The miRNA: a small but powerful RNA for COVID-19

Host factors of SARS-CoV-2 in infection, pathogenesis, and long-term effects

SARS-CoV-2 is the causative virus of the devastating COVID-19 pandemic that results in an unparalleled global health and economic crisis. Despite unprecedented scientific efforts and therapeutic interventions, the fight against COVID-19 continues as the rapid emergence of different SARS-CoV-2 variants of concern and the increasing challenge of long COVID-19, raising a vast demand to understand the pathomechanisms of COVID-19 and its long-term sequelae and develop therapeutic strategies beyond the virus per se. Notably, in addition to the virus itself, the replication cycle of SARS-CoV-2 and clinical severity of COVID-19 is also governed by host factors. In this review, we therefore comprehensively overview the replication cycle and pathogenesis of SARS-CoV-2 from the perspective of host factors and host-virus interactions. We sequentially outline the pathological implications of molecular interactions between host factors and SARS-CoV-2 in multi-organ and multi-system long COVID-19, and summarize current therapeutic strategies and agents targeting host factors for treating these diseases. This knowledge would be key for the identification of new pathophysiological aspects and mechanisms, and the development of actionable therapeutic targets and strategies for tackling COVID-19 and its sequelae.

From molecular pathogenesis to therapy: Unraveling non-coding RNAs/DNMT3A axis in human cancers

Cancer is a comprehensive classification encompassing more than 100 forms of malignancies that manifest in diverse tissues within the human body. Recent studies have provided evidence that aberrant epigenetic modifications are pivotal indicators of cancer. Epigenetics encapsulates DNA methyltransferases as a crucial class of modifiers. DNMTs, including DNMT3A, assume central roles in DNA methylation processes that orchestrate normal biological functions, such as gene transcription, predominantly in mammals. Typically, deviations in DNMT3A function engender distortions in factors that drive tumor growth and progression, thereby exacerbating the malignant phenotype of tumors. Consequently, such abnormalities pose significant challenges in cancer therapy because they impede treatment efficacy. Non-coding RNAs (ncRNAs) represent a group of RNA molecules that cannot encode functional proteins. Recent investigation attests to the crucial significance of regulatory ncRNAs in epigenetic regulation. Notably, recent reports have illuminated the complex interplay between ncRNA expression and epigenetic regulatory machinery, including DNMT3A, particularly in cancer. Recent findings have demonstrated that miRNAs, namely miR-770-5p, miR-101, and miR-145 exhibit the capability to target DNMT3A directly, and their aberration is implicated in diverse cellular abnormalities that predispose to cancer development. This review aims to articulate the interplay between DNMT3A and the ncRNAs, focusing on its impact on the development and progression of cancer, cancer therapy resistance, cancer stem cells, and prognosis. Importantly, the emergence of such reports that suggest a connection between DNMT3A and ncRNAs in several cancers indicates that this connecting axis offers a valuable target with significant therapeutic potential that might be exploited for cancer management.

In Silico Prediction of Functional SNPs Interrupting Antioxidant Defense Genes in Relation to COVID-19 Progression

The excessive production of reactive oxygen species and weakening of antioxidant defense system play a pivotal role in the pathogenesis of different diseases. Extensive differences observed among individuals in terms of affliction with cancer, cardiovascular disorders, diabetes, bacterial, and viral infections, as well as response to treatments can be partly due to their genomic variations. In this work, we attempted to predict the effect of SNPs of the key genes of antioxidant defense system on their structure, function, and expression in relation to COVID-19 pathogenesis using in silico tools. In addition, the effect of SNPs on the target site binding efficiency of SNPs was investigated as a factor with potential to change drug response or susceptibility to COVID-19. According to the predicted results, only six missense SNPs with minor allele frequency (MAF) ≥ 0.1 in the coding region of genes GPX7, GPX8, TXNRD2, GLRX5, and GLRX were able to strongly affect their structure and function. Our results predicted that 39 SNPs with MAF ≥ 0.1 led to the generation or destruction of miRNA-binding sites on target antioxidant genes from GPX, PRDX, GLRX, TXN, and SOD families. The results obtained from comparing the expression profiles of mild vs. severe COVID-19 patients using GEO2R demonstrated a significant change in the expression of approximately 250 miRNAs. The binding efficiency of 21 of these miRNAs was changed due to the elimination or generation of target sites in these genes. Altogether, this study reveals the fundamental role of the SNPs of antioxidant defense genes in COVID-19 progression and susceptibility of individuals to this virus. In addition, different responses of COVID-19 patients to antioxidant defense system enhancement drugs may be due to presence of these SNPs in different individuals.

The influence of epigenetics and inflammation on cardiometabolic risks

Cardiometabolic diseases include metabolic syndrome, obesity, type 2 diabetes mellitus, and hypertension. Epigenetic modifications participate in cardiometabolic diseases through several pathways, including inflammation, vascular dysfunction, and insulin resistance. Epigenetic modifications, which encompass alterations to gene expression without mutating the DNA sequence, have gained much attention in recent years, since they have been correlated with cardiometabolic diseases and may be targeted for therapeutic interventions. Epigenetic modifications are greatly influenced by environmental factors, such as diet, physical activity, cigarette smoking, and pollution. Some modifications are heritable, indicating that the biological expression of epigenetic alterations may be observed across generations. Moreover, many patients with cardiometabolic diseases present with chronic inflammation, which can be influenced by environmental and genetic factors. The inflammatory environment worsens the prognosis of cardiometabolic diseases and further induces epigenetic modifications, predisposing patients to the development of other metabolism-associated diseases and complications. A deeper understanding of inflammatory processes and epigenetic modifications in cardiometabolic diseases is necessary to improve our diagnostic capabilities, personalized medicine approaches, and the development of targeted therapeutic interventions. Further understanding may also assist in predicting disease outcomes, especially in children and young adults. This review describes epigenetic modifications and inflammatory processes underlying cardiometabolic diseases, and further discusses advances in the research field with a focus on specific points for interventional therapy.

COVID-19 in patients with anemia and haematological malignancies: risk factors, clinical guidelines, and emerging therapeutic approaches

Extensive research in countries with high sociodemographic indices (SDIs) to date has shown that coronavirus disease 2019 (COVID-19) may be directly associated with more severe outcomes among patients living with haematological disorders and malignancies (HDMs). Because individuals with moderate to severe immunodeficiency are likely to undergo persistent infections, shed virus particles for prolonged periods, and lack an inflammatory or abortive phase, this represents an overall risk of morbidity and mortality from COVID-19. In cases suffering from HDMs, further investigation is needed to achieve a better understanding of triviruses and a group of related variants in patients with anemia and HDMs, as well as their treatment through vaccines, drugs, and other methods. Against this background, the present study aimed to delineate the relationship between HDMs and the novel COVID-19, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Besides, effective treatment options for HDM cases were further explored to address this epidemic and its variants. Therefore, learning about how COVID-19 manifests in these patients, along with exploiting the most appropriate treatments, may lead to the development of treatment and care strategies by clinicians and researchers to help patients recover faster. Video Abstract.

RVvictor: Virus RNA-directed molecular interactions for RNA virus infection

RNA viruses are major human pathogens that cause seasonal epidemics and occasional pandemic outbreaks. Due to the nature of their RNA genomes, it is anticipated that virus's RNA interacts with host protein (INTPRO), messenger RNA (INTmRNA), and non-coding RNA (INTncRNA) to perform their particular functions during their transcription and replication. In other words, thus, it is urgently needed to have such valuable data on virus RNA-directed molecular interactions (especially INTPROs), which are highly anticipated to attract broad research interests in the fields of RNA virus translation and replication. In this study, a new database was constructed to describe the virus RNA-directed interaction (INTPRO, INTmRNA, INTncRNA) for RNA virus (RVvictor). This database is unique in a) unambiguously characterizing the interactions between viruses RNAs and host proteins, b) providing, for the first time, the most systematic RNA-directed interaction data resources in providing clues to understand the molecular mechanisms of RNA viruses' translation, and replication, and c) in RVvictor, comprehensive enrichment analysis is conducted for each virus RNA based on its associated target genes/proteins, and the enrichment results were explicitly illustrated using various graphs. We found significant enrichment of a suite of pathways related to infection, translation, and replication, e.g., HIV infection, coronavirus disease, regulation of viral genome replication, and so on. Due to the devastating and persistent threat posed by the RNA virus, RVvictor constructed, for the first time, a possible network of cross-talk in RNA-directed interaction, which may ultimately explain the pathogenicity of RNA virus infection. The knowledge base might help develop new anti-viral therapeutic targets in the future. It's now free and publicly accessible at: https://idrblab.org/rvvictor/.

