Skip to main content
Download PDF

Association of the host genetic factors, hypercholesterolemia and diabetes with mild influenza in an Iranian population

Virology Journal volume 18, Article number: 64 (2021) Cite this article

Abstract

Background

Variation in host genetic factors may result in variation in the host immune response to the infection. Some chronic diseases may also affect individuals’ susceptibility to infectious diseases. The aim of this study was to evaluate the association of the host genetic factors mostly involved in inflammation, as well as hypercholesterolemia and diabetes with mild flu in an Iranian population.

Methods

In this cross-sectional study, nasopharyngeal swab samples were collected from 93 patients referred to primary care centers of Markazi, Semnan, and Zanjan provinces (central Iran) due to flu-like symptoms between March 2015 and December 2018. Of these, PCR test identified 49 influenza A/H1N1 and 44 flu-negative individuals. Twelve single-nucleotide polymorphisms (SNPs) in RPAIN, FCGR2A, MBL-2, CD55, C1QBP, IL-10, TNF-α and an unknown gene were genotyped using iPLEX GOLD SNP genotyping analysis. Hypercholesterolemia and diabetes status was determined based on the physician diagnosis. Association of the host genetic variants, hypercholesterolemia and diabetes with mild A/H1N1 flu was assessed with univariable and multivariable logistic regression analysis as implemented in Stata software (v.14). Statistical tests were considered as significant at 0.05 levels.

Results

Frequency of diabetes and hypercholesterolemia, as well as participants mean age was significantly higher in the flu-negative rather than the flu-positive group. Of 12 SNPs, nine did not show any significant association with mild flu in our study (rs1801274, rs1800451, rs2564978, rs361525, rs1800450, rs1800871, rs1800872, rs1800896, rs1800629). Possessing G vs. A allele in two SNPs (rs3786054 and rs8070740) was associated with a threefold increase in the chance of mild flu when compared to flu-negative patients (95% CI: 1.1, 22.0). Possessing C allele (vs. A) in the rs9856661 locus also increased the chance of mild flu up to 2 folds (95% CI: 1.0, 10.0).

Conclusion

The results showed that possessing the G allele in either rs3786054 or rs8070740 loci in C1QBP and RPAIN genes, respectively, increased the risk of H1N1 infection up to 3.3 folds, regardless of the patient’s age, BMI, diabetes, and hypercholesterolemia. Complementary functional genomic studies would shed more light on the underlying mechanism of human immunity associated with these genetic markers. The identified genetic factors may have the same role in susceptibility to similar respiratory infections with RNA viruses, like SARS, MERS and COVID-19. Future genetic association studies targeting these RNA viruses, especially COVID-19 is recommended. Studies on other ethnic groups would also shed light on possible ethnic variations in genetic susceptibility to respiratory RNA viruses.

Trial registry IR.PII.REC.1399.063

Background

Influenza A virus is one of the most important risk factors for human and animal health leading to severe respiratory infections [1]. New strains cause the epidemics among humans and animals. These viruses use the host cell system for their replication and propagation and affect host cell signaling pathways [2]. While most of the antiviral studies have focused on the genetic factors of viral agents that determine the severity of influenza and their impact on host immunity, there is lack of a comprehensive understanding of the host genetics which determine the severity and susceptibility of the respiratory disease [3]. The family clustering of highly pathogenic influenza A /H5N1 cases has led to the hypothesis that host genetic plays a key and critical role in the susceptibility to influenza infection [4,5,6].

The severity of the disease is usually associated with the host factors such as age (over 65 years), pregnancy, obesity and underlying diseases including lung, cardiovascular, renal or hepatic disease, chronic metabolic disorders (including diabetes) or immunodeficiency diseases [7]. Underlying diseases such as diabetes and hypercholesterolemia are associated with the highest risk and the most common contributing factors [8,9,10]. Ethnicity has also been identified as another risk factor for the susceptibility to H1N1 pandemic in multiple populations in the North America and Australia [11]. A key point for the studies of host innate immunity factors is the observation of increased mortality among young adults and children without pre-identified risk factors [12].

It has been shown that host polymorphisms affect the development of certain diseases and disorders by coding altered genes products or altering the transcriptional settings. In order to understand the pathogenicity of influenza virus, several parameters of both host and virus must be considered. These factors mainly include virus pathogenicity, host acquired immunity and host hereditary factors that have been taken into consideration in determining the severity of the disease, particularly after the 2009 epidemic [3, 13].

Several potential genetic polymorphisms have recently been described for the susceptibility to influenza infection; including genes which encode complement decay-accelerating factor (CD55), complement 1 C1q binding protein (C1QBP), Fc fragment of IgG receptor IIa (FCGR2A), interferon induced transmembrane protein 3 (IFITM3), tumor necrosis factor (TNF), replication protein A interacting protein (RPAIN), lymphotoxin alpha (LTA), and killer-cell inhibitory receptor (KIR) [14,15,16,17,18].

Genetic polymorphisms vary among individuals in different populations and usually include single nucleotide polymorphisms (SNP).

For example, the IFITM3 restricts the transcription of some viruses, especially those that enter the cell through endocytosis, such as influenza, HIV, and yellow fever [19,20,21,22]. In a study, the C allele frequency of IFITM3 rs12252 was shown high in Han Chinese population (MAF = 0.528) [23]. This means that IFITM3 gene affects the severity of the disease. The expression of the rare rs12252-C allele in the IFITM3 gene results in a truncated protein that lacks 21 amino acids at the end of the amine, which reduces its impact on influenza infection control [23].

In our previous study, we examined rs12252 in the IFITM3 gene in an Iranian population from the selective provinces [24]. That was the first study to evaluate the association of IFITM3 rs12252 polymorphism with susceptibility to mild influenza with regards to diabetes, hypercholesterolemia and BMI in an Iranian population. Our results showed a significant positive association between mild influenza and the heterozygote C allele at rs12252 (P = 0.008), not the homozygote C allele (P = 0.406). The carriers of the C allele (CT + CC genotype) were 5.92 times more at risk of mild influenza compared to the homozygote T allele (P = 0.007) [24].

