v
Search
Advanced

Publications > Journals > Gene Expression> Article Full Text

  • Original Article
  • OPEN ACCESS

SERF2 (Hero7) Gene Polymorphism is Associated with Severe Course of COVID-19

  • Anna Dorofeeva,
  • Maxim Ivenkov,
  • Ksenia Kobzeva and
  • Olga Bushueva* 
 Author information 

Abstract

Background and objectives

Chaperone-like proteins are involved in the pathogenesis of coronavirus infection through regulation of the viral life cycle, immune response, and antigen presentation. A recently discovered class of chaperones, called heat-resistant obscure proteins (Hero proteins), performs functions similar to other molecular chaperones. This study aimed to investigate the association between the gene encoding the Hero protein SERF2 (Hero7) and the risk of severe COVID-19.

Methods

This case-control study was conducted according to the STROBE protocol. A total of 1,373 unrelated Russians (178 patients with severe COVID-19 and 1,195 controls) were recruited. Genotyping of rs4644832 in the SERF2 gene was performed using a probe-based polymerase chain reaction approach. The effects of the single nucleotide polymorphisms (SNPs) were analyzed using bioinformatics tools, including GTExPortal, eQTLGen, HaploReg, atSNP, Gene Ontology, Lung Disease and Common Metabolic Diseases Knowledge Portals, and the STRING database.

Results

SNP rs4644832 in the SERF2 gene (effect allele G) was associated with a decreased risk of severe COVID-19 in the total sample (odds ratio (OR) = 0.56, 95% confidence interval (CI) 0.39–0.81, P = 0.001), females (OR = 0.51, 95% CI 0.31–0.87, P = 0.006), non-smokers (OR = 0.46, 95% CI 0.29–0.74, P = 0.0004), individuals with body mass index ≥ 25 (OR = 0.42, 95% CI 0.25–0.7, P = 0.0004), individuals with low fruit and vegetable intake (OR = 0.38, 95% CI 0.22–0.67, P = 0.0004), and individuals with low physical activity (OR = 0.41, 95% CI 0.23–0.75, P = 0.002).

Conclusions

The G allele of rs4644832 in the SERF2 gene appears to have a protective effect against severe COVID-19. Functional annotation of rs4644832 suggests that it may influence COVID-19 pathogenesis through regulation of proteostasis, ubiquitination, inflammation-induced protein aggregation, the viral life cycle, and cytoskeletal functions.

Keywords

COVID-19, Heat-resistant obscure, Hero proteins, SERF2, Hero7, SNP, Chaperones, Protein aggregation, Viral life cycle

Introduction

The coronavirus disease (COVID-19) outbreak caused by the SARS-CoV-2 virus has become one of the largest pandemics in modern history. According to the World Health Organization, as of January 2025, COVID-19 has spread to 215 countries and caused more than seven million deaths worldwide.1 The spectrum of clinical forms of COVID-19 varies from asymptomatic infection to severe acute respiratory syndrome.2

It has been discovered that proteins with chaperone-like properties are actively involved in the pathogenesis of COVID-19 by regulating the immune response and viral replication,3 contributing to the development of a cytokine storm,4 and participating in the antigen presentation of viral proteins from infected cells.5 Furthermore, SARS-CoV-2 shares immunogenic epitopes with several human chaperones, which can lead to immune hyperactivation,6,7 multi-organ damage,8 and post-acute sequelae of COVID-19.9 In addition, as obligate intracellular parasites, viruses can seize control over the host cell metabolic machinery, including the chaperoning system, to maintain their life cycle and sustain productive infection.10

A recently discovered class of chaperones, known as heat-resistant obscure (Hero) proteins, is likely to perform functions similar to other molecular chaperones, such as maintaining proteostasis and protecting proteins from pathological aggregation.11,12 Hero proteins are hydrophilic, highly charged, heat-resistant proteins with low molecular weight and disordered structures. These properties allow them to protect other proteins from denaturation under extreme conditions, either through simple “molecular shielding” or by promoting liquid-liquid phase separation.13

Small EDRK-rich factor 2 (SERF2, also known as Hero 7), a member of the Hero protein family, is known for its distinctive role in protein aggregation, which varies depending on the protein “client”.13 Members of the SERF family perform dual functions, both promoting and preventing fibril formation of amyloidogenic proteins.14 Several studies have confirmed that SERF2 increases aggregation of huntingtin, β-amyloid, and α-synuclein, contributing to amyloid proteotoxicity.15–17 However, it also prevents aggregation of TAR DNA-binding protein 43 (TDP-43), which regulates viral RNA expression and has been actively studied in the research on COVID-19.18–20

The SERF protein family has an amino acid composition similar to that of DNA- and RNA-binding proteins, such as zinc-finger proteins.18,21,22 Since zinc-finger proteins are heavily implicated in COVID-19 pathogenesis,23,24 SERF2 may perform similar functions, including the regulation of viral gene expression.18

In our previous studies, we identified an association between the SERF2 SNP rs4644832 and the risk of cerebrovascular diseases.12,25 Because critical illness in COVID-19 and cerebrovascular diseases share several key pathogenic mechanisms, such as increased production of proinflammatory cytokines, hypercoagulation, endothelial dysfunction, inflammation, and oxidative stress,26–34 we hypothesized that SNP rs4644832 correlates with an increased risk of severe COVID-19. Therefore, we set out to investigate the association between SERF2 SNP rs4644832 and the risk of severe COVID-19.

Materials and methods

This study follows the criteria of a case-control study, according to the STROBE guidelines. A total of 1,373 unrelated Russians (178 patients with a severe course of COVID-19 and 1,195 controls with mild or asymptomatic COVID-19) from Central Russia were recruited for the study. The patients were enrolled during the COVID-19 pandemic from 2020 to 2022 at the intensive care units of Kursk Regional Hospital No. 6 and Kursk Regional Tuberculosis Dispensary. The patients were recruited consecutively. Inclusion criteria for the study were self-declared Russian descent and a birthplace within Central Russia. The COVID-19 patients were enrolled under the following exclusion criteria: hepatic or renal failure, endocrine, autoimmune, and/or oncological diseases, which could alter laboratory parameters. All patients in the case group had polymerase chain reaction (PCR)-confirmed COVID-19 and required intensive care unit admission. The control group consisted of healthy volunteers who were diagnosed with COVID-19 but did not require hospitalization (Fig. 1). Baseline and clinical characteristics of the study population are listed in Table S1.

Study design.
Fig. 1  Study design.

Low fruit and vegetable intake was defined according to the World Health Organization guidelines as consuming less than 400 g per day.35 Low physical activity was defined as engaging in less than 180 minutes per week of various physical activities.36

Genomic DNA was extracted from blood samples, and the quality of extracted DNA was assessed using a Nanodrop spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). SNP rs4644832 in SERF2 was genotyped using allele-specific probe-based PCR in accordance with a previously published protocol. Details of primer design, reaction solution, and PCR protocol steps have been previously published.12

STATISTICA software (version 13.3, Informer Technologies, Inc., Santa Clara, CA, USA) was used for statistical analysis. The normality of distribution for quantitative data was assessed using the Shapiro-Wilk test. Most quantitative parameters deviated from a normal distribution; thus, they were presented as the median along with the first and third quartiles [Q1 and Q3]. The Kruskal–Wallis test was applied to compare quantitative variables among three independent groups. Pairwise comparisons were then performed using the Mann-Whitney test. These tests were employed to analyze associations between rs4644832 in SERF2 and clinical features of COVID-19 in the patient group.

To evaluate the independent contribution of the rs4644832 variant in SERF2 to severe COVID-19 risk while adjusting for relevant covariates, we performed multivariate logistic regression in R (version 4.4.1). The analysis included one SNP (rs4644832 in SERF2) and five covariates: age, sex, smoking status, body mass index (BMI), and vegetable intake. Unfortunately, data on physical activity levels were unavailable for the control group and could not be included as a covariate in the regression model.

