Establishment and validation of the autophagy-related ceRNA network in irreversible pulpitis.

BACKGROUND: The molecular mechanisms underlying the onset and progression of irreversible pulpitis have been studied for decades. Many studies have indicated a potential correlation between autophagy and this disease. Against the background of the competing endogenous RNA (ceRNA) theory, protein-coding RNA functions are linked with long noncoding RNAs (lncRNAs) and microRNAs (miRNAs). This mechanism has been widely studied in various fields but has rarely been reported in the context of irreversible pulpitis. The hub genes selected under this theory may represent the key to the interaction between autophagy and irreversible pulpitis. RESULTS: Filtering and differential expression analyses of the GSE92681 dataset, which contains data from 7 inflamed and 5 healthy pulp tissue samples, were conducted. The results were intersected with autophagy-related genes (ARGs), and 36 differentially expressed ARGs (DE-ARGs) were identified. Functional enrichment analysis and construction of the protein‒protein interaction (PPI) network of DE-ARGs were performed. Coexpression analysis was conducted between differentially expressed lncRNAs (DElncRNAs) and DE-ARGs, and 151 downregulated and 59 upregulated autophagy-related DElncRNAs (AR-DElncRNAs) were identified. StarBase and multiMiR were then used to predict related microRNAs of AR-DElncRNAs and DE-ARGs, respectively. We established ceRNA networks including 9 hub lncRNAs (HCP5 and AC112496.1 ↑; FENDRR, AC099850.1, ZSWIM8-AS1, DLX6-AS1, LAMTOR5-AS1, TMEM161B-AS1 and AC145207.5 ↓), which were validated by a qRT‒PCR analysis of pulp tissue from patients with irreversible pulpitis. CONCLUSION: We constructed two networks consisting of 9 hub lncRNAs based on the comprehensive identification of autophagy-related ceRNAs. This study may provide novel insights into the interactive relationship between autophagy and irreversible pulpitis and identifies several lncRNAs that may serve as potential biological markers.

Aid or Antagonize: Nuclear Long Noncoding RNAs Regulate Host Responses and Outcomes of Viral Infections.

Long noncoding RNAs (lncRNAs) are transcripts measuring >200 bp in length and devoid of protein-coding potential. LncRNAs exceed the number of protein-coding mRNAs and regulate cellular, developmental, and immune pathways through diverse molecular mechanisms. In recent years, lncRNAs have emerged as epigenetic regulators with prominent roles in health and disease. Many lncRNAs, either host or virus-encoded, have been implicated in critical cellular defense processes, such as cytokine and antiviral gene expression, the regulation of cell signaling pathways, and the activation of transcription factors. In addition, cellular and viral lncRNAs regulate virus gene expression. Viral infections and associated immune responses alter the expression of host lncRNAs regulating immune responses, host metabolism, and viral replication. The influence of lncRNAs on the pathogenesis and outcomes of viral infections is being widely explored because virus-induced lncRNAs can serve as diagnostic and therapeutic targets. Future studies should focus on thoroughly characterizing lncRNA expressions in virus-infected primary cells, investigating their role in disease prognosis, and developing biologically relevant animal or organoid models to determine their suitability for specific therapeutic targeting. Many cellular and viral lncRNAs localize in the nucleus and epigenetically modulate viral transcription, latency, and host responses to infection. In this review, we provide an overview of the role of nuclear lncRNAs in the pathogenesis and outcomes of viral infections, such as the Influenza A virus, Sendai Virus, Respiratory Syncytial Virus, Hepatitis C virus, Human Immunodeficiency Virus, and Herpes Simplex Virus. We also address significant advances and barriers in characterizing lncRNA function and explore the potential of lncRNAs as therapeutic targets.

Seven bacterial response-related genes are biomarkers for colon cancer.

