Understanding the SARS-CoV-2–Human Liver Interactome Using a Comprehensive Analysis of the Individual Virus–Host Interactions
Abstract
:1. Introduction
2. Materials and Methods
2.1. BioGRID
2.2. STRING
2.3. Protein Enrichment
2.4. Cytoscape and Network Topology Analysis
2.5. CentiScaPe
2.6. GO and KEGG Pathway Analyses
2.7. SARS2-Human Proteome Interaction Database (SHPID)
2.8. Comparison between GO Pairs in Enriched Networks
2.9. Highlighting the Nodes of a STRING Network Involved in the Same Biological Process (GO)
3. Results
3.1. Hub Data of Human Liver during COVID-19
3.2. Comprehensive Liver Interactome during COVID-19
3.3. Metabolic Stress Related to COVID-19 in the Liver
3.4. The Reverse Engineering Actions
3.5. Individual Human Proteins Interacting with Many Viral Proteins and Their Distribution Graph
3.6. Distribution of Viral Proteins Interacting with Single Human Proteins
3.7. Comprehensive Analysis of Liver Metabolic Activities during COVID-19
4. Discussion
5. Conclusions
Supplementary Materials
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
References
- Kariyawasam, J.C.; Jayarajah, U.; Abeysuriya, V.; Riza, R.; Seneviratne, S.L. Involvement of the Liver in COVID-19: A Systematic Review. Am. J. Trop. Med. Hyg. 2022, 106, 1026–1041. [Google Scholar] [CrossRef]
- Beigmohammadi, M.T.; Jahanbin, B.; Safaei, M.; Amoozadeh, L.; Khoshavi, M.; Mehrtash, V.; Jafarzadeh, B.; Abdollahi, A. Pathological findings of postmortem biopsies from lung, heart, and liver of 7 deceased COVID-19 patients. Int. J. Surg. Pathol. 2021, 29, 135–145. [Google Scholar] [CrossRef]
- Ryan, P.M.; Caplice, N.M. Is Adipose Tissue a Reservoir for Viral Spread, Immune Activation, and Cytokine Amplification in Coronavirus Disease 2019? Obesity 2020, 28, 1191–1194. [Google Scholar] [CrossRef]
- Hamming, I.; Timens, W.; Bulthuis, M.L.C.; Lely, A.T.; Navis, G.J.; van Goor, H. Tissue distribution of ACE2 protein, the functional receptor for SARS coronavirus. A first step in understanding SARS pathogenesis. J. Pathol. 2004, 203, 631–637. [Google Scholar] [CrossRef]
- Ding, Y.; He, L.; Zhang, Q.; Huang, Z.; Che, X.; Hou, J.; Wang, H.; Shen, H.; Qiu, L.; Li, Z.; et al. Organ distribution of severe acute respiratory syndrome (SARS) associated coronavirus (SARS-CoV) in SARS patients: Implications for pathogenesis and virus transmission pathways. J. Pathol. 2004, 203, 622–630. [Google Scholar] [CrossRef]
- Birman, D. Investigation of the Effects of COVID-19 on Different Organs of the Body. Eurasian J. Chem. Med. Pet. Res. 2023, 2, 24–36. [Google Scholar]
- Paolini, A.; Borella, R.; De Biasi, S.; Neroni, A.; Mattioli, M.; Tartaro, D.L.; Simonini, C.; Franceschini, L.; Cicco, G.; Piparo, A.M.; et al. Cell Death in Coronavirus Infections: Uncovering Its Role during COVID-19. Cells 2021, 10, 1585. [Google Scholar] [CrossRef]
- Yuan, C.; Ma, Z.; Xie, J.; Li, W.; Su, L.; Zhang, G.; Xu, J.; Wu, Y.; Zhang, M.; Liu, W. The role of cell death in SARS-CoV-2 infection. Signal Transduct. Target. Ther. 2023, 8, 357. [Google Scholar] [CrossRef]
- Jothimani, D.; Venugopal, R.; Abedin, M.F.; Kaliamoorthy, I.; Rela, M. COVID-19 and the liver. J. Hepatol. 2020, 73, 1231–1240. [Google Scholar] [CrossRef]
- Guan, G.W.; Gao, L.; Wang, J.W.; Wen, X.J.; Mao, T.H.; Peng, S.W.; Zhang, T.; Chen, X.M.; Lu, F.M. Exploring the mechanism of liver enzyme abnormalities in patients with novel coronavirus-infected pneumonia. Chin. J. Hepatol. 2020, 28, 100–106. [Google Scholar]
- Shi, J.; Li, G.; Yuan, X.; Wang, Y.; Gong, M.; Li, C.; Ge, X.; Lu, S. Exploration and verification of COVID-19-related hub genes in liver physiological and pathological regeneration. Front. Bioeng. Biotechnol. 2023, 11, 1135997. [Google Scholar] [CrossRef]
- Vandereyken, K.; Van Leene, J.; De Coninck, B.; Cammue, B.P.A. Hub Protein Controversy: Taking a Closer Look at Plant Stress Response Hubs. Front. Plant Sci. 2018, 9, 694. [Google Scholar] [CrossRef]
- Huang, T.; Zheng, D.B.; Song, Y.B.; Pan, H.B.; Qiu, G.; Xiang, Y.B.; Wang, Z.B.; Wang, F. Demonstration of the impact of COVID-19 on metabolic associated fatty liver disease by bioinformatics and system biology approach. Medicine 2023, 102, e34570. [Google Scholar] [CrossRef]
- Luo, H.; Chen, J.; Jiang, Q.; Yu, Y.; Yang, M.; Luo, Y.; Wang, X. Comprehensive DNA methylation profiling of COVID-19 and hepatocellular carcinoma to identify common pathogenesis and potential therapeutic targets. Clin. Epigenetics 2023, 15, 100. [Google Scholar] [CrossRef]
- Jiang, S.-T.; Liu, Y.-G.; Zhang, L.; Sang, X.-T.; Xu, Y.-Y.; Lu, X. Systems biology approach reveals a common molecular basis for COVID-19 and non-alcoholic fatty liver disease (NAFLD). Eur. J. Med. Res. 2022, 27, 251. [Google Scholar] [CrossRef]
- Shen, Q.; Wang, J.; Zhao, L. To investigate the internal association between SARS-CoV-2 infections and cancer through bioinformatics. Math. Biosci. Eng. 2022, 19, 11172–11194. [Google Scholar] [CrossRef]
- Wang, L.; Ding, Y.; Zhang, C.; Chen, R. Target and drug predictions for SARS-CoV-2 infection in hepatocellular carcinoma patients. PLoS ONE 2022, 17, e0269249. [Google Scholar] [CrossRef]
- Abolfazli, P.; Aghajanzadeh, T.; Ghaderinasrabad, M.; Nchama, C.N.A.; Mokhlesi, A.; Talkhabi, M. Bioinformatics analysis reveals molecular connections between non-alcoholic fatty liver disease (NAFLD) and COVID-19. J. Cell Commun. Signal. 2022, 16, 609–619. [Google Scholar] [CrossRef]
