The Genetic Association of Single Nucleotide Polymorphisms of SERPINE1 (rs6092) and IFNAR2 (rs1051393, rs2229207) Genes Is Related to Post Covid-19 Respiratory Syndrome

Yulia Ariani; Indra Muhiardi; Indra Gunawan; Nadhifa Tazkia Ramadhani; Shafa Talitha Risti; Riyadi Sutarto; Agus Dwi Susanto

doi:10.12688/f1000research.167074.1

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Research Article

The Genetic Association of Single Nucleotide Polymorphisms of SERPINE1 (rs6092) and IFNAR2 (rs1051393, rs2229207) Genes Is Related to Post Covid-19 Respiratory Syndrome

[version 1; peer review: awaiting peer review]

Yulia Ariani ¹, Indra Muhiardi¹, Indra Gunawan¹, [...] Nadhifa Tazkia Ramadhani¹, Shafa Talitha Risti¹, Riyadi Sutarto², Agus Dwi Susanto^2,3

Yulia Ariani ¹, Indra Muhiardi¹, [...] Indra Gunawan¹, Nadhifa Tazkia Ramadhani¹, Shafa Talitha Risti¹, Riyadi Sutarto², Agus Dwi Susanto^2,3

PUBLISHED 21 Nov 2025

Author details Author details

¹ Department of Medical Biology, University of Indonesia, Central Jakarta, Jakarta, 10430, Indonesia
² Persahabatan National Respiratory Hospital, East Jakarta, Jakarta, 13230, Indonesia
³ Department of Pulmonology and Respiratory Medicine, University of Indonesia, Central Jakarta, Jakarta, 10430, Indonesia

Yulia Ariani
Roles: Conceptualization, Investigation, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

Indra Muhiardi
Roles: Formal Analysis, Investigation, Writing – Original Draft Preparation, Writing – Review & Editing

Indra Gunawan
Roles: Formal Analysis, Investigation, Writing – Original Draft Preparation, Writing – Review & Editing

Nadhifa Tazkia Ramadhani
Roles: Methodology, Writing – Original Draft Preparation, Writing – Review & Editing

Shafa Talitha Risti
Roles: Methodology, Writing – Original Draft Preparation, Writing – Review & Editing

Riyadi Sutarto
Roles: Investigation, Writing – Original Draft Preparation, Writing – Review & Editing

Agus Dwi Susanto
Roles: Conceptualization, Investigation, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS AWAITING PEER REVIEW

Abstract

Background

Coronavirus disease 2019 (Covid-19) is a disease of the respiratory system caused by the SARS-CoV-2 virus. The severity of Covid-19 can be affected by the presence of single nucleotide polymorphisms (SNPs) in certain genes, such as IFNAR2 and SERPINE-1. This study aimed to identify and analyze the presence of SNP rs6092 in the SERPINE1 gene and rs1051393 and rs2229207 in the IFNAR2 gene relation in Covid-19 respiratory syndrome in Indonesia.

Methods

DNA was isolated from saliva samples of all patients, and a TaqMan Genotyping Assay with a real-time PCR instrument was used to run the samples. The output data were analyzed for demographic data, allele frequency, genotype frequency, and the association of all SNPs with the Covid-19 respiratory syndrome (case) and control subjects. We also analyzed blood laboratory results, blood gas analyses, coagulation factors, and inflammatory factors using SPSS.

Results

This study included 85 subjects comprise with Covid-19 respiratory syndrome and control subjects. Our study found no association between subjects with Covid-19 respiratory syndrome and any of the variants. However, based on the symptoms caused by rs1051393, we found that it had an effect on fever symptoms. In addition, a significant relationship between rs2229207 and chest pain symptoms was observed in patients with case group. Furthermore, our study found significant differences (p < 0.05) in several blood laboratory analyses, such as the level of basophils and eGFR for rs6092 and the potassium level for rs2229207. Furthermore, arterial blood gas analysis showed that pCO2 and pH levels were significantly different for rs2229207.

Conclusion

Our study found an association between rs1051393 and fever and between rs2229207 and chest pain in patients with post Covid-19 respiratory syndrome.

Keywords

covid-19, single nucleotide polymorphisms, Covid-19 respiratory syndrome, SERPINE1, IFNAR2

Corresponding author: Yulia Ariani

Competing interests: No competing interests were disclosed.

Grant information: This study was supported by the Riset Inovasi dan Indonesia Maju–Lembaga Pengelola Dana Pendidikan (RIIM LPDP) grant (Grant No. 36/IV/KS/06/2022).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2025 Ariani Y et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The author(s) is/are employees of the US Government and therefore domestic copyright protection in USA does not apply to this work. The work may be protected under the copyright laws of other jurisdictions when used in those jurisdictions.

