The Association of Programmed Death 1 Gene Polymorphisms of PD1.3 G/A and PD1.5 C/T with Risk of COVID-19 in an Iranian Population: A Case

Abstract

Programmed death 1 (PD-1) has a central role in maintaining T cell tolerance and terminating cellular responses after eliminating antigens. Variation in PD-1 gene products caused by polymorphisms has been linked to several malignancies and autoimmune diseases. However, there is little known about the effects of its single-nucleotide polymorphisms (SNPs) on viral infections, particularly COVID-19. The primary aim of this study was to explore the function of genotypes, alleles, and haplotypes of two SNPs within the programmed cell death protein 1 (PDCD1) gene at PD1.3 G/A and PD1.5 C/T on susceptibility to COVID-19 in an Iranian population. The secondary objective was to evaluate the effects of these SNPs on the outcome of the disease. We got blood samples from COVID-19 patients (n = 195) and healthy subjects (n = 500) for genotypic determination of PD1.3 G/A (rs11568821) and PD1.5 C/T (rs2227981) SNPs, using the polymerase chain reaction-restriction fragment length polymorphism method, and constructed four haplotypes for PDCD1 SNPs. We used Pearson's chi-squared test, Fisher's exact test, and T-test for this study and incorporated effect sizes of odds ratio (OR) and standardized mean difference. The frequency of CT genotype of PD1.5 was meaningfully higher in COVID-19 patients (49.2%) than in healthy subjects (37.4%) (p = 0.005). However, these significant differences were not observed in the frequencies of PD1.3 genotypes between the two groups (p > 0.05). Of all estimated haplotypes for PDCD1, only AT was significantly and largely associated with COVID-19 susceptibility (p = 0.01, OR: 7.79 [95% confidence interval = 1.56–38.79]), however, this finding is inconclusive. In addition, the present study showed that the PD1.3 and PD1.5 SNPs were not associated with the outcome of the disease (p > 0.05). These results may propose that the PD1.5 CT genotype and AT haplotype of PDCD1 indecisively contribute to COVID-19 susceptibility in the Iranian population.

Introduction

Coronavirus disease 2019 (COVID-19) is a respiratory illness caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which first appeared in late 2019, and is concerned with the risk of hospitalization and death (23,25). The virus belongs to the Betacoronavirus genus, sharing similarities to the bat coronaviruses, and harbors the 29.8 kb single-stranded RNA genome (14). The viral entry is mainly mediated by its spike receptor, human angiotensin-converting enzyme 2 (hACE2), which is abundantly expressed by respiratory epithelial cells and may partly explain the respiratory tropism of the virus (21).

Many factors contribute to the susceptibility of individuals toward more severe forms of COVID-19, particularly the host's adaptive immune response (9). The imbalance between mediator molecules and lymphocytes results in dysregulated responses to infections. One of the key mediators in T cell activation and tolerance with inhibitory nature is programmed death 1 (PD-1 or CD279) with its gene programmed cell death protein 1 (PDCD1) (16) and its ligands PD-L1 and PD-L2 (11). It is expressed in T and B cells, monocytes, dendritic cells (DCs), and natural killer (NK) cells.

PD-1 has a polymorphic gene located on chromosome 2, and its single-nucleotide polymorphisms (SNPs) are associated with susceptibility to autoimmune diseases (8) and malignancies (24). PDCD1 polymorphisms have also been investigated in the outcome and clearance of viral infections, including hepatitis C virus (HCV) and cytomegalovirus (CMV) (3,10); however, these data are insufficient to draw conclusions for other viral infections, including SARS-CoV-2.

The role of the PD-1/PD-L1 pathway in the clinical severity of COVID-19 has gained attention since the data show that serum levels of PD-L1 could be a prognostic factor in the patients (18). There is also evidence of increased PD-1 expression in CD4⁺ and CD8⁺ T cells of COVID-19 patients (9), and PD-1 blocking agents could enhance the immune response against the virus (13). Nevertheless, the polymorphic nature of PDCD1 and the uneven distribution of its haplotypes in different populations necessitate exploring the impacts of its SNPs on COVID-19 susceptibility and disease outcome. Therefore, we aimed this study toward finding these associations in Iranian ethnicity.

