• Users Online: 804
  • Print this page
  • Email this page

Table of Contents
Year : 2023  |  Volume : 20  |  Issue : 1  |  Page : 90-94

Gene expression of TNF-α among Iraqi COVID-19 patients with a different severity status

1 Virology Unit, Laboratory Division, Ministry of Health, Baghdad Health Directorate, Baghdad, Iraq
2 Department of Biology, Faculty of Science, Ilam University, Ilam, Iran
3 Department of Biology, College of Science, University of Babylon, Hilla City, Iraq; Department of Biology, Faculty of Science, Ilam University, Ilam, Iran
4 Nursing Home Private Hospital, Baghdad Health Directorate, Baghdad, Iraq

Date of Submission10-Nov-2022
Date of Acceptance09-Jan-2023
Date of Web Publication29-Apr-2023

Correspondence Address:
Hussein O M Al-Dahmoshi
Department of Biology, College of Science, University of Babylon, Hilla 51001
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/MJBL.MJBL_270_22

Rights and Permissions

Background: Coronavirus disease 2019 (COVID-19) individuals with varied severity group are affected by the cytokine storm brought on by SARS-CoV2 infection, which is a significant cause of acute respiratory distress syndrome. Objective: The goal of the current study was to examine tumor necrosis factor (TNF-α) gene expression in COVID-19 at various severity levels. Materials and Methods: The study includes 140 divided into 105 COVID-19-positive patients (35 for each mild, moderate, and severe group) and 35 COVID-19-negative healthy people as control. COVID-19 positive patients had 46 males and 59 females, while COVID-19-negative healthy people included 16 males and 19 females. The separation of peripheral blood mononuclear cells (PBMC) was achieved using Ficoll, and then Ribonucleic acid was extracted and converted to cDNA and the gene expression using glyceraldehyde-3-phosphate dehydrogenase as the housekeeping gene. Results: The results revealed non-significant differences at P < 0.05 in age among different COVID-19 groups and control (F-ratio value is 0.54257 and P-value is 0.65397). The results revealed over-expression of TNF-α gene among COVID-19 patients and the relative quantification (fold change) (mean ± standard deviation) values were 6.542 ± 7.29, 5.740 ± 6.41, 7.306 ± 8.85, and 6.580 ± 6.47 for all, mild, moderate, and severe COVID-19 patients, respectively. One-way analysis of variance test relative quantification (fold change) TNF-α (mean ± standard deviation) for mild, moderate, and severe groups revealed non-significant at P < 0.05, the F-ratio value is 0.39889 and the P-value is 0.672109. Conclusion: The present study concludes upregulation of TNF-α gene in PBMC of COVID-19-positive patients without significant differences among different severity groups.

Keywords: COVID-19, gene expression, severity, TNF-, upregulation

How to cite this article:
Farhan SS, Tahmasebi P, Al-Dahmoshi HO, Gatea HS. Gene expression of TNF-α among Iraqi COVID-19 patients with a different severity status. Med J Babylon 2023;20:90-4

How to cite this URL:
Farhan SS, Tahmasebi P, Al-Dahmoshi HO, Gatea HS. Gene expression of TNF-α among Iraqi COVID-19 patients with a different severity status. Med J Babylon [serial online] 2023 [cited 2023 Jun 10];20:90-4. Available from: https://www.medjbabylon.org/text.asp?2023/20/1/90/375129

  Introduction Top

Coronavirus disease 2019 (COVID-19) is the third coronavirus to cause severe respiratory illness in humans, also known as SARS-CoV-2.[1],[2] The World Health Organization declared this to be a pandemic in March 2020, and it has had a significant negative impact on both health impacts and economic conditions in the world.[3],[4] The COVID-19 outbreak has presented significant difficulties for the fields of public health, research, and medicine.[5] The capacity of the SARS-causing CoV-2 to enter cells depends on both the priming of S proteins by the host membrane serine protease TMPRSS2 and the binding of S proteins covering the virion’s surface to the cellular ACE2 receptor. SARS-CoV-2 causes the generation of inflammatory cytokines after infecting respiratory epithelial cells.[6],[7] One of the most significant researches to determine the most effective treatment modalities is the COVID-19 cytokine trial. Because saving patients with severe COVID-19 may depend on preventing and reducing the cytokine storm.[8],[9] Acute respiratory distress syndrome (ARDS) and multiple organ dysfunction syndromes may also arise in COVID-19 individuals with severe respiratory disease.[10],[11]

