Abstract
Background:
The host response to SARS-CoV-2 depends on multiple factors including age, gender, underlying diseases and genetic background. Viral sensing and activation of the interferon signaling pathway in the early antiviral response could affect disease outcome. This study evaluated gene expression of Toll-like receptor 7 (TLR7), tyrosine kinase 2 (TYK2) and 2’-5’ oligoadenylate synthetase 1 (OAS1) in two early longitudinal samples from mild, moderate and severe cases.
Methods:
Demographic and clinical variables were obtained from 157 COVID-19 patients. Peripheral blood mononuclear cells were used for genotyping and gene expression determination, and plasma for viremia detection, by qPCR and digital PCR, respectively. Gene expression was analyzed using Generalized Linear Mixed Models nested by patient. First, univariate analyses determined which variables were significantly associated with gene expression. Next, multivariate models were built with these variables, following the backward method.
Results:
TLR7 levels were higher in patients with mild disease compared to those with moderate disease and decreased over time. In contrast, patients with severe disease had lower TLR7 expression, which remained stable. OAS1 behavior was similar, whereas TYK2 remained unchanged. Multivariate analysis confirmed the relation of low TLR7 expression with severity and viremia. OAS1 levels directly correlated with higher viremia. TYK2 was associated with OAS1 expression, and OAS1 with both TLR7 and TYK2 levels.
Conclusions:
Low TLR7 levels were associated with severe COVID-19, and together with OAS1 expression were modulated over time according to severity. While TLR7 expression decreased across all severity groups as the disease resolved, OAS1 expression persisted in severe cases with higher viremia.
1 Introduction
SARS-CoV-2 disease (COVID-19) exhibits a variety of clinical manifestations, ranging from asymptomatic infection to severe forms. While mild cases can experience minor symptoms, moderate cases may present with compromised respiratory systems (1), and severe cases are associated with acute pneumonia and organ failure (2). COVID-19 severity outcome is related to risk factors and preexisting conditions, as well as to treatments received during hospitalization. Males have a higher tendency to develop severe forms of COVID-19 compared to females, and the severity risk increases with age (3, 4), obesity (5), and hypertension (6). Our group and others have shown that relevant SARS-CoV-2 RNA levels in peripheral blood, also known as viremia, are associated with poor disease outcome (7–9). Furthermore, several protective factors help lower the severity risk. For example, glucocorticoid or immunosuppressant treatments in hospitalized patients modulate disease response and critical patient evolution (10, 11).
Virus sensing, signal transduction and antiviral effector functions are essential events in the response to SARS-CoV-2. Among these processes, the proteolytic cleavage of the SARS-CoV-2 Spike (S) protein by serine protease 2 (TMPRSS2) facilitates viral endocytosis through the interaction of S protein with the viral cellular receptor angiotensin-converting enzyme 2 (ACE-2) (12). In turn, host cells recognize the viral single-stranded RNA through the Toll-like endosomal receptor 7 (TLR7), inducing type I and III interferon (IFN-I/-III) production (13–15). IFN-I and -III bind to their receptors (IFNAR and IFNLR1/IL10R2, respectively) and activate a signaling cascade that promotes the expression of antiviral resistance-related genes (16, 17). Among them, genes of the 2’-5’-oligoadenylate synthetase (OAS) family activate endoribonuclease L (RNase L), leading to viral RNA degradation (18).
Genome-wide association studies (GWAS) have identified genetic variants associated with COVID-19 severity. These include deficiencies in the X-linked TLR7 that render men more susceptible to severe COVID-19 (19–23), IFNAR (24–26), tyrosine kinase 2 (TYK2) (27) and OAS1 (28, 29). In addition, single nucleotide polymorphisms (SNPs) contribute to disease severity (30). Previous work of our group has validated severity associated SNPs in TMPRSS2, showing that rs75603675 is a severity predictor whereas rs713400 is associated with viremia and a poor prognosis (31, 32). Also, TLR7 rs3853839-GG, TYK2 rs280500-AG and OAS1 rs1131454-AA are associated with severe disease (33).
In addition to genetic background, gene expression impairment might have negative consequences for the patient’s outcome. Although it is known that changes in several genes are associated with severe COVID-19 (34), their modulation over time remains incompletely understood. To assess the contribution of TLR7, TYK2 and OAS1 expression to COVID-19 progression over time, we conducted a longitudinal study on the expression of these three genes in patients with COVID-19. Most of the published works are based on cross-sectional studies of gene expression with data taken at a single time point, although additional longitudinal research has been conducted on long-COVID patients (35). Here, we present a longitudinal study with samples serially collected in the early stages of the disease, which represents a novel contribution compared to that provided by the previously described studies.
This study highlights the role of TLR7 and OAS1 expression in COVID-19, revealing associations with viral load and severity. TLR7 and OAS1 expression was modulated over time and with severity. Low TLR7 and high OAS1 expression were independently associated with viremia, and TLR7, TYK2 and OAS1 levels were independently associated with one another. These insights contribute to understanding the mechanisms that shape the immune response in COVID-19.
2 Methods
2.1 Study population and design
This work is a secondary analysis of samples from a retrospective observational study where 1350 patients with COVID-19 treated in La Princesa University Hospital between March 2020 and December 2021, before the vaccination campaign started in Spain, were recruited (33). Inclusion criteria were: SARS-CoV-2 infection confirmed by RT-PCR or antigen/serum testing, age older than 18 years and informed consent, plus availability of two sequential peripheral blood samples collected at different times during the course of the disease. Under these conditions, 120 patients of the previous work were selected for the current longitudinal study. In order to increase the population size, 37 additional patients fulfilling identical admission criteria and hospitalized in the University Hospital la Princesa were included. In total, we assessed 157 patients and 314 blood samples collected over 2 longitudinal time-points (median: 6 days [IQR 5–7 days]). A schematic representation of the workflow can be found in Supplementary Figure S1.