- Mousavi, S.Z.; Rahmanian, M.; Sami, A. Organ-specific or personalized treatment for COVID-19: Rationale, evidence, and potential candidates. Funct. Integr. Genom. 2022, 22, 429–433. [Google Scholar] [CrossRef]
- Hasankhani, A.; Bahrami, A.; Sheybani, N.; Aria, B.; Hemati, B.; Fatehi, F.; Farahani, H.G.M.; Javanmard, G.; Rezaee, M.; Kastelic, J.P.; et al. Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic. Front. Immunol. 2021, 12, 789317. [Google Scholar] [CrossRef]
- Sokouti, B. A systems biology approach for investigating significantly expressed genes among COVID-19, hepatocellular carcinoma, and chronic hepatitis B. Egypt. J. Med. Hum. Genet. 2022, 23, 146. [Google Scholar] [CrossRef]
- Chen, J.C.; Xie, T.A.; Lin, Z.Z.; Li, Y.Q.; Xie, Y.F.; Li, Z.W.; Guo, X.G. Identification of key pathways and genes in SARS-CoV-2 infecting human intestines by bioinformatics analysis. Biochem. Genet. 2022, 60, 1076–1094. [Google Scholar] [CrossRef]
- Steuer, R. Computational approaches to the topology, stability and dynamics of metabolic networks. Phytochemistry 2007, 68, 2139–2151. [Google Scholar] [CrossRef]
- Hartman, E.; Scott, A.M.; Karlsson, C.; Mohanty, T.; Vaara, S.T.; Linder, A.; Malmström, L.; Malmström, J. Interpreting biologically informed neural networks for enhanced proteomic biomarker discovery and pathway analysis. Nat. Commun. 2023, 14, 5359. [Google Scholar] [CrossRef]
- Wu, S.; Liu, X.; Dong, A.; Gragnoli, C.; Griffin, C.; Wu, J.; Yau, S.-T.; Wu, R. The metabolomic physics of complex diseases. Proc. Natl. Acad. Sci. USA 2023, 120, e2308496120. [Google Scholar] [CrossRef]
- Yang, Y.; Fang, Q.; Shen, H.-B. Predicting gene regulatory interactions based on spatial gene expression data and deep learning. PLoS Comput. Biol. 2019, 15, e1007324. [Google Scholar] [CrossRef]
- Chikofsky, E.; Cross, J. Reverse engineering and design recovery: A taxonomy. IEEE Softw. 1990, 7, 13–17. [Google Scholar] [CrossRef]
- Fornito, A.; Zalesky, A.; Breakspear, M. Graph analysis of the human connectome: Promise, progress, and pitfalls. Neuroimage 2013, 80, 426–444. [Google Scholar] [CrossRef]
- Green, S. Can biological complexity be reverse engineered? Stud. Hist. Philos. Sci. Part C Stud. Hist. Philos. Biol. Biomed. Sci. 2015, 53, 73–83. [Google Scholar] [CrossRef]
- Natale, J.L.; Hofmann, D.; Hernández, D.G.; Nemenman, I. Reverse-engineering biological networks from large data sets. arXiv 2017, arXiv:1705.06370. [Google Scholar]
- de Camargo, R.S.; de Miranda, G.; Løkketangen, A. A new formulation and an exact approach for the many-to-many hub location-routing problem. Appl. Math. Model. 2013, 37, 7465–7480. [Google Scholar] [CrossRef]
- Qu, Y.; Jiang, J.; Liu, X.; Yang, X.; Tang, C. Non-epigenetic mechanisms enable short memories of the environment for cell cycle commitment. BioRxiv 2020. [Google Scholar] [CrossRef]
- Pisco, A.O.; D’hérouël, A.F.; Huang, S. Conceptual Confusion: The case of Epigenetics. BioRxiv 2016, 053009. [Google Scholar] [CrossRef]
- Squire, L.R.; Genzel, L.; Wixted, J.T.; Morris, R.G. Memory consolidation. Cold Spring Harb. Perspect. Biol. 2015, 7, a021766. [Google Scholar] [CrossRef]
- Oughtred, R.; Rust, J.; Chang, C.; Breitkreutz, B.; Stark, C.; Willems, A.; Boucher, L.; Leung, G.; Kolas, N.; Zhang, F.; et al. The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions. Protein Sci. 2020, 30, 187–200. [Google Scholar] [CrossRef]
- Teo, G.; Liu, G.; Zhang, J.; Nesvizhskii, A.I.; Gingras, A.-C.; Choi, H. SAINTexpress: Improvements and additional features in Significance Analysis of INTeractome software. J. Proteom. 2013, 100, 37–43. [Google Scholar] [CrossRef]
- Szklarczyk, D.; Gable, A.L.; Nastou, K.C.; Lyon, D.; Kirsch, R.; Pyysalo, S.; Doncheva, N.T.; Legeay, M.; Fang, T.; Bork, P.; et al. The STRING database in 2021: Customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2020, 49, D605–D612, Erratum in: Nucleic Acids Res. 2021, 49, 10800. [Google Scholar] [CrossRef]
- Szklarczyk, D.; Kirsch, R.; Koutrouli, M.; Nastou, K.; Mehryary, F.; Hachilif, R.; Gable, A.L.; Fang, T.; Doncheva, N.T.; Pyysalo, S.; et al. The STRING database in 2023: Protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2022, 51, D638–D646. [Google Scholar] [CrossRef]
- Kumar, A.; Ingle, Y.S.; Pande, A.; Dhule, P. Canopy clustering: A review on pre-clustering approach to k-means clustering. Int. J. Innov. Adv. Comput. Sci. (IJIACS) 2014, 3, 22–29. [Google Scholar]
- Doncheva, N.T.; Morris, J.H.; Gorodkin, J.; Jensen, L.J. Cytoscape StringApp: Network Analysis and Visualization of Proteomics Data. J. Proteome Res. 2018, 18, 623–632. [Google Scholar] [CrossRef]
- Chung, F.; Lu, L.; Dewey, T.G.; Galas, D.J. Duplication Models for Biological Networks. J. Comput. Biol. 2003, 10, 677–687. [Google Scholar] [CrossRef] [PubMed]
- Scardoni, G.; Tosadori, G.; Faizan, M.; Spoto, F.; Fabbri, F.; Laudanna, C. Biological network analysis with CentiScaPe: Centralities and experimental dataset integration. F1000Research 2015, 3, 139. [Google Scholar] [CrossRef]
- Wuchty, S.; Ravasz, E.; Barabási, A.-L. The architecture of biological networks. In Complex Systems Science in Biomedicine; Springer: Boston, MA, USA, 2006; pp. 165–181. [Google Scholar]
