Next Article in Journal
Exome Sequencing Identifies a Novel FBN1 Variant in a Pakistani Family with Marfan Syndrome That Includes Left Ventricle Diastolic Dysfunction
Previous Article in Journal
The Effects of Sequence Length and Composition of Random Sequence Peptides on the Growth of E. coli Cells
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Genes
Volume 12
Issue 12
10.3390/genes12121914
genes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

TNFA and IL10 Polymorphisms and IL-6 and IL-10 Levels Influence Disease Severity in Influenza A(H1N1)pdm09 Virus Infected Patients

by
Kalichamy Alagarasu
1,
Himanshu Kaushal
1,
Pooja Shinde
1,
Mahadeo Kakade
1,
Urmila Chaudhary
1,
Vikram Padbidri
2,
Shashikala A. Sangle
3,
Sonali Salvi
3,
Ashish R. Bavdekar
4,
Pradeep D’costa
4 and
Manohar Lal Choudhary
1,*
1
ICMR-National Institute of Virology, Pune 411001, India
2
Jehangir Hospital Research Center, Pune 411001, India
3
Department of Medicine, BJ Medical College, Pune 411001, India
4
KEM Hospital Research Center, Pune 411001, India
*
Author to whom correspondence should be addressed.
Genes 2021, 12(12), 1914; https://doi.org/10.3390/genes12121914
Submission received: 27 August 2021 / Revised: 20 November 2021 / Accepted: 26 November 2021 / Published: 28 November 2021
(This article belongs to the Section Molecular Genetics and Genomics)
Browse Figures
Versions Notes

Abstract

:
Cytokines are key modulators of immune response, and dysregulated production of proinflammatory and anti-inflammatory cytokines contributes to the pathogenesis of influenza A(H1N1)pdm09 virus infection. Cytokine production is impacted by single nucleotide polymorphisms (SNPs) in the genes coding for them. In the present study, SNPs in the IL6, TNFA, IFNG, IL17A, IL10, and TGFB were investigated for their association with disease severity and fatality in influenza A(H1N1)pdm09-affected patients with mild disease (n = 293) and severe disease (n = 86). Among those with severe disease, 41 patients had fatal outcomes. In a subset of the patients, levels of IL-2, IL-4, IL-6, IL-10, TNF, IFN-γ, and IL-17 were assayed in the plasma for their association with severe disease. The frequency of TNFA rs1800629 G/A allele was significantly higher in severe cases and survived severe cases group compared to that of those with mild infection (OR with 95% for mild vs. severe cases 2.95 (1.52–5.73); mild vs. survived severe cases 4.02 (1.84–8.82)). IL10 rs1800896-rs1800872 G-C haplotype was significantly lower (OR with 95% 0.34 (0.12–0.95)), while IL10 rs1800896-rs1800872 G-A haplotype was significantly higher (OR with 95% 12.11 (2.23–76.96)) in fatal cases group compared to that of the mild group. IL-6 and IL-10 levels were significantly higher in fatal cases compared to that of survived severe cases. IL-6 levels had greater discriminatory power than IL-10 to predict progression to fatal outcome in influenza A(H1N1)pdm09 virus-infected patients. To conclude, the present study reports the association of TNFA and IL10 SNPs with severe disease in Influenza A(H1N1)pdm09 virus-infected subjects. Furthermore, IL-6 levels can be a potential biomarker for predicting fatal outcomes in Influenza A(H1N1)pdm09 virus infected subjects.

Graphical Abstract

1. Introduction

Pandemic H1N1 influenza (pH1N1), a severe acute respiratory illness (SARI) caused by influenza A(H1N1)pdm09, contributed to increased mortality among those infected with different influenza A subtype viruses [1]. In India, Maharashtra, a state in the western part of the country, accounts for 21% of all the influenza A(H1N1)pdm09 cases and 31% of all pH1N1-related deaths [2]. The progression to SARI depends on multiple factors such as age, obesity, the virulence of the virus, genetics of the host, and host immune response [3].
Cytokines, mediators of the immune response, contribute to disease pathogenesis. Influenza A(H1N1)pdm09-infection is known to induce exuberant proinflammatory cytokine response in both respiratory and extra-respiratory tissues [4]. Dysregulated production of tumor necrosis factor (TNF)-α, interleukin (IL)-1β, IL-6, and IL-17A, which contribute to the proinflammatory response, and IL-10, an anti-inflammatory cytokine, were reported in influenza A(H1N1)pdm09 virus infections [5]. Animal studies showed that inhibition of TNF-α averts lethal disease in influenza A(H1N1)pdm09 virus-infected mice [6].
Production of cytokines is affected by the single nucleotide polymorphisms (SNPs) in the genes coding for them. SNPs in the promoter and intron regions are known to affect the stability of the mRNA, transcriptional efficiency, and splicing of the transcribed RNA, and thereby influence cytokine production. Commonly occurring SNPs that are known to influence cytokine levels were reported in the genes coding for TNF-α, IL-6, interferon (IFN)-γ, IL-17A, IL-10, and tumor growth factor (TGF)-β [7,8,9]. SNPs in the cytokine genes were shown to be associated with disease severity in influenza A(H1N1)pdm09 virus infected subjects [10,11,12]. There is a lack of studies investigating the association between cytokine gene polymorphisms and severe influenza A(H1N1)pdm09 infection in India. In the present study, common SNPs in the IL6 (rs1800795), TNFA (rs1800629), IFNG (rs2430561), IL17A (rs2275913), IL10 (rs1800896 and rs1800872), and TGFB (rs1800470 and rs1800471) were investigated for their association with severe disease and fatality in influenza A(H1N1)pdm09 virus infected patients. In a subset of the patients, levels of IL-2, IL-4, IL-6, IL-10, IL-17, TNF-α, and IFN-γ were assayed in the plasma for their association with severe disease. The effect of SNPs on the plasma level of the cytokines was also studied.

