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Role of interferon regulatory factors in predicting the prognosis of Crimean-Congo hemorrhagic fever

  • Caner Öksüz ORCID logo EMAIL logo , Halef Okan Doğan ORCID logo , Gökmen Zararsız ORCID logo , Nazif Elaldı ORCID logo and Seyit Ali Büyüktuna ORCID logo
Published/Copyright: December 11, 2024

Abstract

Objectives

Crimean-Congo hemorrhagic fever (CCHF) is a severe viral illness with a high fatality rate. The interferon response plays a crucial role in the antiviral defense against the CCHF virus (CCHFV). Interferon regulatory factors (IRFs) are essential for initiating and amplifying the interferon response. In this study, we aimed to evaluate the IRF response in CCHF patients for the first time.

Methods

This study investigated the expression levels of various IRFs (IRF-1, 2, 3, 4, and 7) in CCHF patients and evaluated their potential association with disease prognosis. The research encompassed a cohort of 60 CCHF patients and 30 healthy volunteers. CCHF was diagnosed with CCHFV positivity using PCR method and/or IgM detection using ELISA method. The quantitative sandwich ELISA technique was employed to determine the levels of serum IRF-1, IRF-2, IRF-3, IRF-4, IRF-7, interferon (IFN)-alpha, and IFN-beta.

Results

There were statistically significant differences in the levels of serum IRF-1, IRF-2, IRF-3, IRF-4, IRF-7, IFN-alpha, and IFN-beta between the patient and healthy control groups. Patients showed elevated levels in all these factors except for IRF-1. However, no statistically significant differences were found in IRF-1, IRF-2, IRF-3, IRF-4, IRF-7, IFN-alpha, and IFN-beta levels between patients who survived and those who died.

Conclusions

IFN-alpha and beta likely contribute to the immune response in CCHF. IRF-2, 3, 4, and 7 play crucial roles in IFN-alpha and beta expression, pivotal for the antiviral response in CCHF. Targeted interventions to modulate IRF-1 could alleviate disease severity and overall impact.

Introduction

Crimean-Congo hemorrhagic fever (CCHF) is induced by a negative-sense, single-stranded RNA virus. Despite ongoing research, the intricate mechanisms underlying its pathogenesis remain incompletely understood. The immune system, however, emerges as a pivotal player in the progression of CCHF. Saksida’s findings in 2010 underscored that a delayed and downregulated immune response could potentially lead to heightened viral replication and increased spread, resulting in severe outcomes [1]. This notion gains further support in a study that elucidated how CCHF virus (CCHFV) strategically delays the activation of the innate immune response, thereby facilitating rapid viral dissemination [2]. Highlighting the critical importance of understanding the pathogenesis of CCHF, it has been emphasized the role of the interferon response in controlling CCHFV replication. A notable observation was made in a STAT-1 knockout mouse model, where the absence of this key signaling molecule led to a cytokine storm and delayed activation of immune cells [3]. This emphasizes the significance of comprehending the underlying mechanisms of CCHF pathogenesis, as it not only sheds light on the dynamics of the disease but also provides valuable insights for the development of targeted therapeutic interventions.

Interferons (IFNs) constitute a protein family crucial for antiviral defense, cell growth regulation, and immune activation. Their transcription is facilitated by interferon regulatory factors (IRFs), which form a family of nine members, each serving diverse functions in response to external stimuli [4], 5]. Both IRFs and IFNs play a central role in antiviral defense and immunoregulation [6]. IFNs, being essential proteins in the immune response against viral infections, are released by cells in reaction to viral invasion, thereby inhibiting viral replication and spread. IRFs, as transcription factors, regulate the expression of IFNs and other genes involved in the antiviral response. Activation of IRFs has been observed during human coronavirus infections, including human coronavirus 229E, OC43, and severe acute respiratory syndrome-associated coronavirus 2 (SARS-CoV-2) [7]. However, no previous studies have examined the IFNs and IRFs in patients with CCHF.

