Association of the Interleukin 1B -31*C Proinflammatory Allele with the Severity of COVID-19 Patients: A Preliminary Report

Abstract

Individuals with no known comorbidities or risk factors may develop severe coronavirus disease 2019 (COVID-19). The present study assessed the effect of certain host polymorphisms and viral lineage on the severity of COVID-19 among hospitalized patients with no known comorbidities in Mexico. The analysis included 117 unrelated hospitalized patients with COVID-19. Patients were stratified by whether they required intensive care unit (ICU) admission: the ICU group (n = 40) and non-ICU group (n = 77). COVID-19 was diagnosed on the basis of a positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reverse transcription-polymerase chain reaction (RT-PCR) assay and clinical and radiographic criteria. The presence of the IL1B-31 (T/C) polymorphism was determined for all patients using PCR and nucleotide sequencing. Genotyping of the IL-4 (−590, T/C) and IL-8 (−251, T/A) polymorphisms was performed by the amplification refractory mutation system–PCR method. Genotyping of IL1-RN was performed using PCR. Viral genome sequencing was performed using the ARTIC Network amplicon sequencing protocol using a MinION. Logistic regression analysis identified the carriage of IL-1 B*-31 *C as an independent potential risk factor (odds ratio [OR] = 3.1736, 95% confidence interval [CI] = 1.0748–9.3705, p = 0.0366) for ICU admission and the presence of IL-RN*2 as a protective factor (OR = 0.4371, 95% CI = 0.1935–0.9871, p = 0.0465) against ICU admission. Under the codominant model, the CC genotype of IL1B-31 significantly increased the risk of ICU admission (OR: 6.38, 95% CI: 11.57–25.86, p < 0.024). The IL1B-31 *C—IL-4-590 *T haplotype increased the risk of ICU admission (OR = 2.53, 95% CI = 1.02–6.25, p = 0.047). The 42 SARS-CoV-2 genomes sequenced belonged to four clades, 20A–20D. No association was detected between SARS-CoV-2 clades and ICU admission or death. Thus, in patients with no known comorbidities or risk factors, the IL1B-31*C proinflammatory allele was observed to be associated with the risk of ICU admission owing to COVID-19.

Introduction

The rapid spread of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to a worldwide pandemic (Li et al, 2020). Patients with COVID-19 present a wide spectrum of clinical manifestations, ranging from asymptomatic to critical infections potentially leading to an intensive care unit (ICU) admission and death (Guan et al, 2020). This spectrum of COVID-19 clinical symptoms has been associated with risk factors, such as gender, age, diabetes, chronic pulmonary disorders, renal disease, and cardiovascular disease (Chen et al, 2020; Guan et al, 2020; Hu et al, 2020; Wu et al, 2020; Wu et al., 2019). However, severe disease has also been observed in patients with no known comorbidities.

SARS-CoV-2 infection induces the production of multiple proinflammatory chemokines, including interleukin (IL) 1-B, IL-6, tumor necrosis factor, and the IL-1 receptor antagonist (RA) (Blanco-Melo et al, 2020). In some patients with COVID-19, a dysfunctional response may result in an exacerbated inflammatory response, leading to a cytokine storm manifested clinically by severe acute respiratory distress syndrome (ARDS) and organ failure (Chen et al, 2020; Clay et al, 2012; Guan et al, 2020; Panigrahy et al, 2020; Prompetchara et al, 2020; Tay et al, 2020; Tufan et al, 2020; Wang et al, 2020a; Yang et al, 2020).

The IL-1 gene cluster contains three related genes (IL-1A, IL-1B, and IL-1 RN) that encode the proinflammatory cytokines 1L-1α and IL-1β, and IL-1RA, respectively. IL-1RA is a naturally occurring RA that acts as an inhibitor of IL-1 receptor signaling (El-Omar et al, 2000).