Hepatitis B Virus-Encoded MicroRNA (HBV-miR-3) Inhibits FIH-1 Expression to Promote Tumor Angiogenesis in HBV-Related Hepatocellular Carcinoma

Introduction

Hepatocellular carcinoma (HCC) is a solid tumor with a rich blood supply, and anti-angiogenesis has important clinical significance. Hepatitis B Virus-Encoded MicroRNA 3 (HBV-miR-3) has recently been reported to be involved in HCC development. In this study, we aim to elucidate the role of HBV-miR-3 in promoting HBV-related HCC angiogenesis through Factor Inhibiting Hypoxia-inducible factor 1 (FIH-1).

Results

By analyzing HBV-related HCC tissue samples, we found that high expression of HBV-miR-3 was associated with poor overall survival and HBV-miR-3 expression was significantly correlated with VEGFR2 and FIH-1 expressions. In vitro, HBV-miR-3 agomir repressed FIH-1 expression and promoted HIF-1α/VEGFA signaling activation in HepG2 cells, resulting in increased HUVEC lumen formation in HepG2-HUVEC co-culture model. Conversely, HBV-miR-3 antagomir induced FIH-1 expression and inhibited HIF-1α/VEGFA signaling activation in HepG2.2.15 cells, resulting in decreased HUVEC lumen formation in HepG2.2.15-HUVEC co-culture model. The effect of HBV-miR-3 to HCC angiogenesis was also confirmed by a mouse tumor bearing model. We also confirmed that HBV-miR-3 repressed FIH-1 expression via targeting the 3'-UTR of FIH-1 mRNA by luciferase activity assay.

Conclusion

HBV-miR-3 was related to HCC patients' overall survival and it promoted angiogenesis by repressing FIH-1 expression. HBV-miR-3 may be a new marker for predicting prognosis and a novel target for anti-angiogenic treatment of HBV-related HCC.

The biomarkers' landscape of post-COVID-19 patients can suggest selective clinical interventions

In COVID-19 clinical symptoms can persist even after negativization also in individuals who have had mild or moderate disease. We here investigated the biomarkers that define the post-COVID-19 clinical state analyzing the exhaled breath condensate (EBC) of 38 post COVID-19 patients and 38 sex and age-matched healthy controls via nuclear magnetic resonance (NMR)-based metabolomics. Predicted gene-modulated microRNAs (miRNAs) related to COVID-19 were quantified from EBC of 10 patients and 10 controls. Finally, clinical parameters from all post-COVID-19 patients were correlated with metabolomic data. Post-COVID-19 patients and controls showed different metabolic phenotype ("metabotype"). From the metabolites, by using enrichment analysis we identified miRNAs that resulted up-regulated (hsa-miR146a-5p) and down-regulated (hsa-miR-126-3p and hsa-miR-223-3p) in post-COVID-19. Taken together, our multiomics data indicate that post-COVID-19 patients before rehabilitation are characterized by persistent inflammation, dysregulation of liver, endovascular thrombotic and pulmonary processes, and physical impairment, which should be the primary clinical targets to contrast the post-acute sequelae of COVID-19.

Non-coding RNAs expression in SARS-CoV-2 infection: pathogenesis, clinical significance, and therapeutic targets

The coronavirus disease 2019 (COVID-19) pandemic has been looming globally for three years, yet the diagnostic and treatment methods for COVID-19 are still undergoing extensive exploration, which holds paramount importance in mitigating future epidemics. Host non-coding RNAs (ncRNAs) display aberrations in the context of COVID-19. Specifically, microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) exhibit a close association with viral infection and disease progression. In this comprehensive review, an overview was presented of the expression profiles of host ncRNAs following SARS-CoV-2 invasion and of the potential functions in COVID-19 development, encompassing viral invasion, replication, immune response, and multiorgan deficits which include respiratory system, cardiac system, central nervous system, peripheral nervous system as well as long COVID. Furthermore, we provide an overview of several promising host ncRNA biomarkers for diverse clinical scenarios related to COVID-19, such as stratification biomarkers, prognostic biomarkers, and predictive biomarkers for treatment response. In addition, we also discuss the therapeutic potential of ncRNAs for COVID-19, presenting ncRNA-based strategies to facilitate the development of novel treatments. Through an in-depth analysis of the interplay between ncRNA and COVID-19 combined with our bioinformatic analysis, we hope to offer valuable insights into the stratification, prognosis, and treatment of COVID-19.

COVID-19 Vaccination in Pregnancy: Pilot Study for Maternal and Neonatal MicroRNA Profiles

This pilot study explores alterations in miRNA profiles among pregnant women and their neonates upon receiving different doses of COVID-19 vaccines. Blood samples, including maternal blood (MB) and neonatal cord blood (CB), collected from five pregnant women were scrutinized using the miRNA PanelChip Analysis System, identifying nine distinct miRNAs, including miR-451a and miR-1972, which exhibited significant downregulation with two vaccine doses in both MB and CB. When compared with women vaccinated with four doses, miR-486-5p, miR-451a, and miR-1972 in the two-dose group also showed notable downregulation. Evaluating recipients of three and four doses, miR-423-5p and miR-1972 expression were significantly reduced in both MB and CB. Further comparative analysis highlighted a decline in miR-223-3p expression with increasing vaccine doses, while miR15a-5p, miR-16-5p, and miR-423-5p showed an upward trend. Notably, miR-451a, miR-1972, and miR-423-5p levels varied across doses and were associated with pathways such as "PI3K-Akt", "neurotrophin signaling", and "cortisol synthesis", suggesting the profound influence of vaccination on diverse molecular mechanisms. Our research has uncovered that escalating vaccine dosages impact miRNA profiles, which may be associated with the immunological response mechanisms in both the mother and fetus, thus indicating a substantial impact of vaccination on various molecular processes.

A task-specific encoding algorithm for RNAs and RNA-associated interactions based on convolutional autoencoder

RNAs play essential roles in diverse physiological and pathological processes by interacting with other molecules (RNA/protein/compound), and various computational methods are available for identifying these interactions. However, the encoding features provided by existing methods are limited and the existing tools does not offer an effective way to integrate the interacting partners. In this study, a task-specific encoding algorithm for RNAs and RNA-associated interactions was therefore developed. This new algorithm was unique in (a) realizing comprehensive RNA feature encoding by introducing a great many of novel features and (b) enabling task-specific integration of interacting partners using convolutional autoencoder-directed feature embedding. Compared with existing methods/tools, this novel algorithm demonstrated superior performances in diverse benchmark testing studies. This algorithm together with its source code could be readily accessed by all user at: https://idrblab.org/corain/ and https://github.com/idrblab/corain/.

Adaptive deep propagation graph neural network for predicting miRNA-disease associations

BACKGROUND

A large number of experiments show that the abnormal expression of miRNA is closely related to the occurrence, diagnosis and treatment of diseases. Identifying associations between miRNAs and diseases is important for clinical applications of complex human diseases. However, traditional biological experimental methods and calculation-based methods have many limitations, which lead to the development of more efficient and accurate deep learning methods for predicting miRNA-disease associations.

RESULTS

In this paper, we propose a novel model on the basis of adaptive deep propagation graph neural network to predict miRNA-disease associations (ADPMDA). We first construct the miRNA-disease heterogeneous graph based on known miRNA-disease pairs, miRNA integrated similarity information, miRNA sequence information and disease similarity information. Then, we project the features of miRNAs and diseases into a low-dimensional space. After that, attention mechanism is utilized to aggregate the local features of central nodes. In particular, an adaptive deep propagation graph neural network is employed to learn the embedding of nodes, which can adaptively adjust the local and global information of nodes. Finally, the multi-layer perceptron is leveraged to score miRNA-disease pairs.

CONCLUSION

Experiments on human microRNA disease database v3.0 dataset show that ADPMDA achieves the mean AUC value of 94.75% under 5-fold cross-validation. We further conduct case studies on the esophageal neoplasm, lung neoplasms and lymphoma to confirm the effectiveness of our proposed model, and 49, 49, 47 of the top 50 predicted miRNAs associated with these diseases are confirmed, respectively. These results demonstrate the effectiveness and superiority of our model in predicting miRNA-disease associations.

