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Disponible online el 28 de Septiembre de 2021
The miRNA neuroinflammatory biomarkers in COVID-19 patients with different severity of illness
Los biomarcadores neuroinflamatorios miARN en pacientes con COVID-19 con diferente gravedad de la enfermedad
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R. Keikhaa,b, S.M. Hashemi-Shahria, A. Jebalic,
Autor para correspondencia
alijebal2011@gmail.com

Corresponding author.
a Infectious Diseases and Tropical Medicine Research Center, Resistant Tuberculosis Institute, Zahedan University of Medical Sciences, Zahedan, Iran
b Department of Pathology, Faculty of Medicine, Zahedan University of Medical Sciences, Zahedan, Iran
c Department of Medical Nanotechnology, Faculty of Advanced Sciences and Technology, Tehran Medical Science, Islamic Azad University, Tehran, Iran
Recibido 07 mayo 2021. Aceptado 27 junio 2021
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Table 1. The characteristics of study groups.
Table 2. The human gene targets of pro-neuroinflammatory miRNAs and anti- neuroinflammatory miRNAs, obtained from miRDB online database.
Table 3. Important pathways of pro-neuroinflammatory miRNAs and anti- neuroinflammatory miRNAs, extracted from KEGG molecular pathway.
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Abstract
Introduction

The expression of specific miRNAs and their mRNA targets are changed in infectious disease. The aim of this study was to analyze the expression of pro-neuroinflammatory miRNAs, anti-neuroinflammatory miRNAs, and their mRNA targets in the serum of COVID-19 patients with different grades.

Methods

COVID-19 patients with different grades were enrolled in this study and the expression of pro-neuroinflammatory miRNAs, anti-neuroinflammatory miRNAs, and their target mRNAs was analyzed by q-PCR.

Results

The relative expression of anti- neuroinflammatory miRNAs (mir-21, mir-124, and mir-146a) was decreased and the relative expression of their target mRNAs (IL-12p53, Stat3, and TRAF6) was increased. Also, the relative expression of pro-neuroinflammatory miRNAs (mir-326, mir-155, and mir-27b) was increased and the relative expression of their target mRNA (PPARS, SOCS1, and CEBPA) was decreased in COVID-19 patients with increase of disease grade. A negative significant correlation was seen between mir-21 and IL-12p53 mRNA, mir-124 and Stat3 mRNA, mir-146a and TRAF6 mRNA, mir-27b and PPARS mRNA, mir-155 and SOCS1 mRNA, and between mir-326 and CEBPA mRNA in COVID-19 patients (P<0.05).

Conclusions

This study showed that the relative expression of anti- neuroinflammatory miRNAs was decreased and the relative expression of their targeted mRNAs was increased in COVID-19 patients from asymptomatic to critical illness. Also, this study showed that the relative expression of pro-neuroinflammatory miRNAs was increased and the relative expression of their targeted mRNA was decreased in COVID-19 patients from asymptomatic to critical illness.

Keywords:
miRNAs
COVID-19
Pro-neuroinflammatory
Anti-neuroinflammatory
Resumen
Introducción

La expresión de miARN específicos y sus dianas de ARNm se modifican en las enfermedades infecciosas. El objetivo de este estudio fue analizar la expresión de miARN pro-neuroinflamatorios, miARN anti-neuroinflamatorios y sus ARNm dianas en el suero de pacientes con COVID-19 de diferentes grados.

Métodos

Se incluyeron en este estudio pacientes con COVID-19 de diferentes grados y se analizó la expresión de miARN pro-neuroinflamatorios, miARN anti-neuroinflamatorios y sus ARNm diana mediante q-PCR.

Resultados

La expresión relativa de miARN anti-neuroinflamatorios (mir-21, mir-124 y mir-146a) disminuyó y la expresión relativa de sus ARNm diana (IL-12p53, Stat3 y TRAF6) aumentó. Además, la expresión relativa de miARN pro-neuroinflamatorios (mir-326, mir-155 y mir-27b) aumentó y la expresión relativa de su ARNm diana (PPARS, SOCS1 y CEBPA) disminuyó en pacientes con COVID-19 con aumento del grado de enfermedad. Se observó una correlación negativa significativa entre ARNm de mir-21 e IL-12p53, ARNm de mir-124 y Stat3, ARNm de mir-146a y TRAF6, ARNm de mir-27b y PPARS, ARNm de mir-155 y SOCS1, y entre mir-326 y ARNm de CEBPA en pacientes con COVID-19 (p < 0,05).

Conclusiones

Este estudio mostró que la expresión relativa de miARN anti-neuroinflamatorios disminuyó y la expresión relativa de sus ARNm diana se incrementó en pacientes con COVID-19 de enfermedad asintomática a crítica. Además, este estudio mostró que la expresión relativa de miARN pro-neuroinflamatorios aumentó y la expresión relativa de su ARNm diana disminuyó en pacientes con COVID-19 de enfermedad asintomática a crítica.