The term “cytokine storm” describes the excessive production of inflammatory cytokines from a number of organs and cell types (mainly immune cells). It is, in essence, an ongoing, extremely severe activation and attack of the immune system.[12] tumor necrosis factor (TNF-α) is a COVID-19 cytokine storm pro-inflammatory cytokine that is crucial for multiple organ failure, systemic inflammation, and ARDS.[13] The severity of the disease was found to be correlated with cytokine storm or cytokine release syndrome, which is indicated by higher TNF-α.[14],[15]

The aim of this study was to examine TNF-α gene expression in COVID-19 at various severity levels.

  Materials and Methods Top

Sample collection

The 140 blood samples were collected including 105 patients (COVID-19 positive) and 35 healthy control (COVID-19 negative) from COVID-19 healing center at medical city department and nursing home private hospital of Baghdad, Iraq during a period from November 2021 to June 2022. All COVID-19 positive patients were positive for real-time polymerase chain reaction (PCR) for SARS-CoV2. The Blood samples were taken to separate peripheral blood mononuclear cell (PBMC) and transferred to –20°C. Project No: M220501, Date: 18/5/2022.

PBMC separation

Ficoll density gradient method was used to separate PBMC according to Grievink et al.[16] Briefly, 10 mL of peripheral blood was collected with the anticoagulant. The blood was added to the conical Ficoll tube after being diluted to a Phosphate buffer saline-equivalent ratio. The two layers were then separated and centrifuged at 2500 rpm for approximately 25–20 min. Ribonucleic acid (RNA) was extracted from isolated mononuclear cells after they had been washed with Phosphate buffer saline.

RNA extraction

EX6101-RNX plus solution for total RNA isolation (SinaClon, Iran) was used to extract the total RNA from PBMC in accordance with the manufacturer’s instructions. Briefly, at 4°C, samples that had been prepared in the preceding stage were centrifuged at 1000 rpm for 10 min. The samples were mixed with 1 mL of a cold RNX-Plus solution, vortexed for 30 s, and then left to sit at room temperature for 5 min. Each sample was mixed with 200 mL of chloroform and stirred for 15 s before samples were incubated for 5 min on ice. Following that, the samples were centrifuged at 4°C for 4 min at 12,000 rpm. Each sample’s liquid phase was transferred to an RNase-free tube, and an equivalent volume of this phase was then mixed with isopropanol. The samples were incubated for 15 min on ice. The samples were centrifuged once again for 15 min at 4°C using 12,000 rpm. After discarding the supernatant, each sample received 1 mL of 70% ethanol, which was then a vortex. The supernatant was entirely removed after 8 min of centrifugation at 7500 rpm and 4°C, and the precipitate was then dissolved in 50 L of RNase/DNase-free water for cDNA synthesis.

Quantitative analysis of extracted RNA

After RNA extraction, NanoDrop (NP80 Implen, Germany) device was used to quantify it. In this way, 1–2 µL of the sample was placed in the mentioned device and the samples were read at 260/280 and 260/230 wavelengths.[17]

cDNA synthesis SYBR green real-time PCR reaction

According to earlier investigations, specific primers for the genes encoding TNF-, interferon, and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) were chosen.[18] Following that, the primer combination was validated using the Blast tool.[19],[20][Table 1] lists the primers that were used in the current study. The following ingredients were added to a mixture: 1 µL of oligo dT, 1 µL of dNTP, 3 µL of nuclease-free water, and 5 µL of total RNA. Following a 5-minute incubation period at 65 °C, the mixture was chilled for 2 min and then centrifuged. The following elements were added to the previously described combination: 7 µL of nuclease-free water, 1 µL of M-MulV RT enzyme, and 2 µL of M-Mul V buffer. Next, the mixture was incubated for 5 min at 85°C after 60 min at 42°C. The completed product was stored at –70°C after the real-time PCR experiment was complete.
Table 1: Primer used for this study