2. Materials and Methods

2.1. Study Subjects

Patients with a history of laboratory confirmed influenza A(H1N1)pdm09 virus infection or ongoing treatment were contacted for enrolment in the study, and blood samples were collected from the consenting study subjects. Patients with influenza A(H1N1)pdm09 virus infection admitted to the collaborating hospitals were also included in the study. All the patients included in the study were affected with influenza A(H1N1)pdm09 virus infection during 2013 to 2018. Written informed consent was obtained from all the participants or their legal guardians before sample collection. The study was reviewed and approved by the institutional ethics committee of ICMR-National Institute of Virology and the collaborating hospitals. Influenza A(H1N1)pdm09 virus infection in the study subjects during the acute phase of the disease was confirmed by a real-time RT-PCR assay. DNA was extracted from the blood samples using a commercial DNA extraction kit (QIAamp blood DNA midi kit, Qiagen, Hilden, Germany) according to the protocol given by the manufacturer. Case history sheets filled by the treating physicians were used to classify the patients into those with mild disease and severe disease. Patients admitted to intensive care unit and requiring ventilator support or the patients with fatal outcomes were classified as those with severe influenza A(H1N1)pdm09 virus disease [10,13]. All the other patients were considered as those with mild disease. All the patients were treated according to the clinical management protocols provided by the directorate general of health services, ministry of health and family welfare, Government of India (https://main.mohfw.gov.in/sites/default/files/2366426352.pdf, accessed on 19 November 2021).

2.2. Genotyping of SNPs in the Cytokine Genes

SNPs in the IL6 (rs1800795), and IFNG (rs2430561) genes were assayed using restriction fragment length polymorphism-based method as reported earlier [14,15]. IL17A rs2275913 was studied using the TaqMan SNP genotyping method according to the manufacturer’s instructions. SNPs in the TNFA (rs1800629), IL10 (rs1800896 and rs1800872), and TGFB (rs1800470 and rs1800471) genes were investigated using polymerase chain reaction with allele specific primers, as described previously [16,17,18].

2.3. Investigation of Cytokine Levels in the Plasma of Influenza A(H1N1)pdm09 Virus Infected Cases

The cytokine levels were investigated in a subset of samples. The plasma samples collected during the acute phase (within a week of disease onset) of influenza A(H1N1)pdm09 virus infection from fatal (n = 28) and survived cases (n = 28) were examined for the cytokine profile. Plasma samples collected from patients who recovered from mild A(H1N1)pdm09 infection (n = 21) and healthy controls (n = 11) were also assessed for cytokine levels. The level of cytokines IL-2, IL-4, IL-6, IL-10, TNF, IFN-γ, and IL-17A were analyzed using BD™ Cytometric Bead Array Human Th1/Th2/Th17 Cytokine Kit (BD Biosciences, San Jose, CA, USA), as per manufacturer’s instructions. Briefly, 50 µL plasma was treated with capture beads and phycoerythrin (PE) conjugated detection antibody. The standards provided by the manufacturer were used to prepare the standard curve for each cytokine. Then, the samples, as well as the standards, were acquired and analyzed on a BD FACSCaliburTM flow cytometer (BD Biosciences, San Jose, CA, USA) using BD CellQuestTM Pro software (v5.1). The fcs files were analyzed using flow cytometric analysis program arrayTM software (v3.0). The detection sensitivity of IL-2, IL-4, IL-6, IL-10, IFN-γ, TNF-α, and IL-17A were 2.6, 4.9, 2.4, 4.5, 3.8, 3.7, and 18.9 pg/mL, respectively.

2.4. Statistical Analysis

For genetic association studies, statistical comparisons were carried out using an online SNP stats program [19] or SPSS version 17 as described earlier [20]. The strength of association was represented in terms of odds ratio (OR) with 95% confidence intervals (CI). The OR with 95% CI was adjusted for age and gender. For cytokine levels and their association with genotypes or disease severity, the Mann–Whitney U test was used to compare two groups while the Kruskal–Wallis test with multiple corrections was used to compare multiple groups. Receiver operating curve analysis was performed to assess the utility of cytokines as biomarkers in predicting fatal outcomes. Graphpad prism software version 7 was used for the analysis.

3. Results

3.1. Demographic Characteristics and Disease Profile of the Study Subjects

A total number of 379 influenza A(H1N1)pdm09 virus-infected patients from Pune and its adjoining districts, Maharashtra, western India were included in the study. Among the patients, 86 were categorized as severe cases based on the requirement of ventilator support and/or fatal outcome. Among the severe cases, 41 patients had fatal outcomes, while the remaining 45 survived. The remaining 293 cases were categorized as having a mild disease. The ratio of males to females is 1.15 for mild cases and 0.97 for severe cases. Severe cases had significantly higher age as compared to mild cases (Table 1). Within the severe cases group, the ratio of male to female was 1.03 among survived severe cases and 1.27 in the fatal cases group. The mean age ± standard deviation (sd) was 51 ± 14.6 in the fatal cases group while it was 50 ± 12.6 in the survived severe cases group and the difference was not statistically significant.

3.2. Allele, Genotype, and Haplotype Frequencies in Influenza A(H1N1)pdm09 Virus-Infected Cases

Comparison of allele frequencies among the study groups revealed that the frequency of TNFA rs1800629 A allele was significantly higher in severe cases and survived severe cases group compared to that of those with mild infection (OR with 95% for mild vs. severe cases 2.72 (1.47–4.99); mild vs. survived severe cases 3.49 (1.68–6.99)). Though the frequency of TNFA rs1800629 A allele was higher in fatal cases compared to that of mild cases, it was not statistically significant. The allele frequencies of other SNPs were not different between the influenza A(H1N1)pdm09 infected patient groups (Table 2).
Comparison of genotype frequencies of different SNPs among the study groups revealed that the frequency of TNFA rs1800629 G/A allele was significantly higher in severe cases and survived severe cases group compared to that of those with mild infection (OR with 95% for mild vs. severe cases 2.95 (1.52–5.73); mild vs. survived severe cases 4.02 (1.84–8.82)). Though the frequency of TNFA rs1800629 G/A allele was higher in fatal cases compared to mild cases, it was not statistically significant. The genotype frequencies of other SNPs were comparable between the influenza A(H1N1)pdm09-infected patient groups (Table 3).
Comparison of IL10 haplotype frequencies revealed that IL10 rs1800896–rs1800872 G-C haplotype was significantly lower while IL10 rs1800896–rs1800872 G-A haplotype was significantly higher in fatal cases group compared to mild cases (Table 4). Haplotype frequencies of TGFB SNPs were not different between different categories of influenza A (H1N1)pdm09 virus-infected patient groups.