Palabras clave:
miARN
COVID-19
Pro-neuroinflamatorio
Anti-neuroinflamatorio
Texto completo
Introduction

Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), known as COVID-19, is a new infectious disease first seen in late December 2019 in Wuhan, China, and similar outbreaks occurred in the hospital in neighboring countries. Major clinical symptoms include fever, dry cough, diarrhea, muscle aches, pneumonia, and in severe cases death.1,2 COVID-19 also is associated with neurological manifestations such as encephalopathy and encephalomyelitis, ischemic stroke and intracerebral hemorrhage, anosmia, neuromuscular diseases, and neuroinflammation diseases.3

Since COVID-19 is a new disease, complete information on its etiology, cellular mechanisms, and possible risk factors is not available. COVID-19 may be similar to recent acute respiratory syndromes, such as SARS and MERS.4 Theoretically, after the SARS-CoV-2 enters the human body, different types of immune cells are stimulated. These cells trigger the proper immune response by producing different cytokines, chemokines, antibodies, etc. SARS-CoV-2 can infect the CNS following the entry of the virus into the nose or the eye. The viral particles are transmitted to the olfactory bulb and then to the brainstem, and then all parts of the brain.5 In addition to the direct attack of nerve cells, the SARS-CoV-2 can systematically cross the BBB through the blood vessels and reach the CNS. The main feature of systemic infection in COVID-19 is the massive increase in pro-inflammatory factors in the blood, which is described as a “cytokine stor”.6 This leads to BBB permeability and transmission of SARS-CoV-2 and peripheral immune cells. Once the coronavirus enters the CNS, it is the turn of the astrocytes and microglia to fight it. The immune response of astrocytes and microglia is regulated by different microRNAs (miRNAs). Previous studies showed inflammatory processes in CNS are guided by pro-neuroinflammatory miRNAs (such as mir-155, mir-27b, mir-326) and anti- neuroinflammatory miRNAs (such as mir-146a, mir-124, and mir-21).7,8

This study aimed to analyze the expression of pro-neuroinflammatory miRNAs, anti-neuroinflammatory miRNAs, and their mRNA targets in the serum of COVID-19 patients with different grades.

Materials and methodsMaterials

All primers were provided from Bioneer, South Korea. MirPremier microRNA isolation kit was sourced from Sigma-Aldrich, USA. Mir-X miRNA First-Strand Synthesis kit and cDNA matermix were purchased from Takara bio inc, USA. SYBR® Green Real-Time Master Mix was from Invitrogen, UK.

Bioinformatics

In this study, to determine the miRNAs associated with the COVID-19, we used online bioinformatics Softwares.9 In the first step, mirTarP (https://mcube.nju.edu.cn/jwang/mirTar/docs/mirTar/) was used to the list of appropriate miRNAs.10,11 In the second step, to reduce the number of selected miRNAs, we selected some limited pro-neuroinflammatory and anti-neuroinflammatory miRNAs that were previously reported in other studies. In the third step, the miRDB online database (http://mirdb.org/) was used to find the target of selected miRNAs.12 Target genes of the differentially regulated miRNAs were predicted using the mirPath tool (version 3.0).13 KEGG molecular pathways were also retrieved using the same tool.14 Pathways and processes regulated with P values lower than 0.05 were considered significant.

Study groups

Table 1 shows the full characteristics of 6 study groups enrolled in this study. The licensing committee that approved the experiments, including any relevant details was Zahedan University of Medical Sciences, Zahedan, Iran. All experiments were under the guidelines of the National Institute of Health, and the ethics committee of Zahedan University of Medical Sciences, Zahedan, Iran. (Ethical code: IR.ZAUMS.REC.1399.317). Also, informed consent was obtained from all participants. Five ml of whole blood was collected from each person and their serum was separated by centrifugation at 3000rpm/min for 10min at 4°C. In this study, only COVID-19 patients with English variant of SARS-COV-2 (Lineage B.1.1.7; GISAID accession number: EPI-ISL-2227268) were included.

Table 1.

The characteristics of study groups.

  Study group 1  Study group 2  Study group 3  Study group 4  Study group 5  Control 
Number (n21  20  20  21  21  20 
Age distribution±SD  50±10  50±10  50±10  50±10  50±12  50±12 
Sex percentage±SD  Female (52%±2%)Male (48%±1%)  Female (51%±2%)Male (49%±2%)  Female (50%±3%)Male (50%±1%)  Female (53%±1%)Male (47%±2%)  Female (52%±1%)Male (48%±2%)  Female (50%±3%)Male (50%±1%) 
Severity of illnessa  Grade 5Critical illness: Individuals who have respiratory failure, septic shock, and/or multiple organ dysfunction.  Grade 4Severe illness: Individuals who have SpO2<94% on room air at sea level, a ratio of arterial partial pressure of oxygen to fraction of inspired oxygen (PaO2/FiO2)<300mm Hg, respiratory frequency >30 breaths/min, or lung infiltrates >50%.  Grade 3Moderate illness: Individuals who show evidence of lower respiratory disease during clinical assessment or imaging and who have saturation of oxygen (SpO2) ≥94% on room air at sea level.  Grade 2Mild illness: Individuals who have any of the various signs and symptoms of COVID-19 (e.g., fever, cough, sore throat, malaise, headache, muscle pain, nausea, vomiting, diarrhea, loss of taste and smell) but who do not have shortness of breath, dyspnea, or abnormal chest imaging.  Grade 1Asymptomatic: Individuals who test positive for SARS-CoV-2 using a virologic test (i.e., a nucleic acid amplification test or an antigen test) but who have no symptoms that are consistent with COVID-19.  Healthy people 
Comorbidities  No  No  No  No  No  No 
Inflammatory autoimmune diseases  No  No  No  No  No  No 
Drug treatment  No  No  No  No  No  No 
a

The severity of COVID-19 was categorized according to NIH guidelines, https://www.covid19treatmentguidelines.nih.gov/overview/clinical-spectrum.

