Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action/journalInformation?journalCode=temi20
Emerging Microbes & Infections
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/temi20
Plasma miRNA profile at COVID-19 onset predicts
severity status and mortality
Asier Fernández-Pato, Ana Virseda-Berdices, Salvador Resino, Pablo Ryan,
Oscar Martínez-González, Felipe Pérez-García, María Martin-Vicente, Daniel
Valle-Millares, Oscar Brochado-Kith, Rafael Blancas, Amalia Martínez,
Francisco C. Ceballos, Sofía Bartolome-Sánchez, Erick Joan Vidal-Alcántara,
David Alonso, Natalia Blanca-López, Ignacio Ramirez Martinez-Acitores,
Laura Martin-Pedraza, María Ángeles Jiménez-Sousa & Amanda Fernández-
Rodríguez
To cite this article: Asier Fernández-Pato, Ana Virseda-Berdices, Salvador Resino, Pablo Ryan,
Oscar Martínez-González, Felipe Pérez-García, María Martin-Vicente, Daniel Valle-Millares, Oscar
Brochado-Kith, Rafael Blancas, Amalia Martínez, Francisco C. Ceballos, Sofía Bartolome-Sánchez,
Erick Joan Vidal-Alcántara, David Alonso, Natalia Blanca-López, Ignacio Ramirez Martinez-
Acitores, Laura Martin-Pedraza, María Ángeles Jiménez-Sousa & Amanda Fernández-Rodríguez
(2022) Plasma miRNA profile at COVID-19 onset predicts severity status and mortality, Emerging
Microbes & Infections, 11:1, 676-688, DOI: 10.1080/22221751.2022.2038021
To link to this article:  https://doi.org/10.1080/22221751.2022.2038021
© 2022 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group, on behalf of Shanghai Shangyixun
Cultural Communication Co., Ltd
View supplementary material 
Published online: 27 Feb 2022. Submit your article to this journal 
Article views: 1145 View related articles 
View Crossmark data
RESEARCH ARTICLE
Plasma miRNA profile at COVID-19 onset predicts severity status and mortality
Asier Fernández-Patoa,b, Ana Virseda-Berdices a, Salvador Resino a,c, Pablo Ryanc,d,e,f, Oscar Martínez-
Gonzálezg, Felipe Pérez-Garcíah,i, María Martin-Vicente a,c, Daniel Valle-Millares a, Oscar Brochado-Kith
a,c, Rafael Blancas g, Amalia Martínezd, Francisco C. Ceballos a, Sofía Bartolome-Sáncheza,
Erick Joan Vidal-Alcántaraa, David Alonsoj, Natalia Blanca-Lópezk, Ignacio Ramirez Martinez-Acitoresg,
Laura Martin-Pedrazad, María Ángeles Jiménez-Sousa a,c¥ and Amanda Fernández-Rodríguez a,c¥
aUnit of Viral Infection and Immunity, National Center for Microbiology (CNM), Health Institute Carlos III (ISCIII), Madrid, Spain;
bDepartment of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; cCentro de
Investigación Biomédica en Red de Enfermedades Infecciosas, Instituto de Salud Carlos III, Madrid, Spain; dDepartment of Infectious
Diseases, Hospital Universitario Infanta Leonor, Madrid, Spain; eSchool of Medicine, Complutense University of Madrid, Madrid, Spain;
fGregorio Marañón Health Research Institute, Madrid, Spain; gCritical Care Department, Hospital Universitario del Tajo, Aranjuez, Spain;
hClinical Microbiology Department, Hospital Universitario Príncipe de Asturias, Alcalá de Henares, Spain; iDepartment of Biomedicine and
Biotechnology, Faculty of Medicine, University of Alcalá de Henares, Madrid, Spain; jInternal Medicine Service, Hospital Universitario
Príncipe de Asturias, Alcalá de Henares, Spain; kAllergology Department, University Hospital Infanta Leonor, Madrid, Spain
ABSTRACT
Background: MicroRNAs (miRNAs) have a crucial role in regulating immune response against infectious diseases,
showing changes early in disease onset and before the detection of the pathogen. Thus, we aimed to analyze the
plasma miRNA profile at COVID-19 onset to identify miRNAs as early prognostic biomarkers of severity and survival.
Methods and results: Plasma miRNome of 96 COVID-19 patients that developed asymptomatic/mild, moderate and
severe disease was sequenced together with a group of healthy controls. Plasma immune-related biomarkers were
also assessed. COVID-19 patients showed 200 significant differentially expressed (SDE) miRNAs concerning healthy
controls, with upregulated putative targets of SARS-CoV-2, and inflammatory miRNAs. Among COVID-19 patients, 75
SDE miRNAs were observed in asymptomatic/mild compared to symptomatic patients, which were involved in
platelet aggregation and cytokine pathways, among others. Moreover, 137 SDE miRNAs were identified between
severe and moderate patients, where miRNAs targeting the SARS CoV-2 genome were the most strongly disrupted.
Finally, we constructed a mortality predictive risk score (miRNA-MRS) with ten miRNAs. Patients with higher values
had a higher risk of 90-days mortality (hazard ratio = 4.60; p-value < 0.001). Besides, the discriminant power of miRNA-
MRS was significantly higher than the observed for age and gender (AUROC = 0.970 vs. 0.881; p = 0.042).
Conclusions: SARS-CoV-2 infection deeply disturbs the plasma miRNome from an early stage of COVID-19, making
miRNAs highly valuable as early predictors of severity and mortality.
ARTICLE HISTORY Received 11 November 2021; Revised 21 January 2022; Accepted 31 January 2022
KEYWORDS SARS-CoV2; COVID-19; miRNAs; severity; mortality
Introduction
The pathogenesis of the severe acute respiratory syn-
drome coronavirus 2 (SARS-CoV-2) infection and the
virus-host interactions during COVID-19 are still not
entirely understood. Therefore, it is essential to continue
unraveling the biological and molecular mechanisms
behind the disparity in disease severity. Omics
approaches are indispensable in this scenario, generating
crucial information for decision-making in public health
policies [1] and allowing the identification of new bio-
markers for predicting severity in COVID-19 patients.
MicroRNAs (miRNAs) are small non-coding RNAs
that play a fundamental role in regulating gene
expression by binding the 3´ untranslated region of
target RNAs. This post-transcriptional regulation is
essential in both innate and adaptive immunity [2],
and the fine-tune of the immune response. Several miR-
NAs are key regulators of inflammation-related
mediators, being essential in some inflammatory diseases
[2]. In addition, evidences point to an essential function
of miRNAs in the pathogenesis and therapeutics of
different viral diseases, including those related to the res-
piratory tract [3], such as the respiratory syncytial virus
(RSV) [4], middle east respiratory syndrome coronavirus
(MERS-CoV) [5], SARS-CoV [6], and SARS-CoV-2 [7].
In this regard, several host miRNAs have been
identified to target the SARS-CoV-2 genome, repres-
sing its expression and mitigating the pathogenesis
© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group, on behalf of Shanghai Shangyixun Cultural Communication Co., Ltd
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrest-
ricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
CONTACT María Ángeles Jiménez-Sousa jimenezsousa@isciii.es Unit of Viral Infection and Immunity, National Center for Microbiology (CNM),
Health Institute Carlos III (ISCIII), Majadahonda, Madrid, Spain
¥Both authors contributed equally to this study.
Supplemental data for this article can be accessed https://doi.org/10.1080/22221751.2022.2038021
Emerging Microbes & Infections
2022, VOL. 11
https://doi.org/10.1080/22221751.2022.2038021
of COVID-19. Therefore, the interaction between
SARS-CoV-2 and the cellular miRNome could modify
the outcome of the infection, as some mutations in the
3´UTR region of the viral genome leads to a viral escape
from the host immune system [8]. SARS-CoV-2 infec-
tion induces a robust host miRNA response, as it was
observed in patients admitted to intensive care units
(ICU) [6]. However, it is unknown the miRNA
profile of other severity outcomes, specially at disease
onset, which could give new insights to improve
COVID-19 detection and management.
