Clinical Investigation of Lymphocyte DNA Damage in COVID-19 Patients

doi:10.21203/rs.3.rs-2095176/v1

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Clinical Investigation of Lymphocyte DNA Damage in COVID-19 Patients

https://doi.org/10.21203/rs.3.rs-2095176/v1

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Background: This prospective cross-sectional study, aimed to evaluate lymphocyte DNA damage in COVID-19 patients. In this prospective cross-sectional prospective cohort study, 50 COVID-19- positive patients attending Erzurum City Hospital Internal Medicine Outpatient Clinic and 42 control group patients were included.

Methods: DNA damages were detected in living cells by lymphocyte isolation in 50 COVID-19- positive patients using the comet assay method. DNA tail/head (olive) moments were evaluated and compared. White blood cell (WBC), red blood cell (RBC), hemoglobin (HGB), neutrophil (NEU), lymphocyte (LYM), eosinophil (EO), monocyte (MONO), basophil (BASO), platelet (PLT), neutrophil/lymphocyte ratio (NLR).

Results: The RBC, lymphocyte, eosinophil, and monocyte means were significantly higher in the control group (p < 0.05). Whereas HGB and neutrophile means were significantly higher in the study group (p < 0.05). There were significant negative correlations between COVID-19 and RBC (r = -0.863), LYM (r = -0.542), EO (r = -0.686), and MONO (r = -0.385). Meanwhile, there were significant positive correlations between COVID-19 and HGB (r = 0.863), NEU (r = 0.307), tail moment (r = 0.598), and olive moment (r = 0.582).

Conclusion: Both the tail and olive moment mean differences were significantly higher in the study group with higher ranges (p < 0.05). COVID-19 infection statistically significant is increasing both the tail and olive damage percentage in patients, causing DNA damage. Lastly, the NLR rate was associated with the presence and progression of COVID-19.

COVID-19

Comet assay

DNA damage

Lymphocyte

Lymphopenia

Neutrophil/lymphocyte ratio (NLR).

COVID-19 is a respiratory disease that has turned into a global pandemic and has caused numerous deaths. The incubation period of COVID-19 is five days on average, and the first symptoms appear in 11.5 days in 97.5% of patients [1]. The most common physical symptoms of COVID-19 include fever, cough, dyspnea, myalgia, and fatigue [2–3]. The hospitalization rate of COVID-19- positive patients is 20.7–31.4%, the rate of inpatients in the intensive care unit is 4.9–11.5%, and the mortality rate is 1.8–-3.4% [4]. Studies show that laboratory tests are crucial in terms of recovery rates, severity, malignancy, and treatment follow-up in patients with COVID-19. In patients with COVID-19, especially lymphopenia, thrombocytopenia, and hypoalbuminemia, and increases in D-dimer, CRP, creatinine, aminotransferases, cardiac troponins, total bilirubin, erythrocyte sedimentation rate, prothrombin time, and procalcitonin values come to the fore. These clinical features provide information about the diagnosis and prognosis of the disease [5].

Lymphopenia is a condition in which the number of lymphocytes in the blood is low, and there are studies report that it is associated with the severity of COVID-19. Lymphopenia was detected in 85% of patients with severe COVID-19 [2, 6–7]. Moreover, studies reporting that individuals who died from COVID-19 also had a significantly lower lymphocyte count than survivors [6]. As a lymphopenia criterion in COVID-19- positive patients, those with a lymphocyte count 1.1x109/L were included in the study in four studies [7–10]. In one study, patients with less than a lymphocyte count below 1.5x109/L were evaluated [11]. Although the number of CD4 + and CD8 + T lymphocytes increased in the early stages of the disease [12], it was found that the number of lymphocytes decreased in the following days [7]. In another study, although NK and CTL cell numbers were significantly reduced in COVID-19 patients, the number of these cells were observed to increase again after recovery [13]. Further, it has been shown that the total number of CD8 + and CD4 + T lymphocytes is considerably reduced in patients with COVID-19 infection, especially in those over 60 years of age and in need of intensive care [14]. The genomic similarity of SARS-CoV and SARS-CoV-2 is approximately 88%. SARS-CoV infection has been shown to cause lymphopenia by suppressing the bone marrow or by causing immune-mediated destruction of lymphocytes [15]. SARS-CoV-2 is likely to affect the immune system with a similar internal mechanism as the SARS-CoV virus.