Mannose-Binding Lectin-2 (MBL-2) gene is a member of the collagen-containing C-type lectin family. It is part of the innate immune system which is important for complement activation and opsonization [25]. MBL-2 encodes a calcium-dependent protein that plays an important role in innate immunity. The amount of circulating MBL-2 is mainly due to the single nucleotide polymorphisms occur in exon 1 and the promoter region. Different alleles of MBL-2 are associated with susceptibility to different infections [26,27,28].

Tumor necrosis factor- α (TNF-α) is a potent proinflammatory cytokine that plays role in the pathogenesis and development of various infectious diseases. However, IL-10 is a potent anti-inflammatory cytokine that plays an important role in regulating and reducing the cytotoxic inflammatory responses. Several polymorphisms in the promoter regions of these genes have been shown that affect directly the gene transcription and are associated with the development and progression of autoimmune and infectious diseases [29, 30].

CD55 or DAF (Decay-Accelerating Factor), encodes a glycoprotein involved in complement cascade regulation and is active in protecting host cells from damage of pathogenic microorganisms [31]. Studies have shown that the T/T genotype in the rs2564978 CD55 gene promoter is associated with severe influenza A (H1N1) pdm09 [32].

Two SNPs in the C1QBP (rs3786054) and FCGR2A (rs1801274) genes have been reported to be associated with the cases of severe influenza in Mexico in 2009 [17]. The C1QBP gene encodes a protein that binds to the spherical heads of C1q molecules and activates the classical complement pathway [17], while FCGR2A encodes a member of the Fc immunoglobulin receptor family which are found on the surface of phagocytic cells such as macrophages and neutrophils. It is involved in the process of phagocytosis and clearance of immune complexes [33].

RPAIN encodes a nucleoprotein that is involved in RNA movement and establishment [34]. The rs8070740 SNP in RPAIN has been shown to affect host immune response, or influenza A/H1N1 virus replication [17].

A significant association was also found between rs9856661 from an unknown gene located on chromosome 3 with the incidence of severe pneumonia in the patients with A/H1N1 virus infection [17].

Consequently, based on this background and our previous study findings, further studies are needed to confirm the earlier findings and to explore if further SNPs are associated with susceptibility to mild flu in the Iranian population. Identifying genetic markers associated with mild flu can give us hints and indications about the mechanisms of susceptibility and resistance to this virus. Identifying the pathogenic and resistance mechanisms may have applications in drug and vaccine design. Tracking the host genetic factors in susceptibility and resistance to influenza virus (as a model for the respiratory RNA viruses) may be the basis for understanding the pathogenicity of similar viruses such as coronavirus. Twelve SNPs in 9 genes located on 6 chromosomes with partial global minor allele frequency (GMAF) were selected and analyzed using iPLEX® and MassARRAY® system to genotype the desired alleles.

Methods

Ethics statement

The study objectives and processes were explained to all participants. Written informed consent was then obtained from them. The ethics committee of the Pasteur Institute of Iran reviewed and approved the protocol of this study (Ethics code: IR.PII.REC.1395.3).

Participants and sample collection

Nasopharyngeal swab samples were collected as fluid specimens in Viral Transport Media (VTM). The samples were collected from 93 patients who were suspected to influenza A/H1N1 and referred to primary care centers of Markazi, Semnan, and Zanjan provinces (central Iran) from March 2015 to December 2018. Infection with influenza A/H1N1 was confirmed by real-time PCR test. Our exclusion criteria were respiratory comorbidities, immune suppressive drugs consumption, and previous vaccination against influenza virus [24]. Based on real-time PCR test results, patients were classified into influenza A/H1N1 positive (n = 49) and negative (n = 44) groups. The latter group was considered as having influenza-like illness (ILI).

Participants’ age (year), sex, weight (kg), and height (cm) were assessed at the time of their clinic visit. Diabetes (positive/negative) and hypercholesterolemia (positive/negative) status was also determined by the physician. Twelve SNPs in nine genes located on six chromosomes were genotyped. Genetic data of the SNPs analyzed are shown in Table 1.

Table 1 Genetic data of the SNPs analyzed
Full size table

One-step quantitative real-time PCR for IAV detection

Viral RNA was extracted from fluid specimen using High Pure Viral RNA Kit, according to the manufacturer’s instructions (Roche, Switzerland). The extracted RNA was re-suspended in 50 μl Elution Buffer and stored at – 80 °C for real-time PCR assay. Primers and probes were designed and synthesized by SinaClon Co. (Iran) based on the latest WHO guideline. Real-time PCR reactions were performed as mentioned before [24].

Genotyping of SNPs

Human DNA extraction

All genomic DNA samples were extracted from the specimens using DNP™ High yield DNA Purification Kit, according to the manufacturer’s instructions (Cinnagen, Iran) as stated before [24].

IPLEX® method and MassARRAY® system for genotyping the allelic variants

The extracted DNA samples were transferred to Inqaba Biotech Co, South Africa, to determine the genotypes of the allelic variants using iPLEX® and MassARRAY® platform (Sequenom MassARRAY® System, San Diego, USA). This SNP genotyping method involves an initial PCR reaction specific to the locus followed by single base extension using mass-modified primers containing terminal deoxyribonucleotides that binds immediately upstream of the polymorphism site. Using Matrix-Assisted Laser Desorption/Ionization-Time Of Flight (MALDI-TOF) Mass Spectrometry (MS), the specific mass of the amplified primer determines the SNP allele. This procedure involves the following steps: Pre-PCR for DNA (5 to 50 ng /µl) and oligonucleotides preparation, PCR for target locus amplification using designed primers, Post-PCR for purification of the reaction with SAP (Shrimp alkaline phosphatase), Single base amplification with iPLEX system, Cleaning products with resin, Deployment of amplified products on SpectroCHIPs, and Products tracking with Mass Spectrometry with appropriate MALDI-TOF matrix [35]. The specifications of the oligos for iPLEX SNP genotyping are shown in Table 2.

Table 2 The specifications of the oligos for iPLEX SNP genotyping
Full size table

Genetic and statistical analysis

Normality of continuous variables was confirmed by Kolmogorov Smirnoff test. Continuous and categorical data were described by mean ± SD, and number (percentage), respectively. Distribution of demographic and clinical characteristics was compared between influenza A/H1N1 group and the ILI group. Participants’ mean age and BMI was compared between both groups using Independent t-test. The two groups were compared regarding their gender, diabetes and hypercholesterolemia status using Pearson’s Chi square test.