2.2 Variables
All the variables collected in this study are listed in Supplementary Table S1, and can be classified in the following main groups: demographic data, comorbidities, medical treatment administered before blood sample collection (48h before extraction, increasing to 7 days for special treatments such as immunosuppressants), laboratory findings for both blood samples, COVID-19 severity and viremia, gene expression (TLR7, TYK2 and OAS1) and SNP determination.
COVID-19 severity was classified at both extraction times as mild, moderate or severe based on a modified version of the 8-point World Health Organization (WHO) Ordinal Scale (WOS) for Clinical Improvement (Supplementary Table S2).
Demographic data, comorbidities, treatment and laboratory findings were collected from medical records and included in a codified database removing all identifiable information (Zenodo accession number: 17639534).
2.3 DNA extraction, genotyping and SNP selection
Total DNA was extracted from 0.5 mL of peripheral blood using MagNA Pure 2.0 and MagNA Pure LC DNA Isolation Kit (Roche Life Science, Basel, Switzerland). Purified DNA concentration was determined with NanoDrop ND-1000 (Thermo Fisher Scientific, Waltham, MA, USA). Of the 157 patients included in this study, 120 patients from the previous cohort were genotyped as described (33). The additional 37 patients were genotyped by qPCR using predesigned SNP Genotyping Taqman™ Assays (Applied Biosystems) on a CFX384 Touch Real-Time PCR System. Allelic discrimination was determined using the software CFX 3.1 Manager (BioRad, Hercules, CA, USA). Both genotyping strategies were carried on duplicate and included negative controls.
A total of 21 SNPs were analyzed in the previous study (33). Herein, we selected those which retained their association with COVID-19 severity in the multivariate model (TLR7 rs3853839, TYK2 rs280500 and rs280519, and OAS1 rs1131454).
2.4 RNA extraction and quantification by RT-PCR
Total RNA was extracted from peripheral blood mononuclear cells (PBMCs) isolated from 10 mL. After erythrocyte lysis, a density gradient was performed using Lymphoprep™ (Cedarlane Laboratories, Burlington, ON, Canada), and cell pellets were resuspended in 1 mL of TRIsure™ (Bioline, London, UK) and processed according to manufacturer’s protocol. A total of 2 µg of cDNA were synthesized using the SuperScript® VILO™ cDNA Synthesis Kit (Thermo Fisher Scientific).
TLR7, TYK2 and OAS1 expression was determined by qPCR using TaqMan™ Fast Advanced Master Mix (Applied Biosystems) on a CFX384 Touch Real-Time PCR System (BioRad), following the manufacturer’s recommendations. Predesigned TaqMan™ assays used were: Hs00152971_m1 (TLR7), Hs00177464_m1 (TYK2), Hs00973635_m1 (OAS1), and Hs02800695_m1 (HPRT) and Hs00427620_m1 (TBP) as housekeeping genes. Each sample was evaluated as a triplicate. Relative mRNA levels were calculated by the 2-ΔCt method, and normalization was carried out using HPRT expression due to heterogeneous TBP levels across severity groups.
2.5 SARS-CoV-2 viral load determination by digital PCR
Viral RNA was extracted in an automated EMAG® platform (BioMérieux, Marcy-l’Étoile, France) from 400 µL of plasma obtained by centrifuging EDTA-treated peripheral blood and eluted in 60 µL.
SARS-CoV-2 viremia determination was performed by QuantStudio Absolute Q digital PCR (Applied Biosystems, Thermo Fisher Scientific, Waltham, MA, USA) using the TaqPath™ COVID-19 CE-IVD RT-PCR Kit (Applied Biosystems), adapted for use with the TaqPath™ one Step digital PCR Kit (Applied Biosystems). Quantification of each gene, ORF, N, and S, was calculated using QuantStudio Absolute Q software. Viral load was determined by the average of the quantification of the three genes amplified.
2.6 Statistical analysis
Statistical analyses were conducted using R version 4.4.0.
Quantitative variables with gaussian distribution were expressed as mean and standard deviation (SD), and those with non-normal distribution as median and interquartile range (IQR). Kruskal-Wallis test was used to analyze significant differences. Qualitative variables were described as total counts with frequencies, and Fisher’s exact test or Pearson’s χ2 test were used to compare categorical variables.
TLR7, TYK2 and OAS1 expressions were analyzed using generalized linear mixed models nested by patient with the AR1 variance-covariance matrix to correct for the temporal autocorrelation of repeated measurements over time (glmmTMB package (36)). The distribution that best fitted the expression values of each gene was the log-normal distribution, as estimated by the AIC criterion (rriskDistributions package (37)). First, univariate analyses were performed to determine which demographic, clinical and genetic variables were associated with gene expression, testing all available variables individually. Next, multivariate models were built to better understand the dependence of gene expressions on the set of significant variables identified in the univariate analyses (p<0.10), following the backward method. In the multivariate analyses, variables with more than 15% missing data were excluded. Therefore, no imputations were performed, because the sample size was not sufficiently large. Standardization was applied for comparison of effects on the dependent variable.
