Home Genomic profiling of interferon signaling pathway gene mutations in type 2 diabetic individuals with COVID-19
Article Open Access

Genomic profiling of interferon signaling pathway gene mutations in type 2 diabetic individuals with COVID-19

  • Tuncay Tuluk ORCID logo EMAIL logo , Teoman Kankılıç ORCID logo , İlkay Civelek ORCID logo , Dilara Fatma Akın ORCID logo and Mehmet Bankır ORCID logo
Published/Copyright: December 31, 2024
Download Article (PDF)
Turkish Journal of Biochemistry
From the journal Turkish Journal of Biochemistry Volume 50 Issue 1

Abstract

Objectives

Comorbidities, especially diabetes, significantly contribute to the mortality and morbidity of COVID-19. Studies indicate higher rates of mortality and morbidity among diabetic COVID-19 patients compared to the general population. However, the precise mechanisms underlying this immune response remain incompletely understood. Our study aimed to explore potential disparities in COVID-19 prognosis among type 2 diabetic patients and investigate the genomic-level relationship between key proteins of the interferon signaling pathway: IFNAR1, IFNAR2, IRF3, and IRF7.

Methods

Mutation/polymorphism analysis was conducted to identify potential mutations and polymorphisms in the study group. Predictive assessments of mutation pathogenicity were performed using the PolyPhen-2 bioinformatics tool, while STRING network analysis enhanced our understanding of functional protein relationships in cellular processes.

Results

We detected 10 mutations (3 missense, 2 intronic, 2 indel, 1 nonsense, 1 regulatory, and 1 frameshift mutation), all documented in the Human Gene Mutation Database. PolyPhen2 analysis flagged three missense and 1 nonsense mutations as potential pathogens. The study found no consistent trend in mutation rates across all genes. However, mutation rates in the IFNAR2 and IRF7 genes decreased as disease severity lessened in both patient and control groups. Diabetic and Covid-19 patients exhibited higher mutation rates in the IFNAR2, IRF3, and IRF7 genes compared to non-diabetic controls, suggesting that Type 2 diabetic patients might be more susceptible to genetic mutations when infected with COVID-19.

Conclusions

Understanding these genetic profiles could improve disease severity assessments, enhance preventive measures, and aid in developing effective treatment strategies for coronaviral syndromes and severe acute respiratory infections.

Keywords: COVID-19; type 2 diabetes; mutation; variation; interferon signaling pathway

Introduction

In December 2019, Wuhan, located in Hubei Province, China, experienced an outbreak of pneumonia with an unknown origin [1], caused by the Severe Acute Respiratory Syndrome Coronavirus, which has since developed into a pandemic [2]. On March 12, 2020, the World Health Organization declared a pandemic due to the rapid spread of SARS-CoV-2 and the significant number of deaths attributed to COVID-19 [1].

The COVID-19 pandemic presents challenges for both patients and caregivers in maintaining continuity of care and managing the risks associated with pre-existing chronic conditions [3]. On the other hand, recent evidence indicates that COVID-19 significantly affects individuals with diabetes, exacerbating severity and mortality. Over the past years, the pandemic’s indirect effects on healthcare systems and lingering effects on infected individuals have become apparent. Diabetes exacerbates COVID-19 complications such as acute respiratory distress syndrome (ARDS) and multi-organ failure due to increased viral entry and decreased immunity [4]. On the other hand, this is partially because of an inflammatory condition within the body pro-thrombotic milieu which are observed in metabolic syndrome [5]. There are hypotheses to elucidate the heightened severity of COVID-19 in diabetics, and a considerable percentage of COVID-19 patients experience new-onset hyperglycemia or diabetes [4].

However, the immune response is disrupted in hyperglycemia, affecting both the humoral system, responsible for immediate defense responses, and adaptive immunity. In diabetes mellitus, decreased production of type 1 interferons and interferon gamma by various immune cells may weaken antiviral defenses, potentially increasing infection severity [6] type I interferons play a pivotal role in initiating and enhancing the antiviral response upon infection onset, crucial for effective antiviral immunity. They bind to interferon alpha/beta receptors 1 (IFNAR1) and 2 (IFNAR2), which are found ubiquitously but exhibit varying expression levels depending on the cell type. Interferon pathways play a crucial role in COVID-19 and are linked to disease severity. Various studies, including genome-wide association studies, transcriptomic studies, and single-cell studies, have identified IFNAR2 as a risk factor for severe COVID-19 [7]. In a study analyzing single-cell transcriptomes of over 895,000 peripheral blood mononuclear cells from COVID-19 patients and healthy controls, a risk variant at the IFNAR2 locus (rs13050728) was identified. This variant showed context-specific effects, particularly related to monocyte expression, underscoring the significant role of innate immune cells in the severity of COVID-19. The findings highlight how variations in IFNAR2 may contribute to the dysfunctional immune response observed in severe cases, linking genetic factors with immunological outcomes in the disease [8]. As a part of a large genome-wide association study (GWAS), a study investigated genetic variants linked to severe disease in a cohort of critically ill patients. The study, the relationship between IFNAR2 and critical illness in COVID-19 is emphasized, noting that low expression levels of this gene are associated with life-threatening outcomes. The study also conducted a transcriptomic analysis, revealing that high expression of the CCR2 gene in lung tissue correlates with severe COVID-19 cases [9].

On the other hand, the insulin/IGF signaling pathway is crucial for regulating energy metabolism and cell survival, yet its implications in SARS-CoV-2 infection remain poorly understood [10]. In a study, the findings indicating increased levels of IRF7 and IRF3 mRNA in COVID-19 patients with mild to moderate disease offer further evidence supporting their role in the host’s antiviral response [11]. In the analysis of the most frequent comorbidities in COVID-19, IRF7, a central protein, was found to be upregulated in COVID-19 patients. This upregulation is associated with the pathogenesis of diabetes mellitus and lung cancer [12].