Patients and Methods

Subjects

In this study, a total number of 195 unrelated patients were recruited among those referred to the COVID-19 center located in Khorshid hospital, Isfahan, Iran, and 500 age-matched healthy volunteers without a history of malignancies, autoimmune, allergic, and chronic infectious disorders took part as the control group. The control group was included in the study based on the lack of symptoms at the study time.

We performed the present study from September 2020 to January 2021. All participants were of Persian ethnicity and selected from the Isfahan province of Iran. A pulmonologist confirmed the diagnosis of COVID-19 according to clinical symptoms and laboratory criteria, including (1) blood tests such as complete blood count, C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR); (2) computed tomography scan imaging; and (3) real-time polymerase chain reaction (RT-PCR). Then, we followed up with the patients and compared the PD-1 gene SNPs between patients who survived and expired after COVID-19.

All participants' informed consent was obtained before the study, while the research involved no more than minimal risk to subjects and the Isfahan University of Medical Sciences Ethics Committee approved this study with the ethical code of IR.MUI.MED. REC.1399.750.

Sample collection and DNA extraction

The ethylenediaminetetraacetic acid-treated peripheral blood (5 mL) was collected from participants, and white blood cells were isolated from whole blood using red blood cell lysis buffer. Also, genomic DNA was extracted by a human DNA extraction kit (Biotech Corporation, Beijing, China) according to the manufacturer's instructions. The quantity evaluation of extracted DNAs was performed by the NanoDrop device (Thermo Fisher Scientific, Lithuania).

The investigation of PD-1 SNP and determination of its genotypes

To determine the PD-1 genotypes, PCR-restriction fragment length polymorphism (PCR-RFLP) was used to evaluate the PD1.3 G/A and PD1.5 C/T SNPs. PCR-RFLP was performed on a thermocycler (Bio-Rad, Hercules, CA) in the whole volume of 10 μL having 1 μL (300 ng) of genomic DNA, 0.5 μL of MgCl₂ (60 mM), 0.3 μL of each primer (10 pM), 1 μL of PCR buffer (10 × ), 0.5 μL of dNTP (15 mM), 1 μL of Taq DNA polymerase (5 U/μL), and 5.4 μL of nuclease-free water (all from SinaClon, Tehran, Iran). Each reaction was initiated at 94°C for 5 min followed by 40 cycles of 94°C for 30 sec, 59°C for 25 sec, and 72°C for 30 sec, with a final extension at 72°C for 10 min.

The researchers were blinded to sample information, and randomization was used during the experiments. Some samples (30%) were randomly selected and genotyped twice to avoid genotyping errors. Allele ID primer design software (version 7.5; Premier Biosoft) was used to design the specific primers and checked by Primer-BLAST (NCBI).

The amplified PCR products were digested by the Pst1 (Cat No: ER0611) and PvuII (Cat No: ER0635) restriction endonucleases for PD1.3 and PD1.5 according to the manufacturer's guideline (Thermo Fisher Scientific). Then, after incubating the products at 37°C overnight, the condensation products were stained with the KBC solution (Kowsar Biotech Company, Tehran, Iran). Then stained samples went on a 3% submersed agarose gel electrophoresis system (Invitrogen) to envision the DNA bands with ultraviolet light (Table 1).

Table 1.

Polymerase Chain Reaction Condition, Enzymes, and Primers for Genotyping of PD1.3 and PD1.5 Polymorphisms of PDCD1 Gene

Position	Primer sequence	Annealing temperature	Restriction enzyme	Fragment lengths
PD1.3 (rs1156882, +7146 G/A)	Forward: GCCTGGAGGACTCACATTCT	58	PST 1	G: 381 bp
PD1.3 (rs1156882, +7146 G/A)	Reverse: GTCCCCCTCTGAAATGTCC	58	PST 1	A: 277 bp, 104 bp
PD1.5 (rs2227981, +7785 C/T)	Forward: AGACGGAGTATGCCACCATTGTC	57	PvuII	C: 196 bp
PD1.5 (rs2227981, +7785 C/T)	Reverse: AAATGCGCTGACCCGGGCTCAT	57	PvuII	T: 125 bp, 71 bp

PDCD1 , programmed cell death protein 1.

