Diverse COVID-19 severity levels and a spectrum of clinical manifestations underscore the need to comprehend the underlying genetic mechanisms. Such knowledge is essential for improving disease management and therapeutic approaches. This study aims to explore and uncover pivotal genes and pathways linked to distinct COVID-19 conditions, providing insights into potential therapeutic avenues. Gene expression data from COVID-19 patients across different conditions were analyzed using differential gene expression analysis. Significant genes were subjected to pathway analysis and protein–protein interaction network analysis. Gene ontology was used to identify the functions of these genes. The genes ADAMTS2, PCSK9, and OLAH were upregulated across all disease conditions including SARS-CoV-2 bacterial coinfection, potentially serving as therapeutic targets. The proteins, including RPL and CEACAM, could serve as a potential therapeutic target. The deregulated genes were majorly involved in inflammation, lipid metabolism, and immune regulation. The study's findings reveal significant gene expression differences among COVID-19 disease conditions. These insights guide future research toward targeted therapies and an improved understanding of disease progression and long-term consequences.
Citation: Mairembam Stelin Singh, PV Parvati Sai Arun, Mairaj Ahmed Ansari. Unveiling common markers in COVID-19: ADAMTS2, PCSK9, and OLAH emerged as key differential gene expression profiles in PBMCs across diverse disease conditions[J]. AIMS Molecular Science, 2024, 11(2): 189-205. doi: 10.3934/molsci.2024011
Diverse COVID-19 severity levels and a spectrum of clinical manifestations underscore the need to comprehend the underlying genetic mechanisms. Such knowledge is essential for improving disease management and therapeutic approaches. This study aims to explore and uncover pivotal genes and pathways linked to distinct COVID-19 conditions, providing insights into potential therapeutic avenues. Gene expression data from COVID-19 patients across different conditions were analyzed using differential gene expression analysis. Significant genes were subjected to pathway analysis and protein–protein interaction network analysis. Gene ontology was used to identify the functions of these genes. The genes ADAMTS2, PCSK9, and OLAH were upregulated across all disease conditions including SARS-CoV-2 bacterial coinfection, potentially serving as therapeutic targets. The proteins, including RPL and CEACAM, could serve as a potential therapeutic target. The deregulated genes were majorly involved in inflammation, lipid metabolism, and immune regulation. The study's findings reveal significant gene expression differences among COVID-19 disease conditions. These insights guide future research toward targeted therapies and an improved understanding of disease progression and long-term consequences.
[1] |
Yang H, Lan Y, Yao X, et al. (2020) The chest CT features of coronavirus disease 2019 (COVID-19) in China: A meta-analysis of 19 retrospective studies. Virol J 17: 159. https://doi.org/10.1186/s12985-020-01432-9 ![]() |
[2] |
Muralidar S, Ambi SV, Sekaran S, et al. (2020) The emergence of COVID-19 as a global pandemic: Understanding the epidemiology, immune response and potential therapeutic targets of SARS-CoV-2. Biochimie 179: 85-100. https://doi.org/10.1016/j.biochi.2020.09.018 ![]() |
[3] |
Chiumello D, Modafferi L, Fratti I (2022) Risk factors and mortality in elderly ARDS COVID-19 compared to patients without COVID-19. J Clin Med 11: 5180. https://doi.org/10.3390/jcm11175180 ![]() |
[4] |
Malik YS, Kumar N, Sircar S, et al. (2020) Coronavirus disease pandemic (COVID-19): Challenges and a global perspective. Pathogens 9: 519. https://doi.org/10.3390/pathogens9070519 ![]() |
[5] |
Cunningham AC, Goh HP, Koh D (2020) Treatment of COVID-19: Old tricks for new challenges. Crit Care 24: 91. https://doi.org/10.1186/s13054-020-2818-6 ![]() |