Acute respiratory distress syndrome (ARDS) is the main cause of death caused by COVID-19, and one of its main features is cytokine storm. Cytokine storm is defined as an uncontrolled and fatal systemic inflammatory response resulting from the high production of proinflammatory cytokines such as TNF-α, IL‐6, IL‐1β, and IFN‐α. The inability to eliminate infection and viral contaminated cells leads to an uncontrolled activation of the immune system by the overproduction of different cytokines. These hyperactivated cells can infiltrate various organs and produce extra cytokines and cause clinical symptoms [16–17]. Many studies have confirmed that increased proinflammatory cytokines play a critical role in the induction of lymphopenia. Recent studies, indicate that cytokine storm syndrome causes lymphopenia in COVID-19 patients [16]. It has also been found that COVID-19 patients admitted to intensive care units have higher proinflammatory cytokine concentrations and that the secretion of helper T lymphocyte (Th2) group cytokines that suppress inflammation increases [2]. Among the cases admitted to intensive care units, various inflammatory cytokines and chemokines tumor necrosis factor alpha (TNF-α), interferon-ƴ-induced protein 10 (IP-10), monocyte chemoattractant protein 1 (MCP-1), chemokine (CC motif), ligand 3 (CCL-3) and different interleukins (IL-2, IL-6, IL-7, IL-10 etc. were found to be associated with disease severity [2, 17–21]. Interestingly, lymphopenia with hypercytokinemia was also found prominent in severe patients with SARS-CoV [16, 22–23].

Studies have suggested that cytokines may not be the sole cause of lymphopenia and that more than one mechanism may work together to cause lymphopenia [2, 11]. SARS-CoV-2 can attack lymphocytes directly or destroy lymphoid organs. Since blood lactic acid levels are elevated in patients with the severe COVID-19 phenotype, lymphopenia may also be induced by such metabolic molecules. Although there is no study on the induction of apoptosis of NK cells and T cells by SARS CoV2, it is thought that viral-based factors may cause lymphopenia by inducing early apoptosis [24]. As noted previously, proinflammatory cytokines can induce apoptosis in T cells, especially in chronic viral infections [16]. The factors causing lymphopenia in this context have not been elucidated in the literature, and it is thus unclear as to which mechanisms lead lymphocytes to apoptosis.

DNA damage results in changes in the basic structure of DNA that disrupt the replication mechanism. DNA damage can be caused by the addition of chemical groups to a DNA base, a chemical change (creating an abnormal nucleotide or nucleotide fragment), or a break in one or both strands of the DNA helix. DNA damage occurs spontaneously during normal DNA metabolism or under the influence of environmental factors. A study showed that HIV-1 Tat protein causes DNA damage in human peripheral blood B lymphocytes through mitochondrial reactive oxygen species (ROS) production [25]. In another study, it was revealed that latent hepatitis B virus infection with low viremia caused DNA damage, apoptosis and oxidative stress in peripheral blood lymphocytes [26]. It has also been reported that latent hepatitis C virus triggers mitochondrial oxidative stress in lymphocytes and causes PI3-kinase-mediated DNA damage [27]. The Comet Assay (single-cell gel electrophoresis; SCGE) is a well-known genotoxicity test that is easy to perform. It allows detecting various DNA damages with high sensitivity in eukaryotic cells exposed to genotoxic agents.

In this study, the aim was aimed to evaluate lymphocyte DNA damage in COVID-19.

The present study employed a cross-sectional prospective cohort design. A total of 50 COVID-19-positive patients and 42 control group patients were subjected to the study. The subjects were randomly selected from individuals hospitalized with the diagnosis of COVID-19 in Erzurum City Hospital Internal Medicine Outpatient Clinic. While drawing blood for routine tests from the selected patients, 3 ml of blood was collected in an extra heparin tube for the study. In the study, DNA damages were detected in living cells by lymphocyte isolation in 50 COVID-19 positive patients, including men and women aged 44 and above, and 42 healthy individuals over the age of 42 as the control group. All participants were in included in the the study only after their written consent for participation was obtained through the voluntary consent form. All participants gave informed consent and the local institutional ethics committee approved the study methods.