This study endeavors to augment the current understanding of the pathogenesis of CCHF. Focusing on the intricate interplay between CCHF, IFNs, and IRFs, our investigation aims to shed light on the intricate mechanisms that drive the progression of the disease. Through this exploration, we aspire to provide valuable insights that can inform the development of more effective therapeutic strategies.

Materials and methods

Study population

This prospective study was conducted at the Department of Infectious Diseases and Clinical Microbiology, Sivas Cumhuriyet University Faculty of Medicine, between March 1, 2021, and October 31, 2021. The sample size was calculated using G*Power version 3.1.9.7 software. The minimum sample size required to detect a medium effect size (d=0.65) with a 5 % significance level (alpha), 80 % statistical power (1 − β), and a population ratio of 2 (N1/N2) was calculated to be 86. The research encompassed a cohort of 60 CCHF patients and 30 healthy volunteers devoid of chronic diseases or drug use. CCHF was diagnosed with CCHFV positivity using PCR method and/or IgM detection using ELISA method. Within the patient group, 40 (66.7 %) were males, and 20 (33.3 %) were females, while the control group comprised 21 (70 %) males and 9 (30 %) females. The mean age of the patient group was 55 ± 18 years, and the control group averaged 55 ± 9 years. No statistically significant differences in age and gender were observed between the patient and control groups (p>0.05). Comprehensive demographic data, underlying chronic diseases, treatments administered, and final outcomes (survival or mortality) were meticulously recorded. Following the severity grading system (SGS), participants were categorized into mild (≤4 points), moderate (5–8 points), and severe cases (≥9 points) [8]. Ethical approval for the study was obtained from the local Clinical Research Ethics Committee on December 21, 2020, with approval number 2020-12/06, and the research adhered to the principles outlined in the Declaration of Helsinki.

Sampling

In the patient group, blood samples were collected on the first day of hospitalization. Approximately 5 mL of venous blood from each participant was collected into a serum separator tube. Subsequently, the samples underwent centrifugation at 1,000×g for 20 min. These sera were then carefully aliquoted into secondary containers, intended for subsequent analysis, and were promptly stored in a −40 °C cooler to ensure preservation until further study.

Biochemical analyses

The quantitative sandwich ELISA technique was employed to determine the levels of serum IRF-1, IRF-2, IRF-3, IRF-4, IRF-7, IFN-alpha, and IFN-beta (ELK Biotechnology CO., Ltd., Wuhan, China). Detection ranges and sensitivities of ELISA kit are shown in Table 1. For the assessment of serum ferritin and interleukin-6 (IL-6) levels, the electrochemiluminescent method was utilized, employing the Roche Cobas e801 system from Germany. Additionally, serum blood urea nitrogen (BUN), creatinine, aspartate aminotransferase (AST), alanine aminotransferase (ALT), lactate dehydrogenase (LDH), and creatine kinase (CK) levels were determined using the colorimetric method through the Roche Cobas c702 system (Germany). International normalized ratio (INR), prothrombin time (PT), activated partial thromboplastin time (aPTT), fibrinogen, and D-dimer were assessed using Cobas t511 coagulation analyzers by Roche in Germany. Complete blood count parameters were measured with a hemogram analyzer, specifically the Sysmex XN 1000 system from Japan.

Table 1:

Performance specifications of ELISA kits.

Kit Catalog number Detection range Sensitivity
IFN-alpha, pg/mL ELK1043 7.82–500 3
IFN-beta, pg/mL ELK1101 7.82–500 2.8
IRF-1, ng/mL ELK2069 0.32–20 0.122
IRF-2, pg/mL ELK9111 78.13–5000 36
IRF-3, ng/mL ELK2074 0.16–10 0.065
IRF-4, ng/mL ELK9110 0.32–20 0.122
IRF-7, ng/mL ELK91109 0.16–10 0.048
  1. The kit manufacturer is ELK Biotechnology CO., Ltd., Wuhan, China. IFN, interferon; IRF, interferon regulatory factor.

