F1000Research F1000Research 2046-1402 F1000 Research Limited London, UK 10.12688/f1000research.167074.1 Research Article Articles The Genetic Association of Single Nucleotide Polymorphisms of SERPINE1 (rs6092) and IFNAR 2 (rs1051393, rs2229207) Genes Is Related to Post Covid-19 Respiratory Syndrome

[version 1; peer review: 1 approved with reservations, 1 not approved]

 Ariani Yulia Conceptualization Investigation Supervision Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0002-6640-8530 a 1 Muhiardi Indra Formal Analysis Investigation Writing – Original Draft Preparation Writing – Review & Editing 1 Gunawan Indra Formal Analysis Investigation Writing – Original Draft Preparation Writing – Review & Editing 1 Ramadhani Nadhifa Tazkia Methodology Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0009-0006-5348-4119 1 Risti Shafa Talitha Methodology Writing – Original Draft Preparation Writing – Review & Editing 1 Sutarto Riyadi Investigation Writing – Original Draft Preparation Writing – Review & Editing 2 Susanto Agus Dwi Conceptualization Investigation Supervision Writing – Original Draft Preparation Writing – Review & Editing 2 3 Department of Medical Biology, University of Indonesia, Central Jakarta, Jakarta, 10430, Indonesia Persahabatan National Respiratory Hospital, East Jakarta, Jakarta, 13230, Indonesia Department of Pulmonology and Respiratory Medicine, University of Indonesia, Central Jakarta, Jakarta, 10430, Indonesia yulia.ariani@ui.ac.id

No competing interests were disclosed.

 21 11 2025 2025 14 1292 24 10 2025 Copyright: © 2025 Ariani Y et al. 2025 This is an open access article distributed under the terms of the Creative Commons Attribution Licence, 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. 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.

 covid-19 single nucleotide polymorphisms Covid-19 respiratory syndrome SERPINE1 IFNAR2 Riset Inovasi dan Indonesia Maju–Lembaga Pengelola Dana Pendidikan 36/IV/KS/06/2022 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. 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).

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%)
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.

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.

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
AG 35 (81.40%) 35 (83.33%)
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.

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
GA 35 (81.40%) 35 (83.33%)
rs1051393 (T>G) TT 5 (15.15%) 7 (17.95%) 0.909 (0.226-3.661) 1
TG 11 (33.33%) 14 (35.90%)
TT 5 (15.15%) 7 (17.95%) 0.756 (0.201-2.846) 0.937
GG 17 (51.52%) 18 (46.15%)
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%)
TT 30 (69.77%) 33 (78.57%) 0.303 (0.030-3.073) 0.356
CC 3 (6.98%) 1 (2.38%)

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).

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.

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

Mann Whitney U Test.

Na: Natrium; K: Pottasium; Cl: Chloride; Hb: Hemoglobin; Leu: Leukocytes; PLT: Platelet; Neu: Neutrophil; Eos: Eosinophil; Bas: Basophil, Lim: Lymphocyte; Mo: Monocytes; NLR: Neutrophil Lymphocyte Ratio; BUN: Blood Urea Nitrogen; Cr: Creatinine; SGPT: Serum Glutamic Pyruvic Transaminase; SGOT: Serum Glutamic Oxaloacetic Transaminase; eGFR: Estimated Glomerular Filtration Rate.

 Analysis of blood gas analyses parameter with all SNPs.
Parameters
HCO 3 O 2-Sat pCO 2 pH pO 2 Std-HCO 3 Total CO 2
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)
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)
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)
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)
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)
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)
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 *

HCO 3: bicarbonate; O 2 -Sat, oxygen saturation; pCO 2 , partial pressure of carbon dioxide; pH, potential of hydrogen; pO 2 , partial pressure of oxygen; Std-HCO 3 , standard bicarbonate; Total CO 2 , total carbon dioxide.

Independent T Test.

Mann-Whitney U Test.

 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) 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)

Mann-Whitney U Test.

CRP: C-Reactive Protein.

 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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Surg. 2021 Feb;62:358–361. 10.1016/j.amsu.2021.01.049 Reference Source Alfano G Ferrari A Fontana F : Hypokalemia in Patients with COVID-19. Clin. Exp. Nephrol. 2021 Apr 4;25(4):401–409. 33398605 10.1007/s10157-020-01996-4 PMC7781399 Ariani Y Muhiardi I Gunawan I : The Genetic Association of SNPs of SERPINE1 and IFNAR2 Genes Related to Post Covid-19 Respiratory Syndrome.[dataset]. Figshare. 2025. 10.6084/m9.figshare.30338875 10.5256/f1000research.184154.r441148 Reviewer response for version 1 Saba Abdullah Al 1 Referee University of Dhaka, Dhaka, Dhaka Division, Bangladesh

Competing interests: No competing interests were disclosed.