Therefore, miRNAs could have a potential role in
counteracting the SARS-CoV-2 induction of inflam-
matory response [9]. In this setting, miRNAs are dee-
ply involved in regulating the expression of cytokines,
chemokines, and growth factors [10–12], which is
directly related with the cytokine release syndrome
(CRS) of COVID-19. To date, the plasma miRNome
of COVID-19 patients of different severity grades
has not been massively assessed. The integrative analy-
sis of the miRNome data with plasma cytokine levels
could unravel the miRNA-cytokine interplay in
COVID-19, identifying potential miRNAs as new
therapeutic approaches against CRS and putative sur-
vival biomarkers at disease onset.
This study aimed to characterize the plasma miR-
Nome at the onset of COVID-19 of different severity
statuses and to identify a miRNA signature of
mortality.
Methods
Design and study population
A multicenter observational study was performed in
96 patients with SARS-CoV-2 infection enrolled
from March to August 2020 at three public hospitals
from Madrid: University Hospital Infanta Leonor,
Del Tajo University Hospital, and Príncipe de Asturias
University Hospital. The study protocol was approved
by the Ethics Committee of the Institute of Health
Carlos III (PI 33_2020-v3) and the Ethics Committee
of each hospital. Written, oral, or delegated informed
consent was obtained from patients or legal represen-
tatives whenever possible. The ethics committee, in
some cases, authorized an informed consent waiver.
Patients were classified according to their highest
severity grade during the evolution of COVID-19 as
follows: 1) Severe: i) death during hospitalization, ii)
ICU admission, iii) invasive mechanical ventilation,
or iv) presence of bilateral pulmonary infiltrates,
mechanical ventilation and oxygen saturation (Sat02-
)≤ 93%; 2) Moderate: the remaining patients admitted
to hospital who did not fulfil severe COVID-19 cri-
teria; 3) Asymptomatic/Mild (AM): individuals with
minor or no COVID-19 symptoms; 4) Control:
healthy donors recruited before COVID-19 outbreak.
The STROBE-ID checklist was used to strengthen the
design and conduct the study.
Clinical data and sample collection
Epidemiological and clinical variables were exhaus-
tively collected from medical records using an elec-
tronic case report form (eCRF) built using REDCap
electronic data capture tools [13]. Plasma samples
were collected at hospital entry or within the first
days after hospitalization and before treatment with
specific therapies for COVID-19 such as immunother-
apy (tocilizumab, interferon-beta (IFNβ), corticoids,
or ribavirin, among others).
Analysis of miRNome by high throughput
sequencing
Total RNA, including small RNAs, was isolated from
400μl of plasma with the miRNeasy Serum Plasma
Advanced kit (Qiagen, Hilden, Germany) following
manufacturer’s instructions. RNA quality and quan-
tity were evaluated by the Bioanalyzer 2100 with Agi-
lent RNA 6000 Nano Kit. Small RNA libraries were
constructed using the NEBNext Multiplex Small
RNA Library Prep for Illumina (New England Biolabs,
Ipswich, MA, USA) at Parque Científico of Madrid
(Spain). Next, small RNAs were sequenced in NovaSeq
6000, Illumina, at the National Centre for Micro-
biology (Majadahonda, Spain), estimating over 50
million reads per sample (Supplementary Data 1).
Raw data were analyzed using a specific bioinfor-
matic pipeline detailed in Supplementary Data 2.
Briefly, reads were quality checked with FastQC
(v.0.11.3), and adapter sequences were trimmed with
cutadapt (v.1.13). Reads were then processed with
miRDeep2 (v.0.0.7) to identify and quantify known
miRNAs from the reference human genome
(GRCh38) and miRBase (v2.0) [14].
Cytokines and chemokines quantification
Twenty-six plasma markers previously related to
COVID-19 (Supplementary Data 3) were evaluated
with a custom Procartaplex multiplex immunoassay
(Invitrogen) using the Bio-plex 200TM system (BioRad
Laboratories, Hercules, California, USA) following the
manufacturer’s specifications. We used the raw fluor-
escence intensity (FI) values as a relative quantification
of the analyte abundances, as previously described [15].
Outcome variables
We analyzed several outcomes: i) infection: all SARS-
CoV-2 infected patients were compared to healthy
controls; ii) Symptomatology: symptomatic patients
(moderate plus severe) versus asymptomatic; iii)
Emerging Microbes & Infections 677
Severity: severe versus moderate patients; iv) Mor-
tality: 90-days mortality of SARS-CoV-2 infected
patients, where the baseline was the date of diagnosis
of SARS-CoV-2 infection.
Statistical analysis
The statistical analysis was performed by R statistical
package (v4.0.3). Detailed statistical analysis infor-
mation is available in Supplementary Data 4.
The Kruskal–Wallis test and Pearson’s chi-squared
test were used for descriptive data for continuous and
discrete variables. miRNA counts were normalized
with the DESeq2 method (v1.30.0), and expression
differences between severity groups were analyzed
using a generalized linear model (GLM) with negative
binomial distribution adjusted by age and gender.
miRNAs with fold change (FC)≥ 1.5 and q-value≤
0.05 (p-value corrected for the false discovery rate
(FDR) by Benjamini-Hochberg correction) were con-
sidered significant.
Mortality analysis for 90 days after hospitalization
was performed by a Cox Proportional-Hazard
regression model adjusted by age and gender. Var-
iance stabilizing transformation (VST) normalized
counts of all miRNAs were included in the least absol-
ute shrinkage and selection operator (LASSO) multi-
variate Cox regression model to select the miRNAs
with the best predictive value and construct the
miRNA mortality risk score (miRNA-MRS) within
90 days of hospital admission (see Supplementary
Data 4 for extended information). The predictive per-
formance for miRNA-MRS was assessed using the
area under the receiver-operating characteristic
curve (AUROC) and by calculating sensitivity, specifi-
city and positive and negative predictive value.
Plasma cytokine and chemokine FI values were pre-
processed with a weighted Box–Cox, followed by
quantile normalization [16]. Normalized data were
analyzed with a GLMwith gamma distribution. Corre-
lation analysis between SDE miRNAs of each contrast
and cytokines/chemokines was performed using the
Spearman correlation test (q-value < 0.1).
miRNA-based target prediction and pathway
enrichment analysis
Significant differentially expressed (SDE) miRNAs
were analyzed for experimentally validated miRNA-
target interactions with MIENTURNET (MicroRNA
ENrichment TURned NETwork) [17]. Gene targets
with at least three validated interactions with SDE
miRNAs were selected to perform functional enrich-
ment analysis with the Reactome Pathway database
[18]. Enrichment p-values (Fischer’s exact test with
hypergeometric distribution) were corrected for false
discovery ratio (FDR) (q-value≤ 0.1).
Results
Patient characteristics
Characteristics of the four groups of patients are
shown in Table 1. There were no differences in the
time point at which the plasma samples were obtained
among groups, neither between symptoms onset and
sampling. Ethnicity, smoker status, arterial hyperten-
sion, chronic pulmonary disease, COVID-19-related
symptoms, and basal therapy with nonsteroidal anti-
inflammatory drugs showed significant differences
between groups. COVID-19 hospitalization require-
ments were found significantly different between
groups, as they were used for severity definition (see
methods): i.e. oxygenotherapy, mechanical venti-
lation, presence of bilateral pulmonary infiltrates,
ICU and exitus.
miRNome characterization and correlation with
cytokine release by severity groups
We obtained 50 million reads per sample, roughly
three times over the minimum depth required for
miRNA expression analysis. A total of 2656 miRNAs
(91.97% of miRBase miRNAs) were identified, and
767 miRNAs were successfully filtered with sufficiently
large counts for the differential expression analysis.
The partial least squares discriminant analysis
(PLS-DA) showed differences between severity groups
according to the miRNome expression (Figure 1A-D),
where each group showed a specific expression pattern
(Figure 1E).
We also evaluated the change of cytokine and che-
mokine levels for all comparisons (Supplementary
Data 5). Patients infected with SARS-CoV-2 showed
higher levels of proinflammatory cytokines, which
were increasing with COVID-19 severity. Later, we
analyzed the correlation between SDE miRNAs and
cytokines for each severity group contrast (Figure 2).