How to cite: Ariani Y, Muhiardi I, Gunawan I et al. The Genetic Association of Single Nucleotide Polymorphisms of SERPINE1 (rs6092) and IFNAR2 (rs1051393, rs2229207) Genes Is Related to Post Covid-19 Respiratory Syndrome [version 1; peer review: awaiting peer review]. F1000Research 2025, 14:1292 (https://doi.org/10.12688/f1000research.167074.1) First published: 21 Nov 2025, 14:1292 (https://doi.org/10.12688/f1000research.167074.1) Latest published: 21 Nov 2025, 14:1292 (https://doi.org/10.12688/f1000research.167074.1)

Introduction

Coronavirus Disease 2019 (Covid-19) is a respiratory illness caused by SARS-CoV-2. The disease first emerged in Wuhan, China, in late 2019.¹ As the virus spread globally, the World Health Organization (WHO) declared Covid-19 a pandemic in March 2020.² In Indonesia, Covid-19 cases has steadily increased over time. According to data from the Ministry of Health of the Republic of Indonesia, by March 2022, the number of confirmed Covid-19 cases reached 5,891,022, with 154,221 reported deaths, while 5,658,238 patients had recovered.

Patients infected with Covid-19 may experience a range of symptoms, including asymptomatic, mild, moderate, or severe forms.³ In some cases, individuals also develop post Covid-19 respiratory syndrome. According to the Indonesian Society of Respirology (PDPI), post Covid-19 respiratory syndrome is defined as persistent respiratory symptoms or disorders lasting ≥4 weeks after the initial onset of symptoms. These symptoms may include dry cough, phlegm, shortness of breath, limited physical activity, chest pain, and sore or itchy throats.⁴

The presence of post Covid-19 respiratory syndrome has been reported in several studies. For instance, Zhou et al. (2020) found that among 55 patients, 14.5% experienced shortness of breath that persisted for up to three months.⁵ Another study by Jacob et al. (2020) revealed that 45.5% of 183 patients experienced shortness of breath for up to four weeks.⁶ A study conducted in Indonesia by Susanto et al. (2022) showed that among patients with post Covid-19 respiratory syndrome, 15.5% experienced persistent cough, 11.2% reported shortness of breath, and 3.8% experienced sore throat.⁷ severe Covid-19 symptoms, which can lead to post Covid-19 respiratory syndrome, are often associated with cytokine storms and procoagulant conditions that involve excessive blood clotting.⁸

The severity of Covid-19 is influenced by various factors, including sex, age, lifestyle, and comorbidities. Genetic factors also play a significant role. Specific single nucleotide polymorphisms (SNPs) in certain genes have been linked to more severe Covid-19 outcomes.^9,10 Previous studies have highlighted the relationship between SNPs in genes encoding PAI-1 and Covid-19 severity. For example, Fricke-Galindo et al. (2022) reported that SNPs in SERPINE1 are associated with severe Covid-19 symptoms, characterized by elevated plasma levels of plasminogen activator inhibitor-1 (PAI-1).¹¹ Similarly, SNPs in IFNAR2 have been linked to severe symptoms in Covid-19 patients. However, no studies have investigated the association between SNPs in specific genes and post Covid-19 respiratory syndrome in Indonesia. Therefore, this study aimed to identify and analyze the presence of SNP rs6092 in SERPINE1, as well as rs1051393 and rs2229207 in IFNAR2, in relation to Covid-19 respiratory syndrome symptoms in Indonesian patients.

Methods

Subject criteria of the research

The research is in the form of a retrospective study involving patients who have experienced symptoms of respiratory syndrome after COVID-19. The subjects were patients who had been treated at the Central General Hospital (RSUP) Persahabatan, Jakarta, Indonesia. All participants recruited in this study provided informed consent to participate. The number of participants, based on the prevalence of post COVID-19 respiratory syndrome, was 85 (43 subjects and 42 control subjects). The criteria for the research subjects of post COVID-19 respiratory syndrome (case) were based on PDPI, an abbreviation for patients with symptoms of pulmonary or respiratory disorders that persist ≥4 or 12 weeks after the first onset of symptoms, including dry cough, shortness of breath/heavy breathing/shortness of breath or shortness of breath, limited activity, chest pain, sore or itchy throat, and abnormalities on radiographic examination or pulmonary abnormalities. Written informed consent was obtained by contacting participants through the phone numbers listed in their medical records, as they were no longer under treatment during the study period (they received treatment in 2020–2021 period); the study description was delivered via electronic message, and participants provided explicit agreement or refusal to participate through written confirmation in the same medium.

DNA extraction

DNA was obtained from saliva samples of the participants, using 2 mL of saliva. Saliva sampling was performed using a sterile 10 mL tube filled with 2 mL of DNA/RNA Shield buffer solution (Zymo Research, Irvine, CA, USA; Cat. No. R1100-250). Samples were immediately transported to the laboratory of the Department of Medical Biology, Faculty of Medicine, University of Indonesia (FKUI) for isolation. DNA was isolated using the DNA extraction protocol from the modified Quick-DNA^TM Miniprep Plus Kit (Zymo Research, Irvine, CA, USA; Cat No. D4069). The saliva sample that had been mixed with DNA/RNA Shield was then transferred into two separate 1.5 mL tubes, each containing 700 μL. Next, 25 μL of proteinase K (Elabscience, Houston, TX, USA; Cat. No. E-IR-R109) was added to the sample, which was homogenized using a vortex for 10 s and incubated in a heating block at 60°C for 20 min.