 7 1 2026 Copyright: © 2026 Saba AA 2026 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation reject

The authors investigated the association of rs1051393 and rs2229207 within the IFNAR2 gene and rs6092 within the SERPINE1 gene with post COVID-19 respiratory syndrome. No significant association was observed between post COVID-19 respiratory syndrome and these variants. However, the variants were significantly associated with certain COVID-19 symptoms and clinical parameters. The paper contains major errors. The following points should be addressed by the authors.

Introduction section

The authors did not provide an explicit literature review regarding the association of SERPINE1 and IFNAR2 gene variants with post COVID-19 respiratory syndrome in the Introduction section. Moreover, the rationale behind the selection of these two genes and the specific variants rs1051393, rs2229207, and rs6092 is not adequately justified.

Methods section

The number of study participants is too low. The authors did not provide a priori sample size calculation or a post hoc power analysis. As a result, this study most likely lacks sufficient statistical power to support the conclusions drawn. Moreover, the case and control groups are confusing and poorly defined. The control group is not clearly delineated, which is misleading. The criteria based on which study subjects were classified as cases according to PDPI should be supported with appropriate references from WHO or CDC guidelines.

In the context of COVID-19, the time period of infection is very important, as patients exhibited different symptoms depending on the SARS-CoV-2 variants with which they were infected. Therefore, the timeframe of infection represents a potential confounder in this analysis. No such consideration has been addressed in this study.

Additionally, the authors should justify why saliva samples were collected instead of blood samples.

Results section

There are inconsistencies in the tables. In Tables 7 and 8, commas are used instead of decimal points in the reported p-values. Also in table 1, commas are used instead of decimal points in some instances. In Table 1, the word “symptoms” is misspelled as “symptomps.” 

The authors should have conducted logistic regression analyses to investigate the association between the genetic variants and post COVID-19 respiratory syndrome. Furthermore, linear regression analyses should have been performed to assess the association between the variants and quantitative clinical parameters. Both linear and logistic regression analyses should be adjusted for potential confounding variables.

The authors conducted genotypic association analyses of the variants with post COVID-19 respiratory syndrome. However, genetic inheritance models such as codominant, dominant, recessive, and over-dominant models should have been applied to identify the model that best explains the data.

Discussion section

The Discussion section should be rewritten. The authors should discuss findings from similar studies and compare their results accordingly. The genotypic frequencies of rs1051393, rs2229207, and rs6092 should be compared with frequencies reported in other populations using data from the 1000 Genomes Project, the NCBI ALFA dataset, and other relevant resources. Finally, the strengths and limitations of the study should be clearly stated.

Is the work clearly and accurately presented and does it cite the current literature?

Partly

If applicable, is the statistical analysis and its interpretation appropriate?

No

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

No

Are the conclusions drawn adequately supported by the results?

No

Are sufficient details of methods and analysis provided to allow replication by others?

Partly

Reviewer Expertise:

Genetics and Genomics, Bioinformatics, Immunology, Population Genetics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

Ariani Yulia Medical Biology, University of Indonesia, Depok, West Java, Indonesia

Competing interests: No competing interests were disclosed.

 17 4 2026

Introduction section

The authors did not provide an explicit literature review regarding the association of SERPINE1 and IFNAR2 gene variants with post COVID-19 respiratory syndrome in the Introduction section. Moreover, the rationale behind the selection of these two genes and the specific variants rs1051393, rs2229207, and rs6092 is not adequately justified.  Response:  We thank the reviewer for this valuable comment. Regarding this point, the authors consider that the selected variants may have potential pathomechanistic roles. Therefore, in this study, we attempted to investigate their association with post–COVID-19 respiratory syndrome. This rationale has now been explicitly stated in the Introduction section.

Methods section

The number of study participants is too low. The authors did not provide a priori sample size calculation or a post hoc power analysis. As a result, this study most likely lacks sufficient statistical power to support the conclusions drawn. Moreover, the case and control groups are confusing and poorly defined. The control group is not clearly delineated, which is misleading. The criteria based on which study subjects were classified as cases according to PDPI should be supported with appropriate references from WHO or CDC guidelines.  Response:  This was a retrospective study involving patients who experienced respiratory syndrome after COVID-19. The subjects were patients treated at the Persahabatan Central General Hospital (RSUP), Jakarta, Indonesia, from 2020-2021. The inclusion criteria for this research were divided into case and control. Patients were classified to be in the case population if at least one of the following criteria was met: Hospitalization duration of ≥ 28 days; or < 28 days with pulmonary fibrosis; or < 28 days with no improvement of radiological findings and at least one persistent respiratory symptom (cough, dyspnea, malaise, chest pain, or sore throat) at the time the patient was discharged. The control population consists of patients who met none of the case criteria. The exclusion criteria of this study were patients who did not present with any clinical symptoms, didn’t give consent, or, although they met the inclusion criteria, had passed away at the time of the study. We examined the medical records of COVID-19 patients from that period, did a total sampling, and got 85 participants (43 subjects and 42 control subjects).

In the context of COVID-19, the time period of infection is very important, as patients exhibited different symptoms depending on the SARS-CoV-2 variants with which they were infected. Therefore, the timeframe of infection represents a potential confounder in this analysis. No such consideration has been addressed in this study.  Response:  The regression analysis was performed in the manuscript.

Additionally, the authors should justify why saliva samples were collected instead of blood samples.  Response:  This section has been added to the manuscript, explicitly stated in the method section.

Results section

There are inconsistencies in the tables. In Tables 7 and 8, commas are used instead of decimal points in the reported p-values. Also in table 1, commas are used instead of decimal points in some instances. In Table 1, the word “symptoms” is misspelled as “symptomps.”  Response:  The inconsistency in the use of commas and dots for decimal numbers has been corrected in Tables 7 and 8. And then, the misspelling has been corrected explicitly in Table 7 of the manuscript.

The authors should have conducted logistic regression analyses to investigate the association between the genetic variants and post COVID-19 respiratory syndrome. Furthermore, linear regression analyses should have been performed to assess the association between the variants and quantitative clinical parameters. Both linear and logistic regression analyses should be adjusted for potential confounding variables.  Response:  Thank you for this valuable comment. We agree that the timeframe of infection may act as a potential confounder due to differences in SARS-CoV-2 variants. Therefore, we included the infection period as a covariate in the regression analysis to control for its potential confounding effect. The results have been updated accordingly and are now described in the revised manuscript (Methods and Results sections).

The authors conducted genotypic association analyses of the variants with post COVID-19 respiratory syndrome. However, genetic inheritance models such as codominant, dominant, recessive, and over-dominant models should have been applied to identify the model that best explains the data.  Response:  Thank you for this important suggestion. We agree that applying multiple genetic inheritance models provides a more comprehensive understanding of the association.

Therefore, we have performed analyses under codominant, dominant, recessive, and over-dominant models for all investigated SNPs. The corresponding results have been added to the revised manuscript, and the interpretation has been updated accordingly.

Discussion section

The Discussion section should be rewritten. The authors should discuss findings from similar studies and compare their results accordingly. The genotypic frequencies of rs1051393, rs2229207, and rs6092 should be compared with frequencies reported in other populations using data from the 1000 Genomes Project, the NCBI ALFA dataset, and other relevant resources. Finally, the strengths and limitations of the study should be clearly stated.  Response:  We have thoroughly revised the Discussion section by incorporating comparisons with previous studies and publicly available datasets (NCBI) for the genotypic and allelic frequencies of the investigated SNPs.

10.5256/f1000research.184154.r443744 Reviewer response for version 1 Verma Shrikant 1 Referee https://orcid.org/0000-0002-8356-8448 Era University, Lucknow, India

Competing interests: No competing interests were disclosed.

 6 1 2026 Copyright: © 2026 Verma S 2026 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

Authors mention ‘Severity on Admission’ in Table 1. Please provide a clear description of the criteria used for categorization in the methodology section, including clinical, laboratory, or supportive intervention parameters, to ensure reproducibility and clarity.

Did the authors verify the minor allele frequency (MAF) of the reported SNPs? Please provide the source or database used, and mention whether the frequencies were consistent with the studied population

The authors should clearly specify the inclusion and exclusion criteria in the methodology section to ensure reproducibility and clarify the study population.

The Introduction section requires further improvement. The authors should provide a clearer background, highlight the knowledge gap, and explicitly state the study’s rationale and objectives.

The Discussion section also needs further improvement. The authors should provide a deeper interpretation of the findings, compare results with existing recent literature, discuss potential mechanisms, and address study limitations.

The manuscript requires language improvement. The authors should revise the text for clarity, grammar, and readability to ensure that the scientific content is clearly communicated.

Since multiple variables were analysed, did the authors apply a Bonferroni correction or another method to adjust for multiple comparisons? Please clarify this in the data analysis section.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Molecular Biology and Genetics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Ariani Yulia Medical Biology, University of Indonesia, Depok, West Java, Indonesia

Competing interests: No competing interests were disclosed.

 17 4 2026

Authors mention ‘Severity on Admission’ in  Table 1. Please provide a clear description of the criteria used for categorization in the methodology section, including clinical, laboratory, or supportive intervention parameters, to ensure reproducibility and clarity.  Response:  We divided patients’ severity into mild, moderate, severe, and critical. Mild disease is characterized by symptoms without evidence of viral pneumonia or hypoxia, such as fever, cough, fatigue, myalgia, or anosmia/ageusia, with SpO₂ >95% on room air. Moderate disease is defined by clinical signs of pneumonia, including fever, cough, dyspnea, or tachypnea, without features of severe pneumonia, with SpO₂ >93% on room air. Severe disease (severe pneumonia) is characterized by pneumonia accompanied by at least one of the following: respiratory rate >30 breaths/minute, severe respiratory distress, or SpO₂ <93% on room air. Critical disease includes patients with acute respiratory distress syndrome (ARDS), sepsis or septic shock, or other conditions requiring life-support interventions such as mechanical ventilation or vasopressor therapy.

Did the authors verify the minor allele frequency (MAF) of the reported SNPs? Please provide the source or database used, and mention whether the frequencies were consistent with the studied population.  Response:  In this study, three single nucleotide polymorphisms (SNPs) were analyzed, namely rs6092 in the SERPINE1 gene, rs1051393 in the IFNAR2 gene, and rs2229207 in the IFNAR2 gene. The first SNP analyzed was rs6092 in the SERPINE1 gene. Information on the minor allele frequency (MAF) of this SNP was obtained from the dbSNP database. After verification using the dbSNP database for rs6092 in the SERPINE1 gene, with the reference allele G and the alternative allele A, the minor allele frequency (MAF) was found to be 0.078 (7.8%) in the Asian population and 0.115 (11.5%) in the Southeast Asian population. Based on these MAF data, the rs6092 SNP shows a relatively high frequency in Asian populations, making it a relevant candidate for further investigation. The second SNP analyzed was rs1051393 in the IFNAR2 gene. The minor allele frequency (MAF) data for this variant were obtained from the dbSNP database. The second SNP analyzed was rs1051393 in the IFNAR2 gene. The minor allele frequency (MAF) data for this variant were obtained from the dbSNP database. The third SNP analyzed was rs2229207 in the IFNAR2 gene. for SNP rs2229207 in the IFNAR2 gene, the reference allele is T and the alternative allele is C. In the general Asian population, the minor allele frequency (MAF) of the C allele ranges from approximately 16–17%, indicating that this SNP is relevant and may be considered for inclusion in studies conducted in Indonesia. The three SNPs selected for this study have been verified and are suitable for investigation in the Indonesian population.

The authors should clearly specify the inclusion and exclusion criteria in the methodology section to ensure reproducibility and clarify the study population.  Response:  The inclusion criteria for this research were divided into case and control. Patients were classified to be in the case population if at least one of the following criteria was met: Hospitalization duration of ≥ 28 days; or < 28 days with pulmonary fibrosis; or < 28 days with no improvement of radiological findings and at least one persistent respiratory symptom (cough, dyspnea, malaise, chest pain, or sore throat) at the time the patient was discharged. The control population consists of patients who met none of the case criteria. Patients who did not present with any clinical symptoms were excluded from the study.

The Introduction section requires further improvement. The authors should provide a clearer background, highlight the knowledge gap, and explicitly state the study’s rationale and objectives. Response:  The Introduction has been revised to clarify the background, explicitly define the knowledge gap, and clearly state the study rationale and objectives.

The Discussion section also needs further improvement. The authors should provide a deeper interpretation of the findings, compare results with existing recent literature, discuss potential mechanisms, and address study limitations.  Response:  The discussion section has been revised for improvement about deeper interpretation and compare the result with other studies before.

The manuscript requires language improvement. The authors should revise the text for clarity, grammar, and readability to ensure that the scientific content is clearly communicated.  Response:  The manuscript has been carefully revised to improve the language, grammar, clarity, and readability throughout the text.

Since multiple variables were analysed, did the authors apply a Bonferroni correction or another method to adjust for multiple comparisons? Please clarify this in the data analysis section.  Response:  To adjust for multiple comparisons in the data analysis, we applied the Benjamini–Hochberg correction. Since this study involves genetic association analysis, this method is considered appropriate for controlling the false discovery rate.