miRNAs and COVID-19 Therapy Review

These days, the extreme intense respiratory condition Coronavirus 2 (SARS-CoV-2) disease is recognised on the grounds that the primary cause behind mortality in people. SARS-CoV-2 is transmitted through human-to-human contact and is a symptomless in many patients. furthermore, to approved vaccines against SARS-CoV-2 infection, miRNAs may additionally be promising decisions against the current new virus. miRNAs are small and noncoding RNAs 18–25 nucleotides in length that focus on the mRNAs to degrade them or block their interpretation miRNAs go about as an observer in cells.This review in regards to evaluated the writing on the potential role of cellular miRNAs inside the SARS-CoV-2-have collaboration as a therapeutic option in COVID-19 patients

Predicting microRNAs as Anti-viral Agents in SARS-CoV-2 Infection Based on the Bioinformatics Approach: A Systematic Review

Purpose: The beginning of 2020, the World health organization (WHO) declared severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as responsible for the coronavirus disease 2019 (COVID-19) outbreak. Previous studies showed that microRNAs (miRNAs) are able to inhibit pathogenesis of DNA or RNA viruses by binding the genome. The purpose of the current study is an overview of the anti-viral role of cellular miRNAs against the COVID-19 infection. Methods: Our search was limited to all published original papers in the English language from 2019 to 2021 using several databases including PubMed, Google scholar, Scopus, and Science Direct. A manual search of references for included articled was also performed. Among 66 electronically searched citations, 17 papers met the inclusion criteria. Results: The presence of miRNAs during the COVID-19 infection, reported by several studies, predicts the possibility of using miRNAs as potential tools to eradicate the SARS-CoV-2 infection. In some studies, miRNAs have presented as a tool for targeting SARS-CoV-2 encoded genes which are essential in viral biogenesis, entrance, replication, and infection. Conclusion: The comparison of miRNA between SARS-CoV-2 with other human coronaviruses will help the better understanding of distinct clinical characteristics of them

One-class SVM and supervised machine learning models for uncovering associations of non-coding RNA with diseases

The study of MicroRNAs (miRNAs), long non-coding RNAs (lncRNAs) and gene interactions may be expected to provide new technologies to serve as valuable biomarkers for personalized treatments of diseases and to aid in the prognosis of certain conditions. These molecules act at the genome level by regulating or suppressing their protein expression functions. The primary challenge in the study of these non-coding molecules involves the necessity of finding labeled data indicating positive and negative interactions when predicting interactions using machine-learning or deep-learning techniques. However, usually we end up with a scenario of unbalanced data or unstable scenarios for using these models. An additional problem involves the extraction of features derived from the binding of these non-coding RNAs and genes. This binding process usually occurs fully or partially in animal genetics, which leads to considerable complexity in studying the process. Therefore, the main objective of the present work is to demonstrate that it is possible to use features extracted for miRNAs sequences in the development of diseases such as breast cancer, breast neoplasms, or if there is any influence with immune genes related to the SARS-COV-2. We performed experiments focusing on the erb-b2 receptor tyrosine kinase 2 (ERBB2) gene involved in breast cancer. For this purpose, we gathered miRNA-mRNA information from the binding between these two genetic molecules. In this part of our research, we applied a One-Class SVM and an Isolation Forest to discriminate between weak interactions, outliers given by the one-class model, and strong interactions that could occur between miRNA and mRNA (messenger RNA). Additionally, this study aimed to differentiate between breast cancer cases and breast neoplasm conditions. In this section we used the information encoded in lncRNAs. The additional feature used in this part was the frequency of k-mers, i.e., small portions of nucleotides, along with the data from the energy released in miRNA folding. The models used to discriminate between these diseases were One-Class SVM, SVM, and Random Forest. In the final part of the present work, we described a subset of probable miRNA binding with SARS-COV-2 RNA, focusing on those miRNAs with a relationship with genes involved in the immunological system of the human body. The models used as classifiers were One-Class SVM, SVM, and Random Forest. The results obtained in the present study are comparable to those found in the current literature and demonstrate the feasibility of using one-class models combined with features from the coupling of non-coding genes or mRNAs and their relationships with forms of breast cancer and viral infections. This work is expected to establish a basis for future avenues of research to apply one-class machine-learning models with feature extraction based on genomic sequences to the study of the relationship between non-coding RNAs and various diseases.School of ComputingPh. D. (Computing

