Therapeutic Approaches for COVID-19 Based on the Dynamics of Interferon-mediated Immune Responses

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DOI: 10.20944/preprints202003.0206.v1
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Abstract
As the outbreak of COVID-19 has accelerated, an urgent need for finding strategies to combat the virus is growing. Thus, gaining more knowledge on the pathogenicity mechanism of SARS-CoV-2, the causing agent of COVID-19, and its interaction with the immune system is of utmost importance. Although this novel virus is not well known yet, its structural and genetic similarity with SARS-CoV as well as the comparable pattern of age-mortality relations suggest that the previous findings on SARS can be applicable for COVID-19. Therefore, a systems biology study was conducted to investigate the underlying mechanism for the differences in the age-specific mortality of SARS and the most important signaling pathways activated by the virus. The results were then validated through a literature review on COVID-19 and the other closely related viruses, SARS and MERS. Interferons have shown to possess a crucial role in the defense against coronavirus diseases. The virus can impede the interferon induction in humans. Moreover, STAT1, a key protein in the interferon mediated immune response, is antagonized by the virus. This could explain the increased response threshold of immune cells to IFNs during CoV infections. A vivid correlation between the innate immune response threshold and the fatality rates in COVID-19 can be found. Differences in the dynamics of the interferons-related innate immune responses in children, adults and elderly may explain the reported fatality rates. The increased mortality rates in the elderly can be explained by the higher threshold of interferon-mediated immune responses. Earlier induction of interferons in children and their less developed immune system could be the reason behind their zero or near to zero fatality rate. Administration of interferon-inducing agents, such as Poly (ICLC), could reduce the mortality of SARS at the very early stages of the disease. Adding interferon-γ to an interferon-I, as a synergistic combination therapy, might maximize the benefits.At the later stages of the disease, however, the balance of the immune reactions would be disrupted and the responses would shift toward immnopathogenic over-reactions and probably cytokine storm. Moderating the activity of the immune system and supportive care in such conditions might be the optimum approach.
1
Therapeutic approaches for COVID-19 based on the dynamics of interferon-
mediated immune responses
Farbod Shahabinezhad a,1,2,3,4, Pouria Mosaddeghi a,1,2,3,4, Manica Negahdaripour1,2,*, Zahra
Dehghani1, Mitra Farahmandnejad1,2,3,4, Mohammad Javad Taghipour1, 2,3,4, Mohsen Moghadami5,
Navid Nezafat1,2, Seyed Masoom Masoompour5
a. Co-first authors in alphabetical order
1 Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
2 Department of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical
Sciences, Shiraz, Iran
3 Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
4 Cellular and Molecular Medicine Student Research Group, School of Medicine, Shiraz University of
Medical Science, Shiraz, Iran
5 Non‐Communicable Diseases Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
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© 2020 by the author(s). Distributed under a Creative Commons CC BY license.
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© 2020 by the author(s). Distributed under a Creative Commons CC BY license.
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Abstract
As the outbreak of COVID-19 has accelerated, an urgent need for finding strategies to combat the
virus is growing. Thus, gaining more knowledge on the pathogenicity mechanisms of SARS-CoV-
2, the causing agent of COVID-19, and its interaction with the immune system is of utmost
importance. Although this novel virus is not well known yet, its structural and genetic similarity
with SARS-CoV as well as the comparable pattern of age-mortality relations suggest that the
previous findings on SARS can be applicable for COVID-19. Therefore, a systems biology study
was conducted to investigate the most important signaling pathways activated by the virus. The
results were then validated through a literature review on COVID-19 and the other closely related
viruses, SARS and MERS.
Interferons have shown to play a crucial role in the defense against coronavirus diseases. CoV can
impede the interferon induction in humans. Moreover, STAT1, a key protein in the interferon-
mediated immune response, is antagonized by the virus. This could explain the increased response
threshold of immune cells to IFNs during CoV infections.
A vivid correlation between the innate immune response threshold and the fatality rates in COVID-
19 can be found. Differences in the dynamics of the interferon-related innate immune responses in
children, adults, and elderly may explain the reported fatality rates. The increased mortality rates
in the elderly can be explained by the higher threshold of interferon-mediated immune responses.
Earlier induction of interferons in children and their less developed immune system could
contribute to their near to zero fatality rate. Administration of interferon-inducing agents, such as
poly (ICLC), could reduce the mortality of SARS at the very early stages of the disease. Interferon-
γ combination with an interferon-I might induce synergistic effects and maximize the benefits.
However, in-depth research is needed to validate it and determine the optimum dosage and timing
to prevent unwanted results. Such interventions can act as a double-edged sword and aid the
imbalance of the immune reactions, which may occur at the later stages of the disease. With the
advancement of the disease and the virus overload, the responses would shift toward
immnopathogenic over-reactions and probably cytokine storm. Moderating the activity of the
immune system and supportive care in such conditions might be the optimum approach.
Keywords: 2019 novel coronavirus infection, coronavirus, SARS-CoV, interferon, systems
biology
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Introduction
Coronaviruses (CoVs) are a group of RNA viruses that have the largest RNA genome among all
the viruses known to date (1,2). Their genome is surrounded by a bilayer lipid envelope containing
the spike and membrane proteins (3). CoVs replicate by the attachment of their spike protein to
the host cell receptors resulting in release of the viral genome into the cell (4). They have several
hosts including animals and human (5). They mainly cause respiratory disease and common cold
(6), but can also cause central nervous system (CNS) infection (7).
The recent COVID-19 pandemic has alerted many researchers around the world to find treatment
for this condition. COVID-19 is caused by a novel coronavirus species, named SARS-CoV-2.
Severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory
syndrome coronavirus (MERS-CoV), two other known viruses from the same genera, were
identified in 2002 and 2012, respectively, causing serious respiratory ailments (8).
COVID-19, though a new virus, seems to have a similar pattern to SARS and MERS (9). Despite
the differences in the mortality and epidemiological rates of these three diseases, the pattern of
age-specific mortality is similar; and the mortality rates get higher as the age increases with zero
fatality rate in children under nine years old and the highest mortality rates among the elderly (9)
(Table 1) .
Table 1. COVID-19 fatality rate by age, as stated by the "Worldometers" website (10)
Age
Death rate*
80+ years old
14.8%
70-79 years old
8.0%
60-69 years old
3.6%
50-59 years old
1.3%
40-49 years old
0.4%
30-39 years old
0.2%
20-29 years old
0.2%
10-19 years old
0.2%
0-9 years old
no fatalities
*Death Rate = (number of deaths/number of cases) = probability of dying if infected by the virus (%). This probability
differs depending on the age group. The percentage shown below does NOT represent in any way the share of deaths
by age group. Rather, it represents, for a person in a given age group, the risk of dying if infected with COVID-19.
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It has been shown that the potential first lines of defense against SARS are mediated through
mannose-binding lectin as a pattern recognition molecule (PRM) of innate immunity (12).