BACKGROUND: Colon cancer (CC) is a common tumor that causes significant harm to human health. Bacteria play a vital role in cancer biology, particularly the biology of CC. Genes related to bacterial response were seldom used to construct prognosis models. We constructed a bacterial response-related risk model based on three Molecular Signatures Database gene sets to explore new markers for predicting CC prognosis. METHODS: The Cancer Genome Atlas (TCGA) colon adenocarcinoma samples were used as the training set, and Gene Expression Omnibus (GEO) databases were used as the test set. Differentially expressed bacterial response-related genes were identified for prognostic gene selection. Univariate Cox regression analysis, least absolute shrinkage and selection operator-penalized Cox regression analysis, and multivariate Cox regression analysis were performed to construct a prognostic risk model. The individual diagnostic effects of genes in the prognostic model were also evaluated. Moreover, differentially expressed long noncoding RNAs (lncRNAs) were identified. Finally, the expression of these genes was validated using quantitative polymerase chain reaction (qPCR) in cell lines and tissues. RESULTS: A prognostic signature was constructed based on seven bacterial response genes: LGALS4, RORC, DDIT3, NSUN5, RBCK1, RGL2, and SERPINE1. Patients were assigned a risk score based on the prognostic model, and patients in the TCGA cohort with a high risk score had a poorer prognosis than those with a low risk score; a similar finding was observed in the GEO cohort. These seven prognostic model genes were also independent diagnostic factors. Finally, qPCR validated the differential expression of the seven model genes and two coexpressed lncRNAs (C6orf223 and SLC12A9-AS1) in 27 pairs of CC and normal tissues. Differential expression of LGALS4 and NSUN5 was also verified in cell lines (FHC, COLO320DM, SW480). CONCLUSIONS: We created a seven-gene bacterial response-related gene signature that can accurately predict the outcomes of patients with CC. This model can provide valuable insights for personalized treatment.

Downregulation of MALAT1 is a hallmark of tissue and peripheral proliferative T cells in COVID-19.

T cells play key protective but also pathogenic roles in COVID-19. We studied the expression of long non-coding RNAs (lncRNAs) in COVID-19 T-cell transcriptomes by integrating previously published single-cell RNA sequencing datasets. The long intergenic non-coding RNA MALAT1 was the most highly transcribed lncRNA in T cells, with Th1 cells demonstrating the lowest and CD8+ resident memory cells the highest MALAT1 expression, amongst CD4+ and CD8+ T-cells populations, respectively. We then identified gene signatures that covaried with MALAT1 in single T cells. A significantly higher number of transcripts correlated negatively with MALAT1 than those that correlated. Enriched functional annotations of the MALAT1- anti-correlating gene signature included processes associated with T-cell activation such as cell division, oxidative phosphorylation, and response to cytokine. The MALAT1 anti-correlating gene signature shared by both CD4+ and CD8+ T-cells marked dividing T cells in both the lung and blood of COVID-19 patients. Focussing on the tissue, we used an independent patient cohort of post-mortem COVID-19 lung samples and demonstrated that MALAT1 suppression was indeed a marker of MKI67+ proliferating CD8+ T cells. Our results reveal MALAT1 suppression and its associated gene signature are a hallmark of human proliferating T cells.

Unusual X chromosome inactivation maintenance in female alveolar type 2 cells is correlated with increased numbers of X-linked escape genes and sex-biased gene expression.

Sex differences exist for many lung pathologies, including COVID-19 and pulmonary fibrosis, but the mechanistic basis for this remains unclear. Alveolar type 2 cells (AT2s), which play a key role in alveolar lung regeneration, express the X-linked Ace2 gene that has roles in lung repair and SARS-CoV-2 pathogenesis, suggesting that X chromosome inactivation (XCI) in AT2s might impact sex-biased lung pathology. Here we investigate XCI maintenance and sex-specific gene expression profiles using male and female AT2s. Remarkably, the inactive X chromosome (Xi) lacks robust canonical Xist RNA "clouds" and less enrichment of heterochromatic modifications in human and mouse AT2s. We demonstrate that about 68% of expressed X-linked genes in mouse AT2s, including Ace2, escape XCI. There are genome-wide expression differences between male and female AT2s, likely influencing both lung physiology and pathophysiologic responses. These studies support a renewed focus on AT2s as a potential contributor to sex-biased differences in lung disease.

COVID-19Base v3: Update of the knowledgebase for drugs and biomedical entities linked to COVID-19.

COVID-19 has taken a huge toll on our lives over the last 3 years. Global initiatives put forward by all stakeholders are still in place to combat this pandemic and help us learn lessons for future ones. While the vaccine rollout was not able to curb the spread of the disease for all strains, the research community is still trying to develop effective therapeutics for COVID-19. Although Paxlovid and remdesivir have been approved by the FDA against COVID-19, they are not free of side effects. Therefore, the search for a therapeutic solution with high efficacy continues in the research community. To support this effort, in this latest version (v3) of COVID-19Base, we have summarized the biomedical entities linked to COVID-19 that have been highlighted in the scientific literature after the vaccine rollout. Eight different topic-specific dictionaries, i.e., gene, miRNA, lncRNA, PDB entries, disease, alternative medicines registered under clinical trials, drugs, and the side effects of drugs, were used to build this knowledgebase. We have introduced a BLSTM-based deep-learning model to predict the drug-disease associations that outperforms the existing model for the same purpose proposed in the earlier version of COVID-19Base. For the very first time, we have incorporated disease-gene, disease-miRNA, disease-lncRNA, and drug-PDB associations covering the largest number of biomedical entities related to COVID-19. We have provided examples of and insights into different biomedical entities covered in COVID-19Base to support the research community by incorporating all of these entities under a single platform to provide evidence-based support from the literature. COVID-19Base v3 can be accessed from: https://covidbase-v3.vercel.app/. The GitHub repository for the source code and data dictionaries is available to the community from: https://github.com/91Abdullah/covidbasev3.0.

Long non-coding RNAs in virus-related cancers.

Some viral infections lead to tumourigenesis explained by a variety of underlying molecular mechanisms. Long non-coding RNAs (lncRNAs) have the potential to be added to this list due to their diverse mechanisms in biological functions and disease processes via gene alternation, transcriptional regulation, protein modification, microRNA sponging and interaction with RNA/DNA/proteins. In this review, we summarise the dysregulation and mechanism of lncRNAs in virus-related cancers focussing on Hepatitis B virus, Epstein-Barr virus, Human Papillomavirus. We will also discuss the potential implications of lncRNAs in COVID-19.

LncRNA446 Regulates Tight Junctions by Inhibiting the Ubiquitinated Degradation of Alix after Porcine Epidemic Diarrhea Virus Infection.

Porcine epidemic diarrhea (PED) is a highly contagious disease, caused by porcine epidemic diarrhea virus (PEDV), which causes huge economic losses. Tight junction-associated proteins play an important role during virus infection; therefore, maintaining their integrity may be a new strategy for the prevention and treatment of PEDV. Long noncoding RNAs (lncRNAs) participate in numerous cellular functional activities, yet whether and how they regulate the intestinal barrier against viral infection remains to be elucidated. Here, we established a standard system for evaluating intestinal barrier integrity and then determined the differentially expressed lncRNAs between PEDV-infected and healthy piglets by lncRNA-seq. A total of 111 differentially expressed lncRNAs were screened, and lncRNA446 was identified due to significantly higher expression after PEDV infection. Using IPEC-J2 cells and intestinal organoids as in vitro models, we demonstrated that knockdown of lncRNA446 resulted in increased replication of PEDV, with further damage to intestinal permeability and tight junctions. Mechanistically, RNA pulldown and an RNA immunoprecipitation (RIP) assay showed that lncRNA446 directly binds to ALG-2-interacting protein X (Alix), and lncRNA446 inhibits ubiquitinated degradation of Alix mediated by TRIM25. Furthermore, Alix could bind to ZO1 and occludin and restore the expression level of the PEDV M gene and TJ proteins after lncRNA446 knockdown. Additionally, Alix knockdown and overexpression affects PEDV infection in IPEC-J2 cells. Collectively, our findings indicate that lncRNA446, by inhibiting the ubiquitinated degradation of Alix after PEDV infection, is involved in tight junction regulation. This study provides new insights into the mechanisms of intestinal barrier resistance and damage repair triggered by coronavirus. IMPORTANCE Porcine epidemic diarrhea is an acute, highly contagious enteric viral disease severely affecting the pig industry, for which current vaccines are inefficient due to the high variability of PEDV. Because PEDV infection can lead to severe injury of the intestinal epithelial barrier, which is the first line of defense, a better understanding of the related mechanisms may facilitate the development of new strategies for the prevention and treatment of PED. Here, we demonstrate that the lncRNA446 directly binds one core component of the actomyosin-tight junction complex named Alix and inhibits its ubiquitinated degradation. Functionally, the lncRNA446/Alix axis can regulate the integrity of tight junctions and potentially repair intestinal barrier injury after PEDV infection.

Non-coding RNAs in human health and disease: potential function as biomarkers and therapeutic targets.

Human diseases have been a critical threat from the beginning of human history. Knowing the origin, course of action and treatment of any disease state is essential. A microscopic approach to the molecular field is a more coherent and accurate way to explore the mechanism, progression, and therapy with the introduction and evolution of technology than a macroscopic approach. Non-coding RNAs (ncRNAs) play increasingly important roles in detecting, developing, and treating all abnormalities related to physiology, pathology, genetics, epigenetics, cancer, and developmental diseases. Noncoding RNAs are becoming increasingly crucial as powerful, multipurpose regulators of all biological processes. Parallel to this, a rising amount of scientific information has revealed links between abnormal noncoding RNA expression and human disorders. Numerous non-coding transcripts with unknown functions have been found in addition to advancements in RNA-sequencing methods. Non-coding linear RNAs come in a variety of forms, including circular RNAs with a continuous closed loop (circRNA), long non-coding RNAs (lncRNA), and microRNAs (miRNA). This comprises specific information on their biogenesis, mode of action, physiological function, and significance concerning disease (such as cancer or cardiovascular diseases and others). This study review focuses on non-coding RNA as specific biomarkers and novel therapeutic targets.

Emerging role of microRNAs and long non-coding RNAs in COVID-19 with implications to therapeutics.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection which is commonly known as COVID-19 (COronaVIrus Disease 2019) has creeped into the human population taking tolls of life and causing tremendous economic crisis. It is indeed crucial to gain knowledge about their characteristics and interactions with human host cells. It has been shown that the majority of our genome consists of non-coding RNAs. Non-coding RNAs including micro RNAs (miRNAs) and long non-coding RNAs (lncRNAs) display significant roles in regulating gene expression in almost all cancers and viral diseases. It is intriguing that miRNAs and lncRNAs remarkably regulate the function and expression of major immune components of SARS-CoV-2. MiRNAs act via RNA interference mechanism in which they bind to the complementary sequences of the viral RNA strand, inducing the formation of silencing complex that eventually degrades or inhibits the viral RNA and viral protein expression. LncRNAs have been extensively shown to regulate gene expression in cytokine storm and thus emerges as a critical target for COVID-19 treatment. These lncRNAs also act as competing endogenous RNAs (ceRNAs) by sponging miRNAs and thus affecting the expression of downstream targets during SARS-CoV-2 infection. In this review, we extensively discuss the role of miRNAs and lncRNAs, describe their mechanism of action and their different interacting human targets cells during SARS-CoV-2 infection. Finally, we discuss possible ways how an interference with their molecular function could be exploited for new therapies against SARS-CoV-2.

Transcriptomic study reveals lncRNA-mediated downregulation of innate immune and inflammatory response in the SARS-CoV-2 vaccination breakthrough infections.

Introduction: The emergence of multiple variants of concerns (VOCs) with higher number of Spike mutations have led to enhanced immune escape by the SARS-CoV-2. With the increasing number of vaccination breakthrough (VBT) infections, it is important to understand the possible reason/s of the breakthrough infections. Methods: We performed transcriptome sequencing of 57 VBT and unvaccinated COVID-19 patients, followed by differential expression and co-expression analysis of the lncRNAs and the mRNAs. The regulatory mechanism was highlighted by analysis towards repeat element distribution within the co-expressed lncRNAs, followed by repeats driven homologous interaction between the lncRNAs and the promoter regions of genes from the same topologically associated domains (TAD). Results: We identified 727 differentially expressed lncRNAs (153 upregulated and 574 downregulated) and 338 mRNAs (34 up- and 334 downregulated) in the VBT patients. This includes LUCAT1, MALAT1, ROR1-AS1, UGDH-AS1 and LINC00273 mediated modulation of immune response, whereas MALAT1, NEAT1 and GAS5 regulated inflammatory response in the VBT. LncRNA-mRNA co-expression analysis highlighted 34 lncRNAs interacting with 267 mRNAs. We also observed a higher abundance of Alu, LINE1 and LTRs within the interacting lncRNAs of the VBT patients. These interacting lncRNAs have higher interaction with the promoter region of the genes from the same TAD, compared to the non-interacting lncRNAs with the enrichment of Alu and LINE1 in the gene promoter. Discussion: Significant downregulation and GSEA of the TAD gene suggest Alu and LINE1 driven homologous interaction between the lncRNAs and the TAD genes as a possible mechanism of lncRNA-mediated suppression of innate immune/inflammatory responses and activation of adaptive immune response. The lncRNA-mediated suppression of innate immune/inflammatory responses and activation of adaptive immune response might explain the SARS-CoV-2 breakthrough infections with milder symptoms in the VBT. Besides, the study also highlights repeat element mediated regulation of genes in 3D as another possible way of lncRNA-mediated immune-regulation modulating vaccination breakthroughs milder disease phenotype and shorter hospital stay.

Role of different types of RNA molecules in the severity prediction of SARS-CoV-2 patients.

SARS-CoV-2 pandemic is the current threat of the world with enormous number of deceases. As most of the countries have constraints on resources, particularly for intensive care and oxygen, severity prediction with high accuracy is crucial. This prediction will help the medical society in the selection of patients with the need for these constrained resources. Literature shows that using clinical data in this study is the common trend and molecular data is rarely utilized in this prediction. As molecular data carry more disease related information, in this study, three different types of RNA molecules ( lncRNA, miRNA and mRNA) of SARS-COV-2 patients are used to predict the severity stage and treatment stage of those patients. Using seven different machine learning algorithms along with several feature selection techniques shows that in both phenotypes, feature importance selected features provides the best accuracy along with random forest classifier. Further to this, it shows that in the severity stage prediction miRNA and lncRNA give the best performance, and lncRNA data gives the best in treatment stage prediction. As most of the studies related to molecular data uses mRNA data, this is an interesting finding.

Bioinformatic analysis and preliminary validation of potential therapeutic targets for COVID-19 infection in asthma patients.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 causes coronavirus disease 19 (COVID-19). The number of confirmed cases of COVID-19 is also rapidly increasing worldwide, posing a significant challenge to human safety. Asthma is a risk factor for COVID-19, but the underlying molecular mechanisms of the asthma-COVID-19 interaction remain unclear. METHODS: We used transcriptome analysis to discover molecular biomarkers common to asthma and COVID-19. Gene Expression Omnibus database RNA-seq datasets (GSE195599 and GSE196822) were used to identify differentially expressed genes (DEGs) in asthma and COVID-19 patients. After intersecting the differentially expressed mRNAs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to identify the common pathogenic molecular mechanism. Bioinformatic methods were used to construct protein-protein interaction (PPI) networks and identify key genes from the networks. An online database was used to predict interactions between transcription factors and key genes. The differentially expressed long noncoding RNAs (lncRNAs) in the GSE195599 and GSE196822 datasets were intersected to construct a competing endogenous RNA (ceRNA) regulatory network. Interaction networks were constructed for key genes with RNA-binding proteins (RBPs) and oxidative stress-related proteins. The diagnostic efficacy of key genes in COVID-19 was verified with the GSE171110 dataset. The differential expression of key genes in asthma was verified with the GSE69683 dataset. An asthma cell model was established with interleukins (IL-4, IL-13 and IL-17A) and transfected with siRNA-CXCR1. The role of CXCR1 in asthma development was preliminarily confirmed. RESULTS: By intersecting the differentially expressed genes for COVID-19 and asthma, 393 common DEGs were obtained. GO and KEGG enrichment analyses of the DEGs showed that they mainly affected inflammation-, cytokine- and immune-related functions and inflammation-related signaling pathways. By analyzing the PPI network, we obtained 10 key genes: TLR4, TLR2, MMP9, EGF, HCK, FCGR2A, SELP, NFKBIA, CXCR1, and SELL. By intersecting the differentially expressed lncRNAs for COVID-19 and asthma, 13 common differentially expressed lncRNAs were obtained. LncRNAs that regulated microRNAs (miRNAs) were mainly concentrated in intercellular signal transduction, apoptosis, immunity and other related functional pathways. The ceRNA network suggested that there were a variety of regulatory miRNAs and lncRNAs upstream of the key genes. The key genes could also bind a variety of RBPs and oxidative stress-related genes. The key genes also had good diagnostic value in the verification set. In the validation set, the expression of key genes was statistically significant in both the COVID-19 group and the asthma group compared with the healthy control group. CXCR1 expression was upregulated in asthma cell models, and interference with CXCR1 expression significantly reduced cell viability. CONCLUSIONS: Key genes may become diagnostic and predictive biomarkers of outcomes in COVID-19 and asthma. Video Abstract.

A Concise Review on Dysregulation of LINC00665 in Cancers.

Long Intergenic Non-Protein Coding RNA 665 (LINC00665) is an RNA gene located on the minus strand of chromosome 19. This lncRNA acts as a competing endogenous RNA for miR-4458, miR-379-5p, miR-551b-5p, miR-3619-5p, miR-424-5p, miR-9-5p, miR-214-3p, miR-126-5p, miR-149-3p, miR-379-5p, miR-665, miR-34a-5p, miR-186-5p, miR-138-5p, miR-181c-5p, miR-98, miR-195-5p, miR-224-5p, miR-3619, miR-708, miR-101, miR-1224-5p, miR-34a-5p, and miR-142-5p. Via influencing expression of these miRNAs, it can enhance expression of a number of oncogenes. Moreover, LINC00665 can influence activity of Wnt/ß-Catenin, TGF-ß, MAPK1, NF-κB, ERK, and PI3K/AKT signaling. Function of this lncRNA has been assessed through gain-of-function tests and/or loss-of-function studies. Furthermore, diverse research groups have evaluated its expression levels in tissue samples using microarray and RT-qPCR techniques. In this manuscript, we have summarized the results of these studies and categorized them in three sections, i.e., cell line studies, animal studies, and investigations in clinical samples.

Integrated bioinformatics to identify potential key biomarkers for COVID-19-related chronic urticaria.

Background: A lot of studies have revealed that chronic urticaria (CU) is closely linked with COVID-19. However, there is a lack of further study at the gene level. This research is aimed to investigate the molecular mechanism of COVID-19-related CU via bioinformatic ways. Methods: The RNA expression profile datasets of CU (GSE72540) and COVID-19 (GSE164805) were used for the training data and GSE57178 for the verification data. After recognizing the shared differently expressed genes (DEGs) of COVID-19 and CU, genes enrichment, WGCNA, PPI network, and immune infiltration analyses were performed. In addition, machine learning LASSO regression was employed to identify key genes from hub genes. Finally, the networks, gene-TF-miRNA-lncRNA, and drug-gene, of key genes were constructed, and RNA expression analysis was utilized for verification. Results: We recognized 322 shared DEGs, and the functional analyses displayed that they mainly participated in immunomodulation of COVID-19-related CU. 9 hub genes (CD86, FCGR3A, AIF1, CD163, CCL4, TNF, CYBB, MMP9, and CCL3) were explored through the WGCNA and PPI network. Moreover, FCGR3A, TNF, and CCL3 were further identified as key genes via LASSO regression analysis, and the ROC curves confirmed the dependability of their diagnostic value. Furthermore, our results showed that the key genes were significantly associated with the primary infiltration cells of CU and COVID-19, such as mast cells and macrophages M0. In addition, the key gene-TF-miRNA-lncRNA network was constructed, which contained 46 regulation axes. And most lncRNAs of the network were proved to be a significant expression in CU. Finally, the key gene-drug interaction network, including 84 possible therapeutical medicines, was developed, and their protein-protein docking might make this prediction more feasible. Conclusions: To sum up, FCGR3A, TNF, and CCL3 might be potential biomarkers for COVID-19-related CU, and the common pathways and related molecules we explored in this study might provide new ideas for further mechanistic research.

The lncRNAs involved in regulating the RIG-I signaling pathway.

Understanding the targets and interactions of long non-coding RNAs (lncRNAs) related to the retinoic acid-inducible gene-I (RIG-I) signaling pathway is essential for developing interventions, which would enable directing the host inflammatory response regulation toward protective immunity. In the RIG-I signaling pathway, lncRNAs are involved in the important processes of ubiquitination, phosphorylation, and glycolysis, thus promoting the transport of the interferon regulatory factors 3 and 7 (IRF3 and IRF7) and the nuclear factor kappa B (NF-κB) into the nucleus, and activating recruitment of type I interferons (IFN-I) and inflammatory factors to the antiviral action site. In addition, the RIG-I signaling pathway has recently been reported to contain the targets of coronavirus disease-19 (COVID-19)-related lncRNAs. The molecules in the RIG-I signaling pathway are directly regulated by the lncRNA-microRNAs (miRNAs)-messenger RNA (mRNA) axis. Therefore, targeting this axis has become a novel strategy for the diagnosis and treatment of cancer. In this paper, the studies on the regulation of the RIG-I signaling pathway by lncRNAs during viral infections and cancer are comprehensively analyzed. The aim is to provide a solid foundation of information for conducting further detailed studies on lncRNAs and RIG-I in the future and also contribute to clinical drug development.

Vascular dysfunction in COVID-19 patients: update on SARS-CoV-2 infection of endothelial cells and the role of long non-coding RNAs.

Although COVID-19 is primarily a respiratory disease, it may affect also the cardiovascular system. COVID-19 patients with cardiovascular disorder (CVD) develop a more severe disease course with a significantly higher mortality rate than non-CVD patients. A common denominator of CVD is the dysfunction of endothelial cells (ECs), increased vascular permeability, endothelial-to-mesenchymal transition, coagulation, and inflammation. It has been assumed that clinical complications in COVID-19 patients suffering from CVD are caused by SARS-CoV-2 infection of ECs through the angiotensin-converting enzyme 2 (ACE2) receptor and the cellular transmembrane protease serine 2 (TMPRSS2) and the consequent dysfunction of the infected vascular cells. Meanwhile, other factors associated with SARS-CoV-2 entry into the host cells have been described, including disintegrin and metalloproteinase domain-containing protein 17 (ADAM17), the C-type lectin CD209L or heparan sulfate proteoglycans (HSPG). Here, we discuss the current data about the putative entry of SARS-CoV-2 into endothelial and smooth muscle cells. Furthermore, we highlight the potential role of long non-coding RNAs (lncRNAs) affecting vascular permeability in CVD, a process that might exacerbate disease in COVID-19 patients.