The dependent variable was disease status (0 = control, 1 = severe COVID-19), and all categorical predictors (SNP genotype, sex, smoking status, vegetable intake) were encoded as factors. Genotypes were modeled under a categorical (non-additive) framework. Continuous variables (age, BMI) were used as numeric predictors. Individuals with missing values for any variable were excluded from the analysis.

The dataset was split into a training set (80%) and a testing set (20%) using the caret package to allow model evaluation. The logistic regression model was fitted using the glm function with a binomial link function. Model performance was evaluated by the likelihood ratio test against a null (intercept-only) model, Nagelkerke’s pseudo-R2 (calculated with the DescTools package), and classification accuracy on the training set.

To visualize significant predictors (P < 0.05), odds ratios (OR) and 95% confidence interval (CI) were extracted using the broom package, exponentiated, and plotted on a log scale. Predictors with OR > 1 were classified as “risk” factors, while those with OR < 1 were classified as “protective”.

Compliance of genotype distributions with the Hardy-Weinberg equilibrium was assessed using Fisher’s exact test. Genotype frequencies and their correlation with disease risk were analyzed using SNPStats software (https://www.snpstats.net/start.htm ). A log-additive model was used for genotype association analysis. Associations within the entire group of COVID-19 patients and controls were adjusted for age and sex.

Due to the potential modifying influence of environmental risk factors on the association of genetic markers with disease, associations were analyzed based on the presence or absence of the risk factor. When information about an environmental risk factor was unavailable in the control group (for fruit/vegetable intake, physical activity levels, and BMI), the patient group was compared to the overall control group. In such cases, the Bonferroni correction was applied to account for multiple comparisons.

The following bioinformatics resources were used to analyze the functional effects of SNPs:

  • The bioinformatic tool GTExPortal (http://www.gtexportal.org/ ) was used to analyze the expression levels of the gene in whole blood, blood vessels, and lungs, as well as to analyze the expression of quantitative trait loci (eQTLs).37 The method of eQTL analysis is fully described in our previous article.38

  • The eQTLGen (https://www.eqtlgen.org/ ) was applied for the examination of eQTL expression in peripheral blood.39 eQTLGen incorporates 37 datasets, with a total of over 31,000 individuals.

  • The HaploReg v4.2 (https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php ) was used to assess histone modifications. Histone H3 protein lysine residues at positions 27 and 9 (H3K27ac and H3K9ac, respectively), as well as mono- and tri-methylation at position 4 (H3K4me1 and H3K4me3), were studied.40–42 This resource compiles ChIP-seq data from the Roadmap Epigenomics projects.43

  • The atSNP affinity test (http://atsnp.biostat.wisc.edu/search ) was used to assess the impact of SNPs on the gene’s affinity for transcription factors (TFs).44 In detail, the method of TF analysis is described earlier in our study.45

  • The Gene Ontology online tool (http://geneontology.org/ ) provides a systematic classification of gene functions, which we used to analyze the joint involvement of TFs linked to the reference or SNP alleles in biological processes directly related to the pathogenesis of COVID-19.46

  • The Lung Disease Knowledge Portal (https://cd.hugeamp.org/ ) and Common Metabolic Diseases Knowledge Portal (https://hugeamp.org/ ) were used to analyze the correlation between SNPs and phenotypic risk factors of severe COVID-19.

  • The STRING database’s bioinformatic tools were used to study key functional partners of SERF2. Moreover, the STRING database was utilized to analyze protein-protein interactions between SERF2 and its functional partners.47,48

  • The Comparative Toxicogenomics Database (https://ctdbase.org/ ) was employed to evaluate the influence of various chemicals and hormones on SERF2 expression.49

Results

The distribution of genotype frequencies conformed to Hardy–Weinberg equilibrium (P > 0.05). The log-additive model analysis was performed in the total sample. Additionally, we analyzed groups stratified by sex, BMI, smoking status, fruit and vegetable intake, and physical activity to identify associations between genetic variants and severe COVID-19 depending on the presence or absence of environmental risk factors (Table 1).

Table 1

Statistically significant associations of rs4644832 in SERF2 with severe COVID-19 in subgroups stratified by sex, smoking status, fruit and vegetable intake, physical activity, and BMI

Genetic variantEffect alleleOther alleleNOR [95%CI]1P2 (PBonf)NOR [95%CI]1P2 (PBonf)
MalesFemales
rs4644832 SERF2GA5910.62 [0.36–1.05]0.0597820.51 [0.31–0.87]0.006
SmokersNon-smokers
rs4644832 SERF2GA4070.83 [0.44–1.56]0.559330.46 [0.29–0.74]0.0004
Low fruit and vegetable intakeNormal fruit and vegetable intake
rs4644832 SERF2GA3670.38 [0.22–0.67]0.0002 (0.0004)5250.87 [0.50–1.52]0.63 (1.0)
Low physical activityNormal physical activity
rs4644832 SERF2GA4430.41 [0.23–0.75]0.001 (0.002)4530.71 [0.43–1.20]0.19 (0.4)
BMI < 25BMI ≥ 25
rs4644832 SERF2GA2010.94 [0.49–1.80]0.85 (1.0)8130.42 [0.25–0.70]0.0002 (0.0004)

All calculations were performed relative to the minor alleles (effect allele); 1odds ratio (OR) and 95% confidence interval (CI); 2P-value; statistically significant differences are marked in bold. SERF2 refers to gene encoding small EDRK-rich factor 2.

The analysis revealed significant associations between the polymorphic variant rs4644832 in SERF2 and severe COVID-19. The G allele exhibited a protective effect in the total sample (OR = 0.56, 95% CI 0.39–0.81, P = 0.001) (Table 1), as well as in females (OR = 0.51, 95% CI 0.31–0.87, P = 0.006), non-smokers (OR = 0.46, 95% CI 0.29–0.74, P = 0.0004), individuals with BMI ≥ 25 (OR = 0.42, 95% CI 0.25–0.70, PBonf = 0.0004), individuals with low fruit and vegetable intake (OR = 0.38, 95% CI 0.22–0.67, PBonf = 0.0004), and individuals with low physical activity (OR = 0.41, 95% CI 0.23–0.75, PBonf = 0.002) (Table 1).

Multivariable logistic regression analysis

Multivariable logistic regression adjusted for age, sex, smoking status, BMI, and vegetable intake revealed significant associations between genetic and clinical factors and severe COVID-19 (Likelihood Ratio Test χ2 = 122.5, P < 0.001) (Table S2, Fig. 2). The model explained 25% of the variance (Nagelkerke R2 = 0.25) and correctly classified 86% of cases.

Forest plot of significant predictors of severe COVID-19.
Fig. 2  Forest plot of significant predictors of severe COVID-19.

Carriers of the SNP rs4644832 SERF2 A/G genotype had 45% lower odds of severe COVID-19 compared to A/A homozygotes (OR = 0.55, 95% CI 0.31–0.93, P = 0.03) (Fig. 2). The G/G genotype showed a non-significant protective trend (OR = 0.43, 95% CI 0.06–1.61, P = 0.3). Each additional year of age increased the odds by 7% (OR = 1.07, 95% CI 1.05–1.10, P < 0.001). Females had 60% lower odds than males (OR = 0.40, 95% CI 0.25–0.63, P < 0.001). Regular vegetable consumption was strongly protective (OR = 0.38, 95% CI 0.25–0.58, P < 0.001), whereas smoking showed no significant association (P = 0.6). Each unit increase in BMI increased the odds by 7% (OR = 1.07, 95% CI 1.03–1.11, P = 0.001) (Fig. 2).

Clinical features of COVID-19 and rs4644832 SERF2

We analyzed associations between rs4644832 in SERF2 and clinical features of COVID-19 among patients. In groups stratified by sex, age, smoking status, fruit and vegetable intake, and physical activity, several significant associations were observed. In females, A/A homozygotes were associated with higher C-reactive protein (CRP) levels than heterozygotes (P = 0.02).