Additionally, interleukin (IL)-12 seems to play a vital role in SARS (13). IL-12 activation would
lead to the induction of interferons (IFNs) (14). IFN-γ is a key moderator in linking the innate
immunity to adaptive immune responses (13).
IFNs are a group of cytokines, which communicate between cells against pathogens and have a
critical role in the immune system, such as activating natural killer (NK) cells and macrophages,
in addition to the flu-like symptoms of various diseases. There are three classes of IFNs: I (such
as IFN-α and –β), II (IFN-γ), and III, all of which play roles against viral infections (15).
In SARS-CoV and MERS-CoV, the reaction to viral infections by type I IFNs is suppressed. Both
CoVs use variant strategies to decrease type I IFN production. This dampening approach is highly
associated with the disease severity and increased mortality (16).
On the other hand, in the lethal cases of SARS-CoV or MERS-CoV infection, the increased influx
of inflammatory cells is always observed. In a mouse model of SARS- CoV infection, imbalanced
type I IFN and inflammatory cells were shown as the main causes of fatal pneumonia (17).
Understanding the pattern of the immune system induction in adults and children in the CoV-
associated respiratory syndromes could help to find treatment strategies for these fatal diseases.
Considering the lack of available data on COVID-19, SARS can be a helpful model in this regard.
Because SARS-CoV-2 has the highest similarity in structure and nucleotide sequence to SARS-
CoV among other viruses of this family, showing 96% and 89.6% sequence identity in the proteins
of their envelope and nucleocapsid, respectively (18).
In this study, the most important signaling pathways activated by the virus will be studied using
bioinformatics tools and a systems biology approach (19). The obtained results will be validated
through a literature review on SARS, MERS, and COVID-19 to indicate how the dynamics of
IFN-mediated antiviral response in adults, elderly, and children could determine the severity of the
disease and treatment outcomes.
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Methods
Identification of the deferentially expressed genes using high-throughput RNA-Seq datasets
At first, “coronavirus” was searched through iLINCS at http://ilincs.org (20). iLINCS is an
integrative online platform that brings together different levels of physiological data and integrates
them with a bioinformatics analysis engine aiming to analyze and interpret omics data.
The enquiry identified five non-iLINCS datasets about coronavirus infection. GreinGSE52405
dataset was chosen for further analysis. This dataset contains transcriptomics responses in mice
infected with either PR8 (highly pathogenic mouse-adapted Influenza A virus) or MA15 (mouse-
adapted severe acute respiratory syndrome coronavirus).
Geo RNA-seq experiments Interactive Navigator (GREIN) )http://www.ilincs.org/apps/grein/(
(21) was applied to analyze the differences in the gene expression level (signature) between the
C57BL/6J mice at four days post-infection with MA15 (three experimental groups) and two
control groups.
GREIN is a web application with comprehensive analytical toolbox, which provides manipulation
and analysis of RNA-seq data. The obtained results were then exported to iLINCS (20) for further
analysis.
Gene set enrichment analysis
Gene set enrichment analysis is a method to interpret deferentially expressed genes )DEGs( in
terms of the affected biological pathways and obtain information regarding signature. Gene
ontology (GO) enrichment analysis on DEGs was performed by Enrichr (22,23) method at
http://amp.pharm.mssm.edu/Enrichr/.
GO knowledgebase )http://geneontology.org/( contains comprehensive information about the
function of genes in three main aspects, including biological process (BP), molecular function
(MF), and cellular component (CC).
In addition, pathway enrichment analysis for the top 100 DEGs was done by Enrichr as well.
NCATS BioPlanet (https://tripod.nih.gov/bioplanet) (24), Kyoto encyclopedia of genes and
genomes (KEGG) (https://www.genome.jp/kegg/) (25), and Reactome (https://reactome.org/) (26)
pathway databases were used for pathway enrichment analysis to assess the potential association
of the signature with pathways.
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Protein-protein interactions network reconstruction
In order to find the essential proteins and pathways in the gene set, the protein-protein interaction
(PPI) network of the signature was extracted from the International Molecular Exchange
Consortium (IMEx) protein interactions database (27) through NetworkAnalyst
(https://www.networkanalyst.ca) (28).
NetworkAnalyst is a powerful and user-friendly analytics platform, which assists biologists in the
interpretation of systems-level data. This tool was implemented to visualize and analyze the PPI-
network of top 100 DEGs. Noteworthy, in order to control the network size, the minimum network
tool was selected amongst network tools, which keeps seed proteins and non-seed proteins that are
crucial for network connections.
Results and discussion
Identification of DEGs between control and experimental groups and enrichment analysis
The currently available wealth of omics data prompt the researchers to develop tools and
algorithms to fully exploit the information contained within these data. The list of the DEGs
obtained through iLINCS is represented in the supplementary, Tables S1 (top 100 selected genes)
and S2 (complete signature). As seen on Fig. (1), the results of the enrichment analysis indicated
that the 100-top selected genes are mostly significantly associated with the immune system, IFN
signaling, and viral infections.
The findings of our omics analysis showed good congruence with several studies in which the
importance of the IFN signaling pathways in CoV infections was reported (2931), which supports
the validity of our selected approach and the notion that CoV infection can trigger IFN signaling
pathway.
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a)
b)
c)
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d)
e)
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Figure 1. Gene set enrichment analysis (performed via Enricher) of top DEGs of several subgroups
of GreinGSE52405 dataset obtained through: (a) BioPlanet 2019 (b) KEGG (c) Reactome (d) GO
Biological Process 2018 (e) GO Molecular Function 2018.- The tested subgroups included the
C57BL/6J mice infected with MA15 (mouse-adopted severe acute respiratory syndrome
coronavirus) at four days post-infection. The differences in the gene expression level (signature)
between three experimental groups and two control groups were analyzed in this study.
The PPI network of top100 DEGs
The protein-protein interaction network for the signature was constructed using IMEx protein
interactions database through NetworkAnalyst (Fig. 2), representing the crucial proteins in the
network, which are called hub proteins. It is well established that the virus-host interaction has a
crucial role in the disease outcome, and infecting the host system is mainly mediated through
affecting host’s important proteins. Though studying the related PPI network can clarify some
routes that virus uses in this regard.
The list of all proteins in the network and node centralities (degree and betweenness) is available
in Table S3. Noteworthy, the degree of a node is defined as the number of connections that a node
has to other nodes and betweenness centrality is the number of the shortest paths passing through
the node in a graph (28).
As shown in the PPI network (Fig. 2 and Table S3), STAT1 (Degree: 44, Betweenness: 5916.08),
IRF7 (Degree: 18, Betweenness: 1324.53), and ISG15 (Degree:10, Betweenness: 437.65) could
be considered as major hub proteins in the network.