Genotype frequency of each SNP was compared between influenza A/H1N1 group and the ILI group using Pearson’s Chi square test. Where expected genotype frequencies in more than 20% of the cells were less than five, Fisher’s exact test was used instead. Univariable logistic regression analysis was used to test the size of each genotype crude effect on influenza A/H1N1. Investigated demographic and clinical characteristics whose distribution differed significantly between the two groups were included into a multivariable logistic regression in order to test the adjusted effect of each allele on influenza A/H1N1.

All statistical tests were two-tailed with a type I error of 0.05. Statistical analyses were performed in Stata software (version 11; Stata Corp, College Station, TX, USA).

Results

Demographical and clinical characteristics of influenza A/H1N1 patients and influenza-like illness (ILI) patients are shown in Table 3. In both the A/H1N1 and ILI groups, participants were equally of both genders (PPearson’s chi square = 0.460). The mean age of participants in both groups was 45 years. Participants in the ILI group were significantly of older ages than the A/H1N1 group (mean age: 56.5 vs. 46.2 years, respectively. Pt-test = 0.05). Mean BMI levels was higher in the A/H1N1 group (mean difference = 2.2 kg/m2). This association, however, was not statistically significant (Pt-test = 0.09). There was also a statistically significant association between diabetes (PPearson’s chi square = 0.001) and hypercholesterolemia (PFisher’s exact test = 0.03) statuses and A/H1N1, in a way that the frequency of these comorbidities was lower in the A/H1N1 group rather than the ILI group (Table 3).

Table 3 Demographical and clinical characteristics of influenza A/H1N1 patients and influenza-like illness (ILI) patients
Full size table

Logistic regression analysis showed that possessing G/G genotype in the rs3786054 and rs8070740 loci significantly increases the risk of H1N1 influenza while compared to the A/A genotype in these loci (ORrs3786054 = 7.3; ORrs8070740 = 5.4). The direction of this association was the same for the G/A genotypes, although with smaller magnitude (ORrs3786054 = 2.6 and ORrs8070740 = 1.4); however, these associations were not statistically significant (Table 4). The results of the multivariable logistic regression analysis also showed that possessing the G allele in either rs3786054 or rs8070740 loci, significantly increases the risk of H1N1 influenza up to 3.3 folds, regardless of the patient’s age, BMI, diabetes, and hypercholesterolemia (Table 5).

Table 4 Frequency of 12 SNPs and their association with influenza A/H1N1 infection
Full size table
Table 5 Crude and adjusted allelic risk of influenza A/H1N1 infection in relation to alleles of polymorphisms studied
Full size table

The crude effect of rs9856661 genotypes on the H1N1 influenza risk was not statistically significant, probably due to small number of observations within each genotype category. The allelic OR, however, showed that possessing the A allele (rather than the C allele) significantly increased the risk of H1N1 influenza up to two folds both in the crude analysis and after adjustment for the abovementioned demographics and comorbidities (Table 5). The association of other SNPs with H1N1 influenza was not statistically significant either in crude or adjusted analyses (Tables 4 & 5).

Discussion

In the present study, we investigated the genetic susceptibility of the host to influenza virus infection by incorporating hypercholesterolemia and diabetes factors. The alleles and their genotypes were examined to determine whether the polymorphisms of the studied genes are associated with the susceptibility to influenza virus in this population with a preference for hypercholesterolemia and diabetes. Host and pathogen genetic determinants are important to evaluate pathogenesis of infectious disease. Several polymorphisms which have been known for the susceptibility to influenza infection include CD55, C1QBP, FCGR2A, IFITM3, TNF, RPAIN, LTA and KIR [14,15,16,17,18]. The protein products of the tested SNPs were reported to be associated with inflammatory reactions [36].

In this study, we examined a wide range of human genetic variants in different genes involved in immune response against viral infection in order to explore their potential association with mild influenza in human. Mild influenza causes mild respiratory infection, pulmonary dysfunction and clinical manifestation. It can resolve on its own without significant symptoms and without need for hospitalization. From 12 studied SNPs that are believed to be associated with immune response against viral infection, nine (rs1800450, rs1800629, rs1800871, rs1800872, rs1800896, rs1801274, rs1801274, rs2564978, rs361525) did not show any significant association with mild flu in our study. Three SNPs (rs3786054, rs8070740, rs9856661), however, revealed evidence on such associations. Possessing G allele (vs. A) in rs3786054 and rs8070740 loci is associated with a threefold increase in the chance of mild flu when compared to ILI patients. Possessing C allele (vs. A) in the rs9856661 locus also increases the chance of mild flu up to 2 folds.

In regards to IFITM3, Zhang et al., estimated that patients carrying the CC genotype in their rs12252 IFITM3 were six times more at risk of severe infection than those with the CT and TT genotypes. Higher levels of the CC allele in the Han Chinese population than in White population may put the Chinese at higher risk for developing severe illness following influenza virus infection. IFITM3 may also play an important role in virus replication and proliferation after initial infection [23]. Recently, with extensive analysis, the close association of the rs12252-C allele with influenza susceptibility has been demonstrated in two Han Chinese and English whites populations [37]. However, in some populations such as Caucasian (MAF = 0.041) [23] and infants and children populations [38], little correlation was found between this allele and susceptibility to influenza. Zhang et al., in 2013 demonstrated the association of the C allele at rs12252 in the IFITM3 gene in different populations of Chinese, Japanese, Northern European and English with the 2009 pandemic influenza [23]. In a recent review study, the effect of rs12252 in IFITM3 gene on influenza infection was investigated demographically [39]. In our previous study on IFITM3, BMI, diabetes, and hypercholesterolemia in an Iranian population, no association was found between mild influenza and the homozygote allele rs12252 C and these underlying factors. The carriers of the C allele were more at risk of mild influenza compared to the homozygote T allele [24].

TNF-α functions in homeostasis and disease pathogenesis. It induces pathogenic epigenetic modifications and promotes enhanced inflammatory responses even to minor environmental challenges [40]. IL-10 plays pleiotropic role in immunoregulation and inflammation. It can block NF-kB activity and is involved in the regulation of the JAK-STAT signaling pathway. Mutations in this gene have been shown to be associated with the increased susceptibility to HIV-1 infection [provided by RefSeq, May 2020]. This cytokine selectively impacts the expression of Th17-associated cytokines during influenza infection [41]. IL-10 can impair T cell priming in the early stages of adaptive immunity, a mechanism that viruses use to promote their persistence by infecting antigen presenting cells (APCs). IL-10 effects are more subtle in the immune response during acute infections [42].