Current Perspectives on Viral Disease Outbreaks

The COVID-19 pandemic has reminded the world that infectious diseases are still important. The last 40 years have experienced the emergence of new or resurging viral diseases such as AIDS, ebola, MERS, SARS, Zika, and others. These diseases display diverse epidemiologies ranging from sexual transmission to vector-borne transmission (or both, in the case of Zika). This book provides an overview of recent developments in the detection, monitoring, treatment, and control of several viral diseases that have caused recent epidemics or pandemics

Plasma miRNA profile at COVID-19 onset predicts severity status and mortality

BACKGROUND: MicroRNAs (miRNAs) have a crucial role in regulating immune response against infectious diseases, showing changes early in disease onset and before the detection of the pathogen. Thus, we aimed to analyze the plasma miRNA profile at COVID-19 onset to identify miRNAs as early prognostic biomarkers of severity and survival. METHODS AND RESULTS: Plasma miRNome of 96 COVID-19 patients that developed asymptomatic/mild, moderate and severe disease was sequenced together with a group of healthy controls. Plasma immune-related biomarkers were also assessed. COVID-19 patients showed 200 significant differentially expressed (SDE) miRNAs concerning healthy controls, with upregulated putative targets of SARS-CoV-2, and inflammatory miRNAs. Among COVID-19 patients, 75 SDE miRNAs were observed in asymptomatic/mild compared to symptomatic patients, which were involved in platelet aggregation and cytokine pathways, among others. Moreover, 137 SDE miRNAs were identified between severe and moderate patients, where miRNAs targeting the SARS CoV-2 genome were the most strongly disrupted. Finally, we constructed a mortality predictive risk score (miRNA-MRS) with ten miRNAs. Patients with higher values had a higher risk of 90-days mortality (hazard ratio = 4.60; p-value < 0.001). Besides, the discriminant power of miRNA-MRS was significantly higher than the observed for age and gender (AUROC = 0.970 vs. 0.881; p = 0.042). CONCLUSIONS: SARS-CoV-2 infection deeply disturbs the plasma miRNome from an early stage of COVID-19, making miRNAs highly valuable as early predictors of severity and mortality

It isn't over ‘till it’s over: A continuing concern of the SARS-CoV-2 variants, and miRNAs targeting the S protein as a probable absolute cure

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak which still continues to affect the general population, has mutated day by day and new variants have emerged. More than 40 variants, usually caused by mutations in the spike (S) protein, have been recorded. Observation of S protein mutations in the development of t herapeutic agents will increase success rates. As we identify the three-dimensional (3D) conformation of viruses, it is more and more possible to work on models for understanding molecular interactions. Development of agents for arrays and 3D sequencing of proteins paves the way for potential therapeutic studies against variants. MicroRNAs (miRNAs) seemingly act as a potentially important group of biomolecules in combating uncontrolled cytokine release. Besides antiviral response, miRNAs promise to be&nbsp;&nbsp;powerful therapeutic agents against infections. Studies have shown that miRNAs are able to inhibit the genome directly by miRNA-based treatments as they are sprecific to the SARS-CoV-2 genome. In order to expose this potential, in silico studies before continuing with lab studies are helpful. In our bioinformatics analysis, we proposed to compare the S protein similarities of Delta and Omicron, two of the most common variants, and to detect miRNAs targeting the S protein. The S proteins and coding sequences were compared between the two variants, and differences were determined. Within our analysis, 105 and 109 miRNAs for the Delta and Omicron variants, respectively, were detected. We believe that our study will be a potential guide for deciding on the miRNAs that may most likely have an effect on the management of the infection caused by both variants

MicroRNAs and SARS-CoV-2 life cycle, pathogenesis, and mutations: biomarkers or therapeutic agents?