We observed that several cytokines, chemokines, and
growth factors significantly correlated with the dysre-
gulated miRNAs resulting from each comparison (A,
COVID-19 patients vs. healthy controls; B, asympto-
matic vs. symptomatic; C, severe vs. moderate).
Characterization of COVID-19 infection
We first studied the miRNAs expression differences
between SARS-CoV-2 infected patients and healthy
controls, which showed the presence of 200 SDE miR-
NAs (Figure 3A; Supplementary Data 6). In COVID-
19 patients, 142 miRNAs were upregulated and 58
downregulated. SARS-CoV-2 infected patients had a
strong upregulation of miRNAs such as the hsa-
miR-4665-5p, hsa-miR-3190-3p, hsa-miR-331-3p,
hsa-miR-4525, hsa-miR-431-5p, hsa-miR-6721-5p,
678 A. Fernández-Pato et al.
hsa-miR-4661-5p, hsa-miR-548a-3p, hsa-miR-4745-
5p, and hsa-miR-3150b-3p, which showed a FC > 100.
The functional analysis reported 29 Reactome bio-
logical pathways. The most relevant pathways were
influenza infection, influenza viral RNA transcription
and replication, viral mRNA translation, and estro-
gen-dependent gene expression, among others (Sup-
plementary Data 7; Supplementary Data 8).
SDE miRNAs in COVID-19 patients were signifi-
cantly correlated with proinflammatory cytokines
such as IL-6, IL-12, IP10, and TNFα (Figure 2A),
while healthy and infected patients showed different
correlation patterns (Supplementary Data 9A and
B, respectively). The proinflammatory hsa-miR-320
family was highly expressed in SARS-CoV-2 infected
patients, positively correlated with proinflammatory
cytokines such as IL-12, IL-6, IL-8, IP10, MIG,
TIM3, and TNFα, especially the hsa-miR-320b.
These correlations were not observed in healthy con-
trols, where most of the significant correlations with
proinflammatory cytokines were negative (Sup-
plementary Data 9A).
Asymptomatic COVID-19 patients
We found 75 SDE miRNAs between asymptomatic
(asymptomatic and mild) and symptomatic (moderate
and severe) COVID-19 patients (Figure 3B; Sup-
plementary Data 10). Nineteen of them showed a
higher expression in asymptomatic patients, and the
hsa-miR-1291 was the most upregulated (FC = 14.6).
Table 1. Clinical, epidemiological, and virological characteristics of SARS-CoV-2 infected patients.
Severe Moderate AM Controls p-value
No. 32 52 12 13
Age (years) 63.4 (52.9-78.3) 59.4 (53.0- 71.8) 66.2 (44.4- 72.6) 66.7 (57- 68.9) 0.854
Gender (male) 23 / 32 (71.9%) 26 / 52 (50.0%) 4 / 12 (33.3%) 7 / 13 (53.8%) 0.090
Ethnicity (n = 105)
Caucasian 25 / 32 (78.1%) 29 / 49 (59.2%) 12 / 12 (100%) 12 / 12 (100%) 0.002
Hispanic 7 / 32 (21.9%) 18 / 49 (36.7%) 0 / 12 (0%) 0 / 12 (0%) 0.007
BMI (kg/m2) (n = 49) 28.3 (23.6- 36.3) 29.9 (27.6- 32.8) 28.4 (25.7- 29.3) 28.1 (25.8- 30.4) 0.733
BMI ≥25 (kg/m2) (n = 49) 14 / 20 (70%) 11 / 12 (91.7%) 6 / 8 (75%) 7 / 9 (77.8%) 0.560
Smoker status
Smoker 2 / 32 (6.2%) 4 / 52 (7.7%) 2 / 12 (16.7%) NA 0.522
Ex-smoker 7 / 32 (21.9%) 5 / 52 (9.6%) 4 / 12 (33.3%) NA 0.087
Time between hospitalization to sampling 1.5 (0-5) 2 (0-3.2) 1 (0.5-1.5) NA 0.740
Time between symptoms and sampling 6 (3-8) 7.5 (4-10.2) – NA 0.634
Comorbidities
Arterial hypertension 14 / 32 (43.8%) 23 / 52 (44.2%) 5 / 12 (41.7%) NA 0.987
Cardiopathy 5 / 32 (15.6%) 10 / 52 (19.2%) 2 / 12 (16.7%) NA 0.911
Chronic pulmonary disease 9 / 32 (28.1%) 7 / 52 (13.5%) 0 / 12 (0%) NA 0.055
Chronic renal disease 6 / 32 (18.8%) 4 / 52 (7.7%) 2 / 12 (16.7%) NA 0.296
Chronic liver disease 1 / 32 (3.1%) 1 / 52 (1.9%) 0 / 12 (0%) NA 0.806
Chronic neurological disease 7 / 32 (21.9%) 7 / 52 (13.5%) 0 / 12 (0%) NA 0.177
Neoplasia 3 / 32 (9.4%) 2 / 52 (3.8%) 3 / 12 (25.0%) NA 0.056
Obesity 9 / 32 (28.1%) 9 / 52 (17.3%) 2 / 12 (16.7%) NA 0.461
Diabetes 7 / 32 (21.9%) 9 / 52 (17.3%) 4 / 12 (33.3%) NA 0.461
Chronic inflammatory disease 3 / 32 (9.4%) 1 / 52 (1.9%) 0 / 12 (0%) NA 0.187
Autoinmune diseases 3 / 32 (9.4%) 1 / 52 (1.9%) 0 / 12 (0%) NA 0.187
Therapy
Basal
NSAIDs 4 / 32 (12.5%) 0 / 52 (0%) 0 / 12 (0%) NA 0.015
ACE inhibitors 6 / 32 (18.8%) 10 / 52 (19.2%) 0 / 12 (0%) NA 0.253
ARA II 3 / 32 (9.4%) 5 / 52 (9.6%) 1 / 12 (8.3%) NA 0.991
Corticoids 4 / 32 (12.5%) 6 / 52 (11.5%) 0 / 12 (0%) NA 0.446
HIV antiretroviral therapy 1 / 32 (3.1%) 1 / 52 (1.9%) 0 / 12 (0%) 0 / 13 (0%) 0.858
Treatment
Chloroquine and hidroxychloroquine 23 / 32 (71.9%) 51 / 52 (98.1%) 0 / 12 (0%) NA <0.001
Tocilizumab 13 / 32 (40.6%) 10 / 52 (19.2%) 0 / 12 (0%) NA 0.009
Corticoids 27 / 32 (84.4%) 21 / 52 (40.4%) 0 / 12 (0%) NA <0.001
COVID-19 related symptoms
Dyspnoea 28 / 32 (87.5%) 33 / 52 (63.5%) 1 / 12 (8.3%) NA <0.001
Cough 22 / 32 (68.8%) 40 / 52 (76.9%) 3 / 12 (25%) NA 0.002
Headache 7 / 32 (21.9%) 22 / 52 (42.3%) 0 / 12 (0%) NA 0.007
Diarrhea or abdominal pain 12 / 32 (37.5%) 28 / 52 (53.8%) 2 / 12 (16.7%) NA 0.044
Hospitalization
Hospital stay (days) (n = 86) 18 (10.7- 25.2) 8.5 (6- 12.2) 9 (8.5- 9.5) NA <0.001
Maximum temperature (n = 87) 38.1 (37.5- 38.8) 38 (37.28- 38.5) 36.4 (36.3- 37.7) NA 0.232
Oxygenotherapy 31 / 32 (96.9%) 30 / 52 (57.7%) 2 / 12 (16.7%) NA <0.001
Invasive mechanical ventilation 9 / 32 (28.1%) 0 / 52 (0%) 0 / 12 (0%) NA <0.001
Non- Invasive mechanical ventilation 15 / 32 (46.9%) 3 / 52 (5.8%) 0 / 12 (0%) NA <0.001
Infiltrates 32 / 32 (100%) 47 / 52 (90.4%) 0 / 12 (0%) NA <0.001
ICU 13 / 32 (40.6%) 0 / 52 (0%) 0 / 12 (0%) NA <0.001
Exitus 13 / 32 (40.6%) 0 / 52 (0%) 0 / 12 (0%) NA <0.001
Statistics: The values are expressed as the absolute number (%) and median (interquartile range). P-values were estimated by Kruskal-Wallis test for con-
tinuous variables and Pearson’s chi-squared test for categorical variables. Abbreviations: AM, Asymptomatic/Mild, BMI, body mass index, NSAIDs, non-
steroidal anti-inflammatory drugs; ACE, angiotensin-converting enzyme; ARA, angiotensin receptor antagonists: HIV, human immunodeficiency virus;
ICU, intensive care unit; NA, not available./ not applicable
Emerging Microbes & Infections 679
In contrast, most of the SDE miRNAs were downregu-
lated, showing nine of them over a 10-fold decrease
(hsa-miR-8485, hsa-miR-1229-3p, hsa-miR-412-5p,
hsa-miR-937-3p, hsa-miR-4750-5p, hsa-miR-296-5p,
hsa-miR-26a-2-3p, hsa-miR-939-5p and hsa-miR-
424-5p).