Statistical analysis

Histogram and q-q plots were examined, Shapiro–Wilk’s test was performed to assess the data normality. Levene’s test was applied to test variance homogeneity. To compare the differences between groups, either a two-sided independent samples t-test or Mann–Whitney U test were used for continuous variables, Pearson χ2 analysis, or Fisher’s exact test were used for categorical variables. To compare the difference among groups, either one-way analysis of variance (ANOVA) or Kruskal–Wallis test were applied for continuous variables. Tukey, Tamhane, or Dunn-Bonferroni tests were used for multiple comparisons. The relationship between quantitative data was analyzed with Spearman correlation analysis. Analysis was conducted using R 4.0.1 and GraphPad Prism version 8.3.0 (San Diego, CA, USA, www.graphpad.com). A p-value less than 5 % was considered statistically significant.

Results

A significant difference was observed in the levels of serum IRF-1, IRF-2, IRF-3, IRF-4, IRF-7, IFN-alpha, and IFN-beta between patient and healthy control groups. As shown in Table 2 and Figure 1, the patient group exhibited higher levels of all parameters except IRF-1. Furthermore, significant differences were detected in various markers between patients who survived and those who died, including BUN, creatinine, AST, LDH, CK, PT, aPTT, INR, fibrinogen, D-dimer, white blood cell (WBC), neutrophil (Neu), lymphocyte (Lym), platelet (Plt), ferritin, and IL-6. However, no statistically significant differences were found in IRF-1, IRF-2, IRF-3, IRF-4, IRF-7, IFN-alpha, and IFN-beta levels between patients who survived and those who died. These results are shown in detail in Table 3. As shown in Table 4, notably, a negative correlation was identified between IRF-1 and the SGS. In patients with a severe clinical course, IRF-7 levels were higher compared to mild and moderate groups, while IRF-1 levels were lower in the severe group than in the mild group. Other clinical parameters did not show significant differences among mild, moderate, and severe groups. The strongest and positive correlations between interferons and IRFs were observed between IFN-beta and IRF-4 (Spearman’s rho=0.738) and IFN-beta and IRF-7 (Spearman’s rho=0.699) (Figure 2). Positive correlations were observed between IFN-alpha and lymphocyte count, IRF-1 and platelet count, as well as fibrinogen. Negative correlations were found between IRF-1 and BUN, ALT, AST, LDH, CK, aPTT, and ferritin. Additionally, IRF-2 showed a positive correlation with aPTT, and IRF-3 had a positive correlation with lymphocyte count. These results are expressed in Figure 3.

Table 2:

Comparison of serum interferon and IRF levels between CCHF patients and healthy controls.

Variables Groups Overall (n=90) p-Value
Control (n=30) CCHF patient (n=60)
IFN-alpha, pg/mL 633 (562–712) 741 (660–926) 701 (606–783) <0.001
IFN-beta, pg/mL 52.8 (37.2–200) 231 (121–470) 159 (70.2–394) <0.001
IRF-1, ng/mL 44.4 (38.1–47.2) 38.2 (34.6–45.6) 39.5 (35.3–47.2) 0.049
IRF-2, pg/mL 17.0 (2.4–284) 1241 (94.4–2584) 676 (18.0–1650) <0.001
IRF-3, ng/mL 0.1 (0.1–0.1) 0.9 (0.1–3.4) 0.3 (0.1–2.0) <0.001
IRF-4, ng/mL 0.1 (0.1–4.8) 2.0 (0.1–12.6) 1.0 (0.1–10.4) 0.009
IRF-7, ng/mL 1.1 (0.2–2.9) 4.1 (2.3–7.9) 3.4 (1.3–6.5) <0.001
  1. Values are expressed as median (1st–3rd quartiles). Significant p-values are shown in bold. CCHF, Crimean-Congo hemorrhagic fever; IFN, interferon; IRF, interferon regulatory factor.