- Almaas, E.; Vázquez, A.; Barabási, A.-L. Scale-free networks in biology. In Biological. Networks. Complex Systems and Interdisciplinary Science; Képès, F., Ed.; Word Scientific Publishing Co.: Hackensack, NJ, USA, 2007; Chapter 1; Volume 3, pp. 1–20. ISBN 978-981-270-695-9. [Google Scholar]
- Szklarczyk, D.; Jensen, L.J. Protein-protein interaction databases. Protein-Protein Interact. Methods Appl. 2015, 1278, 39–56. [Google Scholar] [CrossRef]
- Sharma, A.; Colonna, G. System-Wide Pollution of Biomedical Data: Consequence of the Search for Hub Genes of Hepatocellular Carcinoma Without Spatiotemporal Consideration. Mol. Diagn. Ther. 2021, 25, 9–27. [Google Scholar] [CrossRef]
- Yang, S.; Fu, C.; Lian, X.; Dong, X.; Zhang, Z. Understanding Human-Virus Protein-Protein Interactions Using a Human Protein Complex-Based Analysis Framework. mSystems 2019, 4. [Google Scholar] [CrossRef]
- Mishra, P.M.; Verma, N.C.; Rao, C.; Uversky, V.N.; Nandi, C.K. Intrinsically disordered proteins of viruses: Involvement in the mechanism of cell regulation and pathogenesis. Prog. Mol. Biol. Transl. Sci. 2020, 174, 1–78. [Google Scholar]
- Villarreal, L.P. The widespread evolutionary significance of viruses. In Origin and Evolution of Viruses; Elsevier Science Direct.: Amsterdam, The Netherlands, 2008; Chapter 21; pp. 477–516. [Google Scholar] [CrossRef]
- Guidotti, R.; Gardoni, P.; Chen, Y. Network reliability analysis with link and nodal weights and auxiliary nodes. Struct. Saf. 2017, 65, 12–26. [Google Scholar] [CrossRef]
- De Vico Fallani, F.; Richiardi, J.; Chavez, M.; Achard, S. Graph analysis of functional brain networks: Practical issues in translational neuroscience. Philos. Trans. R. Soc. B Biol. Sci. 2014, 369, 20130521. [Google Scholar] [CrossRef]
- Li, V.; Silvester, J. Performance Analysis of Networks with Unreliable Components. IEEE Trans. Commun. 1984, 32, 1105–1110. [Google Scholar] [CrossRef]
- Knight, S.; Nguyen, H.X.; Falkner, N.; Bowden, R.; Roughan, M. The Internet Topology Zoo. IEEE J. Sel. Areas Commun. 2011, 29, 1765–1775. [Google Scholar] [CrossRef]
- Militello, G.; Álvaro, M. Structural and organisational conditions for being a machine. Biol. Philos. 2018, 33, 35. [Google Scholar] [CrossRef]
- Akyildiz, I.F.; Brunetti, F.; Blázquez, C. Nanonetworks: A new communication paradigm. Comput. Netw. 2008, 52, 2260–2279. [Google Scholar] [CrossRef]
- Will, C.L.; Urlaub, H.; Achsel, T.; Gentzel, M.; Wilm, M.; Lührmann, R. Characterization of novel SF3b and 17S U2 snRNP proteins, including a human Prp5p homologue and an SF3b DEAD-box protein. EMBO J. 2002, 21, 4978–4988. [Google Scholar] [CrossRef]
- Wang, C.; Chen, L.; Chen, Y.; Jia, W.; Cai, X.; Liu, Y.; Ji, F.; Xiong, P.; Liang, A.; Liu, R.; et al. Abnormal global alternative RNA splicing in COVID-19 patients. PLoS Genet. 2022, 18, e1010137. [Google Scholar] [CrossRef]
- Wang, E.T.; Sandberg, R.; Luo, S.; Khrebtukova, I.; Zhang, L.; Mayr, C.; Kingsmore, S.F.; Schroth, G.P.; Burge, C.B. Alternative isoform regulation in human tissue transcriptomes. Nature 2008, 456, 470–476. [Google Scholar] [CrossRef]
- Luo, J.; Zhao, H.; Chen, L.; Liu, M. Multifaceted functions of RPS27a: An unconventional ribosomal protein. J. Cell. Physiol. 2022, 238, 485–497. [Google Scholar] [CrossRef]
- Zhang, Y.; Zhang, J.; Chen, X.; Yang, Z. Polymeric immunoglobulin receptor (PIGR) exerts oncogenic functions via activating ribosome pathway in hepatocellular carcinoma. Int. J. Med. Sci. 2021, 18, 364–371. [Google Scholar] [CrossRef]
- Vandelli, A.; Monti, M.; Milanetti, E.; Armaos, A.; Rupert, J.; Zacco, E.; Bechara, E.; Delli Ponti, P.; Tartaglia, G.G. Structural analysis of SARS-CoV-2 genome and predictions of the human interactome. Nucleic Acids Res. 2020, 48, 11270–11283. [Google Scholar] [CrossRef]
- Chiariello, A.M.; Abraham, A.; Bianco, S.; Esposito, A.; Vercellone, F.; Conte, M.; Fontana, A.; Nicodemi, M. Multiscale modelling of chromatin 4D organization in SARS-CoV-2 infected cells. bioRxiv 2023. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
- Chernyak, B.V.; Popova, E.N.; Prikhodko, A.S.; Grebenchikov, O.A.; Zinovkina, L.A.; Zinovkin, R.A. COVID-19 and oxidative stress. Biochemistry 2020, 85, 1543–1553. [Google Scholar] [CrossRef]
- Jana, S.; Heaven, M.R.; Stauft, C.B.; Wang, T.T.; Williams, M.C.; D’Agnillo, F.; Alayash, A.I. HIF-1α-Dependent Metabolic Reprogramming, Oxidative Stress, and Bioenergetic Dysfunction in SARS-CoV-2-Infected Hamsters. Int. J. Mol. Sci. 2022, 24, 558. [Google Scholar] [CrossRef]
- Serebrovska, Z.O.; Chong, E.Y.; Serebrovska, T.V.; Tumanovska, L.V.; Xi, L. Hypoxia, HIF-1α, and COVID-19: From pathogenic factors to potential therapeutic targets. Acta Pharmacol. Sin. 2020, 41, 1539–1546. [Google Scholar] [CrossRef]
- Wing, P.A.; Keeley, T.P.; Zhuang, X.; Lee, J.Y.; Prange-Barczynska, M.; Tsukuda, S.; Morgan, S.B.; Harding, A.C.; Argles, I.L.A.; Kurlekar, S.; et al. Hypoxic and pharmacological activation of HIF inhibits SARS-CoV-2 infection of lung epithelial cells. Cell Rep. 2021, 35, 109020. [Google Scholar] [CrossRef]
- Zhu, Z.; Zheng, Z.; Liu, J. Comparison of COVID-19 and Lung Cancer via Reactive Oxygen Species Signaling. Front. Oncol. 2021, 11, 708263. [Google Scholar] [CrossRef]