Small RNA isolation, first-strand cDNA synthesis, and quantification of miRNAs and mRNAs by qPCR

Here, Small RNA was isolated from blood samples using mirPremier microRNA isolation kit. Briefly, 1000μL of the lysis buffer was added to 100μL of serum samples, vortexed for 2min, and incubated at 55°C for 5min. The samples were then centrifuged for 5min at 14,000×g to remove cellular debris, genomic DNA, and large RNA. The lysate supernatant was filtered through the filtration column and binding column. After binding, the column was first washed with 700μL of 100% ethanol and centrifuged at 14,000×g for 30s and again the flow-through was discarded. The second wash was done by adding 500μl of binding solution into the column and centrifuged at maximum speed (14,000×g) for 1min. Subsequently, 500ml of the ethanol-diluted wash solution 2 was added to the column for a third wash. After centrifugation at maximum speed (14,000×g) for 30s, the flow-through was discarded. Next, the column was dried by centrifuging at maximum speed (14,000×g) for 1min. The column-tube assembly was carefully removed from the centrifuge to avoid splashing of the residual flow-through liquid to the dried column. Small RNA was eluted from the column using 50ml elution solution and by centrifugation at 16,000×g and the process was repeated to improve small RNA yield. The purity of the RNA samples was analyzed by NanoDrop ND-1000 UV-VIS spectrophotometer. The A260nm/A280nm ratio of all samples was between 1.8 and 2.1. The quantity of RNA samples was analyzed by agarose gel electrophoretic separation. For first-strand cDNA synthesis, small RNAs were polyadenylated and reverse transcribed using the Mir-X miRNA First-Strand Synthesis kit. Briefly, 5μl mRQ buffer (2×), 5μg RNA and 1.25μl mRQ enzyme was mixed in a reaction volume of 10μl and incubated in a thermocycler for 1h at 37°C, then terminate at 85°C for 5min to inactivate the enzymes. After reverse transcription, the cDNA was diluted. For quantification of miRNA by qPCR, Mir-X miRNA qPCR SYBR Kit was used. Briefly, 10μl PCR reaction mixture was prepared to comprise of 1× SYBR advantage premix, 0.2mM of both forward and reverse primers, and 50ng of the first-strand cDNA. qPCR reactions were incubated in a 96 well plate at 95°C for 2min, followed by 40 cycles of 95°C for 10s and 60°C for 20s. Amplification cycles were followed by a melting curve analysis ranging from 56 to 95°C. Finally, the threshold cycle (Ct) values were recorded. For mRNA, total RNA was extracted using an RNA extraction kit. Then, the cDNA was synthesized in the presence of the superscript enzyme and hexamers. For real-time PCR, 2μL of cDNA, 2μL of forward primer, and 2μL of reverse primer of each gene were added to 10μL of SYBR® Green Real-Time Master Mix. In this study, the relative expression of mir-155, mir-27b, mir-326, mir-124, mir-146a, mir-21, IL-12p53, Stat3, TRAF6, PPARS, SOCS1, and CEBPA was analyzed. The expression of microRNA and mRNA was normalized to RNU 48 and GAPDH, respectively.

Statistical analysis

All data were reported as the mean±standard deviation. To find significant differences between groups, a one-way ANOVA method was applied. A P-value of less than 0.05 was considered statistically significant. Also, Spearman's correlation coefficient was used to correlate the expression of miRNAs and their mRNA targets.

ResultsBioinformatics analysis

Five-top human mRNA targets for pro-neuroinflammatory miRNAs (mir-155, mir-27b, and mir-326) and anti- neuroinflammatory miRNAs (mir-124, mir-146a, and mir-21) are shown in Table 2. It should be noted that each miRNA has many targets, but here we have listed only 5 important mRNA targets with the highest target score. Theoretically, all of them can be affected by pro-neuroinflammatory and anti-neuroinflammatory miRNAs.

Table 2.

The human gene targets of pro-neuroinflammatory miRNAs and anti- neuroinflammatory miRNAs, obtained from miRDB online database.