Total Lymphocyte Isolation

Blood samples were transferred to can tubes and diluted with phosphate buffer solution (PBS) at a one-to-one ratio. in a 15 ml Falcon tube, 3 ml of ficol was placed and diluted blood samples were added to it. Tubes were centrifuged at 2600 rpm for 20 min. The cloud formed by lymphocytes in the middle of the centrifuged tubes was transferred to a different falcon tube using a thin glass pipette. Thereafter, 10 ml of PBS was added to the tubes and centrifuged at 1900 rpm for 5 minutes. The supernatant portion of the tubes was discarded and the above step was repeated. Lymphocytes obtained after centrifugation were stored in cell storage buffer at + 4°C.

DNA Damage Analysis

It was done with a service purchase. Since the desired amount of patients and control groups could not be collected due to difficulties in sample collection. There were difficulties in the isolation of T and B lymphocytes, since the lymphocyte level in the blood of COVID-19 patients were very low. It was difficult to find patients with high lymphocyte levels. Some of the collected samples stored in the − 80°C deep freeze. DNA damage could be studied in approximately half as many samples as expected.

Preparation of Slides

0.65% prepared agarose gel (HMA) was put into a beaker and heated on the hot plate at 300°C. Then, the grounded slides were quickly dipped into this beaker and removed, and the slides were covered with HMA.

HMA Coated Lama Spread of Cells

100 µL of 0.65% LMA prepared in PBS at 37°C was added into 100 µL of isolated leukocytes and mixed. This mixture was then spread on slides coated with HMA. A coverslip was covered and kept in the refrigerator for 30 minutes to allow the agar to freeze.

Lysis of Cells

After leukocytes were embedded in agarose gel on a slide, the preparations were incubated in a freshly prepared cold lysis solution of high concentration salt and detergent for 1 hour at + 4°C.

Electrophoresis and Neutralization

The slides removed from the lysis buffer were placed in the chalet containing the electrophoresis buffer and incubated at + 4°C for 30 min. After incubation, the preparations were placed in the electrophoresis tank and carried out in an electrical environment at 15 Volts for 30 minutes. After electrophoresis, the slides were placed in a chalet containing neutralization buffer and the slides were washed three times with neutralization 17 buffer. Then the slides were fixed by passing through a series of 50%, 75% and 100% cold ethanol.

Staining

The Slides were stained with ethidium-bromide, and imaging was performed within 30 minutes immediately after staining.

Imaging

A total of 100 cells were photographed per patient by taking 10 separate photographs from an average of 10 different fields randomly selected for each sample at x 20 magnification under a fluorescent microscope.

Evaluation

Tail/head density in each cell was analyzed according to criteria and the mean % damage values (Tail Moment and Olive Moment values) were recorded.

Statistical Methods

Gender distribution was described as frequency and difference analysis was performed by using Fischer’s Exact Test. Other research variables were described with means and standard deviations. The Kolmogorov Smirnov test was used for normality of scale parameters. The Mann Whitney U test was used for non-normally distributed data, and the Mann Whitney U test was utilized for normally distributed data. Spearman’s rho correlation was used for relationship analysis. Lastly, SPSS 17.0 for Windows was used for analysis at 95% Confidence Interval with 0.05 alpha level.

Gender, age, WBC, basophile and PLT differences between the study and control groups were not statistically significant (p > 0.05). The RBC, lymphocyte, eosinophil and monocyte means were significantly higher in the control group (p < 0.05), whereas the HGB and neutrophil means were significantly higher in the study group (p < 0.05) (Table 1).

Table 1

Baseline and clinic characteristics of patient groups. a. Fischer’s Exact Test, b. Independent Samples T-test, c. Mann Whitney U Test, SD: Standard Deviation, WBC: White Blood Cell, RBC: Red Blood Cell, HGB: Hemoglobin, NEU: Neutrophil, LYM: Lymphocyte, EO: Eosinophil, MONO: Monocyte, BASO: Basophil, PLT: Platelet, NLR: Neutrophil/lymphocyte ratio.
Group	Control (n = 42)	Study (n = 50)	p value
Gender, n (I%)
Male	31 (73.8)	30 (60.0)	0.120^a
Female	11 (26.2)	20 (40.0)
Age	59.29 ± 16.99	62.36 ± 18.18	0.408^b
WBC	9.38 ± 5.02	10.08 ± 4.18	0.116^c
RBC	10.24 ± 2.25	4.56 ± 0.76	< 0.05^c
HGB	3.60 ± 0.80	12.91 ± 2.09	< 0.05^c
NEU	6.51 ± 4.38	8.45 ± 4.15	0.032^b
LYM	1.72 ± 0.73	0.99 ± 0.86	< 0.05^c
EO	0.29 ± 0.69	0.03 ± 0.07	< 0.05^c
MONO	0.83 ± 0.55	0.53 ± 0.29	0.001^b
BASO	0.04 ± 0.08	0.02 ± 0.02	0.645^c
PLT	226.67 ± 95.54	236.88 ± 79.49	0.577^b
NLR	4.61 ± 3.79	15.62 ± 17.56	< 0.05^c

Both tail and olive moment means were significantly higher in the study group (p < 0.05) (Table 2).