The emergency situation of the pandemic resulted in differing time intervals between the first and second extractions among patients; therefore, in all analyses, the variable “days from symptom onset to extraction date” was included to correct for the potential effect of the disease duration in each patient. Similarly, to account for interplate variability, the variable “plate” was included in all analyses.
3 Results
3.1 Sociodemographic and clinical variables in the study population
The study population included a total of 157 patients, 52.2% of them were male and the mean age was 69.55 years (SD 15.52). The most frequent comorbidities in the population were hypertension, diabetes mellitus and obesity (Table 1).
Table 1
| Variable | N | % |
|---|---|---|
| Sex | ||
| Male | 82 | 52 |
| Female | 75 | 48 |
| Age (years) | ||
| <45 | 9 | 5.7 |
| 45-70 | 68 | 43 |
| >70 | 80 | 51 |
| Hypertension | 84 | 54 |
| Diabetes mellitus | 31 | 20 |
| Obesity | 23 | 15 |
| Dementia | 6 | 3.8 |
| People living with HIV | 1 | 0.6 |
Demographic data and comorbidities of the study population.
A descriptive analysis of treatments and laboratory findings for each extraction can be found in Supplementary Tables S3 and S4. The variables that showed significant differences among severity groups in both extractions were: treatment with methylprednisolone, immunosuppressants and tocilizumab, and levels of D-dimer, lactate dehydrogenase, C-reactive protein and ferritin. Specifically, at the first extraction, significant variations across severity groups were observed in the OAS1 rs1131454 genotype, TLR7 expression, the number of days from symptom onset to extraction date, treatment with glucocorticoids and fibrinogen levels (Supplementary Table S3). In the second extraction, significant differences between severity groups were observed for the TYK2 rs280519 genotype, TLR7 and OAS1 levels, and treatment with dexamethasone, viremia and lymphocyte levels (Supplementary Table S4).
Considering the longitudinal nature of this study, we took into account the progression of the disease, grouping patients according to their disease severity assessed at each extraction using the WHO severity scale. We then elaborated a Sankey diagram depicting the transitions within severity groups between both extractions. At the initial extraction, 24.8% of patients were classified as having mild disease, 68.8% as having moderate disease, and 6.4% as having severe disease. At the second extraction, the proportion of patients with mild and moderate disease decreased to 24.2% and 56% respectively, while the severe disease percentage increased to 19.7% (Figure 1). Thus, a total of 23 patients improved their condition and 42 worsened, whereas 92 maintained the same COVID-19 severity at both extractions.
Figure 1
COVID-19 severity evolution. Sankey diagram showing the evolution of severity status among patients, between the first (left boxes) and the second (right boxes) extraction. The bandwidths between both extractions represent the status evolution with lane sizes proportional to the percentage of patients. The number of patients are shown in parentheses.
3.2 TLR7 and OAS1 expression varies with COVID-19 severity and shows a time-dependent reduction
In order to determine which variables were associated with gene expression, univariate analyses were performed for each gene, adjusting by plate number and days from symptom onset to extraction date. The association between TLR7 and OAS1 expression with COVID-19 severity was especially relevant among the significant results obtained (Supplementary Table S5). TLR7 levels showed a decreasing trend with increased COVID-19 severity, being lower in patients with moderate disease (β-coeff: -0.45 [95%CI -0.65, -0.25], p<0.001) and further reduced in patients with severe disease (β-coeff: -0.69 [95%CI -0.99, -0.40], p<0.001) compared to those with mild disease.
Another interesting variable collected in our study was viremia, a known factor associated with COVID-19 severity. In our cohort, patients with severe COVID-19 exhibited higher viremia than those with mild or moderate disease at the first extraction, and these elevated levels persisted at the second sampling (Supplementary Figure S2).When evaluating the effect of viremia levels in gene expression, a negative correlation was found between viremia and TLR7 levels, while there was a tendency for OAS1 to show increased expression in patients with higher viremia (Supplementary Table S5).
When gene expression was represented across severity groups, the differences in TLR7 levels remained significant at both extractions (Figure 2A, upper panel). On the other hand, TYK2 levels were not associated with COVID-19 severity (Figure 2A, middle panel). However, OAS1 behaved differently over time within each severity group (Figure 2A, lower panel). Patients with mild and moderate disease showed higher OAS1 levels at the first extraction concurrent with higher viremia, and subsequently reduced its expression at the second extraction, corresponding to a decline in detectable viremia (Supplementary Figure S2). In contrast, patients with severe disease were likely unable to sufficiently upregulate OAS1 to control their higher and more persistent viremia.
Figure 2
Gene expression modulation according to COVID-19 severity groups (A) and over time (B). Data adjusted for plate number and days from symptom onset to extraction date. (A)TLR7, TYK2 and OAS1 expression grouped by COVID-19 severity (mild [blue], moderate [yellow] and severe [red]) for each extraction. The boxes represent the median (line) with interquartile range. Statistics: Kruskal-Wallis and Dunn test. (B) Linear regression of TLR7, TYK2 and OAS1 expression over days from symptom onset to extraction date, grouped by severity levels reported at extraction time. Bold: significant p-values.
Interestingly, time remained significant in all the univariate analyses for TLR7 and OAS1 expression (data not shown). To clarify this modulation, gene expression measured in both extractions was plotted against days from symptom onset to each extraction date across COVID-19 severity levels reached at both extractions (Figure 2B). There was a downward modulation of TLR7 and OAS1 gene expression in patients with mild and moderate disease over time, while levels in patients with severe disease remained stable. Notably, TLR7 levels in the second extraction remained higher in patients with mild compared to moderate and severe disease. On the contrary, OAS1 levels in the second extraction were suppressed in patients with mild and moderate compared to severe disease. As previously stated, TYK2 expression remained unchanged.
