Archives of Academic Emergency Medicine. 2021; 9(1): e27 https://doi.org/10.22037/aaem.v9i1.1108 OR I G I N A L RE S E A RC H Platelet and Haemostasis are the Main Targets in Severe Cases of COVID-19 Infection; a System Biology Study Mona Zamanian-Azodi1, Babak Arjmand2, Mohammadreza Razzaghi3, Mostafa Rezaei Tavirani1∗, Alireza Ahmadzadeh1, Mohammad Rostaminejad4 1. Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 2. Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran. 3. Laser Application in Medical Sciences Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 4. Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Received: January 2021; Accepted: February 2021; Published online: 14 March 2021 Abstract: Introduction: Many proteomics-based and bioinformatics-based efforts are made to detect the molecular mechanism of COVID-19 infection. Identification of the main protein targets and pathways of severe cases of COVID-19 infection is the aim of this study. Methods: Published differentially expressed proteins were screened and the significant proteins were investigated via protein-protein interaction network using Cytoscape software V. 3.7.2 and STRING database. The studied proteins were assessed via action map analysis to determine the relationship between individual proteins using CluePedia. The related biological terms were investigated using ClueGO and the terms were clustered and discussed. Results: Among the 35 queried proteins, six of them (FGA, FGB, FGG, and FGl1 plus TLN1 and THBS1) were identified as critical proteins. A total of 38 biological terms, clustered in 4 groups, were introduced as the affected terms. “Platelet degranulation” and “hereditary factor I deficiency disease” were introduced as the main class of the terms disturbed by COVID-19 virus. Conclusion: It can be concluded that platelet damage and disturbed haemostasis could be the main targets in severe cases of coronavirus infection. It is vital to follow patients’ condition by examining the introduced critical differentially expressed proteins (DEPs). Keywords: COVID-19; Proteins; Bioinformatics; Computational Biology; Network analysis Cite this article as: Zamanian-Azodi M, Arjmand B, Razzaghi M, Rezaei Tavirani M, Ahmadzadeh A, Rostaminejad M. Platelet and Haemosta- sis are the Main Targets in Severe Cases of COVID-19 Infection; a System Biology Study. Arch Acad Emerg Med. 2021; 9(1): e27. 1. Introduction COVID-19 infection resulted in difficulties all over the world and for all the different races of human beings in all coun- tries. In addition, it has imposed complex effects on pa- tients’ lifestyle, which lead to manifestation of other condi- tions such as diabetes, cancers, and other types of disorders, and has thus attracted the attention of researchers and they want to solve this problem (1-3). Since understanding the molecular mechanism of the diseases is fundamental in diag- nosis and therapy of diseases, many efforts are made to study ∗Corresponding Author: Mostafa Rezaei Tavirani; Proteomics Research Cen- ter, Faculty of Paramedical Sciences,Darband Street, Tajrish Square, Tehran, Iran. Email: tavirany@yahoo.com, Tel: 00989122650447, ORCID:????????????? . the molecular aspect of COVID-19 infection (4-6). Proteomics and informatics are two suitable methods for finding the molecular mechanism of different kinds of dis- eases (7, 8). Since proteomics is a high-throughput method, results of proteomics are reliable data that can be interpreted and analyzed via informatics (9, 10). Network analysis based on graph theory is a method in bioinformatics, which is widely applied for evaluating diseases in medical sciences (11, 12). Differentially expressed proteins (DEPs) bind to the other proteins based on affinity, and form a network of nodes, which are linked by edges (13). The constructed net- work contains useful information about the elements of the network (14). Action map is another useful method for deter- mining the relationship between the queried DEPs. Possible inhibition, activation, reaction, binding, and regulation roles of a protein related to the neighbors can be identified via ac- This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem M. Zamanian-Azodi et al. 2 tion map analysis (15). Gene