[6] | Kim GU, Kim MJ, Ra SH, et al. (2020) Clinical characteristics of asymptomatic and symptomatic patients with mild COVID-19. Clin Microbiol infect 26: 948. https://doi.org/10.1016/j.cmi.2020.04.040 |
[7] |
Gandhi RT, Lynch JB, Del Rio C (2020) Mild or moderate Covid-19. N Engl J Med 383: 1757-1766. https://doi.org/10.1056/NEJMcp2009249 ![]() |
[8] |
Sayampanathan AA, Heng CS, Pin PH, et al. (2021) Infectivity of asymptomatic versus symptomatic COVID-19. Lancet 397: 93-94. https://doi.org/10.1016/S0140-6736(20)32651-9 ![]() |
[9] |
Yin SW, Zhou Z, Wang JL, et al. (2021) Viral loads, lymphocyte subsets and cytokines in asymptomatic, mildly and critical symptomatic patients with SARS-CoV-2 infection: A retrospective study. Virol Jl 18: 126. https://doi.org/10.1186/s12985-021-01597-x ![]() |
[10] |
Iwamura APD, Tavares da Silva MR, Hümmelgen AL, et al. (2021) Immunity and inflammatory biomarkers in COVID-19: A systematic review. Rev Med Virol 31: e2199. https://doi.org/10.1002/rmv.2199 ![]() |
[11] |
Sadanandam A, Bopp T, Dixit S, et al. (2020) A blood transcriptome-based analysis of disease progression, immune regulation, and symptoms in coronavirus-infected patients. Cell Death Discov 6: 141. https://doi.org/10.1038/s41420-020-00376-x ![]() |
[12] |
Chakraborty C, Sharma AR, Bhattacharya M, et al. (2021) Understanding gene expression and transcriptome profiling of COVID-19: An initiative towards the mapping of protective immunity genes against SARS-CoV-2 infection. Front Immunol 12: 724936. https://doi.org/10.3389/fimmu.2021.724936 ![]() |
[13] |
Gardinassi LG, Souza COS, Sales-Campos H, et al. (2020) Immune and metabolic signatures of COVID-19 revealed by transcriptomics data reuse. Front Immunol 11: 1636. https://doi.org/10.3389/fimmu.2020.01636 ![]() |
[14] |
Kwan PKW, Cross GB, Naftalin CM, et al. (2021) A blood RNA transcriptome signature for COVID-19. BMC Med Genomics 14: 155. https://doi.org/10.1186/s12920-021-01006-w ![]() |
[15] |
Li G, Ruan S, Zhao X, et al. (2021) Transcriptomic signatures and repurposing drugs for COVID-19 patients: Findings of bioinformatics analyses. Comput Struct Biotec J 19: 1-15. https://doi.org/10.1016/j.csbj.2020.11.056 ![]() |
[16] | El-Aarag SA, Mahmoud A, ElHefnawi M Identifying potential novel insights for COVID-19 pathogenesis and therapeutics using an integrated bioinformatics analysis of host transcriptome (2022)194: 770-780. https://doi.org/10.1016/j.ijbiomac.2021.11.124 |
[17] |
Barrett T, Wilhite SE, Ledoux P, et al. (2012) NCBI GEO: Archive for functional genomics data sets—Update. Nucleic Acids Res 41: D991-D995. https://doi.org/10.1093/nar/gks1193 ![]() |
[18] |
Venugopal BS, Dinesh S, Sharma S, et al. (2023) Exploring the therapeutic potential of green tea phytocompounds for pancreatic cancer: An mRNA differential gene expression and pathway analysis study. Biomedicine 43: 1231-1238. https://orcid.org/0009-0006-2673-1858 ![]() |
[19] |
Vora D, Kapadia H, Dinesh S, et al. (2023) Gymnema sylvestre as a potential therapeutic agent for PCOS: insights from mRNA differential gene expression and molecular docking analysis. Futur J Pharm Sci 9: 76. https://doi.org/10.1186/s43094-023-00529-6 ![]() |
[20] |
Kapadia H, Dinesh S, Sharma S, et al. (2023) Integrating network pharmacology and molecular docking for the identification of key genes and therapeutic targets of Nigella sativa in multiple sclerosis treatment. Biomedicine 43: 936-944. https://doi.org/10.51248/.v43i3.2867 ![]() |
[21] |
Bhowmik R, Nath R, Sharma S, et al. (2022) High-throughput screening and dynamic studies of selected compounds against SARS-CoV-2. Int J Appl Pharm 14: 251-260. ![]() |
[22] | Alabdali AYM, Chinnappan S, Abd Razik BM, et al. Impact of COVID-19 on multiple body organ failure: A review (2021) 54-59. |
[23] |
Abdeen S, Bdeir K, Abu-Fanne R, et al. (2021) Alpha-defensins: Risk factor for thrombosis in COVID-19 infection. Br J Haematol 194: 44-52. https://doi.org/10.1111/bjh.17503 ![]() |
[24] |