Currently, there is a lack of comprehensive studies analyzing the DNA sequences of the genes responsible for protein and enzyme expression in the interferon signaling pathway in patients with both SARS-CoV-2 infection and type 2 diabetes (T2D), particularly concerning disease severity. In our study, our objective was to compare the genetic profiles of key proteins in the interferon signaling pathway, such as IFNAR1, IFNAR2, IRF3, and IRF7, among patients with varying severity levels of T2D who have been infected with SARS-CoV-2, as well as among healthy individuals, to gain insights into disease progression.

Materials and methods

This study consisted of five different patient groups (n=100) based on the severity of COVID-19 pneumonia and the presence of T2D. The patient groups in the study were defined as follows:

T2D patient groups

  1. Intensive care patients (IC-T2D, n=20) with T2D who had severe COVID-19 pneumonia.

  2. Mild-moderate patients (MM-T2D, n=20) with T2D who had mild to moderate COVID-19.

  3. Outpatient treatment COVID-19 patients (OT-T2D, n=20) with T2D who did not have pneumonia.

Control groups

  1. COVID-19 positive control group (PC) (n=20) without T2D.

  2. COVID-19 negative control group (NC) (n=20) without T2D.

The study enrolled adults aged 18 years and older, regardless of whether they had been diagnosed with T2D, and individuals with positive or negative PCR results obtained using the Qiagen Rotor Gene q (Applied Biosystems, Veriti 96-well thermal cycler) device. Participants were also required to meet the Turkish Ministry of Health’s criteria for hospitalized COVID-19 patients with severe pneumonia, which included various indicators such as respiratory rate, oxygen saturation, lung imaging findings, and organ dysfunction parameters. Alternatively, participants could meet the criteria for hospitalized COVID-19 patients with mild to moderate pneumonia, as determined by the Turkish Ministry of Health, which required less extensive lung involvement on radiological imaging.

Throat and nasal swabs were collected from the participants involved in the study and subsequently analyzed through reverse transcriptase polymerase chain reaction (RT-PCR). For RNA extraction from the swab samples, vNAT Transfer Tubes (Bio-speedy) were employed. The detection of COVID-19 was performed using a RT-PCR kit designed for SARS-CoV-2 (Bio-Speedy® SARS-CoV-2 double gene RT-qPCR), which facilitated the generation of complementary DNA and amplification processes. The RT-PCR assay focused on the genes encoding the nucleocapsid protein (N) and open reading frame 1 ab (ORF1ab), conducted according to the specified guidelines provided by the manufacturer. For Real Time-PCR, the Rotor-Gene 5-Plex HRM (Qiagen) device was used. A 15 μL PCR reaction mixture was distributed into 0.1 mL PCR tubes, labeled individually for each sample. After adding 5 μL of extracted samples, the tubes were placed into the real-time PCR device. The amplification of the target genes was performed using 10 µL of PrimeScript mix (DNA polymerase, dNTP mix, reaction buffer solution, reverse transcriptase, and ribonuclease inhibitor), 5 µl of Oligomix mixture for SARS-CoV-2 detection (targeting N and ORF1ab genes – FAM), and an internal control (targeting the RNase P gene – HEX), along with 5 µL of extracted RNA sample. The amplification of gene regions was conducted at 45 °C for 15 min for cDNA synthesis, followed by 95 °C for 3 min for initial denaturation, and 40 cycles of 95 °C for 5 s for denaturation, and 55 °C for 35 s for primer annealing. Based on the manufacturer’s recommendations, the shape of amplification curves obtained from the FAM/HEX channels and the Cq values determined by the device were compared with negative and positive controls. Samples without a sigmoidal curve were considered negative.

Mutation screening

Blood samples were collected from study participants in 5 mL sterile EDTA tubes for DNA extraction. The demographic and clinical details of the study participants are presented in Supplementary Material and DNA was isolated using the Hibrigen Blood DNA isolation kit (Turkey). The DNA concentration was measured, and the coding regions of IFNAR1, IFNAR2, IRF3, and IRF7 genes were amplified using specific primers (Table 1). The primer sequences used in this study were specifically designed for the project. First, genes of interest were selected from the cBioPortal database [13] and the mutated exon regions within these genes were identified. The selected exons were then verified using the Ensembl database [14] and specific primers for these regions were designed using the Primer-tool in The National Center for Biotechnology Information (NCBI) [15].The PCR was performed under the following conditions: an initial denaturation step at 95 °C for 5 min, followed by 40 cycles consisting of denaturation at 94 °C for 4 min, annealing at 56–58 °C for 1 min, and extension at 72 °C for 1 min. The reaction concluded with a final extension at 72 °C for 5 min. After PCR, the purity of DNA was measured at A260/280 wavelengths using a (Geneva NanoJenway) spectrophotometer. Samples with a concentration between 1.8 and 2.0 μg/mL were considered pure and were stored for use in PCR.

Table 1:

Primer sequences and amplicon lengths of the genes.

Gene Forward primer (5′-3′) Reverse primer (5′-3′) Amplicon length, bp
IRF3 CACTGGGCCGGGTCGATAAC CATTCAATTCCCCTCCGACTG 665
IRF7 GGTCGCATCCAATAATAAGAACAGG CCGCTGCTGATCTCTCCAAGGA 837
IFNAR1 TGCTAGCTAGGAGGAAAGGC TTTGGCGCGGCAAAGTGGAC 428
IFNAR2 GGCAGCAGACGATTGTAAATGA CACTAGCCGCATACTCAAGACG 630

The PCR products were then analyzed using a 1 % agarose gel and sequenced to identify any sequence changes compared to reference sequences. The gene sequence results were compared with the reference sequences in the NCBI database [15] and then the identified base changes were investigated in Ensembl database [14] to determine mutations.