Subsequently, 400 μL of Genomic Binding Buffer was added to the sample and homogenized by vortexing for 10 s. Next, the sample from the 1.5 mL tube was aliquoted as much as 700 μL into the GB and Spin column and then centrifuged at 13,000 × g for 1 min. The supernatant was discarded, and the process was repeated until no sample remained. Next, the Spin Column was added to 600 μL of Pre-Wash Buffer and centrifuged at 13,000 × g for 1 min to remove the supernatant. Then, 600 μL of 13,000 × g of centrifuged G-Wash Buffer was added for 1 min (repeated for 2 times). The next step was rinsing by centrifugation at 13,000 × g for 2 min. In the final step, 45 μL Elution Buffer DNA was heated at 60°C for 10 min, incubated for 5 min, and centrifuged at 13,000 × g for 2 min. The DNA samples obtained were stored at -20°C before genotyping.

Quality control of the DNA

Quality Control (QC) of the DNA was performed using Qubit. The QC process with qubits was performed by creating working solutions. The working solution was prepared according to the protocol of the Invitrogen Qubit Assay BR Assay Kit (Thermo Fisher Scientific, USA; Cat. No. THERMO-Q32853). A sterile 1.5 mL tube containing 995 μL of BR reagent and 5 μL of the probe was used. The two components were then vortexed for 5 s and aliquoted into a qubit tube of 198 μL for the sample and 190 μL for the control. Each sample was taken as much as 2 μL of DNA and then inserted into a Qubit tube containing 198 μL of BR assay reagent and homogenized by vortexing for 5 min. Next, the DNA mixed with the reagent was incubated in the dark for 2 min, and the fluorescence intensity was measured.

SNP genotyping method

SNP Genotyping was performed using the TaqMan^TM SNP Genotyping Assay (Applied Biosystems, Waltham, MA, USA; Cat. No. 4351379). SNPs rs6092, rs1051393, and rs2229207 were assessed according to the manufacturer’s instructions. Before genotyping, DNA samples that had been QC using a Qubit were normalized. All samples were normalized to 5 ng/μL. After normalization, 96 well plates were prepared, each well containing 12.5 μL of 2X TaqPath^TM ProAmp^TM Master Mix (Applied Biosystems, Waltham, MA, USA; Cat. No. A30865), 1.25 μL of 20X TaqMan^TM SNP Genotyping Assay, 7.25 μL of Nuclease Free Water (NFW) (Applied Biosystems, Waltham, MA, USA; Cat. No. AM9938), and 4 μL of DNA template.

The components were mixed in each well, and the plate was placed in a Real-Time Polymerase Chain Reaction (RT-PCR) machine (700 Applied Biosystem, USA). The principle of the Taqman Assay is that each assay contains two different forward and reverse primers for each SNP. The TaqMan probes were labeled with two different stains, VIC and FAM, to distinguish only certain SNP points, where one probe was complementary to a wild-type allele, while the other allele was colored by the other probe.^12,13

Data analysis

The data obtained from the study were statistically analyzed using SPSS version 25.0. Demographic data, association between outcome of Covid-19 (case and control), symptoms of Covid-19 respiratory syndrome, and all SNPs in the SERPINE1 and IFNAR2 genes were assessed using Pearson’s chi-square (X2) test. Blood laboratory results, arterial blood gas, and clotting blood factors were analyzed using either the Mann-Whitney U test or the Independent T test.

Results

We examined the demographic and hospitalization history of patients with Covid-19 Respiratory Syndrome (Case) and Covid-19 (Control group) ( Table 1). The average age of the case population tended to be higher than that of the control population, although the difference was not statistically significant (51.23 vs. 47.33, p=0.178). There were no significant differences between the two groups in terms of sex, year of admission, or body mass index (BMI).

Table 1. Demographic data of Covid-19 subjects outcome (Case and control).

	Case	Control	P-value
Age	51.23 ± 13.388	47.33 ± 13.094	0.178*
Sex	M: 34, F: 9, n=43	M:29, F: 13, n=85	0.292**
Year of Admission	2020: 21, 2021: 22, n=43	2020: 18, 2021:24, n=42	0.580**
BMI	26.30 (23,59-28,53)	26,81 (24,05-34,19)	0,401***
Severity on Admission
Mild	2 (4.7%)	0 (0.0%)	0.023***
Moderate	17 (39.5%)	30 (71.4%)
Severe	17 (39.5%)	11 (26.2%)
Critical	6 (14.0%)	1 (2.4%)
Radiology on Admission
Normal or Near Normal	2 (4.7%)	6 (14.3%)	0.013***
Reversible Lession	36 (83.7%)	36 (85.7%)
Ireversible Lession (Fibrosis)	5 (11.6%)	0 (0,0%)
Hospitalization Time	16.00 (11.00-20.00)	9.50 (6.75-12.00)	0.0001***
Worst Clinical Severity Throught Hospitalization
Mild	0 (0.0%)	0 (0.0%)	0,004***
Moderate	16 (37.2%)	28 (66.7%)
Severe	19 (44.2%)	12 (28.6%)
Critical	8 (18.6%)	2 (4.8%)
End of Radiology Series
Normal or Improvement with near normal result	1 (2.3%)	10 (31.3%)	0,001***
Improvement, not normal	33 (25.6%)	22 (68.8%)
Stationary	15 (34.9%)	0 (0.0%)
Deteroriation	16 (37.2%)	0 (0.0%)
Discharge Outcome
Cured	40 (93%)	41 (97,6%)	0.32***
Cured with Complication	3 (7%)	1 (2,4%)	0.32***
Symptomps
Fever	32 (74.4%)	31 (73.85)	0,949**
Cough	35 (81.4%)	32 (76.2%)	0,557**
Malaise	6 (14.0%)	6 (14.3%)	0,965**
Throat Pain	2 (4,7%)	2 (4.8%)	1^
Dyspnoe	36 (83.7%)	29 (69%)	0,111**
ChestPain	1 (2.3%)	2 (4.8%)	0,616^