de Seabra Rodrigues Dias IR, Cao Z, Kwok HF (2022) Adamalysins in COVID-19–Potential mechanisms behind exacerbating the disease. Biomed Pharm 150: 112970. https://doi.org/10.1016/j.biopha.2022.112970 ![]() |
[25] |
Barkas F, Milionis H, Anastasiou G, et al. (2021) Statins and PCSK9 inhibitors: What is their role in coronavirus disease 2019?. Med Hypotheses 146: 110452. https://doi.org/10.1016/j.mehy.2020.110452 ![]() |
[26] |
Wang J, Li Q, Yin Y, et al. (2020) Excessive neutrophils and neutrophil extracellular traps in COVID-19. Front Immunol 11: 2063. https://doi.org/10.3389/fimmu.2020.02063 ![]() |
[27] |
Kouidou S, Malousi A, Andreou AZ (2020) Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection: Triggering a lethal fight to keep control of the ten-eleven translocase (TET)-associated DNA demethylation?. Pathogens 9: 1006. https://doi.org/10.3390/pathogens9121006 ![]() |
[28] |
Couto N, Al-Majdoub ZM, Gibson S, et al. (2020) Quantitative proteomics of clinically relevant drug-metabolizing enzymes and drug transporters and their intercorrelations in the human small intestine. Drug Metab Dispos 48: 245-254. https://doi.org/10.1124/dmd.119.089656 ![]() |
[29] | Hachim M, Hachim I, Hannawi S (2022) Killer cell lectin-like receptor subfamily B member 1 (KLRB1), lymphocyte function-associated antigen 3 (CD58), and Niban1 (FAM129A) are unique markers for developed (central and effector) memory T cells in rheumatoid arthritis. Saudi Med J 43: 646. |
[30] |
Mpekoulis G, Kalliampakou KI, Milona RS, et al. (2022) Significance of catecholamine biosynthetic/metabolic pathway in SARS-CoV-2 infection and COVID-19 severity. Cells 12: 12. https://doi.org/10.3390/cells12010012 ![]() |
[31] |
Hou X, Wang G, Fan W, et al. (2021) T-cell receptor repertoires as potential diagnostic markers for patients with COVID-19. Int J Infect Dis 113: 308-317. https://doi.org/10.1016/j.ijid.2021.10.033 ![]() |
[32] |
Liang X, Li P, Jiang J, et al. (2023) Transcriptomics unveils immune metabolic disruption and a novel biomarker of mortality in patients with HBV-related acute-on-chronic liver failure. JHEP Rep 5: 100848. https://doi.org/10.1016/j.jhepr.2023.100848 ![]() |
[33] |
Rombauts A, Bódalo Torruella M, Abelenda-Alonso G, et al. (2023) Dynamics of gene expression profiling and identification of high-risk patients for severe COVID-19. Biomedicines 11: 1348. https://doi.org/10.3390/biomedicines11051348 ![]() |
[34] |
Rofeal M, Abd El-Malek F (2020) Ribosomal proteins as a possible tool for blocking SARS-CoV 2 virus replication for a potential prospective treatment. Med Hypotheses 143: 109904. https://doi.org/10.1016/j.mehy.2020.109904 ![]() |
[35] |
Huang R, Meng T, Zha Q, et al. (2021) The predicting roles of carcinoembryonic antigen and its underlying mechanism in the progression of coronavirus disease 2019. Crit Care 25: 234. https://doi.org/10.1186/s13054-021-03661-y ![]() |
[36] |
Chan CM, Chu H, Wang Y, et al. (2016) Carcinoembryonic antigen-related cell adhesion molecule 5 is an important surface attachment factor that facilitates entry of Middle East respiratory syndrome coronavirus. J Virol 90: 9114-9127. https://doi.org/10.1128/jvi.01133-16 ![]() |
[37] |
Parthasarathi A, Padukudru S, Arunachal S, et al. (2022) The role of neutrophil-to-lymphocyte ratio in risk stratification and prognostication of COVID-19: A systematic review and meta-analysis. Vaccines 10: 1233. https://doi.org/10.3390/vaccines10081233 ![]() |
[38] |
Yagin FH, Cicek İB, Alkhateeb A, et al. (2023) Explainable artificial intelligence model for identifying COVID-19 gene biomarkers. Comput Biol Med 154: 106619. https://doi.org/10.1016/j.compbiomed.2023.106619 ![]() |
[39] |
Bouchareb Y, Khaniabadi PM, Al Kindi F, et al. (2021) Artificial intelligence-driven assessment of radiological images for COVID-19. Comput Biol Med 136: 104665. ![]() |
[40] | Došilović FK, Brčić M, Hlupić N (2018) Explainable artificial intelligence: A survey. 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) . https://doi.org/10.23919/MIPRO.2018.8400040 |
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