The TNF polymorphisms were shown to be associated with risk of infection by influenza A/H1N1 virus during the pandemic in Mexico in 2009. Carriers of the rs1800629 G/A genotype were associated with high levels of blood urea nitrogen [18]. The role of rs361525 of TNF-α gene located on the short arm of chromosome 6 in the course of infection with 2009 H1N1 influenza virus was confirmed [14]. TNF-α rs361525 G/A genotype was associated with an increased risk of disease severity. TNF-α rs1800629 G/G genotype and IL-10 rs1800872 and rs1800896 were also associated with increased disease severity [43].

Genotype G/G and G/T of IL-10 rs1800872 were associated with increased risk of infection with influenza A/H3N2 virus. This might be due to anti-inflammatory nature of IL-10 that prevents NK and T cell activities to affect the strong inflammatory action after first infection [44]. In the study by Keshavarz et al., the frequencies of IL-10 rs1800872 and IL-28 rs8099917 were shown not to be associated with influenza disease [45] which was in accordance with our results.

The IL-10 rs1800872 is related to increased intensity of autoimmune and infectious diseases which can control the transcription and expression of IL-10 [46]. The same polymorphism was investigated by Ferdinand et al,. They studied the frequency of TNF and MBL SNPs, located on the chromosomes 6 and 10, respectively, in children and adolescents with fatal influenza infection and compared the outcome with the control group. They reported that influenza cases leading to death with concomitant methicillin-resistant Staphylococcus aureus (MRSA) infection had a higher incidence of low-production MBL2 genotype compared to the cases of death without MRSA co-infection [47]. A study conducted on 246 patients infected with A/H1N1pdm09 virus in India, suggested that SNPs in the IL-10 and TNF-α genes might be associated with disease severity in influenza A/H1N1pdm09-infected patients. In this study, TNF-α rs1800629, IL-10 rs1800872, IL-10 rs1800896 were genotyped by PCR along with other genotypes. TNF-α rs1800629 G/A genotype showed significantly higher frequency in severe cases compared to the mild cases. IL-10 rs1800896 G allele, in dominant mode, was significantly negatively associated with the disease severity. IL-10 rs1800896 C/A genotype was significantly associated with fatality in influenza A/H1N1pdm09 infections [48], however, in our study we could not find any association of these SNPS with influenza infection.

MBL-2 gene different alleles are associated with susceptibility to different infections [26,27,28].

This gene encodes the soluble mannose-binding lectin which belongs to the collectin family and is considered as an important element in the innate immune system. This protein binds to mannose and N-acetylglucosamine on different microorganisms and viruses including influenza virus, HIV and SARS-CoV. This binding activates the classical complement pathway [provided by RefSeq, Jun 2020].

Codon 54 of this gene is more prevalent in White population than codons 52 and 57. Individuals with A allele in codon 54 have lower levels of MBL-2 protein compared to those carrying codon 52. This MBL-2 protein is disabled for classical complement activation [49]. Among the polymorphisms of this gene, codon 54 is associated with susceptibility to infection [50, 51]. Recently, in the evaluation of MBL gene variants in pediatric influenza-related illness, MBL deficiency was suggested as no serious risk factor for very severe influenza infection in children and adolescents [52].

In a matched case–control study conducted in Brazil, the association between MBL2, CLEC5A, ITGB3 and CCR5 genes and dengue severity was investigated in children. They found no association in single SNP analysis. However, the allele rs7095891G/rs1800450C/ rs1800451C/ rs4935047A/ rs930509G/ rs2120131G/ rs2099902C were significantly associated to the risk of severe dengue when MBL2 SNPs were combined in haplotypes [53], however, in our study we could not find any association of rs1800450 and rs1800451 SNPs with influenza infection.

FCGR is a glycoprotein which binds to the Fc region of IgG and activates immune response to control pathogens [54]. FCGR2A is one of the five genes located on chromosome 1, which encode receptors with low affinity for this glycoprotein. The activation or inhibition of these receptors regulates the local inflammatory response [55]. This gene encodes a cell surface receptor found on phagocytic cells such as macrophages and neutrophils, and is involved in the process of phagocytosis and clearing of immune complexes [provided by RefSeq, Oct 2008].

The genetic variant of the IgG Fc receptor at position 131 has been shown to be associated with susceptibility to inflammatory and infectious diseases like Kawasaki disease [56] and pneumococcal diseases [57]. Researchers studied the rs1801274 variant of the FCGR2A gene and concluded that this variant may be related to greater susceptibility to severe forms of flu [17]. Maestri and colleagues investigated this SNP for His131Arg substitution in Brazilian admixed population diagnosed with H1N1 pdm09 infection. They detected no association of rs1801274 polymorphism with severe disease in this population [15]. In the study on the patients from northern Greece suffered from influenza A(H1N1)pdm09 disease, no significant difference was either detected for FCGR2A genotypes neither with fatality nor disease severity [58] which was in line with our study outcome. However, this study found relationship between rare T/T genotype of CD55 rs2564978 and rare A/A genotype of C1QBP rs3786054 with increased death risk but not with disease severity [58].

CD55 may play an important role in the presentation of disease. It functions to protect the host cells from complement attack and inhibits the complement cascade. The high expression level of CD55 in human lung may reflect its critical role to prevent lung from complement-mediated tissue damage as a result of long-term evolutionary pressure [16].

C1QBP inhibits the complement activation [59]. It was shown to be involved in HIV-1 replication, probably by contributing to splicing of viral RNA [60].

Carriers of T/T rs2564978 genotype in CD55 gene in Chinese population have been shown to be significantly associated with severe H1N1 infection [16]. Studies have shown that patients with T/T genotype at rs2564978 in CD55 gene contained lower levels of CD55 in peripheral blood mononuclear cells compared to the patients with the C/C and C/T genotypes which cannot regulate complement cascade [32]. The association between IFITM3 rs12252, and CD55 rs2564978 SNPs and influenza H7N9/H1N1 pdm09 clinical outcomes was examined in Chinese population. The over-expression of homozygote CC genotype at rs12252 in IFITM3 among fatal cases was detected and CD55 TT genotype at rs2564978 was linked to severity of the disease [61]. The CD55 rs2564978 SNP did not show significant association with influenza in our study but C1QBP rs3786054 G allele (vs. A) showed association with a threefold increase in the chance of mild flu.