ontology is another molecular analysis that can be used to detect the pathways and biological processes that are re- lated to the studied proteins. Many diseases are assessed via gene ontology method to find the critical dysregulated path- ways and biological processes (16, 17). In the present study, DEPs of severe cases of COVID-19 are extracted from a paper by Ting Shu et al. and are investigated via network analysis, action map assessment, and gene on- tology examination. In the report of Ting Shu et al., plasma protein expression changes of patients in the cases of fatal, severe, and mild conditions are compared with the controls. Here, the severe cases of COVID-19 were selected to be as- sessed and their significant DEPs were investigated. 2. Methods In this bioinformatics study, 35 differentially expressed proteins based on fold change ≥ 1.5 and p-value ≤ 0.01, which were identified by evaluating protein expressions in severe cases of COVID-19 versus healthy people, were extracted from the paper published by Ting Shu et al. (18). The differentially expressed proteins were included in an interactome unit using “protein query” of STRING database via Cytoscape software 3.7.2. The network including a main connected component and two isolated proteins was con- structed. Furthermore, to understand the type of interactions be- tween the nodes, action map analysis was investigated. For this purpose, activation, inhibition, binding, and regula- tion actions were evaluated using CluePedia v1.5.7. The biological terms related to the 35 DEPs were investigated using ClueGO 2.5.7 from REACTOME_Pathways_08.05.2020, CLINVAR_Human-diseases_08.05.2020, KEGG_08.05.2020, and WikiPathways_08.05.2020. In the statistical analysis, protein expression values were de- termined based on mean value of data. Kapa scoring was set to 0.4. Additionally, Term P value corrected with Bonferroni step down, group P value, and group P value corrected with Bonferroni step down were ≤ 0.01 in gene ontology analysis. The protocol of study was approved by Ethics Committee of Shahid Beheshti University of Medical Sciences, Tehran, Iran (Ethics code: IR.SBMU.RETECH.REC.1399.355). 3. Results Except for ARHGDIB and SH3BGRL3 the other DEPs were in- cluded in the network by undirected edges. As shown in fig- ure 1, a compacted region, which is mainly formed by vari- ous types of fibrinogen chains, has appeared as a central part of the constructed network. This finding is confirmed by ac- tion map (see figure 2). FGA, FGB, FGG, and FGl1 plus TLN1 and THBS1 are connected together in action map. YWHAZ, YWHAE, and CFL1 that are located in figure 1, formed a triple unit in the action map. Since the network is not a scale free type, centrality analysis was not applied to find the central nodes such as hubs or bottlenecks. A total of 38 biological terms related to the 35 DEPs are shown in figure 3 and table 1. The terms are grouped in four classes. The smallest group includes only one term (Translocation of SLC2A4 (GLUT4) to the plasma membrane), while Hereditary factor I deficiency disease, as the largest group, includes 29 terms. Frequency of groups of terms is represented in figure 4. 4. Discussion Efforts of researchers to solve COVID-19 infection problems led to production of large numbers of publications. Pro- teomics and bioinformatics are two powerful methods that have been frequently applied in molecular studies of COVID- 19 (19, 20). In the present study, bioinformatics evaluation of plasma proteome of patients with severe COVID-19 revealed a new perspective of the disease. As shown in figure 1, a total of 35 significant DEPs are connected as an interactome unit to create a new concept about COVID-19 pandemic. Apart from two proteins, the other DEPs are interacted in a het- erogeneous way and several nodes form a compact area as a cluster. This compact zone is shown as a cluster includ- ing six proteins in figure 2. It seems that these six proteins (including four varieties of fibrinogen, talin-1 (TLN1), and thrombospondin-1 (THBS1)) play a critical role among the 35 queried DEPs in response to the COVID-19 infection. In- vestigation indicates that regulation of talin-1 effects platelet activation (21). The role of thrombospondin-1 in