Differentially‐regulated miRNAs in COVID‐19: A systematic review

Severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) is responsible for coronavirus disease of 2019 (COVID‐19) that infected more than 760 million people worldwide with over 6.8 million deaths to date. COVID‐19 is one of the most challenging diseases of our times due to the nature of its spread, its effect on multiple organs, and an inability to predict disease prognosis, ranging from being completely asymptomatic to death. Upon infection, SARS‐CoV‐2 alters the host immune response by changing host‐transcriptional machinery. MicroRNAs (miRNAs) are regarded as post‐transcriptional regulators of gene expression that can be perturbed by invading viruses. Several in vitro and in vivo studies have reported such dysregulation of host miRNA expression upon SARS‐CoV‐2 infection. Some of this could occur as an anti‐viral response of the host to the viral infection. Viruses themselves can counteract that response by mounting their own pro‐viral response that facilitates virus infection, an aspect which may cause pathogenesis. Thus, miRNAs could serve as possible disease biomarkers in infected people. In the current review, we have summarised and analysed the existing data about miRNA dysregulation in patients infected with SARS‐CoV‐2 to determine their concordance between studies, and identified those that could serve as potential biomarkers during infection, disease progression, and death, even in people with other co‐morbidities. Having such biomarkers can be vital in not only predicting COVID‐19 prognosis, but also the development of novel miRNA‐based anti‐virals and therapeutics which can become invaluable in case of the emergence of new viral variants with pandemic potential in the future.


| MiRNA biogenesis
Most cellular and viral miRNAs are initially produced as primary (pri)-RNAs hundreds to thousands of nucleotides long with at least one or more~80 nt stem loop structure(s). [4][5][6][7] Over one-third of human miRNAs exist in clusters and transcribed as "polycistronic RNAs." Synthesised in the nucleus by RNA polymerase II that also transcribes other cellular genes, miRNAs are capped and polyadenylated like cellular mRNAs. 6,8 This is followed by processing of these pri-miRNAs into~65-70 nt long pre-miRNA with 2 nt overhangs at the 3 0 end by the Microprocessor Complex that comprises of the nuclear RNase III enzyme Drosha and its cofactor, Pasha/DiGeorge Syndrome Critical 8. 9 This processing maintains the imperfect stem loop structures and these partially processed substrates are then exported to the cytoplasm by the RAN-GTP transporter,  Once in the cytoplasm, they are further processed by another RNase III enzyme, Dicer, with the help of transactivation-responsive RNAbinding protein, which binds to dsRNA, which removes the loop part of the hairpin. 9,10 Now fully mature and~21-24 nts in length, the miRNAs resemble siRNAs of the RNA interference pathway. Each duplex miRNA leads to the generation of two mature miRNA strands termed 5p or 3p, depending upon their location in the pre-miRNA relative to its 5 0 end. Either miRNA strand can be loaded onto the RNA-induced Silencing Complex (RISC) as the "guide" strand for silencing of the target mRNA by the slicer protein, Argonaute (Ago), while the other "passenger" strand is degraded. 11 The cellular environment or cell type predominantly determines strand selection which could either be 5p or 3p exclusively, or either one equally. 8  Silencing (RITS) complex. 14 In the animal cells, miRNAs function primarily by binding through incomplete complementarity with the target sequence at the 3 0 UTR of the mRNAs, leading to inhibition of translation via RISC. miRNAs act by binding to and silencing target mRNAs through base pairing between a group of "seed sequences," the primary determinants of mRNA target recognition in miRNAs. 15 These are located between nts 2-8 at the 5 0 end of the miRNA that interact with complementary seed sequences (MREs) found within the 3 0 UTR of target genes. Other than the 3 0 UTR, regions such as the 5 0 UTR, the promoter region, as well as the coding region of target genes have also been observed to be targeted by miRNAs. 8,15