In regards to rs8070740 and rs9856661 at RPAIN and unknown genes, respectively, it was suggested that these genes polymorphisms might impact the susceptibility to severe pneumonia in A/H1N1 infection [17].

RPAIN in short name; hRIP, mediates the import of replication protein A (RPA) complex into the nucleus, possibly via interaction with importin beta. The isoform 2 of this gene is sumoylated by ubiquitin-like protein and mediates the localization of RPA complex into the nucleus, thereby participating in DNA metabolism [62].

Our study confirmed these results as rs9856661C allele increased the chance of mild flu up to 2 folds in Iranian population.

Our study has notable strengths, including: a) novel method of genotyping; b) study on the Iranian ethnicity for these SNPs for the first time; and c) high number of SNPs analyzed in a single study. In spite of the value of the first genetic association study of its kind in Iran, this study has a number of limitations, particularly its small sample size. Further studies on larger sample sizes will be warranted.

Conclusion

The results showed that possessing the G allele in either rs3786054 or rs8070740 loci in C1QBP and RPAIN genes, respectively, increased the risk of H1N1 infection up to 3.3 folds, regardless of the patient’s age, BMI, diabetes, and hypercholesterolemia. Future functional genomic studies on identified genetic markers may provide clues to the molecular targets that can be used for therapies, as well as effective vaccines. Given multiple factors such as vaccination, virus subtype and host genetic factor play role in the immune response against influenza, systems biology methods are needed to illuminate the interaction between these factors and their role in susceptibility to influenza. The identified genetic and clinical factors may also be associated with susceptibility to other respiratory RNA viruses, like Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS) and COVID-19. Future genetic association studies targeting these RNA viruses, especially COVID-19 is recommended to provide better understanding of host the immune response against the virus [63, 64]. A better understanding of the biological pathways leading to severe viral disease can be helpful for more effective treatments. New discoveries may generate hypotheses for drug repurposing and may estimate the country's need for vaccine design and production. Also, studies on other ethnic groups would also shed the light on possible ethnic variations in genetic susceptibility to respiratory RNA viruses.

Availability of data and materials

All data in case of need are available.

Abbreviations

APCs:

Antigen presenting cells

C1QBP:

Complement 1 C1q binding protein

CD55:

Complement decay-accelerating factor

DAF:

Decay-accelerating factor

FCGR2A:

Fc fragment of IgG receptor IIa

GMAF:

Global minor allele frequency

IAV:

Influenza A virus

IFITM3:

Interferon induced transmembrane protein 3

ILI:

Influenza like illness

KIR:

Killer-cell inhibitory receptor

LTA:

Lymphotoxin alpha

MALDI-TOF:

Matrix-assisted laser desorption/ionization-time of flight

MBL-2:

Mannose-binding Lectin-2

MS:

Mass spectrometry

OR:

Odds ratio

RPA:

Replication protein A

RPAIN:

Replication protein A interacting protein

SAP:

Shrimp alkaline phosphatase

SNP:

Single nucleotide polymorphism

TNF:

Tumor necrosis factor

UTR:

Untranslated region

VTM:

Viral transport media

References

  1. Fedson DS. Confronting an influenza pandemic with inexpensive generic agents: can it be done? Lancet Inf Dis. 2008;8:571–6.

    CAS  Article  Google Scholar 

  2. Herold S, Ludwig S, Pleschka S, Wolff T. Apoptosis signaling in influenza virus propagation, innate host defense, and lung injury. J Leukoc Biol. 2012;92:75–82.

    CAS  PubMed  Article  Google Scholar 

  3. Horby P, Nguyen NY, Dunstan SJ, Baillie JK. The role of host genetics in susceptibility to influenza: a systematic review. PLoS ONE. 2012;7:e33180.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. Aditama, T.Y., Samaan, G., Kusriastuti, R., Purba, W.H., Misriyah, Santoso, H., Bratasena, A., Maruf, A., Sariwati, E., Setiawaty, V., et al. Risk factors for cluster outbreaks of Avian influenza A H5N1 infection, Indonesia. Clinical Infectious Diseases, 2011. 53: 1237–1244.

  5. Horby P, Sudoyo H, Viprakasit V, Fox A, Thai PQ, Yu H, Davila S, Hibberd M, Dunstan SJ, Monteerarat Y, et al. What is the evidence of a role for host genetics in susceptibility to influenza A/H5N1? Epidemiol Infect. 2010;138:1550–8.

    CAS  PubMed  Article  Google Scholar 

  6. Sedyaningsih ER, Isfandari S, Setiawaty V, Rifati L, Harun S, Purba W, Imari S, Giriputra S, Blair PJ, Putnam SD, et al. Epidemiology of cases of H5N1 virus infection in Indonesia, July 2005–June 2006. J Infect Dis. 2007;196:522–7.

    PubMed  Article  Google Scholar 

  7. CDC. People at high risk of developing flu-related complications. 2017 [cited 2017].

  8. Campbell A, Rodin R, Kropp R, Mao Y, Hong Z, Vachon J, Spika J, Pelletier L. Risk of severe outcomes among patients admitted to hospital with pandemic (H1N1) influenza. Can Med Assoc J. 2010;182:349–55.

    Article  Google Scholar 

  9. Hanslik T, Boelle PY, Flahault A. Preliminary estimation of risk factors for admission to intensive care units and for death in patients infected with A(H1N1)2009 influenza virus, France, 2009–2010. PLoS Curr. 2010;5:2157–3999.

    Google Scholar 

  10. Valdez R, Narayan KMV, Geiss LS, Engelgau MM. The impact of diabetes mellitus on mortality associated with pneumonia and influenza among non-hispanic black and white US adults. Am J Public Health. 1999;89:1715–21.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. Webb SA, Pettilä V, Seppelt I, Bellomo R, Bailey M, Cooper DJ, Cretikos M, Davies AR, Finfer S, Harrigan PW, et al. Critical care services and 2009 H1N1 influenza in Australia and New Zealand. N Engl J Med. 2009;361:1925–34.