Target score  miRNA Name  Gene Symbol  Gene description 
98  miR-155  SOCS1  Suppressor Of Cytokine Signaling 1 
99  miR-155  ZNF629  Zinc finger protein 629 
99  miR-155  CREBRF  CREB3 regulatory factor 
99  miR-155  DENND1B  DENN domain containing 1B 
98  miR-155  PTPN21  Protein tyrosine phosphatase, non-receptor type 21 
98  miR-27b  PPARs  Peroxisome Proliferator Activated Receptor Gamma 
97  miR-27b  AFF4  AF4/FMR2 family member 4 
97  miR-27b  GXYLT1  Glucoside xylosyltransferase 1 
97  miR-27b  ARFGEF1  ADP ribosylation factor guanine nucleotide exchange factor 1 
96  miR-27b  GCC2  GRIP and coiled-coil domain containing 2 
99  miR-326  CEBPA  CCAAT Enhancer Binding Protein Alpha 
99  miR-326  ETS1  ETS proto-oncogene 1, transcription factor 
99  miR-326  CEP85  Centrosomal protein 85 
98  miR-326  FGF11  Fibroblast growth factor 11 
98  miR-326  GPD2  Glycerol-3-phosphate dehydrogenase 2 
98  miR-124  Stat3  Signal Transducer And Activator Of Transcription 3 
98  miR-124  OSBPL3  Oxysterol binding protein like 3 
98  miR-124  SLC50A1  Solute carrier family 50 member 1 
98  miR-124  ITGB1  Integrin subunit beta 1 
98  miR-124  SIX4  SIX homeobox 4 
100  miR-146a  TRAF6  TNF Receptor Associated Factor 6 
100  miR-146a  FOXC1  Forkhead box C1 
100  miR-146a  CPLX2  Complexin 2 
100  miR-146a  STXBP6  Syntaxin binding protein 6 
100  miR-146a  ZFX  Zinc finger protein X-linked 
99  miR-21  IL-12p53  Interleukin 12 p53 protein 
99  miR-21  STK38L  Serine/threonine kinase 38 like 
99  miR-21  PCDH19  Protocadherin 19 
99  miR-21  LAMP1  Lysosomal associated membrane protein 1 
99  miR-21  GRIA2  Glutamate ionotropic receptor AMPA type subunit 2 

Based on KEGG database (Table 3), we found that both pro-neuroinflammatory miRNAs and anti-neuroinflammatory miRNAs are significantly enriched in important cellular pathways, such as PI3K-Akt signaling pathway, mRNA surveillance pathway, mTOR signaling pathway, MAPK signaling pathway, Wnt signaling pathway, and AMPK signaling pathway.

Table 3.

Important pathways of pro-neuroinflammatory miRNAs and anti- neuroinflammatory miRNAs, extracted from KEGG molecular pathway.

KEGG pathway  P-value  Genes  miRNAs 
Adherens junction  0.0001  30 
Endometrial cancer  0.0001  24 
Small cell lung cancer  0.0001  36 
Regulation of actin cytoskeleton  0.0001  67  56 
Bladder cancer  0.0002  21 
PI3K-Akt signaling pathway  0.0002  105 
Shigellosis  0.0002  26 
Thyroid hormone signaling pathway  0.0003  44 
Lysine degradation  0.001  15 
Non-small cell lung cancer  0.001  23 
mRNA surveillance pathway  0.001  34 
mTOR signaling pathway  0.002  25 
Oocyte meiosis  0.003  38 
Prolactin signaling pathway  0.003  28 
Melanoma  0.003  25 
Ubiquitin mediated proteolysis  0.004  48 
Fatty acid metabolism  0.004  11 
Fatty acid elongation  0.004 
Arrhythmogenic right ventricular  0.006  16 
Estrogen signaling pathway  0.006  31 
Signaling pathways regulating of stem cells  0.007  42 
Gap junction  0.008  27 
Sphingolipid signaling pathway  0.009  38 
Amoebiasis  0.01  30 
Pantothenate and CoA biosynthesis  0.01 
MAPK signaling pathway  0.01  72 
Insulin signaling pathway  0.02  45 
Wnt signaling pathway  0.02  41 
Axon guidance  0.02  37 
Pathogenic Escherichia coli infection  0.02  21 
AMPK signaling pathway  0.02  41 
Hepatitis C  0.03  20 
Vibrio cholerae infection  0.03  23 
Epithelial cell signaling in Helicobacter pylori infection  0.04  54 
Platelet activation  0.04  39 
Steroid biosynthesis  0.04 
Salmonella infection  0.04  27 
The expression of anti-neuroinflammatory miRNAs and their mRNA targets

We found that the relative expression of anti-neuroinflammatory miRNAs, including mir-21, mir-124, and mir-146a, was significantly decreased with increase of COVID-19 grade (P<0.05) (Fig. 1(a–c)). Interestingly, the relative expression of human mRNA targets, including IL-12p53, Stat3, and TRAF6, of anti-neuroinflammatory miRNAs was significantly increased with increase of COVID-19 grade (P<0.05) (Fig. 2(a–c)). A negative significant correlation was seen between the expression of (mir-21 and IL-12p53 mRNA), (mir-124 and Stat3 mRNA), and (mir-146a and TRAF6 mRNA) in COVID-19 patients at all grades (P<0.05) (Fig. 3(a–c)).

Figure 1.

The relative expression of mir-21 (a), mir-124 (b), and mir-146a (c) in COVID-19 patients with different grades. *P<0.05 compared with Mild Illness and Asymptomatic by one-way ANOVA.

(0,2MB).
Figure 2.

The relative expression of IL-12p53 (a), Stat3 (b), and TRAF6 (c) mRNAs in COVID-19 patients with different grades. *P<0.05 compared with Mild Illness and Asymptomatic by one-way ANOVA.