* T-test,

** Chi-Square,

*** Man-Whitney,

^ Fisher-Exact Test.

When we looked into hospitalization history, there were significant differences in patients’ severity and radiological results on admission, worst clinical severity throughout hospitalization, and results at the end of the radiological examination series. We highlight that the majority of cases upon admission were classified as having severe-to-critical conditions, whereas the majority of controls had moderate symptoms (p=0.023). Throughout hospitalization, the worst recorded severity tended to become more severe in both groups (p=0.078), and the case group had a more severe and critical clinical classification (p=0.004). Significantly worse radiological results (p=0.013 and p=0.001) and longer median and hospitalization durations (p=0.0001) were also found, but these differences may have been influenced by the inherently different classification between the case and control groups.

In terms of hospitalization history, we also examined symptoms (fever, cough, malaise, sore throat, dyspnea, and chest pain) and discharge outcomes in both groups. We found that most patients in both groups had fever, cough, and malaise as their symptoms, and the patients were considered cured with no complications. However, no significant differences were observed between the two groups.

Based on the results (see Table 2) for rs6092 with a pattern of changes in G>A nucleotide bases, it was found that individuals had the GG genotype and 17.65% had the GA genotype, as much as 82.35%. rs1051393 with changes in the T>G base was found to have a TT genotype of 16.67%, TG of 34.72%, and GG of 48.61%. Additionally, for rs2229207 (IFNAR2) with T>C base changes, the TT, TC, and genotype frequencies were 74.12%, 21.8%, and 4.71%, respectively. The data showed that the largest genotype frequencies for rs6092, rs1051393, and rs2229207 were GA, GG, and TT, respectively. The allele frequency in the study population for rs6092 was 58.2% for the G allele and 41.18% for the A allele. Other SNPs, namely rs1051393, were found to carry as many as 34.03% T alleles and 65.97% G alleles. For rs2229207, 84.71% and 15.29% of the patients carried the T and C alleles, respectively.

Table 2. Allele frequency and genotype frequency of Three Single Nucleotide Polymorphisms (SNPs).

SNPs	Allele 1 frequency	Allele 2 frequency	Genotype of homozygote wild-type	Genotype of heterozygote	Genotype of homozygote mutant
rs6092 (G>A)	58.2% (G)	41.18% (A)	17.65% (GG)	82.35% (GA)	0% (AA)
rs105393 (T>G)	34.03% (T)	65.97% (G)	16.67% (TT)	34.72% (TG)	48.61% (GG)
rs2229207 (T>C)	84.71% (T)	15.29% (C)	74.12% (TT)	21.8% (TC)	4.71% (CC)

Based on Table 3, the results of the statistical analysis showed that the three SNPs were not significantly different (p>0.05). This shows that the presence of SNPs rs6092, rs1051393, and rs2229207 was not associated with the condition of patients with post Covid-19 respiratory syndrome or control patients. Furthermore, based on the results of the chi-square value (X2) for rs6092, a figure of 0.055 was obtained with a degree of freedom of 1, which means a critical value of 3.41; thus, the study population was in Hardy-Weinberg equilibrium. Likewise, the SNP rs1051393 with a Chi-Square of 0.223 and rs2229207 of 1.354 with a degree of freedom of 2, which means a critical value of 5.99, indicates that the study population is in Hardy-Weinberg equilibrium.

Table 3. Association between criteria of Covid-19 (case and control) and all SNPs.

SNPs	Genotype	Case	Control	X2	p-value
rs6092 (G>A)	GG	8 (18.60%)	7 (20.00%)	0.055	1
rs6092 (G>A)	AG	35 (81.40%)	35 (83.33%)	0.055	1
rs1051393 (T>G)	TT	5 (15.15%)	7 (17.95%)	0.223	0.894
	TG	11 (33.33%)	14 (35.90%)
	GG	17 (51.52%)	18 (46.15%)
rs2229207 (T>C)	TT	30 (69.77%)	33 (78.57%)	1.354	0.508
	CT	10 (23.26%)	8 (19.05%)
	CC	3 (6.98%)	1 (2.38%)

Based on the results ( Table 4) of the chi-square test for all SNPs, no meaningful relationship (p>0.05) was found between the genotype and the incidence of the case subjects. Further analysis showed that subjects carrying GG and GA genotypes on rs6096 had no effect on the incidence of patients with post Covid-19 respiratory syndrome symptoms (OR=1,143, 95% CI=0.374 – 3,493). In addition, patients with TG and GG genotypes at rs1051393, as well as those with CT and CC genotypes at rs2229207, have protective properties against the incidence of post Covid-19 respiratory syndrome symptoms.