The most studied polymorphisms in the IL-1B gene are rs1143634 (+3954), rs1143627 (−31 T>C), and rs16944 (−511 C>T). The −31 T > C polymorphism disrupts a TATA box with the *C allele (Khazim et al, 2018). A region of the IL-1 RN gene in the same gene cluster contains a variable number of 86 bp tandem repeats; five alleles have been identified. Alleles 1, 2, 3, 4, and 5 exhibits four, two, five, three, and six 86 bp tandem repeats, respectively. Allele 2 has been associated with increased inflammation (Machado et al, 2001). IL-1 and IL-1 RN polymorphisms have also been linked to several malignant tumors (El-Omar et al, 2000; Wang et al, 2003; Yamamoto-Furusho et al, 2011; Zienolddiny et al, 2004). Other single-nucleotide polymorphisms (SNPs) in cytokine genes have been shown to influence the immune response; for example, rs4073 (T/A at the −251) in the IL-8 gene has been associated with higher IL-8 production (Hull et al, 2001), and a promoter polymorphism rs2243250 (T/C at the −590 position) in the IL-4 gene has been associated with a significant decrease in IL-4 levels (Moreno et al, 2007).

Regarding SARS-CoV-2 genetic variability, it has been suggested that certain mutations are associated with higher mortality, for example, the spike mutation D614G and the mutations nsp7 L71F, spike V1176F, and ORF3a P25L (Becerra-Flores and Cardozo, 2020; Eaaswarkhanth et al, 2020; Toyoshima et al, 2020) and that the presence of these mutations explains the severity of the disease. However, studies with a higher number of patients in diverse countries are necessary to confirm these findings.

The present study aimed to assess the effects of certain host polymorphisms and the viral lineage on the need for ICU admissions among hospitalized Mexican patients with COVID-19 with no known comorbidities.

Materials and Methods

Study site and patient population

Patients were recruited at the COVID-19 unit at the Hospital Universitario “Dr. José Eleuterio González” in Nuevo Leon, Mexico. Patients were examined by the medical staff in charge of the COVID-19 unit, including a group of infectious diseases specialists, pulmonologists, and internal medicine physicians. For this evaluation, clinical, laboratory, and image data were used. The diagnosis of COVID-19 was based on a positive SARS-CoV-2 polymerase chain reaction (PCR) result and clinical criteria.

The present study included hospitalized, nonpregnant patients with a diagnosis of COVID-19. Patients with some comorbidities or risk factors were excluded, including obesity, diabetes, hypertension, asthma, chronic obstructive pulmonary disease, cancer, chronic renal disease, hepatic disease, cardiac disease or rheumatic disease, hypothyroidism, cerebral vascular disease, infection by human immunodeficiency virus, substance use disorders, smoking, or alcohol abuse.

Patients were transferred to an ICU if they required oxygen therapy and continuous monitoring of vital parameters (at least oxygen saturation, blood pressure, heart rate, and respiratory rate) and had ARDS or worsening organ dysfunction (Swiss Society of Intensive Care Medicine, 2020).

The analysis included 117 unrelated patients with COVID-19 (median age 47 years) hospitalized between April 14 and August 13, 2020. Patients were stratified by whether they required ICU admission: the ICU group (n = 40) and the non-ICU group (n = 77).

Informed consent was obtained from the patients. The study was approved by the Ethics and Research Committee of the School of Medicine, Universidad Autónoma de Nuevo Leon (number: BI20–0004).

Determination of blood parameters

Determination of blood parameters was performed using the UniCel DxC 800 Synchron instrument (Beckman Coulter, Inc., CA) and CELL-DYN Ruby (Abbott, IL) according to the manufacturer's instructions.

Extraction of nucleic acids

Nucleic acids (RNA and DNA) were extracted from a 250 μL nasopharyngeal swab suspension in universal transport medium. Nucleic acids were extracted using the Microlab NIMBUS IVD system (Hamilton, Reno, NV) with the STARMag96 Virus Kit (Seegene, Seoul, Korea) or with the MagNA Pure LC instrument using the MagNA Pure LC Total Nucleic Acid Isolation Kit (Roche Diagnostics, Mannheim, Germany) according to the manufacturer's instructions.

SARS-CoV-2 PCR

The COVID-19-positive status was determined using real-time reverse transcription-PCR using the Logix Smart COVID-19 Test Kit (Co-Diagnostics, Inc., Salt Lake City, UT) and a Bio-Rad CFX instrument (Bio-Rad, Hercules, CA).

Genotyping of IL-1 B (−31 T/C), IL4 (−590 C/T), IL-8 (−251 A/T), and IL-1 RN polymorphisms

The IL-1 B-31 (T/C) polymorphism was determined by nucleotide sequencing of a specific region using genomic DNA from studied subjects, as described previously^13–16. PCR products were purified by precipitation with 3 M sodium acetate and absolute ethanol at −20°C and sequenced at Macrogen, Korea. Sequencing reactions were performed in the DNA Engine Tetrad 2 Peltier thermal cycler (Bio-Rad) using the ABI BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems). The samples were injected into electrophoresis channels in an ABI 3730xl DNA analyzer (Applied Biosystems), and the sequences were analyzed on the Bio Edit platform (Biological Sequence Alignment Editor Platform). Genotyping of the IL-4-590 and IL-8-251 polymorphisms was performed using the amplification refractory mutation system–PCR method as described previously (Hull et al, 2001; Howell et al, 2003). The variable number of tandem repeats polymorphism of IL-1 RN was assessed by PCR as previously described (El-Omar et al, 2000).

Whole-genome sequencing of SARS-CoV-2

We used the improved ARTIC multiplex PCR method for SARS-CoV-2 genome sequencing using nanopore technology (Tyson et al, 2020). In brief, cDNA was synthesized with LunaScript RT (New England Biolabs, Ipswich, MA) and subsequently used in multiplex PCR using Q5 Hot Start HF polymerase (New England Biolabs). The cDNA Ends were repaired with NEBNext Ultra II end repair/dA-tailing module (New England Biolabs). Native barcodes (Oxford Nanopore Technologies, Oxford, UK) were added to the repaired amplicons using Blunt/TA ligase (New England Biolabs) and subsequently washed with AMPure XP beads (Beckman Coulter, Brea, CA). Adapters (Oxford Nanopore Technologies) were added to the NEBNext quick ligation module (New England Biolabs). The final library was sequenced in a MinION R9.4.1 flow cell (Oxford Nanopore Technologies) for 6 h. Sequences were assembled using the ARTIC network bioinformatics pipeline using Nanopolish and the reference sequence MN908947.3 to obtain the consensus sequence.

Phylogenetic analysis

We used Nextstrain's bioinformatic processing workflow for our analysis (Hadfield et al, 2018). We selected global reference sequences from the global dataset (n = 1,092) from Nextstrain and retrieved them from the Global Initiative for Sharing All Influenza Data (GISAID) database. The final sequence dataset was composed of 1,134 sequences (1,092 from the global dataset and 42 generated in this study). Phylogenetic trees were visualized with ggtree 8 (Wang et al, 2020b; Yu, 2020; Xu et al, 2021).

The authors of the sequences used are listed in Supplementary Table S1. The sequences generated were deposited in GISAID (EPI_ISL_779173, EPI_ISL_779175, EPI_ISL_961760–EPI_ISL_961783, EPI_ISL_961785, EPI_ISL_961786, EPI_ISL_961788–EPI_ISL_961800, and EPI_ISL_965222).

Statistical analyses

The Hardy–Weinberg equilibrium of alleles at individual loci in non-ICU patients was assessed by chi-square statistics. The frequency of each cytokine SNP genotype and allele was calculated and compared between the patient groups (ICU vs. non-ICU) using 2 × 2 tables. Differences between groups were determined by Student's t-test, chi-square, or Fisher's exact test (two-tailed). When appropriate, odds ratios (ORs) with 95% confidence intervals (CIs) were computed. Logistic regression analysis and the Bonferroni method for multiple comparisons were used in the analysis. Statistical analyses were performed using SPSS version 10 (IBM Corp., Armonk, NY).

Associations between SNPs and ICU stay were analyzed under four models of inheritance (codominant, dominant, recessive, overdominant) using the online SNPStats program (Solé et al, 2006). The best model of inheritance for each SNP was selected on the basis of the Akaike information criterion (AIC). A two-sided test with a value <0.05 was considered statistically significant.

Results

Study population characteristics

The demographic, clinical, and laboratory data of the patients included in this study are presented in Table 1. In general, patients admitted to the ICU were older than non-ICU patients (p = 0.015). The distribution of gender was similar (p = 0.523) in both groups, and ICU patients were more frequently intubated. Of all patients, 21 died (17, ICU; 4, non-ICU). All patients in the ICU had ARDS.

Table 1.

Demographics, Blood Parameters, and Clinical Outcomes of Intensive Care Unit (ICU) and Non-ICU Hospitalized Patients with Coronavirus Disease 2019^a

Variable^b	All (n = 117)	ICU (n = 40)	Non-ICU (n = 77)	p
Age (year)	46.0 ± 12.62	50.13 ± 13.54	43.86 ± 11.63	0.015
Age (year): median (IQR)	47 (37–54)	50.0 (42–60)	45 (35.5–51)
Female, n (%)	35 (29.9)	10 (25)	25 (32.5)	0.523
Endotracheal intubation, n (%)	25 (21.4)	25 (62.5)	0 (0.0)	<0.001
Blood parameters
WBC count ( × 10⁹/L)	10.01 ± 5.66	11.15 ± 6.69	9.41 ± 5.00	<0.001
Lymphocyte count ( × 10⁹/L)	1.18 ± 0.84	0.91 ± 0.70	1.33 ± 0.88	<0.001
Hemoglobin (g/L)	12.86 ± 2.69	13.29 ± 2.96	12.62 ± 2.70	<0.001
Platelets ( × 10⁹/L)	232.50 ± 97.05	231.36 ± 96.93	233.09 ± 97.78	<0.001
Creatinine (mg/dL)	0.91 ± 0.55	0.99 ± 0.23	0.87 ± 0.66	<0.001
Blood urea nitrogen (mg/dL)	17.41 ± 18.36	21.48 ± 24.64	15.24 ± 13.62	<0.001
Aspartate aminotransferase (U/L)	67.34 ± 104.65	56.03 ± 39.54	73.63 ± 127.09	<0.001
Alanine aminotransferase (U/L)	57.46 ± 91.38	42.40 ± 32.72	65.83 ± 110.76	<0.001
Alkaline phosphatase (U/L)	96.36 ± 85.51	80.00 ± 55.98	105.44 ± 97.34	<0.001
Death, n (%)	21 (17.94)	17 (42.5)	4 (5.2)	<0.001

Patients had none of the following comorbidities/risk factors: obesity, diabetes mellitus, arterial hypertension, asthma, chronic obstructive pulmonary disease, cancer, renal disease, hepatic disease, cardiac disease, rheumatic disease, hypothyroidism, cerebral vascular disease, infection by human immunodeficiency virus 1–2, drug abuse, tobacco smoking, or alcohol consumption.

Data are expressed as mean ± standard deviation unless stated otherwise. Data were collected upon hospital admission of patients.

ICU, intensive care unit; IQR, interquartile range; WBC, white blood cell.

Genotype distribution

Among non-ICU patients, the genotype frequencies at the individual loci studied were in Hardy–Weinberg equilibrium, with nonsignificant chi-square values (Table 2). A higher proportion of ICU patients had allele *C of IL1B-31 than non-ICU patients (p = 0.0416; OR, 2.927; 95% CI = 1.018–8.415). Moreover, a higher proportion of ICU patients had allele *2 of IL1RN than non-ICU patients, but this difference was not statistically significant (p = 0.0828). These p-values were not significant after using Bonferroni correction.

Table 2.

Genotype and Allele Frequencies Observed for Analyzed Polymorphisms

		Genotype frequency			Allele frequency		Hardy–Weinberg	p-value (OR, 95% CI)
		CC, n (%)	CT, n (%)	TT, n (%)	C	T	Hardy–Weinberg	p-value (OR, 95% CI)
IL1B-31	ICU	12 (30)	23 (57.5)	5 (12.5	0.59	0.41		^* C = 0.0416 (2.927, 1.018–8.415)CC = 0.0538
IL1B-31	Non-ICU	11 (14.3)	43 (55.8)	23 (29.9)	0.42	0.58	0.204

		CC	CT	TT	C	T
IL4	ICU	15 (37.5)	20 (50.0)	5 (12.5)	0.63	0.38		TT = 1
IL4	Non-ICU	29 (37.7)	37 (48.1)	11 (14.3)	0.62	0.38	0.88

		AA	AT	TT	A	T
IL8	ICU	2 (5)	8 (20.0)	30 (75.0)	0.15	0.85		^* A = 0.2981
IL8	Non-ICU	1 (1.3)	27 (35.1)	49 (63.6)	0.19	0.81	0.197

		1,1	1,2	2,2
IL1RN ^a	ICU	23 (59.0)	14 (35.9)	2 (5.1)				^*2 = 0.0828
IL1RN ^a	Non-ICU	28 (37.8)	34 (45.9)	12 (16.2)

One ICU patient was genotype 2 and 3; one non-ICU patient, 1 and 4; and two non-ICU patients, 1 and 3.

The asterisk indicates the allele: C allele, A allele, two copies allele.

CI, confidence interval; IL, interleukin.

Logistic regression analysis

Logistic regression analysis was performed using ICU admission as a dependent variable and the carriage of the IL-1 B-31 *C, IL1-RN*2, IL-4-590 TT, and IL-8-251 *A alleles as independent variables. Logistic regression analysis identified the carriage of IL-1 B*-31 *C as an independent potential risk factor (OR = 3.1736, 95% CI = 1.0748–9.3705, p = 0.0366) for ICU admission and the presence of IL-RN*2 as a protective factor (OR = 0.4371, 95% CI = 0.1935–0.9871, p = 0.0465) against ICU admission (Table 3).

Table 3.

Logistic Regression Analysis Using Intensive Care Unit Admission as an Independent Variable and the Carriage of the IL-1 B-31 ^*C, IL1-RN ^*2, IL-4-590 TT, and IL-8-251 ^*A Markers as Dependent Variables

Marker	p	OR	95% CI
IL1–B31 ^*C	0.0366	3.1736	1.0748–9.3705
IL-RN ^*2	0.0465	0.4371	0.1935–0.9871
IL-8-251 ^*A	0.1400	0.5102	0.2087–1.2471
IL4 TT	0.6412	0.7562	0.2335–2.4489

OR, odds ratio.

According to the sample size for ICU patients (n = 40) and non-ICU patients (n = 77), with a proportion of the ILI-1 B*C of 0.875 in ICU patients (35/40) and of 0.7012 in non-ICU patients (54/77; type I error rate, a, 5%), the statistical power was 0.6539.

Associations between IL1B-31 and ICU admission under four models of inheritance and haplotype association with response

We used four models of inheritance—codominant, dominant, recessive, and overdominant—to evaluate associations between IL1B-31 polymorphisms and risk of ICU admission. On the basis of the AIC value, the codominant model was determined to be the best model for all three SNPs in the analysis of associations between the SNPs and risk of ICU admission. Under the codominant model, the CC genotype of IL1B-31 significantly increased the risk of ICU admission (OR: 6.38, 95% CI: 11.57–25.86, p < 0.024; Table 4).

Table 4.

Associations Between Interleukin1B-31 Genotypes and Intensive Care Unit Admission Under Four Models of Inheritance (Codominant, Dominant, Recessive, and Overdominant)

Model	Genotype	Non-ICU, n (%)	ICU, n (%)	OR (95% CI)	p	AIC
Codominant	T/T	23 (29.9)	5 (12.5)	1.00	0.024	148.1
	C/T	43 (55.8)	23 (57.5)	2.70 (0.83–8.71)
	C/C	11 (14.3)	12 (30)	6.38 (1.57–25.86)
Dominant	T/T	23 (29.9)	5 (12.5)	1.00	0.026	148.6
Dominant	C/T-C/C	54 (70.1)	35 (87.5)	3.32 (1.07–10.31)	0.026	148.6
Recessive	T/T-C/T	66 (85.7)	28 (70)	1.00	0.035	149.1
Recessive	C/C	11 (14.3)	12 (30)	3.03 (1.08–8.54)	0.035	149.1
Overdominant	T/T-C/C	34 (44.2)	17 (42.5)	1.00	0.88	153.5
Overdominant	C/T	43 (55.8)	23 (57.5)	1.06 (0.46–2.46)	0.88	153.5

IL1B-31 association with response status (n = 117, adjusted by folio + IL.RN + IL8).

AIC, Akaike information criterion.

We analyzed haplotype associations and determined that the presence of IL1B-31 *C allele and IL4–590 *T increased the risk ICU admission (OR = 2.53, 95% CI = 1.02–6.25, p = 0.047; Table 6). No other significant haplotype was detected (Table 5).

Table 5.

Haplotype Association with Response (n = 117, Adjusted by Folio + IL.RN + IL8)

IL1B-31	IL-4	Frequency	OR (95% CI)	p
T	C	0.343	1.00	—
C	C	0.2766	1.11 (0.35–3.50)	0.86
C	T	0.202	2.53 (1.02– 6.25)	0.047
T	T	0.1783	0.33 (0.08–1.34)	0.12

Global haplotype association p-value: 0.014.

Table 6.

Correlation Between Severe Acute Respiratory Syndrome Coronavirus 2 Clade and Observed Outcomes

Clade	ICU admission, n (%)	p	Number of deaths, n (%)	p
20A	6 (40)	0.75	5 (33)	1.00
20B	9 (45)	1.00	7 (35)	1.00
20C	3 (50)	1.00	3 (50)	0.64
20D	1 (100)	0.45	0 (0)	1.00

SARS-CoV-2 lineages

To determine whether our findings were attributable to infections caused by a specific SARS-CoV-2 lineage, we performed viral genome sequencing on the basis of nasopharyngeal swabs from 42 patients (19 ICU and 23 non-ICU patients) and obtained 42 complete genome sequences. We selected patients with a Ct value <20 in the SARS-CoV-2 PCR to obtain sufficient viral reads.

The sequenced viruses were grouped into four Nextstrain clades (Figure 1A): 48% of the samples were grouped in clade 20B, 36% in clade 20A, and the remaining 16% in clades 20C and 20D. We did not observe SARS-CoV-2 variants of concern or variants of interest. Figure 1B–E shows that viral clade is not associated with death as an outcome. We detected two viruses in clade 20B (Fig. 1, part D) that were detected only in patients with death as the outcome; however, these viruses were identical to viruses from patients without death as the outcome.

FIG. 1.

Phylogenetic relationship of SARS-CoV-2 lineages, 2020–2021. (A) Phylogenetic tree with 1,134 whole-genome sequences of SARS-CoV-2 from 2019 to December 2021. Highlighted genomes represent posterior amplifications. (B) Section of the phylogenetic tree showing clade 20C and a portion of clade 20A. (C) Section of the phylogenetic tree showing samples from clade 20A. (D, E) Sections of the phylogenetic tree showing samples from clade 20B. Light grey circles indicate sequences from Mexico. Sequences from this study are indicated with grey shapes. Triangles indicate patients with death outcomes. Squares indicate patients without death outcomes. The accession numbers of the sequences used in this tree are listed in Supplementary Table S1. SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.

No association was detected either between SARS-CoV-2 clades and ICU admission (p = 0.698) or between SARS-CoV-2 clades and death (p = 0.770). The clades 20A and 20B were analyzed separately to eliminate empty cases. Nevertheless, these clades were not associated with ICU admission (p = 0.750 and p = 1, respectively) or death (p = 1 and p = 1, respectively). The corresponding results are shown in Table 6.

Discussion

The wide spectrum of clinical manifestations in COVID-19 has been associated with risk factors such as diabetes, cardiovascular disease, chronic pulmonary disorders, and chronic renal disease. None of the patients included in the present study had any known comorbidity. Thus, immunogenic factors likely explain the differences in the severity of the disease.

The SARS-CoV-2 infection causes local and systemic inflammation mediated by proinflammatory cytokines, primarily IL-1 (Conti et al, 2020). Thus, the IL1 promoter polymorphism may be associated with the induction of an intense response. In this study, the IL1B-31*C allele was more frequent in patients who required ICU admission than in those who did not. The IL1B-31 *C allele has been associated with a higher expression of IL-1 (12%), as demonstrated using a luciferase reporter assay that included a fragment containing either C or T allele at −31(Lind et al, 2007). Evidence suggests that the −31 T > C variant is a functional SNP at this locus; however, a molecular mechanism has not yet been identified (Khazim et al, 2018).

An exhaustive analysis of the analyzed SNPs in the RegulomeDB and Genotype-Tissue Expression (GTEx) Portal (Boyle et al, 2012) provided the following information. rs1143627 (ILIB-31) is a binding site for several transcription factors (TFs), for example, CCAAT/enhancer-binding protein alpha (CEBPA), CCAAT/enhancer-binding protein beta (CEBPB), and RNA polymerase II subunit A (POLR2A). CEBPA modulates the expression of genes involved in cell cycle regulation. The binding of this TF was observed in BLaER1 and HepG2 cell lines.

CEBPB regulates genes involved in immune and inflammatory responses, and binding of this TF has been observed in several cell lines. Remarkably, the binding of this TF has been reported in two lung cell lines (A549 and IMR-90) and K562, a lymphoblast cell line. Binding of POLR2A—the largest subunit of RNA polymerase II, which is responsible for synthesizing mRNA in eukaryotes—has been reported in various organs (pancreas, spleen, transverse colon, and lung) and numerous cell lines (HeLaS3, HL-60, and MCF 10A). Moreover, rs1143627 has been associated with single-tissue expression quantitative trait loci (eQTLs), such as IL-36 alpha, IL-36 beta, and IL-36 RA, in the esophageal mucosa; IL-1 alpha, IL-RN, IL-36 beta, IL-37, and cyclin-dependent kinase 8 pseudogene 2 in the skin; and IL-1 beta and X-linked inhibitor of apoptosis pseudogene 3 in the testis. CEBPB may be responsible for the dysregulation of immune response, which manifests as severe disease.

There are limited data on rs2243250 (IL4–590). Only two TFs bind at this site: zinc finger protein X-linked (ZFX) and AT-rich interaction domain 1B (ARID1B).

ZFX is a transcriptional regulator for self-renewal of various types of human stem cells. ARID1B is a component of the SWI/SNF chromatin remodeling complex and plays a role in cell cycle activation. The binding of both TFs has been reported in the K562 cell line. rs2243250 is reported to be associated with the following eQTLs: IL4 in the pituitary, testis, and lung; KIF3A in the esophagus, stomach, nerve, artery, colon, adipose subcutaneous tissue, skin, ovary, skeletal muscle, heart, whole blood, and spleen; and SEPT8 in the skin, esophagus, and aorta. KIF3A is involved in protein localization to cell junction and protein transport.

rs4073 (IL8–251) is a binding site for several general TFs such as JUN, CEBPB, FOSL2, JUNB, BCL3, and EP300. JUN, FOSL2, and JUNB are part of TF AP-1 complex. This complex was reported to be a regulator of cell proliferation, differentiation, and transformation and join in A549 and K562 cells. Only two eQTLs have been associated with this SNP: C-X-C motif chemokine ligand 6 in the pancreas, adipose tissue, and nerve and platelet factor 4 variant 1 in whole blood.

IL1RA prevents the biological response to IL-1. Normally, if IL-1 occupies its receptor, proinflammatory events are initiated, but no such events are initiated when IL1RA occupies the receptor (Dinarello, 2000). In the present study, allele *2 was more frequently observed in ICU-admitted patients; although the difference was not significant, the IL-1 and IL-1 RN combination may influence the immune response.

In this study, the role of the host genetic factors and viral genetics was investigated by sequencing viral genomes derived from 42 patients. Previously, several studies have associated the spike mutation D614G with higher mortality (Becerra-Flores and Cardozo, 2020; Eaaswarkhanth et al, 2020; Toyoshima et al, 2020). In the present study, all sequenced viruses contained the D614G spike mutation. Thus, the present findings do not suggest an association between the mutation and mortality. Furthermore, the mutations nsp7 L71F, spike V1176F, and ORF3a P25L have been associated with higher risk of COVID-19 mortality (Hahn et al, 2020; Majumdar and Niyogi, 2020). Furthermore, all sequenced viruses in the present study contained the D614G spike mutation, and none contained the mutations nsp7 L71F, spike V1176F, and ORF3a P25L, regardless of ICU and non-ICU status. Thus, these results suggest that viral lineage did not play a role in ICU admission or death in this population.

Advanced age and male gender are considered risk factors for ICU admission. A previous study analyzed the risk factors for severe outcomes of 2,491 adults hospitalized with COVID-19 using a geographically diverse surveillance network in the USA; a multivariable analysis showed that independent factors associated with ICU admission included ages 50–64, 65–74, 75–84, and ≥85 years versus 18–39 years (adjusted risk ratios [aRRs]: 1.53, 1.65, 1.84, and 1.43, respectively); male sex (aRR, 1.34); obesity (aRR, 1.31); immunosuppression (aRR, 1.29); and diabetes (aRR, 1.13)(Kim et al, 2021).

In the present study, there was a significant difference in blood parameters—including white blood cell count; lymphocyte count; platelet count; and levels of hemoglobin, creatinine, blood urea nitrogen, aspartate aminotransferase, alanine aminotransferase, and alkaline phosphatase—between ICU and non-ICU patients (p-value <0.001).

A previous study in a group of Italian patients with COVID-19 reported that abnormalities in blood parameters such as white blood cell count; lymphocyte count; and hemoglobin, creatinine, blood urea nitrogen, and aspartate aminotransferase levels are correlated with disease severity (Bonetti et al, 2020); these findings support the clinical differences found among the two groups in the present study.

The present study has certain limitations, for example, a small sample size and differences in age between the groups. A larger sample size and the replication of these assays in other populations are essential to verify the association of the IL1B-31*C allele with the risk of ICU admission. In our study, differences in age may have influenced the incidence of ICU admissions; however, in both groups, risk conditions that may be related to age, such as hypertension, diabetes, and obesity, were excluded.

In conclusion, these preliminary findings suggest that in patients with no known comorbidities or risk factors, the IL1B-31*C proinflammatory allele is associated with the risk of ICU admission owing to COVID-19, indicating that further analysis is warranted. Moreover, the findings suggest that viral lineage was not associated with ICU admission or death in the study population and that host genetic factors affect COVID-19 severity.

Footnotes

Acknowledgments

The authors thank Maria de la Luz Acevedo for technical assistance. The GTEx Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The data used for the analyses described in this article were obtained from the GTEx Portal on October 03, 2022.

Authors' Contributions

D.A.-S., R.O.F.-P., S.F.-T., A.C.-O., and E.P.A. performed literature research, and gathered and analyzed information. M.A.Z.-M., K.A.G.-H., S.A.L.-S., L.N.-S., P.B.-I., and D.S.-T. provided research insight, content examination, and supported some aspects of the article development. E.G.-G. and A.M.G.R.-E., completed the conceptual work, framework, final draft write-up, critical reading, and editing. All authors read and approved the final article.

Author Disclosure Statement

No competing financial interests exist.

Funding Information

This work was funded by Consejo Nacional de Ciencia y Tecnología (CONACyT) through a grant (no. 312328) awarded to K.A.G.-H.

Supplementary Material

Supplementary Table S1

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