To date, proposed therapies and antiviral drugs have been failed to cure coronavirus disease 2019 (COVID-19) patients. However, at least two drug companies have applied for emergency use authorization with the United States Food and Drug Administration for their coronavirus vaccine candidates and several other vaccines are in various stages of development to determine safety and efficacy. Recently, some studies have shown the role of different human and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) microRNAs (miRNAs) in the pathophysiology of COVID-19. miRNAs are non-coding single-stranded RNAs, which are involved in several physiological and pathological conditions, such as cell proliferation, differentiation, and metabolism. They act as negative regulators of protein synthesis through binding to the 3� untranslated region (3� UTR) of the complementary target mRNA, leading to mRNA degradation or inhibition. The databases of Google Scholar, Scopus, PubMed, and Web of Science were searched for literature regarding the importance of miRNAs in the SARS-CoV-2 life cycle, pathogenesis, and genomic mutations. Furthermore, promising miRNAs as a biomarker or antiviral agent in COVID-19 therapy are reviewed. © 2020 Informa UK Limited, trading as Taylor & Francis Group

Identification of MicroRNA-like molecules derived from the antigenome RNA of hepatitis C virus: a bioinformatics approach

MicroRNAs (miRNAs) are small, noncoding RNA molecules that play important roles in the regula-tion of gene expression of the cell. Recent studies have described cytoplasmic RNA virus genome-derived miRNAs. Moreover, miRNAs have also been encountered in the reverse strand of the viral mRNA, revealing the presence of miRNAs in replication intermediaries. In order to get insight into the possible role of Hepatitis C Virus (HCV) antigenome in relation to miRNA coding, we computa-tionally identified potential miRNAs on the antigenome of HCV reference strain H77. By utilizing a series of bioinformatics tools, we identified a miRNA present in the antigenomeof HCV H77 strain. This miRNA maps in the 5’non-translated region (5’UTR) of the HCV genome and is found to be conserved among HCV genotypes and sub-types. In silico target prediction generated 17 cellular genes. These potential targets are involved in apoptosis as well as immune response pathways, suggesting that they could play a role in the pathogenesis caused by viral infection. The results of these studies revealed the presence of a viral miRNA in the negative-sense RNA strand used as a replication template for the HCV genome, as observed for other RNA viruses

MicroRNA Interaction Networks

La tesi di Giorgio Bertolazzi è incentrata sullo sviluppo di nuovi algoritmi per la predizione dei legami miRNA-mRNA. In particolare, un algoritmo di machine-learning viene proposto per l'upgrade del web tool ComiR; la versione originale di ComiR considerava soltanto i siti di legame dei miRNA collocati nella regione 3'UTR dell'RNA messaggero. La nuova versione di ComiR include nella ricerca dei legami la regione codificante dell'RNA messaggero.Bertolazzi’s thesis focuses on developing and applying computational methods to predict microRNA binding sites located on messenger RNA molecules. MicroRNAs (miRNAs) regulate gene expression by binding target messenger RNA molecules (mRNAs). Therefore, the prediction of miRNA binding is important to investigate cellular processes. Moreover, alterations in miRNA activity have been associated with many human diseases, such as cancer. The thesis explores miRNA binding behavior and highlights fundamental information for miRNA target prediction. In particular, a machine learning approach is used to upgrade an existing target prediction algorithm named ComiR; the original version of ComiR considers miRNA binding sites located on mRNA 3’UTR region. The novel algorithm significantly improves the ComiR prediction capacity by including miRNA binding sites located on mRNA coding regions