Notably, rs4644832 influenced clinical features in both smokers and non-smokers. Among smokers, G/G homozygotes had a higher median BMI than A/A homozygotes and heterozygotes (P = 0.015). Among non-smokers, A/A homozygotes were associated with higher CRP levels (P = 0.005) and higher leukocyte counts (P = 0.025) compared to heterozygotes.

In the group aged over 68 years, protective A/A homozygotes were associated with higher thrombocyte counts than heterozygotes (P = 0.01).

In the group with high fruit and vegetable intake, A/A homozygotes had higher CRP levels than heterozygotes (P = 0.0075). Furthermore, in the low physical activity group, A/A homozygotes showed higher thrombocyte (P = 0.02) and leukocyte counts (P = 0.0495) compared to heterozygotes (Fig. 3, Table S3).

https://BioRender.com)." target="_self">https://BioRender.com)." alt="Associations between rs4644832 in SERF2 and C-reactive protein (CRP) levels in females (a); body mass index (BMI) in smokers (b); CRP levels (c) and leukocyte count (d) in non-smokers; thrombocyte count in the group older than 68 years (e); CRP levels in the high fruit and vegetable intake group (f); and thrombocyte (g) and leukocyte counts (h) in the low physical activity group (figure created using https://BioRender.com)." />
Fig. 3  Associations between rs4644832 in SERF2 and C-reactive protein (CRP) levels in females (a); body mass index (BMI) in smokers (b); CRP levels (c) and leukocyte count (d) in non-smokers; thrombocyte count in the group older than 68 years (e); CRP levels in the high fruit and vegetable intake group (f); and thrombocyte (g) and leukocyte counts (h) in the low physical activity group (figure created using https://BioRender.com ).

Molecular correlates of rs4644832 in SERF2

Analysis of cis-eQTL-mediated expression profiles of genes in various tissues revealed several relevant associations. The G allele of rs4644832 in SERF2 decreases expression of ADAL, CATSPER2, CATSPER2P1, MAP1A, STRC, and STRCP1 and increases expression of SERF2, ENSG00000249839, HYPK, PDIA3, and ZSCAN29 in lungs, blood vessels, and whole blood (Fig. 4, Table 2).

https://BioRender.com)." target="_self">https://BioRender.com)." alt="SERF2 molecular effects and their interconnection with local expression quantitative trait locus (cis-eQTL) effects and Reactome pathways (figure created using https://BioRender.com)." />
Fig. 4  SERF2 molecular effects and their interconnection with local expression quantitative trait locus (cis-eQTL) effects and Reactome pathways (figure created using https://BioRender.com ).

Each pathway in Reactome is identified by a stable ID that starts with the specie abbreviation, HSA for homo Sapiens, and ends with a unique ID. MHC, major histocompatibility complex; SERF2 refers to small EDRK-rich factor 2.

Table 2

Effect of the A allele of rs4644832 in SERF2 on gene expression (cis-eQTL) in various tissues (https://gtexportal.org )

SNPEffect alleleGene expressedP-valueEffect (NES)Tissue
rs4644832, SERF2AADAL0.00002↓ (−0.29)Artery - Aorta
2.00×10−7↓ (−0.26)Artery - Tibial
CATSPER25.30×10−7↓ (−0.2)Artery - Tibial
3.20×10−7↓ (−0.27)Lung
0.00052↓ (−0.12)Whole Blood
CATSPER2P10.0001↓ (−0.22)Artery - Tibial
ENSG000002498391.00×10−16↑ (0.68)Artery - Aorta
1.90×10−8↑ (0.6)Artery - Coronary
2.80×10−10↑ (0.41)Artery - Tibial
3.40×10−8↑ (0.4)Lung
HYPK8.1×10−6↑ (0.1)Lung
MAP1A7.8×10−6↓ (−0.17)Artery - Aorta
PDIA32.70×10−7↑ (0.1)Whole Blood
SERF21.1×10−6↑ (0.14)Artery - Aorta
0.00015↑ (0.16)Artery - Coronary
8.60×10−7↑ (0.088)Artery - Tibial
6.40×10−7↑ (0.11)Lung
1.60×10−10↑ (0.1)Whole Blood
STRC1.30×10−8↓ (−0.32)Artery - Aorta
3.90×10−7↓ (−0.27)Artery - Tibial
2.30×10−11↓ (−0.37)Lung
STRCP12.50×10−7↓ (−0.31)Artery - Aorta
5.00×10−8↓ (−0.28)Artery - Tibial
0.00002↓ (−0.24)Lung
ZSCAN290.00014↑ (0.13)Lung
0.00004↑ (0.1)Whole Blood

NES, normalized effect size; SERF2, gene encoding small EDRK-rich factor 2; SNP, single nucleotide polymorphism; ↑, increase the n effect; ↓, decrease in the effect.

In whole blood, the G allele of rs4644832 in SERF2 significantly decreases cis-eQTL–mediated regulation of SERF2, ZSCAN29, TUBGCP4, and PDIA3, while it increases cis-eQTL–mediated regulation of STRCP1, LCMT2, CATSPER2, MAP1A, STRC, CATSPER2P1, ADAL, and TRIM69 expression (Fig. 4, Table 3).

Table 3

Effect of the G allele of rs4644832 in SERF2 on gene expression (cis-eQTL) in whole blood (https://www.eqtlgen.org/cis-eqtls.html )

SNPAlleleGene expressedZ-scoreP-valueFDR
rs4644832, SERF2GZSCAN29↓ (−46.5249)3.27×10−310<0.05
SERF2↓ (−26.1112)2.72×10−150<0.05
TUBGCP4↓ (−23.7631)8.03×10−125<0.05
STRCP1↑ (20.6369)1.28×10−94<0.05
LCMT2↑ (18.2518)2.01×10−74<0.05
CATSPER2↑ (11.8697)1.70×10−32<0.05
PDIA3↓ (−10.7944)3.65×10−27<0.05
MAP1A↑ (9.1256)7.13×10−20<0.05
STRC↑ (9.0382)1.59×10−19<0.05
CATSPER2P1↑ (8.2227)1.99×10−16<0.05
ADAL↑ (6.7174)1.85×10−11<0.05
TRIM69↑ (6.2919)3.14×10−10<0.05

eQTL, expression of quantitative trait locus; FDR, false discovery rate; SNP, single nucleotide polymorphism; ↑, increase in Z-score; ↓, decrease in Z-score.

According to HaploReg v4.2, rs4644832 in SERF2 has a significant effect on histone modifications in COVID-19-related tissues, including blood vessels, lung, and blood cells. This variant is located within a DNA region binding to histone H3, characterized by mono-methylation at lysine 4 of histone H3 (H3K4me1), marking enhancers in blood cells, and tri-methylation at lysine 4 (H3K4me3), marking promoters in lungs, aorta, and blood cells. The effect of these histone marks is enhanced by acetylation of lysine 27 of histone H3 (H3K27ac), marking enhancers in lungs, aorta, and blood cells, and by acetylation of lysine 9 of histone H3 (H3K9ac), marking promoters in blood cells. Furthermore, rs4644832 in SERF2 is located within DNA regions hypersensitive to DNase-1 in blood samples (Table 4).

Table 4

Tissue-specific effects of rs4644832 in SERF2 on histone modifications (https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php )

TissueH3K4me1H3K4me3H3K27acH3K9acDNase
Lung–ProEnh––
Vessels – aorta–ProEnh––
BloodEnhProEnhProDNase

Enh, enhancer; Pro, promoter.