Table 4. Association between Odd Ratio (OR) between genotypes and criteria of Covid-19.

SNPs	Genotype	Case	Control	OR 95% CI	p-value
rs6092 (G>A)	GG	8 (18.60%)	7 (20.00%)	1.143 (0.374-3.493)	1
rs6092 (G>A)	GA	35 (81.40%)	35 (83.33%)	1.143 (0.374-3.493)	1
rs1051393 (T>G)	TT	5 (15.15%)	7 (17.95%)	0.909 (0.226-3.661)	1
	TG	11 (33.33%)	14 (35.90%)	0.909 (0.226-3.661)	1
	TT	5 (15.15%)	7 (17.95%)	0.756 (0.201-2.846)	0.937
	GG	17 (51.52%)	18 (46.15%)	0.756 (0.201-2.846)	0.937
rs2229207 (T>C)	TT	30 (69.77%)	33 (78.57%)	0.727 (0.254-2.085)	0.744
	CT	10 (23.26%)	8 (19.05%)	0.727 (0.254-2.085)	0.744
	TT	30 (69.77%)	33 (78.57%)	0.303 (0.030-3.073)	0.356
	CC	3 (6.98%)	1 (2.38%)	0.303 (0.030-3.073)	0.356

We studied the association between the symptoms of Covid-19 respiratory syndrome throughout hospitalization and SNPs ( Table 5). Overall, there were no statistically significant differences between the SNPs and symptoms (fever, cough, malaise, sore throat, dyspnea, and chest pain). However, the expression of rs1051393 IFNAR2 between patients with fever and the expression of rs2229207 IFNAR2 between patients with chest pain tended to differ in different statistical analyses (p=0.037 and p=0.047, respectively).

Table 5. Association between SNPs with symptoms on persistent of Covid-19 respiratory syndrome.

		rs6092 SERPINE1		rs1051393 IFNAR2			rs2229207 IFNAR2
		GG	AG	TT	TG	GG	TT	CT	CC
Fever	Yes	10	53	9	23	22	46	14	3
	No	5	17	3	2	13	17	4	1
	P-Value	0.468*		0.037 *			0.708***
Cough	Yes	12	55	10	19	28	51	13	3
	No	3	15	2	6	7	12	5	1
	P-Value	1.000**		0.864*			0.430***
Malaise	Yes	11	62	2	2	7	10	1	1
	No	4	8	10	23	28	53	17	3
	P-Value	0.212**		0.427***			0.509***
Throat Pain	Yes	1	3	0	1	3	3	1	0
	No	14	67	12	24	32	60	17	4
	P-Value	0.547**		0.238***			0.924***
Dyspnoe	Yes	11	54	10	18	26	48	13	4
	No	4	16	2	7	9	15	5	0
	P-Value	0.745**		0.751*			0.812***
Chest Pain	Yes	1	2	0	1	2	2	0	1
	No	14	68	12	24	33	61	18	3
	P-Value	0.446**		0.433***			0.047 *

* Chi-Square,

** Fisher-Exact Test,

*** Mann-Whitney.

Based on our research, in the analysis of the blood laboratory (see Table 6), we found that there were significant differences in the basophil level (p=0.027) and eGFR (p=0.036) of rs6092 of the SERPINE1 gene, and the potassium level (p=0.04) of rs2229207 of the IFNAR2 gene. Analysis of arterial blood gas ( Table 7) revealed significant differences in the partial pressure of CO2 (p=0.041) and pH level (p=0.044) of rs2229207 of the IFNAR2 gene. Furthermore, our results regarding the analysis of coagulation and inflammatory factors (see Table 8) showed no significant difference between SNPs in all of the Covid-19 patients.

Table 6. Analysis of blood laboratory parameters with all SNPs.