The functional pathway analysis showed that some of
the most deregulated pathways were platelet aggregation
(plug formation), IL-3, IL-5, and GM-CSF signalling,
VEGFA-VEGFR2 pathway, and signalling by VEGF,
among others (Supplementary Data 11; Supplemen-
tary Data 12). In line with these findings, asymptomatic
patients showed lower levels of proangiogenic markers
such as FGF2 and HGF than symptomatic patients,
which were positively correlated with hsa-miR-4854
and hsa-miR-548t-5p, respectively (Figure 2). This latter
miRNA showed a high positive correlation with IFNγ,
IL-1b, IL-12, IL-1RA, and IL-4 exclusively in asympto-
matic participants (Supplementary Data 13A). Sympto-
matic patients showed a different correlation pattern,
showing a high positive correlation of the hsa-miR-320
with ferritin, HGF, IL-12, IL-4, IL-6, IL-8, IP10, MIG,
TIM3, and TNFα (Figure 13B).
Severe COVID-19 patients
Wefound137 SDEmiRNAs between severe andmoder-
ate COVID-19 patients (Supplementary Data 14). Sev-
enty-sevenmiRNAs were upregulated in severe patients
(Figure 3C), being the hsa-miR-3976, hsa-miR-4488,
hsa-miR-3150b-3p, hsa-miR-7704 and hsa-miR-3168
the most upregulated (FC > 10). Moreover, 60 miRNAs
showed a downregulation in severe patients (Figure 3C)
and the most downregulated miRNAs with a 10-fold
decrease (FC < 0.1) where the hsa-miR-7848-3p, hsa-
miR-7110-3p, hsa-miR-1197, hsa-miR-4686, hsa-miR-
374b-3p, hsa-miR-4473, hsa-miR-758-3p, hsa-miR-
5004-3p, hsa-miR-146a-3p, hsa-miR-329-5p, hsa-miR-
744-3p, hsa-miR-1285-5p and hsa-miR-6516-3p.
The pathway analysis indicated specific deregulation
of several epigenetic pathways (PRC2methylate histones
and DNA, epigenetic regulation of gene expression, and
SUMOylation of DNA methylation proteins) (Sup-
plementary Data 15; Supplementary Data 16).
Severe patients showed an increase of proinflam-
matory cytokines, where the most elevated were IL-
8, IL-6, IP10, MIG, MIP1, and TIM3, among others
(Supplementary Data 5). The proinflammatory hsa-
miR-320 family was highly expressed in severe
patients (Supplementary Data 14), showing a high
positive correlation with proinflammatory cytokines.
Conversely, the hsa-miR-26a-5p (a negative regula-
tor of the inflammatory response) and the miR-98-5p
(a member of the let-7 miRNA family) showed lower
levels in severe patients and were negatively correlated
with all proinflammatory cytokines (Figure 2C), (Sup-
plementary Data 17B).
Mortality predictive model
Characteristics of patients by mortality status are
shown in Supplementary Data 18. We identified 12
Figure 1. Exploratory analysis. A to D. Multivariate analysis
was performed by supervised partial least squares discrimi-
nant analysis (PLS-DA) from normalized log transformed and
scaled miRNA expression data: A) all individuals (healthy con-
trols, asymptomatic, moderate and severe); B) COVID+
patients vs. healthy controls; C) AM vs symptomatic; D) Severe
vs moderate. Each symbol represents the miRNA profile of
each participant of the study. E) Heatmap and hierarchical
clustering of the SDE miRNAs. Top 20 SDE miRNAs for each
comparison are shown: Healthy vs COVID+, AM vs Sympto-
matic and Moderate vs Severe. Study subjects are represented
in columns and SDE miRNAs in rows, with clustering dendo-
grams on the left for miRNAs and at the top for samples.
The colour scale shows the relative expression level of SDE
miRNAs. Red colour indicates a higher expression level and
blue a lower expression level. Patients are grouped by severity.
AM, asymptomatic; SDE, significant differentially expressed.
680 A. Fernández-Pato et al.
Figure 2. Pearson correlation plot showing the correlation between SDE miRNAs and plasma cytokines/chemokines
considering all patients for each comparison: A) COVID+ patients and healthy controls; B) Asymptomatic and symptomatic;
C) Severe and moderate. The size of the circles is proportional to the strength of the correlation, and the colour represents
the direction (colour legends are shown on the right), where large dark blue represents a strong negative correlation, and a
large dark red circle represents a strong positive correlation. SDE miRNAs are on the horizontal axis and cytokines/chemokines
on the vertical axis.
Figure 3. Differential expression analysis of miRNAs. Volcano plots showing the SDE miRNAs for each comparison: A. COVID+
vs healthy controls, B. AM vs symptomatic and C. Moderate vs severe. Red dots show miRNAs with a FDR corrected p-value≤ 0.05
and a FC≥ 1.5; Green dots showmiRNAs with a FDR corrected p-value≤ 0.05 and a FC≤−1.5; Blue dots represent miRNAs with a |
FC|≥ 1.5 that do not present statistical significance; Gray dots show miRNAs without statistical significance and a |FC| < 1.5. AM,
asymptomatic; FDR, false discovery rate; FC, fold change.
Emerging Microbes & Infections 681
miRNAs as predictors of 90-days mortality (hsa-let-7f-
1-3p, hsa-let-7g-5p, hsa-miR-1255a, hsa-miR-140-3p,
hsa-miR-20a-5p, hsa-miR-22-3p, hsa-miR-3180-3p,
hsa-miR-3180, hsa-miR-363-5p, hsa-miR-4510, hsa-
miR-548h-3p and hsa-miR-6130) (Supplementary
Data 19, 20A and 20B), but two of them (hsa-miR-
548h-3p and hsa-miR-3180) did not meet the filtering
criteria (Supplementary Data 19). Among the ten
selected miRNAs, only two were associated with a
lower probability of survival at 90 days (hsa-miR-22-
3p and hsa-miR-3180-3p), whereas eight were associ-
ated with a better prognosis (hsa-let-7f-1-3p, hsa-let-
7g-5p, hsa-miR-1255a, hsa-miR-140-3p, hsa-miR-
20a-5p, hsa-miR-363-5p, hsa-miR-4510, and hsa-
miR-6130).
This ten miRNA-expression signature (Sup-
plementary Data 21) was used to generate the
miRNA-MRS, whose median value was −3.992,
which was used as a threshold to stratify the study sub-
jects into low-risk (risk score < −3.992, n = 42) and
high-risk (risk score≥−3.992, n = 42) (Figure 4A).