3.3 Multivariate models reveal associations of TLR7, TYK2 and OAS1 expression with disease severity, viremia and host factors in COVID-19 patients
In order to better understand TLR7, TYK2 and OAS1 effect on SARS-CoV-2 infection, we fitted multivariate analyses for each gene expression including all the variables that were significant in the univariate analyses, obtaining the final models following the backward method, and adjusting for plate number, extraction and days from symptom onset to extraction date (Figure 3).
Figure 3
Forest plots of the multivariate analysis of TLR7(A), OAS1(B) and TYK2(C) expression. The multivariate model was adjusted for plate number and days from symptom onset to extraction date. The plot represents beta-coefficient (dots and numbers) with 95% confidence intervals (horizontal bars) for the significant variables in the model. *p ≤ 0.1; **p ≤ 0.05; ***p ≤ 0.01.
TLR7 expression was reduced in patients with moderate (β-coeff: -0.28 [95%CI -0.504, -0.056], p=0.014) and severe (β-coeff: -0.345 [95%CI -0.674, -0.015], p=0.04) disease compared to patients with mild disease. Patients with high TLR7 expression also showed higher OAS1 levels (β-coeff: 0.172 [95%CI 0.035, 0.308], p=0.014) and lower TYK2 levels (β-coeff: -0.123 [95%CI -0.220, -0.025], p=0.013). Additionally, angiotensin receptor blocker (ARB) treatment (β-coeff: 0.360 [95%CI 0.061, 0.659], p=0.018) and higher lymphocyte count (β-coeff: 0.15 [95%CI 0.053, 0.247], p=0.002) were associated with greater TLR7 levels. Calcium channel blocker administration (β-coeff: -0.391 [95%CI -0.691, -0.092], p=0.011) and higher viremia (β-coeff: -0.087 [95%CI -0.184, 0.011], p=0.081) were negatively correlated with TLR7 expression (Figure 3A).
Regarding OAS1, a relationship was found between decreased expression and the rs1131454-AA genotype (β-coeff: -0.299 [95%CI -0.62, -0.02], p=0.066). In addition, a positive correlation was detected between OAS1 expression and both TLR7 (β-coeff: 0.288 [95%CI 0.17, 0.40], p<0.001) and TYK2 expression (β-coeff: 0.303 [95%CI 0.21, 0.40], p<0.001). In contrast to TLR7, viremia was higher in patients expressing high OAS1 levels (β-coeff: 0.093 [95%CI -0.01, 0.20], p=0.077) (Figure 3B).
Although TYK2 expression was not associated with severity, we found a positive correlation between TYK2 and OAS1 levels (β-coeff: 0.214 [95%CI 0.164, 0.265], p < 0.001), adjusted by antihypertensive and dexamethasone treatments (Figure 3C).
4 Discussion
This longitudinal study has assessed the expression of TYK2, TLR7 and OAS1 in PBMCs from SARS-CoV-2 infected patients, identifying significant associations between gene expression, severity, viremia and patient biochemical parameters.
In our cohort, patients with moderate and severe disease showed lower TLR7 expression than those with mild disease at the time of the first extraction. Low TLR7 expression has been described to be associated with COVID-19 severity (35, 38), and bronchoalveolar lavage fluids from severe and deceased patients have been reported to contain fewer TLR7-expressing cells than mild and samples from survivor patients, respectively (39, 40). However, these investigations were carried out with samples taken at a single time point. In our study we showed a TLR7 downmodulation at the time of the second extraction in mild and moderate cases. To the best of our knowledge, this is the first longitudinal study focused on investigating the relationship between TLR7 expression during the acute COVID-19 phase and disease outcome.
Regarding the downregulation of TLR7 expression over time, our results suggest that high TLR7 levels protect against severe COVID-19 outcome, and as the disease resolves, TLR7 expression decreases. These data are in agreement with the observation that TLR7 levels in PBMCs from healthy donors are lower than those in COVID-19 patients (41). Furthermore, in mouse models, the early phase of SARS-CoV-2 infection causes an upregulation of the Tlr7, Irf7 and IFN-I pathways in the lungs, whereas Tlr7 and Irf7-deficient mice show increased COVID-19 severity caused by deficient IFN-I and -III activation (42, 43). Based on these results, we can speculate that patients with mild disease, and to a lesser extent in patients with moderate disease, are able to induce TLR7 in early infection stages, which triggers the transcription of genes involved in the antiviral response. In contrast, patients unable to induce TLR7 in early infection stages may develop a severe disease (Figure 4).
Figure 4
Diagram of virus entry into the cell and activation of the IFN-I and -III signaling pathways. Violet: TLR7 signaling pathway; turquoise: IFN signaling cascade. Bold: genes analyzed in this study.
In our analysis, lower TLR7 expression was independently associated with both lymphopenia and severe COVID-19, supporting the link between these observations. In accordance with this, low lymphocyte count on admission has been associated with poor COVID-19 outcome, including ICU admission and acute respiratory distress syndrome (44). Several variables related to blood pressure were associated with the expression of TLR7 and TYK2 in the multivariate analysis. The effect of antihypertensives on TYK2 expression could be related to the fact that hypertensive patients have increased activation of the renin-angiotensin-aldosterone system, and angiotensin II activates TYK2 and JAK2 through its AT1 receptor (45). On the other hand, there is scarce evidence of any relationship of TLR7 with ARB and calcium-channel blockers.
We found a tendency for negative correlation between TLR7 levels and viremia. TLR7 levels could be a major factor in viral clearance, and patients with impaired TLR7 expression might be unable to achieve optimal viral elimination. This association is described in SARS-CoV-2 infected mice, where Tlr7 deficiency leads to higher lung viral load 6–10 days post-infection (43).
TYK2 expression was not associated with COVID-19 severity in our cohort, and its levels were maintained over time. Evidence to date shows controversy about the role of TYK2 in COVID-19 severity, reporting association with its overexpression (34, 46) or its deficiency (47). Additionally, Akbari et al. stated that, although TYK2 expression was suppressed in Iranian COVID-19 patients compared to controls, TYK2 levels could not differentiate properly between non-ICU and ICU admitted patients (48). We have found a positive correlation between TYK2 and OAS1 expression that could be explained by the downstream localization of OAS1 in the IFN-I signaling pathway (49).
Low OAS1 levels have been associated with higher risk of severe COVID-19 (50, 51). In line with these findings, there was a trend in our population in which patients with mild disease had higher OAS1 levels than those with severe disease at the first extraction. However, as the infection resolved, OAS1 was downregulated in patients with mild and moderate disease. Meanwhile, OAS1 expression was maintained in severe cases, indicating a reactive state. Also, OAS1 levels were directly related to viremia, consistent with higher OAS1 expression over time in severe cases.
Furthermore, we detected an association between the rs1131454-AA genotype and a lower OAS1 expression, in agreement with previous results showing the abundance of a transcript with a premature termination codon (28). These authors have shown that loss of genetically regulated OAS1 expression impairs spontaneous clearance of SARS-CoV-2 and increases the risk of hospitalization.
In the multivariate analyses we found relations between TYK2, TLR7 and OAS1 expressions. High TLR7 and TYK2 levels correlated with increased OAS1 expression, and higher TLR7 was associated with low TYK2 levels. These data suggest that SARS-CoV-2 infection increases TLR7 expression in mild and moderate cases, leading to OAS1 transcription (52) either via IRF-7 binding to DNA or via TYK2-mediated IFN-I signaling pathway (Figure 4).
In conclusion, low TLR7 expression correlates with worse outcome and high viremia in COVID-19 patients. TLR7 and OAS1 levels are higher in patients with mild and moderate disease and decrease as disease resolves. In severe cases, OAS1 expression persists. These findings improve our understanding of the molecular mechanisms involved in the early immune response in COVID-19.
Statements
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.
Ethics statement
This study was approved by the Research Ethics Committee of La Princesa University Hospital in Madrid (registration number 4070, 30 March 2020). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
EA-C: Writing – original draft, Writing – review & editing, Investigation, Data curation, Methodology, Validation. MJ-C: Writing – original draft, Writing – review & editing, Investigation, Data curation, Methodology, Validation. PD-W: Methodology, Investigation, Writing – review & editing. NZ-C: Investigation, Writing – review & editing, Validation, Methodology. NM: Formal Analysis, Data curation, Methodology, Validation, Writing – review & editing. ER-V: Investigation, Writing – review & editing. SF-O: Writing – review & editing, Investigation. AN-G: Investigation, Writing – review & editing. RC-R: Writing – review & editing, Investigation. AM-J: Writing – review & editing, Methodology, Investigation. LC-D: Validation, Supervision, Methodology, Writing – review & editing. IG-Á: Writing – review & editing, Supervision, Methodology, Conceptualization, Funding acquisition, Validation, Formal Analysis. EF-R: Writing – review & editing, Conceptualization, Funding acquisition, Supervision, Writing – original draft, Visualization, Methodology.
Funding
The author(s) declared financial support was received for this work and/or its publication. This study was funded with grants: RD21/0002/0027 and PI21/00526 to IG-Á; PI22/00428 to EF-R from Ministerio de Ciencia e Innovación (Instituto de Salud Carlos III, ISCIII), co-funded by European Regional Development Fund (ERDF) “A way to make Europe”. The work of AM-J was funded by Juan Rodés grant (JR23/00062), from the Ministerio de Ciencia e Innovación (Instituto de Salud Carlos III, ISCIII) and co-funded by The ERDF “A way to make Europe”. EA-C and AN-G were financed by INVESTIGO (2022-C23.I01.P03.S0020-0000031) and INVESTIGO CAM (09-PIN1-00015.6/2022), by Ministerio de Ciencia e Innovación and CAM respectively, both financed by the European Union’s Recovery, Transformation and Resilience Plan and NextGenerationEU. MJ-C was supported by Dirección General de Innovación e Investigación Tecnológica de la Comunidad de Madrid (RETAR-A-COVID, Grant P2022/BMD-7274 to EF-R).
Acknowledgments
We thank Dr. Manuel Gómez (IIS-Princesa) for editing the manuscript and the Fundación de Investigación Biomédica (FIB) administrative staff for support.
Conflict of interest
IG-Á reports personal fees from Lilly and Sanofi; personal fees and non-financial support from Abbvie; research support from Gebro Pharma; non-financial support from Biogen, MSD, Novartis and Pfizer, not related to the submitted work.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declare that no Generative AI was used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2025.1713928/full#supplementary-material.
References
1
Ruan Q Yang K Wang W Jiang L Song J . Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Med. (2020) 46:846–8. doi: 10.1007/s00134-020-05991-x
2
Berlin DA Gulick RM Martinez FJ . Severe covid-19. N Engl J Med. (2020) 383:2451–60. doi: 10.1056/NEJMcp2009575
3
Jin JM Bai P He W Wu F Liu XF Han DM et al . Gender differences in patients with COVID-19: focus on severity and mortality. Front Public Health. (2020) 8:152. doi: 10.3389/fpubh.2020.00152
4
Starke KR Reissig D Petereit-Haack G Schmauder S Nienhaus A Seidler A . The isolated effect of age on the risk of COVID-19 severe outcomes: a systematic review with meta-analysis. BMJ Glob Health. (2021) 6:e006434. doi: 10.1101/2021.05.27.21257909
5
Gao F Zheng KI Wang XB Sun QF Pan KH Wang TY et al . Obesity is a risk factor for greater COVID-19 severity. Diabetes Care. (2020) 43:e72–4. doi: 10.2337/dc20-0682
6
Savoia C Volpe M Kreutz R . Hypertension, a moving target in COVID-19: current views and perspectives. Circ Res. (2021) 128:1062–79. doi: 10.1161/CIRCRESAHA.121.318054
7
Cardeñoso Domingo L Roy Vallejo E Zurita Cruz ND Chicot Llano M Ávalos Pérez-Urria E Barrios A et al . Relevant SARS-CoV-2 viremia is associated with COVID-19 severity: Prospective cohort study and validation cohort. J Med Virol. (2022) 94:5260–70. doi: 10.1002/jmv.27989
8
Fajnzylber J Regan J Coxen K Corry H Wong C Rosenthal A et al . SARS-CoV-2 viral load is associated with increased disease severity and mortality. Nat Commun. (2020) 11:5493. doi: 10.1038/s41467-020-19057-5
9
Jacobs JL Bain W Naqvi A Staines B Castanha PMS Yang H et al . Severe acute respiratory syndrome coronavirus 2 viremia is associated with coronavirus disease 2019 severity and predicts clinical outcomes. Clin Infect Dis. (2021) 74:1525. doi: 10.1093/cid/ciab686
10
Rosas IO Bräu N Waters M Go RC Hunter BD Bhagani S et al . Tocilizumab in hospitalized patients with severe covid-19 pneumonia. N Engl J Med. (2021) 384:1503–16. doi: 10.1056/NEJMoa2028700
11
Schoot TS Kerckhoffs APM Hilbrands LB van Marum RJ . Immunosuppressive drugs and COVID-19: A review. Front Pharmacol. (2020) 11:1333. doi: 10.3389/fphar.2020.01333
12
Hoffmann M Kleine-Weber H Schroeder S Krüger N Herrler T Erichsen S et al . SARS-coV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell. (2020) 181:271–280.e8. doi: 10.1016/j.cell.2020.02.052
13
Diebold SS Kaisho T Hemmi H Akira S Reis e Sousa C . Innate antiviral responses by means of TLR7-mediated recognition of single-stranded RNA. Science. (2004) 303:1529–31. doi: 10.1126/science.1093616
14
van der Sluis RM Cham LB Gris-Oliver A Gammelgaard KR Pedersen JG Idorn M et al . TLR2 and TLR7 mediate distinct immunopathological and antiviral plasmacytoid dendritic cell responses to SARS-CoV-2 infection. EMBO J. (2022) 41:e109622. doi: 10.15252/embj.2021109622
15
Zhao X Chen D Li X Griffith L Chang J An P et al . Interferon control of human coronavirus infection and viral evasion: mechanistic insights and implications for antiviral drug and vaccine development. J Mol Biol. (2022) 434:167438. doi: 10.1016/j.jmb.2021.167438
16
Le Pen J Rice CM . The antiviral state of the cell: lessons from SARS-CoV-2. Curr Opin Immunol. (2024) 87:102426. doi: 10.1016/j.coi.2024.102426
17
Schoggins JW . Interferon-stimulated genes: what do they all do? Annu Rev Virol. (2019) 6:567–84. doi: 10.1146/annurev-virology-092818-015756
18
Schwartz SL Conn GL . RNA regulation of the antiviral protein 2′-5′-oligoadenylate synthetase. Wiley Interdiscip Rev RNA. (2019) 4):e1534. doi: 10.1002/wrna.1534
19
Asano T Boisson B Onodi F Matuozzo D Moncada-Velez M Maglorius Renkilaraj MRL et al . X-linked recessive TLR7 deficiency in ~1% of men under 60 years old with life-threatening COVID-19. Sci Immunol. (2021) 6:eabl4348. doi: 10.1126/sciimmunol.abl4348
20
Fallerini C Daga S Mantovani S Benetti E Picchiotti N Francisci D et al . Association of Toll-like receptor 7 variants with life-threatening COVID-19 disease in males: findings from a nested case-control study. Elife. (2021) 10:e67569. doi: 10.7554/eLife.67569.sa2