| MiRNAs in SARS-CoV-2
Soon after the discovery of miRNAs in 1993 and their subsequent role in mRNA expression, several studies focussed their attention on elucidating the mode(s) of miRNA biogenesis and function. 16 Advancements in gene expression analysis made it easy to detect any change in an organism's miRNA expression between control and compromised samples. The recent coronavirus disease 2019  pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has resulted in more than 6.8 million deaths globally so far (https://covid19.who.int/). It is evident that upon infection, viruses hijack host immune system to not only facilitate their replication, but also disable the immune response against the virus. 17,18 To ease their entry and invade host-immune system, SARS-CoV-2 has also been found to change transcriptional profile of numerous pathways associated with host-cell-defence mechanisms. 19 Recent studies have found that upon infection, SARS-CoV-2 significantly alters multiple cellular pathways in several human organs, including heart, lungs, liver, and kidneys. 20 In most of the organs, SARS-CoV-2 infection results in destabilisation of the host cellular immune response and release of proinflammatory cytokines, dysregulated production of inflammatory cells, endothelial dysfunction, and coagulation abnormalities (Table 1).  It has been long known that upon infection, host cells produce miRNAs to counter viral attacks by regulating the host-immune response. 44,45 Moreover, viruses also transcribe miRNAs that may interfere with the host-cellular defence system. 45 Since the start of COVID- 19, several studies have investigated the possible dysregulation of human miRNAs after SARS-CoV-2 infection in vivo, in vitro, and in silico [46][47][48] Initially, human miRNAs were predicted using computer-assisted techniques that were further validated through in vivo or in vitro studies. [48][49][50] In terms of miRNAs related to SARS-CoV-2/COVID-19, within a short span of only~2 years, already hundreds of studies have been published. In this systematic review, we not only summarise the currently available data, but also analyse the available data from patients to examine the performance of currently enumerated miRNAs as biomarkers across the globe. Using networking techniques and cluster analysis, we identify experimentally-verified miRNAs across the globe that may act as possible disease markers for further COVID-19 investigations.

| Data collection
Most of the existing reviews on the role of miRNAs in COVID-19 have summarised the current available data based on dysregulated miRNAs either predicted using in silico techniques or observed experimentally in vivo or in vitro studies. To be considered as a valid biomarker, miRNAs should possess the same expression profiles under certain disease conditions. Unfortunately, none of the recent reviews cross-checked the existing data to validate the specificity of the current proposed biomarkers except for a review from Moatar et al 51 who predicted and grouped possible miRNA targets and associated pathways after SARS-CoV-2 infection reported in a few studies using human patients. In the current review, we only focussed on the miRNA expression results from the studies originating through analysis of human patients enrolled in different healthcare facilities during the pandemic and excluded all other in silico, in vitro and in vivo studies. We did this to ensure that our predicted targets reflected real life scenario. Figure 1 describes the data-search strategy and inclusion-exclusion criteria used in our study. The data included in this review was searched through the PubMed database ranging from December 2020 to November 2022 using words "COVID-19, SARS-CoV-2, miRNA". We also searched other associated databases like Google (https://www.google.com/), Google Scholar (https://scholar.google.com/), ScienceDirect (https://www. sciencedirect.com/) and PubMed Central (https://www.ncbi.nlm.nih. gov/pmc/) to ensure the inclusion of most of the current data. This data was crossed-matched to the data available in PubMed, and most of the data searched on other databases was also available in PubMed.

| Network analysis
As we were interested in miRNAs expressed at various stages of disease progression, we constructed disease stage-specific miRNAs interactions and their interacting networks using Cytoscape v.
3.9.1. 52 Briefly, miRNA lists were constructed from the given literature and grouped based upon disease severity. Healthy controls were designated as "controls" whereas infected patients were grouped into asymptomatic (ASM), mild (MI), moderate (MO), severe (S), critical (Crit) and others, as per the study. Where the authors did not further sub-group the disease severity, data was named as "infected". miRNA interaction networks were created with either overlapping miRNAs in different disease groups or among authors representing their data in similar disease groups. These interacting networks helped to sort out miRNAs that have been identified in various studies under parallel disease conditions.

| MiRNA selection criteria
The miRNAs included within this study were selected based on the differentially expressed genes (DEGs) between the healthy controls and infected group or between groups representing different disease stages specified earlier. We included all the miRNAs considered as DEGs by their publishing authors. While comparing miRNA expression, we found multiple miRNAs expressed commonly in various disease stages based on disease severity (mild, moderate, or severe).
In this scenario, we chose only those miRNAs that were represented in at least 5 or more comparative groups to limit overcrowding. We also removed miRNAs showing opposite expression in the same group or the miRNA not specifying the 3p or 5p strand position. For example, if any study described miR-150 as a dysregulated miRNA and another study mentioned miR-150-5p, both of these miRNAs were not considered the same and excluded. Furthermore, we removed one-time expressing miRNAs in any group during network construction.

| Clinical data selection
Most of the selected studies shared both demographic and clinical data from the patients. However, for the sake of simplicity and AHMAD ET AL.   consistency, we included only gender and age in our study which were common to all studies.

| Data analysis and statistics
Demographic and clinical data was collected from each study and was analysed using Microsoft Excel 2021 and IBM SPSS Statistics software v.26. GraphPad Prism v. 9.0.0 (121) was used to create graphs and analyse data among groups, wherever applicable.

| MiRNA dysregulation in SARS-CoV-2-infected patients
The first step in this study was to collect suitable data. As we were interested in miRNAs dysregulated after SAR-CoV-2 infection, we studies used a study design with two cohorts, discovery and validation. Table 2 summarises the characteristics of the studies included in this review, whereas Table S1 contains the raw data used in this study.