Kaplan-Meier analysis confirmed the significant
differences in the overall mortality (OS) (p-value
<0.001) (Figure 4B). Moreover, ROC curve analysis
confirmed the predictive power of the miRNA-MRS,
which was improved by adjusting with age and gender,
showing a significantly higher AUROC value than the
basic model considering only age and gender (0.968
vs. 0.881, p-value = 0.042), (Figure 4C). Additionally,
we analyzed the diagnostic accuracy of adding the
miRNA-MRS to the model with age and gender by cal-
culating sensitivity, specificity, and positive and nega-
tive predictive value. Considering the cut-off for
maximum sensitivity and specificity, the model
showed sensitivity and specificity values above 90%
(Supplemental Data 22). Additionally, results were
confirmed by the multivariate Cox-regression model,
highlighting the independent prognostic value of
the risk-score (hazard ratio (HR) = 4.599; 95% confi-
dence interval (CI) (1.977− 10.700), p-value <0.001)
(Figure 4D).
Discussion
Our results showed that SARS-CoV-2 infection pro-
duces a strong disturbance of miRNA profile from
the onset of the disease, exhibiting at this time a
specific miRNA signature for each COVID-19 sever-
ity. The identified miRNAs could be used as early
severity markers at hospital admission. To our knowl-
edge, this is the first large-scale study that character-
izes the expression profile of plasma miRNAs in
COVID-19 patients of different clinical statuses, iden-
tifying specific miRNA signature for each severity
group and a specific miRNA mortality risk score
(miRNA-MRS).
miRNA altered profile by SARS-CoV-2 infection
We identified 200 SDE miRNAs between COVID-19
patients and healthy controls, most of them upregu-
lated in SARS-CoV-2 infected patients. Although
scarce studies on human miRNAs during COVID-19
have been published, some of the most upregulated
miRNAs in COVID-19 patients have been identified
to target the SARS-CoV-2 genome, which supports
their functionality. This is the case of hsa-miR-4665-
5p (predicted binding site in SARS-CoV-2 genome)
[19], hsa-miR-3190-3p (predicted targeting of
ORF1ab and ORF8 genes) [20], hsa-miR-4745-5p
(expressed in lung tissue with affinity to the 3′UTR
of SARS-COV-2 genome) [21] and hsa-miR-3150b-
3p (predicted binding site in the leader sequence of
SARS-CoV-2) [22]. Remarkably, other strongly dysre-
gulated miRNAs, such as hsa-miR-431-5p, hsa-miR-
548a-3p, and hsa-miR-331-3p have been previously
linked to several viral infections, including ZIKV
and HBV [23,24]. It has also been suggested that
hsa-miR-431-5p plays a role in restricting HCV repli-
cation after its induction by IFNβ [25]. Due to the
known antiviral activity of IFNβ, constituting the
first line of defence against viral infections, IFNβ-
regulated miRNAs could be used as early biomarkers
of COVID-19 disease, being detected before the tra-
ditional markers of immune activation or
inflammation.
Disrupted miRNAs in COVID-19 patients were
strongly involved in biological processes related to
viral infections, including influenza viral RNA tran-
scription and replication or viral mRNA translation,
suggesting a direct binding/effect of the SDE miRNAs
on the viral genome. Remarkably, biological pathways
involving eukaryotic mRNA translation also appeared
as dysregulated, including cap-dependent translation
initiation, translation initiation complex formation,
elongation, and termination, among others. These
functional alterations agree with in vitro studies that
point to the inhibition of cellular mRNA expression
in cells infected with different coronaviruses, includ-
ing SARS-CoV-2 [26]. Coronaviruses seem to hijack
the host translational machinery, thus favouring viral
protein production. In fact, Kim et al. showed that
host gene expression is almost suppressed in the first
hours of infection [27], which remarks the early dis-
turbance of miRNA machinery, as miRNAs are the
master regulators of gene expression.
Additionally, certain trace elements pathways such
as metabolism and transport of copper (Cu) and sel-
enium (Se) appeared also disrupted. These elements
are essential for a normal immune response, and
their serum levels have been proposed as good predic-
tors of COVID-19 survival [28].
However, the most dysregulated was the estrogen
signalling pathway. Estrogen plays a crucial role in
682 A. Fernández-Pato et al.
innate and adaptive immune responses, showing an
immunoenhancing effect. Estrogen receptors are
involved in tissue repair processes during respiratory
virus infection [29]. A weaker estrogen receptor sig-
nalling is linked to a poor evolution of influenza,
while estrogen treatment help to control the inflam-
matory reactions and improve survival [30]. An anti-
inflammatory effect of estrogens in COVID-19 has
been suggested, reducing SARS-CoV-2 infectivity
through the regulation of proinflammatory signalling
pathways [31]. This evidence, along with our results
and ongoing clinical trials [32], point to the necessity
of continuing to investigate the effects of these
molecules in COVID-19, to discover the potential
use of estrogens as therapy or preventive strategy
against COVID-19.
miRNA profile of asymptomatic infection
We identified a signature of 75 SDE miRNAs in
asymptomatic with respect to symptomatic patients
that could be potentially used as early predictors of a
favourable clinical course. Some of the most upregu-
lated miRNAs in symptomatic patients have pre-
viously been related to viral infections, including
influenza A viruses (IAVs), HIV1, and HIV2 viruses
Figure 4. Survival analysis of COVID-19 patients and 10-miRNA signature risk score. A. Distribution of risk score values for
low-risk and high-risk subjects. B. KM survival curve between the high-risk and low-risk groups stratified by median risk score. C.
ROC curve of survival showing the prognostic ability (AUC) of age + gender (red) and miRNA risk score + age + gender (blue). D.
Forest plot of the multivariate Cox regression analysis for age, gender and 10-miRNA risk score. The HR of each variable is indicated
with 95% CI and statistical significance (p-value). KM, Kaplan-Meier; ROC, receiver operating characteristic; AUC, area under the
curve; HR, hazard ratio; CI, confidence interval.
Emerging Microbes & Infections 683
[33,34]. The hsa-miR-424-5p targets different RNA
viruses, including SARS-CoV-2, and the hsa-miR-
939-5p targets the SARS-CoV-2 genome 3′UTR [21].
Additionally, the hsa-miR-939-5p regulates several
anti-inflammatory genes, including IL-6, VEGFA,
TNFα, NFκB2, and NOS2A, showing a key role as a
mediator in inflammatory networks [35].
We also observed up to 19 upregulated miRNAs in
asymptomatic patients. Among the most upregulated
miRNAs, hsa-miR-708-3p and hsa-miR-1291 were
recently found by Chen et al. as differentially
expressed between patients with mild and severe
symptoms of COVID-19 [36]. The hsa-miR-1291
was more than 14 times expressed in our asympto-
matic patients. This miRNA acts upstream of the che-
mokine receptor CCR2, which is known to induce
monocyte and macrophage recruitment to sites of
inflammation and regulates the immune response by
controlling the proportion of effector and regulatory
T cells [36]. An increased CCR2 expression has been
reported in patients with severe COVID-19 [37], and
similarly, hsa-miR-1291 has been pinpointed as a
potential early biomarker of COVID-19 severity
[36]. Therefore, tight control of inflammation by
hsa-miR-1291 could be behind the COVID-19 control
in asymptomatic patients.
Remarkably, asymptomatic patients showed a
higher expression of hsa-miR-150-5p, a master regula-
tor of the immune system with crucial roles in the
development of immune response, including respirat-
ory viral infections [38]. Hsa-miR-150-5p is involved
in the modulation of B cell differentiation, develop-
ment of natural killer (NK), and invariant NK T
(iNKT) cells by targeting the transcription factor c-
Myb [39], among others. Critically ill patients showed
decreased levels of hsa-miR-150-5p, and its expression
is inversely correlated with the number of days of ICU
stay [37]. Thus, a higher expression of hsa-miR-150-
5p could lead to a fine-tune immune response to suc-
cessfully clear the SARS-CoV-2 virus without an exag-
gerated immune response.
The functional analysis further confirmed these
results, which showed inflammatory and immune pro-
cesses as the most dysregulated biological events by
the SDE miRNAs. In fact, we observed disruption of
platelet aggregation, interleukin-3, −5 and GM-CSF
signalling, VEGFA-VEGFR2, and signalling by
VEGF. Although we could not evaluate VEGF, higher
plasma levels of other angiogenic growth factors such
as HGF and FGF were observed in asymptomatic
patients. Moreover, a strong enrichment of phagocytic
processes, including FcγRIIIa-mediated phagocytosis,
regulation of actin dynamics for phagocytic cup for-
mation, and Fcgamma receptor (FCγR) dependent
phagocytosis was observed. Phagocytosis is one of
the main processes in innate immune response, con-
tributing to the fight against infectious agents, as it
has been detected in COVID-19 severe patients [40].