Figure 1: 
Violin plots of interferons and IRFs in CCHF patients and controls. CCHF, Crimean-Congo hemorrhagic fever; IFN, interferon; IRF, interferon regulatory factor.
Figure 1:

Violin plots of interferons and IRFs in CCHF patients and controls. CCHF, Crimean-Congo hemorrhagic fever; IFN, interferon; IRF, interferon regulatory factor.

Table 3:

Comparison of clinical and laboratory parameters between deceased and surviving CCHF patients.

Variables CCHF patients Overall (n=60) p-Value
Deceased (n=15) Survived (n=45)
SGS 10 (5–12) 4 (2–6) 5 (2–8) <0.001
Ferritin, µg/L 21168 (10768–52188) 6196 (986–21265) 10623 (1187–27063) 0.008
IL-6, ng/L 102 (47.9–156) 23.2 (10.0–39.4) 30.1 (16.2–72.6) <0.001
BUN, mg/dL 41.2 (24.6–53.3) 17.9 (13.7–22.0) 20.2 (14.4–32.3) <0.001
Creatinine, mg/dL 1.68 (1.35–3.59) 0.91 (0.78–1.16) 1.0 (0.82–1.48) <0.001
ALT, U/L 138 (60–320) 79 (33–144) 92 (38–188) 0.062
AST, U/L 513 (260–962) 203 (60–300) 230 (63–393) 0.001
LDH, U/L 2091 (1236–2856) 590 (330–847) 716 (350–1440) <0.001
CK, U/L 520 (212–1532) 425 (138–1233) 486 (169–1255) 0.437
PT, s 16.5 (14.0–23.0) 12.4 (11.3–15.4) 13.2 (11.5–16.2) <0.001
INR 1.5 (1.2–2.1) 1.1 (1.0–1.4) 1.2 (1.0–1.4) <0.001
aPTT, s 60.3 (44.1–71.3) 31.1 (25.3–38.1) 33.4 (26.0–49.0) <0.001
Fibrinogen, mg/dL 148 ± 101 244 ± 68.7 220 (180–273) <0.001
D-dimer, mg/L FEU 35.2 (5.3–72.0) 2.2 (1.1–32.4) 3.9 (1.2–35.2) 0.002
WBC, 10ˆ9/L 13.7 (3.3–16.2) 2.6 (1.8–3.6) 3.0 (1.9–5.5) <0.001
Neu, % 78.5 (65.0–87.6) 66.6 (49.8–81.4) 70.7 (51.1–83.5) 0.103
Neu, 10ˆ9/L 9.0 (2.5–11.9) 1.5 (0.9–2.7) 2.0 (1.1–4.7) <0.001
Lym, % 14.0 (9.0–21.7) 24.1 (13.3–40.0) 21.3 (11.2–36.8) 0.049
Lym, 10ˆ9/L 1.2 (0.7–2.3) 0.5 (0.4–0.9) 0.6 (0.4–1.1) 0.001
Plt, 10ˆ9/L 16.0 (12.0–39.0) 56.0 (28.5–117) 44.0 (22.0–99.5) 0.002
Hgb, g/dL 12.7 ± 2.30 13.9 ± 1.96 13.9 (12.2–15.1) 0.065
Neu/Lym, % 5.5 (2.3–8.9) 3.1 (1.3–6.4) 3.4 (1.4–6.8) 0.144
Neu/Lym, 10ˆ9/L 5.6 (2.3–9.6) 3.0 (1.3–6.3) 3.3 (1.4–7.2) 0.135
IFN-alpha, pg/mL 761 (730–1004) 687 (605–921) 741 (660–926) 0.069
IFN-beta, pg/mL 232 (158–399) 206 (113–510) 231 (121–470) 0.427
IRF-1, ng/mL 34.5 (16.7–45.7) 38.5 (36.0–45.4) 38.2 (34.6–45.6) 0.080
IRF-2, pg/mL 1336 (1019–3690) 1229 (41.0–2159) 1241 (94.4–2584) 0.222
IRF-3, ng/mL 1.6 (0.5–3.4) 0.6 (0.1–3.5) 0.9 (0.1–3.4) 0.141
IRF-4, ng/mL 2.0 (0.1–12.4) 2.1 (0.1–13.6) 2.0 (0.1–12.6) 0.798
IRF-7, ng/mL 5.6 (3.8–7.9) 3.8 (1.9–8.0) 4.1 (2.3–7.9) 0.063
  1. Values are expressed as mean ± standard deviation and median (1st–3rd quartiles). Significant p-values are shown in bold. ALT, alanine aminotransferase; aPTT, activated partial thromboplastin time; AST, aspartate aminotransferase; BUN, blood urea nitrogen; CCHF, Crimean-Congo hemorrhagic fever; CK, creatine kinase; Hgb, hemoglobin; IFN, interferon; IL, interleukin; INR, international normalized ratio; IRF, interferon regulatory factor; LDH, lactate dehydrogenase; Lym, lymphocyte; Neu, neutrophil; Plt, platelet; PT, prothrombin time; SGS, severity grading system; WBC, white blood cell.