Pharmacogenomic Landscape of Ivermectin and Selective Antioxidants: Exploring Gene Interplay in the Context of Long COVID

COVID-19 pandemic has caused widespread panic and fear among the global population. As such, repurposing drugs are being used as viable therapeutic options due to the limited effective treatments for Long COVID symptoms. Ivermectin is one of the emerging repurposed drugs that has been shown effective to have antiviral effects in clinical trials. In addition, antioxidant compounds are also gaining attention due to their capabilities of reducing inflammation and severity of symptoms. Due to the absence of knowledge in pharmacogenomics and modes of actions in the human body for these compounds, this study aims to provide a pharmacogenomic profile for the combination of ivermectin and six selected antioxidants (epigallocatechin gallate (EGCG), curcumin, sesamin, anthocyanins, quercetin, and N-acetylcysteine (NAC)) as potentially effective regimens for long COVID symptoms. Results showed that there were 12 interacting genes found among the ivermectin, 6 antioxidants, and COVID-19. For network pharmacology, the 12 common interacting genes/proteins had the highest associations with Pertussis pathway, AGE-RAGE signaling pathway in diabetic complications, and colorectal cancer in the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Disease analyses also revealed that the top three relevant diseases with COVID-19 infections were diabetes mellitus, ischemia, reperfusion injury. We also identified 6 potential target microRNAs (miRNAs) of the 12 commonly curated genes used as molecular biomarkers for COVID-19 treatments. The established pharmacogenomic network, disease analyses, and identified miRNAs could facilitate developments of effective regimens for chronic sequelae of COVID-19 especially in this post-pandemic era. However, further studies and clinical trials are needed to substantiate the effectiveness and dosages for COVID-19 treatments.

Exosomal microRNA/miRNA Dysregulation in Respiratory Diseases: From Mycoplasma-Induced Respiratory Disease to COVID-19 and Beyond

Respiratory diseases represent a significant economic and health burden worldwide, affecting millions of individuals each year in both human and animal populations. MicroRNAs (miRNAs) play crucial roles in gene expression regulation and are involved in various physiological and pathological processes. Exosomal miRNAs and cellular miRNAs have been identified as key regulators of several immune respiratory diseases, such as chronic respiratory diseases (CRD) caused by Mycoplasma gallisepticum (MG), Mycoplasma pneumoniae pneumonia (MMP) caused by the bacterium Mycoplasma pneumoniae, coronavirus disease 2019 (COVID-19), chronic obstructive pulmonary disease (COPD), asthma, and acute lung injury/acute respiratory distress syndrome (ALI/ARDS). Consequently, miRNAs seem to have the potential to serve as diagnostic biomarkers and therapeutic targets in respiratory diseases. In this review, we summarize the current understanding of the functional roles of miRNAs in the above several respiratory diseases and discuss the potential use of miRNAs as stable diagnostic biomarkers and therapeutic targets for several immune respiratory diseases, focusing on the identification of differentially expressed miRNAs and their targeting of various signaling pathways implicated in disease pathogenesis. Despite the progress made, unanswered questions and future research directions are discussed to facilitate personalized and targeted therapies for patients with these debilitating conditions.

scAAGA: Single cell data analysis framework using asymmetric autoencoder with gene attention

In recent years, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique for investigating cellular heterogeneity and structure. However, analyzing scRNA-seq data remains challenging, especially in the context of COVID-19 research. Single-cell clustering is a key step in analyzing scRNA-seq data, and deep learning methods have shown great potential in this area. In this work, we propose a novel scRNA-seq analysis framework called scAAGA. Specifically, we utilize an asymmetric autoencoder with a gene attention module to learn important gene features adaptively from scRNA-seq data, with the aim of improving the clustering effect. We apply scAAGA to COVID-19 peripheral blood mononuclear cell (PBMC) scRNA-seq data and compare its performance with state-of-the-art methods. Our results consistently demonstrate that scAAGA outperforms existing methods in terms of adjusted rand index (ARI), normalized mutual information (NMI), and adjusted mutual information (AMI) scores, achieving improvements ranging from 2.8% to 27.8% in NMI scores. Additionally, we discuss a data augmentation technology to expand the datasets and improve the accuracy of scAAGA. Overall, scAAGA presents a robust tool for scRNA-seq data analysis, enhancing the accuracy and reliability of clustering results in COVID-19 research.

Theranostic Lipid Nanoparticles for Renal Cell Carcinoma

Renal cell carcinoma (RCC) is a common urological malignancy and represents a leading threat to healthcare. Recent years have seen a series of progresses in the early diagnosis and management of RCC. Theranostic lipid nanoparticles (LNPs) are increasingly becoming one of the focuses in this field, because of their suitability for tumor targeting and multimodal therapy. LNPs can be precisely fabricated with desirable chemical compositions and biomedical properties, which closely match the physiological characteristics and clinical needs of RCC. Herein, a comprehensive review of theranostic LNPs is presented, emphasizing the generic tool nature of LNPs in developing advanced micro-nano biomaterials. It begins with a brief overview of the compositions and formation mechanism of LNPs, followed with an introduction to kidney-targeting approaches, such as passive, active, and stimulus responsive targeting. With examples provided, a series of modification strategies for enhancing the tumor targeting and functionality of LNPs are discussed. Thereafter, research advances on applications of these LNPs for RCC including bioimaging, liquid biopsy, drug delivery, physical therapy, and gene therapy are summarized and discussed from an interdisciplinary perspective. The final part highlights the milestone achievements of translation medicine, current challenges as well as future development directions of LNPs for the diagnosis and treatment of RCC.

In-depth characterization and identification of translatable lncRNAs

Long non-coding RNAs (LncRNAs) are non-protein coding transcripts more than 200 nucleotides in length. Deep sequencing technologies have unveiled lncRNAs can harbor translatable short open reading frames (sORFs). Yet the regulatory mechanisms governing lncRNA translation events remain poorly understood. Here, we exhaustively detected the sequence, functional element, and structure features relevant to lncRNA translation in human. Extensive identification and analysis reveal that translatable lncRNAs contain richer protein-coding related sequence features, cap-dependent and cap-independent translation initiation mechanisms, and more stable secondary structures, as compared to untranslatable lncRNAs. These findings strongly support lncRNAs serve as a repository for the production of new small peptides. Based on the feature fusion affecting translation and the extreme gradient boosting (XGBoost) algorithm, we developed the first computational tool that dedicated for predicting translatable lncRNAs, named TransLncPred. Benchmark experimental results show that our method outperforms several state-of-the-art RNA coding potential prediction tools on the same training and testing datasets. The 100-time 10-fold cross-validation tests also demonstrate that regulatory element-derived features, especially N7-methylguanosine (m7G) and internal ribosome entry site (IRES), contribute to the improvement in predictive performance.

Rm-LR: A long-range-based deep learning model for predicting multiple types of RNA modifications

Recent research has highlighted the pivotal role of RNA post-transcriptional modifications in the regulation of RNA expression and function. Accurate identification of RNA modification sites is important for understanding RNA function. In this study, we propose a novel RNA modification prediction method, namely Rm-LR, which leverages a long-range-based deep learning approach to accurately predict multiple types of RNA modifications using RNA sequences only. Rm-LR incorporates two large-scale RNA language pre-trained models to capture discriminative sequential information and learn local important features, which are subsequently integrated through a bilinear attention network. Rm-LR supports a total of ten RNA modification types (m6A, m1A, m5C, m5U, m6Am, Ψ, Am, Cm, Gm, and Um) and significantly outperforms the state-of-the-art methods in terms of predictive capability on benchmark datasets. Experimental results show the effectiveness and superiority of Rm-LR in prediction of various RNA modifications, demonstrating the strong adaptability and robustness of our proposed model. We demonstrate that RNA language pretrained models enable to learn dense biological sequential representations from large-scale long-range RNA corpus, and meanwhile enhance the interpretability of the models. This work contributes to the development of accurate and reliable computational models for RNA modification prediction, providing insights into the complex landscape of RNA modifications.

Plasma miR-195-5p predicts the severity of Covid-19 in hospitalized patients

Predicting the clinical course of Covid-19 is a challenging task, given the multi-systemic character of the disease and the paucity of minimally invasive biomarkers of disease severity. Here, we evaluated the early (first two days post-admission) level of circulating hsa-miR-195-5p (miR-195, a known responder to viral infections and SARS-CoV-2 interactor) in Covid-19 patients and assessed its potential as a biomarker of disease severity. We show that plasma miR-195 correlates with several clinical and paraclinical parameters, and is an excellent discriminator between the severe and mild forms of the disease. Our Gene Ontology analysis of miR-195 targets differentially expressed in Covid-19 indicates a strong impact on cardiac mitochondria homeostasis, suggesting a possible role in long Covid and chronic fatigue syndrome (CFS) syndromes.