Parameters	rs6092 SERPINE1		P-Value	rs1051393 IFNAR2		P-Value	rs2229207 IFNAR2		P-Value
	GG (n=15)	GA (n=70)		TT (n=12)	TG+GG (n=60)		TT (n=63)	TC+CC (n=22)
	Median (Q1-Q3)	Median (Q1-Q3)		Median (Q1-Q3)	Median (Q1-Q3)		Median (Q1-Q3)	Median (Q1-Q3)
Na	137 (133-139)	134 (131.75-137)	0.065	133 (131-136)	134.5 (132-138)	0.534	135 (132-138)	133.5 (132-136)	0,600
K	3.9 (3.5-4.5)	3.9 (3.5-4.3)	0.477	3.8 (3.4-4.2)	3.9 (3.6-4.3)	0.596	3.8 (3.5-4.1)	4.25 (3.87-4.62)	0,004*
Cl	102 (99-106)	100 (97-103)	0.125	100 (97-102.75)	100 (97.25-103)	0.688	101 (98-103)	99 (96.75-103)	0,435
Hb	13.9 (11.6-15)	13.7 (12.55-14.42)	0.493	14.25 (13.12-15.4)	13.7 (12.2-14.47)	0.149	13.7 (12.4-14.5)	13.9 (12.6-15.15)	0,479
Leu	8.8 (6.28-9.78)	7.85 (6.08-11)	0.584	7.3 (5.25-12.05)	7.9 (6.27-10.8)	0.506	7.82 (6.12-9.59)	9.16 (5.94-11.6)	0,276
AT	273 (226-286)	232 (184.75-312.5)	0.433	217.5 (180-324)	255 (200-317.5)	0.445	238 (187-309)	253 (194-311)	0,696
%Neu	81 (67.1-84.8)	78.35 (66.97-85.1)	0.708	75.55 (66.17-81.85)	79.1 (67.05-86.02)	0.410	77.9 (67-84.2)	80.5 (69.17-90.65)	0,330
%Eos	0 (0-0.3)	0 (0-0.3)	0.747	0 (0-0.2)	0 (0-0.3)	0.832	0 (0-0.4)	0 (0-0.12)	0,250
%Bas	0.1 (0-0.1)	0.1 (0.075-0.2)	0.027*	0.1 (0-0.2)	0.1 (0-0.2)	0.664	0.1 (0-0.2)	0 (1-0.2)	0,851
%Lim	13.3 (9.1-17.8)	13.65 (8.5-23.1)	0.991	15.2 (9.4-23.62)	13.4 (8.6-22.97)	0.751	14 (9.1-23)	13 (5.9-23.5)	0,419
%Mo	5.9 (4.3-8.7)	7.15 (4.45-9.55)	0.461	8.8 (5.92-12.2)	6.65 (4.22-9.4)	0.120	7.2 (4.8-9.5)	5.9 (3.4-8.92)	0,333
OT	50 (31-70)	43.5 (25.75-70)	0.53	45 (23.5-69.75)	44.5 (26.5-72.25)	0.740	44 (25.7-70)	47.5 (32.7-67.25)	0,604
PT	53 (39-67)	43 (28-88)	0.804	40 (29.75-74)	45 (28-87.75)	0.576	40.5 (27.75-88)	58 (36.25-87.75)	0,316
BUN	15.42 (10.28-20.07)	13.6 (10.3-17.42)	0.62	13.03 (8.52-15.2)	13.55 (10.28-18.162)	0.254	13.55 (10.28-17.75)	12.84 (10.27-19.62)	0,984
CR	0.9 (0.7-0.9)	0.9 (0.9-1.1)	0.498	0.9 (0.72-1.125)	0.9 (0.8-1.3)	0.556	0.9 (0.8-1.2)	0.9 (0.77-1.32)	0,622
eGFR	95 (92-108)	87.5 (72-105)	0.036*	94 (70-105)	91 (76.25-106.75)	0.768	92 (77-107)	90.5 (74-102.25)	0,443
NLR	6.18 (3.85-9.03)	5.62 (2.93-10.11)	0.927	4.75 (2.89-8.73)	5.9 (2.8-10.06)	0.618	5.25 (2.97-9.03)	6.18 (2.94-15.42)	0,385

Table 7. Analysis of blood gas analyses parameter with all SNPs.

			Parameters
			HCO₃	O₂-Sat	pCO₂	pH	pO₂	Std-HCO₃	Total CO₂
rs6092 SEPINE1	GG (n=15)	Median (Q1-Q3)	21.9 (19.7-23.9)	96.8 (91.2-99.4)	33.9 (32.3-35.6)	7.4 (7.37-7.43)			23 (20.7-25.2)
	GG (n=15)	Mean (SD)					104.96 (52.49)	22.98 (2.15)
	GA (n=70)	Median (Q1-Q3)	21.45 (19-24.05)	98.2 (95.97-99.4)	32.1 (29.35-36.8)	7.41 (7.39-7.44)			22.65 (19.9-25.75)
	GA (n=70)	Mean (SD)					110.81 (47.32)	23.08 (2.57)
		P-Value	0,665**	0,286**	0,242**	0,328**	0,671*	0,887*	0,729**
rs1051393 IFNAR2	TT (n=12)	Median (Q1-Q3)	22.05 (19.47-23.72)	97.85 (96.52-98.57)		7.4 (7.37-7.44)	92.5 (79.72-115.37)
	TT (n=12)	Mean (SD)			34.59 (5.02)			23.42 (2.13)	24 (4.11)
	TG+GG (n=60)	Median (Q1-Q3)	21.4 (18.82-23.87)	98.05 (95.5-99.47)		7.42 (7.38-7.44)	100 (77.6-142.95)
	TG+GG (n=60)	Mean (SD)			33.15 (6.28)			22.96 (2.58)	22.45 (3.5)
		P-Value	0,373**	0,774**	0,459*	0,539**	0,734**	0,569*	0,180*
rs2229207 IFNAR2	TT (n=63)	Median (Q1-Q3)		98.1 (96-99.4)	32 (29.5-35.5)	7.42 (7.39-7.44)
	TT (n=63)	Mean (SD)	21.34 (3.09)				111.13 (48.7)	22.95 (2.4)	22.66 (3.73)
	TC+CC (n=22)	Median (Q1-Q3)		97.75 (92.52-99.42)	35.5 (30.07-42.57)	7.41 (7.36-7.42)
	TC+CC (n=22)	Mean (SD)	22.49 (3.75)				105.9 (46.79)	23.39 (2.77)	23.59 (3.91)
		P-Value	0,163*	0,460**	0,041**	0,044**	0,662*	0,487*	0,32*

* Independent T Test.