Pathogenic effect analysis of identified mutations

Mutations in IFNAR1-1, IFNAR2, IRF3, and IRF7 genes were assessed for their potential clinical impact using Polymorphism Phenotyping v2 (PolyPhen-2) [16] and SNAP2 [17] bioinformatics tools.

Protein-protein interaction analyses

Protein-protein interaction analyses were carried out utilizing the STRING database [18]. This database found predicted interactions between IFNAR1-1, IFNAR2, IRF3, and IRF7 proteins, defining both direct (physical) and indirect (functional) links between proteins.

Results

Genotyping results

A total of 10 mutations (3 missense, 2 indel, 1 nonsense, 2 intron, 1 frame shift, and 1 regulatory region mutation) have been identified across the four genes. The characteristics of these mutations are detailed in Table 2. All variations are registered in the Human Gene Mutation Database (HGMD) and exhibit a heterozygous genotype. The IFNAR2 gene had the highest number of detected mutations.

Table 2:

Detailed mutations of the IFNAR1, IFNAR2, IRF3, and IRF7 genes.

No Gene Nucleotid change Amino acid change Rs number Variant type Domain Polyphen-2 result
1 IFNAR2 c.841A>T p.Asn281Tyr rs1555862436 Missense variant Exon-9/Intracellular domain Pathogenic
2 IFNAR2 c.908C>T p.Ser303Phe rs1568896969 Missense variant Exon-9/Intracellular domain Pathogenic
3 IFNAR2 c.853G>A p.Ala285Thr rs1131668 Missense variant Exon-9/Intracellular domain Pathogenic
4 IFNAR2 c.841-33C>T – rs9984273 Intron variant – –
5 IFNAR2 c.a59_a73= – rs34865572 Indel variant – –
6 IFNAR2 c.a48_a59= – rs749173983 Indel variant – –
7 IFNAR2 c.966C>A p.Tyr322Ter rs9984273 Nonsense varyant Exon-9/Intracellular domain Pathogenic
8 IFNAR1 c.1441-135C>T – rs2254315 Intron variant – –
9 IRF7 g.612547C>G – rs1350368647 Regulatory variant 3′UTR –
10 IRF3 c.165 + 116del – rs754374657 Frameshift variant Indel insertion and deletion –

  1. aVariants classified as “Missense”, “Nonsense”, “Indel”, “Intron”, “Regulatory” and “Frameshift” correspond to nucleotide and amino acid changes in the genes IFNAR1, IFNAR2, IRF3, and IRF7, with PolyPhen-2 results indicating pathogenicity once applicable.

Table 3 shows the number of mutations detected in each gene for a total of 100 patients and the percentage values for the mutations in each group.

Table 3:

The number of mutations detected in each gene and the percentage values.

Gene Total number of mutations detected in 100 patients IC-T2D group/the total number of mutations, % MM-T2D group/total number of mutations, % OT-T2D group/total number of mutations, % PC group/the total number of mutations, % NC group/the total number of mutations, %
IFNAR1 18 – – 27.8 33.3 38.9
IFNAR2 54 31.5 20.4 18.5 18.5 11.1
IRF3 25 – – 60 8 32
IRF7 29 58.6 34.5 – 6.9 –

For the IFNAR1 gene, no mutations were observed in the IC-T2D and MM-T2D patient groups, while mutations were detected in 27.8 % of the OT-T2D patients. Additionally, higher mutation rates were found in the IFNAR1 gene in the control groups (PC and NC) compared to the T2D patient groups. However, the mutation rate in the PC samples of this gene has shown a decreasing trend compared to the NC group.

The IFNAR2 gene has the highest number of mutations (54) among other genes. The mutations identified in this gene exhibit a decreasing mutation rate with the severity of the disease in patients who experienced severe COVID-19 requiring intensive care, those with moderate COVID-19, and those who received outpatient treatment, respectively. Additionally, this trend is observed to be more pronounced in the COVID-19 positive control group without T2D (PC) compared to the COVID-19 negative control group without T2D (NC), indicating a proportional decrease.

In the IRF3 gene, no mutations were observed in the IC-T2D and MM-T2D patients, while a mutation rate of 18.5 % was observed in the OT-T2D patients. Additionally, unexpectedly, a higher mutation rate was detected in the NC group compared to the PCR group in this gene. Furthermore, while mutations were present in small quantities in the PC group, no mutations were detected in the NC group.

Furthermore, in the IRF7 gene, similar to the IFNAR2 gene, a higher mutation rate was found in the IC-T2D group compared to other groups, which decreased in the MM-T2D group and was not observed in the OT-T2D group. Mutations were observed in the PC group, while no mutations were found in the NC group.

IFNAR1 and IFNAR2

In our study, one intron variant was found as an outcome of mutation/polymorphism analysis in the Ifnar1 gene, and this change was registered in the HGMD e-database. We also found seven mutations (3 missense, 2 indel, 1 silent, and 1 nonsense) in the IFNAR2 gene. The four variations (p.Asn281Tyr, p.Ser303Phe, p.Ala285Thr, and p.Tyr322Ter) are located in intracellular domain of IFNAR2. This domain provides gene activation by activating STAT-JAK2 in the cell. The p.Tyr322Ter nonsense mutation may result in premature termination of the polypeptide encoding 515 amino acids and the production of a truncated protein. The listed indel mutations (c.*59_*73=and c.*48_*59=) may also change the reading frame (Figure 1).