Click here to view

Gene expression by SYBR green real-time PCR

Using the AMPLIQON kit, the device (Rotor-Gene 6000 Corbett) carried out a real-time RT PCR reaction. The final volume of each reaction was calculated to be 25 µL after adding 12 µL of Masterpix AMPLIQON, 2 µL of cDNA (about 100 ng), 1 µL of particular gene primers (200 nM), and 7.5 µL of water without RNase and DNase. Then the device was subjected to a Real-time PCR thermal program in two stages, the first stage (denaturation) lasting 15 min for one cycle and the second stage (annealing and polymerization) lasting 40 cycles, with each cycle lasting 40 cycles at 95°C for 15 s, 60°C for 25 s, and 72°C for 20 s.[21],[22]

Real-time PCR data analysis

After the reaction is completed, the device displays information; the most important of which is the reaction progression curve and the crossing of the threshold. The gene expression ratio in both groups was determined using the delta-delta Ct (ct) computational approach after all Cts for the TNF-α gene and GAPDH for COVID-19 positive and healthy controls were obtained. The relative rate of increase in the expression of the desired gene in COVID-19 patients and healthy individuals is then computed using the equation relative quantification (RQ) = 2-Ct.[23],[24]

Statistical analysis

The Livak and Schmittgen equation (RQ = 2-Ct) was used to calculate gene expression.[25] analysis of variance analysis was performed to compare groups using one (GraphPad Prism version 8.0.0 for Windows, GraphPad Software, San Diego, CA).

Ethical approval

The research related to human use has been complied with all the relevant national regulations and institutional policies and in accordance the tenets of the Helsinki Declaration, and has been approved by the authors’ institutional review board or equivalent committee. The project no. M220501 was approved at 18/05/2022.

  Results Top

Thirty-five healthy controls and 105 COVID-19 patients (35 each for the mild, moderate, and severe categories) were included in the study (COVID-19 negative). The research indicates no age differences between the various COVID-19 groups and the control group at P 0.05 (F-ratio value is 0.54257 and P-value is 0.65397) [Table 2] and [Table 3]. The results of the melting curve for TNF-α and GAPDH primers show that all primers act specifically and only one peak with a specific melting temperature confirms that the primers used are single products [Figure 1] and [Figure 2].
Table 2: Age of COVID-19 groups

Click here to view
Table 3: One way analysis of variance test age (mean ± standard deviation) for COVID-19 groups

Click here to view
Figure 1: Melt curve for GAPDH showing one product has been amplified

Click here to view
Figure 2: Melt curve for tumor necrosis factor-α showing one product has been amplified

Click here to view

[Figure 3] shows the distribution of ∆Ct TNF-α and RQ value among COVID-19 groups and control. When compared to healthy controls (non-COVID-19), the results of gene expression show that TNF- genes are overexpressed in COVID-19 patients. The relative quantification (fold change) (mean ± standard deviation) was 6.5427.29, 5.7406.41, 7.3068.85, and 6.5806.47 for all, mild, moderate, and severe COVID-19 patients, respectively [Table 4]. The results of the one-way analysis of variance test RQ (fold change) TNF-α (mean ± standard deviation) for mild, moderate and severe groups revealed non-significant at P < 0.05, the F-ratio value is 0.39889 and the P-value is 0.672109 [Table 5].
Figure 3: Distribution of ∆Ct tumor necrosis factor-α and relative quantification value among COVID-19 groups

Click here to view
Table 4: Relative quantification (fold) change for tumor necrosis factor-α among different severity groups

Click here to view
Table 5: One way analysis of variance test relative quantification (fold change) tumor necrosis factor-α (mean ± standard deviation) for mild, moderate and sever groups

Click here to view

  Discussion Top

The current study shows that COVID-19 patients have higher levels of TNF- than healthy controls. The findings were in line with other investigations that discovered patients with COVID-19 had higher levels of TNF-α than adults without the disease.[26],[27] Immune cells such as macrophages, NK cells, T cells, and dendritic cells are thought to become activated as a result of SARS-CoV-2 infection. Due to the activation of many inflammatory pathways, including the TNF-, JAK/STAT signaling, and toll-like receptor pathways, pro-inflammatory cytokines were produced.[28],[29] TNF-α is a key pleiotropic facilitator of both acute and chronic systemic inflammatory responses. It also plays a role in a number of physiological processes, including the immune system’s homeostasis, anti-tumor responses, and inflammation management.[30],[31]

TNF-α expression, which is markedly higher in intensive care unit patients, is crucial in determining how severe the COVID-19 cytokine storm will be. Ang-II has the capacity to stimulate ADAM-17’s enzymatic activity, which raises TNF levels and causes the release of numerous other epidermal growth factors, increasing NF-B activity.[32],[33] During lung injury, it has been discovered that TNF-α expression is elevated, and this causes a variety of biological reactions to be triggered in an effort to control the anomaly by increasing the expression of various additional inflammatory mediators.[34] Hyaluronan-synthase-2 is induced by TNF-α in the lungs of COVID-19 patients, specifically in EpCAM+ lung alveolar epithelium, CD31+ lung alveolar endothelium, and fibroblasts. A major contributor to the fluid inflow in the lung alveoli that leads to deoxygenation and ventilator admission is hyaluronan.[35]

The results of the present study were in agreement with those of Merza et al. from Erbil, Iraq, discovered that the concentrations of IL-1 and TNF- were not differ significantly among various severity groups of COVID-19 patients. The present study revealed non-significant differences in the expression of TNF- among mild, moderate, and severe COVID-19 groups of patients.[36],[37] Additionally, Chen et al. discovered that among patients with severe symptoms, the concentration of TNF-, IL-1, IL-8, and IL-10 was not changed.[38] Given that elevated TNF- was dynamically correlated with disease severity as reported by numerous studies, it is possible that differences in TNF- expression between mild, moderate, and severe groups of COVID-19 patients did not reach statistical significance. Alternatively, the small sample size or inaccurate clinical classification of severity groups may also contribute to this situation.[39],[40]

  Conclusion Top

The present study concludes upregulation of TNF-α gene in PBMC of COVID-19 positive patients without significant differences among different severity groups.


We thank the Advanced Microbiology Lab. at Department of Biology, College of Science, University of Babylon to facilities needed to carry out this study.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

Informed consent statement

Informed consent has been obtained from all individuals included in this study.