Table 2

Tail and olive moment levels of the control and the study group. a. Mann Whitney U Test, SD: Standard Deviation.
Group	Control (n = 42)	Study (n = 50)	p^a value
Tail moment	10.30 ± 11.38	30.00 ± 21.21	< 0.05
Olive moment	5.99 ± 4.28	13.87 ± 11.64	< 0.05

The Spearman’s rho correlation analysis results showed that there were significant negative correlations between COVID-19 and RBC (r=-0.863; p < 0.01), LYM (r=-0.542; p < 0.01), EO (r=-0.686; p < 0.01) and MONO (r=-0.385; p < 0.01). Meanwhile, there were significant positive correlations between COVID-19 and HGB (r = 0.863; p < 0.01), NEU (r = 0.307; p < 0.01), tail moment (r = 0.598; p < 0.01) and olive moment (r = 0.582; p < 0.01) (Table 3).

Table 3

Spearman’s rho correlation analysis results between COVID-19 and significantly different parameters. **p < 0.01, RBC: Red Blood Cell, HGB: Hemoglobin, NEU: Neutrophil, LYM: Lymphocyte, EO: Eosinophil, MONO: Monocyte, NLR: Neutrophil/lymphocyte ratio.
Group	r	p
RBC	-0.863^**	< 0.05
HGB	0.863^**	< 0.05
NEU	0.307^**	0.003*
LYM	-0.542^**	< 0.05
EO	-0.686^**	< 0.05
MONO	-0.385^**	< 0.05
Tail moment	0.598^**	< 0.05
Olive moment	0.582^**	< 0.05
NLR	0.525^**	< 0.05

The correlation analysis results showed that tail moment was positively correlated with COVID-19 (r = 0.598; p < 0.01) and HGB (r = 0.573; p < 0.01), but negatively correlated with RBC (r=-0.489; p < 0.01), LYM (r=-0.288; p < 0.01) and EO (r=-0.373; p < 0.01). Olive moment was positively correlated with COVID-19 (r = 0.582; p < 0.01), WBC (r = 0.210; p < 0.05), HGB (r = 0.535; p < 0.01) and NEU (r = 0.263; p > 0.05). Meanwhile, tail moment was negatively correlated with RBC (r=-0.484; p < 0.01), LYM (r=-0.281; p < 0.01) and EO (r=-0.385; p < 0.01) (Table 4).

Table 4

Correlation between baseline characteristics, tail moment, and olive moment. *p < 0.05 **p < 0.01 WBC: White Blood Cell, RBC: Red Blood Cell, HGB: Hemoglobin, NEU: Neutrophil, LYM: Lymphocyte, EO: Eosinophil, MONO: Monocyte, BASO: Basophil, PLT: Platelet, NLR: Neutrophil/lymphocyte ratio
	Tail moment	Olive moment
COVID-19	0.598^**	0.582^**
Gender	0.058	0.107
Age	0.018	0.057
WBC	0.147	0.210^*
RBC	-0.489^**	-0.484^**
HGB	0.573^**	0.535^**
NEU	0.201	0.263^*
LYM	-0.288^**	-0.281^**
EO	-0.373^**	-0.385^**
MONO	-0.154	-0.124
BASO	0.062	0.075
PLT	-0.054	-0.075
NLR	0.297^**	0.330^**

Both tail and olive moment mean differences were significantly higher in the study group with higher ranges (Fig. 1 and Fig. 2).

The comet assay results showed that tail deformations in the COVID-19 group were definite compared with the control group. To provide an objective comparison, the damage sample were selected (Fig. 3).

Although COVID-19 has been examined based on different subjects and aspects in relevant literature, hemogram values were included in a large part of the studies. In these studies, statistically significant changes were observed in the hemogram structures of patients after COVID-19 in general [28–32]. In our study, HGB and NEU values were significantly higher in the COVID-19 group, whereas the mean of RBC, LYM, EO, MONO and BASO were significantly higher statistically in the control group. In this respect, it can be stated that there is a concordance between the results obtained in the study and the results reported in the literature.