Table 4:

Comparison of interferon and IRF levels among SGS groups in CCHF patients.

Variables SGS Groups rho p-Value
Mild (n=25) Moderate (n=22) Severe (n=13)
IFN-alpha, pg/mL 687 (601–1065) 720 (649–1007) 783 (738–848) 0.184 0.196
IFN-beta, pg/mL 280 (115–540) 155 (113–417) 249 (161–527) 0.023 0.313
IRF-1, ng/mL 38.5 (36.5–56.5)a 39.0 (34.1–51.5)ab 35.0 (16.7–39.0)b −0.286c 0.036
IRF-2, pg/mL 1202 (32.0–2011) 1037 (76.8–1698) 2715 (1188–5394) 0.189 0.059
IRF-3, ng/mL 1.0 (0.1–4.1) 0.3 (0.1–2.8) 1.6 (0.7–3.6) 0.047 0.154
IRF-4, ng/mL 4.1 (0.1–15.7) 1.6 (0.1–3.7) 6.2 (0.3–17.4) 0.064 0.382
IRF-7, ng/mL 3.9 (2.3–5.7)a 3.2 (1.7–7.0)a 7.4 (4.7–11.7)b 0.184 0.019
  1. cp<0.05. Values are expressed as median (1st–3rd quartiles). Significant p-values are shown in bold. Different superscripts in the same row indicate a statistically significant difference between groups. CCHF, Crimean-Congo hemorrhagic fever; IFN, interferon; IRF, interferon regulatory factor; rho, Spearman correlation coefficient; SGS, severity grading system.

Figure 2: 
Scatter plots showing the relationships between interferons and IRFs in CCHF patients. Spearman correlation analysis was performed. *p<0.001. CCHF, Crimean-Congo hemorrhagic fever; IFN, interferon; IRF, interferon regulatory factor.
Figure 2:

Scatter plots showing the relationships between interferons and IRFs in CCHF patients. Spearman correlation analysis was performed. *p<0.001. CCHF, Crimean-Congo hemorrhagic fever; IFN, interferon; IRF, interferon regulatory factor.