  References Top

Shrivastava SR, Shrivastava PS Coronavirus disease-2019 infection among children. Med J Babylon 2022;19:102-4.  Back to cited text no. 1
Dalloo FD, Shukur MS, Taha AO The severity of clinical symptoms and paranasal sinuses CT-scan finding in COVID-19 patients in Kirkuk Province. Med J Babylon 2022;19:459-62.  Back to cited text no. 2
Ahmed MH, Maerozy KM, Mohammad JB, Albarwari N Effect of coronavirus disease 2019 (COVID-19) pandemic on catheterization laboratory activity in Azadi Heart Center, Duhok, Iraq. Med J Babylon 2022;19:21-5.  Back to cited text no. 3
Abed TA, Chabuck ZG The interrelationship between diabetes mellitus and COVID-19. Med J Babylon 2022;19:1-4.  Back to cited text no. 4
Tareq AA, Hameed NM, Abdulshaheed TS Impact of lymphopenia on COVID-19 infection severity. Med J Babylon 2022;19:99-101.  Back to cited text no. 5
Shamsi A, Mohammad T, Anwar S, Amani S, Khan MS, Husain FM, et al. Potential drug targets of SARS-CoV-2: From genomics to therapeutics. Int J Biol Macromol 2021;177:1-9.  Back to cited text no. 6
Buchrieser J, Dufloo J, Hubert M, Monel B, Planas D, Rajah MM, et al. Syncytia formation by SARS-CoV-2-infected cells. EMBO J 2020;39:e106267.  Back to cited text no. 7
Nile SH, Nile A, Qiu J, Li L, Jia X, Kai G LCOVID-19: Pathogenesis, cytokine storm and therapeutic potential of interferons. Cytokine Growth Factor Rev 2020;53:66-70.  Back to cited text no. 8
Feldmann M, Maini RN, Woody JN, Holgate ST, Winter G, Rowland M, et al. Trials of anti-tumour necrosis factor therapy for COVID-19 are urgently needed. Lancet 2020;395:1407-9.  Back to cited text no. 9
Ciotti M, Ciccozzi M, Terrinoni A, Jiang WC, Wang CB, Bernardini S The COVID-19 pandemic. Crit Rev Clin Lab Sci 2020;57:365-88.  Back to cited text no. 10
Ayedee N, Kumar A Indian education system and growing number of online conferences: Scenario under COVID-19. Asian J Manag 2020;11:395-401.  Back to cited text no. 11
Mahmudpour M, Roozbeh J, Keshavarz M, Farrokhi S, Nabipour I COVID-19 cytokine storm: The anger of inflammation. Cytokine 2020;133:155151.  Back to cited text no. 12
Caricchio R, Gallucci M, Dass C, Zhang X, Gallucci S, Fleece D, et al. Preliminary predictive criteria for COVID-19 cytokine storm. Ann Rheum Dis 2021;80:88-95.  Back to cited text no. 13
Guo Y, Hu K, Li Y, Lu C, Ling K, Cai C, et al. Targeting TNF-α for COVID-19: Recent advanced and controversies. Front Public Health 2022;10:833967.  Back to cited text no. 14
Kumar A Emotional intelligence can help healthcare professionals and managers: A way deal COVID-19 pandemic. Asian J Manag 2021;12:353-8.  Back to cited text no. 15
Grievink HW, Luisman T, Kluft C, Moerland M, Malone KE Comparison of three isolation techniques for human peripheral blood mononuclear cells: Cell recovery and viability, population composition, and cell functionality. Biopreserv Biobank 2016;14:410-5.  Back to cited text no. 16
Landolt L, Marti HP, Beisland C, Flatberg A, Eikrem OS RNA extraction for RNA sequencing of archival renal tissues. Scand J Clin Lab Invest 2016;76:426-34.  Back to cited text no. 17
Tao X, Ning Y, Zhao X, Pan T The effects of cordycepin on the cell proliferation, migration and apoptosis in human lung cancer cell lines A549 and NCI-H460. J Pharm Pharmacol 2016;68:901-11.  Back to cited text no. 18
Asp L, Johansson AS, Mann A, Owe-Larsson B, Urbanska EM, Kocki T, et al. Effects of pro-inflammatory cytokines on expression of kynurenine pathway enzymes in human dermal fibroblasts. J Inflamm (Lond) 2011;8:25.  Back to cited text no. 19
Hampel B, Fortschegger K, Ressler S, Chang MW, Unterluggauer H, Breitwieser A, et al. Increased expression of extracellular proteins as a hallmark of human endothelial cell in vitro senescence. Exp Gerontol 2006;41:474-81.  Back to cited text no. 20
Peinnequin A, Mouret C, Birot O, Alonso A, Mathieu J, Clarençon D, et al. Rat pro-inflammatory cytokine and cytokine related mRNA quantification by real-time polymerase chain reaction using SYBR Green. BMC Immunol 2004;5:3.  Back to cited text no. 21
Ahrberg CD, Ilic BR, Manz A, Neužil P Handheld real-time PCR device. Lab Chip 2016;16:586-92.  Back to cited text no. 22