SARS-CoV-2-associated organs failure and inflammation: a focus on the role of cellular and viral microRNAs

SARS-CoV-2 has been responsible for the recent pandemic all over the world, which has caused many complications. One of the hallmarks of SARS-CoV-2 infection is an induced immune dysregulation, in some cases resulting in cytokine storm syndrome, acute respiratory distress syndrome and many organs such as lungs, brain, and heart that are affected during the SARS-CoV-2 infection. Several physiological parameters are altered as a result of infection and cytokine storm. Among them, microRNAs (miRNAs) might reflect this poor condition since they play a significant role in immune cellular performance including inflammatory responses. Both host and viral-encoded miRNAs are crucial for the successful infection of SARS-CoV-2. For instance, dysregulation of miRNAs that modulate multiple genes expressed in COVID-19 patients with comorbidities (e.g., type 2 diabetes, and cerebrovascular disorders) could affect the severity of the disease. Therefore, altered expression levels of circulating miRNAs might be helpful to diagnose this illness and forecast whether a COVID-19 patient could develop a severe state of the disease. Moreover, a number of miRNAs could inhibit the expression of proteins, such as ACE2, TMPRSS2, spike, and Nsp12, involved in the life cycle of SARS-CoV-2. Accordingly, miRNAs represent potential biomarkers and therapeutic targets for this devastating viral disease. In the current study, we investigated modifications in miRNA expression and their influence on COVID-19 disease recovery, which may be employed as a therapy strategy to minimize COVID-19-related disorders.

The Genomic and Proteomic Profiles of NTRK Genes and Trk Receptors in Liver Hepatocellular Carcinoma

Background

The neurotrophic tyrosine kinase (NTRK) gene family includes NTRK1, NTRK2, and NTRK3, which encode tropomyosin receptor kinases TrkA, TrkB, and TrkC, respectively. This study aimed to initially assess the genomic and proteomic profiles of NTRK genes and Trk receptors in liver hepatocellular carcinoma (LIHC).

Methods

The ONCOMINE, UALCAN, GEPIA, cBioPortal, FusionGDB, SurvivalMeth, and the Human Protein Atlas databases were searched for NTRK gene expression and protein data in LIHC. Immunohistochemistry was used to detect pan-Trk expression across a commercial microarray containing 96 hepatocellular carcinoma (HCC) and 94 para-cancerous tissue spots. A modified histological score (H-score) with a maximum score of 300 was used to quantify immunohistochemical staining for pan-Trk. Student's t- and chi-square tests were the main statistical analyses used.

Results

The transcriptional levels of NTRK genes in LIHC were not significantly different from healthy controls. Using UALCAN and GEPIA, only high expression of NTRK2 was significantly associated with longer disease-free survival (P = 0.004). The alteration frequencies were low (7% in NTRK1, 1.7% in NTRK2, and 2% in NTRK3). The methylation levels of NTRK genes were all significantly different as analyzed by UALCAN; the high-risk group displayed an unfavorable prognosis compared with the low-risk group for NTRK1 (P = 0.033) and NTRK3 (P = 0.005). The median H-score of pan-Trk in HCC and para-cancerous tissues was not statistically different (186.31 ± 23.86 and 192.38 ± 21.06, P = 0.065). No differences were observed in clinicopathological features of HCC with the median H-score for pan-Trk expression (p > 0.05). The survival rate of patients with pan-Trk expression was also not significantly different.

Conclusion

The alteration frequency was low in NTRK genes, including gene fusion and methylation levels. Therefore, pan-Trk expression in HCC tissue has limited value in clinicopathological features and prognosis.

Clinically informative microRNAs for SARS-CoV-2 infection

COVID-19 is a viral respiratory infection induced by the newly discovered coronavirus SARS-CoV-2. miRNA is an example of a strong and direct regulator of a gene's transcriptional activity. The interaction between miRNAs and their target molecules is responsible for homeostasis. Virus-derived and host-derived miRNAs are involved in the activity of hiding from immune system cells, inducing the inflammatory reaction through interplay with associated genes, during SARS-COV-2 infection. Interest in miRNAs has raised the comprehension of the machinery and pathophysiology of SARS-COV-2 infection. In this review, the effects and biological roles of miRNAs on SARS-CoV-2 pathogenicity and life cycle are described. The therapeutic potential of miRNAs against SARS-CoV-2 infection are also mentioned.

High Expression Levels of miR-21-5p in Younger Hospitalized COVID-19 Patients Are Associated with Mortality and Critical Disease

In COVID-19, critical disease and invasive mechanical ventilation (IMV) increase the risk of death, mainly in patients over 60 years of age.

OBJECTIVES

To find the relationship between miR-21-5p and miR-146a-5p in terms of the severity, IMV, and mortality in hospitalized COVID-19 patients younger than 55 years of age.

METHODS

The patients were stratified according to disease severity using the IDSA/WHO criteria for severe and critical COVID-19 and subclassified into critical non-survivors and critical survivors.

RESULTS

Ninety-seven severe/critical COVID-19 patients were included; 81.3% of the deceased were male and 18.8% were female. Higher expression miR-21-5p levels were associated as follows: severe vs. critical disease (p = 0.007, FC = 0.498), PaO₂/FiO₂ index, mild vs. severe (p = 0.027, FC = 0.558), and survivors vs. non-survivors (p = 0.03, FC = 0.463). Moreover, we identified correlations with clinical variables: CRP (rho = -0.54, p < 0.001), D-dimer (rho = -0.47, p < 0.05), related to damage in the kidney (rho = 0.60, p < 0.001), liver (rho = 0.41, p < 0.05), and lung (rho = 0.54, p < 0.001). Finally, miR-21-5p thresholds were calculated according to severity (8.191), IMV (8.191), and mortality (8.237); these values increased the risk of developing a critical disease (OR = 4.19), the need for IMV (OR = 5.63), and death (OR = 6.00).

CONCLUSION

Increased expression levels of miR-21-5p are related to worse outcome of COVID-19 in younger hospitalized patients.

In Silico Identification of Potential Inhibitors of the SARS-CoV-2 Main Protease among a PubChem Database of Avian Infectious Bronchitis Virus 3CLPro Inhibitors

Remarkable structural homologies between the main proteases of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the avian infectious bronchitis virus (IBV) were revealed by comparative amino-acid sequence and 3D structural alignment. Assessing whether reported IBV 3CLPro inhibitors could also interact with SARS-CoV-2 has been undertaken in silico using a PubChem BioAssay database of 388 compounds active on the avian infectious bronchitis virus 3C-like protease. Docking studies of this database on the SARS-CoV-2 protease resulted in the identification of four covalent inhibitors targeting the catalytic cysteine residue and five non-covalent inhibitors for which the binding was further investigated by molecular dynamics (MD) simulations. Predictive ADMET calculations on the nine compounds suggest promising pharmacokinetic properties.

Interplay of host and viral factors in inflammatory pathway mediated cytokine storm during RNA virus infection

RNA viruses always have been a serious concern for human health by causing several outbreaks, often pandemics. The excessive mortality and deaths associated with the outbreaks caused by these viruses were due to the excessive induction of pro-inflammatory cytokines leading to cytokine storm. Cytokines are important for cell-to-cell communication to maintain cell homeostasis. Disturbances of this homeostasis can lead to intricate chain reactions resulting in a massive release of cytokines. This could lead to a severe self-reinforcement of several feedback processes, which could eventually cause systemic harm, multiple organ failure, or death. Multiple inflammation-associated pathways were involved in the cytokine production and its regulation. Different RNA viruses induce these pathways through the interplay with their viral factors and host proteins and miRNAs regulating these pathways. This review will discuss the interplay of host proteins and miRNAs that can play an important role in the regulation of cytokine storm and the possible therapeutic potential of these molecules for the treatment and the challenges associated with the clinical translation.

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.

