The miRNA neuroinflammatory biomarkers in COVID-19 patients with different severity of illness

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.

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.

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 methods

Materials

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 3000 rpm/min for 10 min 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 (n)	21	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 illness^a	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) < 300 mm 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, 1000 μL of the lysis buffer was added to 100 μL of serum samples, vortexed for 2 min, and incubated at 55 °C for 5 min. The samples were then centrifuged for 5 min 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 700 μL of 100% ethanol and centrifuged at 14,000 × g for 30 s and again the flow-through was discarded. The second wash was done by adding 500 μl of binding solution into the column and centrifuged at maximum speed (14,000 × g) for 1 min. Subsequently, 500 ml 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 30 s, the flow-through was discarded. Next, the column was dried by centrifuging at maximum speed (14,000 × g) for 1 min. 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 50 ml 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 A260 nm/A280 nm 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, 5 μl mRQ buffer (2×), 5 μg RNA and 1.25 μl mRQ enzyme was mixed in a reaction volume of 10 μl and incubated in a thermocycler for 1 h at 37 °C, then terminate at 85 °C for 5 min 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, 10 μl PCR reaction mixture was prepared to comprise of 1× SYBR advantage premix, 0.2 mM of both forward and reverse primers, and 50 ng of the first-strand cDNA. qPCR reactions were incubated in a 96 well plate at 95 °C for 2 min, followed by 40 cycles of 95 °C for 10 s and 60 °C for 20 s. 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, 2 μL of cDNA, 2 μL of forward primer, and 2 μL of reverse primer of each gene were added to 10 μL 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.

Results

Bioinformatics 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	6
Endometrial cancer	0.0001	24	6
Small cell lung cancer	0.0001	36	6
Regulation of actin cytoskeleton	0.0001	67	56
Bladder cancer	0.0002	21	4
PI3K-Akt signaling pathway	0.0002	105	5
Shigellosis	0.0002	26	4
Thyroid hormone signaling pathway	0.0003	44	4
Lysine degradation	0.001	15	3
Non-small cell lung cancer	0.001	23	4
mRNA surveillance pathway	0.001	34	4
mTOR signaling pathway	0.002	25	4
Oocyte meiosis	0.003	38	4
Prolactin signaling pathway	0.003	28	4
Melanoma	0.003	25	5
Ubiquitin mediated proteolysis	0.004	48	5
Fatty acid metabolism	0.004	11	3
Fatty acid elongation	0.004	6	3
Arrhythmogenic right ventricular	0.006	16	3
Estrogen signaling pathway	0.006	31	5
Signaling pathways regulating of stem cells	0.007	42	5
Gap junction	0.008	27	5
Sphingolipid signaling pathway	0.009	38	5
Amoebiasis	0.01	30	4
Pantothenate and CoA biosynthesis	0.01	6	3
MAPK signaling pathway	0.01	72	5
Insulin signaling pathway	0.02	45	5
Wnt signaling pathway	0.02	41	4
Axon guidance	0.02	37	4
Pathogenic Escherichia coli infection	0.02	21	5
AMPK signaling pathway	0.02	41	5
Hepatitis C	0.03	20	5
Vibrio cholerae infection	0.03	23	4
Epithelial cell signaling in Helicobacter pylori infection	0.04	54	4
Platelet activation	0.04	39	5
Steroid biosynthesis	0.04	5	3
Salmonella infection	0.04	27	5

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.

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.

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.

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.

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.

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.

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.