Linja MJ, Savinainen KJ, Saramäki OR, Tammela TL, Vessella RL, Visakorpi T Amplification and overexpression of androgen receptor gene in hormone-refractory prostate cancer. Cancer Res 2001;61:3550-5.  Back to cited text no. 23
Grace MB, McLeland CB, Blakely WF Real-time quantitative RT-PCR assay of GADD45 gene expression changes as a biomarker for radiation biodosimetry. Int J Radiat Biol 2002;78: 1011-21.  Back to cited text no. 24
Livak KJ, Schmittgen TD Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods 2001;25:402-8.  Back to cited text no. 25
Chen G, Wu D, Guo W, Cao Y, Huang D, Wang H, et al. Clinical and immunological features of severe and moderate coronavirus disease 2019. J Clin Invest 2020;130:2620-9.  Back to cited text no. 26
Karki R, Sharma BR, Tuladhar S, Williams EP, Zalduondo L, Samir P, et al. Synergism of TNF-α and IFN-γ triggers inflammatory cell death, tissue damage, and mortality in SARS-CoV-2 infection and cytokine shock syndromes. Cell 2021;184:149-168.e17.  Back to cited text no. 27
Choudhary S, Sharma K, Silakari O The interplay between inflammatory pathways and COVID-19: A critical review on pathogenesis and therapeutic options. Microb Pathog 2021;150:104673.  Back to cited text no. 28
Tan LY, Komarasamy TV, Rmt Balasubramaniam V Hyperinflammatory immune response and COVID-19: A double edged sword. Front Immunol 2021;12:742941.  Back to cited text no. 29
Ragab D, Salah Eldin H, Taeimah M, Khattab R, Salem R The COVID-19 cytokine storm; what we know so far. Front Immunol 2020;11:1446.  Back to cited text no. 30
Negi R, Saharan K, Pillai K Knowledge regarding immunity boosting measures for self-care during covid-19 crisis: Survey of urban community in Haryana. Asian J Nurs Educ Res 2021;11:488-94.  Back to cited text no. 31
Hojyo S, Uchida M, Tanaka K, Hasebe R, Tanaka Y, Murakami M, et al. How COVID-19 induces cytokine storm with high mortality. Inflam Regen 2020;40:37.  Back to cited text no. 32
Chann AAK, Jyoti D, Kataria M, Pebma KM, Thakur JS Health outcomes and comorbidities among Covid-19 patients from a Peri urban community of Chandigarh. Asian J Nurs Edu Res 2022;12:235-8.  Back to cited text no. 33
Thomson EM, Williams A, Yauk CL, Vincent R Overexpression of tumor necrosis factor-α in the lungs alters immune response, matrix remodeling, and repair and maintenance pathways. Am J Pathol 2012;180:1413-30.  Back to cited text no. 34
Shi Y, Wang Y, Shao C, Huang J, Gan J, Huang X, et al. COVID-19 infection: The perspectives on immune responses. Cell Death Differ 2020;27:1451-4.  Back to cited text no. 35
Merza MY, Hwaiz RA, Hamad BK, Mohammad KA, Hama HA, Karim AY Analysis of cytokines in SARS-CoV-2 or COVID-19 patients in Erbil city, Kurdistan Region of Iraq. PLoS One 2021;16:e0250330.  Back to cited text no. 36
Talele SG, Ahire ED, Surana KR, Sonawane VN, Talele GS Corona virus disease (COVID-19): A past and present prospective. Asian J Pharm Res 2022;12:45-53.  Back to cited text no. 37
Chen L, Liu HG, Liu LW, Liu J, Shang KJ, et al. Analysis of clinical features of 29 patients with 2019 novel coronavirus pneumonia. Zhonghua Jie He He Hu Xi Za Zhi 2020;43:E005.  Back to cited text no. 38
Mehta P, McAuley DF, Brown M, Sanchez E, Tattersall RS, Manson JJ, et al. COVID-19: Consider cytokine storm syndromes and immunosuppression. Lancet 2020;395:1033-4.  Back to cited text no. 39
Chen X, Zhao B, Qu Y, Chen Y, Xiong J, Feng Y, et al. Detectable serum severe acute respiratory syndrome coronavirus 2 viral load (RNAemia) is closely correlated with drastically elevated interleukin 6 level in critically ill patients with coronavirus disease 2019. Clin Infect Dis 2020;71:1937-42.  Back to cited text no. 40


  [Figure 1], [Figure 2], [Figure 3]

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]


    Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
    Access Statistics
    Email Alert *
    Add to My List *
* Registration required (free)  

  In this article
Materials and Me...
Article Figures
Article Tables

 Article Access Statistics
    PDF Downloaded34    
    Comments [Add]    

Recommend this journal