Figure 3: 
Heatmap graphs of Spearman correlation coefficients among interferons, IRFs, and laboratory parameters in CCHF patients. ALT, alanine aminotransferase; aPTT, activated partial thromboplastin time; AST, aspartate aminotransferase; BUN, blood urea nitrogen; CCHF, Crimean-Congo hemorrhagic fever; CK, creatine kinase; Hgb, hemoglobin; IFN, interferon; IL, interleukin; INR, international normalized ratio; IRF, interferon regulatory factor; LDH, lactate dehydrogenase; Lym, lymphocyte; Neu, neutrophil; Plt, platelet; PT, prothrombin time; WBC, white blood cell.
Figure 3:

Heatmap graphs of Spearman correlation coefficients among interferons, IRFs, and laboratory parameters in CCHF patients. ALT, alanine aminotransferase; aPTT, activated partial thromboplastin time; AST, aspartate aminotransferase; BUN, blood urea nitrogen; CCHF, Crimean-Congo hemorrhagic fever; CK, creatine kinase; Hgb, hemoglobin; IFN, interferon; IL, interleukin; INR, international normalized ratio; IRF, interferon regulatory factor; LDH, lactate dehydrogenase; Lym, lymphocyte; Neu, neutrophil; Plt, platelet; PT, prothrombin time; WBC, white blood cell.

Discussion

IFN-alpha and beta are produced by infected cells in viral infections and trigger a cascade of signals leading to the activation of numerous interferon-stimulated genes (ISGs). These ISGs encode proteins with various antiviral functions, including hindering viral replication and bolstering the immune response [9], 10]. The presence of certain gene polymorphisms in IFN-alpha has been linked to susceptibility to viral infections, including CCHF [10]. Additionally, the activation of interferon-stimulated genes during CCHF infection suggests the involvement of both type I and II interferon-mediated antiviral mechanisms [11]. Research has shown that type I interferon effectively inhibits CCHF virus in human cells, and studies in mice lacking the interferon α/β receptor have highlighted the importance of interferon signaling in controlling viral loads and inflammation [12]. Despite these insights, clinical trials specifically investigating the effect of IFN-alpha and beta on CCHF are lacking, representing a significant gap in knowledge. In this study, which fills this gap, we observed increased levels of IFN-alpha and IFN-beta in patients with CCHF compared to healthy individuals. On the other hand, our analysis did not reveal statistically significant differences between surviving and non-surviving patients. Additionally, no significant correlations were found among IFN-alpha, beta levels, coagulopathy indicators, and hepatic injury biomarkers. The upregulation of IFN-alpha and beta is believed to have a significant impact on the immune response, bolstering the body’s antiviral defenses in cases of CCHF. Understanding the complex interplay between interferons, viral pathogenesis, and clinical outcomes holds promise for the development of targeted therapies. Our study contributes to this understanding by demonstrating elevated levels of IFN-alpha and beta in CCHF patients. Despite this observation, we did not find a significant correlation between these biomarkers and coagulopathy or hepatic biomarkers. This suggests that while IFN-alpha and beta likely contribute to the pathophysiology of CCHF by bolstering the immune response, they may not directly influence the development of coagulopathy or hepatic dysfunction in affected individuals.

In this study, elevated levels of IRF-2, 3, 4, and 7 were detected in patients compared to healthy controls. Moreover, we found a weak positive correlation between IRF-2 and aPTT levels. Previous research has explored the involvement of IRFs in various viral infectious diseases [13], [14], [15], [16]. Ousman et al. observed increased expression of the IRF-7 gene in a mouse model of central nervous system viral infection [13]. Ainsua-Enrich demonstrated the regulatory role of IRF4 in T-cell differentiation during influenza infection [14]. Li et al. identified a protective function of IRF-2 in viral neuroinfection [15]. Furthermore, IRF-3 has been shown to induce apoptosis in virus-infected cells, thereby limiting virus propagation within the host [16], 17]. IRFs are a family of transcription factors that play a central role in regulating the expression of interferons and other genes involved in the immune response. Various pattern recognition receptors (PRRs) detect viral nucleic acids and activate signaling pathways that lead to the production of IFN-alpha and IFN-beta [13], 18], 19]. This activation involves the recruitment and activation of IRF proteins. These IRFs translocate to the nucleus and bind to specific DNA sequences in the promoters of the IFN-alpha and IFN-beta genes, leading to their transcription. IRFs are critical regulators of the IFN response to viral infections. They control the induction, amplification, and regulation of IFN-alpha and IFN-beta production, as well as the triggering signaling pathways that mediate the antiviral effects of these cytokines. Therefore, we think that IRF-2, 3, 4, and 7 have a role in the expression of IFN alpha and beta expressions and these IRFs are crucial for antiviral response in CCHF.