Bioinformatic resources including the Lung Disease Knowledge Portal (https://lung.hugeamp.org/ ) and Common Metabolic Diseases Knowledge Portal (https://hugeamp.org/ ) were used to analyze correlations between rs4644832 in SERF2 and phenotypic risk factors for severe COVID-19. It was found that the risk allele A of SNP rs4644832 SERF2 significantly increased the likelihood of hospitalization in COVID-19 patients. It was also associated with increased CRP levels and monocyte percentage, and decreased eosinophil count, eosinophil percentage, and insulin-like growth factor 1 levels (Table 5).

Table 5

Summary of associations between rs4644832 in SERF2 and phenotypes linked to a severe course of COVID-19

№SNPPhenotypeP-valueBeta (OR)Sample Size
1rs4644832, SERF2 (AHospitalized vs non-hospitalized COVID-1910.008OR▲1.064152 246
2Eosinophil percentage22.27×10−16Beta▼−0.00331 334 180
3Eosinophil count22.00×10−12Beta▼−0.00271 794 490
5Insulin-like growth factor (IGF-1) 20.00001Beta▼−0.0045445 573
7Plasma C-reactive protein20.0002Beta▲0.00521 215 140
9Monocyte percentage20.03Beta▲0.0053853 181

The effect allele is marked in bold. 1Data from Lung Disease Knowledge Portal; 2Data from Common Metabolic Diseases Knowledge Portal. OR, odds ratio; SERF2, gene encoding small EDRK-rich factor 2; SNP, single nucleotide polymorphism.

The protective G allele of rs4644832 in SERF2 creates DNA binding sites for 46 TFs involved in the following biological processes: positive regulation of CD8-positive, alpha-beta T cell differentiation (GO:0043378; false discovery rate (FDR) = 0.00301), negative regulation of CD4-positive, alpha-beta T cell differentiation (GO:0043371; FDR = 0.0399), cellular response to glucocorticoid stimulus (GO:0071385; FDR = 0.00763), nuclear receptor-mediated steroid hormone signaling pathway (GO:0030518; FDR = 0.01), and lung development (GO:0030324; FDR = 0.0144) (Table S4).

Protein-protein interactions of SERF2

Analysis of primary functional partners of SERF2 using the STRING database (protein-protein interactions (PPI) enrichment P-value: 3.92×10−5) revealed ten proteins with the most prominent interactions: actin related protein 2/3 complex subunit 2 (ARPC2), boule RNA binding protein (BOLL), huntingtin interacting protein K (HYPK), myosin light chain 6 (MYL6), ribosomal protein L23a (RPL23A), ribosomal protein lateral stalk subunit P1 (RPLP1), ribosomal protein S19 (RPS19), ribosomal protein S27 (RPS27), signal recognition particle 14 (SRP14), and translation machinery associated 7 homolog (TMA7) (Fig. 5, Table S5).

Predicted functional partners of SERF2 according to the STRING database (PPI enrichment <italic>P</italic>-value: 3.92 × 10<sup>−5</sup>).
Fig. 5  Predicted functional partners of SERF2 according to the STRING database (PPI enrichment P-value: 3.92 × 10−5).

ARPC2, actin related protein 2/3 complex subunit 2; BOLL, boule RNA binding protein; HYPK, huntingtin interacting protein K; MYL6, myosin light chain 6; RPL23A, ribosomal protein L23a; PPI, protein-protein interactions; RPLP1, ribosomal protein lateral stalk subunit P1; RPS19, ribosomal protein S19; RPS27, ribosomal protein S27; SRP14, signal recognition particle 14; TMA7, translation machinery associated 7 homolog.

The full list of biological processes and Reactome pathways involving SERF2 and its PPI-partners is presented in Table S6. Key pathways include cytoplasmic translation (GO:0002181; FDR = 0.0082), ribosome assembly (GO:0042255; FDR = 0.0395), viral mRNA translation (HSA-192823; FDR = 0.00016), infectious disease (HSA-5663205; FDR = 0.0062), and SARS-CoV-2 modulation of host translation machinery (HSA-9754678; FDR = 0.0254), among others.

Discussion

In this study, we provide genetic evidence that rs4644832 of SERF2 is associated with severe COVID-19. We found that the G allele of rs4644832 SERF2 has a protective effect against severe COVID-19, and this effect is modified by sex, smoking status, level of fresh fruit and vegetable intake, physical activity, and BMI. We observed the protective effect of allele G rs4644832 SERF2 exclusively in females and non-smokers. This can be explained by the positive regulation of SERF2 expression by tobacco components and androgens,50–53 as previously mentioned.12 Moreover, several studies confirm that estrogen decreases SERF2 expression.54,55

Additionally, the protective effect of rs4644832 SERF2 was also observed in subgroups with low fruit and vegetable intake, low physical activity, and overweight (BMI ≥ 25). We hypothesize that the manifestation of the G allele’s protective effect occurs in these subgroups with predisposing risk factors for severe COVID-19 due to the predominance of protective effects from lower BMI, normal fruit and vegetable intake, and regular physical activity.

The role of SERF2 in human pathology remains to be fully elucidated. Our research group recently discovered a link between rs4644832 SERF2 and ischemic stroke.12 In our previous study, we suggested that SERF2 influences ischemic stroke development through its ability to maintain proteome quality,12 which is also essential in the context of COVID-19.56

SERF2 plays a dual role in proteostasis. On one hand, it accelerates amyloid formation.15–17 Persistent fibrin amyloid microclots obstruct small vessels and inhibit tissue oxygenation, potentially leading to thrombotic complications and post-acute sequelae of COVID-19.57 On the other hand, SERF2 prevents TDP-43 aggregation.13 TDP-43 acts as a regulator of viral RNA expression due to its ability to interact with ribonucleoprotein complexes.19,58,59 Viral infections trigger TDP-43 aggregation; for instance, the SARS-CoV-2 main protease induces neurotoxic TDP-43 aggregation,20 and its spike S1 protein receptor-binding domain (SARS-CoV-2 S1 RBD) binds to TDP-43,60 suggesting TDP-43 involvement in the neurological symptoms of COVID-19.19

In cis-eQTL analysis, we have found that the risk allele A of rs4644832 upregulates SERF2 expression in blood vessels, lung, and whole blood. Therefore, it may be associated with increased proteotoxicity and amyloid clot formation. Furthermore, increased TDP-43 stability caused by high SERF2 expression can promote the viral life cycle.59

Some genes influenced by rs4644832 SERF2 cis-eQTL effects are linked to ubiquitination, which is essential for protein synthesis, signaling, and innate and adaptive immune responses.61 Efficient ubiquitination followed by degradation of SARS-CoV-2 spike protein ensures an antiviral response.62 Recent studies have shown that high ubiquitination levels are associated with a favorable COVID-19 prognosis due to proper immune regulation and prevention of immune damage.63 The G allele of rs4644832 SERF2 upregulates TRIM69, which encodes a human E3 ubiquitin-protein ligase critical for antiviral immunity, mediating major histocompatibility complex class I (MHC I) antigen processing and presentation.64–67 It is worth mentioning that SERF2 itself, conversely, prevents degradation of ubiquitinated proteins.68

According to GTExPortal data, the risk allele A of rs4644832 SERF2 increases PDIA3 expression via cis-eQTL effects. PDIA3 encodes a molecular chaperone that is a core component of major histocompatibility complex class I and is heavily implicated in antigen presentation to cytotoxic T lymphocytes.69–72 Overexpression of PDIA3 enhances Wnt/β-catenin signaling,73,74 which is involved in triggering the cytokine storm.75 Furthermore, recent studies confirm that PDIA3 disulfide isomerase activity participates in the viral life cycle76; its deletion significantly decreases viral load and inflammatory cytokine levels.77,78 Considering that the G allele of rs4644832 SERF2 reduces PDIA3 levels, this may explain the SNP’s protective effect against a severe course of COVID-19.

Moreover, in cis-eQTL analysis, we discovered that rs4644832 of SERF2 modulates the expression of several genes involved in maintaining cytoskeleton function, including MAP1A,79TUBGCP4,80 and STRC.81 The cytoskeleton ensures the structural integrity of the endothelial barrier; thus, endothelial dysfunction, one of the major pathological features of severe COVID-19,27,28 is believed to be associated with cytoskeleton disorganization.82 Viruses frequently depend on microtubules at multiple stages of their life cycle. SARS-CoV-2 employs the host cytoskeleton for virion transport, cell-to-cell spread, and disruption of the immune response.83

Notably, SERF2 and its functional partners in the PPI network are associated with viral mRNA translation and modulation of the host translation machinery by SARS-CoV-2. This discovery provides evidence of SERF2’s pivotal role in COVID-19 pathogenesis.

We accessed data from the Lung Disease Knowledge Portal and the Common Metabolic Diseases Knowledge Portal to identify associations between rs4644832 SERF2 and phenotypical risk factors for severe COVID-19. It was found that the risk A allele is associated with an increased likelihood of hospitalization in COVID-19 patients. Moreover, the A allele of rs4644832 SERF2 increases certain laboratory traits typical of severe COVID-19, such as monocyte percentage and CRP levels.

Elevation in monocyte proportion can be linked to the COVID-19-associated cytokine storm. The cytokine storm in COVID-19 differs considerably from the canonical cytokine storm seen in macrophage activation syndrome in other infectious diseases.84 In COVID-19, the atypical cytokine storm is orchestrated mainly by monocytes, whereas in macrophage activation syndrome, macrophages play the predominant role.85

Our analysis of COVID-19 clinical features corresponds with data from the Common Metabolic Diseases Knowledge Portal. It shows a correlation between the protective G allele of rs4644832 SERF2 and decreased CRP levels in females and non-smokers. CRP is one of the most accurate and sensitive markers of inflammation.86,87 It is synthesized in the liver in response to pro-inflammatory cytokines, such as interleukin-6, interleukin-1β, and tumor necrosis factor-α.88 In COVID-19, CRP levels have an indisputable prognostic and diagnostic value.89

To further explore the molecular mechanisms by which rs4644832 SERF2 influences COVID-19 pathogenesis, we analyzed TFs that bind to the DNA region containing the studied SNP and their associated biological processes. These TFs are involved in positive regulation of CD8-positive, alpha-beta T-cell differentiation (GO:0043378), negative regulation of CD4-positive, alpha-beta T-cell differentiation (GO:0043371), cellular response to glucocorticoid stimulus (GO:0071385), nuclear receptor-mediated steroid hormone signaling pathway (GO:0030518), and lung development (GO:003032).

CD8+ T-cells, or cytotoxic T-cells, are involved in eliminating cells infected by intracellular pathogens.90 CD4+ T-cells, or T-helper cells, play an important role in the adaptive immune response against intracellular pathogens.91

The efficacy of glucocorticoids in managing hyperimmune responses in COVID-19 patients is well established. They are effective immunosuppressors and are widely used to treat hypoxic respiratory failure and ARDS.92 Glucocorticoids reduce inflammation by regulating cytokine gene transcription and proinflammatory pathways.93 Activation of the cellular response to glucocorticoid stimulus and the nuclear receptor-mediated steroid hormone signaling pathway, promoted by the protective G allele of rs4644832 SERF2, may consequently enhance the efficacy of glucocorticoid treatment in severe COVID-19 cases.

Our study has several limitations. Firstly, since a COVID-19 diagnosis can be based on clinical evidence, the control group includes both PCR-confirmed and clinically confirmed cases, which may lead to misclassification. Secondly, our study is limited to the Russian population; therefore, confirming our results requires studies in diverse populations. Furthermore, we did not measure gene expression levels in the blood of SERF2 genotype carriers. Instead, we used bioinformatic tools to assess cis-eQTL effects of SNPs on gene expression. Similarly, histone modifications and TFs binding were analyzed exclusively using bioinformatic resources.

Conclusions

The present study reveals the protective effect of the G allele of rs4644832 SERF2 against severe COVID-19. These results provide novel insights into the involvement of Hero proteins in the pathogenesis of viral infections. Further studies may help uncover the detailed role of SERF2 in COVID-19 pathogenesis.

Supporting information

Supplementary material for this article is available at https://doi.org/10.14218/GE.2025.00057 .

Table S1

Baseline and clinical characteristics of the studied groups.

(DOCX)

Click here for additional data file.

Table S2

Odds ratios from multivariable logistic regression analyzing genetic and clinical predictors of severe COVID-19 (568 controls vs. 138 COVID-19 patients).

(DOCX)

Click here for additional data file.

Table S3

Clinical features associated with rs4644832 SERF2.

(DOCX)

Click here for additional data file.

Table S4

Analysis of the impact of rs4644832 SERF2 on the binding of transcription factors with DNA (data from Gene Ontology resources http://geneontology.org/ ).

(DOCX)

Click here for additional data file.

Table S5

Main functional characteristics of predicted functional partners of Small EDRK-rich factor 2 (SERF2).

(DOCX)

Click here for additional data file.

Table S6

Functional enrichments in the SERF2 network.

(DOCX)

Click here for additional data file.

Declarations

Acknowledgement

None.

Ethical statement

The study was conducted according to the guidelines of the Declaration of Helsinki (as revised in 2024) and was approved by the Ethical Review Committee of Kursk State Medical University (protocol №1 from January 11, 2022). Informed consent was obtained from all individual participants included in the study.

Data sharing statement

No additional data are available.

Funding

No funding was received to assist with the preparation of this manuscript.

Conflict of interest

All authors declare no conflicts of interest.

Authors’ contributions

Study design (OB), performance of experiments, analysis and interpretation of data, statistical analysis (AD, MI, KK), manuscript writing (AD), critical revision, administrative, technical, and material support, and study supervision (OB). All authors have made significant contributions to this study and have approved the final manuscript.

References

  1. World Health Organization. COVID-19 deaths | WHO COVID-19 dashboard. COVID-19 Cases, World. Available from: https://data.who.int/dashboards/covid19/cases. Accessed January 12, 2025
  2. Montazersaheb S, Hosseiniyan Khatibi SM, Hejazi MS, Tarhriz V, Farjami A, Ghasemian Sorbeni F, et al. COVID-19 infection: an overview on cytokine storm and related interventions. Virol J 2022;19(1):92 View Article PubMed/NCBI
  3. Bolhassani A, Agi E. Heat shock proteins in infection. Clin Chim Acta 2019;498:90-100 View Article PubMed/NCBI
  4. Danladi J, Sabir H. Innate immunity, inflammation activation and heat-shock protein in COVID-19 pathogenesis. J Neuroimmunol 2021;358:577632 View Article PubMed/NCBI
  5. Binder RJ. Functions of heat shock proteins in pathways of the innate and adaptive immune system. J Immunol 2014;193(12):5765-5771 View Article PubMed/NCBI
  6. Cappello F, Gammazza AM, Dieli F, Conway de Macario AJ. Does SARS-CoV-2 Trigger Stress-InducedAutoimmunity by Molecular Mimicry? A Hypothesis. J Clin Med 2020;9(7):2038 View Article PubMed/NCBI
  7. Lucchese G, Flöel A. SARS-CoV-2 and Guillain-Barré syndrome: molecular mimicry with human heat shock proteins as potential pathogenic mechanism. Cell Stress Chaperones 2020;25(5):731-735 View Article PubMed/NCBI
  8. Angileri F, Legare S, Marino Gammazza A, Conway de Macario E, JL Macario A, Cappello F. Molecular mimicry may explain multi-organ damage in COVID-19. Autoimmun Rev 2020;19(8):102591 View Article PubMed/NCBI
  9. Marino Gammazza A, Légaré S, Lo Bosco G, Fucarino A, Angileri F, Oliveri M, et al. Molecular mimicry in the post-COVID-19 signs and symptoms of neurovegetative disorders?. Lancet Microbe 2021;2(3):e94 View Article PubMed/NCBI
  10. Paladino L, Vitale AM, Caruso Bavisotto C, Conway de Macario E, Cappello F, Macario AJL, et al. The Role of Molecular Chaperones in Virus Infection and Implications for Understanding and Treating COVID-19. J Clin Med 2020;9(11):3518 View Article PubMed/NCBI
  11. Morimoto E, Tsuboyama K, Tomari Y. Fusion with heat-resistant obscure (Hero) proteins have the potential to improve the molecular property of recombinant proteins. PLoS One 2022;17(6):e0270097 View Article PubMed/NCBI
  12. Belykh AE, Soldatov VO, Stetskaya TA, Kobzeva KA, Soldatova MO, Polonikov AV, et al. Polymorphism of SERF2, the gene encoding a heat-resistant obscure (Hero) protein with chaperone activity, is a novel link in ischemic stroke. IBRO Neurosci Rep 2023;14:453-461 View Article PubMed/NCBI
  13. Tsuboyama K, Osaki T, Matsuura-Suzuki E, Kozuka-Hata H, Okada Y, Oyama M, et al. A widespread family of heat-resistant obscure (Hero) proteins protect against protein instability and aggregation. PLoS Biol 2020;18(3):e3000632 View Article PubMed/NCBI
  14. Qi S, Peng Y, Wang G, Zhang X, Liu M, He L. A tale of dual functions of SERF family proteins in regulating amyloid formation. Chembiochem 2024;25(5):e202300727 View Article PubMed/NCBI
  15. Cleverley K, Lee WC, Mumford P, Collins T, Rickman M, Cunningham TJ, et al. A novel knockout mouse for the small EDRK-rich factor 2 (Serf2) showing developmental and other deficits. Mamm Genome 2021;32(2):94-103 View Article PubMed/NCBI
  16. van Ham TJ, Holmberg MA, van der Goot AT, Teuling E, Garcia-Arencibia M, Kim HE, et al. Identification of MOAG-4/SERF as a regulator of age-related proteotoxicity. Cell 2010;142(4):601-612 View Article PubMed/NCBI
  17. Falsone SF, Meyer NH, Schrank E, Leitinger G, Pham CL, Fodero-Tavoletti MT, et al. SERF protein is a direct modifier of amyloid fiber assembly. Cell Rep 2012;2(2):358-371 View Article PubMed/NCBI
  18. Sahoo BR, Bardwell JCA. SERF, a family of tiny highly conserved, highly charged proteins with enigmatic functions. FEBS J 2023;290(17):4150-4162 View Article PubMed/NCBI
  19. Rahic Z, Buratti E, Cappelli S. Reviewing the Potential Links between Viral Infections and TDP-43 Proteinopathies. Int J Mol Sci 2023;24(2):1581 View Article PubMed/NCBI
  20. Yang J, Li Y, Wang S, Li H, Zhang L, Zhang H, et al. The SARS-CoV-2 main protease induces neurotoxic TDP-43 cleavage and aggregates. Signal Transduct Target Ther 2023;8(1):109 View Article PubMed/NCBI
  21. Bartas M, Červeň J, Guziurová S, Slychko K, Pečinka P. Amino Acid Composition in Various Types of Nucleic Acid-Binding Proteins. Int J Mol Sci 2021;22(2):922 View Article PubMed/NCBI
  22. Sahoo BR, Kocman V, Clark N, Myers N, Deng X, Wong EL, et al. Protein G-quadruplex interactions and their effects on phase transitions and protein aggregation. Nucleic Acids Res 2024;52(8):4702-4722 View Article PubMed/NCBI
  23. Nchioua R, Kmiec D, Müller JA, Conzelmann C, Groß R, Swanson CM, et al. SARS-CoV-2 Is Restricted by Zinc Finger Antiviral Protein despite Preadaptation to the Low-CpG Environment in Humans. mBio 2020;11(5):e01930-20 View Article PubMed/NCBI
  24. Esposito S, D’Abrosca G, Antolak A, Pedone PV, Isernia C, Malgieri G. Host and Viral Zinc-Finger Proteins in COVID-19. Int J Mol Sci 2022;23(7):3711 View Article PubMed/NCBI
  25. Shilenok VV, Shilenok IV, Soldatov VO, Orlov YL, Kobzeva KA, Deykin AV, et al. Bioinformatic analysis of the regulatory potential of tagging SNPs provides evidence of the involvement of genes encoding the heat-resistant obscure (Hero) proteins in the pathogenesis of cardiovascular diseases. J Integr Bioinform 2025;22(1):20240043 View Article PubMed/NCBI
  26. Chernyak BV, Popova EN, Prikhodko AS, Grebenchikov OA, Zinovkina LA, Zinovkin RA. COVID-19 and Oxidative Stress. Biochemistry (Mosc) 2020;85(12):1543-1553 View Article PubMed/NCBI
  27. Fodor A, Tiperciuc B, Login C, Orasan OH, Lazar AL, Buchman C, et al. Endothelial Dysfunction, Inflammation, and Oxidative Stress in COVID-19-Mechanisms and Therapeutic Targets. Oxid Med Cell Longev 2021;2021:8671713 View Article PubMed/NCBI
  28. McAlpine LS, Zubair AS, Maran I, Chojecka P, Lleva P, Jasne AS, et al. Ischemic Stroke, Inflammation, and Endotheliopathy in COVID-19 Patients. Stroke 2021;52(6):e233-e238 View Article PubMed/NCBI
  29. Valencia I, Lumpuy-Castillo J, Magalhaes G, Sánchez-Ferrer CF, Lorenzo Ó, Peiró C. Mechanisms of endothelial activation, hypercoagulation and thrombosis in COVID-19: a link with diabetes mellitus. Cardiovasc Diabetol 2024;23(1):75 View Article PubMed/NCBI
  30. Soldatov V, Venediktov A, Belykh A, Piavchenko G, Naimzada MD, Ogneva N, et al. Chaperones vs. oxidative stress in the pathobiology of ischemic stroke. Front Mol Neurosci 2024;17:1513084 View Article PubMed/NCBI
  31. Loktionov AV, Kobzeva KA, Karpenko AR, Sergeeva VA, Orlov YL, Bushueva OY. GWAS-significant loci and severe COVID-19: analysis of associations, link with thromboinflammation syndrome, gene-gene, and gene-environmental interactions. Front Genet 2024;15:1434681 View Article PubMed/NCBI
  32. Kobzeva KA, Gurtovoy DE, Polonikov AV, Pokrovsky VM, Patrakhanov EA, Bushueva OY. Polymorphism in Genes Encoding HSP40 Family Proteins is Associated with Ischemic Stroke Risk and Brain Infarct Size: A Pilot Study. J Integr Neurosci 2024;23(12):211 View Article PubMed/NCBI
  33. Shilenok I, Kobzeva K, Stetskaya T, Freidin M, Soldatova M, Deykin A, et al. SERPINE1 mRNA Binding Protein 1 Is Associated with Ischemic Stroke Risk: A Comprehensive Molecular-Genetic and Bioinformatics Analysis of SERBP1 SNPs. Int J Mol Sci 2023;24(10):8716 View Article PubMed/NCBI
  34. Loktionov A, Kobzeva K, Dorofeeva A, Sergeeva V, Bushueva O. GWAS-Identified Loci are Associated with Obesity and Type 2 Diabetes Mellitus in Patients with Severe COVID-19. Front Biosci (Schol Ed) 2024;16(3):14 View Article PubMed/NCBI
  35. World Health Organization. Diet, nutrition and the prevention of chronic diseases. World Health Organ Tech Rep Ser 2003;916:i-viii, 1-149, backcover PubMed/NCBI
  36. World Health Organization. Physical activity. Available from: https://www.who.int/europe/news-room/fact-sheets/item/physical-activity. Accessed February 11, 2025
  37. GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 2020;369(6509):1318-1330 View Article PubMed/NCBI
  38. Shilenok I, Kobzeva K, Soldatov V, Deykin A, Bushueva O. C11orf58 (Hero20) Gene Polymorphism: Contribution to Ischemic Stroke Risk and Interactions with Other Heat-Resistant Obscure Chaperones. Biomedicines 2024;12(11):2603 View Article PubMed/NCBI
  39. Võsa U, Claringbould A, Westra HJ, Bonder MJ, Deelen P, Zeng B, et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genet 2021;53(9):1300-1310 View Article PubMed/NCBI
  40. Ward LD, Kellis M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res 2012;40(Database issue):D930-D934 View Article PubMed/NCBI
  41. Kobzeva KA, Soldatova MO, Stetskaya TA, Soldatov VO, Deykin AV, Freidin MB, et al. Association between HSPA8 Gene Variants and Ischemic Stroke: A Pilot Study Providing Additional Evidence for the Role of Heat Shock Proteins in Disease Pathogenesis. Genes (Basel) 2023;14(6):1171 View Article PubMed/NCBI
  42. Kobzeva KA, Shilenok IV, Belykh AE, Gurtovoy DE, Bobyleva, Krapiva AB, et al. C9orf16 (BBLN) gene, encoding a member of Hero proteins, is a novel marker in ischemic stroke risk. Research Results in Biomedicine 2022;8(3):278-292 View Article
  43. Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, Heravi-Moussavi A, et al. Integrative analysis of 111 reference human epigenomes. Nature 2015;518(7539):317-330 View Article PubMed/NCBI
  44. Shin S, Hudson R, Harrison C, Craven M, Keleş S. atSNP Search: a web resource for statistically evaluating influence of human genetic variation on transcription factor binding. Bioinformatics 2019;35(15):2657-2659 View Article PubMed/NCBI
  45. Shilenok I, Kobzeva K, Deykin A, Pokrovsky V, Patrakhanov E, Bushueva O. Obesity and Environmental Risk Factors Significantly Modify the Association between Ischemic Stroke and the Hero Chaperone C19orf53. Life (Basel) 2024;14(9):1158 View Article PubMed/NCBI
  46. The Gene Ontology Consortium. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res 2019;47(D1):D330-D338 View Article PubMed/NCBI
  47. Szklarczyk D, Nastou K, Koutrouli M, Kirsch R, Mehryary F, Hachilif R, et al. The STRING database in 2025: protein networks with directionality of regulation. Nucleic Acids Res 2025;53(D1):D730-D737 View Article PubMed/NCBI
  48. Stetskaya TA, Kobzeva KA, Zaytsev SM, Shilenok IV, Komkova GV, Goryainova NV, et al. HSPD1 gene polymorphism is associated with an increased risk of ischemic stroke in smokers. Research Results in Biomedicine 2024;10:175-186 View Article
  49. Davis AP, Wiegers TC, Johnson RJ, Sciaky D, Wiegers J, Mattingly CJ. Comparative Toxicogenomics Database (CTD): update 2023. Nucleic Acids Res 2023;51(D1):D1257-D1262 View Article PubMed/NCBI
  50. Takami T, Kaposi-Novak P, Uchida K, Gomez-Quiroz LE, Conner EA, Factor VM, et al. Loss of hepatocyte growth factor/c-Met signaling pathway accelerates early stages of N-nitrosodiethylamine induced hepatocarcinogenesis. Cancer Res 2007;67(20):9844-9851 View Article PubMed/NCBI
  51. Jiang CL, He SW, Zhang YD, Duan HX, Huang T, Huang YC, et al. Air pollution and DNA methylation alterations in lung cancer: A systematic and comparative study. Oncotarget 2017;8(1):1369-1391 View Article PubMed/NCBI
  52. Gonzalez-Rivera JC, Baldridge KC, Wang DS, Patel K, Chuvalo-Abraham JCL, Hildebrandt Ruiz L, et al. Post-transcriptional air pollution oxidation to the cholesterol biosynthesis pathway promotes pulmonary stress phenotypes. Commun Biol 2020;3(1):392 View Article PubMed/NCBI
  53. Bereketoglu C, Modig C, Pradhan A, Andersson PL, Stasinopoulou S, Mitsiou DJ, et al. The brominated flame retardants TBECH and DPTE alter prostate growth, histology and gene expression patterns in the mouse. Reprod Toxicol 2021;102:43-55 View Article PubMed/NCBI
  54. Gertz J, Reddy TE, Varley KE, Garabedian MJ, Myers RM. Genistein and bisphenol A exposure cause estrogen receptor 1 to bind thousands of sites in a cell type-specific manner. Genome Res 2012;22(11):2153-2162 View Article PubMed/NCBI
  55. Wang Q, Gao S, Chen B, Zhao J, Li W, Wu L. Evaluating the Effects of Perinatal Exposures to BPSIP on Hepatic Cholesterol Metabolism in Female and Male Offspring ICR Mice. Environ Health Perspect 2024;132(9):97011 View Article PubMed/NCBI
  56. Khomari F, Nabi-Afjadi M, Yarahmadi S, Eskandari H, Bahreini E. Effects of Cell Proteostasis Network on the Survival of SARS-CoV-2. Biol Proced Online 2021;23(1):8 View Article PubMed/NCBI
  57. Pretorius E, Venter C, Laubscher GJ, Kotze MJ, Oladejo SO, Watson LR, et al. Prevalence of symptoms, comorbidities, fibrin amyloid microclots and platelet pathology in individuals with Long COVID/Post-Acute Sequelae of COVID-19 (PASC). Cardiovasc Diabetol 2022;21(1):148 View Article PubMed/NCBI
  58. Fiesel FC, Weber SS, Supper J, Zell A, Kahle PJ. TDP-43 regulates global translational yield by splicing of exon junction complex component SKAR. Nucleic Acids Res 2012;40(6):2668-2682 View Article PubMed/NCBI
  59. Avendaño-Vázquez SE, Dhir A, Bembich S, Buratti E, Proudfoot N, Baralle FE. Autoregulation of TDP-43 mRNA levels involves interplay between transcription, splicing, and alternative polyA site selection. Genes Dev 2012;26(15):1679-1684 View Article PubMed/NCBI
  60. Idrees D, Kumar V. SARS-CoV-2 spike protein interactions with amyloidogenic proteins: Potential clues to neurodegeneration. Biochem Biophys Res Commun 2021;554:94-98 View Article PubMed/NCBI
  61. Oh E, Akopian D, Rape M. Principles of Ubiquitin-Dependent Signaling. Annu Rev Cell Dev Biol 2018;34:137-162 View Article PubMed/NCBI
  62. Zhao M, Zhang M, Yang Z, Zhou Z, Huang J, Zhao B. Role of E3 ubiquitin ligases and deubiquitinating enzymes in SARS-CoV-2 infection. Front Cell Infect Microbiol 2023;13:1217383 View Article PubMed/NCBI
  63. Che Y, Jiang D, Zhang Y, Zhang J, Xu T, Sun Y, et al. Elevated ubiquitination contributes to protective immunity against severe SARS-CoV-2 infection. Clin Transl Med 2022;12(12):e1103 View Article PubMed/NCBI
  64. Kueck T, Bloyet LM, Cassella E, Zang T, Schmidt F, Brusic V, et al. Vesicular Stomatitis Virus Transcription Is Inhibited by TRIM69 in the Interferon-Induced Antiviral State. J Virol 2019;93(24):e01372-19 View Article PubMed/NCBI
  65. Bagga T, Tulsian NK, Mok YK, Kini RM, Sivaraman J. Mapping of molecular interactions between human E3 ligase TRIM69 and Dengue virus NS3 protease using hydrogen-deuterium exchange mass spectrometry. Cell Mol Life Sci 2022;79(5):233 View Article PubMed/NCBI
  66. Han Y, Li R, Gao J, Miao S, Wang L. Characterisation of human RING finger protein TRIM69, a novel testis E3 ubiquitin ligase and its subcellular localisation. Biochem Biophys Res Commun 2012;429(1-2):6-11 View Article PubMed/NCBI
  67. Rihn SJ, Aziz MA, Stewart DG, Hughes J, Turnbull ML, Varela M, et al. TRIM69 Inhibits Vesicular Stomatitis Indiana Virus. J Virol 2019;93(20):e00951-19 View Article PubMed/NCBI
  68. Balasubramaniam M, Ayyadevara S, Shmookler Reis RJ. Structural insights into pro-aggregation effects of C. elegans CRAM-1 and its human ortholog SERF2. Sci Rep 2018;8(1):14891 View Article PubMed/NCBI
  69. Dick TP, Bangia N, Peaper DR, Cresswell P. Disulfide bond isomerization and the assembly of MHC class I-peptide complexes. Immunity 2002;16(1):87-98 View Article PubMed/NCBI
  70. Peaper DR, Wearsch PA, Cresswell P. Tapasin and ERp57 form a stable disulfide-linked dimer within the MHC class I peptide-loading complex. EMBO J 2005;24(20):3613-3623 View Article PubMed/NCBI
  71. Müller IK, Winter C, Thomas C, Spaapen RM, Trowitzsch S, Tampé R. Structure of an MHC I-tapasin-ERp57 editing complex defines chaperone promiscuity. Nat Commun 2022;13(1):5383 View Article PubMed/NCBI
  72. Domnick A, Winter C, Sušac L, Hennecke L, Hensen M, Zitzmann N, et al. Molecular basis of MHC I quality control in the peptide loading complex. Nat Commun 2022;13(1):4701 View Article PubMed/NCBI
  73. Mutze K, Vierkotten S, Milosevic J, Eickelberg O, Königshoff M. Enolase 1 (ENO1) and protein disulfide-isomerase associated 3 (PDIA3) regulate Wnt/β-catenin-driven trans-differentiation of murine alveolar epithelial cells. Dis Model Mech 2015;8(8):877-890 View Article PubMed/NCBI
  74. Yang CL, Wang FX, Luo JH, Rong SJ, Lu WY, Chen QJ, et al. PDIA3 orchestrates effector T cell program by serving as a chaperone to facilitate the non-canonical nuclear import of STAT1 and PKM2. Mol Ther 2024;32(8):2778-2797 View Article PubMed/NCBI
  75. Yudhawati R, Shimizu K. PGE2 Produced by Exogenous MSCs Promotes Immunoregulation in ARDS Induced by Highly Pathogenic Influenza A through Activation of the Wnt-β-Catenin Signaling Pathway. Int J Mol Sci 2023;24(8):7299 View Article PubMed/NCBI
  76. Kumar A, Souza De Lima D, Mark Z, Anathy V. Lung Epithelial Redox Chaperones in Human Coronavirus Propagation. Am J Respir Crit Care Med 2023;207:A5663 View Article
  77. Chamberlain N, Anathy V. Pathological consequences of the unfolded protein response and downstream protein disulphide isomerases in pulmonary viral infection and disease. J Biochem 2020;167(2):173-184 View Article PubMed/NCBI
  78. Chamberlain N, Korwin-Mihavics BR, Nakada EM, Bruno SR, Heppner DE, Chapman DG, et al. Lung epithelial protein disulfide isomerase A3 (PDIA3) plays an important role in influenza infection, inflammation, and airway mechanics. Redox Biol 2019;22:101129 View Article PubMed/NCBI
  79. Fink JK, Jones SM, Esposito C, Wilkowski J. Human microtubule-associated protein 1a (MAP1A) gene: genomic organization, cDNA sequence, and developmental- and tissue-specific expression. Genomics 1996;35(3):577-585 View Article PubMed/NCBI
  80. Fava F, Raynaud-Messina B, Leung-Tack J, Mazzolini L, Li M, Guillemot JC, et al. Human 76p: A new member of the gamma-tubulin-associated protein family. J Cell Biol 1999;147(4):857-868 View Article PubMed/NCBI
  81. Han S, Zhang D, Guo Y, Fu Z, Guan G. Prevalence and Characteristics of STRC Gene Mutations (DFNB16): A Systematic Review and Meta-Analysis. Front Genet 2021;12:707845 View Article PubMed/NCBI
  82. Karki P, Birukova AA. Microtubules as Major Regulators of Endothelial Function: Implication for Lung Injury. Front Physiol 2021;12:758313 View Article PubMed/NCBI
  83. Aminpour M, Hameroff S, Tuszynski JA. How COVID-19 Hijacks the Cytoskeleton: Therapeutic Implications. Life (Basel) 2022;12(6):814 View Article PubMed/NCBI
  84. Merad M, Martin JC. Pathological inflammation in patients with COVID-19: a key role for monocytes and macrophages. Nat Rev Immunol 2020;20(6):355-362 View Article PubMed/NCBI
  85. Vanderbeke L, Van Mol P, Van Herck Y, De Smet F, Humblet-Baron S, Martinod K, et al. Monocyte-driven atypical cytokine storm and aberrant neutrophil activation as key mediators of COVID-19 disease severity. Nat Commun 2021;12(1):4117 View Article PubMed/NCBI
  86. Ahnach M, Zbiri S, Nejjari S, Ousti F, Elkettani C. C-reactive protein as an early predictor of COVID-19 severity. J Med Biochem 2020;39(4):500-507 View Article PubMed/NCBI
  87. Smilowitz NR, Kunichoff D, Garshick M, Shah B, Pillinger M, Hochman JS, et al. C-reactive protein and clinical outcomes in patients with COVID-19. Eur Heart J 2021;42(23):2270-2279 View Article PubMed/NCBI
  88. Wang L. C-reactive protein levels in the early stage of COVID-19. Med Mal Infect 2020;50(4):332-334 View Article PubMed/NCBI
  89. Chen W, Zheng KI, Liu S, Yan Z, Xu C, Qiao Z. Plasma CRP level is positively associated with the severity of COVID-19. Ann Clin Microbiol Antimicrob 2020;19(1):18 View Article PubMed/NCBI
  90. Zhang N, Bevan MJ. CD8(+) T cells: foot soldiers of the immune system. Immunity 2011;35(2):161-168 View Article PubMed/NCBI
  91. Malyshkina A, Brüggemann A, Paschen A, Dittmer U. Cytotoxic CD4(+) T cells in chronic viral infections and cancer. Front Immunol 2023;14:1271236 View Article PubMed/NCBI
  92. Alexaki VI, Henneicke H. The Role of Glucocorticoids in the Management of COVID-19. Horm Metab Res 2021;53(1):9-15 View Article PubMed/NCBI
  93. Bruscoli S, Puzzovio PG, Zaimi M, Tiligada K, Levi-Schaffer F, Riccardi C. Glucocorticoids and COVID-19. Pharmacol Res 2022;185:106511 View Article PubMed/NCBI
Copyright © 2025 Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 4.0 License (CC BY-NC 4.0), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

About this Article

Cite this article
Dorofeeva A, Ivenkov M, Kobzeva K, Bushueva O. SERF2 (Hero7) Gene Polymorphism is Associated with Severe Course of COVID-19. Gene Expr. 2025;24(4):e00057. doi: 10.14218/GE.2025.00057.
Copy        Export to RIS        Export to EndNote
Article History
Received Revised Accepted Published
June 27, 2025 August 23, 2025 September 15, 2025 October 30, 2025
DOI http://dx.doi.org/10.14218/GE.2025.00057
  • Gene Expression
  • eISSN 1555-3884
  • © 2025 Xia & He Publishing Inc.
Back to Top

SERF2 (Hero7) Gene Polymorphism is Associated with Severe Course of COVID-19

Anna Dorofeeva, Maxim Ivenkov, Ksenia Kobzeva, Olga Bushueva
  • Reset Zoom
  • Download TIFF