    PubMed  Article  Google Scholar 

  12. Ribeiro, A.F., Pellini, A.C.G., Kitagawa, B.Y., Marques, D., Madalosso, G., N.F.G., d.C., Fred, J., Albernaz, R.K.M., Carvalhanas, T.R.M.P., Zanetta, D.M.T. Risk factors for death from influenza A(H1N1)pdm09, State of São Paulo, Brazil, 2009. PLoS ONE, 2015. 10: e0118772.

  13. Horby P, Nguyen NY, Dunstan SJ, Kenneth Baillie J. An updated systematic review of the role of host genetics in susceptibility to influenza. Influ Other Respir Viruses. 2013;7:37–41.

    Article  Google Scholar 

  14. Antonopoulou A, Baziaka F, Tsaganos T, Raftogiannis M, Koutoukas P, Spyridaki A, Mouktaroudi M, Kotsaki A, Savva A, Georgitsi M, et al. Role of tumor necrosis factor gene single nucleotide polymorphisms in the natural course of 2009 influenza A H1N1 virus infection. Int J Inf Dis. 2012;16:e204–8.

    CAS  Article  Google Scholar 

  15. Maestri, A., Sortica, V.A., Tovo-Rodrigues, L., Santos, M.C., Barbagelata, L., Moraes, M.R., Alencar de Mello, W., Gusmão, L., Sousa, R.C.M., Emanuel Batista dos Santos, S. Siaα2–3Galβ1- receptor genetic variants are associated with influenza A(H1N1)pdm09 severity. PLoS ONE, 2015. 10: e0139681.

  16. Zhou J, To KK, Dong H, Cheng ZS, Lau CC, Poon VK, Fan YH, Song YQ, Tse H, Chan KH, et al. A functional variation in CD55 increases the severity of 2009 pandemic H1N1 influenza A virus infection. J Inf Dis. 2012;206:495–503.

    CAS  Article  Google Scholar 

  17. Zúñiga, J., Buendía-Roldán, I., Zhao, Y., Jiménez, L., Torres, D., Romo, J., Ramírez, G., Cruz, A., Vargas-Alarcon, G., Sheu, C.C., et al. Genetic variants associated with severe pneumonia in A/H1N1 influenza infection. Eur Respir J, 2012. 39: https://doi.org/10.1183/09031936.00020611.

  18. Morales-García G, Falfán-Valencia R, García-Ramírez RA, Camarena Á, Ramirez-Venegas A, Castillejos-López M, Pérez-Rodríguez M, González-Bonilla C, Grajales-Muñíz C, Borja-Aburto V, et al. Pandemic influenza A/H1N1 virus infection and TNF, LTA, IL1B, IL6, IL8, and CCL polymorphisms in Mexican population: a case-control study. BMC Infect Dis. 2012;12:1–9.

    Article  CAS  Google Scholar 

  19. Bailey CC, Huang IC, Kam C, Farzan M. IFITM3 limits the severity of acute influenza in mice. PLoS Pathog. 2012;8:e1002909.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. Brass AL, Huang IC, Benita Y, John SP, Krishnan MN, Feeley EM, Ryan B, Weyer JL, van der Weyden L, Fikrig E, et al. IFITM proteins mediate the innate immune response to influenza A H1N1 virus, West Nile Virus and Dengue Virus. Cell. 2009;139:1243–54.

    PubMed  PubMed Central  Article  Google Scholar 

  21. Huang IC, Bailey CC, Weyer JL, Radoshitzky SR, Becker MM, Chiang JJ, Brass AL, Ahmed AA, Chi X, Dong L, et al. Distinct patterns of IFITM-mediated restriction of filoviruses, SARS coronavirus, and influenza A virus. PLoS Pathog. 2011;7:e1001258.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. Lu J, Pan Q, Rong L, He W, Liu SL, Liang C. The IFITM proteins inhibit HIV-1 infection. J Virol. 2011;85:2126–37.

    CAS  PubMed  Article  Google Scholar 

  23. Zhang YH, Zhao Y, Li N, Peng YC, Giannoulatou E, Jin RH, Yan HP, Wu H, Liu JH, Liu N, et al. Interferon-induced transmembrane protein-3 genetic variant rs12252-C is associated with severe influenza in Chinese individuals. Nat Commun. 2013;4:1418.

    PubMed  Article  CAS  Google Scholar 

  24. Mehrbod P, Eybpoosh S, Fotouhi F, Shokouhi Targhi H, Mazaheri V, Farahmand B. Association of IFITM3 rs12252 polymorphisms, BMI, diabetes, and hypercholesterolemia with mild flu in an Iranian population. Virol J. 2017;14:1–8.

    Article  CAS  Google Scholar 

  25. Turner DM, Williams DM, Sankaran D, Lazarus M, Sinnott PJ, Hutchinson IV. An investigation of polymorphism in the interleukin-10 gene promoter. Eur J Immunogenet. 1997;24:1–8.

    CAS  PubMed  Article  Google Scholar 

  26. Garred P, Pressler T, Madsen HO, Frederiksen B, Svejgaard A, Høiby N, Schwartz M, Koch C. Association of mannose-binding lectin gene heterogeneity with severity of lung disease and survival in cystic fibrosis. J Clin Invest. 1999;104:431–7.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. Ip WK, Chan KH, Law HK, Tso GH, Kong EK, Wong WH, To YF, Yung RW, Chow EY, Au KL, et al. Mannose-binding lectin in severe acute respiratory syndrome coronavirus infection. J Infect Dis. 2005;191:1697–704.

    CAS  PubMed  Article  Google Scholar 

  28. Tang YW, Cleavinger PJ, Li H, Mitchell PS, Smith TF, Persing DH. Analysis of candidate-host immunogenetic determinants in herpes simplex virus-associated Mollaret’s meningitis. Clin Infect Dis. 2000;30:176–8.

    CAS  PubMed  Article  Google Scholar 

  29. McGuire W, Hill AV, Allsopp CE, Greenwood BM, Kwiatkowski D. Variation in the TNF-alpha promoter region associated with susceptibility to cerebral malaria. Nature. 1994;371:508–10.

    CAS  PubMed  Article  Google Scholar 

  30. Mira JP, Cariou A, Grall F, Delclaux C, Losser MR, Heshmati F, Cheval C, Monchi M, Teboul JL, Riché F, et al. Association of tnf2, a tnf-α promoter polymorphism, with septic shock susceptibility and mortality: A multicenter study. J Am Med Assoc. 1999;282:561–8.