The number of genetic studies related to COVID-19 is small. it has been done. These studies suggest that there is a relationship between COVID-19 and genetic structure in general and that the COVID-19 virus destroys in the genetic structure [33–35]. However, among these studies, none examined direct DNA damage hence, there is inadequate data to directly compare our research in this regard. According to the data obtained in our study, both tail moment and olive moment averages were statistically significantly higher in the COVID-19 group. When the distributions of tail and olive moment values were examined, the range of variation was higher in the COVID-19 group for both variables. In general, the tail moment change interval was greater than the olive moment variation interval in both groups. In other words, the DNA damage level in the tail part was more pronounced than that in the body part.

Neutrophil/lymphocyte ratio (NLR) is an indicator of acute inflammation, and many studies are currently examining whether the NLR has diagnostic value against various diseases. In these studies, NLR generally contributes to much more effective results than the diagnostic value or clinical predictive value of neutrophil or lymphocyte alone. On the other hand, since NLR is also an indicator of acute inflammation, the effect of steroid use on NLR should also be considered. In addition, there have been studies in the literature examining the relationship between COVID-19 and NLR. Macchi et al. [36] reported that NLR is an indicator of cytokine storm that COVID-19 causes cytokine storm, therefore the NLR level may have diagnostic value for COVID-19. In another study, Qin et al [37] reported that NLR has prognostic value in determining the severity of COVID-19.

In our study, the relationships between COVID-19 and RBC, HGB, NEU, LYM, EO, MONO, tail moment, olive moment and NLR were statistically significant. Among these correlations, the direction of the relationships between COVID-19 and RBC, LYM, EO and MONO was negative, whereas the direction of the relationships between COVID-19 and HGB, NEU, tail moment, olive moment and NLR was positive. While the increase in the NLR level may be an indicator of acute inflammation, the change in blood values can also be expressed by the hematological changes during the COVID-19 treatment. However, the fact that tail moment and olive moment changes were significantly related to COVID-19 and the direction of this relationship being positive mean that DNA damage occurs in both the tail and body parts of COVID-19 patients.

According to the results obtained in the present study, the relationships between tail moment and COVID-19, RBC, HGB, LYM, EO and NLR were significant. The relationship between COVID-19, HGB and NLR and tail moment was positive, while the relationship between RBC, LYM, EO and tail moment was negative. The relationship between olive moment and COVID-19, WBC, RBC, HGB, NEU, LYM, EO and NLR was significant. Among these relationships, those between COVID-19, WBC, HGB, NEU and NLR were positive, while the others were negative. Compared with tail moment, olive moment had a greater effect on hemogram value. On the other hand, COVID-19 affected tail moment more than olive moment. This supports the expected hypothesis put forward in our study. While COVID-19 causes serious DNA damage in patients, the damage is much more evident in the DNA tail region. Meanwhile, considering that the structure of the COVID-19 virus is mutated and constantly changing, it would be beneficial to evaluate this effect together with new mutations and variations.

The COVID-19 pandemic has affected many domains, including daily life, and caused many deaths. In this context, knowing and understanding the structure of the COVID-19 virus is important for vaccine studies, while comprehending the mechanism of action in patients infected with the virus will contribute to preventing the disease’s undesirable effects. Although many studies have been conducted on vaccine and prevention during the acute period of the pandemic, those focusing on the effects of the disease have been relatively lacking. Therefore, studies on the short and long term effects of COVID-19 on the human body are needed. In this respect, this study can be a foundation for post-COVID-19 DNA and genetic studies. Thus, the study makes an important contribution to the literature.

Our study has strengths as well as limitations. In the literature review a study related to COVID-19 and DNA damage such as this was identified to be the first of its kind and if it published, its impact value would be quite high. However, difficulties in sample collection and isolation of live lymphocytes limited the study. Nonetheless, highly statistically significant and valuable results were obtained.

Research results showed that COVID-19 infection led to a statistically significant increase in both tail and olive damage percentage in patients, causing DNA damage. In the study, the NLR was associated with the presence and progression of COVID-19. In future studies, conducting research on a larger sample and examining the effect of DNA damage on different types of COVID-19 mutations and different demographic groups would be beneficial.

