Iranian Journal of Medical Sciences

Document Type : Original Article(s)

Authors

1 Department of Aquatic Biotechnology, Artemia and Aquaculture Research Institute, Urmia University, Urmia, Iran

2 Department of Infectious Diseases, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran

Abstract

Background: Following the coronavirus disease 2019 outbreak (COVID-19), it became a worrisome health burden worldwide. COVID-19-associated mucormycosis emergence, characterized by dysregulated inflammation and iron metabolism, exacerbated the prognosis of affected patients. Given the significance of hepcidin in regulating inflammation and iron metabolism, this study investigated the significance of hepcidin single nucleotide polymorphisms (SNP) in COVID-19-associated mucormycosis development, along with the association between the clinical and laboratory factors and COVID-19-associated mucormycosis. 
Methods: From September 2021 to November 2021, COVID-19 patients with and without mucormycosis were enrolled in this cross-sectional study. Their medical records and laboratory results were investigated. SNP genotyping was performed using Sanger sequencing. Hardy-Weinberg Equilibrium, Pearson’s Chi square, and student t test were used for analyzing the data using SPSS software version 25. P<0.05 was regarded as statistically significant.
Results: Here, 110 COVID-19 patients with and without mucormycosis were investigated. Elevated levels of urea, aspartate aminotransferase, lactate dehydrogenase, and increased ratio of polymorphonuclear neutrophil to lymphocytes were associated with decreased risk of COVID-19-associated mucormycosis in patients (all P<0.05). Moreover, diabetes mellitus increased the risk of mucormycosis (P=0.028). In contrast to patients without mucormycosis, patients with mucormycosis did not display 442 GA and SNP335 GT genotypes. Unlike patients without mucormycosis, none of the patients with mucormycosis had SNP442 GA and SNP335 GT genotypes. Regarding SNP 443 C>T, and the combination of SNPs 582 A>G and 443 C>T, CC genotype and AA+CC genotypes were associated with increased lactate dehydrogenase levels in COVID-19 patients, respectively. 
Conclusion: Regarding SNP 443 C>T, the CC genotype was associated with increased lactate dehydrogenase levels in COVID-19 patients. In terms of SNP 582 A>G and SNP 443 C>T, COVID-19 patients with AA+CC genotypes had higher levels of LDH. None of the patients with mucormycosis had SNP442 GA and SNP335 GT genotypes.

Keywords

What’s Known

The emergence of COVID-19-associated mucormycosis, which is characterized by dysregulated inflammation and iron metabolism, exacerbated the prognosis of affected patients. Studies have shown that one of the important genes involved in dysregulated inflammation and iron metabolism is hepcidin.

What’s New

The results revealed the potential of some factors and genotypes in mucormycosis susceptibility. In contrast to patients without mucormycosis, patients with mucormycosis did not display SNP442GA and SNP335GT genotypes. Increased levels of urea, aspartate aminotransferase, lactate dehydrogenase, and increased ratio of polymorphonuclear neutrophils to lymphocytes are associated with a decreased risk of mucormycosis.

Introduction

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to the coronavirus disease 2019 (COVID-19) outbreak in 2019. 1 The initial pneumonia-resembling symptoms of COVID-19 can lead to the development of hypoxia, acute respiratory disease syndrome (ARDS), and systemic inflammatory response syndrome in some patients. 2

Inflammation is highly implicated in developing severe clinical manifestations of COVID-19. 3 The dysregulation of pro-inflammatory cytokines and chemokines, e.g., IL-6, pave the way for cytokine storm and multi-organ failure in affected patients. 4 Aside from the systemic inflammatory response syndrome, the development of mucormycosis has been another worrisome concern in patients. 5 Mucormycosis is an opportunistic fungal infection that highly affects the blood vessels; low pH, hyperglycemia, and iron metabolism dysregulation have been identified for the emergence of mucormycosis. 6 Rhizopus oryzae and Mucor circinelloides are among the causative fungal organisms for mucormycosis. Fungal spores inhalation is the main route for developing mucormycosis in immunocompromised patients. It has been reported that immunocompromised patients, such as patients with organ transplants, leukemia, and those on chronic use of steroid-based agents, are more prone to develop mucormycosis. 6 Besides the hyperglycemic complication of glucocorticoid administration for treating COVID-19 patients, COVID-19-related tissue injury and dysregulated inflammation can increase free iron and ferritin levels, facilitating mucormycosis development. 7 Therefore, dysregulated inflammation and iron metabolism have substantial roles in developing mucormycosis in COVID-19 patients.