stimulating platelet aggregation is reported by Jeff S. Isenberg et al. (22). The biological terms that are connected to the DEPs are shown in figure 3 and table 1. To find the terms that are connected to the six critical DEPs, the terms that were re- lated to at least one node of these critical proteins were deter- mined. Findings indicate that among the 31 terms in cluster 4, there are 29 terms (about 94%) that are linked to the sev- eral members of the six DEPs. All terms of cluster 2 are linked to the members of the critical DEPs, while the single term of cluster 1 has no connection to the critical set of DEPs. Four terms (80%) of cluster 3 members have no connection to the mentioned DEPs. Based on the analysis, it can be concluded that, clusters 2 and 4 (“platelet degranulation” and “heredi- tary factor I deficiency disease”, respectively) are the promi- nent terms that are dysregulated in response to COVID-19 in- fection. Hereditary factor I deficiency disease or fibrinogen defi- ciency is a blood disorder that is accompanied with de- creased level of fibrinogen (afibrinogenemia, hypofibrino- genemia) or disturbed quality of fibrinogen (dysfibrinogene- This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 3 Archives of Academic Emergency Medicine. 2021; 9(1): e27 mia) in circulation (23). As previously known, fibrinogen has a noticeable role in normal haemostasis in human body. It is the key element of promotion of fibrinolysis, clot formation, and platelet aggregation processes (24). As depicted in figure 4, about 76% of the determined biological terms are related to the “Hereditary factor I deficiency disease”. The second cluster of terms is “platelet degranulation” class of pathway, which includes 3 terms. Participation in haemostasis is the well-known role of platelet in blood. An essential process in response to vascular damage is platelet adhesion, which leads to initiation of thrombus creation at the time of hemorrhage and promotes wound healing (25, 26). There is a similar function that the two biological terms (“Hereditary factor I deficiency disease” and “platelet degran- ulation”) are involved in: haemostasis. It can be concluded that disturbed haemostasis is the main dysfunction in se- vere cases of COVID-19. What is more, clinical features of infection with coronavirus (1, 27) support the findings of our study. Since COVID-19 is a new disease, more data and sufficient patients are required to analyze the molecular events re- lated to the promotion of infection. Complementary in- vestigations regarding different parameters such as age, ge- ography, race, and other conditions are recommended to achieve a better understanding of the molecular mechanism of COVID-19. 5. Conclusion It can be concluded that platelet damage and disturbed haemostasis could be the main targets in severe cases of coronavirus infection. It is vital to follow patients’ condition by examining the introduced critical DEPs. 6. Declarations 6.1. Conflict of interest There is no conflict of interest. 6.2. Acknowledgment This project was supported by Shahid Beheshti University of Medical Sciences. 6.3. Authors’ contributions Project was designed by Mostafa Rezaei Tavirani and Mona Zamanian Azodi. All authors had equal roles in the other ac- tions. 6.4. Funding and supports This project was supported by Shahid Beheshti University of Medical Sciences. References 1. Marietta M, Ageno W, Artoni A, De Candia E, Gresele P, Marchetti M, et al. COVID-19 and haemostasis: a po- sition paper from Italian Society on Thrombosis and Haemostasis (SISET). Blood Transfusion. 2020;18(3):167. 2. Madsbad S. COVID-19 infection in people with diabetes. Endocrinology. 2020;2020:1. 3. Wang Y, Duan Z, Ma Z, Mao Y, Li X, Wilson A, et al. Epi- demiology of mental health problems among patients with cancer during COVID-19 pandemic. Translational psychiatry. 2020;10(1):1-10. 4. Cazzola M, Skoda RC. Translational pathophysiology: a novel molecular mechanism of human disease. Blood, The Journal of the American Society of Hematology. 2000;95(11):3280-8. 5. Li X, Geng M, Peng Y, Meng L, Lu S. Molecular im- mune pathogenesis and diagnosis of COVID-19. Journal of Pharmaceutical Analysis. 2020. 