3.3. Elevated Plasma IL-6 and IL-10 Level among Fatal Cases during the Acute Phase of Influenza A(H1N1)pdm09 Virus Infection

The proinflammatory cytokine, IL-6 was found to be significantly higher during the acute phase of fatal cases (Mean ± SE, 812 pg/mL ± 451.3, p <0.001) and survived severe cases (Mean ± SE, 112.6 pg/mL ± 53, p < 0.05) compared to that of healthy controls (Mean ± SE, 5.039 pg/mL ± 1.69) and mild recovered cases (Mean ± SE, 6.11 pg/mL ± 3.57). Also, IL-6 level was found significantly higher (p < 0.05) in fatal cases compared to that of survived severe cases (Figure 1A).The plasma IL-10 level was found significantly higher during the acute phase of fatal cases (Mean ± SE, 36.44 pg/mL ± 12.18, p < 0.001) and survived severe cases (Mean ± SE, 6.567 pg/mL ± 1.43, p < 0.05) compared to that of healthy controls (Mean ± SE, 1.20 pg/mL ± 0.90) and mild recovered cases (Mean ± SE, 0.76 pg/mL ± 0.41). IL-10 level was significantly higher in fatal cases compared to that of survived severe cases (p < 0.05) (Figure 1B). The other evaluated cytokines such as IL-2, IL-4, IFN-γ, TNF-α, and IL-17A, were observed only in few patients, and the levels were comparable between the study groups.

3.4. IL-6 and IL-10 Levels among Influenza A(H1N1)pdm09 Virus-Infected Subjects with Different Genotypes of IL-6 and IL-10

When IL-6 and IL-10 levels were compared between levels among influenza A(H1N1)pdm09 virus-infected subjects with different genotypes of IL-6 and IL-10, the cytokine levels were not significantly different between subjects with different IL6 or IL10 genotypes. However, irrespective of the IL6 and IL10 genotypes, the cytokine levels were elevated in fatal cases compared to that of survived severe cases (Figure 2A–C).

3.5. Utility of IL-6 and IL-10 Levels as A Marker for Predicting Fatality in Influenza A(H1N1)pdm09 Virus-Infected Subjects

The utility of IL-6 and IL-10 levels in predicting fatal outcomes in influenza A(H1N1)pdm09 virus infected subjects with severe disease was investigated using receiver operating curve analysis. The analysis revealed that the area under curve (AUC) value for IL-6 in discriminating survived severe cases and fatal cases was 0.81 (p < 0.0001) while it was 0.73 (p < 0.01)for IL-10 (Figure 3A,B).

4. Discussion

Cytokines are the key mediators of inflammation and were associated with the disease severity in influenza A(H1N1)pdm09 virus infection [21,22,23]. In the present study, the SNPs in the IL6, TNFA, IFNG, IL17A, IL10, and TGFB genes were investigated for their association with disease severity and fatality in influenza A(H1N1)pdm09 virus-infected subjects. The results revealed that TNFArs1800629 A allele and G/A genotype were associated with severe disease but not fatal outcomes. Our earlier study [15] with a smaller sample size also reported the association of TNFArs1800629 A allele and G/A genotype with severe disease, and this was also confirmed in the present study. Contradicting our study, a study of a Mexican population showed the association of TNFArs1800629G allele with severe disease [24]. TNFA rs1800629 is located in the promoter region, and this SNP, in combination with other SNPs, might influence TNF-α production. TNFA rs361525 was shown to be associated with severe disease in Mexican population [10]. Thus, the haplotype structure might vary between populations and influence genetic associations of TNFA SNPs with the disease in different populations. The lack of association of TNFArs1800629 with fatality suggests the role of additional factors including other genetic factors and comorbidities in progression to a fatal outcome and needs investigation.
The results also suggested thatIL10 rs1800896-rs1800872 G-C haplotype was associated with protection to fatal outcome, while IL10 rs1800896-rs1800872 G-A haplotype was associated with fatal outcomes in influenza A(H1N1)pdm09 virus infected-subjects. Similar to our study, IL10 rs1800896-rs1800872 G-A haplotype was reported to be associated with protection against severe disease in a Mexican population [24]. IL10 rs1800896-rs1800872 G-A haplotype might be associated with dysregulated production of IL-10 while IL10 rs1800896-rs1800872 G-C haplotype might be associated with optimal IL-10 production. Thus, these IL10 haplotypes might be associated with contrasting outcomes. The present study also reported the lack of association between SNPs in the IL6, IFNG, IL17A, and TGFB genes and disease severity of Influenza A(H1N1)pdm09 virus infection.
The present study demonstrated an elevated level of IL-6, indicative of raised inflammatory responses during the acute phase of influenza A(H1N1)pdm09 virus infection. Previous studies also indicated a higher level of IL-6 in severe influenza A(H1N1)pdm09 disease [21,22,25]. Besides, our study also revealed an increased level of plasma IL-10 during the acute phase of the infection and is in line with the previous findings [23,26]. Together, our study reported that there is a surge in plasma IL-6 and IL-10 level during the acute phase of influenza A(H1N1)pdm09 virus infection and also, this cytokine surge is more pronounced in fatal cases, as compared to that of severe survived cases, indicating their critical role in the pathogenesis of severe influenza A(H1N1)pdm09 disease. In some instances, IL-6 may get altered to resemble IL-10, a potent inhibitor of dendritic cells and macrophages [27,28], and thereby suppressing their microbicidal activities and production of proinflammatory cytokines. Thus, the immunosuppressive effect of IL-10 in synergy with IL-6, probably supports viral persistence or secondary infection, resulting in the more severe clinical outcome in influenza A(H1N1)pdm09 virus [23]. The utility of IL-6 and IL-10 in discriminating fatal and nonfatal cases was also investigated. The results revealed that IL-6 has greater discriminatory power than IL-10 and can be utilized as a biomarker to predict fatal outcomes in influenza A(H1N1)pdm09 virus-infected subjects and undertake better treatment strategies to prevent the same. Since IL-6 is nonspecifically upregulated in all type of infections [29], it cannot be used as a biomarker of influenza A(H1N1)pdm09 virus infection but can be used as a biomarker to predict fatal outcomes in subjects with confirmed influenza A(H1N1)pdm09 virus infection.
The study also revealed that investigated IL6 and IL10 SNPs were not associated with plasma IL-6 and IL-10 levels. An earlier study from the Mexican population also reported no association between plasma cytokine levels and cytokine gene SNPs [24]. However, a study using respiratory syncytial virus stimulated macrophages showed the association between cytokine levels and cytokine SNPs [8]. It is plausible that association between plasma cytokine levels and cytokine SNPs are not usually observed since plasma cytokines represent total cytokines produced by different cell types under specific stimulation as well as nonspecific production of cytokines.
It was shown that age, obesity, underlying other chronic diseases, secondary bacterial coinfections, and early administration of oseltamivir influence disease severity in influenza A(H1N1)pdm09 virus-infected subjects [30]. In the present study, details of the existing comorbidities in the study subjects were not available and are a limitation.