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Several studies revealed that CoV implements some strategies to evade the immune response by
antagonizing the arms of IFN signaling pathway (32). In this regard, Frieman et al. indicated that
SARS-CoV ORF6 protein blocks the expression of STAT1-activated genes and finally acts as an
IFN antagonist (33). Yang and colleagues indicated that MERS-CoV ORF4b antagonizes the
antiviral IFN-β response by inhibiting IRF3 and IRF7 (34). In addition, it is well established that
SARS-CoV and MERS-CoV encode papain-like proteases (PLPs) that are able to impede the
immune response function (35). Daczkowski et al. demonstrated that CoV could interact with IFN-
stimulated gene 15 (ISG15) and antagonize the IFN-mediated antiviral response (36).
Therefore, it seems that coronavirus targets the most important proteins in the IFN signaling
pathway to evade the immune system. This highlights the key role of the IFN-mediated antiviral
responses in the CoV infections.
Figure 2. Network analysis of the tested subgroups of GreinGSE52405 dataset (the C57BL/6J
mice at four days post-infection with MA15, as three experimental groups, and two control
groups) . Nodes with bigger size are considered as hubs in this network.
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Dynamics of antiviral response determines the severity of the disease
The dynamics of IFN-related antiviral responses may be the lost circle in understanding the
virulence of CoVs. There are some observations and facts supporting this notion.
The zero fatality in children under nine years old (Table 1) seems contradictory to the fact that the
immune system gets stronger when a child grows up. However, there are differences at the timing
of the initiation of immune responses in children versus adults (11).
Considering the immunologic differences between adults and children, the IFN-γ induction by NK
cells are higher in adults but has a lower threshold in children (11,37). It seems that children
respond faster to the virus in the incubation period (38), so that their immune system inhibits the
virus replication and prevents high virus titers. On the other hand, in adults, the immunologic
response is postponed as the virus impairs the innate immune response by shutting down the
signaling pathways.
In a study (39) on the dynamics of the innate immune responses of human cells to SARS-CoV
infection, it was indicated that the activation of the IFN regulatory factor )IRF(-3/7 pathway did
not occur until 48 hours post-infection. The authors concluded that the delayed IFN-related
antiviral response is a possible strategy implemented by CoV to evade the immune response (40).
The virus also circumvents the immune system by hiding its double-stranded RNA in vesicles,
causing less IFN induction (32).
In a study by Reghunathan et al. (41), the immunologic responses of 10 adult hospitalized patients
with SARS were investigated. It was concluded that the immune response of SARS-affected
patients is mainly innate immune response rather than specific antiviral responses. Even though
their finding may seem contradictory to the uncovered crucial role of IFN signaling in CoV
infections (40) at first, it affirms that SARS-CoV can impede IFN induction and the IFN mediated
responses may be suppressed in severe cases of the infection. In other words, the lack of proper
antiviral immune responses in the affected patients can be due to antagonization of the sensing and
signaling arms of IFN pathways following a high level of virus replication and the consequent
impairment in the immune system functions at several days post-infection. Thus, it can be
concluded that a delayed IFN-related antiviral response could debilitate the host immunity to
inhibit rapid virus replication at the early stages of infection.
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This immune system inhibition might happen through affecting STAT1 and other important
discussed proteins or some other strategies that CoV implement to antagonize IFN signaling (31),
which finally compromises effective IFN-mediated antiviral immune responses (42).
To validate this assumption, the dataset of the above-mentioned study (41), GDS1028, was
searched and reanalyzed with the help of iLINCS (43). Noteworthy, this dataset contains the
expression profiling of peripheral blood mononuclear cells (PBMC) from 10 adult hospitalized
patients with SARS and four healthy controls.
The pathway (24,25,44,45) and diseases (46,47) enrichment analysis of the top 100 DEGs was
also performed via Enrichr (22,23). Interestingly, disease enrichment analysis demonstrated that
the signature was highly associated with some diseases that are the result of the immune system
malfunction, including septic shock, obstructive pulmonary bronchitis, allergic diseases, and
autoimmune diseases.
Besides, the pathway enrichment analysis revealed that the signature is highly associated with the
apoptosis pathway. This result is consistent with the recent study, which represented that the
number of T cells in patients with COVID-19 were reduced and functionally exhausted, especially
among elderly patients (≥60) and in patients requiring Intensive Care Unit (ICU) (48).
The enrichment analysis results are represented in Fig. S2 and Tables S3. Moreover, the list of the
DEGs obtained through iLINCS is represented in the supplementary, Tables S4 (top 100 selected
genes) and S5 (complete signature).
The importance of T cell-mediated immune response in respiratory CoV is well established (49).
Type I IFN response is shown crucial in T cell survival (50,51). Moreover, the phosphorylation of
STAT1 and STAT5 was increased in the activated naïve CD4+ T cells taken from young adults
leading to their lower response threshold to type I IFN stimulation. However, this mechanism was
subdued in the elderly naïve CD4+ T cells (52). In addition, systemically reduced immune cell
responsiveness to cytokines in older adults was demonstrated in a striking study (53). Likewise,
the impaired innate IFN secretion in the elderly is well documented in several studies (4850).
As mentioned above, SARS-CoV ORF6 protein antagonizes the function of STAT1. Therefore,
STAT1 inactivation by ORF6 protein might be the cause of reduced and functionally-exhausted T
cells in patients with COVID-19, especially in the elderly patients (33).
Channappanavar et al. (29) also demonstrated that suboptimal T cell responses occurred in SARS-
CoV-infected BALB/c mice. Although the authors concluded that IFN-I-mediated inflammatory
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responses caused impaired T cell function, it seems that antagonizing the function of STAT1 by
SARS-CoV can play a crucial role in this impairment. Considering the fact that T cells reduce
cytokine storm by modulating the innate immune response (54), it seems that the higher response
threshold in the elderly, which is aggravated by the antagonizing effects of ORF6 on STAT1 (33),
leads to their poor clinical outcome.
Altogether, it might be concluded that the patients with SARS-CoV-2 at the late stages of the
disease suffer from many abnormalities, which are the result of immune system imbalance and
malfunction and lack of on-time effective IFN-specific immune responses that can lead to
proinflammatory reactions and immunopathological conditions, presented by lethal inflammations
in the lungs, vascular leakage, and cytokine storm (29).
Approaches to control COVID19: a systems biology perspective
Induction of IFNs can play a key role in the body defense against CoVs infections, as supported
by several studies mentioned in the following.
Numerous studies have presented the success in defeating CoVs by the direct administration of
IFNs. A combination of type I IFN and either IFN-γ or IFN-λ, was shown to synergistically inhibit
the virus replication in vitro (40,55,56). Larkin et al. (57) indicated that a combination of IFN-α
and IFN-γ in vitro provided strong synergistic antiviral activities at much lower dosages of IFN
than normally required. Lowering the dose of IFNs in combination therapy offers the advantage
of reduction in undesired adverse reactions for the patients. Nagata et al. (58) has described the
destructive effect of cytokine storm in adult mice after SARS-CoV infection. While IV injections
of TNF-α was not beneficial, intraperitoneal IFN-γ injection showed a protective effect. Cinatl et
al. (59) reported the in vitro superiority of IFN-β over -α and -γ, while suggesting the effectiveness
of IFN-γ over IFN-α in Vero cell cultures of SARS-CoV infection. Bellomi et al. (60) also reported
the synergistic effects of IFN-γ and -β on Vero cells infected with SARS-CoV. Another study,
demonstrated that IFN-α and -γ co-administration caused hyper-activated IRF-1 and STAT1,
which finally led to a more robust antiviral symphony against virus replication (61).
Altogether, it seems that combinational IFN therapy could significantly inhibit virus replication
and overcome the increased response threshold of IFN induction that has been resulted by STAT1
inhibition in the immune cells by CoVs, especially in the elderlies
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Additionally, another study uncovered that the timing of IFN therapy would be of great
importance. In an in vivo study, mice were protected against the virus when IFN-I was given before
the maximum rise of the virus, during one day after the infection, though the expression of the
ISGs and inflammatory cytokine genes was reduced. On the other hand, treatment failure was seen
in case of later injection of IFN-β, because the virus titer went up, and monocytes, macrophages,
and neutrophils were accumulated and activated in the lungs, and proinflammatory cytokines were
induced, which finally led to severe lethal pneumonia (62).
As another approach, the IFN level can be increased indirectly. Noteworthy, toll like receptor
(TLR)3 activation, could induce these IFNs (6366).
In this regard, two distinct studies (67,68) demonstrated that the pretreatment of mice with TLR3
agonists protected them from mouse-adapted SARS-CoV infection. The used TLR3 agonists, poly
ICLC and poly (I:C) (polyinosinic:polycytidylic acid) could augment the production of IFN-α, -β,
and -γ, which consequently inhibited CoV replication and compensated for the inhibitory effects
of CoV on IFN signaling pathways (64). Poly (I:C) is a synthetic double-stranded RNA
immunostimulant, which is used as adjuvant in vaccine production (69). Poly (I:C) treatment
induces not only IFN-α and -β, but also IFN-γ (67). The intranasal or aerosols of poly (I:C) within
48 hours of infection were shown beneficial in mice (67,68). In another study on the infected mice,
IFN-α or poly (I:C) were suggested as the only compounds able to inhibit SARS-CoV replication
when compared to several different compounds including nelfinavir, calpain inhibitor VI, 3-
deazaneplanocin A, β-D-N4-hydroxycytidine, and Alferon® (human leukocyte IFN-α-n3) as well
as some anti-inflammatory agents, i.e. chloroquine, amodiaquin, and pentoxifylline (70).
Interestingly, chloroquine, a recently proposed medication for COVID-19 (71), interacts with poly
(I:C) as an endosomal acidification inhibitor, which inhibits poly (I:C)-mediated IFN-β expression
(72). Therefore, it can be concluded that TLR3 agonists can be a proper option for employment in
the development of vaccines against COVID-19.
In addition to TLR-3 agonists (poly ICLC (68), poly (I:C) (73), and rintatolimod (32)) and
phytohaemagglutinin (PHA) can induce IFN production in the human body as well (74).
Nevertheless, some of them may strongly stimulate the immune system and lead to unwanted
reactions or toxicity. For example, PHA is a natural compound found in high concentrations in red
kidney beans and with lower concentrations in other beans (75). The oral consumption of uncooked
red kidney bean has been announced to induce gastrointestinal toxicity and mitogenicity (75)
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because of high levels of PHA. However, whether a low concentration of PHA could be beneficial
at the early stages of the disease or incubation period to stimulate IFN production can be a subject
for further research.
Although IFNs are available as medicinal products, some adverse effects such as flu-like syndrome
and bone marrow suppression should be considered for their direct indication (76). Moreover, the
protocol for their indication including proper timing and dosing should be confirmed.
All in all, IFN induction in the incubation period and at the very early stages of the disease could
be the key to prevent CoV-associated mortalities, yet the proper dosing needs further
investigations. Research and clinical trials for finding the right timing for such interventions as
well as introducing the proper dose-adjustment protocols are urgently needed.
On the other hand, at the later stages of the disease, the balance of the immune system becomes
impaired, hence probable inflammatory over-reactions, cytokine storm, and possible autoimmune
responses should be considered. In such circumstances, therapeutic approaches to reduce possible
lung inflammations may be needed.
Conclusion
A vivid correlation between the innate immune response threshold and the fatality rates in COVID-
19 can be found. The increased mortality rates in the elderly can be explained by the higher
threshold of IFN immune responses. Differences in the dynamics of the IFN-related innate immune
responses in children, adults, and elderly may explain the different reported fatality rates. Earlier
induction of IFNs in children and their less developed immune system could contribute to their
near to zero mortality.
The key for success in reducing the disease fatality might be stimulation of the innate immune
responses to trigger IFN production at the very early stages of the disease, which might be done
through administration of agents that are able to augment IFN production such as poly ICLC.
Despite the evidences for the efficacy of IFNs in treating CoV-induced infections, the proper
dosing and ideal timing for such interventions needs to be verified in clinical trials. Moreover,
interferon-γ combination with an interferon-I might induce synergistic benefits. However, in-depth
research is needed to validate it and determine the optimum dosage and timing to prevent unwanted
results. Such interventions can act as a double-edged sword and aid the imbalance of the immune
reactions, which may occur at the later stages of the disease. With the advancement of the disease
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16
and the virus overload, the responses would shift toward immnopathogenic over-reactions and
probably cytokine storm presented by severe respiratory syndrome, which might indicate a need
for tempering the immune system activity. Although this suggestion might need more clinical
evidences.
All of the stated theories are based on the assumption that the immune response against COVID-
19 is similar to other coronaviruses especially SARS, which should be validated through future
insights on SARS-CoV-2.
Acknowledgment
This study was supported by Grant from the Research Council of Shiraz University of Medical
Sciences, Shiraz University of Medical Sciences, Shiraz, Iran. The authors would like to thank
Dr. Mohammadreza Dorvash for his help on selection of the methods.
Conflict of interest
Authors declare no conflict of interests.
Appendix A. Supplementary data
Figure S1. Gene set enrichment analysis of top DEGs of GDS1028 dataset.
Table S1. Top 100 selected DEGs (deferentially expressed genes) of some subgroups from
GreinGSE52405 dataset.
Table S2. The complete signature of the MA15 group data from GreinGSE52405 dataset.
Table S3. The list of all proteins in the network and node centralities (degree and betweenness) of
the tested subgroups of GreinGSE52405 dataset.
Table S4. Top 100 selected DEGs (deferentially expressed genes) of GDS1028 dataset.
Table S5. The complete signature of GDS1028 dataset.
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Figure S1. Gene set enrichment analysis of top DEGs of GDS1028 dataset (performed via
Enricher). (a) BioPlanet(b) KEGG (c) WikiPathways (d) PheWeb 2019 (e) GWAS catalog 2019
(f) Jensen disease. This dataset contains the expression profiling of peripheral blood mononuclear
cells (PBMC) from 10 adult hospitalized patients with SARS and four healthy controls (41).
Supplementary tables legends
Table S1. Top 100 selected DEGs (deferentially expressed genes) of some subgroups from
GreinGSE52405 dataset. The data is obtained through iLINCS and analyzed by Grein. The tested
subgroups included the C57BL/6J mice infected with MA15 (mouse-adopted severe acute
respiratory syndrome coronavirus) at four days post-infection. The differences in the gene
expression level (signature) between three experimental groups and two control groups were
analyzed in this study.
Table S2. The complete signature of the MA15 group data from GreinGSE52405 dataset obtained
through iLINCS and analyzed by Grein. The tested subgroups included the C57BL/6J mice
infected with MA15 (mouse-adopted severe acute respiratory syndrome coronavirus) at four days
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18
post-infection. The differences in the gene expression level (signature) between three experimental
groups and two control groups were analyzed in this study.
Table S3. The list of all proteins in the network and node centralities (degree and betweenness) of
the tested subgroups of GreinGSE52405 dataset (the C57BL/6J mice at four days post-infection
with MA15, as three experimental groups, and two control groups) obtained through analysis of
the protein-protein interaction network.
Table S4. Top 100 selected DEGs (deferentially expressed genes) of GDS1028 dataset, obtained
through iLINCS. This dataset contains the expression profiling of peripheral blood mononuclear
cells (PBMC) from 10 adult hospitalized patients with SARS and four healthy controls (41).
Table S5. The complete signature of GDS1028 dataset, obtained through iLINCS and analyzed
by Grein. This dataset contains the expression profiling of peripheral blood mononuclear cells
(PBMC) from 10 adult hospitalized patients with SARS and four healthy controls (41).
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References
1. Ziebuhr J. The coronavirus replicase. Curr Top Microbiol Immunol. 2005;287:5794.
2. Anand K, Ziebuhr J, Wadhwani P, Mesters JR, Hilgenfeld R. Coronavirus main proteinase
(3CLpro) Structure: Basis for design of anti-SARS drugs. Science (80- ).
2003;300(5626):17637.
3. de Haan CAM, Smeets M, Vernooij F, Vennema H, Rottier PJM. Mapping of the
Coronavirus Membrane Protein Domains Involved in Interaction with the Spike Protein. J
Virol. 1999;73(9):744152.
4. Weiss SR, Navas-Martin S. Coronavirus Pathogenesis and the Emerging Pathogen Severe
Acute Respiratory Syndrome Coronavirus. Microbiol Mol Biol Rev. 2005;69(4):63564.
5. Lim Y, Ng Y, Tam J, Liu D. Human Coronaviruses: A Review of VirusHost
Interactions. Diseases. 2016;4(4):26.
6. Nicholson KG, Kent J, Ireland DC. Respiratory viruses and exacerbations of asthma in
adults. Br Med J. 1993;307(6910):9826.
7. Bergmann CC, Lane TE, Stohlman SA. Coronavirus infection of the central nervous
system: Host-virus stand-off. Vol. 4, Nat Rev Microbiol. 2006. p. 12132.
8. Wu Z, McGoogan JM. Characteristics of and Important Lessons From the Coronavirus
Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases
From the Chinese Center for Disease Control and Prevention. [Internet]. Jama. 2020 [cited
2020 Mar 2]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/32091533
9. Novel Coronavirus Pneumonia Emergency Response Epidemiology Team. [The
epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases
(COVID-19) in China]. Zhonghua Liu Xing Bing Xue Za Zhi [Internet]. 2020;41(2):145
51. Available from: http://www.ncbi.nlm.nih.gov/pubmed/32064853
10. Coronavirus Age, Sex, Demographics (COVID-19) - Worldometer [Internet]. [cited 2020
Mar 2]. Available from: https://www.worldometers.info/coronavirus/coronavirus-age-sex-
demographics/
11. Simon AK, Hollander GA, McMichael A. Evolution of the immune system in humans
from infancy to old age. Proc R Soc B Biol Sci. 2015;282(1821):18.
12. Ip WKE, Chan KH, Law HKW, Tso GHW, Kong EKP, Wong WHS, et al. Mannose‐
Binding Lectin in Severe Acute Respiratory Syndrome Coronavirus Infection. J Infect
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 23 March 2020 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 23 March 2020 doi:10.20944/preprints202003.0206.v2
20
Dis. 2005;191(10):1697704.
13. Tang F, Liu W, Zhang F, Xin ZT, Wei MT, Zhang PH, et al. IL-12 RB1 genetic variants
contribute to human susceptibility to severe acute respiratory syndrome infection among
Chinese. PLoS One. 2008;3(5):e2183.
14. Brown J, Wang H, Hajishengallis GN, Martin M. TLR-signaling networks: An integration
of adaptor molecules, kinases, and cross-talk. J Dent Res. 2011;90(4):41727.
15. Samuel CE. Antiviral actions of interferons. Clin Microbiol Rev. 2001;14(4):778809.
16. Perlman S, Dandekar AA. Immunopathogenesis of coronavirus infections: Implications
for SARS. Vol. 5, Nat Rev Immunol. 2005. p. 91727.
17. Channappanavar R, Perlman S. Pathogenic human coronavirus infections: causes and
consequences of cytokine storm and immunopathology. Vol. 39, Seminars in
Immunopathology. 2017. p. 52939.
18. Zhou Y, Hou Y, Shen J, Huang Y, Martin W, Cheng F. Network-based Drug Repurposing
for Human Coronavirus. medRxiv [Internet]. 2020 Jan 1;2020.02.03.20020263. Available
from:
https://www.medrxiv.org/content/medrxiv/early/2020/02/05/2020.02.03.20020263.full.pdf
19. Dorvash M, Farahmandnia M, Mosaddeghi P, Farahmandnejad M, Saber H,
Khorraminejad-Shirazi M, et al. Dynamic modeling of signal transduction by mTOR
complexes in cancer. J Theor Biol [Internet]. 2019 Dec 21 [cited 2020 Mar
4];483:109992. Available from: http://www.ncbi.nlm.nih.gov/pubmed/31493485
20. Pilarczyk M, Najafabadi MF, Kouril M, Vasiliauskas J, Niu W, Shamsaei B, et al.
Connecting omics signatures of diseases, drugs, and mechanisms of actions with iLINCS.
bioRxiv [Internet]. 2019;826271. Available from:
http://biorxiv.org/content/early/2019/10/31/826271.abstract
21. Mahi N Al, Najafabadi MF, Pilarczyk M, Kouril M, Medvedovic M. GREIN: An
Interactive Web Platform for Re-analyzing GEO RNA-seq Data. Sci Rep. 2019;9(1):7580.
22. Kuleshov M V., Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr:
a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids
Res. 2016;44(W1):W907.
23. Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles G V., et al. Enrichr: Interactive
and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics.
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 23 March 2020 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 23 March 2020 doi:10.20944/preprints202003.0206.v2
21
2013;14.
24. Huang R, Grishagin I, Wang Y, Zhao T, Greene J, Obenauer JC, et al. The NCATS
BioPlanet An integrated platform for exploring the universe of cellular signaling
pathways for toxicology, systems biology, and chemical genomics. Front Pharmacol.
2019;10(APR):445.
25. Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M. KEGG: Kyoto encyclopedia
of genes and genomes. Vol. 27, Nucleic Acids Res. 1999. p. 2934.
26. Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, et al. The
Reactome Pathway Knowledgebase. Nucleic Acids Res. 2018;46(D1):D64955.
27. Orchard S, Kerrien S, Abbani S, Aranda B, Bhate J, Bidwell S, et al. Protein interaction
data curation: The International Molecular Exchange (IMEx) consortium. Vol. 9, Nature
Methods. NIH Public Access; 2012. p. 34550.
28. Zhou G, Soufan O, Ewald J, Hancock REW, Basu N, Xia J. NetworkAnalyst 3.0: a visual
analytics platform for comprehensive gene expression profiling and meta-analysis.
Nucleic Acids Res. 2019;47(W1):W23441.
29. Channappanavar R, Fehr AR, Vijay R, Mack M, Zhao J, Meyerholz DK, et al.
Dysregulated Type I Interferon and Inflammatory Monocyte-Macrophage Responses
Cause Lethal Pneumonia in SARS-CoV-Infected Mice. Cell Host Microbe.
2016;19(2):18193.
30. Clementz MA, Chen Z, Banach BS, Wang Y, Sun L, Ratia K, et al. Deubiquitinating and
Interferon Antagonism Activities of Coronavirus Papain-Like Proteases. J Virol.
2010;84(9):461929.
31. Kindler E, Thiel V, Weber F. Interaction of SARS and MERS Coronaviruses with the
Antiviral Interferon Response. In: Advances in Virus Research. 2016. p. 21943.
32. Strayer D, Dickey R, Carter W. Sensitivity of SARS/MERS CoV to Interferons and Other
Drugs Based on Achievable Serum Concentrations in Humans. Infect Disord - Drug
Targets [Internet]. 2014 Sep 11 [cited 2020 Mar 3];14(1):3743. Available from:
http://www.ncbi.nlm.nih.gov/pubmed/25019238
33. Frieman M, Yount B, Heise M, Kopecky-Bromberg SA, Palese P, Baric RS. Severe Acute
Respiratory Syndrome Coronavirus ORF6 Antagonizes STAT1 Function by Sequestering
Nuclear Import Factors on the Rough Endoplasmic Reticulum/Golgi Membrane. J Virol.
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 23 March 2020 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 23 March 2020 doi:10.20944/preprints202003.0206.v2
22
2007;81(18):981224.
34. Yang Y, Ye F, Zhu N, Wang W, Deng Y, Zhao Z, et al. Middle East respiratory syndrome
coronavirus ORF4b protein inhibits type i interferon production through both cytoplasmic
and nuclear targets. Sci Rep. 2015;5:17554.
35. Sun L, Xing Y, Chen X, Zheng Y, Yang Y, Nichols DB, et al. Coronavirus papain-like
proteases negatively regulate antiviral innate immune response through disruption of
STING-mediated signaling. PLoS One. 2012;7(2):e30802.
36. Daczkowski CM, Dzimianski J V., Clasman JR, Goodwin O, Mesecar AD, Pegan SD.
Structural Insights into the Interaction of Coronavirus Papain-Like Proteases and
Interferon-Stimulated Gene Product 15 from Different Species. J Mol Biol.
2017;429(11):166183.
37. Dobbs K, Tabellini G, Calzoni E, Patrizi O, Martinez P, Giliani SC, et al. Natural killer
cells from patients with recombinase-activating gene and non-homologous end joining
gene defects comprise a higher frequency of CD56bright NKG2A+++ cells, and yet
display increased degranulation and higher perforin content. Front Immunol.
2017;8(JUL):798.
38. Ivarsson MA, Loh L, Marquardt N, Kekäläinen E, Berglin L, Björkström NK, et al.
Differentiation and functional regulation of human fetal NK cells. J Clin Invest.
2013;123(9):3889901.
39. Okabe Y, Kawane K, Nagata S. IFN regulatory factor (IRF) 3/7-dependent and -
independent gene induction by mammalian DNA that escapes degradation. Eur J Immunol
[Internet]. 2008 Nov [cited 2020 Mar 4];38(11):31508. Available from:
http://www.ncbi.nlm.nih.gov/pubmed/18991290
40. Yoshikawa T, Hill TE, Yoshikawa N, Popov VL, Galindo CL, Garner HR, et al. Dynamic
innate immune responses of human bronchial epithelial cells to severe acute respiratory
syndrome-associated coronavirus infection. PLoS One. 2010;5(1):e8729.
41. Reghunathan R, Jayapal M, Hsu LY, Chng HH, Tai D, Leung BP, et al. Expression profile
of immune response genes in patients with severe acute respiratory syndrome. BMC
Immunol. 2005;6:2.
42. Kindrachuk J, Ork B, Hart BJ, Mazur S, Holbrook MR, Frieman MB, et al. Antiviral
potential of ERK/MAPK and PI3K/AKT/mTOR signaling modulation for Middle East
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 23 March 2020 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 23 March 2020 doi:10.20944/preprints202003.0206.v2
23
respiratory syndrome coronavirus infection as identified by temporal kinome analysis.
Antimicrob Agents Chemother [Internet]. 2015 Feb 1 [cited 2020 Mar 19];59(2):108899.
Available from: http://www.ncbi.nlm.nih.gov/pubmed/25487801
43. Pilarczyk M, Najafabadi MF, Kouril M, Vasiliauskas J, Niu W, Shamsaei B, et al.
Connecting omics signatures of diseases, drugs, and mechanisms of actions with iLINCS.
bioRxiv [Internet]. 2019 Oct 31 [cited 2020 Mar 3];826271. Available from:
http://biorxiv.org/content/early/2019/10/31/826271.abstract
44. Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M. KEGG as a reference
resource for gene and protein annotation. Nucleic Acids Res. 2016;44(D1):D45762.
45. Slenter DN, Kutmon M, Hanspers K, Riutta A, Windsor J, Nunes N, et al. WikiPathways:
A multifaceted pathway database bridging metabolomics to other omics research. Nucleic
Acids Res. 2018;46(D1):D6617.
46. Buniello A, Macarthur JAL, Cerezo M, Harris LW, Hayhurst J, Malangone C, et al. The
NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted
arrays and summary statistics 2019. Nucleic Acids Res. 2019;47(D1):D100512.
47. Pletscher-Frankild S, Pallejà A, Tsafou K, Binder JX, Jensen LJ. DISEASES: Text mining
and data integration of disease-gene associations. Methods. 2015;74(3):839.
48. Diao B, Wang C, Tan Y, Chen X, Liu Y, Ning L, et al. Reduction and Functional
Exhaustion of T Cells in Patients with Coronavirus Disease 2019 (COVID-19). medRxiv.
2020 Feb 20;2020.02.18.20024364.
49. Channappanavar R, Zhao J, Perlman S. T cell-mediated immune response to respiratory
coronaviruses. Immunol Res. 2014;59(13):11828.
50. Lombardi G, Dunne PJ, Scheel-Toellner D, Sanyal T, Pilling D, Taams LS, et al. Type 1
IFN Maintains the Survival of Anergic CD4 + T Cells . J Immunol. 2000;165(7):37829.
51. Marrack P, Kappler J, Mitchell T. Type I interferons keep activated T cells alive. J Exp
Med. 1999;189(3):5219.
52. Li G, Ju J, Weyand CM, Goronzy JJ. Age-Associated Failure To Adjust Type I IFN
Receptor Signaling Thresholds after T Cell Activation. J Immunol. 2015;195(3):86574.
53. Shen-Orr SS, Furman D, Kidd BA, Hadad F, Lovelace P, Huang YW, et al. Defective
Signaling in the JAK-STAT Pathway Tracks with Chronic Inflammation and
Cardiovascular Risk in Aging Humans. Cell Syst. 2016;3(4):374-384.e4.
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 23 March 2020 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 23 March 2020 doi:10.20944/preprints202003.0206.v2
24
54. Dong Kim K, Zhao J, Auh S, Yang X, Du P, Tang H, et al. Adaptive immune cells temper
initial innate responses. Nat Med. 2007;13(10):124852.
55. Sainz B, Mossel EC, Peters CJ, Garry RF. Interferon-beta and interferon-gamma
synergistically inhibit the replication of severe acute respiratory syndrome-associated
coronavirus (SARS-CoV). Virology [Internet]. 2004 Nov 10 [cited 2020 Mar
1];329(1):117. Available from: http://www.ncbi.nlm.nih.gov/pubmed/15476870
56. Mossel EC, Sainz B, Garry RF, Peters CJ. Synergistic inhibition of SARS-coronavirus
replication by type I and type II IFN. Adv Exp Med Biol. 2006;581:5036.
57. Larkin J, Jin L, Farmen M, Venable D, Huang Y, Tan SL, et al. Synergistic antiviral
activity of human interferon combinations in the hepatitis C virus replicon system. J Interf
Cytokine Res [Internet]. 2003 May 1 [cited 2020 Mar 4];23(5):24757. Available from:
http://www.ncbi.nlm.nih.gov/pubmed/12804067
58. Nagata N, Iwata N, Hasegawa H, Fukushi S, Harashima A, Sato Y, et al. Mouse-passaged
severe acute respiratory syndrome-associated coronavirus leads to lethal pulmonary
edema and diffuse alveolar damage in adult but not young mice. Am J Pathol. 2008 Jun
1;172(6):162537.
59. Cinatl J, Morgenstern B, Bauer G, Chandra P, Rabenau H, Doerr HW. Treatment of SARS
with human interferons. Lancet [Internet]. 2003 Jul 26 [cited 2020 Mar 4];362(9380):293
4. Available from: http://www.ncbi.nlm.nih.gov/pubmed/12892961
60. Scagnolari C, Vicenzi E, Bellomi F, Stillitano MG, Pinna D, Poli G, et al. Increased
sensitivity of SARS-coronavirus to a combination of human type I and type II interferons.
Antivir Ther. 2004;9(6):100311.
61. Zhang XN, Liu JX, Hu YW, Chen H, Yuan ZH. Hyper-activated IRF-1 and STAT1
contribute to enhanced interferon stimulated gene (ISG) expression by interferon alpha
and gamma co-treatment in human hepatoma cells. Biochim Biophys Acta. 2006;1759(8
9):41725.
62. Channappanavar R, Fehr AR, Zheng J, Wohlford-Lenane C, Abrahante JE, Mack M, et al.
IFN-I response timing relative to virus replication determines MERS coronavirus infection
outcomes. J Clin Invest. 2019;129(9):362539.
63. Li J, Ye L, Wang X, Hu S, Ho W. Induction of interferon-λ contributes to toll-like
receptor 3-mediated herpes simplex virus type 1 inhibition in astrocytes. J Neurosci Res.
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 23 March 2020 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 23 March 2020 doi:10.20944/preprints202003.0206.v2
25
2012;90(2):399406.
64. Matsumoto M, Seya T. TLR3: Interferon induction by double-stranded RNA including
poly(I:C). Vol. 60, Adv Drug Deliver Rev. 2008. p. 80512.
65. Negishi H, Osawa T, Ogami K, Ouyang X, Sakaguchi S, Koshiba R, et al. A critical link
between Toll-like receptor 3 and type II interferon signaling pathways in antiviral innate
immunity. Proc Natl Acad Sci U S A. 2008;105(51):2044651.
66. Uematsu S, Akira S. Toll-like receptors and type I Interferons. Vol. 282, J Biol Chem.
2007. p. 1531924.
67. Zhao J, Wohlford-Lenane C, Zhao J, Fleming E, Lane TE, McCray PB, et al. Intranasal
Treatment with Poly(I{middle dot}C) Protects Aged Mice from Lethal Respiratory Virus
Infections. J Virol. 2012;86(21):1141624.
68. Kumaki Y, Salazar AM, Wandersee MK, Barnard DL. Prophylactic and therapeutic
intranasal administration with an immunomodulator, Hiltonol® (Poly IC:LC), in a lethal
SARS-CoV-infected BALB/c mouse model. Antiviral Res. 2017;139:112.
69. Ngoi SM, Tovey MG, Vella AT. Targeting Poly(I:C) to the TLR3-Independent Pathway
Boosts Effector CD8 T Cell Differentiation through IFN-α/β. J Immunol.
2008;181(11):767080.
70. Barnard DL, Day CW, Bailey K, Heiner M, Montgomery R, Lauridsen L, et al. Evaluation
of immunomodulators, interferons and known in vitro SARS-CoV inhibitors for inhibition
of SARS-CoV replication in BALB/c mice. Antivir Chem Chemother. 2006;17(5):275
84.
71. Gao J, Tian Z, Yang X. Breakthrough: Chloroquine phosphate has shown apparent
efficacy in treatment of COVID-19 associated pneumonia in clinical studies. [Internet].
Bioscience trends. 2020. Available from: http://www.ncbi.nlm.nih.gov/pubmed/32074550
72. Kumar A, Zhang J, Yu FSX. Toll-like receptor 3 agonist poly(I:C)-induced antiviral
response in human corneal epithelial cells. Immunology. 2006 Jan;117(1):1121.
73. Perrot I, Deauvieau F, Massacrier C, Hughes N, Garrone P, Durand I, et al. TLR3 and
Rig-Like Receptor on Myeloid Dendritic Cells and Rig-Like Receptor on Human NK
Cells Are Both Mandatory for Production of IFN-γ in Response to Double-Stranded RNA.
J Immunol. 2010;185(4):20808.
74. Miyawaki T, Seki H, Taga K, Sato H, Taniguchi N. Dissociated production of interleukin-
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 23 March 2020 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 23 March 2020 doi:10.20944/preprints202003.0206.v2
26
2 and immune (γ) interferon by phytohaemagglutinin stimulated lymphocytes in healthy
infants. Clin Exp Immunol. 1985;59(2):50511.
75. Zhang J, Shi J, Ilic S, Jun Xue S, Kakuda Y. Biological properties and characterization of
lectin from red kidney bean (Phaseolus Vulgaris). Food Rev Int. 2009;25(1):1227.
76. Sleijfer S, Bannink M, Van Gool AR, Kruit WHJ, Stoter G. Side effects of interferon-α
therapy. Vol. 27, Pharmacy World and Science. Springer; 2005. p. 42331.
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 23 March 2020 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 23 March 2020 doi:10.20944/preprints202003.0206.v2
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    The GWAS Catalog delivers a high-quality curated collection of all published genome-wide association studies enabling investigations to identify causal variants, understand disease mechanisms, and establish targets for novel therapies. The scope of the Catalog has also expanded to targeted and exome arrays with 1000 new associations added for these technologies. As of September 2018, the Catalog contains 5687 GWAS comprising 71673 variant-trait associations from 3567 publications. New content includes 284 full P-value summary statistics datasets for genome-wide and new targeted array studies, representing 6 × 109 individual variant-trait statistics. In the last 12 months, the Catalog's user interface was accessed by ∼90000 unique users who viewed >1 million pages. We have improved data access with the release of a new RESTful API to support high-throughput programmatic access, an improved web interface and a new summary statistics database. Summary statistics provision is supported by a new format proposed as a community standard for summary statistics data representation. This format was derived from our experience in standardizing heterogeneous submissions, mapping formats and in harmonizing content. Availability: https://www.ebi.ac.uk/gwas/.
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    Topical application of Aldara cream, containing the Toll-like receptor 7/8 agonist Imiquimod, is a widely used mouse model for investigating the pathogenesis of psoriasis. We have previously used this model to study the effects of peripheral inflammation on the brain, and reported a brain-specific response characterised by increased transcription, infiltration of immune cells and anhedonic-like behavior. Here, we perform a more robust characterisation of the systemic response to Aldara application and find a potent but transient response in the periphery, followed by a prolonged response in the brain. Mass spectrometry analysis of plasma and brain samples identified significant levels of Imiquimod in both compartments at molar concentrations likely to evoke a biological response. Indeed, the association of Imiquimod with the brain correlated with increased Iba1 and GFAP staining, indicative of microglia and astrocyte reactivity. These results highlight the potency of this model and raise the question of how useful it is for interpreting the systemic response in psoriasis-like skin inflammation. In addition, the potential impact on the brain should be considered with regards to human use and may explain why fatigue, headaches and nervousness have been reported as side effects following prolonged Aldara use.
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    WikiPathways (wikipathways.org) captures the collective knowledge represented in biological pathways. By providing a database in a curated, machine readable way, omics data analysis and visualization is enabled. WikiPathways and other pathway databases are used to analyze experimental data by research groups in many fields. Due to the open and collaborative nature of the WikiPathways platform, our content keeps growing and is getting more accurate, making WikiPathways a reliable and rich pathway database. Previously, however, the focus was primarily on genes and proteins, leaving many metabolites with only limited annotation. Recent curation efforts focused on improving the annotation of metabolism and metabolic pathways by associating unmapped metabolites with database identifiers and providing more detailed interaction knowledge. Here, we report the outcomes of the continued growth and curation efforts, such as a doubling of the number of annotated metabolite nodes in WikiPathways. Furthermore, we introduce an OpenAPI documentation of our web services and the FAIR (Findable, Accessible, Interoperable and Reusable) annotation of resources to increase the interoperability of the knowledge encoded in these pathways and experimental omics data. New search options, monthly downloads, more links to metabolite databases, and new portals make pathway knowledge more effortlessly accessible to individual researchers and research communities.
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    Mutations of the recombinase-activating genes 1 and 2 (RAG1 and RAG2) in humans are associated with a broad range of phenotypes. For patients with severe clinical presentation, hematopoietic stem cell transplantation (HSCT) represents the only curative treatment; however, high rates of graft failure and incomplete immune reconstitution have been observed, especially after unconditioned haploidentical transplantation. Studies in mice have shown that Rag−/− natural killer (NK) cells have a mature phenotype, reduced fitness, and increased cytotoxicity. We aimed to analyze NK cell phenotype and function in patients with mutations in RAG and in non-homologous end joining (NHEJ) genes. Here, we provide evidence that NK cells from these patients have an immature phenotype, with significant expansion of CD56bright CD16−/int CD57− cells, yet increased degranulation and high perforin content. Correlation was observed between in vitro recombinase activity of the mutant proteins, NK cell abnormalities, and in vivo clinical phenotype. Addition of serotherapy in the conditioning regimen, with the aim of depleting the autologous NK cell compartment, may be important to facilitate engraftment and immune reconstitution in patients with RAG and NHEJ defects treated by HSCT.
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    Human coronaviruses (hCoVs) can be divided into low pathogenic and highly pathogenic coronaviruses. The low pathogenic CoVs infect the upper respiratory tract and cause mild, cold-like respiratory illness. In contrast, highly pathogenic hCoVs such as severe acute respiratory syndrome CoV (SARS-CoV) and Middle East respiratory syndrome CoV (MERS-CoV) predominantly infect lower airways and cause fatal pneumonia. Severe pneumonia caused by pathogenic hCoVs is often associated with rapid virus replication, massive inflammatory cell infiltration and elevated pro-inflammatory cytokine/chemokine responses resulting in acute lung injury (ALI), and acute respiratory distress syndrome (ARDS). Recent studies in experimentally infected animal strongly suggest a crucial role for virus-induced immunopathological events in causing fatal pneumonia after hCoV infections. Here we review the current understanding of how a dysregulated immune response may cause lung immunopathology leading to deleterious clinical manifestations after pathogenic hCoV infections.