(0,23MB).
Figure 3.

The correlation between the relative expression of mir-21 and IL-12p53 mRNA (a), mir-124 and Stat3 mRNA (b), and mir-146a and TRAF6 mRNA (c) in COVID-19 patients with different grades. Spearman's correlation coefficient was used to correlate these parameters.

(0,26MB).
The expression of pro-neuroinflammatory miRNAs and their mRNA targets

The relative expression of pro-neuroinflammatory miRNAs, including mir-326, mir-155, and mir-27b, was significantly increased with increase of COVID-19 grade (P<0.05) (Fig. 4(a–c)). Interestingly, the relative expression of human mRNA targets, including PPARS, SOCS1, and CEBPA, of pro-neuroinflammatory miRNAs was significantly decreased with increase of COVID-19 grade (P<0.05) (Fig. 5(a–c)). A negative significant correlation was also seen between the expression of (mir-27b and PPARS mRNA), (mir-155 and SOCS1 mRNA), and (mir-326 and CEBPA mRNA) in COVID-19 patients at all grades (P<0.05) (Fig. 6(a–c)).

Figure 4.

The relative expression of mir-27b (a), mir-155 (b), and mir-326 (c) in COVID-19 patients with different grades. *P<0.05 compared with Mild Illness and Asymptomatic by one-way ANOVA.

(0,22MB).
Figure 5.

The relative expression of PPARS (a), SOCS1 (b), and CEBPA (c) mRNAs in COVID-19 patients with different grades. *P<0.05 compared with Mild Illness and Asymptomatic by one-way ANOVA.

(0,21MB).
Figure 6.

The correlation between the relative expression of mir-27b and PPARS mRNAs (a), mir-155 and SOCS1 mRNAs (b), and mir-326 and CEBPA mRNA (c) in COVID-19 patients with different grades. Spearman's correlation coefficient was used to correlate these parameters.

(0,28MB).
Discussion

This study showed that the relative expression of anti-neuroinflammatory miRNAs (mir-21, mir-124, and mir-146a) was decreased and the relative expression of their target mRNAs (IL-12p53, Stat3, and TRAF6) was increased in COVID-19 patients with increase of disease grade from asymptomatic to critical illness. Also, this study showed that the relative expression of pro-neuroinflammatory miRNAs (mir-326, mir-155, and mir-27b) was increased and the relative expression of their target mRNA (PPARS, SOCS1, and CEBPA) was decreased in COVID-19 patients with increase of disease grade. A negative significant correlation was seen between each miRNA and its target mRNA. Based on bioinformatics analysis, some important pathways are affected by these pro-neuroinflammatory and anti-neuroinflammatory miRNAs, including PI3K-Akt, mRNA surveillance, mTOR, MAPK, Wnt, and AMPK signaling pathways. What we have found is that in patients with high severity of illness, the expression of pro-inflammatory miRNAs is increased, and conversely, the expression of anti-inflammatory miRNAs is decreased. Of course, it is clear that this situation follows a cytokine storm. Unfortunately, we have to say that this special condition not only causes serious damage to the brain but also causes damage to several organs and leads to multiple organ failure. We think that when immune cells are highly stimulated, cytokines and miRNAs can travel through the bloodstream to the whole body. This phenomenon has been mentioned by some researchers.15,16

Mir-155 is a central pro-inflammatory mediator in CNS by NF-κB dependent TLR signaling. It is synthesized inside macrophages and microglia.17–19mir-155 targets anti-inflammatory regulators such as SOCS1,17,19SHIP1,20C/EBP-β21 and IL13Rα1.22mir-155 inhibits the suppression of anti-inflammatory signaling and induces neuroinflammation. When mir-155 is expressed, it stimulates the transcription factor p53, and it targets the c-Maf transcription factor, which induces differentiation and inflammatory responses.23Mir-146a is an anti-inflammatory regulator in nerve cells, microglia, and astrocytes. It activates by NF-κB dependent TLR signaling.24,25 The Mir-146a targets MyD88 signaling complex, including IRAK1 and TRAF6, and acts as an NF-κB signaling regulator. In addition, Mir-146a targets other pro-inflammatory mediators including STAT-1,26,27IRF-527 and CFH.28,29 The polarization of macrophages and microglia are also altered by mir-146a.30mir-124 is also an anti-inflammatory miRNA and has a major role in neuronal differentiation31 and is highly expressed in microglia under normal conditions, but is not expressed in peripheral macrophages.32 Expression of mir-124 in microglia leads to anti-inflammatory effects33 by M2 phenotype.34 It is clear that mir-124 has anti-inflammatory activity by reducing inflammatory mediators and limiting microglia to activity. The role of mir-21 is very prominent in different types of CNS cells such as microglia35 and astrocytes,36 neurons,37 and oligodendrocytes.38Mir-21 is an anti-inflammatory regulator activated by TLR signaling. This induces the expression of the anti-inflammatory cytokine such as IL-10.39 In addition, mir-21 decreases TNF-α secretion in macrophages and microglia.40mir-27b targets an anti-inflammatory transcriptional activator, PPAR-γ; in human macrophages, this interaction blocks the induction of an anti-inflammatory phenotype. Inhibiting mir-27b also limits inflammatory signaling. It leads to produce inflammatory cytokines including IL-6 and TNF-α.41mir-326 is another pro-inflammatory miRNAs and can affect on differentiation of IL-17-producing Th17 cells. It was found that silencing mir-326 reduced EAE pathology.42 miRNAs have a cumulative effect on neuronal signaling and act together in inflammatory or anti-inflammatory pathways. For example, both mir-146a and mir-21 target different components of the TLR/MyD88/NF-κB and JAK-STAT pathways.26,28 In contrast, mir-155, mir-27b, and mir-326 activate the JAK-STAT pathway by targeting SOCS1 and SHIP1.19 It is interesting to note that miRNAs are also present in extracellular exosomes and can participate in intercellular communication.43 For example, mir-124, mir-21, and let-7 are found in exosomes and stimulate and regulate adjacent cells such as microglia and contribute to inflammatory signaling.44