21
van der Made CI Simons A Schuurs-Hoeijmakers J van den Heuvel G Mantere T Kersten S et al . Presence of genetic variants among young men with severe COVID-19. JAMA. (2020) 324:1. doi: 10.1001/jama.2020.13719
22
Martínez-Gómez LE Martinez-Armenta C Medina-Luna D Ordoñez-Sánchez ML Tusie-Luna T Ortega-Peña S et al . Implication of myddosome complex genetic variants in outcome severity of COVID-19 patients. J Microbiol Immunol Infect. (2023) 56:939–50. doi: 10.1016/j.jmii.2023.06.002
23
Naushad SM Mandadapu G Ramaiah MJ Almajhdi FN Hussain T . The role of TLR7 agonists in modulating COVID-19 severity in subjects with loss-of-function TLR7 variants. Sci Rep. (2023) 13:13078. doi: 10.1038/s41598-023-40114-8
24
Al Qureshah F Le Pen J de Weerd NA Moncada-Velez M Materna M Lin DC et al . A common form of dominant human IFNAR1 deficiency impairs IFN-α and -ω but not IFN-β-dependent immunity. J Exp Med. (2025) 222:e20241413. doi: 10.1084/jem.20241413
25
Zhang Q Bastard P Liu Z Le Pen J Moncada-Velez M Chen J et al . Inborn errors of type I IFN immunity in patients with life-threatening COVID-19. Science. (2020) 370:eabd4570. doi: 10.1126/science.abd4570
26
Zhang Q Matuozzo D Le Pen J Lee D Moens L Asano T et al . Recessive inborn errors of type I IFN immunity in children with COVID-19 pneumonia. J Exp Med. (2022) 219:e20220131. doi: 10.1084/jem.20220131
27
Pairo-Castineira E Rawlik K Bretherick AD Qi T Wu Y Nassiri I et al . GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19. Nature. (2023) 617:764. doi: 10.1038/s41586-023-06034-3
28
Banday AR Stanifer ML Florez-Vargas O Onabajo OO Papenberg BW Zahoor MA et al . Genetic regulation of OAS1 nonsense-mediated decay underlies association with COVID-19 hospitalization in patients of European and African ancestries. Nat Genet. (2022) 54:1103–16. doi: 10.1038/s41588-022-01113-z
29
Huffman JE Butler-Laporte G Khan A Pairo-Castineira E Drivas TG Peloso GM et al . Multi-ancestry fine mapping implicates OAS1 splicing in risk of severe COVID-19. Nat Genet. (2022) 54:125. doi: 10.1038/s41588-021-00996-8
30
Colona VL Vasiliou V Watt J Novelli G Reichardt JKV . Update on human genetic susceptibility to COVID-19: susceptibility to virus and response. Hum Genomics. (2021) 15:57. doi: 10.1186/s40246-021-00356-x
31
Roy-Vallejo E Fernández De Córdoba-Oñate S Delgado-Wicke P Triguero-Martínez A Montes N Carracedo-Rodríguez R et al . Occurrence of SARS-CoV-2 viremia is associated with genetic variants of genes related to COVID-19 pathogenesis. Front Med (Lausanne). (2023) 10:1215246. doi: 10.3389/fmed.2023.1215246
32
Villapalos-García G Zubiaur P Rivas-Durán R Campos-Norte P Arévalo-Román C Fernández-Rico M et al . Transmembrane protease serine 2 (TMPRSS2) rs75603675, comorbidity, and sex are the primary predictors of COVID-19 severity. Life Sci Alliance. (2022) 5:e202201396. doi: 10.26508/lsa.202201396
33
Delgado-Wicke P Fernández de Córdoba-Oñate S Roy-Vallejo E Alegría-Carrasco E Rodríguez-Serrano DA Lamana A et al . Genetic variants regulating the immune response improve the prediction of COVID-19 severity provided by clinical variables. Sci Rep. (2024) 14:1–10. doi: 10.1038/s41598-024-71476-2
34
Pairo-Castineira E Clohisey S Klaric L Bretherick AD Rawlik K Pasko D et al . Genetic mechanisms of critical illness in COVID-19. Nature. (2021) 591:92–8. doi: 10.1038/s41586-020-03065-y
35
Gómez-Carballa A Pardo-Seco J Pischedda S Rivero-Calle I Butler-Laporte G Richards JB et al . Sex-biased expression of the TLR7 gene in severe COVID-19 patients: Insights from transcriptomics and epigenomics. Environ Res. (2022) 215:114288. doi: 10.1016/j.envres.2022.114288
36
Brooks ME Kristensen K van Benthem KJ Magnusson A Berg CW Nielsen A et al . glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. (2017) 9:378–400. doi: 10.32614/RJ-2017-066
37
Belgorodski N Greiner M Tolksdorf K Schueller K . rriskDistributions: fitting distributions to given data or known quantiles (2017). Available online at: https://CRAN.R-project.org/package=rriskDistributions (Accessed September 19, 2025).