The FCγR is also involved in cell activation and
increased proinflammatory cytokine production [41],
which could be promoting the outbreak of COVID-
19 cytokine storm.
Early miRNA signature predicting the severity
of COVID-19
Certain risk factors linked to a more severe course of
COVID-19 have already been identified, such as age
or male gender. However, these factors cannot predict
the evolution of the disease by themselves, and it
remains essential to find additional biological factors
decisive to trigger COVID-19 severity. In this sense,
we identified the molecular players in the early stages
that could be behind the progression to a severe
COVID-19.
We identified 137 SDE miRNAs between severe
patients and those with moderate symptoms. Sev-
enty-seven of these miRNAs were upregulated in
severe patients, with hsa-miR-3976, hsa-miR-4488,
hsa-miR-3150b-3p, hsa-miR-7704, and hsa-miR-
3168 showing the strongest disruption. Most of them
showed some evidence of playing a role in different
viral infections [42,43]. Moreover, hsa-miR-3150b-3p
and hsa-miR-7704 are putative targets to the leader
sequence and ORF3a gene of the SARS-CoV-2 gen-
ome, respectively [20,22]. Meanwhile, 60 miRNAs
showed downregulation in severe patients. Again,
some of the strongly disrupted miRNAs potentially
target the SARS-CoV-2 genome, such as hsa-miR-
1197, hsa-mir-5004-3p, and hsa-miR-1285-5p
[19,44], which could be involved in inhibiting virus
replication. In this regard, our functional enrichment
results suggest a strong involvement of SDE miRNAs
in biological processes such as viral RNA transcrip-
tion, replication and translation, further supporting
this hypothesis. Several examples of host miRNA
implication on RNA viruses replication and pathogen-
esis have been described, as these viruses may modify
miRNAs profile within the cell to establish a proviral
environment [45]. Our data suggest that the altered
miRNA profile observed in COVID-19 patients
could lead to a differential viral load according to
the severity of the disease. Similarly, RNAemia and
viral load on admission have been related to mortality
[46]. However, future studies would be necessary to
clarify the interplay between miRNAs, RNAemia and
viral load.
Additionally, we observed a strong disturbance of
miRNAs that modulate the immune response,
suggesting that the differential severity status could
be a result of both the altered regulation of viral trans-
lation/replication processes but also the differential
modulation of immune / inflammatory responses fol-
lowing infection.
684 A. Fernández-Pato et al.
In fact, other strongly downregulated miRNAs in
severe patients directly regulate antiviral response,
such as the hsa-miR-744-3p, against IAV and RSV
through targeting MAPK-activated protein kinase 2
[47]. On the contrary, hsa-miR-758-3p downregulates
the expression of the viral RNA receptors Toll-like
receptors 3 (TLR3) and 7 (TLR7) following HCV
infection [48]. Additionally, we observed a deep
downregulation of hsa-miR-146a-3p, involved in sev-
eral autoimmune and inflammatory diseases such as
cystic fibrosis, where it controls the IL-6 production,
contributing to the restriction of the inflammatory
response [49]. Thus, the reduced expression of this
miRNA in severe patients is related to an increase in
IL-6 production, which has been reported in severe
and critical COVID-19 patients, where IL-6 acts as a
vital amplifier [50]. Besides, we also observed an
increase in additional proinflammatory cytokines
such as IL-8, IP10, MIG, MIP-1, and TIM3, among
others.
The dysregulated miRNAs in severe patients were
mainly involved in epigenetic pathways, which have
been reported as altered in COVID-19 [51].
Ten miRNA-based risk score as a predictor of
COVID-19 mortality
The overall mortality rate of COVID-19 is around 1-
3% in most countries, and it is hugely affected by cer-
tain epidemiological factors, such as age, gender, and
comorbidities [52]. However, these estimates are
highly dependent on the countries healthcare systems
and vaccination progress. While no differences in
comorbidities or treatments by mortality status were
observed in this study, we identified early biomarkers
that allowed predicting an increased risk of death. We
screened 767 miRNAs using a LASSO Cox regression
model and identified a signature of ten SDE miRNAs
that can be used as good predictors of COVID-19
mortality.
While little is known about the potential role of
hsa-miR-1255a and hsa-miR-6130 in COVID-19 dis-
ease, some of the selected miRNAs have already
been predicted to target the SARS-CoV-2 genome,
including hsa-let-7g-5p, hsa-miR-363-5p, and hsa-
miR-4510 [20]. Moreover, a decreased hsa-miR-20a-
5p expression in peripheral blood was observed in
patients with COVID-19 [53]. This miRNA has been
computationally predicted to target several proteins
that may play a role in SARS-CoV-2 induced cell
death, including SMAD3, SMAD4, and TGFBR1
[54]. In fact, the binding of SARS-CoV N protein to
SMAD3 has been shown to interfere with the complex
formation between SMAD3 and SMAD4, thus inhibit-
ing the apoptosis of SARS-CoV-infected cells
mediated by the TGF-signalling pathway [55], in
favour of virus packaging and replication.
A link between COVID-19 disease and the miRNAs
hsa-let-7f-1-3p and hsa-miR-140-3p has also been
reported, showing a downregulation in cells infected
with SARS-CoV-2 [19,56]. Kim et al. proposed that
hsa-miR-140-3p targets the serine protease TMPRSS2,
which processes the viral S protein that binds to the
ACE2 host receptor [56]. Moreover, several studies
have described its potential to modulate Bcl-2
expression, which has an essential role in regulating
several cellular processes that could have an impact
on viral infection progression [57].
Although both age and gender have been widely
reported as risk factors for the greater severity and
mortality of COVID-19, we found that the sensitivity
and specificity in the prediction of COVID-19 mor-
tality significantly increased when including the ten-
miRNA signature-based risk score in our model.
Moreover, we confirmed the independent predictive
value of the risk-score by a multivariate Cox-
regression model including both epidemiological
variables.
Although further research is still needed to deci-
pher the biological mechanisms that could be behind
the potential effect of these human miRNAs in
COVID-19 mortality, our results provide a new prom-
ising diagnostic tool that could contribute to the
improved management of patients with a higher risk
of death at disease onset.
This miRNA-MRS could be implemented in rou-
tine diagnostics through an amplification-based detec-
tion method such as multiplex real time quantitative
PCR, or even through emerging technologies such as
hybridization-based detection methods [58]. In this
latter case, low amount of starting material and no
reverse transcription or cDNA amplification is
needed, which make this medium-throughput tech-
nology highly suitable for a scalable use [59]. These
systems can be easily combined with an analysis soft-
ware to give a simple readout indicating the disease
probability score based on the concentration of each
miRNA [60].
Nevertheless, this study has several limitations.
First, our cohort included a limited number of deaths
among participants. Thus, we decided to include all
the study samples as a training set in order to obtain
more reliable and representative results. Therefore,
our findings would need to be validated in an external
cohort of COVID-19 patients. Nevertheless, at the
time of manuscript submission, there were no high-
throughput miRNA sequencing data available from
studies of COVID-19 patients reporting mortality
analysis results to perform the validation of our
miRNA-based model. Moreover, most of the partici-
pants in our cohort were Caucasian and Hispanic,
while other ethnic groups were underrepresented.
Due to the differences in the genetic background
and miRNA expression, the predictive value of the
Emerging Microbes & Infections 685
miRNA-based risk score should also be confirmed in
studies with ethnically more diverse populations.
Additionally, it would also be interesting to study
whether miRNA profiles correlate with SARS-CoV-2
viral load or RNAemia. However, we were unable to
analyze it because no remaining sample was available.
Finally, further studies would be needed to explore
how the duration of illness influences the microRNA
profiles.
