix Organization"
  35. )
  37. # Extract gene-to-pathway mapping (one term per gene)
  38. er <- react %>%
  39. filter(Term %in% relevant_pathways) %>%
  40. separate_rows(Genes, sep = ";") %>%
  41. group_by(Genes) %>%
  42. arrange(Adjusted.P.value) %>%
  43. slice(1) %>%
  44. ungroup() %>%
  45. select(Term, Genes)
  46. gene <- er$Genes
  47. pathway <- er$Term
  49. # Term id
  50. # <chr> <chr>
  51. # 1 Extracellular Matrix Organization HSA-1474244
  52. # 2 Diseases Of Glycosylation HSA-3781865
  53. # 3 Post-translational Protein Modification HSA-597592
  54. # 4 COPI-dependent Golgi-to-ER Retrograde Traffic HSA-6811434
  55. # 5 Unfolded Protein Response (UPR) HSA-381119
  56. # 6 Golgi Cisternae Pericentriolar Stack Reorganization HSA-162658
  57. # 7 Lipophagy HSA-9613354
  60. # ========================
  61. # 2. Prepare expression matrix and z-score transform
  62. # ========================
  63. mat <- read.csv("sva_corrected_expression_matrix_annotated.csv")
  64. expr_mat <- mat %>%
  65. filter(SYMBOL %in% gene) %>%
  66. distinct(SYMBOL, .keep_all = TRUE)
  68. expr_mat_named <- expr_mat %>%
  69. select(SYMBOL, starts_with("Ctrl"), starts_with("IMP")) %>%
  70. column_to_rownames("SYMBOL")
  72. # Z-score by gene
  73. zscore_mat <- t(scale(t(as.matrix(expr_mat_named))))
  74. zscore_mat <- t(zscore_mat) # rows = samples, columns = genes
  76. # # Collapse replicates by condition
  77. # ctrl_avg <- rowMeans(zscore_mat[, grep("^Ctrl", colnames(zscore_mat))])
  78. # imp_avg <- rowMeans(zscore_mat[, grep("^IMP", colnames(zscore_mat))])
  79. # collapsed_mat <- rbind(CTRL = ctrl_avg, IMP = imp_avg)
  81. # ========================
  82. # 3. Column annotation (Pathway per gene)
  83. # ========================
  84. column_annot <- data.frame(
  85. SYMBOL = colnames(zscore_mat),
  86. Pathway = pathway[match(colnames(zscore_mat), gene)]
  87. )
  89. # Recode pathway names for plotting
  90. column_annot$Pathway <- recode(column_annot$Pathway,
  91. "COPI-dependent Golgi-to-ER Retrograde Traffic" = "COPI-dependent\nGolgi-to-ER\nRetrograde Traffic",
  92. "Golgi Cisternae Pericentriolar Stack Reorganization" = "Golgi Cisternae\nPericentriolar \nStack\nReorganization",
  93. "Diseases Of Glycosylation" = "Diseases of\nGlycosylation",
  94. "Post-translational Protein Modification" = "Post-translational\nProtein Modification",
  95. "Extracellular Matrix Organization" = "Extracellular Matrix\nOrganization",
  96. "Unfolded Protein Response (UPR)" = "Unfolded Protein\nResponse (UPR)"
  97. )
  99. # Define pathway order
  100. desired_order <- c(
  101. "COPI-dependent\nGolgi-to-ER\nRetrograde Traffic",
  102. "Golgi Cisternae\nPericentriolar \nStack\nReorganization",
  103. "Lipophagy",
  104. "Diseases of\nGlycosylation",
  105. "Post-translational\nProtein Modification",
  106. "Extracellular Matrix\nOrganization",
  107. "Unfolded Protein\nResponse (UPR)"
  108. )
  110. column_annot$Pathway <- factor(column_annot$Pathway, levels = desired_order, ordered = TRUE)
  112. # Reorder genes based on pathway
  113. gene_order <- order(column_annot$Pathway)
  114. zscore_mat <- zscore_mat[, gene_order]
  115. column_annot <- column_annot[gene_order, , drop = FALSE]
  117. # ========================
  118. # 4. Top annotation (Pathway)
  119. # ========================
  120. color_palette <- brewer.pal(length(levels(column_annot$Pathway)), "Set3")
  121. top_ha <- HeatmapAnnotation(
  122. Pathway = column_annot$Pathway,