| Demographic and clinical data analysis
Out of 35 studies, 30 mentioned participant ages, a mixed-age range from 3.5 to 93 years, while thirty-one mentioned gender of the F I G U R E 1 Study design. Literature search strategy and exclusion and inclusion criteria for this review.
participants (Table 2). In all of these studies, the ethnicity of the participants was not disclosed and a written consent form approved from ethical committees was signed by each participant. In most of the studies, infected patients after testing positive for COVID-19 infection were further categorised into the following stages: (i) mild, (ii) moderate, (iii) severe, (iv) critical, and (v) asymptomatic.
Other common stages were patients with or without mechanical ventilation, recovered, or deceased. Mechanical ventilation was defined as falling within the severe stage, intensive care unit patients without ventilation as moderate, and asymptomatic as mild, in this review. Some studies combined these stages as one group, as given in Table 2. A few studies also examined the effect of co-morbidities like diabetes, cerebrovascular issues, pregnancy, common cold, influenza, and bone fractures in infected patients.

| MiRNA dysregulation in "Inf vs C" studies
Our data showed that 13 studies 53 to create an miRNA-study network in infected  Table S2).

Author(s) main conclusions
Year Country Crit vs S vs MO vs MI vs ASM vs C

Down-regulated
Latini et al 80 The involvement of hsa-let7b-5p in the regulation of genes necessary for SARS-CoV-2 infection and its putative role as a therapeutic target for COVID-19.   Table S3); interestingly, a majority of these shared miRNAs (67%; n = 56) showed an opposite expression profile in these studies. This suggests the need for further study of expression of these miRNAs in the severe group compared to uninfected individuals since these results came from only three studies.

| Unique miRNAs that distinguish "severe" from "infected" patients
During data analysis, we observed that most of the differentiallyregulated miRNAs found in the available data were not present in all studies, filtering out many miRNAs that could have been of importance. We were especially interested in those miRNAs that could distinguish severe disease in infected patients without further disease sub-groupings like mild or moderate. To achieve this goal, we first compared and removed those miRNAs from "Inf vs C" and "S vs  Table 3 summarises the targets and principle cellular pathways associated with miRNAs regulated during Inf vs C and S vs C stages and cited previously. 46,56,73, Most of the miRNAs were involved in targeting virus-host interactions, viral replication, and host-immune responses.

| MiRNA expression analysis in all stages of disease severity after SARS-CoV-2 infection
As mentioned earlier, most of the studies included in this review either represented their miRNA dysregulation data as "Inf vs C" (n = 13) or "S vs C" (n = 5) comparison. However, some of the studies also further elaborated their results based on disease severity. To

| MiRNA regulation in "deceased" versus patients that "survived"
There were four studies 55

| MiRNA regulation in SARS-CoV-2 infected patients with other co-morbidities
Among the studies analysed, there were six studies 62-67 that also examined the role of other co-morbidities and conditions in SARS-CoV-2-infected patients. These included patients with bone fractures, community acquire pneumonia, common cold, diabetes, pregnancy, patients recovered from SARS-CoV-2 infection, and any patient treated with Tocilizumab (TCZ). We found 110 miRNAs being differentially-regulated within these groups. First, we filtered out 14 miRNAs that appeared in more than one of these groups followed by 61 more miRNAs that were also present in infected patients only.
The remaining 35 miRNAs were unique and can potentially be considered as being regulated owing to the presence of other conditions in COVID-19 infected patients (Table 4).     The overall goal of this study was to find miRNAs that could act as biomarkers in SARS-CoV-2-infected patients, especially to differentiate between disease stage/severity. Although we found many miRNAs that were reported in multiple studies, we chose the ones with consistent expression profiles across studies. Our efforts identified 40 miRNAs that were differentially-regulated in SARS-CoV-2 infected patients compared to healthy controls ( Figure 3).

MiRNAs have been reported as potential biomarkers in various dis
The frequently reported up-regulated miRNAs included miR-1299, miR-15a-5p, miR-27b-3p, miR-320b, and miR-320c, while the   As miRNA expression is associated with disease progression, we were also interested in identifying miRNAs that could distinguish intermediate disease stages. Towards this end, we identified 71 miRNAs which appeared in more than 5 comparative disease severity groups (Figures 6 and 7). Our analysis showed that the expression of miR-320b, miR-18a-5p, miR-320c, miR-144-5p, miR-15a-5p, miR-342-3p, miR-451a, and miR-548k was consistent during progression of disease severity. Interestingly, miR-150-5p was reported 12 times in seven comparative groups; however, its expression was not consistent within groups. Similar findings were observed for miR-16-