It was notable that patients showed decreased levels of IRF-1 compared to the healthy control group. Negative associations were observed between IRF-1 and various biomarkers, including BUN, ALT, AST, LDH, CK, aPTT, and ferritin. Additionally, IRF-1 exhibited a negative correlation with SGS. Moreover, IRF-1 levels were lower in the severe group compared to the mild group. IRF-1 plays a crucial role in the innate immune response against viral infections [20]. It’s known to activate antiviral defenses and induce IFN-β and other protective genes during viral infections [20]. Moreover, IRF-1 has been found to protect against diseases caused by various viruses, such as Chikungunya and vesicular stomatitis virus, by inhibiting viral replication [21], 22]. In accordance with our study, previous studies have also highlighted diminished IRF-1 expression in viral infections. For instance, hepatitis C virus core proteins, human immunodeficiency virus-1 transactivator protein, and rotavirus NSP1 proteins have been shown to suppress IRF-1 expression or degrade it, affecting its antiviral activity [23], [24], [25]. The decreased IRF-1 levels observed in patients with CCHF could stem from various factors. It’s possible that the CCHF virus actively suppresses IRF-1 to evade the immune response or the infection disrupts the interferon pathway, where IRF-1 plays a vital role. Additionally, individual factors such as underlying health conditions or genetic variations might influence IRF-1 production during viral infections. Further research is necessary to understand the impact of CCHF infection on IRF-1 and the interferon response pathway. Overall, these findings suggest that the diminished levels of IRF-1 in CCHF patients may play a role in the pathogenesis and prognosis of the disease, potentially contributing to immune suppression or dysregulation during infection. Targeted interventions, particularly those aimed at modulating IRF-1, could be beneficial in reducing the severity of disease symptoms and lessening the overall impact of the illness.

In conclusion, our findings shed light on the complex interplay between the immune response and CCHFV. While type I interferons, IFN-alpha and beta, and IRF-2, 3, 4, and 7 contribute to the immune response against CCHF, their direct impact on patient prognosis might be limited. Our study’s intriguing finding of diminished IRF-1 levels in CCHF patients warrants further investigation. This downregulation of IRF-1 could potentially play a significant role in the disease’s pathogenesis. Insufficient IRF-1 might lead to impaired IFN-alpha and beta production, ultimately resulting in compromised antiviral immunity and dysregulation of the immune response during CCHF infection. These findings open new avenues for exploring targeted interventions. By modulating IRF-1 function, we might be able to develop therapeutic strategies to mitigate disease severity and improve patient outcomes in CCHF.


Corresponding author: Caner Öksüz, MD, Department of Infectious Diseases and Clinical Microbiology, Sivas State Hospital, Sivas, Türkiye, E-mail:

Acknowledgments

We would like to thank Sivas Cumhuriyet University Scientific Research Projects Coordination Unit for providing financial support to our study with the project number T-2021-920.

  1. Research ethics: The research related to human use has complied with all the relevant national regulations, institutional policies, and in accordance with the tenets of the Helsinki Declaration and has been approved by the authors’ Institutional Review Board or equivalent committee (Ethical Committee of Sivas Cumhuriyet University approval number 2020-12/06).

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

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

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

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

  6. Research funding: Financial support for this study was received from Sivas Cumhuriyet University Scientific Research Projects Coordination Unit with project number T-2021-920.

  7. Data availability: The raw data can be obtained on request from the corresponding author.

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Received: 2024-10-18
Accepted: 2024-11-19
Published Online: 2024-12-11

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

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

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