One of main limitations of this study was to find and to collect COVID-19 patients with no comorbidities, no inflammatory autoimmune diseases, and no drug treatments. Theoretically, these factors can affect the expression of mRNAs and miRNAs. Second limitation was that we did not include COVID-19 patients caused by different variants of SARS-COV-2. Here, only COVID-19 patients with English variant (Lineage B.1.1.7) were included. We think that the expression of mRNAs and miRNAs may also be affected by virus variants. The third limitation was that we evaluated only 6 neuroinflammatory miRNAs in COVID-19 patients and it is suggested that other neuroinflammatory miRNAs could be studied in future studies.

Conclusions

This study showed that the relative expression of anti-neuroinflammatory miRNAs (mir-21, mir-124, and mir-146a) was decreased and the relative expression of their mRNAs (IL-12p53, Stat3, and TRAF6) was increased in COVID-19 patients from asymptomatic to critical illness. Also, this study showed that the relative expression of pro-neuroinflammatory miRNAs (mir-326, mir-155, and mir-27b) was increased and the relative expression of their mRNA (PPARS, SOCS1, and CEBPA) was decreased in COVID-19 patients from asymptomatic to critical illness. A negative significant correlation was seen between mir-21 and IL-12p53 mRNA, mir-124 and Stat3, between mir-146a and TRAF6, between mir-27b and PPARS, between mir-155 and SOCS1, and between mir-326 and CEBPA mRNA in COVID-19 patients (P<0.05).

Authors’ contributions

(I) Conception and design: R.K. and A.J., (II) Administrative support: R.K. and A.J., (III) Provision of study materials or patients: R.K., (IV) Collection and assembly of data: A.J., (V) Data analysis and interpretation: R.K. and A.J., (VI) Manuscript writing: All authors, (VII) Final approval of manuscript: All authors.

Ethics approval and consent to participate

“The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.” All experiments were under the guidelines of the National Institute of Health, the provisions of the Declaration of Helsinki,and the ethics committee of Zahedan University of Medical Sciences, Zahedan, Iran. (Ethical code: IR.ZAUMS.REC.1399.317).

Consent for publication

Not.

Availability of data and material

Not.

Funding

This article was financially supported by Zahedan University of Medical Sciences, Zahedan, Iran (grant number: 9937).

Conflict of interest

There is no conflict of interest.

Acknowledgements

We thank the Reference Laboratory of Zahedan University of Medical Sciences.