5. Conclusions

The present study suggests that TNFA and IL10 SNPs and IL-6 and IL-10 levels are associated with disease severity in influenza A(H1N1)pdm09 virus-infected subjects. Further, IL-6 levels might have utility as a biomarker for predicting fatal outcomes in influenza A(H1N1)pdm09 virus-infected subjects.

Author Contributions

M.L.C. and K.A. conceived and designed the project. M.L.C., H.K., P.S., M.K., U.C. performed the work. V.P., S.A.S., S.S., A.R.B. and P.D. were involved in the recruitment of patients. K.A. and H.K. performed the statistical analysis. H.K., K.A. and M.L.C. wrote the manuscript. All the authors approved the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

The study was funded by a grant from the Department of Biotechnology, Government of India (BT/PR8957/MED/12/630/2013 dated 8 February 2017).

Institutional Review Board Statement

The study was reviewed and approved by the institutional ethics committee of ICMR-National Institute of Virology (NIV/IHEC/2013/D-1873 dated 13 June 2013).

Informed Consent Statement

Written informed consent was obtained from all the participants or their legal guardians before sample collection.

Data Availability Statement

The data presented in this study are available in the article.

Acknowledgments

We thank the study participants and Vasant Walkoli for his help in sample collection.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Bhat, S.P.S.; Bhat, M.K.P.; Kumar, R.; Hosmane, G.B.; D’Sa, I. Burden of H1N1 Influenza Related Mortality in a Tertiary Care Centre. Jemds 2020, 9, 3917–3920. [Google Scholar] [CrossRef]
  2. Chatterjee, P.; Seth, B.; Biswas, T. Hotspots of H1N1 Influenza in India: Analysis of Reported Cases and Deaths (2010-2017). Trop. Doct. 2020, 50, 166–169. [Google Scholar] [CrossRef]
  3. Pérez-Rubio, G.; Ponce-Gallegos, M.A.; Domínguez-Mazzocco, B.A.; Ponce-Gallegos, J.; García-Ramírez, R.A.; Falfán-Valencia, R. Role of the Host Genetic Susceptibility to 2009 Pandemic Influenza A H1N1. Viruses 2021, 13, 344. [Google Scholar] [CrossRef]
  4. Short, K.R.; Veeris, R.; Leijten, L.M.; van den Brand, J.M.; Jong, V.L.; Stittelaar, K.; Osterhaus, A.D.M.E.; Andeweg, A.; van Riel, D. Proinflammatory Cytokine Responses in Extra-Respiratory Tissues During Severe Influenza. J. Infect. Dis. 2017, 216, 829–833. [Google Scholar] [CrossRef]
  5. Thomas, M.; Mani, R.S.; Philip, M.; Adhikary, R.; Joshi, S.; Revadi, S.S.; Buggi, S.; Desai, A.; Vasanthapuram, R. Proinflammatory Chemokines Are Major Mediators of Exuberant Immune Response Associated with Influenza A (H1N1) Pdm09 Virus Infection. J. Med. Virol. 2017, 89, 1373–1381. [Google Scholar] [CrossRef] [PubMed]
  6. Shi, X.; Zhou, W.; Huang, H.; Zhu, H.; Zhou, P.; Zhu, H.; Ju, D. Inhibition of the Inflammatory Cytokine Tumor Necrosis Factor-Alpha with Etanercept Provides Protection against Lethal H1N1 Influenza Infection in Mice. Crit. Care 2013, 17, R301. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Warlé, M.C.; Farhan, A.; Metselaar, H.J.; Hop, W.C.J.; Perrey, C.; Zondervan, P.E.; Kap, M.; Kwekkeboom, J.; Ijzermans, J.N.M.; Tilanus, H.W.; et al. Are Cytokine Gene Polymorphisms Related to in Vitro Cytokine Production Profiles? Liver Transpl. 2003, 9, 170–181. [Google Scholar] [CrossRef]
  8. Patel, J.A.; Nair, S.; Ochoa, E.E.; Huda, R.; Roberts, N.J.; Chonmaitree, T. Interleukin-6−174 and Tumor Necrosis Factor A−308 Polymorphisms Enhance Cytokine Production by Human Macrophages Exposed to Respiratory Viruses. J. Interferon Cytokine Res. 2010, 30, 917–921. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Li, W.; Shi, W.; Yin, Y.; Chen, J.; Luo, L. Association of IL-17 and IL-23 Gene Variants with Plasma Levels and Risk of Vulvovaginal Candidiasis in a Chinese Han Population. Pharmgenomics Pers. Med. 2020, 13, 725–733. [Google Scholar] [CrossRef]
  10. García-Ramírez, R.A.; Ramírez-Venegas, A.; Quintana-Carrillo, R.; Camarena, Á.E.; Falfán-Valencia, R.; Mejía-Aranguré, J.M. TNF, IL6, and IL1B Polymorphisms Are Associated with Severe Influenza A (H1N1) Virus Infection in the Mexican Population. PLoS ONE 2015, 10, e0144832. [Google Scholar] [CrossRef] [Green Version]
  11. 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. Virol. J. 2019, 16, 79. [Google Scholar] [CrossRef]