Conclusion
SARS-CoV-2 infection deeply disturbs the plasma
miRNA expression profile from an early stage of
COVID-19, making miRNAs highly valuable as early
predictors of severity and mortality.
Acknowledgements
This study would not have been possible without the collab-
oration of all the patients, their families, medical and nur-
sery staff, and data managers who have taken part in the
project. We would like also to acknowledge the Genomics
Unit, and the Bioinformatics Unit at Instituto de Salud Car-
los III, whose assistance was essential.
Author contribution
Funding body: AFR and MAJS.
Study concept and design: AFR and MAJS
Patients’ selection and clinical data acquisition: PR,
RB, MMV, AVB, OBK, FCC, FPG, OMG, AM, NBL,
JT, DA, LMP, IRMA, NBL.
Sample preparation, and plasma biomarker analy-
sis: MMV, AVB, EJVA, SBS, OBK.
Statistical analysis and interpretation of data: AFP,
AVB, OBK, DVM, AFR, and MAJS.
Writing of the manuscript: AFP, AFR, and MAJS.
Critical revision of the manuscript for relevant
intellectual content: SR, PR, RB, FPG, OMG.
Supervision and visualization: AFR and MAJS.
All authors read and approved the final manuscript.
Funding
This work was supported by Instituto de Salud Carlos III:
[Grant Number COV20/1144,CP14CIII/00010,CP17CIII/
00007,CPII20CIII/0001]. The study was also supported by
the Centro de Investigación Biomédica en Red (CIBER)
en Enfermedades Infecciosas (CB21/13/00044).
Disclosure statement
No potential conflict of interest was reported by the
author(s).
ORCID
Ana Virseda-Berdices http://orcid.org/0000-0002-9549-
567X
Salvador Resino http://orcid.org/0000-0001-8783-0450
María Martin-Vicente http://orcid.org/0000-0001-9148-
1476
Daniel Valle-Millares http://orcid.org/0000-0002-5205-
855X
Oscar Brochado-Kith http://orcid.org/0000-0001-8372-
2604
Rafael Blancas http://orcid.org/0000-0002-7559-710X
Francisco C. Ceballos http://orcid.org/0000-0001-7113-
7387
María Ángeles Jiménez-Sousa http://orcid.org/0000-0002-
1945-6169
Amanda Fernández-Rodríguez http://orcid.org/0000-
0002-5110-2213
References
[1] Oude Munnink BB, Nieuwenhuijse DF, Stein M, et al.
Rapid SARS-CoV-2 whole-genome sequencing and
analysis for informed public health decision-making
in the Netherlands. Nat Med. 2020 09;26(9):1405–
1410.
[2] Chandan K, Gupta M, Sarwat M. Role of Host and
Pathogen-Derived MicroRNAs in Immune
Regulation During Infectious and Inflammatory
Diseases. Front Immunol. 2019;10:3081.
[3] Hum C, Loiselle J, Ahmed N, et al. MicroRNAMimics
or Inhibitors as Antiviral Therapeutic Approaches
Against COVID-19. Drugs. 2021 Apr;81(5):517–531.
[4] Feng S, Zeng D, Zheng J, et al. MicroRNAs: Mediators
and Therapeutic Targets to Airway Hyper Reactivity
After Respiratory Syncytial Virus Infection. Front
Microbiol. 2018;9:2177.
[5] Hasan MM, Akter R, Ullah MS, et al. A
Computational Approach for Predicting Role of
Human MicroRNAs in MERS-CoV Genome. Adv
Bioinformatics. 2014;2014:967946.
[6] de Gonzalo-Calvo D, Benítez ID, Pinilla L, et al.
Circulating microRNA profiles predict the severity
of COVID-19 in hospitalized patients. Transl Res.
2021 Oct;236:147–159..
[7] Ying H, Ebrahimi M, KeivanM, et al. miRNAs; a novel
strategy for the treatment of COVID-19. Cell Biol Int.
2021 Oct;45(10):2045–2053.
[8] Chan AP, Choi Y, Schork NJ. Conserved Genomic
Terminals of SARS-CoV-2 as Coevolving Functional
Elements and Potential Therapeutic Targets.
mSphere. 2020 11 25;5(6):e00754–20.
[9] NarożnaM, Rubiś B. Anti-SARS-CoV-2 Strategies and
the Potential Role of miRNA in the Assessment of
COVID-19 Morbidity, Recurrence, and Therapy. Int
J Mol Sci. 2021 Aug 12;22(16):8663.
[10] Chen L, Xie W,Wang L, et al. miRNA-133a aggravates
inflammatory responses in sepsis by targeting SIRT1.
Int Immunopharmacol. 2020 Nov;88:106848.
[11] Goodwin AJ, Li P, Halushka PV, et al. Circulating
miRNA 887 is differentially expressed in ARDS and
modulates endothelial function. Am J Physiol Lung
Cell Mol Physiol. 2020 Jun 1;318(6):L1261–L1269.
[12] Song X, Wang CT, Geng XH. MicroRNA-29a pro-
motes apoptosis of monocytes by targeting STAT3
during sepsis. Genet Mol Res. 2015 Oct 29;14
(4):13746–13753.
686 A. Fernández-Pato et al.
[13] Harris PA, Taylor R, Minor BL, et al. The REDCap
consortium: Building an international community of
software platform partners. J Biomed Inform. 2019
07;95:103208.
[14] Kozomara A, Birgaoanu M, Griffiths-Jones S.
miRBase: from microRNA sequences to function.
Nucleic Acids Res. 2019 01 08;47(D1):D155–D162.
[15] Breen EJ, TanW, Khan A. The Statistical Value of Raw
Fluorescence Signal in Luminex xMAP Based
Multiplex Immunoassays. Sci Rep. 2016 05;6:26996.
[16] Rausch TK, Schillert A, Ziegler A, et al. Comparison of
pre-processing methods for multiplex bead-based
immunoassays. BMC Genomics. 2016 Aug 11;17
(1):601.
[17] Licursi V, Conte F, Fiscon G, et al. MIENTURNET: an
interactive web tool for microRNA-target enrichment
and network-based analysis. BMC Bioinformatics.
2019 Nov 04;20(1):545.
[18] Jassal B, Matthews L, Viteri G, et al. The reactome
pathway knowledgebase. Nucleic Acids Res. 2020 01
08;48(D1):D498–D503.
[19] Chow JT, Salmena L. Prediction and Analysis of
SARS-CoV-2-Targeting MicroRNA in Human Lung
Epithelium. Genes (Basel). 2020 08 26;11(9):1002.
[20] Saçar Demirci MD, Adan A. Computational analysis
of microRNA-mediated interactions in SARS-CoV-2
infection. PeerJ. 2020;8:e9369.
[21] Balmeh N, Mahmoudi S, Mohammadi N, et al.
Predicted therapeutic targets for COVID-19 disease
by inhibiting SARS-CoV-2 and its related receptors.
Inform Med Unlocked. 2020;20:100407.
[22] Natarelli L, Parca L, Mazza T, et al. MicroRNAs and
Long Non-Coding RNAs as Potential Candidates to
Target Specific Motifs of SARS-CoV-2. Noncoding
RNA. 2021 Feb 18;7(1):14.
[23] Seong RK, Lee JK, Cho GJ, et al. mRNA and miRNA
profiling of Zika virus-infected human umbilical
cord mesenchymal stem cells identifies miR-142-5p
as an antiviral factor. Emerg Microbes Infect. 2020
Dec;9(1):2061–2075.
[24] Cao Y, Chen J, Wang D, et al. Upregulated in Hepatitis
B virus-associated hepatocellular carcinoma cells,
miR-331-3p promotes proliferation of hepatocellular
carcinoma cells by targeting ING5. Oncotarget. 2015
Nov 10;6(35):38093–38106.
[25] Pedersen IM, Cheng G, Wieland S, et al. Interferon
modulation of cellular microRNAs as an antiviral
mechanism. Nature. 2007 Oct 18;449(7164):919–922.
[26] Banerjee AK, Blanco MR, Bruce EA, et al. SARS-CoV-
2 Disrupts Splicing, Translation, and Protein
Trafficking to Suppress Host Defenses. Cell. 2020 11
25;183(5):1325–1339.e21.