    CAS  Article  Google Scholar 

  31. Zhang Y, Zhang Z, Cao L, Lin J, Yang Z, Zhang X. A common CD55 rs2564978 variant is associated with the susceptibility of non-small cell lung cancer. Oncotarget. 2017;8:6216–21.

    PubMed  Article  Google Scholar 

  32. Shang Y, Chai N, Gu Y, Ding L, Yang Y, Zhou J, Ren G, Hao X, Fan D, Wu K, et al. Systematic immunohistochemical analysis of the Eexpression of CD46, CD55, and CD59 in colon cancer. Arch Pathol Lab Med. 2014;138:910–9.

    PubMed  Article  Google Scholar 

  33. Bermejo-Martin JF, Martin-Loeches I, Rello J, Antón A, Almansa R, Xu L, Lopez-Campos G, Pumarola T, Ran L, Ramirez P, et al. Host adaptive immunity deficiency in severe pandemic influenza. Crit Care. 2010;14:R167–R167.

    PubMed  PubMed Central  Article  Google Scholar 

  34. Chen JZ, Huang SD, Ji CN, Pang RY, Xie Y, Xue JL. Identification, expression pattern, and subcellular location of human RIP isoforms. DNA Cell Biol. 2005;24:464–9.

    CAS  PubMed  Article  Google Scholar 

  35. Gabriel, S., Ziaugra, L., Tabbaa, D. SNP genotyping using the Sequenom MassARRAY iPLEX platform. Current Protocols in Human Genetics, 2009. 60: 2.12.1–2.12.18.

  36. Tang YW, Li H, Wu H, Shyr Y, Edwards KM. Host single-nucleotide polymorphisms and altered responses to inactivated influenza vaccine. J Infect Dis. 2007;196:1021–5.

    CAS  PubMed  Article  Google Scholar 

  37. Yang X, Tan B, Zhou X, Xue J, Zhang X, Wang P, Shao C, Li Y, Li C, Xia H, et al. Interferon-inducible transmembrane protein 3 genetic variant rs12252 and influenza susceptibility and severity: a meta-analysis. PLoS ONE. 2015;10:e0124985.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  38. Randolph AG, Yip WK, Allen EK, Rosenberger CM, Agan AA, Ash SA, Zhang Y, Bhangale TR, Finkelstein D, Cvijanovich NZ, et al. Evaluation of IFITM3 rs12252 association with severe pediatric influenza Iinfection. J Infect Dis. 2017;216:14–21.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. Wellington D, Laurenson-Schafer H, Abdel-Haq A, Dong T. IFITM3: How genetics influence influenza infection demographically. Biomedical Journal. 2019;42:19–26.

    PubMed  PubMed Central  Article  Google Scholar 

  40. Kalliolias GD, Ivashkiv LB. TNF biology, pathogenic mechanisms and emerging therapeutic strategies. Nat Rev Rheumatol. 2016;12:49–62.

    CAS  PubMed  Article  Google Scholar 

  41. McKinstry KK, Strutt TM, Buck A, Curtis JD, Dibble JP, Huston G, Tighe M, Hamada H, Sell S, Dutton RW, et al. IL-10 deficiency unleashes an influenza-specific Th17 response and enhances survival against high-dose challenge. Journal of Immunology. 2009;182:7353–63.

    CAS  Article  Google Scholar 

  42. Rojas JM, Avia M, Martín V, Sevilla N. IL-10: A multifunctional cytokine in viral infections. J Immunol Res. 2017;2017:6104054–6104054.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  43. García-Ramírez RA, Ramírez-Venegas A, Quintana-Carrillo R, Camarena ÁE, Falfán-Valencia R, Mejía-Aranguré JM. TNF, IL6, and IL1B polymorphisms are associated with severe influenza A (H1N1) virus infection in the Mexican population. PLoS ONE. 2015;10:e0144832.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  44. Rogo LD, Rezaei F, Marashi SM, Yekaninejad MS, Naseri M, Ghavami N, Mokhtari-Azad T. Seasonal influenza A/H3N2 virus infection and IL-1Β, IL-10, IL-17, and IL-28 polymorphisms in Iranian population. J Med Virol. 2016;88:2078–84.

    CAS  PubMed  Article  Google Scholar 

  45. Keshavarz M, Namdari H, Farahmand M, Mehrbod P, Mokhtari-Azad T, Rezaei F. Association of polymorphisms in inflammatory cytokines encoding genes with severe cases of influenza A/H1N1 and B in an Iranian population. Virology Journal. 2019;16:1–10.

    CAS  Article  Google Scholar 

  46. Zhao N, Chen H-L, Chen Z-Z, Li J, Chen Z-B. IL-10-592 polymorphism is associated with IL-10 expression and severity of enterovirus 71 infection in chinese children. J Clin Virol. 2017;95:42–6.

    CAS  PubMed  Article  Google Scholar 

  47. Ferdinands JM, Denison AM, Dowling NF, Jost HA, Gwinn ML, Liu L, Zaki SR, Shay DK. A Pilot study of host genetic variants associated with influenza-associated deaths among children and young adults. Emerg Infect Dis. 2011;17:2294–302.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. Choudhary ML, Alagarasu K, Chaudhary U, Kawale S, Malasane P, Gurav YK, Padbidri V, Kadam D, Sangle SA, Salvi S, et al. Association of single nucleotide polymorphisms in TNFA and IL10 genes with disease severity in influenza A/H1N1pdm09 virus infections: A study from Western India. Viral Immunol. 2018;31:683–8.

    CAS  PubMed  Article  Google Scholar 

  49. Super M, Gillies SD, Foley S, Sastry K, Schweinle JE, Silverman VJ, Ezekowitz RAB. Distinct and overlapping functions of allelic forms of human mannose binding protein. Nat Genet. 1992;2:50–5.

    CAS  PubMed  Article  Google Scholar 

  50. Gallagher PM, Lowe G, Fitzgerald T, Bella A, Greene CM, McElvaney NG, O’Neill SJ. Association of IL-10 polymorphism with severity of illness in community acquired pneumonia. Thorax. 2003;58:154–6.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. Gomi K, Tokue Y, Kobayashi T, Takahashi H, Watanabe A, Fujita T, Nukiwa T. Mannose-binding lectin gene polymorphism is a modulating factor in repeated respiratory infections. Chest. 2004;26:95–9.