6. Hosoki K, Chakraborty A, Sur S. Molecular mechanisms and epidemiology of COVID-19 from an allergist’s per- spective. Journal of Allergy and Clinical Immunology. 2020. 7. Chambers G, Lawrie L, Cash P, Murray GI. Proteomics: a new approach to the study of disease. The Journal of pathology. 2000;192(3):280-8. 8. Brusic V, Marina O, Wu CJ, Reinherz EL. Proteome infor- matics for cancer research: from molecules to clinic. Pro- teomics. 2007;7(6):976-91. 9. Blueggel M, Chamrad D, Meyer HE. Bioinformatics in proteomics. Current pharmaceutical biotechnology. 2004;5(1):79-88. 10. Guingab-Cagmat J, Cagmat E, Hayes RL, Anagli J. Inte- gration of proteomics, bioinformatics, and systems biol- ogy in traumatic brain injury biomarker discovery. Fron- tiers in neurology. 2013;4:61. 11. Zamanian-Azodi M, Rezaei-Tavirani M, Rahmati-Rad S, Hasanzadeh H, Tavirani MR, Seyyedi SS. Protein-Protein Interaction Network could reveal the relationship be- tween the breast and colon cancer. Gastroenterology and Hepatology from bed to bench. 2015;8(3):215. 12. Safari-Alighiarloo N, Taghizadeh M, Rezaei-Tavirani M, Goliaei B, Peyvandi AA. Protein-protein interaction net- works (PPI) and complex diseases. Gastroenterology and Hepatology from bed to bench. 2014;7(1):17. 13. Rual J-F, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, et al. Towards a proteome-scale map of the human protein–protein interaction network. Nature. 2005;437(7062):1173-8. 14. Bu D, Zhao Y, Cai L, Xue H, Zhu X, Lu H, et al. Topo- logical structure analysis of the protein–protein interac- tion network in budding yeast. Nucleic acids research. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem M. Zamanian-Azodi et al. 4 2003;31(9):2443-50. 15. Rezaei-Tavirani M, Tavirani MR, Azodi MZ, Farshi HM, Razzaghi M. Evaluation of skin response after erbium: yttrium–aluminum–garnet laser irradiation: a network analysis approach. Journal of lasers in medical sciences. 2019;10(3):194. 16. Rezaei Tavirani M, Mansouri V, Rezaei Tavirani S, Hesami Tackallou S, Rostami-Nejad M. Gliosarcoma protein- protein interaction network analysis and gene on- tology. International Journal of Cancer Management. 2018;11(5). 17. Schlicker A, Lengauer T, Albrecht M. Improving disease gene prioritization using the semantic similarity of Gene Ontology terms. Bioinformatics. 2010;26(18):i561-i7. 18. Shu T, Ning W, Wu D, Xu J, Han Q, Huang M, et al. Plasma proteomics identify biomarkers and pathogene- sis of COVID-19. Immunity. 2020;53(5):1108-22. e5. 19. Messner CB, Demichev V, Wendisch D, Michalick L, White M, Freiwald A, et al. Ultra-high-throughput clini- cal proteomics reveals classifiers of COVID-19 infection. Cell systems. 2020;11(1):11-24. e4. 20. Whetton AD, Preston GW, Abubeker S, Geifman N. Pro- teomics and informatics for understanding phases and identifying biomarkers in COVID-19 disease. Journal of proteome research. 2020;19(11):4219-32. 21. Zhang D, Qiao W, Zhao Y, Fang H, Xu D, Xia Q. Curdione attenuates thrombin-induced human platelet activation: β1-tubulin as a potential therapeutic target. Fitoterapia. 2017;116:106-15. 22. Isenberg JS, Romeo MJ, Yu C, Yu CK, Nghiem K, Monsale J, et al. Thrombospondin-1 stimulates platelet aggrega- tion by blocking the antithrombotic activity of nitric ox- ide/cGMP signaling. Blood, The Journal of the American Society of Hematology. 2008;111(2):613-23. 23. Peyvandi F. Epidemiology and treatment of con- genital fibrinogen deficiency. Thrombosis research. 2012;130:S7-S11. 24. Bornikova L, Peyvandi F, Allen G, Bernstein J, Manco- Johnson M. Fibrinogen replacement therapy for con- genital fibrinogen deficiency. Journal of Thrombosis and Haemostasis. 2011;9(9):1687-704. 25. Ruggeri ZM, Mendolicchio GL. Adhesion mecha- nisms in platelet function. Circulation research. 2007;100(12):1673-85. 26. Marcus AJ. Platelet function. New England Journal of Medicine. 1969;280(22):1213-20. 27. White D, MacDonald S, Edwards T, Bridgeman C, Hay- man M, Sharp M, et al. Evaluation of COVID-19 co- agulopathy; laboratory characterization using thrombin generation and nonconventional haemostasis assays. In- ternational journal of laboratory hematology. 2020. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 5 Archives of Academic Emergency Medicine. 2021; 9(1): e27 Table 1: List of 38 biological terms related to the queried differentially expressed proteins (DEPs) GO Term N AGs % Associated Genes Found Translocation of SLC2A4 (GLUT4) to the plasma membrane 1 4.17 [ACTB, YWHAE, YWHAZ] Platelet degranulation 2 9.30 [APOH, CFL1, FGA, FGB, FGG, ORM1, ORM2, PF4, PPBP, TAGLN2, THBS1, TLN1] Platelet activation, signaling and aggregation 2 4.94 [APOH, CFL1, FGA, FGB, FGG, ORM1, ORM2, PF4, PPBP, TAGLN2, THBS1, TLN1, YWHAZ] Response to elevated platelet cytosolic Ca2+ 2 8.96 [APOH, CFL1, FGA, FGB, FGG, ORM1, ORM2, PF4, PPBP, TAGLN2, THBS1, TLN1] Hemolytic-uremic syndrome 3 33.33 [CFHR1, CFHR3, CFI] Atypical hemolytic uremic syndrome 3 33.33 [CFHR1, CFHR3, CFI] Complement and coagulation cascades 3 7.06 [CFHR1, CFHR3, CFI, FGA, FGB, FGG] Complement cascade 3 8.62 [CFHR1, CFHR3, CFI, CFP, CRP] Regulation of Complement cascade 3 8.51 [CFHR1, CFHR3, CFI, CFP] Hemolytic-uremic syndrome 4 33.33 [CFHR1, CFHR3, CFI] Hereditary factor I deficiency disease 4 100.00 [CFI, FGA, FGB, FGG] Dysfibrinogenemia, congenital 4 100.00 [FGA, FGB, FGG] Afibrinogenemia, congenital 4 100.00 [FGA, FGB, FGG] Atypical hemolytic uremic syndrome 4 33.33 [CFHR1, CFHR3, CFI] Complement and coagulation cascades 4 7.06 [CFHR1, CFHR3, CFI, FGA, FGB, FGG] Platelet activation 4 4.03 [ACTB, FGA, FGB, FGG, TLN1] Common Pathway of Fibrin Clot Formation 4 18.18 [FGA, FGB, FGG, PF4] Formation of Fibrin Clot (Clotting Cascade) 4 10.26 [FGA, FGB, FGG, PF4] Integrin cell surface interactions 4 4.71 [FGA, FGB, FGG, THBS1] Integrin signaling 4 14.81 [FGA, FGB, FGG, TLN1] GRB2:SOS provides linkage to MAPK signaling for Integrins 4 26.67 [FGA, FGB, FGG, TLN1] p130Cas linkage to MAPK signaling for integrins 4 26.67 [FGA, FGB, FGG, TLN1] MAP2K and MAPK activation 4 12.50 [ACTB, FGA, FGB, FGG, TLN1] Regulation of TLR by endogenous ligand 4 21.05 [FGA, FGB, FGG, S100A8] Signaling by moderate kinase activity BRAF mutants 4 10.64 [ACTB, FGA, FGB, FGG, TLN1] Signaling by high-kinase activity BRAF mutants 4 13.89 [ACTB, FGA, FGB, FGG, TLN1] Signaling by RAS mutants 4 10.64 [ACTB, FGA, FGB, FGG, TLN1] Signaling by BRAF and RAF fusions 4 7.46 [ACTB, FGA, FGB, FGG, TLN1] Paradoxical activation of RAF signaling by kinase inactive BRAF 4 10.64 [ACTB, FGA, FGB, FGG, TLN1] Oncogenic MAPK signaling 4 6.76 [ACTB, FGA, FGB, FGG, TLN1] Platelet Aggregation (Plug Formation) 4 10.26 [FGA, FGB, FGG, TLN1] Signaling downstream of RAS mutants 4 10.64 [ACTB, FGA, FGB, FGG, TLN1] Regulation of Complement cascade 4 8.51 [CFHR1, CFHR3, CFI, CFP] Selenium Micronutrient Network 4 5.43 [CRP, FGA, FGB, FGG, SAA2] Folate Metabolism 4 6.85 [CRP, FGA, FGB, FGG, SAA2] Blood Clotting Cascade 4 13.04 [FGA, FGB, FGG] Human Complement System 4 7.07 [CFI, CFP, CRP, FGA, FGB, FGG, THBS1] Fibrin Complement Receptor 3 Signaling Pathway 4 7.14 [FGA, FGB, FGG] The terms are extracted from Ontology Source; REACTOME_Pathways_08.05.2020, CLINVAR_Human-diseases_08.05.2020, KEGG_08.05.2020, WikiPathways_08.05.2020. Term P Value, term P Value Corrected with Bonferroni step down, group P Value, and term P Value Corrected with Bonferroni step down ≤ 0.01 were considered. GO: gene ontology; N: number of group; AGs: associated genes. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem M. Zamanian-Azodi et al. 6 Figure 1: The queried 35 differentially expressed proteins (DEPs) are included in a network using STRING database and Cytoscape software. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 7 Archives of Academic Emergency Medicine. 2021; 9(1): e27 Figure 2: The action map for the 35 queried differentially expressed proteins (DEPs) via CluePedia. The blue and red colors of edges refer to binding and inhibition actions. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem M. Zamanian-Azodi et al. 8 Figure 3: Gene ontology results related to the 35 queried differentially expressed proteins (DEPs). The 38 terms are classified in the four groups. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 9 Archives of Academic Emergency Medicine. 2021; 9(1): e27 Figure 4: Frequency of four classes of biological terms as a pie chart. Different colors indicate designated groups of terms. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem Introduction Methods Results Discussion Conclusion Declarations References