38
Lee N Ko R Lee SY . Differential expression patterns of toll-like receptors in COVID-19 patients. FBL. (2023) 28:307. doi: 10.31083/j.fbl2811307
39
Sorrentino L Fracella M Frasca F D’Auria A Santinelli L Maddaloni L et al . Alterations in the expression of IFN lambda, IFN gamma and toll-like receptors in severe COVID-19 patients. Microorganisms. (2023) 11:689. doi: 10.3390/microorganisms11030689
40
Wu D Yang XO . Dysregulation of pulmonary responses in severe COVID-19. Viruses. (2021) 13:957. doi: 10.3390/v13060957
41
Arefinia N Banafi P Zarezadeh MA Mousawi HS Yaghobi R Farokhnia M et al . TLR3, TLR7, and TLR8 genes expression datasets in COVID-19 patients: Influences of the disease severity and gender. Data Brief. (2024) 54:110498. doi: 10.1016/j.dib.2024.110498
42
Ghimire R Shrestha R Amaradhi R Liu L More S Ganesh T et al . Toll-like receptor 7 (TLR7)-mediated antiviral response protects mice from lethal SARS-CoV-2 infection. J Virol. (2025) 99:e0166824. doi: 10.1128/jvi.01668-24
43
Wang C Khatun MS Ellsworth CR Chen Z Islamuddin M Nisperuza Vidal AK et al . Deficiency of Tlr7 and Irf7 in mice increases the severity of COVID-19 through the reduced interferon production. Commun Biol. (2024) 7:1162. doi: 10.1038/s42003-024-06872-5
44
Huang I Pranata R . Lymphopenia in severe coronavirus disease-2019 (COVID-19): systematic review and meta-analysis. J Intensive Care. (2020) 8:36. doi: 10.1186/s40560-020-00453-4
45
Marrero MB Schieffer B Paxton WG Heerdt L Berk BC Delafontaine P et al . Direct stimulation of Jak/STAT pathway by the angiotensin II AT1 receptor. Nature. (1995) 375:247–50. doi: 10.1038/375247a0
46
Hadjadj J Yatim N Barnabei L Corneau A Boussier J Smith N et al . Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients. Science. (2020) 369:718–24. doi: 10.1126/science.abc6027
47
Zabihi Rizi F Ghorbani A Zahtab P Darbaghshahi NN Ataee N Pourhamzeh P et al . TYK2 single-nucleotide variants associated with the severity of COVID-19 disease. Arch Virol. (2023) 168:119. doi: 10.1007/s00705-023-05729-2
48
Akbari M Akhavan-Bahabadi M Shafigh N Taheriazam A Hussen BM Sayad A et al . Expression analysis of IFNAR1 and TYK2 transcripts in COVID-19 patients. Cytokine. (2022) 153:155849. doi: 10.1016/j.cyto.2022.155849
49
Silverman RH . A scientific journey through the 2-5A/RNase L system. Cytokine Growth Factor Rev. (2007) 18:381. doi: 10.1016/j.cytogfr.2007.06.012
50
Gajate-Arenas M Fricke-Galindo I García-Pérez O Domínguez-de-Barros A Pérez-Rubio G Dorta-Guerra R et al . The immune response of OAS1, IRF9, and IFI6 genes in the pathogenesis of COVID-19. Int J Mol Sci. (2024) 25:4632. doi: 10.3390/ijms25094632
51
Zhou S Butler-Laporte G Nakanishi T Morrison DR Afilalo J Afilalo M et al . A Neanderthal OAS1 isoform protects individuals of European ancestry against COVID-19 susceptibility and severity. Nat Med. (2021) 27:659–67. doi: 10.1038/s41591-021-01281-1
52
Yan Q Li P Ye X Huang X Feng B Ji T et al . Longitudinal peripheral blood transcriptional analysis reveals molecular signatures of disease progression in COVID-19 patients. J Immunol. (2021) 206:2146–59. doi: 10.4049/jimmunol.2001325
Summary
Keywords
COVID-19, SARS-CoV-2, longitudinal study, severity, TLR7 , TYK2 , OAS1 , viremia
Citation
Alegría-Carrasco E, Jaén-Castaño M, Delgado-Wicke P, Zurita-Cruz ND, Montes N, Roy-Vallejo E, Fernández de Córdoba-Oñate S, Nicolao-Gómez A, Carracedo-Rodríguez R, Marcos-Jiménez A, Cardeñoso-Domingo L, González-Álvaro I and Fernández-Ruiz E (2025) Modulation of TLR7, TYK2 and OAS1 expression during SARS-CoV-2 infection. Front. Immunol. 16:1713928. doi: 10.3389/fimmu.2025.1713928
Received
26 September 2025
Revised
19 November 2025
Accepted
24 November 2025
Published
09 December 2025
Volume
16 - 2025
Edited by
Samuel Pushparaj Robert Jeyasingh, Oklahoma State University, United States
Reviewed by
Parul Suri, St. John’s University, United States
Jeeviya Murugesan, Oklahoma State University, United States
Updates
Copyright
© 2025 Alegría-Carrasco, Jaén-Castaño, Delgado-Wicke, Zurita-Cruz, Montes, Roy-Vallejo, Fernández de Córdoba-Oñate, Nicolao-Gómez, Carracedo-Rodríguez, Marcos-Jiménez, Cardeñoso-Domingo, González-Álvaro and Fernández-Ruiz.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Isidoro González-Álvaro, isidoro.ga@ser.es; Elena Fernández-Ruiz, efruiz@salud.madrid.org
†These authors have contributed equally to this work and share senior authorship
ORCID: Estíbaliz Alegría-Carrasco, orcid.org/0000-0001-7119-4920; Marta Jaén-Castaño, orcid.org/0000-0002-2088-8807; Pablo Delgado-Wicke, orcid.org/0000-0003-3988-3704; Nelly D. Zurita-Cruz, orcid.org/0000-0002-3679-4709; Nuria Montes, orcid.org/0000-0001-9526-2772; Emilia Roy-Vallejo, orcid.org/0000-0001-5253-5785; Sara Fernández de Córdoba-Oñate, orcid.org/0009-0004-5686-3486; Ana Nicolao-Gómez, orcid.org/0009-0009-9912-5260; Ana Marcos-Jiménez, orcid.org/0000-0002-7606-3580; Laura Cardeñoso-Domingo, orcid.org/0000-0002-8395-3370; Isidoro González-Álvaro, orcid.org/0000-0001-9614-5199; Elena Fernández-Ruiz, orcid.org/0000-0001-5380-1686
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.