  12. Romanova, E.N.; Govorin, A.V. [TNF-α, IL-10, and eNOS gene polymorphisms in patients with influenza A/H1N1 complicated by pneumonia]. Ter Arkh 2013, 85, 58–62. [Google Scholar]
  13. Zhou, J.; To, K.K.-W.; Dong, H.; Cheng, Z.-S.; Lau, C.C.-Y.; Poon, V.K.M.; Fan, Y.-H.; Song, Y.-Q.; Tse, H.; Chan, K.-H.; et al. A Functional Variation in CD55 Increases the Severity of 2009 Pandemic H1N1 Influenza A Virus Infection. J. Infect. Dis. 2012, 206, 495–503. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Frade, A.F.; de Oliveira, L.C.; Costa, D.L.; Costa, C.H.N.; Aquino, D.; Van Weyenbergh, J.; Barral-Netto, M.; Barral, A.; Kalil, J.; Goldberg, A.C. TGFB1 and IL8 Gene Polymorphisms and Susceptibility to Visceral Leishmaniasis. Infect. Genet. Evol. 2011, 11, 912–916. [Google Scholar] [CrossRef]
  15. Wu, F.; Qu, Y.; Tang, Y.; Cao, D.; Sun, P.; Xia, Z. Lack of Association between Cytokine Gene Polymorphisms and Silicosis and Pulmonary Tuberculosis in Chinese Iron Miners. J. Occup. Health 2008, 50, 445–454. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Warzocha, K.; Ribeiro, P.; Bienvenu, J.; Roy, P.; Charlot, C.; Rigal, D.; Coiffier, B.; Salles, G. Genetic Polymorphisms in the Tumor Necrosis Factor Locus Influence Non-Hodgkin’s Lymphoma Outcome. Blood 1998, 91, 3574–3581. [Google Scholar] [CrossRef]
  17. Kinnunen, A.H.; Surcel, H.-M.; Lehtinen, M.; Karhukorpi, J.; Tiitinen, A.; Halttunen, M.; Bloigu, A.; Morrison, R.P.; Karttunen, R.; Paavonen, J. HLA DQ Alleles and Interleukin-10 Polymorphism Associated with Chlamydia Trachomatis-Related Tubal Factor Infertility: A Case-Control Study. Hum. Reprod. 2002, 17, 2073–2078. [Google Scholar] [CrossRef] [PubMed]
  18. Perrey, C.; Turner, S.J.; Pravica, V.; Howell, W.M.; Hutchinson, I.V. ARMS-PCR Methodologies to Determine IL-10, TNF-Alpha, TNF-Beta and TGF-Beta 1 Gene Polymorphisms. Transpl. Immunol. 1999, 7, 127–128. [Google Scholar] [CrossRef]
  19. Solé, X.; Guinó, E.; Valls, J.; Iniesta, R.; Moreno, V. SNPStats: A Web Tool for the Analysis of Association Studies. Bioinformatics 2006, 22, 1928–1929. [Google Scholar] [CrossRef] [Green Version]
  20. Choudhary, M.L.; Alagarasu, K.; Chaudhary, U.; Kawale, S.; Malasane, P.; Gurav, Y.K.; Padbidri, V.; Kadam, D.; Sangle, S.A.; 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, 683–688. [Google Scholar] [CrossRef]
  21. Hagau, N.; Slavcovici, A.; Gonganau, D.N.; Oltean, S.; Dirzu, D.S.; Brezoszki, E.S.; Maxim, M.; Ciuce, C.; Mlesnite, M.; Gavrus, R.L.; et al. Clinical Aspects and Cytokine Response in Severe H1N1 Influenza A Virus Infection. Crit. Care 2010, 14, R203. [Google Scholar] [CrossRef] [Green Version]
  22. Almansa, R.; Anton, A.; Ramirez, P.; Martin-Loeches, I.; Banner, D.; Pumarola, T.; Xu, L.; Blanco, J.; Ran, L.; Lopez-Campos, G.; et al. Direct Association between Pharyngeal Viral Secretion and Host Cytokine Response in Severe Pandemic Influenza. BMC Infect. Dis. 2011, 11, 232. [Google Scholar] [CrossRef]
  23. Yu, X.; Zhang, X.; Zhao, B.; Wang, J.; Zhu, Z.; Teng, Z.; Shao, J.; Shen, J.; Gao, Y.; Yuan, Z.; et al. Intensive Cytokine Induction in Pandemic H1N1 Influenza Virus Infection Accompanied by Robust Production of IL-10 and IL-6. PLoS ONE 2011, 6, e28680. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Martinez-Ocaña, J.; Olivo-Diaz, A.; Salazar-Dominguez, T.; Reyes-Gordillo, J.; Tapia-Aquino, C.; Martínez-Hernández, F.; Manjarrez, M.E.; Antonio-Martinez, M.; Contreras-Molina, A.; Figueroa-Moreno, R.; et al. Plasma Cytokine Levels and Cytokine Gene Polymorphisms in Mexican Patients during the Influenza Pandemic A(H1N1)Pdm09. J. Clin. Virol. 2013, 58, 108–113. [Google Scholar] [CrossRef]
  25. Paquette, S.G.; Banner, D.; Zhao, Z.; Fang, Y.; Huang, S.S.H.; Leόn, A.J.; Ng, D.C.K.; Almansa, R.; Martin-Loeches, I.; Ramirez, P.; et al. Interleukin-6 Is a Potential Biomarker for Severe Pandemic H1N1 Influenza A Infection. PLoS ONE 2012, 7, e38214. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Arriaga-Pizano, L.; Ferat-Osorio, E.; Rodríguez-Abrego, G.; Mancilla-Herrera, I.; Domínguez-Cerezo, E.; Valero-Pacheco, N.; Pérez-Toledo, M.; Lozano-Patiño, F.; Laredo-Sánchez, F.; Malagón-Rangel, J.; et al. Differential Immune Profiles in Two Pandemic Influenza A(H1N1)Pdm09 Virus Waves at Pandemic Epicenter. Arch. Med. Res. 2015, 46, 651–658. [Google Scholar] [CrossRef] [Green Version]