- Bhandari, V.; Hoey, C.; Liu, L.Y.; Lalonde, E.; Ray, J.; Livingstone, J.; Lesurf, R.; Shiah, Y.-J.; Vujcic, T.; Huang, X.; et al. Molecular landmarks of tumor hypoxia across cancer types. Nat. Genet. 2019, 51, 308–318. [Google Scholar] [CrossRef]
- Cimmino, F.; Avitabile, M.; Lasorsa, V.A.; Montella, A.; Pezone, L.; Cantalupo, S.; Visconte, F.; Corrias, M.V.; Iolascon, A.; Capasso, M. HIF-1 transcription activity: HIF1A driven response in normoxia and in hypoxia. BMC Med. Genet. 2019, 20, 37. [Google Scholar] [CrossRef]
- Varghese, B.; Chianese, U.; Capasso, L.; Sian, V.; Bontempo, P.; Conte, M.; Benedetti, R.; Altucci, L.; Carafa, V.; Nebbioso, A. SIRT1 activation promotes energy homeostasis and reprograms liver cancer metabolism. J. Transl. Med. 2023, 21, 627. [Google Scholar] [CrossRef]
- Wang, X.; Simpson, E.R.; Brown, K.A. p53: Protection against Tumor Growth beyond Effects on Cell Cycle and Apoptosis. Cancer Res. 2015, 75, 5001–5007. [Google Scholar] [CrossRef]
- Moll, U.M.; Petrenko, O. The MDM2-p53 interaction. Mol. Cancer Res. 2003, 1, 1001–1008. [Google Scholar]
- Liu, Y.; Deisenroth, C.; Zhang, Y. RP–MDM2–p53 pathway: Linking ribosomal biogenesis and tumor surveillance. Trends Cancer 2016, 2, 191–204. [Google Scholar] [CrossRef]
- Halehalli, R.R.; Nagarajaram, H.A. Molecular principles of human virus protein–protein interactions. Bioinformatics 2014, 31, 1025–1033. [Google Scholar] [CrossRef]
- Gordon, D.E.; Jang, G.M.; Bouhaddou, M.; Xu, J.; Obernier, K.; White, K.M.; O’Meara, M.J.; Rezelj, V.V.; Guo, J.Z.; Swaney, D.L.; et al. A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature 2020, 583, 459–468. [Google Scholar] [CrossRef]
- Gordon, D.E.; Hiatt, J.; Bouhaddou, M.; Rezelj, V.V.; Ulferts, S.; Braberg, H.; Jureka, A.S.; Obernier, K.; Guo, J.Z.; Batra, J.; et al. Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms. Science 2020, 370, eabe9403. [Google Scholar] [CrossRef]
- Komarova, A.V.; Combredet, C.; Sismeiro, O.; Dillies, M.-A.; Jagla, B.; David, R.Y.S.; Vabret, N.; Coppée, J.-Y.; Vidalain, P.-O.; Tangy, F. Identification of RNA partners of viral proteins in infected cells. RNA Biol. 2013, 10, 943–956. [Google Scholar] [CrossRef]
- Li, J.; Guo, M.; Tian, X.; Wang, X.; Yang, X.; Wu, P.; Liu, C.; Xiao, Z.; Qu, Y.; Yin, Y.; et al. Virus–host interactome and proteomic survey reveal potential virulence factors influencing SARS-CoV-2 pathogenesis. Med 2021, 2, 99–112. [Google Scholar] [CrossRef]
- Stukalov, A.; Girault, V.; Grass, V.; Karayel, O.; Bergant, V.; Urban, C.; Haas, D.A.; Huang, Y.; Oubraham, L.; Wang, A.; et al. Multilevel proteomics reveals host perturbations by SARS-CoV-2 and SARS-CoV. Nature 2021, 594, 246–252. [Google Scholar] [CrossRef]
- Zhou, Y.; Liu, Y.; Gupta, S.; Paramo, M.I.; Hou, Y.; Mao, C.; Luo, Y.; Judd, J.; Wierbowski, S.; Bertolotti, M.; et al. A comprehensive SARS-CoV-2–human protein–protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets. Nat. Biotechnol. 2022, 41, 128–139. [Google Scholar] [CrossRef]
- Khorsand, B.; Savadi, A.; Naghibzadeh, M. SARS-CoV-2-human protein-protein interaction network. Inform. Med. Unlocked 2020, 20, 100413. [Google Scholar] [CrossRef]
- Ghosh, N.; Saha, I.; Sharma, N. Interactome of human and SARS-CoV-2 proteins to identify human hub proteins associated with comorbidities. Comput. Biol. Med. 2021, 138, 104889. [Google Scholar] [CrossRef]
- Srinivasan, S.; Cui, H.; Gao, Z.; Liu, M.; Lu, S.; Mkandawire, W.; Narykov, O.; Sun, M.; Korkin, D. Structural Genomics of SARS-CoV-2 Indicates Evolutionary Conserved Functional Regions of Viral Proteins. Viruses 2020, 12, 360. [Google Scholar] [CrossRef]
- Shuler, G.; Hagai, T. Rapidly evolving viral motifs mostly target biophysically constrained binding pockets of host proteins. Cell Rep. 2022, 40, 111212. [Google Scholar] [CrossRef] [PubMed]
- Mendez-Rios, J.; Uetz, P.; Luo, Y.; Muesing, M.A.; E Ballestas, M.; Kaye, K.M.; Moyano, D.F.; Rotello, V.M.; Gazzé, G.; Rodland, K.D.; et al. Global approaches to study protein–protein interactions among viruses and hosts. Futur. Microbiol. 2010, 5, 289–301. [Google Scholar] [CrossRef] [PubMed]
- Goh, G.K.-M.; Dunker, A.K.; Uversky, V.N. Understanding Viral Transmission Behavior via Protein Intrinsic Disorder Prediction: Coronaviruses. J. Pathog. 2012, 2012, 738590. [Google Scholar] [CrossRef] [PubMed]
- Anjum, F.; Mohammad, T.; Asrani, P.; Shafie, A.; Singh, S.; Yadav, D.K.; Uversky, V.N.; Imtaiyaz Hassan, C.M. Identification of intrinsically disorder regions in non-structural proteins of SARS-CoV-2: New insights into drug and vaccine resistance. Mol. Cell. Biochem. 2022, 477, 1607–1619. [Google Scholar] [CrossRef]
- Anger, A.M.; Armache, J.-P.; Berninghausen, O.; Habeck, M.; Subklewe, M.; Wilson, D.N.; Beckmann, R. Structures of the human and Drosophila 80S ribosome. Nature 2013, 497, 80–85. [Google Scholar] [CrossRef]
- Singh, S.; Broeck, A.V.; Miller, L.; Chaker-Margot, M.; Klinge, S. Nucleolar maturation of the human small subunit processome. Science 2021, 373, eabj5338. [Google Scholar] [CrossRef]
- Baranov, P.V.; Henderson, C.M.; Anderson, C.B.; Gesteland, R.F.; Atkins, J.F.; Howard, M.T. Programmed ribosomal frameshifting in decoding the SARS-CoV genome. Virology 2005, 332, 498–510. [Google Scholar] [CrossRef]
- Rehfeld, F.; Eitson, J.L.; Ohlson, M.B.; Chang, T.C.; Schoggins, J.W.; Mendell, J.T. CRISPR screening reveals a dependency on ribosome recycling for efficient SARS-CoV-2 programmed ribosomal frameshifting and viral replication. Cell Rep. 2023, 42, 112076. [Google Scholar] [CrossRef]
- Khrustalev, V.V.; Giri, R.; Khrustaleva, T.A.; Kapuganti, S.K.; Stojarov, A.N.; Poboinev, V.V. Translation-associated mutational U-pressure in the first ORF of SARS-CoV-2 and other coronaviruses. Front. Microbiol. 2020, 11, 559165. [Google Scholar] [CrossRef]
- Kusakabe, T.; Motoki, K.; Hori, K. Mode of Interactions of Human Aldolase Isozymes with Cytoskeletons. Arch. Biochem. Biophys. 1997, 344, 184–193. [Google Scholar] [CrossRef]
- Esposito, G.; Vitagliano, L.; Costanzo, P.; Borrelli, L.; Barone, R.; Pavone, L.; Izzo, P.; Zagari, A.; Salvatore, F. Human aldolase A natural mutants: Relationship between flexibility of the C-terminal region and enzyme function. Biochem. J. 2004, 380 Pt 1, 51–56. [Google Scholar] [CrossRef] [PubMed]
- Guittet, O.; Håkansson, P.; Voevodskaya, N.; Fridd, S.; Gräslund, A.; Arakawa, H.; Nakamura, Y.; Thelander, L. Mammalian p53R2 Protein Forms an Active Ribonucleotide Reductasein Vitro with the R1 Protein, Which Is Expressed Both in Resting Cells in Response to DNA Damage and in Proliferating Cells. J. Biol. Chem. 2001, 276, 40647–40651. [Google Scholar] [CrossRef] [PubMed]
- Yamaguchi, T.; Matsuda, K.; Sagiya, Y.; Iwadate, M.; Fujino, M.A.; Nakamura, Y.; Arakawa, H. p53R2-dependent pathway for DNA synthesis in a p53-regulated cell cycle checkpoint. Cancer Res. 2001, 61, 8256–8262. [Google Scholar] [PubMed]
- Rauch, J.N.; Gestwicki, J.E. Binding of Human Nucleotide Exchange Factors to Heat Shock Protein 70 (Hsp70) Generates Functionally Distinct Complexes in Vitro. J. Biol. Chem. 2014, 289, 1402–1414. [Google Scholar] [CrossRef] [PubMed]
- Takayama, S.; Xie, Z.; Reed, J.C. An Evolutionarily Conserved Family of Hsp70/Hsc70 Molecular Chaperone Regulators. J. Biol. Chem. 1999, 274, 781–786. [Google Scholar] [CrossRef] [PubMed]
- Yu, Z.; Zeng, J.; Wang, J.; Cui, Y.; Song, X.; Zhang, Y.; Cheng, X.; Hou, N.; Teng, Y.; Lan, Y.; et al. Hepatocyte growth factor-regulated tyrosine kinase substrate is essential for endothelial cell polarity and cerebrovascular stability. Cardiovasc. Res. 2020, 117, 533–546. [Google Scholar] [CrossRef] [PubMed]
- Wu, L.; Cheng, Y.; Geng, D.; Fan, Z.; Lin, B.; Zhu, Q.; Li, J.; Qin, W.; Yi, W. O-GlcNAcylation regulates epidermal growth factor receptor intracellular trafficking and signaling. Proc. Natl. Acad. Sci. USA 2022, 119, e2107453119. [Google Scholar] [CrossRef]
- Han, J.; Goldstein, L.A.; Hou, W.; Watkins, S.C.; Rabinowich, H. Involvement of CASP9 (caspase 9) in IGF2R/CI-MPR endosomal transport. Autophagy 2020, 17, 1393–1409. [Google Scholar] [CrossRef]
- Vázquez, A. Growing network with local rules: Preferential attachment, clustering hierarchy, and degree correlations. Phys. Rev. E 2003, 67, 056104. [Google Scholar] [CrossRef]
- Giuraniuc, C.V.; Hatchett, J.P.L.; Indekeu, J.O.; Leone, M.; Castillo, I.P.; Van Schaeybroeck, B.; Vanderzande, C. Trading Interactions for Topology in Scale-Free Networks. Phys. Rev. Lett. 2005, 95, 098701. [Google Scholar] [CrossRef]
- Caetano-Anollés, D.; Caetano-Anollés, K.; Caetano-Anollés, G. Evolution of Macromolecular Structure: A ‘Double Tale’ of Biological Accretion and Diversification. Sci. Prog. 2018, 101, 360–383. [Google Scholar] [CrossRef] [PubMed]
- Caetano-Anollés, G.; Aziz, M.F.; Mughal, F.; Gräter, F.; Koç, I.; Caetano-Anollés, K.; Caetano-Anollés, D. Emergence of Hierarchical Modularity in Evolving Networks Uncovered by Phylogenomic Analysis. Evol. Bioinform. 2019, 15. [Google Scholar] [CrossRef]
- Benson, A.R.; Gleich, D.F.; Leskovec, J. Higher-order organization of complex networks. Science 2016, 353, 163–166. [Google Scholar] [CrossRef] [PubMed]
- Michoel, T.; Joshi, A.; Nachtergaele, B.; Van de Peer, Y. Enrichment and aggregation of topological motifs are independent organizational principles of integrated interaction networks. Mol. Biosyst. 2011, 7, 2769–2778. [Google Scholar] [CrossRef]
- Almaas, E. Biological impacts and context of network theory. J. Exp. Biol. 2007, 210, 1548–1558. [Google Scholar] [CrossRef]
- Modell, A.E.; Blosser, S.L.; Arora, P.S. Systematic targeting of protein–protein interactions. Trends Pharmacol. Sci. 2016, 37, 702–713. [Google Scholar] [CrossRef]
- Subramanian, S.; Kumar, S. Gene Expression Intensity Shapes Evolutionary Rates of the Proteins Encoded by the Vertebrate Genome. Genetics 2004, 168, 373–381. [Google Scholar] [CrossRef]
- Szaflik, T.; Smolarz, B.; Mroczkowska, B.; Kulig, B.; Soja, M.; Romanowicz, H.; Bryś, M.; Forma, E.; Szyłło, K. An Analysis of ESR2 and CYP19A1 Gene Expression Levels in Women with Endometriosis. Vivo 2020, 34, 1765–1771. [Google Scholar] [CrossRef] [PubMed]
- Zeng, C.; Evans, J.P.; King, T.; Zheng, Y.-M.; Oltz, E.M.; Whelan, S.P.J.; Saif, L.J.; Peeples, M.E.; Liu, S.-L. SARS-CoV-2 spreads through cell-to-cell transmission. Proc. Natl. Acad. Sci. USA 2021, 119, e2111400119. [Google Scholar] [CrossRef]
- Colonna, G. Molecular mechanisms driving the action of the Spike S1 subunit of the SARS-CoV-2 virus in human metabolism by interactomic analysis. (manuscript in preparation).
- Reményi, A.; Good, M.C.; Bhattacharyya, R.P.; Lim, W.A. The Role of Docking Interactions in Mediating Signaling Input, Output, and Discrimination in the Yeast MAPK Network. Mol. Cell 2005, 20, 951–962. [Google Scholar] [CrossRef]
- Staley, J.P.; Woolford, J.L. Assembly of ribosomes and spliceosomes: Complex ribonucleoprotein machines. Curr. Opin. Cell Biol. 2009, 21, 109–118. [Google Scholar] [CrossRef]
- Abbasian, M.H.; Mahmanzar, M.; Rahimian, K.; Mahdavi, B.; Tokhanbigli, S.; Moradi, B.; Sisakht, M.M.; Deng, Y. Global landscape of SARS-CoV-2 mutations and conserved regions. J. Transl. Med. 2023, 21, 152. [Google Scholar] [CrossRef]
- Markov, P.V.; Ghafari, M.; Beer, M.; Lythgoe, K.; Simmonds, P.; Stilianakis, N.I.; Katzourakis, A. The evolution of SARS-CoV-2. Nat. Rev. Microbiol. 2023, 21, 361–379. [Google Scholar] [CrossRef]
- Telenti, A.; Hodcroft, E.B.; Robertson, D.L. The evolution and biology of SARS-CoV-2 variants. Cold Spring Harb. Perspect. Med. 2022, 12, a041390. [Google Scholar] [CrossRef]
- Gebhardt, R.; MAatz-Soja, M. Liver zonation: Novel aspects of its regulation and its impact on homeostasis. World J. Gastroenterol. WJG 2014, 20, 8491. [Google Scholar] [CrossRef]
- Burley, S.K.; Bhikadiya, C.; Bi, C.; Bittrich, S.; Chen, L.; Crichlow, G.V.; Christie, C.H.; Dalenberg, K.; Di Costanzo, L.; Duarte, J.M.; et al. RCSB Protein Data Bank: Powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic Acids Res. 2020, 49, D437–D451. [Google Scholar] [CrossRef]
- Burke, D.F.; Bryant, P.; Barrio-Hernandez, I.; Memon, D.; Pozzati, G.; Shenoy, A.; Zhu, W.; Dunham, A.S.; Albanese, P.; Keller, A.; et al. Towards a structurally resolved human protein interaction network. Nat. Struct. Mol. Biol. 2023, 30, 216–225. [Google Scholar] [CrossRef]
- Baek, M.; DiMaio, F.; Anishchenko, I.; Dauparas, J.; Ovchinnikov, S.; Lee, G.R.; Wang, J.; Cong, Q.; Kinch, L.N.; Schaeffer, R.D.; et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 2021, 373, 871–876. [Google Scholar] [CrossRef]
- Jumper, J.; Evans, R.; Pritzel, A.; Green, T.; Figurnov, M.; Ronneberger, O.; Tunyasuvunakool, K.; Bates, R.; Žídek, A.; Potapenko, A.; et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021, 596, 583–589. [Google Scholar] [CrossRef]
- Bryant, P.; Pozzati, G.; Elofsson, A. Improved prediction of protein-protein interactions using AlphaFold2. Nat. Commun. 2022, 13, 1265. [Google Scholar] [CrossRef] [PubMed]
- Evans, R.; O’Neill, M.; Pritzel, A.; Antropova, N.; Senior, A.; Green, T.; Žídek, A.; Bates, R.; Blackwell, S.; Yim, J.; et al. Protein complex prediction with AlphaFold-Multimer. bioRxiv 2021. [Google Scholar] [CrossRef]
- Humphreys, I.R.; Pei, J.; Baek, M.; Krishnakumar, A.; Anishchenko, I.; Ovchinnikov, S.; Zhang, J.; Ness, T.J.; Banjade, S.; Bagde, S.R.; et al. Computed structures of core eukaryotic protein complexes. Science 2021, 374, eabm4805. [Google Scholar] [CrossRef]
- Shannon, C. A mathematical theory of communication. Bell Syst. Tech. J. 1948, 27, 379–423. [Google Scholar] [CrossRef]
- Cover, T.; Thomas, J. Elements of Information Theory; Wiley: New York, NY, USA, 1991. [Google Scholar]
- Orchard, S.; Kerrien, S.; Abbani, S.; Aranda, B.; Bhate, J.; Bidwell, S.; Bridge, A.; Briganti, L.; Brinkman, F.S.L.; Cesareni, G.; et al. Protein interaction data curation: The International Molecular Exchange (IMEx) consortium. Nat. Methods 2012, 9, 345–350. [Google Scholar] [CrossRef]
- Tyson, J.J.; Chen, K.C.; Novak, B. Sniffers, buzzers, toggles and blinkers: Dynamics of regulatory and signaling pathways in the cell. Curr. Opin. Cell Biol. 2003, 15, 221–231. [Google Scholar] [CrossRef]
- Kremling, A.; Saez-Rodriguez, J. Systems biology—An engineering perspective. J. Biotechnol. 2007, 129, 329–351. [Google Scholar] [CrossRef]
Article Title | HUB Genes |
---|---|
Demonstration of the impact of COVID-19 on metabolic associated fatty liver disease by bioinformatics and system biology approach [13]. | SERPINE1, IL1RN, THBS1, TNFAIP6, GADD45B, TNFRSF12A, PLA2G7, PTGES, PTX3, and GADD45G. |
Comprehensive DNA methylation profiling of COVID-19 and hepatocellular carcinoma to identify common pathogenesis and potential therapeutic targets [14]. | MYLK2, FAM83D, STC2, CCDC112, EPHX4, and MMP1. |
Exploration and verification of COVID-19-related hub genes in liver physiological and pathological regeneration [11]. | ASPM, BUB1B, CDC20, CENPF, CEP55, KIF11, KIF4, NCAPG, NUF2, NUSAP1, PBK, PTTG1, RRM2, TPX2, and UBE2C. |
Systems biology approach reveals a common molecular basis for COVID-19 and non-alcoholic fatty liver disease [NAFLD] [15]. | IL6, IL1B, PTGS2, JUN, FOS, ATF3, SOCS3, CSF3, NFKB2, and HBEGF. |
To investigate the internal association between SARS-CoV-2 infections and cancer through bioinformatics [16]. | MMP9, FOS, COL1A2, COL2A1, DKK3, IHH, CYP3A4, PPARGC1A, MMP11, and APOD. |
Target and drug predictions for SARS-CoV-2 infection in hepatocellular carcinoma patients [17]. | Upregulated, PDGFRB, MMP14, VWF, CD34, NES, MCAM, CSPG4, MMP1, SPARCL1, and MMP10. Downregulated, IL1B, S100A12, FCGR3B, CCR1, S100A8, CCL3, CCL2, CCL4, CLEC4D, and LILRA1. |