City-scale model for COVID-19 epidemiology with mobility and social activities represented by a set of hidden Markov models

BACKGROUND AND OBJECTIVE

SARS-CoV-2 emerged by the end of 2019 and became a global pandemic due to its rapid spread. Various outbreaks of the disease in different parts of the world have been studied, and epidemiological analyses of these outbreaks have been useful for developing models with the aim of tracking and predicting the spread of epidemics. In this paper, an agent-based model that predicts the local daily evolution of the number of people hospitalized in intensive care due to COVID-19 is presented.

METHODS

An agent-based model has been developed, taking into consideration the most relevant characteristics of the geography and climate of a mid-size city, its population and pathology statistics, and its social customs and mobility, including the state of public transportation. In addition to these inputs, the different phases of isolation and social distancing are also taken into account. By means of a set of hidden Markov models, the system captures and reproduces virus transmission associated with the stochastic nature of people's mobility and activities in the city. The spread of the virus in the host is also simulated by following the stages of the disease and by considering the existence of comorbidities and the proportion of asymptomatic carriers.

RESULTS

As a case study, the model was applied to Paraná city (Entre Ríos, Argentina) in the second half of 2020. The model adequately predicts the daily evolution of people hospitalized in intensive care due to COVID-19. This adequacy is reflected by the fact that the prediction of the model (including its dispersion), as with the data reported in the field, never exceeded 90% of the capacity of beds installed in the city. In addition, other epidemiological variables of interest, with discrimination by age range, were also adequately reproduced, such as the number of deaths, reported cases, and asymptomatic individuals.

CONCLUSIONS

The model can be used to predict the most likely evolution of the number of cases and hospital bed occupancy in the short term. By adjusting the model to match the data on hospitalizations in intensive care units and deaths due to COVID-19, it is possible to analyze the impact of isolation and social distancing measures on the disease spread dynamics. In addition, it allows for simulating combinations of characteristics that would lead to a potential collapse in the health system due to lack of infrastructure as well as predicting the impact of social events or increases in people's mobility.

Comprehensive analysis of lncRNA-mRNA co-expression networks in HPV-driven cervical cancer reveals the pivotal function of LINC00511-PGK1 in tumorigenesis

BACKGROUND

Mounting evidence suggests that noncoding RNAs (lncRNAs) were involved in various human cancers. However, the role of these lncRNAs in HPV-driven cervical cancer (CC) has not been extensively studied. Considering that HR-HPV infections contribute to cervical carcinogenesis by regulating the expression of lncRNAs, miRNAs and mRNAs, we aim to systematically analyze lncRNAs and mRNAs expression profile to identify novel lncRNAs-mRNAs co-expression networks and explore their potential impact on tumorigenesis in HPV-driven CC.

METHODS

LncRNA/mRNA microarray technology was utilized to identify the differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) in HPV-16 and HPV-18 cervical carcinogenesis compared to normal cervical tissues. Venn diagram and weighted gene co-expression network analysis (WGCNA) were used to identify the hub DElncRNAs/DEmRNAs that were both significantly correlated with HPV-16 and HPV-18 CC patients. LncRNA-mRNA correlation analysis and functional enrichment pathway analysis were performed on these key DElncRNAs/DEmRNAs in HPV-16 and HPV-18 CC patients to explore their mutual mechanism in HPV-driven CC. A lncRNA-mRNA co-expression score (CES) model was established and validated by using the Cox regression method. Afterward, the clinicopathological characteristics were analyzed between CES-high and CES-low groups. In vitro, functional experiments were performed to evaluate the role of LINC00511 and PGK1 in cell proliferation, migration and invasion in CC cells. To understand whether LINC00511 play as an oncogenic role partially via modulating the expression of PGK1, rescue assays were used.

RESULTS

We identified 81 lncRNAs and 211 mRNAs that were commonly differentially expressed in HPV-16 and HPV-18 CC tissues compared to normal tissues. The results of lncRNA-mRNA correlation analysis and functional enrichment pathway analysis showed that the LINC00511-PGK1 co-expression network may make an important contribution to HPV-mediated tumorigenesis and be closely associated with metabolism-related mechanisms. Combined with clinical survival data, the prognostic lncRNA-mRNA co-expression score (CES) model based on LINC00511 and PGK1 could precisely predict patients' overall survival (OS). CES-high patients had a worse prognosis than CES-low patients and the enriched pathways and potential targets of applicable drugs were explored in CES-high patients. In vitro experiments confirmed the oncogenic functions of LINC00511 and PGK1 in the progression of CC, and revealed that LINC00511 functions in an oncogenic role in CC cells partially via modulating the expression of PGK1.

CONCLUSIONS

Together, these data identify co-expression modules that provide valuable information to understand the pathogenesis of HPV-mediated tumorigenesis, which highlights the pivotal function of the LINC00511-PGK1 co-expression network in cervical carcinogenesis. Furthermore, our CES model has a reliable predicting ability that could stratify CC patients into low- and high-risk groups of poor survival. This study provides a bioinformatics method to screen prognostic biomarkers which leads to lncRNA-mRNA co-expression network identification and construction for patients' survival prediction and potential drug applications in other cancers.

MicroRNAs and cytokines as potential predictive biomarkers for COVID-19 disease progression

Host microRNAs can influence the cytokine storm associated SARS-CoV-2 infection and proposed as biomarkers for COVID-19 disease. In the present study, serum MiRNA-106a and miRNA-20a were quantified by real time-PCR in 50 COVID-19 patients hospitalized at Minia university hospital and 30 healthy volunteers. Profiles of serum inflammatory cytokines (TNF-α, IFN-γ, and IL-10) and TLR4 were analyzed by Eliza in patients and controls. A highly significant decrease (P value = 0.0001) in the expressions of miRNA-106a and miRNA-20a was reported in COVID-19 patients compared to controls. A significant decrease in the levels of miRNA-20a was also reported in patients with lymphopenia, patients having chest CT severity score (CSS) > 19 and in patients having O₂ saturation less than 90%. Significantly higher levels of TNF-α, IFN-γ, IL-10 and TLR4 were reported in patients compared to controls. IL-10 and TLR4 levels were significantly higher in patients having lymphopenia. TLR-4 level was higher in patients with CSS > 19 and in patients with hypoxia. Using univariate logistic regression analysis, miRNA-106a, miRNA-20a, TNF-α, IFN-γ, IL-10 and TLR4 were identified as good predictors of disease. Receiver operating curve showed that the downregulation of miRNA-20a in patients having lymphopenia, patients with CSS > 19 and patients with hypoxia could be a potential biomarker with AUC = 0.68 ± 0.08, AUC = 0.73 ± 0.07 and AUC = 0.68 ± 0.07 respectively. Also, ROC curve showed accurate association between the increase of serum IL-10 and TLR-4 and lymphopenia among COVID-19 patients with AUC = 0.66 ± 0.08 and AUC = 0.73 ± 0.07 respectively. ROC curve showed also that serum TLR-4 could be a potential marker for high CSS with AUC = 0.78 ± 0.06. A negative correlation was detected between miRNA-20a with TLR-4 (r = - 0.30, P value = 0.03). We concluded that, miR-20a, is a potential biomarker of COVID-19 severity and blockade of IL-10 and TLR4 may constitute a novel therapy for COVID-19 patients.

Non-classical HLA class I molecules and their potential role in viral infections

Human Leukocyte Antigens (HLA) are classified in three different classes I, II and III, and represent the key mediators of immune responses, self-tolerance development and pathogen recognition. Among them, non-classical subtypes (HLA-Ib), e.g. HLA-E and HLA-G, are characterize by tolerogenic functions that are often exploited by viruses to evade the host immune responses. In this perspective, we will review the main current data referred to HLA-G and HLA-E and viral infections, as well as the impact on immune response. Data were selected following eligibility criteria accordingly to the reviewed topic. We used a set of electronic databases (Medline/PubMed, Scopus, Web of Sciences (WOS), Cochrane library) for a systematic search until November 2022 using MeSH keywords/terms (i.e. HLA, HLA-G, HLA-E, viral infection, SARS-CoV-2, etc.…). Recent studies support the involvement of non-classical molecules, such as HLA-E and HLA-G, in the control of viral infection. On one side, viruses exploit HLA-G and HLA-E molecule to control host immune activation. On the other side, the expression of these molecules might control the inflammatory condition generated by viral infections. Hence, this review has the aim to summarize the state of art of literature about the modulation of these non-classical HLA-I molecules, to provide a general overview of the new strategies of viral immune system regulation to counteract immune defenses.

A systematic review of state-of-the-art strategies for machine learning-based protein function prediction

New drug discovery is inseparable from the discovery of drug targets, and the vast majority of the known targets are proteins. At the same time, proteins are essential structural and functional elements of living cells necessary for the maintenance of all forms of life. Therefore, protein functions have become the focus of many pharmacological and biological studies. Traditional experimental techniques are no longer adequate for rapidly growing annotation of protein sequences, and approaches to protein function prediction using computational methods have emerged and flourished. A significant trend has been to use machine learning to achieve this goal. In this review, approaches to protein function prediction based on the sequence, structure, protein-protein interaction (PPI) networks, and fusion of multi-information sources are discussed. The current status of research on protein function prediction using machine learning is considered, and existing challenges and prominent breakthroughs are discussed to provide ideas and methods for future studies.

COVID-19: Mechanisms, risk factors, genetics, non-coding RNAs and neurologic impairments

The novel coronavirus infection (COVID-19) causes a severe acute illness with the development of respiratory distress syndrome in some cases. COVID-19 is a global problem of mankind to this day. Among its most important aspects that require in-depth study are pathogenesis and molecular changes in severe forms of the disease. A lot of literature data is devoted to the pathogenetic mechanisms of COVID-19. Without dwelling in detail on some paths of pathogenesis discussed, we note that at present there are many factors of development and progression. Among them, this is the direct role of both viral non-coding RNAs (ncRNAs) and host ncRNAs. One such class of ncRNAs that has been extensively studied in COVID-19 is microRNAs (miRNAs) and long non-coding RNAs (lncRNAs). Moreover, Initially, it was believed that this COVID-19 was limited to damage to the respiratory system. It has now become clear that COVID-19 affects not only the liver and kidneys, but also the nervous system. In this review, we summarized the current knowledge of mechanisms, risk factors, genetics and neurologic impairments in COVID-19. In addition, we discuss and evaluate evidence demonstrating the involvement of miRNAs and lnRNAs in COVID-19 and use this information to propose hypotheses for future research directions.

Altered microRNA Transcriptome in Cultured Human Airway Cells upon Infection with SARS-CoV-2

Numerous proteomic and transcriptomic studies have been carried out to better understand the current multi-variant SARS-CoV-2 virus mechanisms of action and effects. However, they are mostly centered on mRNAs and proteins. The effect of the virus on human post-transcriptional regulatory agents such as microRNAs (miRNAs), which are involved in the regulation of 60% of human gene activity, remains poorly explored. Similar to research we have previously undertaken with other viruses such as Ebola and HIV, in this study we investigated the miRNA profile of lung epithelial cells following infection with SARS-CoV-2. At the 24 and 72 h post-infection time points, SARS-CoV-2 did not drastically alter the miRNome. About 90% of the miRNAs remained non-differentially expressed. The results revealed that miR-1246, miR-1290 and miR-4728-5p were the most upregulated over time. miR-196b-5p and miR-196a-5p were the most downregulated at 24 h, whereas at 72 h, miR-3924, miR-30e-5p and miR-145-3p showed the highest level of downregulation. In the top significantly enriched KEGG pathways of genes targeted by differentially expressed miRNAs we found, among others, MAPK, RAS, P13K-Akt and renin secretion signaling pathways. Using RT-qPCR, we also showed that SARS-CoV-2 may regulate several predicted host mRNA targets involved in the entry of the virus into host cells (ACE2, TMPRSS2, ADAM17, FURIN), renin-angiotensin system (RAS) (Renin, Angiotensinogen, ACE), innate immune response (IL-6, IFN1β, CXCL10, SOCS4) and fundamental cellular processes (AKT, NOTCH, WNT). Finally, we demonstrated by dual-luciferase assay a direct interaction between miR-1246 and ACE-2 mRNA. This study highlights the modulatory role of miRNAs in the pathogenesis of SARS-CoV-2.

MicroRNAs Improve Cancer Treatment Outcomes Through Personalized Medicine

MicroRNAs (miRNAs) are short non-coding RNAs that repress or degrade mRNA targets to downregulate genes. In cancer occurrence, the expression of miRNAs is altered. Depending on the involvement of a certain miRNA in the pathogenetic growth of a tumor, It may be up or downregulated. The "oncogenic" action of miRNAs corresponds with upregulation, which leads to tumor proliferation and spread meanwhile the miRNAs that have been downregulated bring tumorsuppressive outcomes. Oncogenes and tumor suppressor genes are among the genes whose expression is under their control, demonstrating that classifying them solely as oncogenes or tumor suppressor genes alone is not only hindering but also incorrect. Apart from basic tumors, miRNAs may be found in nearly all human fluids and can be used for cancer diagnosis as well as clinical outcome prognostics and better response to treatment strategies. The overall variance of these tiny noncoding RNAs influences patient-specific pharmacokinetics and pharmacodynamics of anti-cancer medicines, driving a growing demand for personalized medicine. By now, microRNAs from tumor biopsies or blood are being widely investigated as substantial biomarkers for cancer in time diagnosis, prognosis, and, progression. With the rise of COVID-19, this paper also attempts to study recent research on miRNAs involved with deaths in lung cancer COVID patients. With the discovery of single nucleotide polymorphisms, personalized treatment via microRNAs has lately become a reality. The present review article describes the highlights of recent knowledge of miRNAs in various cancers, with a focus on miRNA translational applications as innovative potential diagnostic and prognostic indicators that expand person-to-person therapy options.

Circulating microRNAs as emerging regulators of COVID-19

Coronavirus disease 2019 (COVID-19), an infectious disease caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global pandemic that has high incidence rates, spreads rapidly, and has caused more than 6.5 million deaths globally to date. Currently, several drugs have been used in the clinical treatment of COVID-19, including antivirals (e.g., molnupiravir, baricitinib, and remdesivir), monoclonal antibodies (e.g., etesevimab and tocilizumab), protease inhibitors (e.g., paxlovid), and glucocorticoids (e.g., dexamethasone). Increasing evidence suggests that circulating microRNAs (miRNAs) are important regulators of viral infection and antiviral immune responses, including the biological processes involved in regulating COVID-19 infection and subsequent complications. During viral infection, both viral genes and host cytokines regulate transcriptional and posttranscriptional steps affecting viral replication. Virus-encoded miRNAs are a component of the immune evasion repertoire and function by directly targeting immune functions. Moreover, several host circulating miRNAs can contribute to viral immune escape and play an antiviral role by not only promoting nonstructural protein (nsp) 10 expression in SARS coronavirus, but among others inhibiting NOD-like receptor pyrin domain-containing (NLRP) 3 and IL-1β transcription. Consequently, understanding the expression and mechanism of action of circulating miRNAs during SARS-CoV-2 infection will provide unexpected insights into circulating miRNA-based studies. In this review, we examined the recent progress of circulating miRNAs in the regulation of severe inflammatory response, immune dysfunction, and thrombosis caused by SARS-CoV-2 infection, discussed the mechanisms of action, and highlighted the therapeutic challenges involving miRNA and future research directions in the treatment of COVID-19.

Cardiovascular Implications of microRNAs in Coronavirus Disease 2019

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic continues to be a global challenge due to resulting morbidity and mortality. Cardiovascular (CV) involvement is a crucial complication in coronavirus disease 2019 (COVID-19), and no strategies are available to prevent or specifically address CV events in COVID-19 patients. The identification of molecular partners contributing to CV manifestations in COVID-19 patients is crucial for providing early biomarkers, prognostic predictors, and new therapeutic targets. The current report will focus on the role of microRNAs (miRNAs) in CV complications associated with COVID-19. Indeed, miRNAs have been proposed as valuable biomarkers and predictors of both cardiac and vascular damage occurring in SARS-CoV-2 infection. SIGNIFICANCE STATEMENT: It is essential to identify the molecular mediators of coronavirus disease 2019 (COVID-19) cardiovascular (CV) complications. This report focused on the role of microRNAs in CV complications associated with COVID-19, discussing their potential use as biomarkers, prognostic predictors, and therapeutic targets.

A systematic review on the state-of-the-art strategies for protein representation

The study of drug-target protein interaction is a key step in drug research. In recent years, machine learning techniques have become attractive for research, including drug research, due to their automated nature, predictive power, and expected efficiency. Protein representation is a key step in the study of drug-target protein interaction by machine learning, which plays a fundamental role in the ultimate accomplishment of accurate research. With the progress of machine learning, protein representation methods have gradually attracted attention and have consequently developed rapidly. Therefore, in this review, we systematically classify current protein representation methods, comprehensively review them, and discuss the latest advances of interest. According to the information extraction methods and information sources, these representation methods are generally divided into structure and sequence-based representation methods. Each primary class can be further divided into specific subcategories. As for the particular representation methods involve both traditional and the latest approaches. This review contains a comprehensive assessment of the various methods which researchers can use as a reference for their specific protein-related research requirements, including drug research.

Identification of Human miRNA Biomarkers Targeting the SARS-CoV-2 Genome

SARS-CoV-2 poses a great challenge toward mankind, majorly due to its evolution and frequently occurring variants. On the other hand, in human hosts, microRNA (miRNA) plays a vital role in replication and propagation during a viral infection and can control the biological processes. This may be essential for the progression of viral infection. Moreover, human miRNAs can play a therapeutic role in treatment of different viral diseases by binding to the target sites of the virus genome, thereby hindering the essential functioning of the virus. Motivated by this fact, we have hypothesized a new approach in order to identify human miRNAs that can target the mRNA (genome) of SARS-CoV-2 to degrade their protein synthesis. In this regard, the multiple sequence alignment technique Clustal Omega is used to align a complement of 2656 human miRNAs with the SARS-CoV-2 reference genome (mRNA). Thereafter, ranking of these aligned human miRNAs is performed with the help of a new scoring function that takes into account the (a) total number of nucleotide matches between the human miRNA and the SARS-CoV-2 genome, (b) number of consecutive nucleotide matches between the human miRNA and the SARS-CoV-2 genome, (c) number of nucleotide mismatches between the human miRNA and the SARS-CoV-2 genome, and (d) the difference in length before and after alignment of the human miRNA. As a result, from the 2656 ranked miRNAs, the top 20 human miRNAs are reported, which are targeting different coding and non-coding regions of the SARS-CoV-2 genome. Moreover, molecular docking of such human miRNAs with virus mRNA is performed to verify the efficacy of the interactions. Furthermore, 4 miRNAs out of the top 20 miRNAs are identified to have the seed region. In order to inhibit the virus, the key human targets of the seed regions may be targeted. Repurposable drugs like carfilzomib, bortezomib, hydralazine, and paclitaxel are identified for such purpose.

Pharmacological Mechanism of NRICM101 for COVID-19 Treatments by Combined Network Pharmacology and Pharmacodynamics

Symptom treatments for Coronavirus disease 2019 (COVID-19) infection and Long COVID are one of the most critical issues of the pandemic era. In light of the lack of standardized medications for treating COVID-19 symptoms, traditional Chinese medicine (TCM) has emerged as a potentially viable strategy based on numerous studies and clinical manifestations. Taiwan Chingguan Yihau (NRICM101), a TCM designed based on a medicinal formula with a long history of almost 500 years, has demonstrated its antiviral properties through clinical studies, yet the pharmacogenomic knowledge for this formula remains unclear. The molecular mechanism of NRICM101 was systematically analyzed by using exploratory bioinformatics and pharmacodynamics (PD) approaches. Results showed that there were 434 common interactions found between NRICM101 and COVID-19 related genes/proteins. For the network pharmacology of the NRICM101, the 434 common interacting genes/proteins had the highest associations with the interleukin (IL)-17 signaling pathway in the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Moreover, the tumor necrosis factor (TNF) was found to have the highest association with the 30 most frequently curated NRICM101 chemicals. Disease analyses also revealed that the most relevant diseases with COVID-19 infections were pathology, followed by cancer, digestive system disease, and cardiovascular disease. The 30 most frequently curated human genes and 2 microRNAs identified in this study could also be used as molecular biomarkers or therapeutic options for COVID-19 treatments. In addition, dose-response profiles of NRICM101 doses and IL-6 or TNF-α expressions in cell cultures of murine alveolar macrophages were constructed to provide pharmacodynamic (PD) information of NRICM101. The prevalent use of NRICM101 for standardized treatments to attenuate common residual syndromes or chronic sequelae of COVID-19 were also revealed for post-pandemic future.

Identification of potent compounds against SARs-CoV-2: An in-silico based drug searching against Mpro

The worldwide pandemic of coronavirus disease 2019 (COVID-19) along with the various newly discovered major SARS-CoV-2 variants, including B.1.1.7, B.1.351, and B.1.1.28, constitute the Variant of Concerns (VOC). It's difficult to keep these variants from spreading over the planet. As a result of these VOCs, the fifth wave has already begun in several countries. The rapid spread of VOCs is posing a serious threat to human civilization. There is currently no specific medicine available for the treatment of COVID-19. Here, we present the findings of methods that used a combination of structure-assisted drug design, virtual screening, and high-throughput screening to swiftly generate lead compounds against Mpro protein of SARs-CoV-2. Therapeutics, in addition to vaccinations, are an essential element of the healthcare response to COVID-19's persistent threat. In the current study, we designed the efficient compounds that may combat all emerging variants of SARs-CoV-2 by targeting the common Mpro protein. The present study was aimed to discover new compounds that may be proposed as new therapeutic agents to treat COVID-19 infection without any adverse effects. For this purpose, a computational-based virtual screening of 352 in-house synthesized compounds library was performed through molecular docking and Molecular Dynamics (MD) simulation approach. As a result, four novel potent compounds were successfully shortlisted by implementing certain pharmacological, physiological, and ADMET criteria i.e., compounds 3, 4, 21, and 22. Furthermore, MD simulations were performed to evaluate the stability and dynamic behavior of these compounds with Mpro complex for about 30 ns. Eventually, compound 22 was found to be highly potent against Mpro protein and was further evaluated by applying 100 ns simulations. Our findings showed that these shortlisted compounds may have potency to treat the COVID-19 infection for which further experimental validation is proposed as part of a follow-up investigation.

Bioinformatics analysis reveals molecular connections between non-alcoholic fatty liver disease (NAFLD) and COVID-19

The ongoing coronavirus disease 2019 (COVID-19) pandemic caused by SARS-CoV-2 has devastatingly impacted people's lives. Non-alcoholic fatty liver disease (NAFLD) is fatal comorbidity of COVID-19 seen with potential risk factors to develop severe symptoms. This research focuses on determining and elucidating the molecular factors and connections that might contribute to the severity of SARS-CoV-2 infection in NAFLD patients. Here, we comprehensively inspected the genes involved in NAFLD and SARS-CoV-2 entry factors (SCEFs) found by searching through the DisGeNet database and literature review, respectively. Further, we identified the SCEFs-related proteins through protein-protein interaction (PPI) network construction, MCODE, and Cytohubba. Next, the shared genes involved in NAFLD and SARS-CoV-2 entry, and hub gene were determined, followed by the GO and KEGG pathways analysis. X2K database was used to construct the upstream regulatory network of hub genes, as well as to identify the top ten candidates of transcription factors (TFs) and protein kinases (PKs). PPI analysis identified connections between 4 top SCEFs, including ACE, ADAM17, DPP4, and TMPRSS2 and NAFLD-related genes such as ACE, DPP4, IL-10, TNF, and AKT1. GO and KEGG analysis revealed the top ten biological processes and pathways, including cytokine-mediated signaling, PI3K-Akt, AMPK, and mTOR signaling pathways. The upstream regulatory network revealed that AKT1 and MAPK14 as important PKs and HIF1A and SP1 as important TFs associated with AKT1, IL-10, and TNF. The molecular connections identified between COVID-19 and NAFLD may shed light on discovering the causes of the severity of SARS-CoV-2 infected NAFLD patients.

Cellular miR-6741-5p as a Prognostic Biomarker Predicting Length of Hospital Stay among COVID-19 Patients

Wide variability exists with host response to SARS-CoV-2 infection among individuals. Circulatory micro RNAs (miRNAs) are being recognized as promising biomarkers for complex traits, including viral pathogenesis. We hypothesized that circulatory miRNAs at 48 h post hospitalization may predict the length of stay (LOS) and prognosis of COVID-19 patients. Plasma miRNA levels were compared between three groups: (i) healthy volunteers (C); (ii) COVID-19 patients treated with remdesivir (an antiviral) plus dexamethasone (a glucocorticoid) (with or without baricitinib, a Janus kinase inhibitor) on the day of hospitalization (I); and COVID-19 patients at 48 h post treatment (T). Results showed that circulatory miR-6741-5p expression levels were significantly different between groups C and I (p < 0.0000001); I and T (p < 0.0000001); and C and T (p = 0.001). Our ANOVA model estimated that all patients with less than 12.42 Log2 CPM had a short LOS, or a good prognosis, whereas all patients with over 12.42 Log2 CPM had a long LOS, or a poor prognosis. In sum, we show that circulatory miR-6741-5p may serve as a prognostic biomarker effectively predicting mortality risk and LOS of hospitalized COVID-19 patients.

In silico analysis of SARS-CoV-2 genomes: Insights from SARS encoded non-coding RNAs

The recent pandemic caused by Severe Acute Respiratory Syndrome Coronavirus-2 has resulted in enormous deaths around the world. Clues from genomic sequences of parent and their mutants can be obtained to understand the evolving pathogenesis of this virus. Apart from the viral proteins, virus-encoded microRNAs (miRNAs) have been shown to play a vital role in regulating viral pathogenesis. Thus we sought to investigate the miRNAs encoded by SARS-CoV-2, its mutants, and the host. Here, we present the results obtained using a dual approach i.e (i) identifying host-encoded miRNAs that might regulate viral pathogenesis and (ii) identifying viral-encoded miRNAs that might regulate host cell signaling pathways and aid in viral pathogenesis. Analysis utilizing the first approach resulted in the identification of ten host-encoded miRNAs that could target the SARS, SARS-CoV-2, and its mutants. Interestingly our analysis revealed that there is a significantly higher number of host miRNAs that could target the SARS-CoV-2 genome as compared to the SARS reference genome. Results from the second approach resulted in the identification of a set of virus-encoded miRNAs which might regulate host signaling pathways. Our analysis further identified a similar "GA" rich motif in the SARS-CoV-2 and its mutant genomes that was shown to play a vital role in lung pathogenesis during severe SARS infections. In summary, we have identified human and virus-encoded miRNAs that might regulate the pathogenesis of SARS coronaviruses and describe similar non-coding RNA sequences in SARS-CoV-2 that were shown to regulate SARS-induced lung pathology in mice.

LargeMetabo: an out-of-the-box tool for processing and analyzing large-scale metabolomic data

Large-scale metabolomics is a powerful technique that has attracted widespread attention in biomedical studies focused on identifying biomarkers and interpreting the mechanisms of complex diseases. Despite a rapid increase in the number of large-scale metabolomic studies, the analysis of metabolomic data remains a key challenge. Specifically, diverse unwanted variations and batch effects in processing many samples have a substantial impact on identifying true biological markers, and it is a daunting challenge to annotate a plethora of peaks as metabolites in untargeted mass spectrometry-based metabolomics. Therefore, the development of an out-of-the-box tool is urgently needed to realize data integration and to accurately annotate metabolites with enhanced functions. In this study, the LargeMetabo package based on R code was developed for processing and analyzing large-scale metabolomic data. This package is unique because it is capable of (1) integrating multiple analytical experiments to effectively boost the power of statistical analysis; (2) selecting the appropriate biomarker identification method by intelligent assessment for large-scale metabolic data and (3) providing metabolite annotation and enrichment analysis based on an enhanced metabolite database. The LargeMetabo package can facilitate flexibility and reproducibility in large-scale metabolomics. The package is freely available from https://github.com/LargeMetabo/LargeMetabo.

Computational approach investigation bioactive molecules from Saussurea Costus plant as SARS-CoV-2 main protease inhibitors using reverse docking, molecular dynamics simulation, and pharmacokinetic ADMET parameters

SARS-COV-2 virus causes (COVID-19) disease; it has become a global pandemic since 2019 and has negatively affected all aspects of human life. Scientists have made great efforts to find a reliable cure, vaccine, or treatment for this emerging disease. Efforts have been directed towards using medicinal plants as alternative medicines, as the active chemical compounds in them have been discovered as potential antiviral or anti-inflammatory agents. In this research, the potential of Saussurea costus (S. Costus) or QUST Al Hindi chemical consistent as potential antiviral agents was investigated by using computational methods such as Reverse Docking, ADMET, and Molecular Dynamics with different proteases COVID-19 such as PDB: 2GZ9; 6LU7; 7AOL, 6Y2E, 6Y84. The results of Reverse Docking the complex between 6LU7 proteases and Cynaropicrin compound being the best complex, as the same result, is achieved by molecular dynamics. Also, the toxicity testing result from ADMET method proved that the complex is the least toxic and the safest possible drug. In addition, 6LU7-Cynaropicrin complex obeyed Lipinski rule; it formed ≤5 H-bond donors and ≤10 H bond acceptors, MW < 500 Daltons, and octanol/water partition coefficient <5.

Computational pharmacology: New avenues for COVID-19 therapeutics search and better preparedness for future pandemic crises

The COVID-19 pandemic created an unprecedented global healthcare emergency prompting the exploration of new therapeutic avenues, including drug repurposing. A large number of ongoing studies revealed pervasive issues in clinical research, such as the lack of accessible and organised data. Moreover, current shortcomings in clinical studies highlighted the need for a multi-faceted approach to tackle this health crisis. Thus, we set out to explore and develop new strategies for drug repositioning by employing computational pharmacology, data mining, systems biology, and computational chemistry to advance shared efforts in identifying key targets, affected networks, and potential pharmaceutical intervention options. Our study revealed that formulating pharmacological strategies should rely on both therapeutic targets and their networks. We showed how data mining can reveal regulatory patterns, capture novel targets, alert about side-effects, and help identify new therapeutic avenues. We also highlighted the importance of the miRNA regulatory layer and how this information could be used to monitor disease progression or devise treatment strategies. Importantly, our work bridged the interactome with the chemical compound space to better understand the complex landscape of COVID-19 drugs. Machine and deep learning allowed us to showcase limitations in current chemical libraries for COVID-19 suggesting that both in silico and experimental analyses should be combined to retrieve therapeutically valuable compounds. Based on the gathered data, we strongly advocate for taking this opportunity to establish robust practices for treating today's and future infectious diseases by preparing solid analytical frameworks.

Computational molecular interaction between SARS-CoV-2 main protease and theaflavin digallate using free energy perturbation and molecular dynamics

Our objective was to identify the molecule which can inhibit SARS-CoV-2 main protease and can be easily procured. Natural products may provide such molecules and can supplement the current custom chemical synthesis-based drug discovery for this objective. A combination of docking approaches, scoring functions, classical molecular dynamic simulation, binding pose metadynamics, and free energy perturbation calculations have been employed in this study. Theaflavin digallate has been observed in top-scoring compounds after the three independent virtual screening simulations of 598435 compounds (unique 27256 chemical entities). The main protease-theaflavin digallate complex interacts with critical active site residues of the main protease in molecular dynamics simulation independent of the explored computational framework, simulation time, initial structure, and force field used. Theaflavin digallate forms approximately three hydrogen bonds with Glutamate166 of main protease, primarily through hydroxyl groups in the benzene ring of benzo(7)annulen-6-one, along with other critical residues. Glu166 is the most critical amino acid for main protease dimerization, which is necessary for catalytic activity. The estimated binding free energy, calculated by Amber and Schrodinger MMGBSA module, reflects a high binding free energy between theaflavin digallate and main protease. Binding pose metadynamics simulation shows the highly persistent H-bond and a stable pose for the theaflavin digallate-main protease complex. Using method control, experimental controls, and test set, alchemical transformation studies confirm high relative binding free energy of theaflavin digallate with the main protease. Computational molecular interaction suggests that theaflavin digallate can inhibit the main protease of SARS-CoV-2.

CovInter: interaction data between coronavirus RNAs and host proteins

Coronavirus has brought about three massive outbreaks in the past two decades. Each step of its life cycle invariably depends on the interactions among virus and host molecules. The interaction between virus RNA and host protein (IVRHP) is unique compared to other virus-host molecular interactions and represents not only an attempt by viruses to promote their translation/replication, but also the host's endeavor to combat viral pathogenicity. In other words, there is an urgent need to develop a database for providing such IVRHP data. In this study, a new database was therefore constructed to describe the interactions between coronavirus RNAs and host proteins (CovInter). This database is unique in (a) unambiguously characterizing the interactions between virus RNA and host protein, (b) comprehensively providing experimentally validated biological function for hundreds of host proteins key in viral infection and (c) systematically quantifying the differential expression patterns (before and after infection) of these key proteins. Given the devastating and persistent threat of coronaviruses, CovInter is highly expected to fill the gap in the whole process of the 'molecular arms race' between viruses and their hosts, which will then aid in the discovery of new antiviral therapies. It's now free and publicly accessible at: https://idrblab.org/covinter/.