    Article  Google Scholar 

  52. Dhand, N.K. and Khatkar, M.S. Statulator: An online statistical calculator. Sample Size Calculator for Estimating a Single Proportion. Accessed 14 August 2019 at http://statulator.com/SampleSize/ss1P.html; 2014.

  53. Ornelas, A.M.d.M., Xavier-de-Carvalho, C., Alvarado-Arnez, L.E., Ribeiro-Alves, M., Rossi, Á.D., Tanuri, A., de Aguiar, R.S., Moraes, M.O., and Cardoso, C.C. Association between MBL2 haplotypes and dengue severity in children from Rio de Janeiro, Brazil. Memorias do Instituto Oswaldo Cruz, 2019. 114: e190004.

  54. Ravetch JV, Bolland S. IgG Fc receptors. Annu Rev Immunol. 2001;19:275–90.

    CAS  PubMed  Article  Google Scholar 

  55. Boruchov AM, Heller G, Veri M-C, Bonvini E, Ravetch JV, Young JW. Activating and inhibitory IgG Fc receptors on human DCs mediate opposing functions. J Clin Investig. 2005;115:2914–23.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  56. Duan J, Lou J, Zhang Q, Ke J, Qi Y, Shen N, Zhu B, Zhong R, Wang Z, Liu L, et al. A genetic variant rs1801274 in FCGR2A as a potential risk marker for Kawasaki disease: a case-control study and meta-analysis. PLoS ONE. 2014;9:e103329.

    PubMed  PubMed Central  Article  Google Scholar 

  57. Bouglé A, Max A, Mongardon N, Grimaldi D, Pène F, Rousseau C, Chiche J-D, Bedos J-P, Vicaut E, Mira J-P. Protective effects of FCGR2A polymorphism in invasive pneumococcal diseases. Chest. 2012;142:1474–81.

    PubMed  Article  Google Scholar 

  58. Chatzopoulou F, Gioula G, Kioumis I, Chatzidimitriou D, Exindari M. Identification of complement-related host genetic risk factors associated with influenza A(H1N1)pdm09 outcome: challenges ahead. Med Microbiol Immunol. 2019;208:631–40.

    CAS  PubMed  Article  Google Scholar 

  59. Arcanjo AC, Mazzocco G, de Oliveira SF, Plewczynski D, Radomski JP. Role of the host genetic variability in the influenza A virus susceptibility. Acta Biochim Pol. 2014;61:403–19.

    PubMed  Article  Google Scholar 

  60. Zheng YH, Yu HF, Peterlin BM. Human p32 protein relieves a post-transcriptional block to HIV replication in murine cells. Nat Cell Biol. 2003;5:611–8.

    CAS  PubMed  Article  Google Scholar 

  61. Lee N, Cao B, Ke C, Lu H, Hu Y, Tam CHT, Ma RCW, Guan D, Zhu Z, Li H, et al. IFITM3, TLR3, and CD55 gene SNPs and cumulative genetic risks for severe outcomes in Chinese patients with H7N9/H1N1pdm09 influenza. J Infect Dis. 2017;216:97–104.

    CAS  PubMed  Article  Google Scholar 

  62. Park J, Seo T, Kim H, Choe J. Sumoylation of the novel protein hRIP{beta} is involved in replication protein A deposition in PML nuclear bodies. Mol Cell Biol. 2005;25:8202–14.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. Ganna A. The Covid-Host Genetics Initiative; a global initiative to elucidate the role of host genetic factors in susceptibility and severity of the SARS-CoV-2 virus pandemic. Eur J Hum Genet. 2020;28:715–8.

    CAS  Article  Google Scholar 

  64. Kachuri, L., Francis, S.S., Morrison, M., Boss, amp, amp, eacute, Y., Cavazos, T.B., Rashkin, S.R., Ziv, E., et al. The landscape of host genetic factors involved in infection to common viruses and SARS-CoV-2. MedRxiv, 2020. Preprint: 2020.05.01.20088054.

Download references

Acknowledgements

The authors would like to thank Inqaba Biotech Co., South Africa for their cooperation in SNP genotyping using iPLEX GOLD SNP genotyping platform.

Funding

This study was funded by Pasteur institute of Iran, Iran (grant number 1188).

Author information

Authors and Affiliations

  1. Influenza and Respiratory Viruses Department, Pasteur Institute of Iran, Tehran, Iran

    Parvaneh Mehrbod, Behrokh Farahmand & Fatemeh Fotouhi

  2. Department of Epidemiology and Biostatistics, Research Centre for Emerging and Reemerging Infectious Diseases, Pasteur Institute of Iran, Tehran, Iran

    Sana Eybpoosh

  3. Private Practitioner, Mashhad, Iran

    Majid Khanzadeh Alishahi

Authors
  1. Parvaneh Mehrbod
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sana Eybpoosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Behrokh Farahmand
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fatemeh Fotouhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Majid Khanzadeh Alishahi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceived and designed the experiments: PM; performed the experiments: PM; Analyzed the data: SE, PM; Contributed reagents/materials: PM, BF, FF; Wrote the paper: PM, SE; Comprehensive reading the manuscript: all authors. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Parvaneh Mehrbod.

Ethics declarations

Ethics approval and consent to participate

Clinical investigation was conducted according to the principles expressed in the Declaration of Pasteur Institute of IRAN. This research was reviewed and approved by the Ethics Committee of Pasteur Institute of IRAN (Protocol Number IR.PII.REC.1399.063).

Consent for publication

Written informed consent was obtained from the patients or the guardians of the patients participated in this research.

Competing interests

Authors (Parvaneh Mehrbod, Sana Eybpoosh, Fatemeh Fotouhi, Behrokh Farahmand) declare that they have no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mehrbod, P., Eybpoosh, S., Farahmand, B. et al. Association of the host genetic factors, hypercholesterolemia and diabetes with mild influenza in an Iranian population. Virol J 18, 64 (2021). https://doi.org/10.1186/s12985-021-01486-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12985-021-01486-3

Keywords

  • Influenza
  • Genetic polymorphisms
  • Hypercholesterolemia
  • Diabetes
  • Iran

Virology Journal

ISSN: 1743-422X