  27. Croker, B.A.; Krebs, D.L.; Zhang, J.-G.; Wormald, S.; Willson, T.A.; Stanley, E.G.; Robb, L.; Greenhalgh, C.J.; Förster, I.; Clausen, B.E.; et al. SOCS3 Negatively Regulates IL-6 Signaling in Vivo. Nat. Immunol. 2003, 4, 540–545. [Google Scholar] [CrossRef]
  28. Yasukawa, H.; Ohishi, M.; Mori, H.; Murakami, M.; Chinen, T.; Aki, D.; Hanada, T.; Takeda, K.; Akira, S.; Hoshijima, M.; et al. IL-6 Induces an Anti-Inflammatory Response in the Absence of SOCS3 in Macrophages. Nat. Immunol. 2003, 4, 551–556. [Google Scholar] [CrossRef]
  29. Zhu, S.; Zeng, C.; Zou, Y.; Hu, Y.; Tang, C.; Liu, C. The Clinical Diagnostic Values of SAA, PCT, CRP, and IL-6 in Children with Bacterial, Viral, or Co-Infections. Int. J. Gen. Med. 2021, 14, 7107–7113. [Google Scholar] [CrossRef] [PubMed]
  30. Viasus, D.; Paño-Pardo, J.R.; Pachón, J.; Campins, A.; López-Medrano, F.; Villoslada, A.; Fariñas, M.C.; Moreno, A.; Rodríguez-Baño, J.; Oteo, J.A.; et al. Factors Associated with Severe Disease in Hospitalized Adults with Pandemic (H1N1) 2009 in Spain. Clin. Microbiol. Infect. 2011, 17, 738–746. [Google Scholar] [CrossRef] [Green Version]
Genes 12 01914 g001 550
Figure 1. (A). IL-6 levels in healthy controls (HC), mild recovered cases (MRC), survived severe cases (SSC), and fatal cases (FC). (B). IL-10 levels in HC, MRC, SSC, and FC. Circles represent the cytokine level of individual subjects and lines indicate median and interquartile range. * p < 0.05; ** p < 0.01; *** p < 0.001.
Figure 1. (A). IL-6 levels in healthy controls (HC), mild recovered cases (MRC), survived severe cases (SSC), and fatal cases (FC). (B). IL-10 levels in HC, MRC, SSC, and FC. Circles represent the cytokine level of individual subjects and lines indicate median and interquartile range. * p < 0.05; ** p < 0.01; *** p < 0.001.
Genes 12 01914 g001
Genes 12 01914 g002 550
Figure 2. (A) IL-6 levels in survived severe cases (SSC) and fatal cases (FC) with different IL6 rsrs1800795 genotypes. C/C and G/C genotypes were clubbed together due to fewer subjects with C/C genotype. Circles represent cytokine level of individual subjects and lines indicate median and interquartile range. (B) IL-10 levels in survived severe cases (SSC) and fatal cases (FC) with different IL10 rs1800896 genotypes. (C) IL-10 levels in survived severe cases (SSC) and fatal cases (FC) with different IL10 rs1800872 genotypes.
Figure 2. (A) IL-6 levels in survived severe cases (SSC) and fatal cases (FC) with different IL6 rsrs1800795 genotypes. C/C and G/C genotypes were clubbed together due to fewer subjects with C/C genotype. Circles represent cytokine level of individual subjects and lines indicate median and interquartile range. (B) IL-10 levels in survived severe cases (SSC) and fatal cases (FC) with different IL10 rs1800896 genotypes. (C) IL-10 levels in survived severe cases (SSC) and fatal cases (FC) with different IL10 rs1800872 genotypes.
Genes 12 01914 g002
Genes 12 01914 g003 550
Figure 3. Receiver operating curve based on (A) IL-6 and (B) IL-10 levels in survived severe cases and fatal cases.
Figure 3. Receiver operating curve based on (A) IL-6 and (B) IL-10 levels in survived severe cases and fatal cases.
Genes 12 01914 g003
Table
Table 1. Demographic characteristics of influenza A(H1N1)pdm09-infected patients with different grades of disease severity.
Table 1. Demographic characteristics of influenza A(H1N1)pdm09-infected patients with different grades of disease severity.
Demographic CharacteristicsMild Cases
(n = 293)		Severe Cases (n = 86)p-Value
Age [mean years ± standard deviation]
Males [n (%)]
Females [n (%)]		39.1 ± 18.0
144 (49.1%)
149 (50.8%)		50.0 ± 13.0
46 (53.5%)
40 (46.5%)		<1 × 10−6
−
0.6447
Table
Table 2. Percent allele frequencies of cytokine gene polymorphisms in different Influenza A(H1N1)pdm09 patient groups.
Table 2. Percent allele frequencies of cytokine gene polymorphisms in different Influenza A(H1N1)pdm09 patient groups.
SNPAllelesPercent Minor Allele Frequency n(%) AMild Cases vs. Severe Cases/Severe Survived Cases/Fatal Cases
Odds Ratio (95% CI)p-Value
IL6 rs1800795
Mild cases
Severe cases
Survived severe cases
Fatal cases
 