Statistical analysis

The genotypic and allelic frequencies were compared between the COVID-19 patients and the controls using Epi Info 2000 statistical software for epidemiology (CDC). The Arlequin software package (version 3.1; CMPG, University of Berne, Switzerland) was used to determine the Hardy–Weinberg equilibrium for the haplotype analyses. The Pearson's chi-squared and Fisher's exact tests were performed to evaluate the association of PDCD-1 gene polymorphisms with COVID-19. All the analyses were performed in SPSS software version 16.0 (IBM Corp., Armonk, NY). All the data are shown as mean ± standard deviation (SD), and p-values <0.05 were considered statistically significant. Corrected Bonferroni p-value was calculated using the chi-squared test on 2 × 2 table

We followed the interpretation zones for the effect size of odds ratio (OR) as follows: (1) small for 1.22 to 1.85, (2) medium for 1.86 to 2.99, and (3) large for above 3.00 values (15). Our findings for the effect size of standardized mean difference (SMD, Cohen's d) are in alignment with the interpretation zones as follows: (1) −0.19 to 0.19 for trivial, (2) −0.49 to −0.2 and 0.2 to 0.49 for small, (3) −0.79 to −0.50 and 0.50 to 0.79 for medium, and (4) lower than −0.8 and above 0.8 for large effect (2).

Results

Description of patients

All patients were positive for the RT-PCR test for COVID-19. About 112 (57.4%) patients were ground-glass opacity (GGO) positive, and 69 (38.5%) were 2+ positive for the CRP test. The assessment of hemoglobin levels (mean ± SD of 12.82 ± 2.02 g/dL in patients vs. 14.64 ± 1.42 g/dL in control, SMD [Cohen's d]: −1.13 confidence interval [95% CI] = −1.31 to −0.95, p < 0.001) and the mean ± SD of total lymphocyte count (987.24 ± 401.93 cells/μL in patients against 3429.88 ± 1690.25 cells/μL in control, SMD [Cohen's d]: −1.68 [95% CI = −1.87 to −1.50], p < 0.001) revealed that these values were significantly and considerably lower in patients than the control group.

Besides, the ESR was considerably higher in patients (mean ± SD of 33.63 ± 25.08 mm/h) in comparison with the control group (mean ± SD of 11.58 ± 6.06 mm/h), SMD (Cohen's d): 1.55 (95% CI = 1.37–1.73), (p < 0.001). Afterward, in a follow-up, 179 (91.8%) of the patients were released from the hospital with no health-threatening difficulty, and 16 (8.2%) had died (Table 2).

Table 2.

Baseline Demographic and Clinical Characteristics of COVID-19 Patients and Control Group

	Patients (n = 195)	Control (n = 500)	p	Standardized mean difference (Cohen's d)
Sex, n (%)	Male: 105 (53.8%) Female: 90 (46.2%)	Male: 272 (54.4%) Female: 228 (45.6%)	0.48	—
Age: mean ± SD (years)	64.82 ± 11.7	63.02 ± 11.35	0.85	—
Outcome, n (%)	Survived: 179 (91.8%) Expired: 16 (8.2%)	—	—	—
RT-PCR, n (%)	Positive: 195 (100%)	—	—	—
GGO, n (%)	Yes: 112 (57.4%) No: 83 (42.6%)	—	—	—
Hemoglobin: mean ± SD (g/dL)	12.82 ± 2.02	14.64 ± 1.42	<0.001	−1.13 (−1.31 to −0.95)
Lymphocyte count: mean ± SD (cell/μL)	987.24 ± 401.93	3429.88 ± 1690.25	<0.001	−1.68 (−1.87 to −1.50)
CRP, n (%)	Negative: 16 (8.2%) Positive: 179 (91.8%) 1+: 62 (34.6%) 2+: 69 (38.5%) 3+: 36 (20.1%) 4+: 12 (6.7%)	Negative: 500 (100%)	—	—
ESR: mean ± SD (mm/h)	33.63 ± 25.08	11.58 ± 6.06	<0.001	1.55 (1.37 to 1.73)
Background, disease, n (%)	No: 114 (58.5%) Yes: 81 (41.5%) Hypertension: 25 (30.9%) Diabetes: 18 (22.2%) Kidney disease (ESRD and CKD): 16 (19.8%) Lung disease (COPD, asthma): 15 (18.5%) IHD: 5 (6.2%) Hypothyroidism: 2 (2.5%)	No: 500 (100%)	—	—
Anorexia, n (%)	Yes: 36 (18.5%) No: 159 (81.5%)	—	—	—
Fever, n (%) Temperature: mean ± SD (°C)	Yes: 121 (62.0%) No: 74 (38.0%) 37.89 ± 1.03	—	—	—
Headache, n (%)	Yes:133 (68.2%) No: 62 (31.8%)	—	—	—
Dyspnea, n (%)	Yes: 139 (71.3%) No: 56 (28.7%)	—	—	—
Cough, n (%)	Yes: 135 (69.2%) No: 60 (30.8%)	—	—	—
Sore throat, n (%)	Yes:128 (65.6%) No: 67 (34.4%)	—	—	—
Diarrhea, n (%)	Yes: 51 (26.2%) No: 144 (73.8%)	—	—	—
Vomiting, n (%)	Yes: 41 (21.0%) No: 154 (79.0%)	—	—	—
Smoking, n (%)	Yes: 69 (35.4%) No: 126 (64.6%)	Yes: 185 (37.0%) No: 315 (63.0%)	0.38	—
O2 saturation: mean ± SD (%)	87.17 ± 8.91	—	—	—

CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; CRP, C-reactive protein; ESR, erythrocyte sedimentation rate; ESRD, end-stage renal failure; GGO, ground-glass opacity; IHD, ischemic heart disease; RT-PCR, real-time polymerase chain reaction; SD, standard deviation.

Association of PD-1 genotype and allele frequencies with COVID-19 susceptibility

To determine the association between PD-1 genetic variations and COVID-19 in the Iranian patients, PD-1 SNPs were evaluated in the extracted genomic DNA from patients and control groups using the PCR-RFLP method. The frequencies of all genotypes of the patients and control subjects were in line with the Hardy–Weinberg equilibrium (p > 0.05).

The significant differences in PD1.5 C/T genotype frequencies between COVID-19 and control groups were observed (Table 3, p = 0.016). The results of multiple comparisons indicated that the distribution frequencies of PD1.5 C/C and C/T genotypes versus other genotypes in COVID-19 patients significantly differed from healthy subjects (Table 3, p = 0.01 and p = 0.005, respectively). Bonferroni correction revealed that the pairwise comparison of CT versus CC and TT was still significant (p = 0.005). Allelic analysis revealed no significant association between PD1.3 G/A and PD1.5 C/T gene allele frequency and COVID-19 susceptibility (Table 3, p > 0.05). The frequencies of genotypes and alleles in two positions (PD1.3 and PD1.5) of the PDCD1 gene are shown in Table 3.

Table 3.

Genotype and Allele Frequencies in PD1.3 and PD1.5 Positions of PDCD1 Gene in COVID-19 and Control Groups

Position	Genotype and allele frequencies	Patient (n = 195)	Control (n = 500)	OR (95% CI)	p
PD1.3 (rs11568821, +7146 G/A)	GG	162 (83.1%)	408 (81.6%)	—	0.84
	GA	29 (14.9%)	83 (16.6%)
	AA	4 (2.0%)	9 (1.8%)		0.72
	GG vs. others (GA+AA)	162 (83.0%) vs. 33 (16.9%)	408 (81.6%) vs. 92 (18.4%)		0.65
	GA vs. others (GG+AA)	29 (14.9%) vs. 166 (85.1%)	83 (16.6%) vs. 417 (83.4%)		1
	AA vs. others (GG+GA)	4 (2.0%) vs. 191 (98.0%)	9 (1.8%) vs. 491 (98.2%)
	G	353 (90.5%)	899 (89.9%)	1.07 (0.72–1.59)	0.73
	A	37 (9.5%)	101 (10.1%)	1.07 (0.72–1.59)
PD1.5 (rs2227981, +7785 C/T)	CC	82 (42.1%)	263 (52.6%)		0.0165
	CT	96 (49.2%)	187 (37.4%)	—
	TT	17 (8.7%)	50 (10.0%)
	CC vs. others (CT+TT)	82 (42.1%) vs. 113 (57.9%)	263 (52.6%) vs. 237 (47.4%)	0.65 (0.46–0.91)	0.0157
	CT vs. others (CC+TT)	96 (49.2%) vs. 99 (50.8%)	187 (37.4%) vs. 313 (62.6%)	1.62 (1.16–2.26)	0.005
	TT vs. others (CC+CT)	17 (8.7%) vs. 178 (91.3%)	50 (10.0%) vs. 450 (90.0%)		0.71
	C	260 (66.6%)	713 (71.3%)	0.81 (0.63–1.03)	0.09
	T	130 (33.4%)	287 (28.7%)	0.81 (0.63–1.03)	0.09

After applying Bonferroni correction, p-values <0.0125 were considered significant.