Acknowledgements

Not applicable.

Ethics approval and consent to participate

All participants gave informed consent and the local institutional ethics committee approved the study methods (Ataturk University Faculty of Medicine Clinical Research Ethical Box. No: B.30.2.ATA.0.01.00/62)" ‘Declarations Section’- ‘Ethics approval and consent to participate’ sub-section.

We declare that written consent has been obtained from all participants.

Consent for publication

Not applicable.

Availability of data and materials

All data analysed during this study are included in this published article.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by the Atatürk University BAP [Grant TSA-2021-8869

number].

Author Contributions

Conceptualization: HD and EÇ. Data curation: EB and AK. Formal analysis: MAG, MK and MA. Funding acquisition: HD and AK. Writing: HD and EÇ contributed equally to this work. All authors read and approved the final manuscript.

Author informations

¹Department of Medical Biology, Faculty of Medicine, Ataturk University, Erzurum, Turkey

²Department of Internal Medicine, Erzurum Regional Training and Research Hospital, Health Sciences University, Erzurum, Turkey.

³Department of Nephrology, Faculty of Medicine, Ataturk University, Erzurum, Turkey

⁴Department of Histology and Embryology, Faculty of Medicine, Ataturk University, Erzurum, Turkey

Lauer SA, Grantz KH, Bi Q, Jones FK, Zheng Q, Meredith HR, Azman AS, Reich NG, Lessler J. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Ann Intern Med. 2020;172:577–82.
Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, Xiao Y, Gao H, Guo L, Xie J, Wang G, Jiang R, Gao Z, Jin Q, Wang J, Cao B. Clinical features of patients infected with 2019 novel coronavirus in Wuhan. China Lancet. 2020;395:497–506.
Rodriguez-Morales AJ, Cardona-Ospina JA, Gutiérrez-Ocampo E, Villamizar-Peña R, Holguin-Rivera Y, Escalera-Antezana JP, Alvarado-Arnez LE, Bonilla-Aldana DK, Franco-Paredes C, Henao-Martinez AF, Paniz-Mondolfi A, Lagos-Grisales GJ, Ramírez-Vallejo E, Suárez JA, Zambrano LI, Villamil-Gómez WE, Balbin-Ramon GJ, Rabaan AA, Harapan H, Dhama K, Nishiura H, Kataoka H, Ahmad T, Sah R. Latin American Network of Coronavirus Disease 2019-COVID-19 Research (LANCOVID-19). Electronic address: https://www.lancovid.org. Clinical, laboratory and imaging features of COVID-19: A systematic review and meta-analysis. Travel Med Infect Dis 2020;34:101623.
CDC COVID-19 Response Team. Severe Outcomes Among Patients with Coronavirus Disease 2019 (COVID-19) - United States, February 12-March 16, 2020. MMWR Morb Mortal Wkly Rep. 2020;69:343–6.
Henry BM. COVID-19, ECMO, and lymphopenia: a word of caution. Lancet Respir Med. 2020;8:e24.
Ruan Q, Yang K, Wang W, Jiang L, Song J. Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Med. 2020;46:846–8.
Yang X, Yu Y, Xu J, Shu H, Xia J, Liu H, Wu Y, Zhang L, Yu Z, Fang M, Yu T, Wang Y, Pan S, Zou X, Yuan S, Shang Y. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. Lancet Respir Med. 2020;8:475–81.
Liu W, Tao ZW, Wang L, Yuan ML, Liu K, Zhou L, Wei S, Deng Y, Liu J, Liu HG, Yang M, Hu Y. Analysis of factors associated with disease outcomes in hospitalized patients with 2019 novel coronavirus disease. Chin Med J (Engl). 2020;133:1032–8.
Liu Y, Yang Y, Zhang C, Huang F, Wang F, Yuan J, Wang Z, Li J, Li J, Feng C, Zhang Z, Wang L, Peng L, Chen L, Qin Y, Zhao D, Tan S, Yin L, Xu J, Zhou C, Jiang C, Liu L. Clinical and biochemical indexes from 2019-nCoV infected patients linked to viral loads and lung injury. Sci China Life Sci. 2020;63:364–74.
Zhang JJ, Dong X, Cao YY, Yuan YD, Yang YB, Yan YQ, Akdis CA, Gao YD. Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan, China. Allergy. 2020;75:1730–41.
Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, Liu L, Shan H, Lei CL, Hui DSC, Du B, Li LJ, Zeng G, Yuen KY, Chen RC, Tang CL, Wang T, Chen PY, Xiang J, Li SY, Wang JL, Liang ZJ, Peng YX, Wei L, Liu Y, Hu YH, Peng P, Wang JM, Liu JY, Chen Z, Li G, Zheng ZJ, Qiu SQ, Luo J, Ye CJ, Zhu SY, Zhong NS. China Medical Treatment Expert Group for Covid-19. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med. 2020;382:1708–20.
Thevarajan I, Nguyen THO, Koutsakos M, Druce J, Caly L, van de Sandt CE, Jia X, Nicholson S, Catton M, Cowie B, Tong SYC, Lewin SR, Kedzierska K. Breadth of concomitant immune responses prior to patient recovery: a case report of non-severe COVID-19. Nat Med. 2020;26:453–5.
Zheng M, Gao Y, Wang G, Song G, Liu S, Sun D, Xu Y, Tian Z. Functional exhaustion of antiviral lymphocytes in COVID-19 patients. Cell Mol Immunol. 2020;17:533–5.
Diao B, Wang C, Tan Y, Chen X, Liu Y, Ning L, Chen L, Li M, Liu Y, Wang G, Yuan Z, Feng Z, Zhang Y, Wu Y, Chen Y. Reduction and Functional Exhaustion of T Cells in Patients With Coronavirus Disease 2019 (COVID-19). Front Immunol. 2020;11:827.
He Z, Zhao C, Dong Q, Zhuang H, Song S, Peng G, Dwyer DE. Effects of severe acute respiratory syndrome (SARS) coronavirus infection on peripheral blood lymphocytes and their subsets. Int J Infect Dis. 2005;9:323–30.
Fathi N, Rezaei N. Lymphopenia in COVID-19: Therapeutic opportunities. Cell Biol Int. 2020;44:1792–7.
Chen L, Liu HG, Liu W, Liu J, Liu K, Shang J, Deng Y, Wei S. Analysis of clinical features of 29 patients with 2019 novel coronavirus pneumonia. Zhonghua Jie He He Hu Xi Za Zhi. 2020;43:E005.
Wang Z, Yang B, Li Q, Wen L, Zhang R. Clinical Features of 69 Cases With Coronavirus Disease 2019 in Wuhan, China. Clin Infect Dis. 2020;71:769–77.
Mehta P, McAuley DF, Brown M, Sanchez E, Tattersall RS, Manson JJ. HLH Across Speciality Collaboration, UK. COVID-19: consider cytokine storm syndromes and immunosuppression. Lancet. 2020;395:1033–4.
Gao Y, Li T, Han M, Li X, Wu D, Xu Y, Zhu Y, Liu Y, Wang X, Wang L. Diagnostic utility of clinical laboratory data determinations for patients with the severe COVID-19. J Med Virol. 2020;92:791–6.
Mo P, Xing Y, Xiao Y, Deng L, Zhao Q, Wang H, Xiong Y, Cheng Z, Gao S, Liang K, Luo M, Chen T, Song S, Ma Z, Chen X, Zheng R, Cao Q, Wang F, Zhang Y. Clinical Characteristics of Refractory Coronavirus Disease 2019 in Wuhan, China. Clin Infect Dis. 2021;73:e4208–13.
Cameron MJ, Ran L, Xu L, Danesh A, Bermejo-Martin JF, Cameron CM, Muller MP, Gold WL, Richardson SE, Poutanen SM, Willey BM, DeVries ME, Fang Y, Seneviratne C, Bosinger SE, Persad D, Wilkinson P, Greller LD, Somogyi R, Humar A, Keshavjee S, Louie M, Loeb MB, Brunton J, McGeer AJ. Canadian SARS Research Network, Kelvin DJ. Interferon-mediated immunopathological events are associated with atypical innate and adaptive immune responses in patients with severe acute respiratory syndrome. J Virol. 2007;81:8692–706.
Lee N, Hui D, Wu A, Chan P, Cameron P, Joynt GM, Ahuja A, Yung MY, Leung CB, To KF, Lui SF, Szeto CC, Chung S, Sung JJ. A major outbreak of severe acute respiratory syndrome in Hong Kong. N Engl J Med. 2003;348:1986–94.
Tan L, Wang Q, Zhang D, Ding J, Huang Q, Tang YQ, Wang Q, Miao H. Lymphopenia predicts disease severity of COVID-19: a descriptive and predictive study. Signal Transduct Target Ther. 2020;5:33.