TNFA rs1800629
Mild cases
Severe cases
Survived severe cases
Fatal cases
 
INFGrs2430561
Mild cases
Severe cases
Survived severe cases
Fatal cases
 
IL17A rs2275913
Mild cases
Severe cases
Survived severe cases
Fatal cases
 
IL10 rs1800896
Mild cases
Severe cases
Severe survived cases
Fatal cases
 
IL10 rs1800872
Mild cases
Severe cases
Severe survived cases
Fatal cases
 
TGFB rs1800470
Mild cases
Severe cases
Severe survived cases
Fatal cases
 
TGFBrs1800471
Mild cases
Severe cases
Severe survived cases
Fatal cases		G>C
 
 
 
 
 
G>A
 
 
 
 
 
A>T
 
 
 
 
 
G>A
 
 
 
 
 
G>A
 
 
 
 
 
C>A
 
 
 
 
 
T>C
 
 
 
 
 
G>C		 
75 (12.8)
22 (12.8)
10 (11.1)
12 (14.6)
 
 
27 (4.6)
20 (13.1)
13 (14.4)
7 (8.2)
 
 
218 (37.2)
61 (35.4)
25 (27.8)
36 (43.9)
 
 
220 (39.1)
58 (34.1)
31 (35.2)
27 (32.9)
 
 
150 (25.6)
40 (23.2)
23 (25.5)
17 (20.7)
 
 
252 (43.0)
80 ( 46.5)
42 (46.7)
38 (46.3)
 
 
278 (47.6)
80 (47.0)
43 (47.8)
37 (46.2)
 
 
45 (7.7)
16 (9.7)
8 (8.9)
8 (9.7)		 
−
0.99 (0.59–1.64)
0.85 (0.40–1.66)
1.16 (0.58–2.20)
 
 
−
2.72 (1.47–4.99)
3.49 (1.68–6.99)
1.93 (0.75–4.45)
 
 
−
0.93 (0.65–1.32)
0.65 (0.39–1.05)
1.32 (0.82–2.11)
 
 
−
0.85 (0.59–1.22)
0.89 (0.55–1.42)
0.81 (0.49–1.31)
 
 
−
0.88 (0.59–1.31)
0.99 (0.59–1.65)
0.76 (0.42–1.32)
 
 
−
1.15 (0.82–1.62)
1.16 (0.74–1.81)
1.14 (0.72–1.82)
 
 
−
0.98 (0.69–1.38)
1.01 (0.64–1.57)
0.95 (0.59–1.51)
 
 
−
1.23 (0.66–2.22)
1.17 (0.50–2.50)
1.30 (0.55–2.78)		 
−
0.999
0.668
0.639
 
 
−
0.002
0.001
0.155
 
 
−
0.682
0.081
0.246
 
 
−
0.384
0.649
0.397
 
 
−
0.539
0.994
0.346
 
 
−
0.416
0.515
0.569
 
 
−
0.901
0.974
0.823
 
 
−
0.489
0.672
0.508
A: numbers represent allelic count. Numbers in parenthesis represent percentage.
Table
Table 3. Percent genotype frequencies of cytokine gene polymorphisms in Influenza A(H1N1)pdm09 patient groups.
Table 3. Percent genotype frequencies of cytokine gene polymorphisms in Influenza A(H1N1)pdm09 patient groups.
SNP and GenotypesGenotypic Count (%) in Mild vs. Severe CasesMild vs. Severe Survived CasesMild vs. Fatal Cases
Mild CasesSevere CasesSurvived Severe CasesFatal CasesOdds Ratio with 95% Confidence Intervals
IL6 rs1800795
G/G
G/C
C/C
 
TNFA rs1800629
G/G
G/A
 
INFGrs2430561
A/A
A/T
T/T
 
IL17A rs2275913
G/G
G/A
A/A
 
IL10 rs1800896
A/A
A/G
G/G
 
IL10 rs1800872
C/C
C/A
A/A
 
TGFB rs1800470
T/T
T/C
C/C
 
 
TGFBrs1800471
G/G
G/C
224 (76.7)
61 (20.9)
7 (2.4)
 
 
266 (90.8)
27 (9.2)
 
 
114 (38.9)
140 (47.8)
39 (13.3)
 
 
115 (39.5)
132 (45.4)
44 (15.1)
 
 
168 (57.3)
100 (34.1)
25 (8.4)
 
 
101 (34.5)
132 (45.0)
60 (20.0)
 
 
87 (29.8)
132 (45.2)
73 (25.0)
 
 
 
248 (84.6)
45 (15.4)
65 (75.6)
20 (23.3)
1 (1.2)
 
 
66 (76.7)
20 (23.3)
 
 
34 (39.5)
43 (50.0)
9 (10.5)
 
 
40 (47.1)
32 (37.6)
13 (15.3)
 
 
55 (64.0)
22 (25.6)
9 (10.5)
 
 
22 (25.6)
48 (55.8)
16 (18.6)
 
 
25 (29.4)
40 (47.1)
20 (23.5)
 
 
 
70 (81.4)
16 (18.6)
35 (77.8)
10 (22.2)
0
 
 
32 (71.1)
13 (28.9)
 
 
23 (51.1)
19 (42.2)
3 (6.7)
 
 
20 (45.5)
17 (38.6)
7 (15.9)
 
 
28 (62.2)
11 (24.4)
6 (13.3)
 
 
12 (26.7)
24 (53.3)
9 (20.0)
 
 
13 (28.9)
21 (46.7)
11 (24.4)
 