CI, confidence interval; OR, odds ratio; PD-1, programmed death 1.

Evaluation of PD-1 haplotype frequencies in patients and control groups

The Arlequin software package constructed four haplotypes. GC haplotype was the most common in COVID-19 (58.7%) patients and control (61.4%) group. AT haplotype was significantly and largely, but inconclusively, overpresented in COVID-19 patients compared with the control group (p = 0.01, OR = 7.79, 95% CI = 1.56–38.79) (Table 4). However, there was no significant difference in the frequencies of other haplotypes between COVID-19 and control groups. The frequencies of haplotypes are shown in Table 4.

Table 4.

Haplotype Frequencies in PD1.3 and PD1.5 Positions of PDCD1 Gene in COVID-19 and Control Groups

Haplotype		Frequency		OR (95% CI)	χ²	p
PD1.3 (rs1156882, +7146 G/A)	PD1.5 (rs2227981, +7785 C/T)	Patient (n = 195)	Control (n = 500)	OR (95% CI)	χ²	p
G	C	229 (58.7%)	614 (61.4%)	0.89 (0.7–1.13)	0.73	0.39
G	T	124 (31.8%)	285 (28.5%)	1.17 (0.90–1.51)	1.31	0.25
A	C	31 (7.9%)	99 (9.9%)	0.78 (0.51–1.19)	1.04	0.3
A	T	6 (1.5%)	2 (0.2%)	7.79 (1.56–38.79)	6.6	0.01

There was no significant association between the PD1 gene SNP at PD1.3 G/A and PD1.5 C/T with clinical and laboratory criteria (p > 0.05).

Evaluation of PD-1 gene polymorphisms in patients who survived and expired

Next, we analyzed PD1.3 and PD1.5 gene polymorphisms between survived and expired patients. Our results indicated that the genotype and allele frequencies of the PD-1 gene were not significantly different in survived and expired patients (Table 5, p > 0.05). Similarly, haplotype frequency was no different in survived and expired groups (Table 6, p > 0.05).

Table 5.

Genotype and Allele Frequencies in PD1.3 and PD1.5 Positions of PDCD1 Gene in Survived and Expired COVID-19 Patients

Position	Genotype and allele frequencies	Survived patients (n = 179)	Expired patients (n = 16)	OR (95% CI)	p
PD1.3 (rs11568821, +7146 G/A)	GG	149 (83.2%)	13 (81.3%)	—	0.98
	GA	26 (14.5%)	3 (18.7%)
	AA	4 (2.3%)	0 (0.00%)
	G	324 (90.5%)	29 (90.6%)	0.99 (0.29–3.41)	1
	A	34 (9.5%)	3 (9.4%)	0.99 (0.29–3.41)	1
PD1.5 (rs2227981, +7785 C/T)	CC	76 (42.5%)	6 (37.5%)	—	0.33
	CT	89 (49.7%)	7 (43.8%)
	TT	14 (7.8%)	3 (18.7%)
	C	241 (67.3%)	19 (59.4%)	1.40 (0.67–2.95)	0.47
	T	117 (32.7%)	13 (40.6%)	1.40 (0.67–2.95)	0.47

Table 6.

Haplotype Frequencies in PD1.3 and PD1.5 Positions of PDCD1 Gene in Survived and Expired COVID-19 Patients

Haplotype		Frequency		OR (95% CI)	χ²	p
PD1.3 (rs1156882, +7146 G/A)	PD1.5 (rs2227981, +7785 C/T)	Survived patients (n = 179)	Expired patients (n = 16)	OR (95% CI)	χ²	p
G	C	212 (59.2%)	20 (62.5%)	0.87 (0.41–1.83)	0.03	0.39
G	T	112 (31.3%)	9 (28.1%)	1.16 (0.52–2.59)	0.02	0.84
A	C	29 (8.1%)	3 (9.4%)	0.85 (0.24–2.96)	0.00	1
A	T	5 (1.4%)	0	Undefined	0.00	1

Association of PD-1 gene polymorphisms with demographics of patients

Furthermore, we investigated the association of patients' demographic information with each genotype and haplotype, and the analysis revealed no significant associations (p > 0.05).

Discussion

Amid the novel coronavirus pandemic, the position of immune response mechanisms against viral infections has been elevated. Several checkpoint molecules in the immune system maintain balanced responses to the infection and make the backbone of the immune homeostasis. PD-1 is one of the checkpoint molecules that captured lots of attention in the battle with COVID-19. Through numerous roles of PD-1 in immune regulation, speculation around its gene polymorphisms has propelled some studies to investigate how genetic variants of PD-1 loci contribute to different outcomes in terms of viral infection (19). Similarly, our study is dedicated to seeking a link between PD-1 gene polymorphisms and COVID-19 vulnerability.

PD-1 allelic and genotypic frequencies and COVID-19 susceptibility

While mutations are investigated alone, they may not be attributed to susceptibility to specific diseases, however, the collective set of mutations or SNPs may alter the structure or function of the protein that further suppresses or sensitizes immune responses. For the PD-1 gene, the allelic analysis revealed no relationship between their frequency and COVID-19 susceptibility.

In the present study, we found that among several distinct PD-1 SNPs, the PD1.5 CT genotype harbors the most association with susceptibility to COVID-19 compared with other variants (CC+TT) and CC compared with others (CT+TT). However, an allelic frequency for PD1.5 C/T yielded no significant relationship to COVID-19 vulnerability.

Our investigation for detecting an association between COVID-19 vulnerability and PD-1 gene polymorphisms is the first of its kind, and no prior study was performed in this area to the best of our knowledge. Nevertheless, there is evidence on the association of PD-1 gene polymorphisms with viral infections and certain types of carcinomas. The role of specific PD-1 SNPs on CMV infection has been discussed in the studies of Forconi et al. (7) and Hoffmann et al. (10) in lung and kidney transplantations. Forconi et al. identified that PD1.3 A allele increased the survival of lung allograft recipients who received donations from donors infected with CMV. On the risk of CMV infection in the kidney transplant recipient, Hoffmann et al. showed that carrying the PD1.3 A allele may be a risk factor.

Despite our result on COVID-19 disease severity and PD-1 SNPs, Zheng et al. and Hou et al. both revealed that the PD-1.5 T allele is related to a lower blood viral load in chronic hepatitis B infection (26,27). The study of Sarvari et al. that evaluated hepatitis C infection outcome and PD-1 gene variants demonstrated that the A allele of PD-1.3 G/A polymorphism conferred some resistance to HCV infection in individuals who share this genotype. Still, other alleles or genotypes of PD1.3 G/A and also PD1.5 C/T did not contribute to how patients may end up with an infection (19).

Similar to Sarvari et al.'s findings, Vidal-Castiñeira et al. found that the PD1.3 A allele is considerably associated with a better outcome for HCV infection after treatment, known as the sustained virological response (22). The expression of PD1.3 A and PD1.7 G mRNA was higher in patients involved in hepatocellular carcinoma related to HCV infection in the analysis by de Re et al. (3). Our recent study (5) analyzed three different polymorphisms of the PD-1 gene at locations PD1.3, PD1.5, PD1.9 and showed that neither of these polymorphisms at these loci turned individuals prone to head and neck squamous cell carcinoma.

PD-1 haplotypes and COVID-19 susceptibility

Our findings suggest that between four PD-1 haplotypes, AT haplotype frequency among COVID-19 patients and healthy controls differed significantly and considerably. However, this is inconclusive due to the extension of OR's CI to more than one interpretation zone partly caused by the small sample size. Also, different haplotypes of PD-1 could be a potential risk or protective factor for developing several cancers. Ren et al. (17) displayed a considerable decrease in breast cancer risk in individuals carrying PD1.9 genotypes (CC, CT, and TT), and on developing epithelial ovarian cancer, PD-1 genotypes of AG and GG were risk factors in the study of Li et al. (12). However, in colon cancer susceptibility, PD-1.1 GG genotype and G allele frequencies were considerably higher in colon cancer patients compared with the control group (20).

Two studies on the association of PD-1 gene variants with risk factors for basal cell carcinoma (BCC) by Fathi et al. (4,6) revealed that the PD1.3 G allele and GA, AG, and AC haplotypes contributed to BCC in the Iranian population.