References
[1]
H.A. Rothan, S.N. Byrareddy.
The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak.
J Autoimmun, (2020), pp. 102433
[2]
Z.Y. Zu, M.D. Jiang, P.P. Xu, W. Chen, Q.Q. Ni, G.M. Lu, et al.
Coronavirus disease 2019 (COVID-19): a perspective from China.
Radiology, 200490 (2020),
[3]
P. Berlit, J. Bösel, G. Gahn, S. Isenmann, S.G. Meuth, C.H. Nolte, et al.
“Neurological manifestations of COVID-19”-guideline of the German society of neurology.
Neurol Res Pract, 2 (2020), pp. 1-14
[4]
N.C. Peeri, N. Shrestha, M.S. Rahman, R. Zaki, Z. Tan, S. Bibi, et al.
The SARS MERS and novel coronavirus (COVID-19) epidemics, the newest and biggest global health threats: what lessons have we learned?.
Int J Epidemiol, (2020),
[5]
K. Li, C. Wohlford-Lenane, S. Perlman, J. Zhao, A.K. Jewell, L.R. Reznikov, et al.
Middle East respiratory syndrome coronavirus causes multiple organ damage and lethal disease in mice transgenic for human dipeptidyl peptidase 4.
J Infect Dis, 213 (2016), pp. 712-722
[6]
F. Coperchini, L. Chiovato, L. Croce, F. Magri, M. Rotondi.
The cytokine storm in COVID-19: an overview of the involvement of the chemokine/chemokine-receptor system.
Cytokine Growth Factor Rev, 53 (2020), pp. 25-32
[7]
A.D. Gaudet, L.K. Fonken, L.R. Watkins, R.J. Nelson, P.G. Popovich.
MicroRNAs: roles in regulating neuroinflammation.
Neuroscientist, 24 (2018), pp. 221-245
[8]
J.A. Slota, S.A. Booth.
MicroRNAs in neuroinflammation: implications in disease pathogenesis, biomarker discovery and therapeutic applications.
Non-coding RNA, 5 (2019), pp. 35
[9]
S. Ekimler, K. Sahin.
Computational methods for microRNA target prediction.
Genes, 5 (2014), pp. 671-683
[10]
X. Shu, X. Zang, X. Liu, J. Yang, J. Wang.
Predicting microRNA mediated gene regulation between human and viruses.
Cells, 7 (2018), pp. 100
[11]
R. Sardar, D. Satish, D. Gupta.
Identification of novel SARS-CoV-2 drug targets by host microRNAs and transcription factors co-regulatory interaction network analysis.
Front Genet, 11 (2020), pp. 1105
[12]
N. Wong, X. Wang.
miRDB: an online resource for microRNA target prediction and functional annotations.
Nucleic Acids Res, 43 (2015), pp. D146-D152
[13]
I.S. Vlachos, K. Zagganas, M.D. Paraskevopoulou, G. Georgakilas, D. Karagkouni, T. Vergoulis, et al.
DIANA-miRPath v3.0 deciphering microRNA function with experimental support.
Nucleic Acids Res, 43 (2015), pp. W460-W466
[14]
M. Kanehisa, S. Goto, S. Kawashima, A. Nakaya.
The KEGG databases at GenomeNet.
Nucleic Acids Res, 30 (2002), pp. 42-46
[15]
G. Latimer, C. Corriveau, R.L. DeBiasi, B. Jantausch, M. Delaney, C. Jacquot, et al.
Cardiac dysfunction and thrombocytopenia-associated multiple organ failure inflammation phenotype in a severe paediatric case of COVID-19.
Lancet Child Adolescent Health, 4 (2020), pp. 552-554
[16]
T. Wang, Z. Du, F. Zhu, Z. Cao, Y. An, Y. Gao, et al.
Comorbidities and multi-organ injuries in the treatment of COVID-19.
[17]
P. Wang, J. Hou, L. Lin, C. Wang, X. Liu, D. Li, et al.
Inducible microRNA-155 feedback promotes type I IFN signaling in antiviral innate immunity by targeting suppressor of cytokine signaling 1.
J Immunol, 185 (2010), pp. 6226-6233
[18]
S. Bala, M. Marcos, K. Kodys, T. Csak, D. Catalano, P. Mandrekar, et al.
Up-regulation of microRNA-155 in macrophages contributes to increased tumor necrosis factor α (TNFα) production via increased mRNA half-life in alcoholic liver disease.
J Biol Chem, 286 (2011), pp. 1436-1444
[19]
A.L. Cardoso, J.R. Guedes, L. Pereira de Almeida, M.C. Pedroso de Lima.
miR-155 modulates microglia-mediated immune response by down-regulating SOCS-1 and promoting cytokine and nitric oxide production.
[20]
R.M. O’Connell, A.A. Chaudhuri, D.S. Rao, D. Baltimore.
Inositol phosphatase SHIP1 is a primary target of miR-155.
Proc Natl Acad Sci USA, 106 (2009), pp. 7113-7118
[21]
J. Worm, J. Stenvang, A. Petri, K.S. Frederiksen, S. Obad, J. Elmen, et al.
Silencing of microRNA-155 in mice during acute inflammatory response leads to derepression of c/ebp Beta and down-regulation of G-CSF.
Nucleic Acids Res, 37 (2009), pp. 5784-5792
[22]
R.T. Martinez-Nunez, F. Louafi, T. Sanchez-Elsner.
The interleukin 13 (IL-13) pathway in human macrophages is modulated by microRNA-155 via direct targeting of interleukin 13 receptor α1 (IL13Rα1).
J Biol Chem, 286 (2011), pp. 1786-1794
[23]
W. Su, S. Hopkins, N.K. Nesser, B. Sopher, A. Silvestroni, S. Ammanuel, et al.
The p53 transcription factor modulates microglia behavior through microRNA-dependent regulation of c-Maf.
J Immunol, 192 (2014), pp. 358-366
[24]
K.D. Taganov, M.P. Boldin, K-J. Chang, D. Baltimore.