El-Amine R, Germini D, Zakharova VV, Tsfasman T, Sheval EV, Louzada RAN, Dupuy C, Bilhou-Nabera C, Hamade A, Najjar F, Oksenhendler E, Lipinski M, Chernyak BV, Vassetzky YS. HIV-1 Tat protein induces DNA damage in human peripheral blood B-lymphocytes via mitochondrial ROS production. Redox Biol. 2018;15:97–108.
Bhargava A, Khan S, Panwar H, Pathak N, Punde RP, Varshney S, et al. Occult hepatitis B virus infection with low viremia induces DNA damage, apoptosis and oxidative stress in peripheral blood lymphocytes. Virus Res. 2010;153(1):143–50.
Bhargava A, Khan S, Panwar H, Pathak N, Punde RP, Varshney S, Mishra PK. Occult hepatitis B virus infection with low viremia induces DNA damage, apoptosis and oxidative stress in peripheral blood lymphocytes. Virus Res. 2010;153:143–50.
Ghaebi M, Tahmasebi S, Jozghorbani M, Sadeghi A, Thangavelu L, Zekiy AO, Esmaeilzadeh A. Risk factors for adverse outcomes of COVID-19 patients: Possible basis for diverse responses to the novel coronavirus SARS-CoV-2. Life Sci. 2021;277:119503.
Gu X, Sha L, Zhang S, Shen D, Zhao W, Yi Y. Neutrophils and Lymphocytes Can Help Distinguish Asymptomatic COVID-19 From Moderate COVID-19. Front Cell Infect Microbiol. 2021;11:654272.
Singh K, Mittal S, Gollapudi S, Butzmann A, Kumar J, Ohgami RS. A meta-analysis of SARS-CoV-2 patients identifies the combinatorial significance of D-dimer, C-reactive protein, lymphocyte, and neutrophil values as a predictor of disease severity. Int J Lab Hematol. 2021;43:324–8.
Han H, Xu Z, Cheng X, Zhong Y, Yuan L, Wang F, Li Y, Liu F, Jiang Y, Zhu C, Xia Y. Descriptive, Retrospective Study of the Clinical Characteristics of Asymptomatic COVID-19 Patients. mSphere 2020;5:e00922-20.
Tomo S, Karli S, Dharmalingam K, Yadav D, Sharma P. The Clinical Laboratory: A Key Player in Diagnosis and Management of COVID-19. EJIFCC 2020;31:326 – 46.
Jia Z, Gong W. Will Mutations in the Spike Protein of SARS-CoV-2 Lead to the Failure of COVID-19 Vaccines? J Korean Med Sci. 2021;36:e124.
Sharma A, Ahmad Farouk I, Lal SK. COVID-19: A Review on the Novel Coronavirus Disease Evolution, Transmission, Detection, Control and Prevention. Viruses. 2021;13:202.
Zella D, Giovanetti M, Cella E, Borsetti A, Ciotti M, Ceccarelli G, D'Ettorre G, Pezzuto A, Tambone V, Campanozzi L, Magheri M, Unali F, Bianchi M, Benedetti F, Pascarella S, Angeletti S, Ciccozzi M. The importance of genomic analysis in cracking the coronavirus pandemic. Expert Rev Mol Diagn. 2021;21:547–62.
Machhi J, Herskovitz J, Senan AM, Dutta D, Nath B, Oleynikov MD, Blomberg WR, Meigs DD, Hasan M, Patel M, Kline P, Chang RC, Chang L, Gendelman HE, Kevadiya BD. The Natural History, Pathobiology, and Clinical Manifestations of SARS-CoV-2 Infections. J Neuroimmune Pharmacol. 2020;15:359–86.
Qin C, Zhou L, Hu Z, Zhang S, Yang S, Tao Y, Xie C, Ma K, Shang K, Wang W, Tian DS. Dysregulation of Immune Response in Patients With Coronavirus 2019 (COVID-19) in Wuhan, China. Clin Infect Dis. 2020;71:762–8.

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Clinical Investigation of Lymphocyte DNA Damage in COVID-19 Patients

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Abstract

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Introduction

Methods

Total Lymphocyte Isolation

DNA Damage Analysis

Statistical Methods

Results

Discussion

Conclusion

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References

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