Figure 1: Variants in the IFNAR2, IFNAR1, IRF7 and IRF3 genes.
Figure 1:

Variants in the IFNAR2, IFNAR1, IRF7 and IRF3 genes.

IRF3 and IRF7

As a result of mutation analysis in IRF3 gene, a frameshift variation was detected in 5′UTR region. On the other hand, a regulatory variation was detected in 3′UTR region of IRF7 gene (Table 1). Figure 1 shows the sequencing electropherograms of IRF3 and IRF7 genes’ mutations and variants. Arrows indicate localizations of the mutation and variant.

Results of functional/pathogenic effect analysis of identified mutations

PolyPhen-2 analysis were showed a pathogenic effect on bone regeneration in I FNAR2 gene due to the scores of the detected four mutations (p.H176Y, p.F142L, p.L234V, p.P273S) were near to 1 (Figure 2). The amino acid sequences affected by mutations were compared across different species, revealing that four mutations remained conserved throughout the evolutionary process. These mutations resulted in changes to critical amino acids.

Figure 2: Prediction of possible functional effects of detected IFNAR2 missense mutations with PolyPhen2, and analysis with STRING database.
Figure 2:

Prediction of possible functional effects of detected IFNAR2 missense mutations with PolyPhen2, and analysis with STRING database.

Protein-protein interaction results

To detect the functional interactions in cellular processes of IFNAR1, IFNAR2, and IRF7 proteins, STRING interaction network analysis was conducted. The STRING tool was showed that IFNAR1 and IFNAR2 proteins in interaction with JAK-STAT (Figure 2).

Discussion

In COVID-19, characteristics such as older age, gender, and comorbidities influence the disease’s clinical progression and outcomes [19], 20]. However, they only explain just a part of the documented diversity in disease severity among people. Variations found among populations contribute to variances in susceptibility to disease and immune response results. SNPs are one of the most common types of genetic variations. These SNPs can appear in either the coding or non-coding regions of genes, potentially causing gene function loss or change. They may also affect amino acid sequences and protein structure, or influence the use of alternative splicing methods. Mutations in the IFNAR2 and IFNAR1 genes have been linked to severe COVID-19 in populations across the UK, Africa, and Latin America. These genes encode type I interferon receptor (IFNAR) subunits. SNPs found in the IFNAR genes have the potential to interfere with protein function, thereby affecting the body’s antiviral response. This disruption can result in a range of disease phenotypes [20]. On the other hand, the role of IRF7 In COVID-19 was evaluated as controversial. While IRF7 is known to provide protection against viral infections, there are conflicting findings. Researcher indicated that individuals deficient in IRF7 are more susceptible to severe respiratory viral infections, including influenza and COVID-19 [21], 22].

In this study, we found several genetic variants across important interferon-related genes that may affect susceptibility to severe infections, particularly COVID-19. We found seven variations in the IFNAR2 gene and rs1555862436, rs1568896969 and rs1131668 missense variants, were identified in this gene as potential pathogenic. The SNPs rs1555862436, rs1568896969 are available in the dbSNP database; however, particular information on its biological importance and associations with diseases is limited. Despite the lack of established linkages to specific health disorders, the possible pathogenic consequences of this variation increase the possibility that it may act as a risk factor in our study, particularly among T2D patients infected with COVID-19. Given the classification as possibly harmful, additional studies into this SNP might provide light on its role in modifying immune responses and impacting illness outcomes in this vulnerable group.

On the other hand, the missense variant rs1131668 in the IFNAR2 gene was also identified as pathogenic, suggesting a possible impairment in interferon signaling pathways that may raise the risk of severe COVID-19 outcomes. In a study focusing on IFNAR2, variants such as rs1051393, rs1131668, and rs12482556 were found to exhibit increased allelic frequencies in indigenous communities. Some investigations have highlighted the link between the IFNAR2 gene and the most severe forms of COVID-19, along with the correlation of genetic variations with increased susceptibility to critical cases of the disease [23], [24], [25]. Our findings align with previous studies that demonstrate the significance of genetic variations in the IFNAR2 gene in modulating immune responses to viral infections. Given that the presence of this variant could hinder the effective functioning of interferon signaling, we propose that individuals carrying rs1131668 may indeed exhibit heightened susceptibility to severe manifestations of COVID-19, consistent with existing literature indicating associations between IFNAR2 polymorphisms and adverse disease outcomes.

The variant rs9984273 identified in our study highlights the importance of investigating genetic variations in IFNAR2 among individuals with COVID-19. The detected indel mutations may have potential effects on altering the reading frame. According to the results of Poly-Phen2 analysis, the score of the three missense mutations identified in our study being close to 1 suggests their potentially pathogenic nature in the pathogenesis of COVID-19. A novel disease association of rs9984273 SNP, located within the IFNAR2 gene, was identified by Jalkanen and colleagues. The minor allele of rs9984273 was associated with higher IFNAR expression, faster reduction in IFN γ and interleukin-6 (IL-6) levels, and better outcomes in IFN β-treated patients with acute respiratory distress syndrome (ARDS). Conversely, the major allele was linked to worse outcomes, particularly when administered alongside IFN β and glucocorticoid therapy. Moreover, according to the COVID-19 Host Genetics Initiative database [26], the minor allele of rs9984273 was linked with less severe forms of COVID-19. Our findings are consistent with reports indicating that genome-wide association studies have demonstrated associations between IFNAR2 polymorphisms and severe infections, including COVID-19 [27], [28], [29]. Furthermore, the identification of SNP rs9984273 within the IFNAR2 gene highlights the significance of genetic variations in impacting treatment outcomes. Given that T2D is linked to altered immunological responses, understanding the implications of this variation can help in identifying high-risk patients and personalizing treatment approaches accordingly. As a result, the diversity in mutation rates and their potential influence on disease severity highlight the need for larger sample counts in future investigations to understand the function of these genetic parameters in the context of COVID-19 and diabetes.

In addition, rs9984273 (c.841-33C>A) variation in IFNAR2 as shown in a study which research the comparison of IRF7, TBK1, IFNAR1 IFNAR2, and TLR3 gene variants between the African/African American, Ashkenazi Jewish, East Asian, European, Latino/American Admixed, and South Asian populations and Turkish individuals and their importance in infectious diseases. This variation was found to begin, as in our study. In the same study, one splice-associated variant (SAV) (rs34865572 – c.841-4delT) was determined in IFNAR2. This particular variation exhibited a lower minimum allele frequency (MAF) in the Turkish population in contrast to its frequency in other ethnic groups [30]. In our study, the same SNP, rs34865572, was identified as a distinct variation (c.*59_*73=) with a benign characteristic, consistent with the findings of Karacan and colleagues’ study. On the other hand, our study identified a SNP, rs749173983. It exhibited both an indel variation feature and a benign characteristic.

On the other hand, we determined an intronic SNP (rs2254315) in IFNAR1 gene. The same polymorphism was also studied in a study. The study focused on genotyping a cohort called Genetics of Resistance to Immunodeficiency Virus (GRIV), which includes individuals with various rates of AIDS progression and seronegative controls. Among the identified SNPs, IFNAR1 rs2254315 newly discovered. The findings suggested potential links between certain SNP alleles and AIDS development, particularly highlighting one of the two SNPs, IFNAR1 rs2254315. This SNP showed strong associations with both slow and rapid AIDS progression compared to controls. This suggested its possible role in AIDS progression or susceptibility to HIV-1 infection [31]. Related to this, certain studies have documented the infection of mice with mouse hepatitis virus, a coronavirus closely related with type 2 coronaviruses [32].

In addition, we detected an SNP (rs1350368647) in 3′ UTR region of the IRF7 gene that did not have pathogenic importance according to the PolyPhen-2 analysis. Considering the potential existence of several risk polymorphisms within the IRF7 gene, it’s plausible that other studied genetic variants of IRF7 could also play a role in systemic lupus erythematosus (SLE) development. For instance, SLE patients with the KIAA1542 SNP rs702966, situated in the 3′ UTR region and associated with the risk genotype, exhibited heightened serum IFNα activity when expressing anti-dsDNA [33], [34], [35]. Finally, a SNP (rs754374657) was found in IRF3 gene. As a frameshift mutation, it featured both insertion and deletion of nucleotides. In another study involving 784 Chinese patients with systemic lupus erythematosus (SLE) and 899 controls, four SNPs (rs4963128, rs1131665 (Q412R), and rs1061502 (K179E) within KIAA1542/IRF7 were genotyped using Taqman genotyping assay. The IRF7 3′UTR SNP rs702966 exhibited an association with renal involvement in the study, with a statistically significant p-value of 0.01 and an odds ratio of 0.46 [0.25–0.85]. It was also provided evidence that variants rs702966 and rs4963128 are associated with SLE disease activity [33]. Although the polymorphisms in this gene has been linked to other diseases in the literature, we focused on its implications in COVID-19 prognosis among type 2 diabetic patients. Due to the complexities of the immune response in T2D, investigating how genetic variants in the IRF7 gene could affect these mechanisms is crucial. While this specific SNP (rs1350368647) may not be harmful, it is critical to investigate how it, in combination with other genetic factors, may contribute to the severity of viral infections such as COVID-19 in diabetic patients.

We searched that if the mutations altering the functionality of the IFNAR1, IFNAR2, IRF3, and IRF7 signaling pathways may play a significant role in COVID-19 for the patients with T2D. According to the mutation rates detected in all genes, it cannot be said that there is a consistent trend of increase or decrease among all of them. However, overall, in the IFNAR2 and IRF7 genes, a decrease in the mutation rate was observed in both patient and control groups as the severity of the disease decreased. Although not clear in other groups, in the IFNAR2, IRF3, and IRF7 genes, a higher mutation rate was observed in all diabetic and COVID-19 patients regardless of the severity of the disease compared to the non-Type 2 Diabetic control groups. This may indicate that Type 2 diabetic patients are more susceptible to mutations when infected with COVID-19. Further study with a larger sample size may lead to an increase in the mutation rates detected in this study. This study also identified candidate genes in the immune system that have infection susceptibility and pathogenic potential, along with those harboring pathogenic variants. Investigating the relationship between life-threatening viral infections and immune system gene variants, as well as the functional effects of these variants, demonstrates the variability in variant frequencies among populations and its impact on them. Disease progression can arise from point mutations, gene deletions, and chromosomal translocations in various genes, highlighting their significance in COVID-19 biology through conducted studies. Identifying these differences may lead to the discovery of treatment options affecting signaling pathway steps and the development of personalized treatment methods.

Our study revealed novel findings that could be reported for the first time in individuals with T2D in the literature. We attempted to determine the impact of genetic anomalies in the interferon signaling pathway, crucial in immunity, and their role in the severity of SARS-CoV-2 infection in T2D patients who experienced severe COVID-19. The genomic structure of the IFN pathway in an individual could indicate its candidacy for protective/personalized medical treatment strategies for COVID-19 and similar diseases. Mutations identified in T2D patients in our population could be investigated for their impact on the immune response to viruses such as SARS-CoV-2, and their contributions to the severity of infections across different disease groups could be clinically assessed. Understanding the genetic makeup of individuals facing coronaviral syndromes and those expected to encounter them in the future is crucial. This understanding, during outbreaks of severe acute respiratory infections, will allow for determining the severity of the disease, facilitating prevention, and enabling effective treatment approaches.

Corresponding author: Tuncay Tuluk, MSc, Department of Biotechnology, Faculty of Science, Niğde Ömer Halisdemir University, 51310, Niğde, Türkiye, E-mail:
Tuncay Tuluk and Teoman Kankılıç contributed equally to this work.

Funding source: Niğde Ömer Halisdemir University

Award Identifier / Grant number: FMT 2021/3-LÜTEP

Funding source: The Scientific and Technological Research Council of Türkiye (TÜBİTAK)

Award Identifier / Grant number: Project No: 222S190

  1. Research Ethics: The research protocol was approved by the Non-Interventional Clinical Research Ethics Committee of Niğde Ömer Halisdemir University Faculty of Medicine (Project No: 2021/55).

  2. Informed consent: Informed consent was obtained from all individuals included in this study.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: This study was supported by the Scientific and Technological Research Council of Türkiye (TÜBİTAK) 1002 Hızlı Destek-B Project (No: 222S190) and by Niğde Ömer Halisdemir University, Scientific Research Projects Coordination Unit, FMT 2021/3-LÜTEP project.

  7. Data availability: Not applicable.

References

1. Ciotti, M, Ciccozzi, M, Terrinoni, A, Jiang, WC, Wang, CB, Bernardini, S. The COVID-19 pandemic. Crit Rev Clin Lab Sci 2020;57:365–88. https://doi.org/10.1080/10408363.2020.1783198.Search in Google Scholar PubMed

2. Sharma, A, Tiwari, S, Deb, MK, Marty, JL. Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2): a global pandemic and treatment strategies. Int J Antimicrob Agents 2020;56. https://doi.org/10.1016/j.ijantimicag.2020.106054.Search in Google Scholar PubMed PubMed Central

3. Orioli, L, Hermans, MP, Thissen, JP, Maiter, D, Vandeleene, B, Yombi, JC. COVID-19 in diabetic patients: related risks and specifics of management. Ann Endocrinol-Paris 2020;81:101–9. https://doi.org/10.1016/j.ando.2020.05.001.Search in Google Scholar PubMed PubMed Central

4. Dallavalasa, S, Tulimilli, SV, Prakash, J, Ramachandra, R, Madhunapantula, SV, Veeranna, RP. COVID-19: diabetes perspective–pathophysiology and management. Pathogens 2023;12:184. https://doi.org/10.3390/pathogens12020184.Search in Google Scholar PubMed PubMed Central

5. Gangadharan, C, Ahluwalia, R, Sigamani, A. Diabetes and COVID-19: role of insulin resistance as a risk factor for COVID-19 severity. World J Diabetes 2021;12. https://doi.org/10.4239/wjd.v12.i9.1550.Search in Google Scholar PubMed PubMed Central

6. Chee, YJ, Tan, SK, Yeoh, E. Dissecting the interaction between COVID-19 and diabetes mellitus. J Diabetes Investig 2020;11:1104–14. https://doi.org/10.1111/jdi.13326.Search in Google Scholar PubMed PubMed Central

7. Yaugel-Novoa, M, Bourlet, T, Longet, S, Botelho-Nevers, E, Paul, S. Association of IFNAR1 and IFNAR2 with COVID-19 severity. The Lancet Microbe 2023;4:e487. https://doi.org/10.1016/s2666-5247(23)00095-2.Search in Google Scholar

8. Edahiro, R, Shirai, Y, Takeshima, Y, Sakakibara, S, Yamaguchi, Y, Murakami, T, et al.. Single-cell analyses and host genetics highlight the role of innate immune cells in COVID-19 severity. Nat Genet 2023;55:753–67. https://doi.org/10.1038/s41588-023-01375-1.Search in Google Scholar PubMed PubMed Central

9. Pairo-Castineira, E, Clohisey, S, Klaric, L, Bretherick, AD, Rawlik, K, Pasko, D, et al.. Genetic mechanisms of critical illness in COVID-19. Nature 2021;591:92–8. https://doi.org/10.1038/s41586-020-03065-y.Search in Google Scholar PubMed

10. Shin, J, Toyoda, S, Nishitani, S, Onodera, T, Fukuda, S, Kita, S, et al.. SARS-CoV-2 infection impairs the insulin/IGF signaling pathway in the lung, liver, adipose tissue, and pancreatic cells via IRF1. Metabolism 2022;133. https://doi.org/10.1016/j.metabol.2022.155236.Search in Google Scholar PubMed PubMed Central

11. Li, S, Duan, X, Li, Y, Li, M, Gao, Y, Li, T, et al.. Differentially expressed immune response genes in COVID-19 patients based on disease severity. Aging (Albany NY) 2021;13:9265. https://doi.org/10.18632/aging.202877.Search in Google Scholar PubMed PubMed Central

12. Nain, Z, Barman, SK, Sheam, MM, Syed, SB, Samad, A, Quinn, JM, et al.. Transcriptomic studies revealed pathophysiological impact of COVID-19 to predominant health conditions. Briefings Bioinf 2021;22. https://doi.org/10.1093/bib/bbab197.Search in Google Scholar PubMed PubMed Central

13. https://www.cbioportal.org/.Search in Google Scholar

14. The Ensembl website. [Online]. Available from: https://www.ensembl.org.Search in Google Scholar

15. NCBI website. https://www.ncbi.nlm.nih.gov/.Search in Google Scholar

16. The PolyPhen-2 website. [Online]. Available from: http://genetics.bwh.harvard.edu/pph2/.Search in Google Scholar

17. SNAP2 (https://service.rostlab.org/snap2web/).Search in Google Scholar

18. String website: https://string-db.org/.Search in Google Scholar

19. Santesmasses, D, Castro, JP, Zeni, AA, Shindyapina, AV, Gerashchenko, MV, Zhang, B, et al.. COVID-19 is an emergent disease of aging. Aging Cell 2020;19:e13230. https://doi.org/10.1111/acel.13230.Search in Google Scholar PubMed PubMed Central

20. López-Bielma, MF, Falfán-Valencia, R, Abarca-Rojano, E, Pérez-Rubio, G. Participation of single-nucleotide variants in IFNAR1 and IFNAR2 in the immune response against SARS-CoV-2 Infection: a systematic review. Pathogens 2023;12:1320. https://doi.org/10.3390/pathogens12111320.Search in Google Scholar PubMed PubMed Central

21. Campbell, TM, Liu, Z, Zhang, Q, Moncada-Velez, M, Covill, LE, Zhang, P, et al.. Respiratory viral infections in otherwise healthy humans with inherited IRF7 deficiency. J Exp Med 2022;219. https://doi.org/10.1084/jem.20220202.Search in Google Scholar PubMed PubMed Central

22. Ma, W, Huang, G, Wang, Z, Wang, L, Gao, Q. IRF7: role and regulation in immunity and autoimmunity. Front Immonol 2023;14. https://doi.org/10.3389/fimmu.2023.1236923.Search in Google Scholar PubMed PubMed Central

23. Coelho, RDCC, Martins, CLELP, Pastana, LF, Rodrigues, JCG, Aguiar, KEC, Cohen-Paes, ADN, et al.. Molecular profile of variants potentially associated with severe forms of COVID-19 in amazonian indigenous populations. Viruses 2024;6:359. https://doi.org/10.3390/v16030359.Search in Google Scholar PubMed PubMed Central

24. Fricke-Galindo, I, Martínez-Morales, A, Chávez-Galán, L, Ocaña-Guzmán, R, Buendía-Roldán, I, Pérez-Rubio, G, et al.. IFNAR2 relevance in the clinical outcome of individuals with severe COVID-19. Front Immunol 2022;13. https://doi.org/10.3389/fimmu.2022.949413.Search in Google Scholar PubMed PubMed Central

25. Kanai, M, Andrews, SJ, Cordioli, M, Stevens, C, Neale, BM, Daly, M, et al.. A second update on mapping the human genetic architecture of COVID-19. Nature 2023;621:E7–26. https://doi.org/10.1038/s41586-023-06355-3.Search in Google Scholar PubMed PubMed Central

26. Jalkanen, J, Khan, S, Elima, K, Huttunen, T, Wang, N, Hollmén, M, et al.. Polymorphism in interferon alpha/beta receptor contributes to glucocorticoid response and outcome of ARDS and COVID-19. Crit Care 2023;27:112. https://doi.org/10.1186/s13054-023-04388-8.Search in Google Scholar PubMed PubMed Central

27. Abdelhafez, M, Nasereddin, A, Shamma, OA, Abed, R, Sinnokrot, R, Marof, O, et al.. Association of IFNAR2 rs2236757 and OAS3 rs10735079 polymorphisms with susceptibility to COVID-19 infection and severity in Palestine. Interdiscip Perspect Infect Dis 2023;2023. https://doi.org/10.1155/2023/9551163.Search in Google Scholar PubMed PubMed Central

28. Akter, S, Roy, AS, Tonmoy, MIQ, Islam, MS. Deleterious single nucleotide polymorphisms (SNPs) of human IFNAR2 gene facilitate COVID-19 severity in patients: a comprehensive in silico approach. J Biomol Struct Dyn 2022;40:11173–89. https://doi.org/10.1080/07391102.2021.1957714.Search in Google Scholar PubMed

29. Dieter, C, de Almeida Brondani, L, Lemos, NE, Schaeffer, AF, Zanotto, C, Ramos, DT, et al.. Polymorphisms in ACE1, TMPRSS2, IFIH1, IFNAR2, and TYK2 genes are associated with worse clinical outcomes in COVID-19. Genes 2022;14:29. https://doi.org/10.3390/genes14010029.Search in Google Scholar PubMed PubMed Central

30. Karacan, A, Toksoy, G, Uyguner, O, Karaman, B, Başaran, S, Komurcu-Bayrak, E. Inter-population comparisons and the importance in infectious diseases of the IRF7, TBK1, IFNAR1, IFNAR2 and TLR3 gene variants in Turkish individuals. J Ist Faculty Med 2022;85:344–54.10.26650/IUITFD.1060030Search in Google Scholar

31. Diop, G, Hirtzig, T, Do, H, Coulonges, C, Vasilescu, A, Labib, T, et al.. Exhaustive genotyping of the interferon alpha receptor 1 (IFNAR1) gene and association of an IFNAR1 protein variant with AIDS progression or susceptibility to HIV-1 infection in a French AIDS cohort. Biomed Pharmacother 2006;60:569–77. https://doi.org/10.1016/j.biopha.2006.08.002.Search in Google Scholar PubMed

32. Devaraj, SG, Wang, N, Chen, Z, Tseng, M, Barretto, N. Regulation of IRF-3-dependent innate immunity by the papain-like protease domain of the severe acute respiratory syndrome coronavirus. J Biol Chem 2007;282:32208–21. https://doi.org/10.1074/jbc.m704870200.Search in Google Scholar PubMed PubMed Central

33. Salloum, R, Franek, BS, Kariuki, SN, Rhee, L, Mikolaitis, RA, Jolly, M, et al.. Genetic variation at the IRF7/PHRF1 locus is associated with autoantibody profile and serum interferon‐α activity in lupus patients. Arthritis Rheumatol 2010;62:553–61. https://doi.org/10.1002/art.27182.Search in Google Scholar PubMed PubMed Central

34. Fu, Q, Zhao, J, Qian, X, Wong, JL, Kaufman, KM, Yu, CY, et al.. Association of a functional IRF7 variant with systemic lupus erythematosus. Arthritis Rheum 2011;63:749-54, https://doi.org/10.1002/art.30193,Search in Google Scholar PubMed PubMed Central

35. Xu, WD, Zhang, YJ, Xu, K, Zhai, Y, Li, BZ, Pan, HF, et al.. IRF7, a functional factor associates with systemic lupus erythematosus. Cytokine 2012;58:317–20. https://doi.org/10.1016/j.cyto.2012.03.003.Search in Google Scholar PubMed

Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/tjb-2024-0139).

Received: 2024-06-12
Accepted: 2024-10-20
Published Online: 2024-12-31

© 2024 the author(s), published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

  1. Frontmatter
  2. Review
  3. Targeting oxidative stress, iron overload and ferroptosis in bone-degenerative conditions
  4. Research Articles
  5. Assessing medical biochemistry professionals’ knowledge, attitudes, and behaviors regarding green and sustainable medical laboratory practices in Türkiye
  6. The efficacy of high pressure liquid chromatography (HPLC) in detecting congenital glycosylation disorders (CDG)
  7. Atypical cells parameter in sysmex UN automated urine analyzer: a single center study
  8. The frequency of single specific immunoglobulin E and allergen mixes with a MAST (multiple-antigen simultaneous test) technique
  9. Differences in second trimester risk estimates for trisomy 21 between Maglumi X3/Preaccu and Immulite/Prisca systems
  10. Comparison of classical and flowcytometric osmotic fragility and flowcytometric eosin-5-maleimide binding tests in diagnosis of hereditary spherocytosis
  11. Casticin inhibits the hedgehog signaling and leads to apoptosis in AML stem-like KG1a and mature KG1 cells
  12. Trimethylamine N-oxide, S-equol, and indoxyl sulfate inflammatory microbiota players in ocular Behçet’s disease
  13. Genomic profiling of interferon signaling pathway gene mutations in type 2 diabetic individuals with COVID-19
  14. CDR1as/miR-7-5p/IGF1R axis contributes to the suppression of cell viability in prostate cancer
  15. Role of interferon regulatory factors in predicting the prognosis of Crimean-Congo hemorrhagic fever
  16. The significance of taurine for patients with Crimean-Congo hemorrhagic fever and COVID-19 diseases: a cross-sectional study
  17. Gene mining, recombinant expression and enzymatic characterization of N-acetylglucosamine deacetylase
  18. Ethanol inhibited growth hormone receptor-mediated endocytosis in primary mouse hepatocytes
  19. Gypsophila eriocalyx roots inhibit proliferation, migration, and TGF-β signaling in melanoma cells
  20. The role of kynurenine and kynurenine metabolites in psoriasis
  21. Tobacco induces abnormal metabolism of tryptophan via the kynurenine pathway
  22. Effect of vitamin D and omega-3 on the expression of endoplasmic reticulum-associated protein degradation and autophagic proteins in rat brain
https://doi.org/10.1515/tjb-2024-0139
Keywords for this article
COVID-19; type 2 diabetes; mutation; variation; interferon signaling pathway
Creative Commons
BY 4.0

Articles in the same Issue

  1. Frontmatter
  2. Review
  3. Targeting oxidative stress, iron overload and ferroptosis in bone-degenerative conditions
  4. Research Articles
  5. Assessing medical biochemistry professionals’ knowledge, attitudes, and behaviors regarding green and sustainable medical laboratory practices in Türkiye
  6. The efficacy of high pressure liquid chromatography (HPLC) in detecting congenital glycosylation disorders (CDG)
  7. Atypical cells parameter in sysmex UN automated urine analyzer: a single center study
  8. The frequency of single specific immunoglobulin E and allergen mixes with a MAST (multiple-antigen simultaneous test) technique
  9. Differences in second trimester risk estimates for trisomy 21 between Maglumi X3/Preaccu and Immulite/Prisca systems
  10. Comparison of classical and flowcytometric osmotic fragility and flowcytometric eosin-5-maleimide binding tests in diagnosis of hereditary spherocytosis
  11. Casticin inhibits the hedgehog signaling and leads to apoptosis in AML stem-like KG1a and mature KG1 cells
  12. Trimethylamine N-oxide, S-equol, and indoxyl sulfate inflammatory microbiota players in ocular Behçet’s disease
  13. Genomic profiling of interferon signaling pathway gene mutations in type 2 diabetic individuals with COVID-19
  14. CDR1as/miR-7-5p/IGF1R axis contributes to the suppression of cell viability in prostate cancer
  15. Role of interferon regulatory factors in predicting the prognosis of Crimean-Congo hemorrhagic fever
  16. The significance of taurine for patients with Crimean-Congo hemorrhagic fever and COVID-19 diseases: a cross-sectional study
  17. Gene mining, recombinant expression and enzymatic characterization of N-acetylglucosamine deacetylase
  18. Ethanol inhibited growth hormone receptor-mediated endocytosis in primary mouse hepatocytes
  19. Gypsophila eriocalyx roots inhibit proliferation, migration, and TGF-β signaling in melanoma cells
  20. The role of kynurenine and kynurenine metabolites in psoriasis
  21. Tobacco induces abnormal metabolism of tryptophan via the kynurenine pathway
  22. Effect of vitamin D and omega-3 on the expression of endoplasmic reticulum-associated protein degradation and autophagic proteins in rat brain
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 19.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/tjb-2024-0139/html
Scroll to top button