COVID-19 survival and clinical features

Regarding clinical findings for patients, no remarkable link between PD1.5 C/T and PD1.3 G/A SNPs and both clinical and laboratory criteria were found. Also, differences in PD-1 polymorphisms, genotypes, and allelic frequency were not significant between survived and expired patients; thus, PD-1 genetic variants might not contribute to the COVID-19 patient's survival chance.

Limitations

This study should address several potential limitations. We selected 195 patients with COVID-19 in our sample design, which is a relatively low number for finding most PD-1 gene polymorphisms. In addition, the number of patients in each group may not be adequate to infer the results. Another limitation is that we solely scanned Iranian ethnicity and from one peculiar province for PD-1 SNPs; therefore, our data may not be translated to other ethnicities and populations, and further studies need to be conducted to explore the prevalence of different unknown variations of PD-1 gene in broader populations.

Conclusion

Despite some restrictions and biases, our robust data with clear outcomes in clinical evaluation and genome-level sequencing support the hypothesis that the PD-1 gene variation of PD1.5 C/T is significantly but inconclusively related to COVID-19 susceptibility compared with the other genotypes. Future studies should shed light on the association of all polymorphisms in immune checkpoint molecules such as PD-1 with novel coronavirus infection (COVID-19) and assess the diverse aftermath of viral infection in individuals.

Footnotes

Acknowledgments

The authors thank all the subjects who participated in the study.

Authors' Contributions

F.F.: Contributed to all the experimental work, data and statistical analysis, and interpretation of data. P.Z.B.: Sample collection and data analysis. B.K.: Data analysis. M.G. and B.G.: Sample collection and first draft. A.R.D.: Final draft. H.F. and S.A.: Sample collection and statistical analysis. N.E.: Editing the first version of the article and responsible for the overall supervision. S.M.M.: Article final version editing.

Authors' Confirmation Statement

A.R.D. and S.A. are from Tehran University of Medical Sciences (Tehran, Iran); P.Z.B., H.F., B.G., N.E., S.M.M., and F.F. are from Isfahan University of Medical Sciences (Isfahan, Iran); B.K. is from Kermanshah University of Medical Sciences (Kermanshah, Iran); and M.G. is from Shahid Sadoughi University of Medical Sciences (Yazd, Iran), all where education and research are the primary functions.

Ethics Approval and Consent to Participate

Ethical approval for this study was obtained from Isfahan University of Medical Sciences (ethics code: (IR.MUI.MED. REC.1399.750). Written informed consent was obtained from all subjects before the study.

Consent for Publication

Not applicable.

Availability of Data and Materials

The data used in this study are available on request from the corresponding author upon reasonable request.

Author Disclosure Statement

No competing financial interests exist.

Funding Information

This study was financially supported by the Isfahan University of Medical Sciences (grant number: 199422).

References

Bellesi

, Metafuni

, Hohaus

, et al. Increased CD95 (Fas) and PD-1 expression in peripheral blood T lymphocytes in COVID-19 patients. Br J Haematol, 2020; 191:207–211.

Cohen

. Statistical Power Analysis for the Behavioral Sciences. Hillsdale, NJ: Lawrence Erlbaum Associates, 2013.

de Re

, Tornesello

, de Zorzi

, et al. PDCD1 and IFNL4 genetic variants and risk of developing hepatitis C virus-related diseases. Liver Int, 2021; 41:133–149.

Fathi

, Ebrahimi

, Eslami

, et al. Association of programmed death-1 gene polymorphisms with the risk of basal cell carcinoma. Int J Immunogenet, 2019; 46:444–450.

Fathi

, Faghih

, Khademi

, et al. PD-1 haplotype combinations and susceptibility of patients to squamous cell carcinomas of head and neck. Immunol Investig, 2019; 48:1–10.

Fathi

, Zamani

, Piroozmand

, et al. Programmed cell death 1 (PDCD1) gene haplotypes and susceptibility of patients to basal cell carcinoma. Mol Biol Rep, 2021; 48:2047–2052.

Forconi

, Gatault

, Miquelestorena-Standley

, et al. Polymorphism in programmed cell death 1 gene is strongly associated with lung and kidney allograft survival in recipients from CMV-positive donors. J Heart Lung Transplant, 2017; 36:315–324.