Hepcidin, encoded by hepcidin antimicrobial peptide (HAMP), is well-known for its significant role in regulating iron homeostasis. Increased iron levels upregulate hepcidin expression, leading to decreased iron levels. 8 Hepcidin is produced from the hepatocytes and acts as a receptor of ferroportin, which is highly expressed in enterocytes and macrophages. The hepcidin-mediated signaling pathways block the iron absorption and iron export in macrophages. 9 Besides, increased levels of hepcidin reported in inflammation and infection, likely reduce iron availability for the microorganisms. IL-6-mediated hepcidin expression can be the underlying mechanism for hepcidin upregulation in inflammations and infections. 10 Considering the substantial role of dysregulated inflammation in the pathogenicity of COVID-19, it has been reported that hepcidin is considerably elevated in non-survivor COVID-19 patients admitted to ICU compared to survivor ones. 11 Recently, it was shown that Rhizopus arrhizus var. delemar, which is responsible for mucormycosis, can secrete peptides that regulate hepcidin expression in blood-derived monocytic macrophages. 12 Although hepcidin has a significant role in iron hemostasis and inflammation, and inflammation and iron dysregulation have been implicated in COVID-19 pathogenesis and COVID-19-associated mucormycosis development, to the best of our knowledge there is no study to investigate the role of hepcidin polymorphisms in COVID-19-associated mucormycosis development.

Genetic footprint has been identified in most human conditions. Different expression patterns have been implicated in developing and progressing human diseases such as COVID-19. 13 In this regard, polymorphism is highlighted as one of the main culprits for altering the expression of genes. Specific nucleotide changes in the promotor of genes can affect its expression, leading to the altered function of that gene product. 14 It has been reported that single-nucleotide polymorphism (SNPs) of HAMP can also alter iron metabolism. 15 The SNP582 A>G, SNP 443 C>T, SNP 442 G>A, SNP 335 G>T, and SNP 153 C>T are the polymorphisms of the promotor region of the HAMP gene, and the number mentioned in SNP names refer to their exact location in the promotor. SNP582 A>G is one of the well-studied polymorphisms of HAMP, wherein the presence of G allele decreases hepcidin expression. 16 SNP 153 C>T variant is located in a bone morphogenetic protein (BMP)-responsive element and reduces basal hepcidin gene expression by disrupting its response to BMPs and IL-6. 17 SNP 443 C>T, SNP 442 G>A, and SNP 335 G>T were later included in the study when sequencing data revealed heterozygosity in those loci.

Despite the impact of SNPs on hepcidin expression and the significant role of hepcidin in COVID-19 pathogenesis and COVID-19-associated mucormycosis development, there is no study investigating the effect of HAMP SNPs on COVID-19-associated mucormycosis development.

The current study investigated the clinical and laboratory characteristics of COVID-19 patients who developed COVID-19-associated mucormycosis. Moreover, the significance of hepcidin polymorphisms in the clinical and laboratory features of COVID-19-associated mucormycosis was investigated.

Patients and Methods

Study Participants

From September 2021 to November 2021, COVID-19 patients with and without mucormycosis who were admitted at Emam Reza and Sina Hospitals, the teaching Hospitals affiliated with Tabriz University of Medical Sciences (Tabriz, Iran), were enrolled in this cross-sectional study. Based on the statistical power analysis, a sample size of 110 participants was selected (error margin of 5% with a 95% confidence interval). As inclusion criteria, patients who were admitted to the hospitals due to COVID-19-associated mucormycosis during the delta wave were included as cases, and people who had a history of hospitalization due to COVID-19 without mucormycosis were included in the study as the control group. Those who had previous history of mucormycosis or were not positive for SARS-CoV-2 were excluded. This study was performed after approval by the Ethics Committee of Tabriz University of Medical Sciences (Ethical approval no: IR.TBZMED.REC.1400.1086). Besides, the protocol of this study was performed following the Declaration of Helsinki. The included patients’ demographical, clinical, and laboratory characteristics were collected for further analyses.

Hepcidin Gene Polymorphisms

Following written informed consent, 5 mL of peripheral venous blood was collected from each patient into separate EDTA-coated blood collection tubes. Following red blood cell lysis and centrifuging, the DNA of white blood cells was extracted using the salting out method. 18 The extracted DNA was preserved in an RNAse and DNAase-free microtube at -20 °C. The quality and quantity of the extracted DNA were assessed using agarose gel (2%) and spectrophotometry. Using the Gene Runner, the related primers for amplifying a fragment containing SNP 582 A>G, SNP 443 C>T, SNP 442 G>A, SNP 335 G>T, and SNP 153 C>T polymorphisms were designed, and the specificity of the primers was studied using the NCBI website (https://www.ncbi.nlm.nih.gov/). The fragment was amplified using the Polymerase Chain Reaction (PCR) technique. The primers for the amplification of the 666 bp target sequence containing the polymorphic regions of interest were forward 5ʹ-TATTACTGCTGTCATTTATGGC-3ʹ and 5ʹ-reverse TCTGTCTGGCTGTCCCACT-3ʹ. PCR on the target fragment was conducted in a reaction mixture including 12.5 μL Taq DNA Polymerase Master Mix RED (Ampliqon, Denmark), 1 μL of each mentioned primer (10 pmol), 1 μL template DNA (30-60 ng), and finally 9.5 μL sterile distilled H2O.

PCR was carried out in a thermal cycler (Sensoquest, GmbH, Germany) after setting the following cycling program: an initial denaturation for 5 min at 94 °C followed by 35 cycles of denaturation for 35 sec at 94 °C, primer annealing for 30 sec at 60 °C, extension for 35 sec at 72 °C, and a final extension for 7 min at 72 °C. Then, 5 μL of each PCR product was loaded on a 2% agarose gel to evaluate the PCR reaction. To obtain high-quality sequencing results, PCR reactions should be specific. A 50 bp DNA ladder, as a molecular size marker (Fermentas, USA), was also loaded into a well of gel. The amplified fragments were sequenced using the Sanger method (Bio Magic Gene (BMG) Co., China) to identify the SNPs. The sequencing data were interpreted by Chromas 2.6 software (Nucleics Pty Ltd., Australia).

Statistical Analysis

After SNP genotyping, the statistical analysis was conducted using SPSS software V.25 (SPSS Inc., Chicago, USA). The Hardy-Weinberg Equilibrium (HWE) was applied to assess the genotype distributions in the case and control populations using an HWE calculator, available at http://www.oege.org/software/hwe-mr-calc.shtml. Pearson’s Chi square or Fisher exact test was used to study the possible associations between COVID-19-associated mucormycosis with these polymorphisms and patients’ clinical and laboratory characteristics. The odds ratio was calculated using the binary logistic regression, and the 95% CI was calculated. The Student t test was used to compare the two groups, and the normality of data distribution was studied via the Kolmogorov-Smirnov or Shapiro-Wilk tests. Levene’s test was used to study the equality of variances. A P<0.05 was considered statistically significant.

Results

The Association between Clinical and Laboratory Findings and COVID-19-Associated Mucormycosis

Table 1 shows the associations between the studied clinical and laboratory variables of included patients with the development of mucormycosis. It was found that increased levels of urea, aspartate aminotransferase (AST), lactate dehydrogenase (LDH), iron, and elevated ratio of polymorphonuclear neutrophil (PMN) to lymphocytes were associated with decreased risk of COVID-19-associated mucormycosis in COVID-19 patients. Table 2 demonstrates the association between the studied clinical and laboratory variables of COVID-19 patients with mucormycosis. Our results showed that diabetes mellitus significantly increases the risk of mucormycosis development in COVID-19 patients.

Laboratory characteristics Total (n=110) Patients without mucormycosis# (n=60) Patients with mucormycosis (n=50) P value (95% CI) OR (95% CI)
mean±SEM mean±SEM mean±SEM
WBC (110) 9217.09±470.18 (60) 9925±776.71 (50) 8367.6±428.01 0.419 0.37-0.557 1 (1-1)
Hb (110) 12.15±0.26 (60) 12.8±0.39 (50) 11.4±0.29 0.005* 0.48-2.41 0.807 (0.69-0.94)
Plt (110) 239163.6±9724.99 (60) 233383.3±15042.45 (50) 246100±11580.01 0.504 -5.6-4.12 1 (1-1)
MCV (92) 85.49±0.99 (46) 85.02±1.39 (46) 85.95±1.43 0.225 0.133-0.285 1.011 (0.97-1.06)
RBC (91) 4.09±0.084 (45) 4.24±0.14 (46) 3.94±0.09 0.083 -0.039-0.637 0.625 (0.37-1.07)
Cr (107) 1.77±0.19 (60) 2.11±0.39 (50) 1.36±0.14 0.104 0.04-0.156 0.719 (0.51-1.03)
LDH (75) 547.42±35.63 (60) 608.45±49.83 (50) 432.42±32.38 0.007* 0.000-0.27 0.996 (0.993-0.999)
Ferritin (15) 515±84.16 (7) 521.57±124.12 (8) 509.87±122.47 0.948 0.48-2.41 1 (0.997-1.003)
Iron (8) 83.88±11.71 (5) 101.6±11.94 (3) 54.33±10.33 0.036* -4.17-90.36 0.85 (0.61-1.19)
Alt (71) 37.14±8.53 (37) 48.86±16.04 (34) 24.38±2.7 0.333 0.291-0.473 0.986 (0.96-1.003)
AST (70) 30±3.81 (37) 35.97±6.71 (33) 23.3±2.64 0.031* 0.007-0.84 0.969 (0.94-1.004)
ESR (46) 49.4±4.89 (27) 47.22±5.84 (19) 52.53±8.6 0.639 0.546-0.726 1.005 (0.99-1.02)
CRP (76) 60.16±7.46 (46) 61.18±9.25 (30) 58.6±12.68 0.857 0.8-0.228 0.999 (0.992-1.007)
Polymorpho nuclear leukocytes (62) 73.78±1.7 (26) 78.52±2.4 (36) 70.37±2.22 0.017* 1.52-14.78 0.95 (0.909-0.993)
Lymphocytes (68) 19.93±1.44 (31) 15.98±1.86 (37) 23.24±2.01 0.01* -12.73-1.79 1.06 (1.01-1.11)
HCT (81) 35.21±0.76 (37) 36.19±1.34 (44) 34.38±0.82 0.252 -1.32-4.95 0.961 (0.91-1.02)
Urea (100) 53.9±4.07 (54) 62.67±6.51 (46) 43.61±4.03 0.015* 3.84-34.28 0.985 (0.97-0.99)
*P value is significant at P<0.05. All Ps were calculated by t test. #In all OR logistic regression models, patients without mucormycosis were considered as a reference. WBC: White blood cell; Hb: Hemoglobin; Plt: Platelet; MCV: Mean corpuscular volume; RBC: Red blood cell; Cr: Creatinine; LDH: Lactate dehydrogenase; ALT: Alanine transaminase; AST: Aspartate transferase; ESR: Erythrocyte sedimentation rate; CRP: C-reactive protein; HCT: Hematocrit
Table 1.Laboratory characteristics of patients and estimated P values for COVID-19 and mucormycosis association with laboratory status
Character (n) Groups Patients without mucormycosis Patients with mucormycosis P value Ref
n (%) n (%) OR (95% CI)
Sex (110) Female 25 (41.7) 24 (48) 0.506 1
0.807 (0.29-2.34)
Male 35 (58.3) 26 (52)
Marital status (77) Single 5 (16.7) 5 (10.6) 0.44 1
1.68 (0.44-6.38)
Married 25 (83.3) 42 (89.4)
PMH (75) Yes 26 (86.7) 35 (77.8) 0.333 1
0.146 (0.03-0.69)
No 4 (13.3) 10 (22.2)
DM (74) No 18 (60.0) 15 (34.1) 0.028* 1
6.86 (2.01-23.4)
Yes 12 (40.0) 29 (65.9)
Age (years) (108) 58.16±1.44 (60) 57.5±2.21# (48) 58.68±1.72 0.673 1#
Range (22-95) 0.995 (0.97-1.02)
*P value is significant at P<0.05. All Ps were calculated by t test. PMH: Past medical history, comorbidities other than DM including hypertension, hyperlipidemia, cerebrovascular accident, ischemic heart disease, chronic renal failure, hyperthyroidism, and cancer; DM: Diabetes mellitus; Ref: Reference line
Table 2.Clinical characteristics of patients and estimated P values for COVID-19 and mucormycosis association with clinical status

The Frequency of HAMP Genotypes in COVID-19 Patients with and without Mucormycosis

The sequencing diagrams of the polymorphisms are shown in figure 1. Based on the HWE test, the genotype frequency distribution of the control and case individuals followed the Hardy-Weinberg equilibrium for the studied polymorphisms (table 3).

Figure 1. The sequencing diagrams of identified polymorphisms is shown in A to J. Diagrams A, B, and C show SNP582 C>G; diagrams D and E show SNP443 C>T; diagram F shows SNP153 C>T; diagrams G and H show SNP442 G>A, and diagrams I and J show SNP335 G>T. The red arrows indicate the detected polymorphisms. All diagrams were drawn by Chromas 2.6 software.

SNP582 A>G
Group AA AG GG Total 95% CI P value HWE
n (%) n (%) n (%) P value
MUCOR 28 (57.10) 19 (38.80) 2 (4.10) 49 (100) 0.807 -0.821 0.771 0.58
COVID 32 (57.10) 23 (41.10) 1 (1.80) 56 (100) 0.17
Total 60 (57.1) 42 (40) 3 (2.9) 105 (100) 0.17
SNP443 C>T
Group CC CT Total 95% CI P value HWE
n (%) n (%) P value
MUCOR 43 (87.8) 6 (12.2) 49 (100) 0.724-19.611 0.90 0.21
COVID 54 (96.4) 2 (3.6) 56 (100) 0.89
Total 97 (92.1) 8 (7.9) 105 (100) 0.68
SNP442 G>A
Group GG GA Total 95% CI P value HWE
n (%) n (%) P value
MUCOR 49 (100) 0 (0) 49 (100) 0.983-1.055 0.261 -
COVID 55 (98.2) 1 (1.8) 56 (100) 0.94
Total 104 (99.1) 1 (0.9) 105 (100) 0.96
SNP335 G>T
Group GG GT Total 95% CI P value HWE
n (%) n (%) P value
MUCOR 49 (100) 0 (0) 49 (100) 0.993-1.125 0.05* -
COVID 53 (94.6) 3 (5.4) 56 (100) 0.83
Total 102 (97.3) 3 (2.7) 105 (100) 0.88
**SNP 153C>T
Group CC P value HWE
n (%) P value
MUCOR 49 (100) - -
- -
COVID 56 (100)
* P value is significant at P<0.05. All Ps were calculated by t test. **Computed only for a 2×2 table; 95 % confidence intervals (95% CI), and P values for the association between SNPs and mucormycosis risk; HWE: Hardy-Weinberg
Table 3.Frequency of genotypes in COVID-19 patients with and without mucormycosis performed by HWE analysis

Afterward, the frequency of hepcidin gene polymorphisms in COVID-19 patients with and without mucormycosis was studied (table 3). Significant differences were observed in the frequency of SNP335 G>T in COVID-19 patients with mucormycosis compared to those without mucormycosis. Some COVID-19 patients without mucormycosis had the T allele, which seems to reduce the risk of mucormycosis. However, since there was no T allele in COVID-19 patients with mucormycosis, we could not compute OR value. Indeed, none of our COVID-19 patients with mucormycosis were GT in terms of SNP335; while three COVID-19 patients without mucormycosis were GT. There were no significant differences between the studied polymorphisms of SNP582 A>G, SNP443 C>T, SNP442 G>A, and the risk of mucormycosis. Since all included patients were CC regarding SNP 153C>T, the related statistical analysis was not performed. Although the SNP442 GA genotype was found in one of our COVID-19 patients without mucormycosis, none of our COVID-19 patients with mucormycosis had this genotype.

The Frequency of Alleles in COVID-19 Patients with and without Mucormycosis

The allele frequency of studied SNPs in COVID-19 patients with and without mucormycosis was also studied here (table 4). There were no significant differences in the allele frequency of SNP582 and SNP443 in COVID-19 patients with and without mucormycosis. The related statistical analyses for SNP442, SNP335, and SNP153 could not be performed due to the limited number of cases, and the analysis could only be computed for the 2×2 table.

SNP 582 A>G
Group A n (%) G n (%) OR 95% CI P value
MUCOR 75 (78.1) 21(21.9) 1.01 0.524 -1.968 0.97
COVID 87 (78.4) 24 (21.6) 1
SNP 443 C>T
Group C n (%) T n (%) OR 95% CI P value
MUCOR 92 (93.9) 6 (6.1) 3.43 0.707-18.2 0.1
COVID 110 (98.2) 2(1.8) 1
SNP 442 G>A
Group G n (%) A n (%) OR* 95% CI P value
MUCOR 98 (100) 0 (0) - - -
COVID 111 (99.1) 1 (0.9)
SNP 335 G>T
Group G n (%) T n (%) OR* 95% CI P value
MUCOR 98 (100) 0 (0) - - -
COVID 109 (97.3) 3 (2.7)
SNP 153 C>T
Group C n (%) OR* 95% CI P value
MUCOR 98 (100) - - -
COVID 112 (100)
*Computed only for a 2×2 table; Estimated relative risks with odds ratios (OR)
Table 4.Allele frequency in COVID-19 patients with and without mucormycosis

The Association Between SNP582 A>G with the Risk of Mucormycosis and the Clinical and Laboratory Findings of COVID-19 Patients with Mucormycosis

Since SNP582 A>G is the well-studied pleomorphism of the hepcidin gene, the association between this polymorphism and the clinical and laboratory findings of COVID-19 patients with mucormycosis was investigated. There was a trend in which older patients were AA and younger patients were AG+GG in terms of SNP582 A>G. In our patients, there were no significant differences between the studied clinical and laboratory variables with this polymorphism (table 5).

Clinical and laboratory characteristics Groups AA AG+GG Total P value Ref
n (%) n (%) OR (95% CI)
Age <60 27 (49.1) 28 (50.9) 55 0.072 1
0.48 (0.22-1.07)
60 32(66.7) 16 (33.3) 48
Sex Female 38 (63.3) 22 (36.7) 60 0.139 1
1.8 (0.82-3.96)
Male 22 (48.9) 23 (51.1) 45
Marital status Single 6 (60) 4 (40) 10 0.792 1
1.2 (0.31-0.47)
Married 35 (55.6) 28 (44.4) 63
PMH No 9 (64.3) 5 (35.7) 14 0.842 1
1.13 (0.34-3.82)
Yes 35 (61.4) 22 (38.6) 57
DM No 19 (61.3) 12 (38.7) 31 0.983 1
0.99 (0.38-2.61)
Yes 24(61.5) 15 (38.5) 39
Involved site Rhinosinusitis 7 (70) 3 (30) 10 0.465 1
1.94 (0.32-11.8)
Rhino-orbital-cerebral 6 (54.5) 5 (45.5) 11
WBC Normal 49 (59) 34 (41) 83 0.446 1
0.28 (0.11-1.2)
Leukocytosis 11 (50) 11 (50) 22
Hb Normal 34 (63) 20 (37) 54 0.215 1
1.64 (0.75-3.56)
Anemia 26 (51) 25 (49) 51
Plt Normal 50 (58.1) 36 (49.9) 86 0.661 1
1.25 (0.46-3.39)
Thrombocytopenia 10 (52.6) 9 (47.4) 19
Cr Normal 40 (56.3) 31 (43.7) 71 0.888 1
1.06 (0.46-2.48)
High 17 (54.8) 14 (45.2) 31
LDH Normal 19 (51.4) 18 (48.6) 37 0.194 1
0.53 (0.20-1.39)
High 22 (66.7) 11 (33.3) 33
LFT Normal 29 (65.9) 15 (34.1) 44 0.95 1
0.98 (0.34-2.77)
High 16 (66.7) 8 (33.3) 24
ESR Normal 10 (62.5) 6 (37.5) 16 0.306 1
1.91 (0.55-6.59)
High 14 (46.7) 16 (53.3) 30
CRP Normal 7 (62.5) 8 (37.5) 15 0.328 1
0.57 (0.18-1.78)
High 34 (46.7) 22 (53.3) 56
Estimated relative risks with odds ratios (OR) and 95 % confidence intervals (95 % CI) and P values for the association between SNP582 A>G and mucormycosis risk. The P value is significant at P<0.05. All Ps were calculated by t test. PMH: Past medical history, comorbidities other than DM including hypertension, hyperlipidemia, cerebrovascular accident, ischemic heart disease, chronic renal failure, hyperthyroidism, and cancer; DM: Diabetes mellitus; Ref: Reference line
Table 5.Clinical and laboratory characteristics of patients and estimated P values for genotype–disease associations by clinical and laboratory status

Discussion

The results showed that increased levels of urea, AST, LDH, and PMN to lymphocytes were associated with a decreased risk of COVID-19-associated mucormycosis in COVID-19 patients. Furthermore, diabetes mellitus increased the risk of mucormycosis development in COVID-19 patients. It was found that none of the COVID-19 patients with mucormycosis displayed the SNP442 GA and SNP335 GT genotypes. However, some patients without mucormycosis demonstrated these genotypes. In terms of SNP 443 C>T, the CC genotype is associated with increased LDH levels in COVID-19 patients. Regarding SNP 582 A>G and SNP 443 C>T, COVID-19 patients with AA+CC genotypes had higher levels of LDH.

Mucormycosis is a rare, angio-invasive, opportunistic fungal infection, which has become a worrisome burden for COVID-19 patients following corticosteroid therapy. R. oryzae is the well-established causative fungus of mucormycosis in immunocompromised patients, including patients with diabetes mellitus. Hyperglycemia substantially impairs the function of immune cells, and low pH decreases the binding of iron to transferrin, shifting iron for fungal metabolism and the development of COVID-19-associated mucormycosis. 19 Iron is reported to be vital for the development of mucormycosis, and iron starvation activates programmed cell death in R. oryzae. 20 In line with this, it was reported that iron depletion can be an effective strategy for mucormycosis treatment. 21 Additionally, it was shown that iron chelation has a considerable protection effect against R. oryzae and improves the survival of diabetic ketoacidosis animal models. 22 Therefore, iron metabolism has a pivotal role in the development of COVID-19-associated mucormycosis. Regarding other clinical and laboratory factors, it was found that LDH, AST, and segmentation were positively associated with this condition, while increased levels of urea and lymphocytes were negatively associated with mucormycosis in patients with COVID-19. A positive association between ICU hospitalization and increased levels of LDH in COVID-19 patients with mucormycosis was shown. 23 Consistent with previous findings, 24 diabetes mellitus significantly increased the risk of mucormycosis development in our COVID-19 patients. Our results indicated that elevated levels of urea, AST, LDH, and increased ratio of PMN to lymphocytes were associated with decreased risk of COVID-19-associated mucormycosis in COVID-19 patients.

Hepcidin is a major regulator of iron homeostasis, which is produced in hepatocytes. Hepcidin expression is regulated according to the level of iron in a negative feedback loop manner. 25 In infections and inflammations, hepcidin expression is increased to modulate inflammation and decrease the available iron for pathogens. In COVID-19 patients and patients with septic shock, the serum level of hepcidin is substantially elevated compared to healthy individuals. 26 A recent meta-analysis study indicated that severe COVID-19 patients have higher hepcidin levels than those with non-severe conditions. 27 Aside from regulating iron homeostasis, hepcidin increases inflammation via the IL-6 effect. 10 Moreover, Rhizopus arrhizus var. delemar can express peptides that regulate hepcidin expression in blood-derived monocytic macrophages. 12 In addition to the association of hepcidin with inflammation, increased levels of hepcidin decrease iron availability for mucormycosis development. Therefore, the role of hepcidin gene polymorphisms in the context of COVID-19-associated mucormycosis was investigated in this study.

Polymorphisms are genetic variations that can interact with epigenetic factors, such as DNA methylations, microRNAs, and transcription factors, and modulate gene expression. 28

The SNP 443 C>T, SNP 442 G>A, SNP 335 G>T, and SNP 153 C>T are the polymorphisms located in the promotor of the HAMP gene. SNP582 A>G, one of the well-studied polymorphisms of HAMP, in the presence of G allele decreases hepcidin expression. 16 Regarding the SNP582 A>G, Liang and colleagues found that CD14+ monocytes with the GG genotype had decreased hepcidin expression compared to cells with at least one A allele. 15 Our results showed that none of the COVID-19 patients with mucormycosis display the SNP442 GA and SNP335 GT genotypes. However, some patients without mucormycosis demonstrate these genotypes. In terms of clinical and laboratory findings, SNP443 C>T (rs117345431) was associated with LDH levels in COVID-19 patients, and the CC genotype was significantly associated with higher levels of LDH in the affected patients (figure 2). Regarding the rs10421768 and rs117345431 polymorphisms, COVID-19 patients with AA+CC genotypes have higher levels of LDH and patients with AG+CT genotypes have normal levels of LDH (figure 3A). Moreover, it is worth mentioning that there was a trend toward significance for sex (figure 3B).

Figure 2. Association of SNP 443 C>T with LDH levels. As indicated in the chart, the CC genotype was significantly associated with higher levels of LDH in the affected patients.

Figure 3. Association of the combination effect of SNP582 A>G and SNP 443 C>T on LDH levels and sex. Figure 3A shows that COVID-19 patients with AA+CC genotypes had higher levels of LDH, and the patients with AG+CT genotypes had normal levels of LDH. Figure 3B depicts that there is a trend toward significance for males with AA+CC genotypes.

The current study has several strengths. Given the significance of hepcidin and iron metabolism in infection, inflammation, and COVID-19-associated mucormycosis development, it is the first study to investigate the significance of hepcidin polymorphisms in COVID-19-associated mucormycosis. Second, we thoroughly investigated included patients’ clinical and laboratory features and medical history to study the potential risk factors in COVID-19-associated mucormycosis. However, the current study has several limitations as well. First, this study is retrospective. Moreover, we were unable to investigate the prognostic values of hepcidin polymorphism in affected patients.

Overall, the current study provides valuable insights into the role of hepcidin polymorphisms in developing COVID-19-associated mucormycosis.

Conclusion

Unlike COVID-19 patients without mucormycosis, none of the COVID-19 patients with mucormycosis had the SNP442 GA and SNP335 GT genotypes. Regarding SNP 443 C>T, the CC genotype was associated with increased LDH levels in COVID-19 patients. In terms of SNP 582 A>G and SNP 443 C>T, COVID-19 patients with AA+CC genotypes had higher levels of LDH. Regarding the clinical and laboratory results, increased levels of urea, AST, LDH, and PMN to lymphocytes were associated with a decreased risk of COVID-19-associated mucormycosis in COVID-19 patients. Besides, diabetes mellitus increased the risk of mucormycosis development in COVID-19 patients.

Acknowledgment

The present work was financially supported by the Infectious and Tropical Diseases Research Center affiliated with Tabriz University of Medical Sciences (Grant number: 69240). The Authors would like to thank the Infectious and Tropical Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran, for their financial support and the Clinical Research Development Unit of Sina Educational, Research and Treatment Center, Tabriz University of Medical Sciences, Tabriz, Iran for their assistance. This work is derived from the thesis of Dr. Yalda Farhand.

Authors’ Contribution

R.R, Y.F, F.R.G: conception and design of the work; Y.F, F.R.G: the acquisition of Data; R.R, F.R.G: analysis and interpretation of data; Y.F, F.R.G: drafting the work; R.R, F.R.G: reviewing it critically for important intellectual content. All authors have read and approved the final version of the manuscript and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Conflict of Interest:

None declared.

References

  1. Boni MF, Lemey P, Jiang X, Lam TT, Perry BW, Castoe TA, et al. Evolutionary origins of the SARS-CoV-2 sarbecovirus lineage responsible for the COVID-19 pandemic. Nat Microbiol. 2020; 5:1408-17. DOI | PubMed
  2. Rana R, Tripathi A, Kumar N, Ganguly NK. A Comprehensive Overview on COVID-19: Future Perspectives. Front Cell Infect Microbiol. 2021; 11:744903. Publisher Full Text | DOI | PubMed
  3. Derakhshani A, Hemmat N, Asadzadeh Z, Ghaseminia M, Shadbad MA, Jadideslam G, et al. Arginase 1 (Arg1) as an Up-Regulated Gene in COVID-19 Patients: A Promising Marker in COVID-19 Immunopathy. J Clin Med. 2021; 10Publisher Full Text | DOI | PubMed
  4. Moore JB, June CH. Cytokine release syndrome in severe COVID-19. Science. 2020; 368:473-4. DOI | PubMed
  5. Gupta I, Baranwal P, Singh G, Gupta V. Mucormycosis, past and present: a comprehensive review. Future Microbiol. 2023; 18:217-34. DOI | PubMed
  6. Sharma R, Kumar P, Rauf A, Chaudhary A, Prajapati PK, Emran TB, et al. Mucormycosis in the COVID-19 Environment: A Multifaceted Complication. Front Cell Infect Microbiol. 2022; 12:937481. Publisher Full Text | DOI | PubMed
  7. Cheng L, Li H, Li L, Liu C, Yan S, Chen H, et al. Ferritin in the coronavirus disease 2019 (COVID-19): A systematic review and meta-analysis. J Clin Lab Anal. 2020; 34:e23618. Publisher Full Text | DOI | PubMed
  8. Nemeth E, Ganz T. Hepcidin-Ferroportin Interaction Controls Systemic Iron Homeostasis. Int J Mol Sci. 2021; 22Publisher Full Text | DOI | PubMed
  9. Pagani A, Nai A, Silvestri L, Camaschella C. Hepcidin and Anemia: A Tight Relationship. Front Physiol. 2019; 10:1294. Publisher Full Text | DOI | PubMed
  10. Rodriguez R, Jung CL, Gabayan V, Deng JC, Ganz T, Nemeth E, et al. Hepcidin induction by pathogens and pathogen-derived molecules is strongly dependent on interleukin-6. Infect Immun. 2014; 82:745-52. Publisher Full Text | DOI | PubMed
  11. Ciotti M, Nuccetelli M, Pieri M, Petrangeli CM, Giovannelli A, Cosio T, et al. Evaluation of Hepcidin Level in COVID-19 Patients Admitted to the Intensive Care Unit. Diagnostics (Basel).. 2022; 12Publisher Full Text | DOI | PubMed
  12. Soliman SSM, El-Labbad EM, Abu-Qiyas A, Fayed B, Hamoda AM, Al-Rawi AM, et al. Novel Secreted Peptides From Rhizopus arrhizus var. delemar With Immunomodulatory Effects That Enhance Fungal Pathogenesis. Front Microbiol. 2022; 13:863133. Publisher Full Text | DOI | PubMed
  13. Hashemi SMA, Thijssen M, Hosseini SY, Tabarraei A, Pourkarim MR, Sarvari J. Human gene polymorphisms and their possible impact on the clinical outcome of SARS-CoV-2 infection. Arch Virol. 2021; 166:2089-108. Publisher Full Text | DOI | PubMed
  14. Chiarella P, Capone P, Sisto R. Contribution of Genetic Polymorphisms in Human Health. Int J Environ Res Public Health. 2023; 20Publisher Full Text | DOI | PubMed
  15. Liang L, Liu H, Yue J, Liu LR, Han M, Luo LL, et al. Association of Single-Nucleotide Polymorphism in the Hepcidin Promoter Gene with Susceptibility to Extrapulmonary Tuberculosis. Genet Test Mol Biomarkers. 2017; 21:351-6. DOI | PubMed
  16. Stachowska L, Koziarska D, Karakiewicz B, Kotwas A, Knyszynska A, Folwarski M, et al. Hepcidin (rs10421768), Transferrin (rs3811647, rs1049296) and Transferrin Receptor 2 (rs7385804) Gene Polymorphism Might Be Associated with the Origin of Multiple Sclerosis. Int J Environ Res Public Health. 2022; 19Publisher Full Text | DOI | PubMed
  17. Zarghamian P, Azarkeivan A, Arabkhazaeli A, Mardani A, Shahabi M. Hepcidin gene polymorphisms and iron overload in beta-thalassemia major patients refractory to iron chelating therapy. BMC Med Genet. 2020; 21:75. Publisher Full Text | DOI | PubMed
  18. Wang XY, Yu CX. Research advances on DNA extraction methods from peripheral blood mononuclear cells. Zhongguo Shi Yan Xue Ye Xue Za Zhi. 2014; 22:1495-8. DOI | PubMed
  19. Balushi AA, Ajmi AA, Sinani QA, Menon V, Berieki ZA, Shezawi AA, et al. COVID-19-Associated Mucormycosis: An Opportunistic Fungal Infection. A Case Series and Review. Int J Infect Dis. 2022; 121:203-10. Publisher Full Text | DOI | PubMed
  20. Tabassum T, Araf Y, Moin AT, Rahaman TI, Hosen MJ. COVID-19-associated-mucormycosis: possible role of free iron uptake and immunosuppression. Mol Biol Rep. 2022; 49:747-54. Publisher Full Text | DOI | PubMed
  21. Shirazi F, Kontoyiannis DP, Ibrahim AS. Iron starvation induces apoptosis in Rhizopus oryzae in vitro. Virulence. 2015; 6:121-6. Publisher Full Text | DOI | PubMed
  22. Mahalmani V, Sarma P, Prakash A, Medhi B. Role of Iron Chelators in Mucormycosis. Indian J Pharmacol. 2021; 53:261-3. Publisher Full Text | DOI | PubMed
  23. Shahcheraghi SH, Hamidfar M, Heidari F, Shahmohammadi S, Akhondimeybodi Z, Fallah T, et al. Evaluation of ESR, CRP, LDH, CPK, D-Dimer, BS and FBS levels in hospitalized COVID-19 patients with mucormycosis. Health Biotechnology and Biopharma (HBB).. 2022; 6:43-51.
  24. Kumar HM, Sharma P, Rudramurthy SM, Sehgal IS, Prasad KT, Pannu AK, et al. Serum iron indices in COVID-19-associated mucormycosis: A case-control study. Mycoses. 2022; 65:120-7. Publisher Full Text | DOI | PubMed
  25. Liu J, Sun B, Yin H, Liu S. Hepcidin: A Promising Therapeutic Target for Iron Disorders: A Systematic Review. Medicine (Baltimore).. 2016; 95:e3150. Publisher Full Text | DOI | PubMed
  26. Hortova-Kohoutkova M, Skotakova M, Onyango IG, Slezakova M, Panovsky R, Opatril L, et al. Hepcidin and ferritin levels as markers of immune cell activation during septic shock, severe COVID-19 and sterile inflammation. Front Immunol. 2023; 14:1110540. Publisher Full Text | DOI | PubMed
  27. Peng D, Gao Y, Zhang L, Liu Z, Wang H, Liu Y. The Relationship Between Hepcidin-Mediated Iron Dysmetabolism and COVID-19 Severity: A Meta-Analysis. Front Public Health. 2022; 10:881412. Publisher Full Text | DOI | PubMed
  28. Vohra M, Sharma AR, Prabhu BN, Rai PS. SNPs in Sites for DNA Methylation, Transcription Factor Binding, and miRNA Targets Leading to Allele-Specific Gene Expression and Contributing to Complex Disease Risk: A Systematic Review. Public Health Genomics. 2020; 23:155-70. DOI | PubMed