[27] Kim D, Lee JY, Yang JS, et al. The Architecture of
SARS-CoV-2 Transcriptome. Cell. 2020 05 14;181
(4):914–921.e10.
[28] Hackler J, Heller RA, Sun Q, et al. Relation of Serum
Copper Status to Survival in COVID-19. Nutrients.
2021 May 31;13(6):1898.
[29] Kadel S, Kovats S. Sex Hormones Regulate Innate
Immune Cells and Promote Sex Differences in
Respiratory Virus Infection. Front Immunol.
2018;9:1653.
[30] Robinson DP, Lorenzo ME, Jian W, et al. Elevated
17β-estradiol protects females from influenza A virus
pathogenesis by suppressing inflammatory responses.
PLoS Pathog. 2011 Jul;7(7):e1002149.
[31] Al-Kuraishy HM, Al-Gareeb AI, Faidah H, et al. The
Looming Effects of Estrogen in Covid-19: A Rocky
Rollout. Front Nutr. 2021;8:649128.
[32] Adel Ganaw MPF, Madhani M, Montgomery H, et al.
Oestrogen Treatment for COVID-19 Symptoms. In:
Corporation HM, editor. Clinical Trial. Qatar:
Hamad Medical Corporation; 2021.
[33] Gao J, Gao L, Li R, et al. Integrated analysis of
microRNA-mRNA expression in A549 cells infected
with influenza A viruses (IAVs) from different host
species. Virus Res. 2019 04 02;263:34–46.
[34] Biswas S, Chen E, Haleyurgirisetty M, et al.
Comparison of miRNA Expression Profiles between
HIV-1 and HIV-2 Infected Monocyte-Derived
Macrophages (MDMs) and Peripheral Blood
Mononuclear Cells (PBMCs). Int J Mol Sci. 2020 Sep
22;21(18):6970.
[35] McDonald MK, Ramanathan S, Touati A, et al.
Regulation of proinflammatory genes by the circulat-
ing microRNA hsa-miR-939. Sci Rep. 2016 08
08;6:30976.
[36] Chen Z, Wang X, Li L, et al. Construction of an autop-
hagy interaction network based on competitive
endogenous RNA reveals the key pathways and central
genes of SARS-CoV-2 infection in vivo. Microb
Pathog. 2021 Jun 19;158:105051.
[37] Kim CW, Oh JE, Lee HK. Single Cell Transcriptomic
Re-analysis of Immune Cells in Bronchoalveolar
Lavage Fluids Reveals the Correlation of B Cell
Characteristics and Disease Severity of Patients with
SARS-CoV-2 Infection. Immune Netw. 2021 Feb;21
(1):e10.
[38] Morán J, Ramírez-Martínez G, Jiménez-Alvarez L,
et al. Circulating levels of miR-150 are associated
with poorer outcomes of A/H1N1 infection. Exp
Mol Pathol. 2015 Oct;99(2):253–261.
[39] Bezman NA, Chakraborty T, Bender T, et al. miR-150
regulates the development of NK and iNKT cells. J Exp
Med. 2011 Dec 19;208(13):2717–2731.
[40] Chakraborty S, Gonzalez J, Edwards K, et al.
Proinflammatory IgG Fc structures in patients with
severe COVID-19. Nat Immunol. 2021 Jan;22(1):67–
73.
[41] Vogelpoel LT, Baeten DL, de Jong EC, et al. Control of
cytokine production by human fc gamma receptors:
implications for pathogen defense and autoimmunity.
Front Immunol. 2015;6:79.
[42] Shabani M, Nasr Esfahani B, Sadegh Ehdaei B, et al.
Inhibition of herpes simplex virus type 1 replication
by novel hsa-miR-7704. Res Pharm Sci. 2019 Apr;14
(2):167–174.
[43] Chahar HS, Corsello T, Kudlicki AS, et al. Respiratory
Syncytial Virus Infection Changes Cargo Composition
of Exosome Released from Airway Epithelial Cells. Sci
Rep. 2018 01 10;8(1):387.
[44] Mohammadi-Dehcheshmeh M, Moghbeli SM,
Rahimirad S, et al. A Transcription Regulatory
Sequence in the 5’ Untranslated Region of SARS-
CoV-2 Is Vital for Virus Replication with an Altered
Evolutionary Pattern against Human Inhibitory
MicroRNAs. Cells. 2021 02 04;10(2):319.
[45] Trobaugh DW, Klimstra WB. MicroRNA Regulation
of RNA Virus Replication and Pathogenesis. Trends
Mol Med. 2017 01;23(1):80–93.
[46] Bermejo-Martin JF, González-Rivera M, Almansa R,
et al. Viral RNA load in plasma is associated with
Emerging Microbes & Infections 687
critical illness and a dysregulated host response in
COVID-19. Crit Care. 2020 12 14;24(1):691.
[47] McCaskill JL, Ressel S, Alber A, et al. Broad-Spectrum
Inhibition of Respiratory Virus Infection by
MicroRNA Mimics Targeting p38 MAPK Signaling.
Mol Ther Nucleic Acids. 2017 Jun 16;7:256–266.
[48] Yang Q, Fu S, Wang J. Hepatitis C virus infection
decreases the expression of Toll-like receptors 3 and
7 via upregulation of miR-758. Arch Virol. 2014
Nov;159(11):2997–3003.
[49] Luly FR, Lévêque M, Licursi V, et al. MiR-146a is over-
expressed and controls IL-6 production in cystic fibro-
sis macrophages. Sci Rep. 2019 11 07;9(1):16259.
[50] Sabbatinelli J, Giuliani A, Matacchione G, et al.
Decreased serum levels of the inflammaging marker
miR-146a are associated with clinical non-response
to tocilizumab in COVID-19 patients. Mech Ageing
Dev. 2021 01;193:111413.
[51] Herbein G. An epigenetic signature to fight COVID-
19. EBioMedicine. 2021 May;67:103385.
[52] Wiersinga WJ, Rhodes A, Cheng AC, et al.
Pathophysiology, Transmission, Diagnosis, and
Treatment of Coronavirus Disease 2019 (COVID-
19): A Review. JAMA. 2020 Aug 25;324(8):782–793.
[53] Li C, Hu X, Li L, et al. Differential microRNA
expression in the peripheral blood from human
patients with COVID-19. J Clin Lab Anal. 2020
Oct;34(10):e23590.
[54] Yousefi H, Poursheikhani A, Bahmanpour Z, et al.
SARS-CoV infection crosstalk with human host cell
noncoding-RNA machinery: An in-silico approach.
Biomed Pharmacother. 2020 Oct;130:110548.
[55] Zhao X, Nicholls JM, Chen YG. Severe acute respirat-
ory syndrome-associated coronavirus nucleocapsid
protein interacts with Smad3 and modulates trans-
forming growth factor-beta signaling. J Biol Chem.
2008 Feb 08;283(6):3272–3280.
[56] Kim WR, Park EG, Kang KW, et al. Expression
Analyses of MicroRNAs in Hamster Lung Tissues
Infected by SARS-CoV-2. Mol Cells. 2020 Nov 30;43
(11):953–963.
[57] Zhu ZR, He Q, Wu WB, et al. MiR-140-3p is Involved
in In-Stent Restenosis by Targeting C-Myb and BCL-2
in Peripheral Artery Disease. J Atheroscler Thromb.
2018 Nov 01;25(11):1168–1181.
[58] Gustafson D, Tyryshkin K, Renwick N. microRNA-
guided diagnostics in clinical samples. Best Pract Res
Clin Endocrinol Metab. 2016 10;30(5):563–575.
[59] Kappel A, Keller A. miRNA assays in the clinical lab-
oratory: workflow, detection technologies and auto-
mation aspects. Clin Chem Lab Med. 2017 May
01;55(5):636–647.
[60] Gliddon HD, Herberg JA, Levin M, et al. Genome-
wide host RNA signatures of infectious diseases: dis-
covery and clinical translation. Immunology. 2018
02;153(2):171–178.
688 A. Fernández-Pato et al.