Gianchecchi

, Delfino

, Fierabracci

. Recent insights into the role of the PD-1/PD-L1 pathway in immunological tolerance and autoimmunity. Autoimmun Rev, 2013; 12:1091–1100.

, Lu

, Zhang

, et al. The clinical course and its correlated immune status in COVID-19 pneumonia. J Clin Virol, 2020; 127:104361.

10.

Hoffmann

, Halimi

, Büchler

, et al. Association between a polymorphism in the human programmed death-1 (PD-1) gene and cytomegalovirus infection after kidney transplantation. J Med Genet, 2010; 47:54–58.

11.

Keir

, Butte

, Freeman

, et al. PD-1 and its ligands in tolerance and immunity. Annu Rev Immunol, 2008; 26:677–704.

12.

, Zhang

H lan

, Kang

, et al. The effect of polymorphisms in PD-1 gene on the risk of epithelial ovarian cancer and patients' outcomes. Gynecol Oncol, 2017; 144:140–145.

13.

Loretelli

, Abdelsalam

, D'Addio

, et al. PD-1 blockade counteracts post-COVID-19 immune abnormalities and stimulates the anti-SARS-CoV-2 immune response. JCI Insight, 2021; 6:e146701.

14.

, Zhao

, Li

, et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet, 2020; 395:565–574.

15.

Olivier

, May

, Bell

. Relative effect sizes for measures of risk. Commun Stat Theory Methods, 2017; 46:6774–6781.

16.

PDCD1 programmed cell death 1 [Homo sapiens (human)]—Gene—NCBI. www.ncbi.nlm.nih.gov (accessed February 7, 2022 ).

17.

Ren

, Li

, Wang

, et al. PD-1 rs2227982 polymorphism is associated with the decreased risk of breast cancer in Northwest Chinese women: a hospital-based observational study. Medicine, 2016; 95:e3760.

18.

Sabbatino

, Conti

, Franci

, et al. PD-L1 Dysregulation in COVID-19 patients. Front Immunol, 2021; 12:695242.

19.

Sarvari

, Dowran

, Hosseini

, et al. Association of PD-1 gene with outcome of hepatitis C virus infection. EXCLI J, 2018; 17:935–944.

20.

Shamsdin

, Karimi

, Hosseini

, et al. Associations of ICOS and PD.1 gene variants with colon cancer risk in the Iranian population. Asian Pac J Cancer Prev, 2018; 19:693–698.

21.

Sungnak

, Huang

, Bécavin

, et al. SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes. Nat Med 2020, 26:5; 2020;26:681–687.

22.

Vidal-Castiñeira

, López-Vázquez

, Alonso-Arias

, et al. A predictive model of treatment outcome in patients with chronic HCV infection using IL28B and PD-1 genotyping. J Hepatol, 2012; 56:1230–1238.

23.

Wang

, Hu

, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. JAMA, 2020; 323:1061–1069.

24.

Zhang

, Song

, Zhang

. Relationship of programmed death-1 (PD-1) and programmed death ligand-1 (PD-L1) polymorphisms with overall cancer susceptibility: an updated meta-analysis of 28 studies with 60 612 subjects. Med Sci Monit, 2021; 27:e932146.

25.

Zhu

, Zhang

, Wang

, et al. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med, 2020; 382:727–733.

The Association of Programmed Death 1 Gene Polymorphisms of PD1.3 G/A and PD1.5 C/T with Risk of COVID-19 in an Iranian Population: A Case–Control Study

Abstract

Introduction

Patients and Methods

Subjects

Sample collection and DNA extraction

The investigation of PD-1 SNP and determination of its genotypes

Statistical analysis

Results

Description of patients

Association of PD-1 genotype and allele frequencies with COVID-19 susceptibility

Evaluation of PD-1 haplotype frequencies in patients and control groups

Evaluation of PD-1 gene polymorphisms in patients who survived and expired

Association of PD-1 gene polymorphisms with demographics of patients

Discussion

PD-1 allelic and genotypic frequencies and COVID-19 susceptibility

PD-1 haplotypes and COVID-19 susceptibility

COVID-19 survival and clinical features

Limitations

Conclusion

Footnotes

Acknowledgments

Authors' Contributions

Authors' Confirmation Statement

Ethics Approval and Consent to Participate

Consent for Publication

Availability of Data and Materials

Author Disclosure Statement

Funding Information

References