NF-κB-dependent induction of microRNA miR-146, an inhibitor targeted to signaling proteins of innate immune responses.
Proc Natl Acad Sci USA, 103 (2006), pp. 12481-12486
[25]
J.G. Cui, Y.Y. Li, Y. Zhao, S. Bhattacharjee, W.J. Lukiw.
Differential regulation of interleukin-1 receptor-associated kinase-1 (IRAK-1) and IRAK-2 by microRNA-146a and NF-κB in stressed human astroglial cells and in Alzheimer disease.
J Biol Chem, 285 (2010), pp. 38951-38960
[26]
X. He, R. Tang, Y. Sun, Y-G. Wang, K-Y. Zhen, Zhang D-M., et al.
MicroR-146 blocks the activation of M1 macrophage by targeting signal transducer and activator of transcription 1 in hepatic schistosomiasis.
EBioMedicine, 13 (2016), pp. 339-347
[27]
Y. Tang, X. Luo, H. Cui, X. Ni, M. Yuan, Y. Guo, et al.
MicroRNA-146a contributes to abnormal activation of the type I interferon pathway in human lupus by targeting the key signaling proteins.
Arthritis Rheumat, 60 (2009), pp. 1065-1075
[28]
Y.Y. Li, J.G. Cui, P. Dua, A.I. Pogue, S. Bhattacharjee, W.J. Lukiw.
Differential expression of miRNA-146a-regulated inflammatory genes in human primary neural, astroglial and microglial cells.
Neurosci Lett, 499 (2011), pp. 109-113
[29]
W.J. Lukiw, Y. Zhao, J.G. Cui.
An NF-κB-sensitive micro RNA-146a-mediated inflammatory circuit in Alzheimer disease and in stressed human brain cells.
J Biol Chem, 283 (2008), pp. 31315-31322
[30]
C. Huang, X-j. Liu, J. Xie, T-t. Ma, X-m. Meng, J. Li.
MiR-146a modulates macrophage polarization by inhibiting Notch1 pathway in RAW264 7 macrophages.
Int Immunopharmacol, 32 (2016), pp. 46-54
[31]
E.V. Makeyev, J. Zhang, M.A. Carrasco, T. Maniatis.
The MicroRNA miR-124 promotes neuronal differentiation by triggering brain-specific alternative pre-mRNA splicing.
Mol Cell, 27 (2007), pp. 435-448
[32]
E.D. Ponomarev, T. Veremeyko, N. Barteneva, A.M. Krichevsky, H.L. Weiner.
MicroRNA-124 promotes microglia quiescence and suppresses EAE by deactivating macrophages via the C/EBP-α-PU. 1 pathway.
Nat Med, 17 (2011), pp. 64-70
[33]
C. Ma, Y. Li, M. Li, G. Deng, X. Wu, J. Zeng, et al.
microRNA-124 negatively regulates TLR signaling in alveolar macrophages in response to mycobacterial infection.
Mol Immunol, 62 (2014), pp. 150-158
[34]
T. Veremeyko, S. Siddiqui, I. Sotnikov, A. Yung, E.D. Ponomarev.
IL-4/IL-13-dependent and independent expression of miR-124 and its contribution to M2 phenotype of monocytic cells in normal conditions and during allergic inflammation.
[35]
L. Zhang, L.Y. Dong, Y.J. Li, Z. Hong, W.S. Wei.
miR-21 represses FasL in microglia and protects against microglia-mediated neuronal cell death following hypoxia/ischemia.
Glia, 60 (2012), pp. 1888-1895
[36]
H.-J. Li, Y.-B. Pan, Z.-L. Sun, Y.-Y. Sun, X.-T. Yang, D.-F. Feng.
Inhibition of miR-21 ameliorates excessive astrocyte activation and promotes axon regeneration following optic nerve crush.
Neuropharmacology, 137 (2018), pp. 33-49
[37]
Z. Han, F. Chen, X. Ge, J. Tan, P. Lei, J. Zhang.
miR-21 alleviated apoptosis of cortical neurons through promoting PTEN-Akt signaling pathway in vitro after experimental traumatic brain injury.
Brain Res, 1582 (2014), pp. 12-20
[38]
J.J. Miguel-Hidalgo, K.O. Hall, H. Bonner, A.M. Roller, M. Syed, C.J. Park, et al.
MicroRNA-21: expression in oligodendrocytes and correlation with low myelin mRNAs in depression and alcoholism.
Prog Neuropsychopharmacol Biol Psychiatry, 79 (2017), pp. 503-514
[39]
F.J. Sheedy, E. Palsson-McDermott, E.J. Hennessy, C. Martin, J.J. O’leary, Q. Ruan, et al.
Negative regulation of TLR4 via targeting of the proinflammatory tumor suppressor PDCD4 by the microRNA miR-21.
Nat Immunol, 11 (2010), pp. 141-147
[40]
R.E. Barnett, D.J. Conklin, L. Ryan, R.C. Keskey, V. Ramjee, E.A. Sepulveda, et al.
Anti-inflammatory effects of miR-21 in the macrophage response to peritonitis.
J Leukoc Biol, 99 (2016), pp. 361-371
[41]
C. Jennewein, A. von Knethen, T. Schmid, B. Brüne.
MicroRNA-27b contributes to lipopolysaccharide-mediated peroxisome proliferator-activated receptor γ (PPARγ) mRNA destabilization.
J Biol Chem, 285 (2010), pp. 11846-11853
[42]
C. Du, C. Liu, J. Kang, G. Zhao, Z. Ye, S. Huang, et al.
MicroRNA miR-326 regulates T H-17 differentiation and is associated with the pathogenesis of multiple sclerosis.
Nat Immunol, 10 (2009), pp. 1252
[43]
R. Bayraktar, K. Van Roosbroeck, Calin G.A.
Cell-to-cell communication: microRNAs as hormones.
Mol Oncol, 11 (2017), pp. 1673-1686
[44]
S. Pinto, C. Cunha, M. Barbosa, A.R. Vaz, D. Brites.
Exosomes from NSC-34 cells transfected with hSOD1-G93A are enriched in miR-124 and drive alterations in microglia phenotype.
Front Neurosci, 11 (2017), pp. 273
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