** Mann-Whitney U Test.

Table 8. Analysis of coagulation and inflammatory factors with all SNPs.

Parameters	rs6092 SERPINE1		P-Value	rs1051393 IFNAR2		P-Value	rs2229207 IFNAR2		P-Value
	GG (n=15)	GA (n=70)	P-Value	TT (n=12)	TG+GG (n=60)		TT (n=63)	TC+CC (n=22)
	Median (Q1-Q3)	Median (Q1-Q3)		Median (Q1-Q3)	Median (Q1-Q3)		Median (Q1-Q3)	Median (Q1-Q3)
CRP	80.1 (75.8-97.3)	73.4 (21.67-114)	0,44	78.9 (21.32-148.5)	78.8 (25.95-114.8)	0,373	75.5 (21.7-109.2)	95.15 (51.15-149.62)	0,211
D-dimer	820	605	0,721	480	655	1	560	770	0,231
	(420-2060)	(360-1060)		(312.5-1842.5)	(420-1240)		(360-1280)	(427.5-1302)

Discussion

Post Covid-19 respiratory syndrome refers to respiratory symptoms that persist for at least four weeks following Covid-19 infection. The results of the study on sex differences did not show any significant variation between the sexes or between the case and control categories. However, when considering the percentages, the number of male patients exceeded that of female patients. This aligns with previous studies suggesting that men are more susceptible to Covid-19 infection than women.¹⁴ This higher susceptibility is thought to be due to the greater expression of ACE2 receptors in males. ACE2 is the main receptor through which SARS-CoV-2 infects host cells.^15,16

Covid-19 patients with more severe symptoms are at an increased risk of developing post Covid-19 respiratory syndrome.¹⁷ Our study found a significant association between disease severity and the control group (p<0.05). Severe symptoms in these patients are often associated with a cytokine storm. This storm occurs when SARS-CoV-2 infects the body and induces an immune response that releases pro-inflammatory cytokines, such as IL-6, GM-CSF, and IFN-γ. The increased cytokine levels were further exacerbated by the binding of Gal-3 to the CD147 and CD26 receptors. Cytokine storms lead to the infiltration of macrophages and neutrophils into lung tissue.^18,19 The persistence of the virus in tissues and an inadequate immune response during the acute phase can impair the enzymatic function of the ACE2 receptor and worsen respiratory symptoms. Histopathological studies have demonstrated that SARS-CoV-2 can persist in tissues for up to 230 days, potentially triggering repeated immune responses and chronic symptoms.^20,21

Our study also found a significant prevalence of fibrosis in Covid-19 patients. Approximately 11.6% of patients showed irreversible fibrotic lesions (p=0.013). A higher prevalence of worsening lung radiological findings (P=0.001) was also observed in the case group. While this difference may be partly attributed to the specific criteria of the case population, previous research indicates that individuals infected with Covid-19 who developed acute respiratory distress syndrome (ARDS) and require intubation during the acute phase have a threefold increased risk of developing fibrosis. Pathological repair of the alveolar epithelium, excessive extracellular collagen matrix formation, and loss of normal lung architecture during the acute phase contribute to fibrosis.²² Macrophage and neutrophil infiltration into lung tissue is a key factor in fibrosis. Cytokine storms and immunothrombosis exacerbate this condition. Immunothrombosis occurs when IL-6 is released by cells, activating platelets that express blood clotting factors, such as tissue factor (TF) and PAI-1.^20,23

Another contributing factor to lung fibrosis is the SARS-CoV-2 entry into alveolar cells via the ACE2 receptor. This infection leads to the downregulation of ACE2 and upregulation of Angiotensin II, which is harmful to cells. This cascade of events triggers inflammation and activates Transforming Growth Factor-beta (TGF-β), which in turn activates fibroblasts. These fibroblasts differentiate into myofibroblasts, which produce extracellular matrix (ECM) components, such as collagen and fibronectin, contributing to pulmonary fibrosis.^24,25 Additionally, fibrosis can result from ARDS, which occurs when viral infections cause significant damage to lung cells and provoke excessive inflammatory cytokine release. Mechanical ventilation may further aggravate this condition, leading to Ventilator-Induced Lung Injury (VILI), a known contributor to pulmonary fibrosis.²⁶