 
 
37 (82.2)
8 (17.8)
30 (73.2)
10 (24.4)
1 (2.4)
 
 
34 (82.9)
7 (17.1)
 
 
11 (26.8)
24 (58.5)
6 (14.6)
 
 
20 (48.8)
15 (36.6)
6 (14.6)
 
 
27 (65.8)
11 (26.8)
3 (7.3)
 
 
10 (24.4)
24 (58.5)
7 (17.1)
 
 
12 (30.0)
19 (47.5)
9 (22.5)
 
 
 
33 (80.5)
8 (19.5)
1.00
1.14 (0.63–2.07)
0.80 (0.10–6.77)
 
 
1.00
2.95 (1.52–5.73) *
 
 
1.00
1.08 (0.63–1.84)
0.94 (0.40–2.19)
 
 
1.00
0.72 (0.42–1.24)
0.87 (0.41–1.85)
 
 
1.00
0.63 (0.35–1.1)
0.94 (0.40–2.21)
 
 
1.00
1.77 (0.98–3.21)
1.50 (0.71–3.18)
 
 
1.00
1.06 (0.59–1.91)
0.86 (0.43–1.73)
 
 
 
1.00
1.21 (0.63–2.32)
1.00
1.02 (0.47–2.21)
0.00
 
 
1.00
4.02 (1.84–8.82) @
 
 
1.00
0.67 (0.34–1.31)
0.46 (0.13–1.66)
 
 
1.00
0.81 (0.40–1.65)
0.97 (0.37–2.50)
 
 
1.00
0.62 (0.29–1.32)
1.29 (0.47–3.54)
 
 
1.00
1.61 (0.75–3.45)
1.50 (0.58–3.88)
 
 
1.00
1.06 (0.50–2.28)
0.97 (0.40–2.35)
 
 
 
1.00
1.15 (0.49–2.68)
1.00
1.28 (0.58–2.81)
1.76 (0.20–15.31)
 
 
1.00
2.04 (0.80–5.17)
 
 
1.00
1.85 (0.85–4.03)
2.04 (0.68–6.10)
 
 
1.00
0.67 (0.32–1.39)
0.79 (0.29–2.18)
 
 
1.00
0.61 (0.28–1.33)
0.60 (0.16–2.21)
 
 
1.00
1.95 (0.87–4.36)
1.47 (0.51–4.18)
 
 
1.00
1.07 (0.49–2.37)
0.79 (0.31–2.02)
 
 
 
1.00
1.30 (0.55–3.09)
* p = 0.0018; @ p = 9 × 10−4.
Table
Table 4. Percent haplotype frequencies of IL10 and TGFB polymorphisms in influenza A(H1N1)pdm09 patient groups.
Table 4. Percent haplotype frequencies of IL10 and TGFB polymorphisms in influenza A(H1N1)pdm09 patient groups.
HaplotypesPercent Predicted Haplotype Mild vs. Severe CasesMild vs. Severe Survived CasesMild vs. Fatal Cases
Mild CasesSevere CasesSevere Survived CasesFatal CasesOdds Ratio with 95% Confidence Intervals

IL10 rs1800896-rs1800872
A-A
A-C
G-C
G-A
 
TGFB rs1800470-rs1800471
T-G
C-G
C-C
T-C
 
41.9
32.5
24.5
1.1
 
 
50.8
41.5
6.1
1.6
 
43.2
33.6
19.9
3.3
 
 
49.9
40.8
6.2
3.1
 
45.3
29.2
24.2
1.4
 
 
48.0
43.1
4.7
4.2
 
39.4
39.9
13.8
7.0
 
 
51.8
38.4
7.7
2.00
 
1.00
0.92 (0.60–1.40)
0.66 (0.41–1.07)
3.21 (0.75–13.69)
 
 
1.00
0.98 (0.68–1.40)
0.87 (0.36–2.12)
2.84 (0.60–13.50)
 
1.00
0.77 (0.44–1.34)
0.83 (0.48–1.45)
1.19 (0.12–11.98)
 
 
1.00
1.08 (0.68–1.71)
0.68 (0.17–2.65)
3.91 (0.67–22.86)
 
1.00
1.25 (0.69–2.25)
0.34 (0.12–0.95) *
12.11 (2.23–76.96) @
 
 
1.00
0.88 (0.55–1.43)
1.12 (0.39–3.24)
1.74 (0.17–18.18)
* p = 0.041; @ p = 0.005.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Alagarasu, K.; Kaushal, H.; Shinde, P.; Kakade, M.; Chaudhary, U.; Padbidri, V.; Sangle, S.A.; Salvi, S.; Bavdekar, A.R.; D’costa, P.; et al. TNFA and IL10 Polymorphisms and IL-6 and IL-10 Levels Influence Disease Severity in Influenza A(H1N1)pdm09 Virus Infected Patients. Genes 2021, 12, 1914. https://doi.org/10.3390/genes12121914

AMA Style

Alagarasu K, Kaushal H, Shinde P, Kakade M, Chaudhary U, Padbidri V, Sangle SA, Salvi S, Bavdekar AR, D’costa P, et al. TNFA and IL10 Polymorphisms and IL-6 and IL-10 Levels Influence Disease Severity in Influenza A(H1N1)pdm09 Virus Infected Patients. Genes. 2021; 12(12):1914. https://doi.org/10.3390/genes12121914

Chicago/Turabian Style

Alagarasu, Kalichamy, Himanshu Kaushal, Pooja Shinde, Mahadeo Kakade, Urmila Chaudhary, Vikram Padbidri, Shashikala A. Sangle, Sonali Salvi, Ashish R. Bavdekar, Pradeep D’costa, and et al. 2021. "TNFA and IL10 Polymorphisms and IL-6 and IL-10 Levels Influence Disease Severity in Influenza A(H1N1)pdm09 Virus Infected Patients" Genes 12, no. 12: 1914. https://doi.org/10.3390/genes12121914

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop