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Micro RNA-based regulation of genomics and transcriptomics of
inflammatory cytokines in COVID-19
Manoj Khokhar , Sojit Tomo , Purvi Purohit*
1
1
1
Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur 342005, India
*Corresponding author and address
Dr Purvi Purohit
Additional Professor
Department of Biochemistry
All India Institute of Medical Sciences,
Basni Industrial Area, Phase-2
Jodhpur-342005, India.
Tel: 09928388223
NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.
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It is made available under a CC-BY-NC-ND 4.0 International license .
Abstract:
Background: Coronavirus disease 2019 is characterized by the elevation of a
wide spectrum of inflammatory mediators, which are associated with poor
disease outcomes. We aimed at an in-silico analysis of regulatory microRNA and
their transcription factors (TF) for these inflammatory genes that may help to
devise potential therapeutic strategies in the future.
Methods: The cytokine regulating immune-expressed genes (CRIEG) was sorted
from literature and the GEO microarray dataset. Their co-differentially
expressed miRNA and transcription factors were predicted from publicly
available databases. Enrichment analysis was done through mienturnet, MiEAA,
Gene Ontology, and pathways predicted by KEGG and Reactome pathways.
Finally, the functional and regulatory features were analyzed and visualized
through Cytoscape.
Results: Sixteen CRIEG were observed to have a significant protein-protein
interaction network. The ontological analysis revealed significantly enriched
pathways for biological processes, molecular functions, and cellular
components. The search performed in the MiRNA database yielded 10 (ten)
miRNAs that are significantly involved in regulating these genes and their
transcription factors.
Conclusion: An in-silico representation of a network involving miRNAs, CRIEGs,
and TF which take part in the inflammatory response in COVID-19 has been
elucidated. These regulatory factors may have potentially critical roles in the
inflammatory response in COVID-19 and may be explored further to develop
targeted therapeutic strategies and mechanistic validation.
Keywords: Cytokine storm; immuno-interactomics; COVID-19; Cytokines;
MicroRNA, SARS-CoV-2
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It is made available under a CC-BY-NC-ND 4.0 International license .
1.
Introduction
Cytokine storm in severe or critically ill coronavirus disease 2019 (COVID-19) patients
is characterized by the elevation of a wide spectrum of inflammatory mediators. These
include cytokines and chemokines, originating from airway epithelial cells as well as
various immune cells, and act as independent risk factors for disease severity and
mortality (Liu et al. 2020, p. 19).
Various cytokines and chemokines have been observed to play dominant roles in
different stages of the COVID-19 disease. Association of COVID-19 severity and
mortality with higher levels of interleukin-6 (IL-6) have been corroborated in various
studies (Cummings et al. 2020, Hajifathalian et al. 2020, Ruan et al. 2020). However,
depending upon the stage in the natural history of COVID-19 disease SARS CoV-2 has
the menacing feature of longer persistence in the environment and various inanimate
surfaces (Khokhar, Roy, et al. 2020) and different inflammatory mediators have been
observed to play a dominant role in Acute kidney injury pathophysiology (Khokhar,
Purohit, et al. 2020). Control this disease Newer diagnostic tools, based on the
Clustered Regularly Interspaced Short Palindromic Repeats/Cas (CRISPR-Cas) system is
used for better diagnostic accuracy.(Gadwal et al. 2021, p. 19) In the initial stage,
when clinical symptoms are mild, the severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2) replicates rapidly in blood (Chen, Lan, et al. 2020). Chemokines are the
inflammatory mediators that characterize this initial stage. Chemokine (C-C motif)
ligands (CCL), namely CCL8, CCL9, and CCL2 expression were found to be increased in
the initial phase (Blanco-Melo et al. 2020, p. 19). In severe COVID-19 patients, serum
CCL5 levels were elevated even before the occurrence of an IL-6 peak (Zhao et al.
2020). Further, in bronchoalveolar lavage (BAL) fluid of COVID-19 patients, a
chemokine-rich signature was observed characterized by the expression of CCL2, CCL3,
CCL4, CCL7, CCL8, chemokine (C-X-C motif) ligand 2 (CXCL2), CXCL8, CXCL17, and
interferon-inducible protein 10 (IP-10) (Lu et al. 2020, Xiong et al. 2020).
During the amplification phase, inflammatory immune responses get aggravated and
the disease progresses rapidly to severe/critical illness. The chemokines secreted in
the initial phase recruit inflammatory innate and adaptive immune cells resulting in an
exaggerated inflammatory immune response. Peripheral levels of inflammatory
mediators including IL-2, IL-6, IL-7, IL-10, tumor necrosis factor-alpha (TNF-α), CCL-2,
and CCL-3 were highly elevated in this phase (Chen, Wu, et al. 2020, Huang et al.
2020). The unchecked elevation of inflammatory mediators leads to vascular leakage,
complement cascade activation, and cytokine storm in this consummation phase
(Wang et al. 2020, Yang et al. 2020, Zhou et al. 2020). Apart from the inflammatory
mediators, it had also been observed that the mRNA expression levels of inflammatory
genes peaked as the respiratory function deteriorated (Ong et al. 2020). Further,
COVID-19 patients had also shown the possibility of alterations in transcription factors,
affecting both cytokines as well as immune cells, adding another layer of dimension to
the regulation and release of cytokines in a cytokine storm (Claverie 2020, p. 19, De
Biasi et al. 2020).
This study aimed to search the various databases for miRNAs that can affect the
genetic expression of these inflammatory mediators and the transcription factors that
regulate the expression. The insight into the regulation of the expression of these
cytokine genes by miRNAs and transcription factors will help in devising better
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targeted therapies to address the complications in severe COVID-19 disease due to
cytokine storm.
2. Methodology
2.1 Identification of cytokine responsible for inflammation and cytokine storm
in SARS-CoV-2:
Several keywords including "Inflammation", "Immunity", "Immunogenetics", "Cytokine
storm", "Acute respiratory distress syndrome", "ARDS", "COVID-19", "cytokines",
"Coronavirus disease", "SARS-CoV-2" and "Severe Acute Respiratory Syndrome" and
“19990101 to 20200706” were searched in PubMed (Figure 1, Supplementary Tables
1 & Table-1A).
2.2 Microarray Data collection:
We have searched in GEO database by several keywords including "SARS", "Corona
Virus", "Blood", "Homo sapiens", "Expression profiling by array", "bronchial epithelial
cells" from 01/01/2012 to 17/12/2020. Selected one gene series expressions (GSEs)
data were for further study. GSE17400 contain bronchial epithelial cells of 09 (nine)
samples. (Table-1B).
2.3 Identification of co-differentially expressed mRNAs responsible for
inflammation and cytokine storm in SARS-CoV:
GEO2R is an online interactive web tool used to compare two or more groups of
samples in a GEO Series to identify genes that are differentially expressed across
experimental conditions. We obtain differentially expressed genes (DEGs) from two
datasets (GSE17400) for innate immune responses of human bronchial epithelial cells
against SARS-CoV with the help of GEO2R with the cutoff criteria of p<0.05. Common
genes in both datasets were identified and isolated with the use of the Venn-diagram.
(Figure 1)
2.4 Identification of common transcriptome related to cytokine regulating immune
expressed genes (CRIEGs):
Cytokines are mostly regulated at the transcriptional level by specific combinations of
TFs that recruit cofactors and the transcriptional machinery (Carrasco Pro et al. 2018).
We identified the common transcription factors of CRIEGs through five different
databases- TRRUST, RegNetwork, ENCODE, JASPAR, and CHEA. The targeted
transcription factor of 16 cytokines were identified by well-established TF - target
prediction database miRNet Version 2 (a miRNA-centric network visual analytics
platform) (Chang et al. 2020). A co-regulatory transcriptome network was created
based on inter-correlation in Cytoscape software. (Table 2)
2.5 Identification and assortment of co-differentially regulated miRNAs of
CRIEGs and transcriptome of CRIEGs
Evolutionary conserved small non-coding RNA or MicroRNA affect the gene expression
by binding to specific mRNAs and regulate cell growth, differentiation, and death.
miRNAs regulate multiple functions of T-cell subsets through immune homeostasis and
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immune tolerance that control the development, survival and activation (Garavelli et
al. 2018). The targeted miRNA of cytokine and its transcriptome genes were predicted
by well-established different miRNA target prediction databases miRDB, miRBase,
miRNet Version 2 and TargetScan (Chang et al. 2020). A co-expressed network was
created based on regulatory correlation analysis of Cytoscape software. (Tables 1 & 2)
2.6 Protein-protein interaction, functional enrichment and KEGG pathway
analysis of CRIEGs and transcriptome of CRIEGs
The Search Tool for the Retrieval of Interacting Genes/Protein [STRING] (http://stringdb.org/) was used to construct a protein-protein interaction (PPI) network using only
overlapped DEGs and greater than 0.4 confidence score cut-off. The interaction
networks for all 16 cytokines were constructed by Cytoscape (Shannon 2003, Otasek et
al. 2019).
Analysis of the functional and regulatory features was carried out through gene
ontology (GO), KEGG pathways through DAVID (the database for annotation,
visualization and integrated discovery) and STRING (functional protein association
networks) biological databases. (Supplementary Table 2)
2.7 Functional enrichment, disease relationship, and KEGG pathway analysis
of co-differentially regulated miRNAs of CRIEGs and transcriptome of CRIEGs
Cytokine regulating immune expressed genes (CRIEGs) and transcriptome of CRIEGs
are regulated by ten (10) common microRNAs. We will identify the all ten microRNAs
enrichment of two database MIENTURNET and MiEAA (Licursi et al. 2019, Kern et al.
2020). MIENTURNET and miEAA (miRNA Enrichment Analysis and Annotation tool)
perform both statistical and network-based analyses pathways and disease-related
activity in cellular processes.
3.
Results
3.1 Identification of - Co Expressed DEGs responsible for inflammation
and cytokine storm in SARS-CoV:
We searched for genes that were obtained from the literature search and their
transcription factors that control them in the SARS-CoV dataset (GSE17400),
most of which were expressed here. (Figure 1B).
3.2 Identification of cytokine regulating immune expressed genes and
construction of PPI Network
We found 16 cytokine regulating immune expressed genes (IL-1b, IL-2, IL-7, IL-8, IL-9,
IL-10, IL-17, G-CSF, GM-CSF, IFN-γ, TNF-α, CXCL10, MCP1, MIP1A, MIP1B, and IL-6)
from literature which is responsible for acute respiratory distress syndrome (ARDS) in
COVID-19. Over-expression of these genes in a short time increases the severity of the
disease. All 16 CRIEGs show interactions among themselves, based on the STRING
database (Figure 2B). The PPI network consisted of 16 nodes and 117 edges, the
average local clustering coefficient was 0.977, and PPI enrichment p-value was highly
significant (p<0.001).
3.3 Ontological analysis of CRIEGs
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To get insights into the biomolecular significance of the identified CRIEGs, we
performed gene ontology analysis by various databases and obtained enriched GO
terms. STRING and DAVID were used to conduct the gene ontology analysis for CRIEGs
within three different categories like biological process, molecular function and
cellular component. Common statistically significant (p<0.05) ontological processes
have been identified through DAVID.
Molecular process like Chemokine, Cytokine and Growth factor activity, Granulocyte
colony-stimulating factor receptor binding, CC chemokine receptor (CCR), and C-X-C
chemokine receptor (CXCR) binding play significant roles. Some crucial biological
process such as immune and inflammatory response, chemokine-mediated signaling
pathway, negative regulation of extrinsic apoptotic signaling pathway in absence of
ligand, protein kinase B signaling, cellular response to interleukin-1, chemotaxis,
receptor biosynthetic process, positive regulation of interleukin-23 production,
estradiol secretion, myeloid cell differentiation, podosome assembly, osteoclast
differentiation, tyrosine phosphorylation of Stat5 protein, cytokine secretion involved
in immune response, cytokine secretion, transcription from RNA polymerase II
promoter, lymphocyte, monocyte, neutrophil chemotaxis, cell proliferation, B cell
proliferation, interferon-gamma production, inflammatory response response to
glucocorticoid, regulation of cell proliferation, interleukin-6 production, negative
regulation of growth of symbiont in host lipopolysaccharide-mediated signaling
pathway, negative regulation of myoblast differentiation are regulated by CRIEGs.
Cellular components (CC) found very limited organelles of the cell-like external side of
the plasma membrane, extracellular region and extracellular space (Figure 2 &
Supplimentry Tables 3A, 3C and 3D).
3.4 Common Pathway enrichment analysis CRIEGs
STRING and DAVID were assessed to acquire KEGG pathways enriched by CRIEGs. Both
databases were selected for preferred and significant (p<0.01) common pathways.
Important selected pathways are enlisted in (Supplimentry Table 3B).
3.5 Identification of common transcription factors of Cytokine Genes
We identified the common transcription factors of CRIEGs through TRRUST,
RegNetwork, ENCODE, JASPAR and CHEA databases. During this identification process,
we found a total of 32 transcription regulators. All transcription regulators AHR,
CEBPA, CREM, DDIT3, E2F1, EGR1, EP300, ESR1, ETS2, FOXP3, HDAC1, HDAC2, HSF1,
IRF1, JUN, KLF4, NFAT5, NFKB1, NFKBIA, NR1I2, REL, RELA, RUNX1, SIRT1, SP1, SPI1,
STAT1, STAT3, VDR, XBP1, ZFP36, ZNF300 commonly regulated the transcription of 16
CRIEGs (Tables 1 & 2).
3.6 Ontological analysis of common transcription factors
To get deep insights into the biomolecular significance of the identified common TFs.
We performed gene ontology analysis by various databases and obtained enriched GO
terms. STRING and DAVID have been used to conduct the GO analysis of common TFs
within three different categories like BP, MF and CC. Common statistically significant
(p<0.05) ontological processes have been identified through DAVID.
Important biological processes like cellular response to interleukin-1, interleukin-6,
Interferon-gamma-mediated signaling pathway, macrophage differentiation, negative
regulation by host of viral transcription, cell proliferation, fat cell differentiation,
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interleukin-2 biosynthetic process, transcription from RNA polymerase II promoter,
Positive regulation by host of viral transcription, interleukin-12 biosynthetic process,
miRNA metabolic process, pri-miRNA transcription from RNA polymerase II promoter,
transcription, Regulation of inflammatory response, miRNA mediated inhibition of
translation are involved in the metabolic regulatory process of TFs.
Most of the TFs play significant roles in different MF like RNA polymerase II core
promoter proximal region and distal enhancer sequence-specific DNA binding, core
promoter binding, transcriptional repressor activity, chromatin DNA binding,
transcriptional activator activity, RNA polymerase II transcription regulatory region
sequence-specific binding, chromatin binding, Histone deacetylase activity, and ligandactivated sequence-specific DNA binding.
Various transcription factors localization in multiple CC like cytoplasm, nucleus,
nuclear euchromatin, nuclear chromatin, Transcription factor complex, Sin3 complex,
nucleoplasm, NuRD complex, ESC/E(Z) complex, and cytosol (figure 5 and
Supplementary Table 4A-4C).
3.7 Common Pathway enrichment analysis of transcription factors
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of common
transcription regulating genes identified above was carried out. A p-value<0.05 was set
as the threshold for the significantly enriched pathways.
Crucial KEGG pathways involved in the common TFs were HTLV-I infection, Hepatitis B,
Hepatitis C, Influenza A, Viral carcinogenesis, Herpes simplex infection, Measles, Tolllike receptor signaling pathway, B cell receptor signaling pathway, chemokine signaling
pathway, HIF-1 signaling pathway, T cell receptor signaling pathway, TNF signaling
pathway, cytosolic DNA-sensing pathway, NF-kappa B signaling pathway, MAPK
signaling pathway, and FoxO signaling pathway. (Supplementary Table 4D)
3.8 Identification of common targeting MicroRNAs of CRIEGs and TFs
We identified the common MicroRNAs targeting CRIEGs and TFs from various
microRNA databases like miRNet, TargetScan, miRDB, miRanda, miRWalk. During this
data identification process, common CRIEGs and TFs multi-targeting 10 miRNAs- hsamiR-106a-5p, hsa-miR-155-5p, hsa-miR-98-5p, hsa-miR-24-3p, hsa-miR-204-5p, hsamiR-124-3p, hsa-miR-203a-3p, hsa-miR-335-5p, hsa-let-7c-5p, and hsa-miR-1-3p were
obtained. All these miRNAs target the CRIEGs and its same targeting TFs (Tables 1 &
2).
3.9 Disease category, RNA localization and Ontological analysis of frequent
targeting MicroRNAs
We identified the microRNA enrichment analysis from two different databases
MIENTURNET and miEA. We found out the localization of cellular components, miRNAdisease relationship and ontological functions of these important microRNAs. These
ten MicroRNAs are found in different parts of the cell, such as microvesicle, nucleus,
exosome, cytoplasm, and mitochondrion. These miRNAs also correlate in many
diseases such as SARS, lymphoma, inflammatory bowel disease, hepatitis B, hepatitis
C, asthma, Acute Lung Injury, sepsis, HIV, Adenoviridae infections, aortic valve disease,
Acute Kidney Failure, prion disease, chronic obstructive pulmonary disease, and
human influenza.
Involvement of miRNA in various GO terms such as macrophage chemotaxis, positive
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regulation of B-cell activation, regulation of inflammatory response cytokine binding,
IL-6 receptor binding, IL-6 Mediated Signaling Pathway, T-cell differentiation in
thymus, IL-1 binding, response To IL-1, cellular response to IL-4, regulation of T-cell
proliferation, viral entry into host cell via membrane fusion with the plasma
membrane, positive regulation of IL-6 biosynthetic process, T-cell chemotaxis, IL-10
production, mast cell chemotaxis viral reproduction, positive regulation of viral
genome replication, viral entry into host cell. (Figure 4 & Tables 5B-5D)
3.10 Common Pathway enrichment analysis of MicroRNA
These ten crucial MicroRNAs regulate Graft-versus-host disease, asthma, intestinal
immune network for IgA production, IBD, viral protein interaction with cytokine and
cytokine receptor, viral myocarditis, Toll-like receptor signaling pathway, B-cell
receptor signaling pathway, Th17 cell differentiation and signaling pathway, natural
killer cell mediated cytotoxicity, Butanoate metabolism, longevity regulating pathway,
T-cell receptor signaling pathway, cytokine-cytokine receptor interaction, Influenza A,
JAK-STAT signaling pathway, Hepatitis B, Hepatitis C- that are responsible for
progression of many metabolic and pathological regulatory process. (Table 5A)
3.11
PPIs network of CRIEGs, TFs and common targeting MicroRNAs
interactions network with CRIEGs, TFs
With the help of Cytoscape, we created a network of common targeting miRNAs
of all the 14 CRIEGs and their 32 TFs. These all play important correlations that
affect the entire network due to the influence of an external specific virus which
contributes to increase the severity of the disease. (Table 1 & Figure 7, 8 & 9)
4.
Discussion
MicroRNAs post-transcriptionally regulate the expression of target mRNA. RNA viruses
are known to utilize the host miRNA machinery for their benefit. Hence, various
studies have identified miRNAs as key players in the pathogenesis and therapeutics of
viral diseases. Also, miRNAs can target viral genes as well as the host inflammatory
machinery, as part of the host-pathogen interactions, to counter-act the impairing
effects of infection (Ghosh et al. 2008). Demirci et al. have identified 67 different
human miRNA that targets the spike protein of the SARS-CoV-2 virus (Saçar Demirci
and Adan 2020). The inflammatory cascades involved in the pathophysiological
pathways of COVID-19 are crucial in the development of complications in COVID-19.
These pathogenetic pathways constitute, but are not limited to, receptor tyrosine
kinases, the JAK/STAT pathway, TNF-α receptor and toll-like receptors, IL-6 and IFN-γ,
cytokine storm, and macrophage activation (Yarmohammadi et al. 2020). Hence,
exploring the regulatory networks of these inflammatory markers have the two-fold
advantages of discovering the interconnected nature of these dysregulated pathways
and unlocking the potential of novel mechanistic-based treatment strategies.
However, a clear understanding of the miRNA response in SARS-CoV-2 is still elusive.
Here, we have identified the miRNAs and transcription factors of the target mRNAs
which provide the necessary insight into the genetic regulation of the inflammatory
response in COVID-19.
Our in-silico analysis revealed ten miRNAs involved in the regulation of the common
inflammatory genes and their transcription factors.
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The miR-155-5p has been widely studied in viral inflammatory pathways. It is a
regulator of the HCV-induced TLR3/NF-κB pathway mediated inflammatory response.
Further, elevated circulating levels of miR-155 were also observed in HBV infection
(Bala et al. 2012, Wang et al. 2015). The role of miR-155-5p in the cytokine response
through the TLR4/NF-κB/miR-155-5p/SOCS-1 axis in monocyte-derived macrophages
has been demonstrated in dengue (Arboleda et al. 2019). It has further been observed
to be upregulated in B cells in EBV infection and in PBMC of HIV-1 infected patients
(Gao et al. 2015, Dey et al. 2016). In JHMV-infected (a coronavirus) mice models, miR155 enhanced the T-cell trafficking, cytokine secretion, and cellular effectors functions
(Dickey et al. 2016). Woods et al. studied 1908 mature murine miRNA expressions in
influenza A virus (IAV)-infected type II alveolar cells and miR-155-5p was showed to
have the highest expression (Woods et al. 2020). In FeAE cells, miR-155-5p expression
induced IL-6 and IP-10 production, which is responsible for recruitment of leukocytes
(McAdams et al. 2015). It also regulates the NF-κB and MAPK signaling pathways (Shi
et al. 2020). In our analysis, we found miR-155-5p to be one of the ten identified
miRNAs that possibly regulates the cytokine expression and triggers inflammatory
response in COVID-19. Further, miR-155-5p was found to affect the expression of
multiple transcription factors including CEBPA, JUN, NFAT5, NFKB1, SP1, SPI1, STAT1,
STAT3, CEBPB, ZP36, ZNF300, ZFP36.
Another targeting miRNA identified in this study, miR-124-3p, was observed to be
downregulated in JEV-infected human neural stem/progenitor cells (Mukherjee et al.
2019). A mice model showed downregulation in miR-124-3p expression in ARDS.
Treatment with miR-124-3p agomir attenuated the pulmonary injury and the levels of
pro-inflammatory cytokines IL-6 and TNF-α by directly targeting p65, thus showing
promise in in-vitro management of pulmonary injury (Liang et al. 2020, p. 65).
Yet another miR-203a has been demonstrated to have an antagonistic role in foot-andmouth disease virus (FMDV) infection (Gutkoska et al. 2017). This miRNA was further
studied in IAV infection where an upregulated miR-203a modulated the antiviral
response by targeting DR1 gene (Zhang et al. 2018, p. 1). However, further studies are
needed to consolidate the its role in corona-virus infections. Our in-silico analysis
showed miR-203a targets transcription factors NFkB1, RELA, CREBPB, ATF4, ETS1 and
2.
miR-335-5p had the most predicted targets in the response against the Porcine
Reproductive and Respiratory Syndrome Virus (PRRSV) of alveolar macrophages.
Contrastingly, no effects on the cytokine expression was observed in this study
(Dhorne-Pollet et al. 2019). This may be attributed to the low level of expression of
miR-335-5p in most tissues, which renders its effect to be negligible despite the
abundant number of predicted targets (The FANTOM Consortium et al. 2017).
However, miR-24-3p facilitated PRRSV replication via suppression of heme oxygenase1 (HO-1) (Xiao et al. 2015), and HO-1 has been reported to play role in anti-viral
activity in several viral infections including HIV, hepatitis C virus, hepatitis B
virus, enterovirus 71, influenza virus, respiratory syncytial virus, dengue virus,
and Ebola virus (Espinoza et al. 2017, p. 1). Thus a high expression of miR-24-3p may
be pathognomic for the worsening of viral infection.
Another targeting miRNA identified in this study, hsa-let-7c-5p, directly affects ACE2
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and TMPRSS2; two key players in the SARS-CoV-2 infection (Chauhan et al. 2020). In
rhabdomyosarcoma cells, hsa-let-7c-5p promoted the replication of enterovirus 71
(EV71) by inhibition of the MAPK4K expression (Zhou et al. 2017). Overexpression of
miR-let-7c attenuated the replication of HCV through HO-1 induction (Chen et al.
2019). A differential expression of hsa-let-7 in-silico as a CRIEG indicates it has a role in
immunomodulation in COVID-19.
Collectively, these studies demonstrate that mainly ten microRNAs (hsa-miR-106a-5p,
hsa-miR-155-5p, hsa-miR-98-5p, hsa-miR-24-3p, hsa -miR-204-5p, hsa-miR-124-3p,
hsa-miR-203a-3p, hsa-miR-335-5p, hsa-let-7c-5p, hsa-miR-1-3p) regulate the role of
inflammatory mechanism in viral infection. Our in-silico analysis points towards a
similar potential regulatory role of miRNA in SARS-CoV-2 mediated inflammatory
cascades. Many of the target miRNA found in this study, namely miR-106a-5p, miR-13p, miR-98-5p, miR-24-3p, and miR-204-5p have been observed to orchestrate the
gene expression of IL-1β, IL-6, IL-10, IFNγ, IL-2, and IL-17 through TFs such as ERK,
STAT1, and STAT3 (Ye et al. 2014, Srivastava et al. 2017, Shen et al. 2019, Xiu et al.
2020).
Although most of these data are derived from cancer and transplantation studies, a
similar mechanism of regulation of expression may be functional in the case of COVID19. The disease severity in COVID-19 had been associated with an influx of innate
immune cells and inflammatory cytokines (Hadjadj et al. 2020, Yale IMPACT Team et
al. 2020, p. 19). The cytokine storm in COVID-19 leads to lung injury, multiple organ
failure and had poor prognosis (Jose and Manuel 2020, Mehta et al. 2020). TNF-α and
IFN-γ together had showed to incite the cells to PANoptosis, inflammatory cell death
involving the components of pyroptosis, apoptosis, and necroptosis. Further,
JAK/STAT1/IRF1 axis was also involved in regulation of inflammatory cell death due to
PANoptosis (Karki et al. 2020). Further, IL-6 have also been shown to activate the
Janus kinase-Signal Transducer and Activator of Transcription (JAK-STAT) pathway
leading to immune activation (Luo et al. 2020, p. 19).
Apart from the above mentioned transcription factors, NF-κB also plays a crucial role
in poor prognosis of severe COVID-19 disease. NF-κB leads to accelerated
inflammatory response with increased secretion of TNF-α and IL-6. This autoamplified pro- inflammatory loop with impaired type I IFN response culminates in
Viral replication within the lungs and tissue damage (Hadjadj et al. 2020). Recent
studies have demonstrated the up regulation of various miRNAs in COVID-19 patients
thus confirming our prediction. In comparison to healthy controls, miRNAs, including
miR-21, miR-155, miR-208a and miR-499, had been demonstrated to be up-regulated
in the COVID-19 patients (Mahesh and Biswas 2019, Garg et al. 2021). The varying
functional significance and organ specificty of these upregulated miRNAs,i.e, miR-155
(inflammatory miRNA), miR-208a (heart-muscle specific) and miR-499 ( muscle
function) and miR-21 ( fibrosis-associated) denotes the involvement of multiple
pathways and organs in the pathophysiology of COVID-19 (van Rooij et al. 2007, 2009,
Thum et al. 2008). MicroRNAs such as miR-125b, miR-138, miR-199a and miR-21 are
also responsible for cytokine storms in the acute respiratory distress syndrome and
COPD(Guterres et al. 2020). In addition, miR-26a-5p, miR-29b-3p, and miR-34a-5p
have been shown to be involved in endothelial dysfunction and inflammatory response
medRxiv preprint doi: https://doi.org/10.1101/2021.06.08.21258565; this version posted June 12, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .
in patients with SARS–CoV-2 infection(Centa et al. 2021). Further, the upregulation
miRNAs in post-COVID-19 complications such as (miR-21, miR-155, miR-208a and miR499) in chronic myocardial damage and inflammation(Garg et al. 2021), (let-7b-3p,
miR-29a-3p, miR-146a-3p and miR-155-5p) in post-acute COVID-19 phase ; also
indicates the extent of the alteration of these pathways in Post-COVID-19 sequelae.
The downregulation of these miRNAs may be targeted to improve acute symptoms
and distress by regulating the production of pro-inflammatory cytokines and apoptotic
proteins (Guterres et al. 2020). Apart from the effect on host cellular pathways,
miRNAs can also inhibit the viral infectivity by different ways like blocking the viral
replication, cellular receptors and the function of viral proteins in SARS-CoV-2 (Fani et
al. 2021). MicroRNAs such as miR-21-3p, miR-195-5p, miR-16-5p, miR-3065-5p, miR424-5p and miR-421 potentially regulate the infectivity of viruses belonging to human
coronavirus family through direct binding to the viral genome (Chan et al. 2020).
Interestingly, newer miRNA-based therapy including antimiR-18 and antimir-125b,
which potentially targets ACE2-related genes, have been proposed for nephropathy
associated with COVID-19 (Widiasta et al. 2020).
We have identified other target CRIEGs of transcription factors STAT1 , IRF1 & NF-κB
and the miRNAs regulating their expression. This would help to better understand the
cross talk and regulation of various cytokines in COVID-19 and the possible role played
by them in regulation of inflammatory cell death leading to multiple organ failure. The
main limitation of this study is that the data is derived from the publicly available
databases, and needs experimental substantiation to prove its clinical efficacy.
Moreover, our study highlights the interaction and the pathways concerning the
miRNA, immune-expressed and TFs. Such expression data of all these three entities
together are not available in COVID-19 patients or in-vitro models, which can establish
a better understanding of the mechanisms involved.
5.
Conclusion
The present study identifies an in-silico representation of a network involving miRNAs
(hsa-miR-106a-5p, hsa-miR-155-5p, hsa-miR-98-5p, hsa-miR-24-3p, hsa -miR-204-5p,
hsa-miR-124-3p, hsa-miR-203a-3p, hsa-miR-335-5p, hsa-let-7c-5p, hsa-miR-1-3p),
CRIEGs (CCL2, CCL4, CXCL10, CXCL8, IL6, IL7, JAK2, TNF), and TF (AHR, CREM, DDIT3,
E2F1, EGR1, EP300, ESR1, ETS2, HDAC1, HDAC2, IRF1, JUN, KLF4, NFAT5, NFKB1,
NFKBIA, REL, RUNX1, SIRT1, SP100, SP140L, STAT1, XBP1, ZFP36) which take part in the
inflammatory response in COVID-19. This study has identified the CRIEGs and miRNA,
the interactions between them, which are potentially critical and can be studied
further for the development of targeted therapeutic strategies. The data can also be
used in exploring novel pathways, which occur following SARS-CoV-2 infection.
However, the data needs to be experimentally validated in vitro and in vivo.
6.
Declaration of Competing Interest:
The authors declare that they have no known competing financial interests or personal
relationships that could have appeared to influence the work reported in this paper.
medRxiv preprint doi: https://doi.org/10.1101/2021.06.08.21258565; this version posted June 12, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .
7. Acknowledgements:
The authors thanks to Dr. Dipayan Roy (MD Biochemistry) for language editing and
review of the manuscript.
medRxiv preprint doi: https://doi.org/10.1101/2021.06.08.21258565; this version posted June 12, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .
Figure legends:
Figure 1. Flow Chart of the data processing and Analysis.
Figure 1B. (A). Boxplot is represent the total DEGs of diffrent groups for study samples. (B).
Mean-variance trend plot is applicable to check the mean-variance relationship of the DEGs
data, after fitting a linear model. (C). A mean difference (MD) plot displays log2 fold change
versus average log2 expression values of DEGs. (D) A volcano plot displays statistical
significance (-log10 P value) versus magnitude of change (log2 fold change) DEGs.
Figure 2A. Venn diagram of enriched gene ontology
Figure 2B. Protein-Protein Interaction between Cytokine storm genes.
Figure 3. Enriched Gene Ontology terms of Cytokine storm genes obtained; Biological Process
(BP), Molecular Function (MF), Cellular components (CC), KEGG Pathway.
Figure 4. Common targeting MicroRNAs responsible for KEGG Pathway; RNA localization in
cellular components (BP), Gene ontology (GO), Disease Category (DC).
Figure 5. Enriched Gene Ontology terms of Cytokine storm genes obtained; KEGG Pathway;
RNA localization in cellular components (BP), Gene ontology (GO), Disease Category (DC).
Figure 6. Show the PPI network of Cytokine and Transcription interaction network.
Figure 7. Show the Cytokine-Transcription factor and MicroRNAs interaction network.
Figure 8. The Cytokine and MicroRNAs interaction network.
medRxiv preprint doi: https://doi.org/10.1101/2021.06.08.21258565; this version posted June 12, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .
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medRxiv preprint doi: https://doi.org/10.1101/2021.06.08.21258565; this version posted June 12, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .
Table: 1 A. List of Cytokine Strom genes and there regulating transcription factors expressed
in GSE17400.
Cytokine Strom Gene
Gene
Symbol
P Value
CCL2
CCL4
CXCL10
CXCL8
IL6
IL7
JAK2
TNF
0.01
0.03
0.00
0.00
0.00
0.00
0.00
0.02
Fold Change
Transcription Factors of Cytokine Gene
Gene
P Value
Fold Change
AHR
0.03
4.25
CREM
0.05
3.75
DDIT3
0.00
52.60
Symbol
6.07
4.59
98.60
56.40
139.00
24.60
16.40
5.26
E2F1
0.00
10.50
EGR1
0.00
75.60
EP300
0.01
6.13
ESR1
0.00
7.64
ETS2
0.00
82.10
HDAC1
0.03
4.49
HDAC2
0.00
9.29
IRF1
0.00
132.00
JUN
0.00
45.20
KLF4
0.00
58.10
NFAT5
0.03
4.46
NFKB1
0.00
12.70
NFKBIA
0.00
81.60
REL
0.00
14.20
RUNX1
0.04
4.10
SIRT1
0.00
26.40
SP100
0.00
111.00
SP140L
0.00
53.80
STAT1
0.00
80.60
XBP1
0.02
4.81
ZFP36
0.00
14.40
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(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .
Table: 2B. List of Cytokine Strom genes, transcription factors and common targeting
microRNAs.
Cytokine Gene
IL1B
IL2
IL7
CXCL8
IL9
IL10
IL17A
CSF3
CSF2
JAK2
TNF
CXCL10
CCL2
CCL3
CCL4
IL6
Transcription Factor
AHR
CEBPA
CREM
DDIT3
E2F1
EGR1
EP300
ESR1
ETS2
FOXP3
HDAC1
HDAC2
HSF1
IRF1
JUN
KLF4
NFAT5
NFKB1
NFKBIA
NR1I2
REL
RELA
RUNX1
SIRT1
SP1
SPI1
STAT1
STAT3
VDR
XBP1
ZFP36
ZNF300
MicroRNAs
hsa-miR-106a-5p
hsa-miR-155-5p
hsa-miR-98-5p
hsa-miR-24-3p
hsa-miR-204-5p
hsa-miR-124-3p
hsa-miR-203a-3p
hsa-miR-335-5p
hsa-let-7c-5p
hsa-miR-1-3p
medRxiv preprint doi: https://doi.org/10.1101/2021.06.08.21258565; this version posted June 12, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .
Table 3: Cytokine and regulating transcription factor and common targeting MiroRNA of
Cytokine and Its Transcription factor.
Target
CCL2
CCL2 targeting MicroRNA
hsa-mir-124-3p
hsa-mir-1-3p
hsa-mir-155-5p
hsa-mir-206
hsa-mir-24-3p
hsa-mir-26b-5p
hsa-mir-495-3p
hsa-mir-518a-5p
hsa-mir-98-5p
CCL3
hsa-mir-24-3p
hsa-mir-223-3p
CCL4
hsa-mir-195-5p
Transcription
Factor
TF Targeting MicroRNA
APEX1
ATF4
CEBPA
hsa-mir-124-3p
hsa-mir-1-3p
hsa-mir-155-5p
HDAC2
hsa-mir-1-3p
IRF3
JUN
hsa-mir-155-5p
hsa-mir-26b-5p
NFAT5
hsa-mir-1-3p
hsa-mir-155-5p
hsa-mir-206
hsa-mir-24-3p
NFIC
NFKB1
hsa-mir-155-5p
hsa-mir-26b-5p
NR1I2
PREB
REL
hsa-mir-518a-5p
hsa-mir-98-5p
RELA
hsa-mir-124-3p
SP1
hsa-mir-124-3p
hsa-mir-1-3p
hsa-mir-155-5p
hsa-mir-24-3p
hsa-mir-518a-5p
SP1
SPI1
hsa-mir-155-5p
STAT1
hsa-mir-155-5p
STAT2
STAT3
hsa-mir-124-3p
hsa-mir-155-5p
XBP1
hsa-mir-124-3p
E2F1
hsa-mir-223-3p
hsa-mir-24-3p
NFKB1
RELA
RUNX1
STAT1
hsa-mir-223-3p
CREM
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(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .
hsa-mir-24-3p
CSF2
CXCL8
hsa-let-7a-5p
hsa-let-7b-5p
hsa-let-7c-5p
hsa-let-7e-5p
hsa-let-7f-5p
hsa-mir-106a-5p
hsa-mir-124-3p
hsa-mir-1-3p
hsa-mir-146a-5p
hsa-mir-155-5p
hsa-mir-203a-3p
hsa-mir-204-5p
hsa-mir-23a-3p
hsa-mir-302c-3p
hsa-mir-302d-3p
hsa-mir-335-5p
hsa-mir-520b
hsa-mir-93-5p
NFKB1
RELA
CREBBP
CTCF
ETS1
ETS2
JUN
NFKB1
RELA
RUNX1
ATF4
CEBPB
DDIT3
DEK
EGR1
ELF4
EP300
ERG
ETS2
FOS
FOSB
HDAC1
HDAC2
IKBKB
ING4
JUN
NEAT1
NFE2L2
NFKB1
NFKBIA
hsa-mir-124-3p
hsa-mir-1-3p
hsa-let-7c-5p
hsa-mir-155-5p
hsa-mir-106a-5p
hsa-mir-203a-3p
hsa-mir-204-5p
hsa-mir-302c-3p
hsa-mir-302d-3p
hsa-mir-520b
hsa-mir-93-5p
hsa-mir-335-5p
hsa-mir-124-3p
hsa-let-7b-5p
hsa-let-7f-5p
hsa-mir-155-5p
hsa-mir-203a-3p
hsa-mir-93-5p
hsa-let-7a-5p
hsa-mir-146a-5p
hsa-mir-155-5p
hsa-let-7b-5p
hsa-mir-335-5p
hsa-mir-93-5p
medRxiv preprint doi: https://doi.org/10.1101/2021.06.08.21258565; this version posted June 12, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .
NR4A2
RELA
SFPQ
STAT3
STAT6
VDR
XBP1
CXCL10
IL1B
hsa-mir-15a-5p
hsa-mir-21-5p
hsa-mir-6849-3p
hsa-mir-106a-5p
hsa-mir-204-5p
hsa-mir-21-5p
hsa-mir-24-3p
ZFP36
ZNF300
IRF1
IRF3
IRF7
NFKB1
RELA
STAT1
AHR
CEBPB
E2F1
HMGA1
HSF1
IRF8
JUN
JUNB
KLF4
NFIL3
NFKB1
NFKBIA
REL
RELA
SIRT1
SPI1
hsa-mir-124-3p
hsa-let-7a-5p
hsa-let-7c-5p
hsa-let-7e-5p
hsa-mir-106a-5p
hsa-mir-124-3p
hsa-mir-155-5p
hsa-mir-23a-3p
hsa-mir-93-5p
hsa-let-7a-5p
hsa-mir-124-3p
hsa-let-7c-5p
hsa-mir-124-3p
hsa-mir-93-5p
hsa-mir-155-5p
hsa-mir-155-5p
hsa-mir-15a-5p
hsa-mir-21-5p
hsa-mir-6849-3p
hsa-mir-21-5p
hsa-mir-106a-5p
hsa-mir-204-5p
hsa-mir-106a-5p
hsa-mir-21-5p
hsa-mir-24-3p
hsa-mir-21-5p
hsa-mir-24-3p
hsa-mir-204-5p
medRxiv preprint doi: https://doi.org/10.1101/2021.06.08.21258565; this version posted June 12, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .
IL2
IL6
hsa-mir-155-5p
hsa-let-7a-5p
hsa-let-7c-5p
hsa-let-7f-5p
hsa-mir-106a-5p
hsa-mir-107
hsa-mir-124-3p
hsa-mir-125a-3p
hsa-mir-1-3p
hsa-mir-146a-5p
hsa-mir-146b-5p
hsa-mir-149-5p
hsa-mir-155-5p
hsa-mir-203a-3p
hsa-mir-223-3p
hsa-mir-26a-5p
hsa-mir-335-5p
hsa-mir-365a-3p
hsa-mir-9-5p
hsa-mir-98-5p
STAT1
SUGP1
YY1
BACH2
CREB1
CREM
EGR1
ETS1
FOXK2
FOXP3
HDAC1
ILF2
ILF3
JUN
KAT5
KLF2
NFATC1
NFKB1
POU2F1
REL
RELA
RUNX1
SATB1
SP1
STAT3
TOB1
VDR
AHR
ATF4
CEBPA
CEBPB
CREB1
DDIT3
EGR1
hsa-mir-155-5p
hsa-mir-155-5p
hsa-mir-155-5p
hsa-mir-155-5p
hsa-let-7a-5p
hsa-let-7c-5p
hsa-let-7f-5p
hsa-mir-124-3p
hsa-mir-26a-5p
hsa-mir-98-5p
hsa-mir-124-3p
hsa-mir-1-3p
hsa-let-7c-5p
hsa-mir-155-5p
hsa-mir-106a-5p
hsa-mir-203a-3p
hsa-mir-9-5p
hsa-mir-335-5p
hsa-let-7a-5p
hsa-mir-106a-5p
medRxiv preprint doi: https://doi.org/10.1101/2021.06.08.21258565; this version posted June 12, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .
hsa-mir-203a-3p
hsa-mir-223-3p
hsa-mir-98-5p
EGR2
EP300
FOXO1
GBX2
JUN
JUND
KLF4
MYC
NFKB1
OTX2
PPARA
RBPJ
REL
RELA
SP1
STAT1
STAT3
hsa-mir-149-5p
hsa-mir-26a-5p
hsa-mir-9-5p
hsa-mir-149-5p
hsa-mir-155-5p
hsa-mir-203a-3p
hsa-mir-107
hsa-mir-124-3p
hsa-mir-335-5p
hsa-let-7a-5p
hsa-mir-146a-5p
hsa-mir-146b-5p
hsa-mir-155-5p
hsa-mir-9-5p
hsa-mir-107
hsa-mir-365a-3p
hsa-mir-98-5p
hsa-mir-124-3p
hsa-let-7a-5p
hsa-let-7f-5p
hsa-mir-124-3p
hsa-mir-1-3p
hsa-mir-149-5p
hsa-mir-155-5p
hsa-mir-223-3p
hsa-mir-335-5p
hsa-mir-146a-5p
hsa-mir-155-5p
hsa-mir-203a-3p
hsa-mir-223-3p
hsa-let-7a-5p
hsa-let-7c-5p
hsa-mir-106a-5p
hsa-mir-124-3p
hsa-mir-155-5p
medRxiv preprint doi: https://doi.org/10.1101/2021.06.08.21258565; this version posted June 12, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .
TBP
VDR
XBP1
IL10
IL2
hsa-let-7c-5p
hsa-mir-106a-5p
hsa-mir-34a-5p
hsa-mir-98-5p
hsa-mir-155-5p
ZFP36
ZMYND11
ZNF300
ATF1
CEBPA
CEBPB
CREB1
FLI1
FOXP3
GATA3
HDAC11
HDAC4
IRF1
IRF8
MSC
NFKB1
NR1I2
PARP1
PGR
REL
RELA
SP1
STAT1
STAT3
TBX21
VDR
BACH2
CREB1
CREM
EGR1
ETS1
FOXK2
FOXP3
HDAC1
ILF2
ILF3
hsa-mir-223-3p
hsa-let-7a-5p
hsa-mir-124-3p
hsa-let-7c-5p
hsa-mir-124-3p
hsa-mir-125a-3p
hsa-mir-155-5p
hsa-mir-155-5p
hsa-let-7c-5p
hsa-mir-34a-5p
hsa-mir-106a-5p
hsa-let-7c-5p
hsa-mir-34a-5p
hsa-mir-98-5p
hsa-mir-34a-5p
hsa-let-7c-5p
hsa-mir-106a-5p
medRxiv preprint doi: https://doi.org/10.1101/2021.06.08.21258565; this version posted June 12, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .
IL17A
JAK2
TNF
hsa-mir-125a-5p
hsa-mir-125b-5p
hsa-mir-375
hsa-mir-17-5p
hsa-mir-203a-3p
hsa-mir-24-3p
hsa-mir-34a-5p
JUN
KAT5
KLF2
NFATC1
NFKB1
POU2F1
REL
RELA
RUNX1
SATB1
SP1
STAT3
TOB1
VDR
HDAC11
NFKB1
RELA
RORC
BRCA1
ESR1
STAT1
STAT3
STAT3
ATF2
CEBPB
CEBPD
E2F1
E2F1
E2F1
E2F1
EGR1
ETV4
HDAC11
HDAC3
HMGB2
HSF1
IRF5
JUN
LRRFIP1
NFAT5
NFKB1
NR4A1
RELA
hsa-mir-155-5p
hsa-mir-155-5p
hsa-mir-155-5p
hsa-mir-155-5p
hsa-mir-125a-5p
hsa-mir-125b-5p
hsa-mir-375
hsa-mir-17-5p
hsa-mir-203a-3p
hsa-mir-24-3p
hsa-mir-34a-5p
hsa-mir-203a-3p
hsa-mir-17-5p
hsa-mir-24-3p
hsa-mir-34a-5p
medRxiv preprint doi: https://doi.org/10.1101/2021.06.08.21258565; this version posted June 12, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .
SIRT1
hsa-mir-34a-5p