| Current status of already proposed biomarkers miRNAs in COVID-19
The published studies included in this review proposed multiple miR-NAs as possible biomarkers and most of them were validated in the secondary cohorts of the same study. We found that most of the Based on this analysis, we highlighted 10 possible miRNAs as COVID-19 biomarkers, showing consistent expression profile among several studies ( Table 6). Four of these were up-regulated (miR-193a-5p, miR-320b, miR-423-5p, miR-6721-5p) and five were downregulated (miR-150-5p, miR-342-3p, miR-144-3p, miR-144-5p, miR-29b-3p) during disease progression, whereas the expression of one miRNA (miR-15a-5p) was down-regulated during the severe stage.
Most of these miRNAs were associated with host-immune response after infection or the virus itself. MiR-193a-5p has been found to regulate TOMM70 receptor and it is possible that this miRNA may be associated with SARS-CoV-2 life cycle during its pathogenesis. 108 Mir-320 family has been well studied in COVID-19 pathogenesis and found to be associated with TGF-β signalling pathway that may further regulate pro-inflammatory and thromboembolic processes in infected patients. 77,129 Another up-regulated miRNA after infection was miR-423-5p. This miRNA regulates MALAT1 expression and has been found to induce survival and decrease metastases in mice model by inhibiting MALTA1-mediated proliferation, tumour growth and metastasis. 130 As this miRNA also induces apoptosis and autophagy in cancer cells, 131   Interestingly, treatment with the anti-inflammatory drug "simvastatin" induced miR-150-5p levels and decreased disease severity in SARS-CoV-2 infected patients, supporting this hypothesis. 132 We also observed a consistent down-regulation of miR-342-5p that is mainly involved in inflammatory stimulation of macrophages. 133 Targeting analysis showed that this miRNA might be able to target SARS-CoV-2 nucleocapsid, ORF1ab, and ORF3a domains and thus may be involved in regulating virus replication. 134 There is evidence that decreased miR-144 levels could indicate compromised immune response and could be used as a biomarker to predict COVID-19 disease severity and mortality. 104 Decreased expression of miR-29b-3p is associated with airway inflammation and regulate inflammatory cytokine IL-8 expression. 135 Down-regulated expression of miR-29b-3p might be a biomarker of disease severity in SARS-CoV-2 infection. 105 We found miR-15a-5p as a suitable biomarker to distinguish severe stage from others during SARS-CoV-2 infection.
Down-regulation of miR-15a-5p may be a sign of uncontrolled immune-thrombosis and/or thrombo-inflammation. 136 A current study by Wu et al suggests that down-regulated expression of miR-15a-5p could induce/activate interferon-1 signalling pathway to overcome SARS-CoV-2 severity. 105 Overall, if these miRNAs continue to show consistent expression in future studies, these could be considered as possible biomarkers in COVID-19 prognosis.
Meanwhile, targeting these miRNAs may be helpful to create future therapies against SARS-CoV-2 infection.

| CONCLUSIONS
In this systematic review, we were able to identify miRNAs from published literature that not only distinguished infected patients from healthy controls, but also were able to discriminate stages of disease severity, poor disease prognosis, and even death. These miRNAs showed consistent expressions within groups and can potentially be used as possible biomarkers. Furthermore, we also identified unique miRNAs associated with patients with specific co-morbidities.
Although any change in the expression of these miRNAs could be used as specific biomarkers of SARS-CoV-2 infection, COVID-19 disease progression, and mortality, further validation is needed. Considering that SARS-CoV-2 has become endemic in the human population and is here to stay, the emergence of new SARS-CoV-2 variants with pandemic potential exists. Thus, this study offers a valuable addition to the literature towards the identification of miRNA-based biomarkers that could eventually be used in the development of miRNAbased antivirals and therapeutics for COVID-19.

| Limitations and future perspectives
In this review, we included only those studies originating from human patients with the hope that our effort will help identify a list of miRNAs that could be used as potential biomarkers in SARS-CoV-2 infected patients as prognostic markers. The results extracted from these studies needs proper validation as we found vast differences in miRNA expression profiles within same groups between studies. It is not surprising that validated biomarkers in one study might not be the same as those in another study conducted elsewhere since ethnicity, gender, age, presence of co-morbidities, effect of medications taken for such co-morbidities, types of COVID-19 treatments and vaccines taken, and other environmental factors (such as the strain of SARS-CoV-2) may influence miRNAs expression profiles in COVID-19 patients. We anticipate that data gathered from other in vitro, in vivo, or in silico studies as well as future studies in humans could help confirm some of these miRNAs as biomarkers and/or clarify the mechanistic aspects of the function of the identified miRNAs.