Our study did not find significant differences in symptoms between the post Covid-19 respiratory syndrome phenotype group (cases) and the Covid-19 population at the time of admission. The three most common symptoms in both groups were shortness of breath (83.7% vs. 69%), cough (81.4% vs. 76.2%), and fever (74.4% vs. 73.8%, respectively). The trend towards a higher incidence of dyspnea in the case population (83.7% vs. 69%, p = 0.111) may reflect the more severe clinical condition of these patients, who likely required ICU care upon admission.²² The severity of symptoms, both at admission and at their peak during hospitalization, was significantly worse in the post Covid-19 respiratory syndrome group, which could influence radiological outcomes, and vice versa.²²

SERPINE1 encodes plasminogen activator inhibitor 1 (PAI-1), which is involved in blood clotting processes.²⁷ PAI-1 inhibits tissue plasminogen activator (tPA), which converts plasminogen to plasmin. The inhibition of plasmin prevents fibrinolysis, leading to excessive clotting in Covid-19 patients, often evidenced by high levels of D-dimer.²⁸ Previous studies have shown an association between the rs6092 SNP of SERPINE1 and coagulation abnormalities in Covid-19 patients.¹¹ However, our study did not find an association between this SNP and the incidence of post Covid-19 respiratory syndrome, even though the GA genotype was present in 41% of patients. A separate study examining SNPs of the SERPINE1 gene, specifically rs1799889, found a correlation with PAI-1 expression in other diseases, indicating potential risk or protective effects.²⁹

IFNAR2 encodes Interferon Alpha and Beta Receptor 2, which play crucial roles in the antiviral response by binding to type I interferons (IFN-α and IFN-β). When SARS-CoV-2 infects the body, lymphocytes release interferons that bind to IFNAR2 receptors. This binding activates the Janus Kinase (JAK) pathway, resulting in the phosphorylation of STAT1 and STAT2. These transcription factors, in combination with IRF9, form the ISGF3 complex, which enters the nucleus and activates genes involved in the antiviral response, including MX1, OAS, and IRF7.^30,31

Previous studies have demonstrated a relationship between variations in IFNAR2 and severe Covid-19 symptoms.¹¹ However, our study found no significant association between SNPs rs1051393 and rs2229207 of the IFNAR2 gene and post Covid-19 respiratory syndrome in case and control patients. We observed a correlation between rs1051393 and fever symptoms (p=0.037) and between rs2229207 and chest pain symptoms (p=0.047). A study by Nhung et al. in a Vietnamese population also found an association between rs2229207 and the risk of contracting Covid-19.³² SNP rs1051393, which causes a thymine (T) to guanine (G) substitution, leads to an amino acid change from phenylalanine to valine. Similarly, rs2229207 causes a thymine (T) to cysteine (C) substitution, leading to a phenylalanine-to-serine change in the IFNAR2 receptor. These structural changes are believed to interfere with antiviral processes.³³

Additionally, our findings suggest a relationship between the rs2229207 SNP and blood potassium levels. We hypothesized that disruption of the antiviral response involving IFNAR2 receptors may lead to persistent viral infections, which in turn disturb the Renin-Angiotensin-Aldosterone System (RAAS). When SARS-CoV-2 infects host cells, it binds to ACE2 and impairs its function. This downregulation of ACE2 prevents the breakdown of Angiotensin II, which stimulates aldosterone release. In turn, aldosterone promotes sodium reabsorption and potassium excretion in the kidneys, contributing to hypokalemia in severe Covid-19 cases. While this condition may not directly affect the patient’s health, it highlights the potential impact of the IFNAR2 rs2229207 SNP on potassium levels in Covid-19 patients. Prior studies have reported that Covid-19 patients can experience hypokalemia for up to five months.^34,35

Conclusion

Based on the results of studies related to the relationship between gene variation or SNPs rs6092, rs1051393, and rs2229207, it was found that there was no association with patients with symptoms of post Covid-19 respiratory syndrome. However, further studies showed that the SNP rs1051393 IFNAR2 gene had an effect on fever symptoms, and the SNP rs222907 IFNAR2 gene had an effect on patients with chest pain.

Ethical consideration

All patients involved in this study have agreed, and the study has passed the ethical review, based on the Letter of Approval of the Research and Health Ethics Committee of the Faculty of Medicine, University of Indonesia and Dr. Cipto Mangunkusumo National Central General Hospital (FKUI-RSCM), Jakarta: KET-872/UN2. F1/ETIK/PPM.002.02/2023. This research was also supported by the Persahabatan National Respiratory Hospital, Jakarta (No. 98/KEPK-RSUPP/06/2023).

Data availibility statement

Figshare: The Genetic Association of Single Nucleotide Polymorphisms of SERPINE1 (rs6092) and IFNAR2 (rs1051393, rs2229207) Genes Is Related to Post Covid-19 Respiratory Syndrome. https://doi.org/10.6084/m9.figshare.30338875.³⁶

This project contains the following underlying data:

• Long Covid-19_F1000_dataset.xlsx – This file contains research data including: demographic information, clinical data, laboratory data, and genotyping results.

The data are available under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Acknowledgement

The authors acknowledge the support of Persahabatan National Respiratory Hospital for enabling access to the study population and assisting with data collection. The authors also gratefully acknowledge the National Research and Innovation Agency (BRIN – Badan Riset dan Inovasi Nasional), Indonesia, for their support and for facilitating the RIIM LPDP Research Grant.

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