  123. gp = gpar(fontsize = rel(26), fontfamily = "Times", col = "black"),
  124. col = list(Pathway = setNames(color_palette, levels(column_annot$Pathway))),
  125. show_annotation_name = TRUE,
  126. annotation_name_side = "left",
  127. show_legend = FALSE,
  128. annotation_name_gp = gpar(fontsize = rel(20), fontface = "bold", fontfamily = "Times")
  129. )
  131. # ========================
  132. # 5. Row annotation (Sample condition)
  133. # ========================
  134. # Define sample group with descriptive labels
  135. sample_group <- ifelse(grepl("^Ctrl", rownames(zscore_mat)), "DMSO", "IMP-1088")
  136. sample_group <- factor(sample_group, levels = c("DMSO", "IMP-1088"))
  140. # ========================
  141. # 6. Plot heatmap
  142. # ========================
  143. col_fun <- colorRamp2(seq(-2, 2, length.out = 11), rev(brewer.pal(11, "RdYlBu")))
  145. # ===============================
  146. # 🎨 Font Setup for Publication
  147. # ===============================
  148. font_add("Times", regular = "C:/Windows/Fonts/times.ttf")
  149. showtext_auto()
  151. CairoPNG("Heatmap_pathways.png", width = 2600, height = 500)
  153. # CairoPDF("Heatmap_pathways.pdf", width = 26, height = 8)
  154. Heatmap(
  155. zscore_mat,
  156. name = "Z-score",
  157. row_split = sample_group,
  158. col = col_fun,
  159. top_annotation = top_ha,
  160. # left_annotation = row_ha,
  161. cluster_rows = FALSE,
  162. cluster_columns = TRUE,
  163. column_split = column_annot$Pathway,
  164. column_gap = unit(10, "mm"),
  165. show_row_names = FALSE,
  166. show_column_names = TRUE,
  167. # width = unit(60, "cm"),
  168. # height = unit(8, "cm"),
  169. row_title_gp = gpar(fontsize = rel(20), fontfamily = "Times"), # this is DMSO and IMP
  170. column_names_gp = gpar(fontsize = rel(20), fontfamily = "Times"), # this is the gene name at the bottom
  171. column_title_gp = gpar(fontsize = rel(20), fontfamily = "Times"), # this is the pathway names on the top
  172. heatmap_legend_param = list(
  173. legend_height = unit(6, "cm"),
  174. title_gp = gpar(fontsize = 12, fontface = "bold"),
  175. labels_gp = gpar(fontsize = 10)
  176. )
  177. )
  178. dev.off()
  181. #
  182. # ========================
  183. # 1. Define pathway and extract genes
  184. # ========================
  185. # Load proteomics expression matrix
  186. matrix <- read.csv("sva_corrected_expression_matrix_annotated.csv")
  188. # Define databases to use for enrichment
  189. dbs_to_use <- c("KEGG_2021_Human", "Reactome_2022", "WikiPathways_2024_Human")
  191. # Pull all gene symbols from expression matrix
  192. genes <- matrix %>% dplyr::pull(SYMBOL)
  194. # Exit if no genes found
  195. if (length(genes) == 0) stop("No gene symbols found in matrix")
  197. # Run enrichment
  198. results <- enrichr(genes, dbs_to_use)
  200. # Combine enrichment results into one dataframe
  201. combined_results <- bind_rows(
  202. lapply(names(results), function(db_name) {
  203. if (!is.null(results[[db_name]]) && nrow(results[[db_name]]) > 0) {
  204. results[[db_name]] %>% mutate(Database = db_name)
  205. } else {
  206. NULL
  207. }
  208. }),
  209. .id = NULL
  210. )
  212. # ========================
  213. # Extract SARS-CoV-2–related terms
  214. # ========================
  215. sars <- combined_results %>%
  216. filter(Database == "WikiPathways_2024_Human", grepl("SARS", Term)) %>%
  217. separate_rows(Genes, sep = ";") %>%
  218. group_by(Genes) %>%