Bioinformatics analysis reveals molecular connections between non-alcoholic fatty liver disease [NAFLD] and COVID-19 [18]. | ACE, ADAM17, DPP4, TMPRSS2 and NAFLD-related genes such as TNF, AKT1, MAPK14, HIF1A, SP1, and IL10. |
Organ-specific or personalized treatment for COVID-19: rationale, evidence, and potential candidates [19]. | CCL2, CCL5, CXCL10, HAO2, BAAT, and SLC27A2. |
Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic [20]. | IL6, IL18, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A. |
A systems biology approach for investigating significantly expressed genes among COVID-19, hepatocellular carcinoma, and chronic hepatitis B [21]. | ACTB, ATM, CDC42, DHX15, EPRS, GAPDH, HIF1A, HNRNPA1, HRAS, HSP90AB1, HSPA8, IL1B, JUN, POLR2B, PTPRC, RPS27A, SFRS1, SMARCA4, SRC, TNF, UBE2I, and VEGFA. |
Identification of Key Pathways and Genes in SARS-CoV-2 Infecting Human Intestines by Bioinformatics Analysis [22] | AKT1, TIMP1, NOTCH, CCNA2, RRM2, TTK, BUB1B, KIF20A, and PLK1. |
GO-Term Biological Process | Description | p-Value | Node Color |
---|---|---|---|
GO:0043620 | Regulation of DNA-template transcription in response to stress | 1.90 × 10−3 | |
GO:0080135 | Regulation to cellular response stress | 9.77 × 10−37 | |
GO:1900407 | Regulation of cellular response to oxidative stress | 8.69 × 10−5 | |
GO:0034599 | Cellular response to oxidative stress | 1.93 × 10−11 | |
GO:0080134 | Regulation of response to stress | 7.11 × 10−67 | |
GO:0006979 | Response to oxidative stress | 2.99 × 10−12 | |
GO:0033554 | Cellular response to stress | 2.98 × 10−45 | |
GO:0006950 | Response to stress | 5.86 × 10−85 |
Human Protein | Number of Interacting Viral Proteins ** |
---|---|
RPL18A * (84) | 20 |
RPL13 (84) | 19 |
ALDOA (4), CDC42 (52), EIF2S1 (45) | 18 |
RRM2B (3) | 17 |
RPL13A (98), RPL21 * (87), RPL30 * (85) | 16 |
PSMC1 (30), RPL26 * (96), RPL7A (85), RPL (9) | 15 |
BUB3 (19), RPL7 (95), RPL8 (95), RPS24 (90), RPS6 (93), RPS9 * (102), SNRPD1 (38), SRC (97), STIP1 (12) | 14 |
BAG2 (7), RAC1 (11), RPL12 (93), RPL27A (85), RPS27L (82). | 13 |
EIF6 (46), MCM7 (20), HYOU1, PTGES3 (23), RPL27 (84), RPL13 (84), RPL35A (84), RPS10 (87), RPS11 * (108), RPSA (99). | 12 |
1—Normal biological processes related to nodes certified by reverse engineering in the liver infected by COVID-19 | |||||
GO Term Biological Process | Description | P | p-Value | Strength | |
GO:0019221 | Cytokine-mediated signaling pathway | 47.50 | 8.51 × 10−57 | 0.82 | |
GO:0002181 | Cytoplasmic translation | 46.53 | 2.05 × 10−44 | 1.05 | |
GO:0071345 | Cellular response to cytokine stimulus | 42.97 | 1.59 × 10−63 | 0.68 | |
GO:0033044 | Regulation of chromosome separation | 37.73 | 9.62 × 10−36 | 1.02 | |
GO:0010965 | Regulation of mitotic sister chromatid separation | 36.30 | 8.03 × 10−34 | 1.04 | |
GO:0033045 | Regulation of sister chromatid segregation | 36.20 | 6.46 × 10−34 | 1.02 | |
GO:0051983 | Regulation of chromosome segregation | 34.60 | 4.60 × 10−35 | 0.97 | |
GO:0030071 | Regulation of mitotic metaphase/anaphase transition | 33.87 | 3.68 × 10−32 | 1.04 | |
GO:0033044 | Regulation of chromosome organization | 32.37 | 3.03 × 10−39 | 0.82 | |
GO:0007346 | Regulation of mitotic cell cycle | 32.25 | 1.18 × 10−46 | 0.70 | |
GO:1901987 | Regulation of cell cycle phase transition | 30.16 | 2.98 × 10−42 | 0.71 | |
GO:0006412 | Translation | 29.30 | 4.59 × 10−40 | 0.72 | |
GO:1901990 | Regulation of mitotic cell cycle phase transition | 27.66 | 2.42 × 10−37 | 0.74 | |
GO:1990869 | Cellular response to chemokine | 23.92 | 8.36 × 10−24 | 0.96 | |
GO:0034243 | Regulation of transcript. elongat. from RNA polym. II | 17.94 | 5.25 × 10−19 | 0.91 | |
GO:0007088 | Regulation of mitotic nuclear division | 17.50 | 3.89 × 10−20 | 0.85 | |
2—Negative regulation of biological processes related to nodes certified by reverse engineering in the liver infected by COVID-19 | |||||
GO Term Biological Process | Description | P | p-Value | Strength | |
GO:0043069 | Negative regulation of programmed cell death | 18.94 | 2.65 × 10−36 | 0.52 | |
GO:0043066 | Negative regulation of apoptotic process | 18.31 | 7.95 × 10−35 | 0.51 | |
GO:1901988 | Negative regulation of cell cycle phase transition | 15.97 | 3.11 × 10−22 | 0.71 | |
GO:0045786 | Negative regulation of cell cycle | 15.25 | 1.63 × 10−24 | 0.63 | |
GO:0010948 | Negative regulation of cell cycle process | 14.98 | 1.08 × 10−22 | 0.68 | |
GO:0009892 | Negative regulation of metabolic process | 14.36 | 3.19 × 10−43 | 0.33 | |
GO:0010605 | Neg. regulation of macromolecule metabolic process | 14.22 | 6.61 × 10−41 | 0.34 | |
GO:1901991 | Neg. regulation of mitotic cell cycle phase transition | 13.83 | 8.82 × 10−18 | 0.73 | |
GO:0045930 | Negative regulation of mitotic cell cycle | 13.33 | 2.12 × 10−19 | 0.69 | |
GO:0031324 | Negative regulation of cellular metabolic process | 12.03 | 2.37 × 10−34 | 0.35 | |
GO:0060548 | Negative regulation of cell death | 11.95 | 1.43 × 10−34 | 0.35 | |
GO:2000816 | Neg. regulation of mitotic sister chromatid separation | 11.88 | 7.56 × 10−11 | 1.0 | |
GO:0045841 | Neg. regulation mitotic metaphase/anaphase transition | 10.46 | 2.29 × 10−10 | 1.01 | |
GO:2001237 | Neg. regulation of extrinsic apoptotic signaling pathway | 9.67 | 5.60 × 10−12 | 0.76 | |
GO:0051348 | Negative regulation of transferase activity | 8.90 | 1.17 × 10−15 | 0.59 | |
3—Dysregulated biological processes related to nodes certified by reverse engineering in the liver infected by COVID-19 | |||||
3A—Local Network Clustering (STRING) | Description | P | p-Value | Strength | |
CL.152 | Viral mRNA translation | 89.03 | 7.21 × 10−46 | 1.19 | |
CL:159 | Viral mRNA translation | 55.38 | 1.06 × 10−45 | 1.23 | |
CL:162 | Cytoplasmic ribosomal proteins | 54.16 | 1.41 × 10−43 | 1.23 | |
CL.143 | Viral mRNA transl. and Sec61 translocon complex | 53.10 | 6.93 × 10−47 | 1.11 | |
3B—Reactome Pathways | Description | P | p-Value | Strength | |
HSA-192823 | Viral mRNA translation | 64.09 | 2.56 × 10−53 | 1.2 | |
HSA-72764 | Eukaryotic translational termination | 61.79 | 2.32 × 10−52 | 1.18 | |
HSA-72689 | Formation of a pool of free 40S subunits | 58.97 | 1.91 × 10−51 | 1.15 | |
HSA-72737 | CAP-dependent translation initiation | 53.73 | 1.98 × 10−49 | 1.09 | |
HSA-1799339 | SRP-dependent co-translational prot. targeting to member | 53.17 | 2.20 × 10−48 | 1.1 | |
HSA-9679506 | SARS-CoV-1 infections | 38.58 | 5.77 × 10−50 | 0.76 | |
HSA-9754678 | SARS-CoV-2 modulation of host translational machinery | 26.18 | 2.39 × 10−23 | 1.12 | |
HSA-9692914 | SARS-CoV-1 host interactions | 32.98 | 1.06 × 10−32 | 1.03 | |
HSA-9705683 | SARS-CoV-2 host interactions | 31.14 | 1.61 × 10−36 | 0.86 | |
HSA-9678108 | SARS-CoV-1 infection | 30.73 | 1.12 × 10−33 | 0.93 | |
HSA-9735869 | SARS-CoV-1 modulates host translational machinery | 28.19 | 1.28 × 10−23 | 1.22 | |
HAS-9754678 | SARS-CoV-2 modulation of host translational machinery | 26.18 | 2.39 × 10−23 | 1.12 | |
HSA-9694516 | SARS-CoV-2 infections | 25.52 | 1.07 × 10−34 | 0.75 | |
HSA-9705671 | SARS-CoV-2 activates/modulates innate/adaptative immune responses | 11.06 | 5.57 × 10−14 | 0.75 | |
HSA-597592 | Post-translational protein modification | 7.95 | 1.28 × 10−22 | 0.36 | |
HSA-9772572 | Early SARS-CoV-2 infection events | 3.68 | 1.3 × 10−5 | 0.72 | |
4—Protein domain characteristics in the liver infected by COVID-19 | |||||
4A—Prot. Domains (InterPro) | Description | Count in Network | P | p-Value | Strength |
IPR036048 | Chemokine interleukin-8-like superfamily | 29 of 44 | 15.03 | 1.11 × 10−14 | 1.07 |
IPR039809 | Chemokine beta/gamma/delta | 15 of 26 | 8.03 | 8.90 × 10−7 | 1.01 |
IPR033899 | CXC chemokine domain | 12 of 14 | 7.30 | 1.54 × 10−6 | 1.18 |
IPR011332 | Zinc-binding ribosomal protein | 9 of 10 | 7.01 | 6.92 × 10−5 | 1.2 |
IPR011029 | Death-like domain superfamily | 29 of 97 | 6.01 | 2.23 × 10−8 | 0.72 |
IPR008271 | Serine/threonine-protein kinase, active site | 52 of 310 | 4.21 | 9.00 × 10−8 | 0.47 |
IPR001875 | Death effector domain | 5 of 7 | 3.84 | 3.10 × 10−3 | 1.1 |
IPR0000488 | Death domain | 11 of 35 | 2.81 | 6.3 × 10−3 | 0.74 |
4B—Prot. Domains (SMART) | Description | Count in Network | P | p-Value | Strength |
SM00199 | Intercrine alpha family (small cyt/chem CXC) | 28 of 42 | 16.80 | 5.07 × 10−15 | 1.07 |
SM00252 | Src homology 2 domains | 22 of 104 | 3.24 | 2.5 × 10−5 | 0.6 |
SM00219 | Tyrosine kinase, catalytic domain | 20 of 88 | 2.64 | 2.5 × 10−4 | 0.6 |
4C—Annotated Keywords (UniProt) | Description | Count in Network | P | p-Value | Strength |
KW-0689 | Ribosomal protein | 90 of 175 | 44.83 | 5.05 × 10−46 | 0.96 |
KW-0687 | Ribonucleoprotein | 112/278 | 42.17 | 4.14 × 10−49 | 0.85 |
KW-0945 | Host–virus interaction | 148/540 | 33.03 | 3.81 × 10−48 | 0.68 |
KW-0747 | Spliceosome | 50 of 138 | 16.77 | 5.14 × 10−20 | 0.81 |
KW-0395 | Inflammatory response | 56 of 163 | 16.56 | 1.73 × 10−21 | 0.78 |
KW-0132 | Cell division | 88 of 384 | 14.25 | 2.31 × 10−23 | 0.61 |
KW-0498 | Mitosis | 69 of 75 | 13.43 | 4.53 × 10−20 | 0.65 |
KW-0131 | Cell cycle | 137/651 | 13.13 | 1.09 × 10−23 | 0.57 |
KW-0647 | Proteasome | 25 of 52 | 11.57 | 2.74 × 10−12 | 0.93 |
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Colonna, G. Understanding the SARS-CoV-2–Human Liver Interactome Using a Comprehensive Analysis of the Individual Virus–Host Interactions. Livers 2024, 4, 209-239. https://doi.org/10.3390/livers4020016
Colonna G. Understanding the SARS-CoV-2–Human Liver Interactome Using a Comprehensive Analysis of the Individual Virus–Host Interactions. Livers. 2024; 4(2):209-239. https://doi.org/10.3390/livers4020016
Chicago/Turabian StyleColonna, Giovanni. 2024. "Understanding the SARS-CoV-2–Human Liver Interactome Using a Comprehensive Analysis of the Individual Virus–Host Interactions" Livers 4, no. 2: 209-239. https://doi.org/10.3390/livers4020016