  219. arrange(Adjusted.P.value) %>%
  220. slice(1) %>%
  221. ungroup()
  223. # ========================
  224. # Isolate genes from specific SARS-CoV-2 autophagy pathway
  225. # ========================
  226. virus_pathway <- "Perturbations Host Cell Autophagy Induced By SARS CoV 2 Prots WP4936"
  228. View(virus)
  229. virus <- sars %>%
  230. filter(Term == virus_pathway) %>%
  231. select(Term, Genes) %>%
  232. separate_rows(Genes, sep = ";") %>%
  233. distinct()
  235. # Output clean gene and pathway lists
  236. gene <- virus$Genes
  237. pathway <- virus$Term
  238. # ========================
  239. # 2. Prepare expression matrix and Z-score transform
  240. # ========================
  241. mat <- read.csv("sva_corrected_expression_matrix_annotated.csv")
  243. expr_mat <- mat %>%
  244. filter(SYMBOL %in% gene) %>%
  245. distinct(SYMBOL, .keep_all = TRUE) %>%
  246. select(SYMBOL, starts_with("Ctrl"), starts_with("IMP")) %>%
  247. column_to_rownames("SYMBOL")
  249. # Remove genes with zero variance to avoid NaN in z-score
  250. expr_mat_clean <- expr_mat[rowSds(as.matrix(expr_mat)) != 0, ]
  252. # Z-score transform
  253. zscore_mat <- t(scale(t(as.matrix(expr_mat_clean))))
  254. zscore_mat <- t(zscore_mat)
  256. # ========================
  257. # 3. Column annotation (Pathway per gene)
  258. # ========================
  259. column_annot <- data.frame(
  260. SYMBOL = colnames(zscore_mat),
  261. Pathway = rep("Perturbation of Host Cell Autophagy (WP4936)", ncol(zscore_mat))
  262. )
  263. rownames(column_annot) <- column_annot$SYMBOL
  264. column_annot$Pathway <- factor(column_annot$Pathway)
  266. # ========================
  267. # 4. Top annotation
  268. # ========================
  269. top_ha <- HeatmapAnnotation(
  270. Pathway = column_annot$Pathway,
  271. col = list(Pathway = setNames(brewer.pal(3, "Set3")[1], levels(column_annot$Pathway))),
  272. annotation_name_side = "left",
  273. annotation_name_gp = gpar(fontsize = rel(20), fontface = "bold", fontfamily = "Times"),
  274. show_annotation_name = TRUE,
  275. show_legend = FALSE
  276. )
  278. # ========================
  279. # 5. Row annotation (sample group)
  280. # ========================
  281. sample_group <- ifelse(grepl("^Ctrl", rownames(zscore_mat)), "DMSO", "IMP-1088")
  282. sample_group <- factor(sample_group, levels = c("DMSO", "IMP-1088"))
  284. # ========================
  285. # 6. Plot heatmap
  286. # ========================
  287. col_fun <- colorRamp2(seq(-2, 2, length.out = 11), rev(brewer.pal(11, "RdYlBu")))
  289. font_add("Times", regular = "C:/Windows/Fonts/times.ttf")
  290. showtext_auto()
  292. CairoPDF("Heatmap_SARS_perturbation_final.pdf", width = 26, height = 8)
  294. Heatmap(
  295. zscore_mat,
  296. name = "Z-score",
  297. row_split = sample_group,
  298. col = col_fun,
  299. top_annotation = top_ha,
  300. cluster_rows = FALSE,
  301. cluster_columns = TRUE,
  302. column_gap = unit(10, "mm"),
  303. show_row_names = FALSE,
  304. show_column_names = TRUE,
  305. row_title_gp = gpar(fontsize = rel(20), fontfamily = "Times"),
  306. column_names_gp = gpar(fontsize = rel(20), fontfamily = "Times"),
  307. column_title_gp = gpar(fontsize = rel(20), fontfamily = "Times"),
  308. heatmap_legend_param = list(
  309. legend_height = unit(6, "cm"),
  310. title_gp = gpar(fontsize = 12, fontface = "bold"),
  311. labels_gp = gpar(fontsize = 10)
  312. )
  313. )
  315. dev.off()
  316. sessionInfo()

05_visualization.R at commit 3c8548a, no license · at the source

Overview

Authors: Saber H. Saber1,2,3, Nyakuoy Yak1,2, Konstantin Dolski4, Sanna Mäki4, Lev Levanov4, Levina A. Willenbrink4, Julian D. J. Sng5, Mohammed R. Shaker1, Sean D. Morrison1, Huiwen Zheng1, Selin Pars1, Giovanni Pietrogrande1, Yih Tyng Bong4, Tania Vane-Tempest4, Teemu Smura4, Tomas Strandin4, Ravi Ojha4, Ravi Kant4,6,7, Janika Ruuska4, Francesco Topi4
and 19 other authorsDiana Vaskiv4, Lauri Kareinen4,8, Tobias Binder1, Siyuan Lu1, Matthias Floetenmeyer9, Bahaa Al-mhanawi1, Yanshan Zhu5, Tarja Sironen4,6, Gert Hoy Talbo1, Kirsty R. Short5, Wouter W. Kallemeijn10,11, Roberto Solari12, Jessica Mar1, Edward W. Tate10,11, Ashley J. van Waardenberg13, Olli Vapalahti4,6,14, Ernst Wolvetang1, Giuseppe Balistreri4,6, Merja Joensuu1
ORCID iDs: Nyakuoy Yak, Konstantin Dolski, Sanna Mäki, Julian D. J. Sng, Mohammed R. Shaker, Sean D. Morrison, Huiwen Zheng, Selin Pars, Giovanni Pietrogrande, Yih Tyng Bong, Teemu Smura, Ravi Kant, Janika Ruuska, Francesco Topi, Lauri Kareinen, Tarja Sironen, Kirsty R. Short, Edward W. Tate, Ashley J. van Waardenberg, Olli Vapalahti, Ernst Wolvetang, Giuseppe Balistreri, Merja Joensuu
14 affiliations
  1. Australian Institute for Bioengineering and Nanotechnology, The University of Queensland,Brisbane, Australia
  2. Queensland Brain Institute, The University of Queensland,Brisbane, Australia
  3. Department of Zoology, Faculty of Science, Assiut University,Assiut, Egypt
  4. Department of Virology, Medicum Research Program, Faculty of Medicine, University of Helsinki,Helsinki, Finland
  5. School of Chemistry and Molecular Biosciences, The University of Queensland,Brisbane, Australia
  6. Department of Veterinary Biosciences, University of Helsinki,Helsinki, Finland
  7. Department of Tropical Parasitology, Institute of Maritime and Tropical Medicine, Medical University of Gdansk,Gdańsk, Poland
  8. Finnish Food Authority,Helsinki, Finland
  9. Centre for Microscopy and Microanalysis, The University of Queensland,Brisbane, Australia
  10. The Francis Crick Institute,London, United Kingdom
  11. Department of Chemistry, Imperial College London,London, United Kingdom
  12. Myricx Bio, London, United Kingdom
  13. i-Synapse, Cairns, Australia
  14. HUS Diagnostic Center, Clinical Microbiology, Helsinki University Hospital, University of Helsinki,Helsinki, Finland
Institutions: The University of Queensland (Australia); Assiut University (Egypt); University of Helsinki (Finland); Gdańsk Medical University (Poland); Finnish Food Authority (Finland); The Francis Crick Institute (United Kingdom); Imperial College London (United Kingdom)
Journal: Nature communications, volume 17, issue 1, article 7055
Dates: received 16 July 2024; accepted 28 April 2026; published online 11 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-72938-z · PMID 42115621 · PMCID PMC13392107 · OpenAlex W7160864965
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), other condition (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, Preprocessing, Evoked potentials, fMRI & imaging
Keywords: Cellular microbiology, Endoplasmic reticulum, SARS-CoV-2
MeSH: Acyltransferases*, Golgi Apparatus*, SARS-CoV-2*, Virus Release*, Animals, Antiviral Agents, Cell Line, Tumor, Chlorocebus aethiops, COVID-19, Endoplasmic Reticulum, Humans, Lysosomes, Spike Glycoprotein, Coronavirus, Vero Cells, Virion (* major topic)
Topic: Cellular transport and secretion (Cell Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: UQ’s Amplify Fellowship; Instrumentarium Science Foundation Grant (240024); PREPARE-TID and VEO (European Union’s Horizon 2020; grant number 101137132, 874735); PREPARE-TID and VEO (European Union’s Horizon 2020; grant number 101137132, 874735)and Sigrid Juselius Foundation 2022-2025; Myricx Pharma Ltd and Cancer Research UK, with support from the Engineering & Physical Sciences Research Council (C29637/A21451, C29637/A20183 and DRCNPG-Nov21\100001 to E.W.T.). Work in E.W.T.’s laboratories are supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK, the UK Medical Research Council and the Wellcome Trust (FC001057 and FC001097); Research Council of Finland (351010), Jane and Aatos Erkko foundation, and Helsinki University Hospital Funding; Research Council of Finland (335527), the European Union’s Horizon Europe Research and Innovation Program (101057553), the Helsinki Institute for Life Sciences (HiLIFE) Grants, and the Sigrid Juselius Foundation Senior Investigator Award
Citations: not cited yet (Europe PMC); 122 references in the paper
Research resources: RRID:Addgene_12260, RRID:Addgene_138479102, RRID:Addgene_39554, which was a gift from Charles Gersbach RRID:Addgene_71237101

Abstract

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bongrita/IMP1088-proteomics-analysis

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 3c8548a0b7d117ae6ef370232ba21329833a8b89, 21 April 2026
Languages: R (5)
Size: 13 files, 5 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (3 files), clusterProfiler (2 files), circlize (1 file), ComplexHeatmap (1 file), limma (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
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At the source: github.com/bongrita/IMP1088-proteomics-analysis

Zenodo 19685659

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Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (3 files), clusterProfiler (2 files), circlize (1 file), ComplexHeatmap (1 file), limma (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
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6 files
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Recorded: type, language, journal, volume, issue, pages, dates, 39 authors, 3 keywords, 15 MeSH terms, 7 funders, 119 references, 4 RRIDs.

Cite

This paper

Saber, S. H., Yak, N., Dolski, K., Mäki, S., Levanov, L., Willenbrink, L. A., Sng, J. D. J., Shaker, M. R., Morrison, S. D., Zheng, H., Pars, S., Pietrogrande, G., Bong, Y. T., Vane-Tempest, T., Smura, T., Strandin, T., Ojha, R., Kant, R., Ruuska, J., . . . Joensuu, M. (2026). Inhibition of host N-myristoylation compromises the infectivity of SARS-CoV-2 due to Golgi-bypassing egress. Nature communications, 17(1), 7055. https://doi.org/10.1038/s41467-026-72938-z

BibTeX

@article{saber2026inhibition,
author = {Saber, Saber H. and Yak, Nyakuoy and Dolski, Konstantin and Mäki, Sanna and Levanov, Lev and Willenbrink, Levina A. and Sng, Julian D. J. and Shaker, Mohammed R. and Morrison, Sean D. and Zheng, Huiwen and Pars, Selin and Pietrogrande, Giovanni and Bong, Yih Tyng and Vane-Tempest, Tania and Smura, Teemu and Strandin, Tomas and Ojha, Ravi and Kant, Ravi and Ruuska, Janika and Topi, Francesco and Vaskiv, Diana and Kareinen, Lauri and Binder, Tobias and Lu, Siyuan and Floetenmeyer, Matthias and Al-mhanawi, Bahaa and Zhu, Yanshan and Sironen, Tarja and Talbo, Gert Hoy and Short, Kirsty R. and Kallemeijn, Wouter W. and Solari, Roberto and Mar, Jessica and Tate, Edward W. and van Waardenberg, Ashley J. and Vapalahti, Olli and Wolvetang, Ernst and Balistreri, Giuseppe and Joensuu, Merja},
title = {{Inhibition of host N-myristoylation compromises the infectivity of SARS-CoV-2 due to Golgi-bypassing egress}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {7055},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-72938-z},
url = {https://doi.org/10.1038/s41467-026-72938-z},
pmid = {42115621},
pmcid = {PMC13392107}
}

RIS

TY - JOUR
AU - Saber, Saber H.
AU - Yak, Nyakuoy
AU - Dolski, Konstantin
AU - Mäki, Sanna
AU - Levanov, Lev
AU - Willenbrink, Levina A.
AU - Sng, Julian D. J.
AU - Shaker, Mohammed R.
AU - Morrison, Sean D.
AU - Zheng, Huiwen
AU - Pars, Selin
AU - Pietrogrande, Giovanni
AU - Bong, Yih Tyng
AU - Vane-Tempest, Tania
AU - Smura, Teemu
AU - Strandin, Tomas
AU - Ojha, Ravi
AU - Kant, Ravi
AU - Ruuska, Janika
AU - Topi, Francesco
AU - Vaskiv, Diana
AU - Kareinen, Lauri
AU - Binder, Tobias
AU - Lu, Siyuan
AU - Floetenmeyer, Matthias
AU - Al-mhanawi, Bahaa
AU - Zhu, Yanshan
AU - Sironen, Tarja
AU - Talbo, Gert Hoy
AU - Short, Kirsty R.
AU - Kallemeijn, Wouter W.
AU - Solari, Roberto
AU - Mar, Jessica
AU - Tate, Edward W.
AU - van Waardenberg, Ashley J.
AU - Vapalahti, Olli
AU - Wolvetang, Ernst
AU - Balistreri, Giuseppe
AU - Joensuu, Merja
TI - Inhibition of host N-myristoylation compromises the infectivity of SARS-CoV-2 due to Golgi-bypassing egress
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/11
VL - 17
IS - 1
SP - 7055
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-72938-z
UR - https://doi.org/10.1038/s41467-026-72938-z
LA - en
ER -

CSL-JSON

{
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"type": "article-journal",
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"container-title": "Nature communications",
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"given": "Saber H."
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{
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"given": "Nyakuoy"
},
{
"family": "Dolski",
"given": "Konstantin"
},
{
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"given": "Sanna"
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{
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{
"family": "Willenbrink",
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{
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},
{
"family": "Shaker",
"given": "Mohammed R."
},
{
"family": "Morrison",
"given": "Sean D."
},
{
"family": "Zheng",
"given": "Huiwen"
},
{
"family": "Pars",
"given": "Selin"
},
{
"family": "Pietrogrande",
"given": "Giovanni"
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{
"family": "Bong",
"given": "Yih Tyng"
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{
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{
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"family": "Kareinen",
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{
"family": "Binder",
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},
{
"family": "Lu",
"given": "Siyuan"
},
{
"family": "Floetenmeyer",
"given": "Matthias"
},
{
"family": "Al-mhanawi",
"given": "Bahaa"
},
{
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{
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},
{
"family": "Talbo",
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},
{
"family": "Short",
"given": "Kirsty R."
},
{
"family": "Kallemeijn",
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},
{
"family": "Solari",
"given": "Roberto"
},
{
"family": "Mar",
"given": "Jessica"
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{
"family": "Tate",
"given": "Edward W."
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},
{
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"given": "Merja"
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],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "7055",
"DOI": "10.1038/s41467-026-72938-z",
"PMID": "42115621",
"PMCID": "PMC13392107",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-72938-z",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
11
]
]
}
}

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