Circular RNA hsa_circ_0001380 in peripheral blood as a potential diagnostic biomarker for active pulmonary tuberculosis

CircRNA: a rising therapeutic strategy for lung injury induced by pulmonary toxicants

Lung injury has been a serious medical problem that requires new therapeutic approaches and biomarkers. Circular RNAs (circRNAs) are non-coding RNAs (ncRNAs) that exist widely in eukaryotes. CircRNAs are single-stranded RNAs that form covalently closed loops. CircRNAs are significant gene regulators that have a role in the development, progression, and therapy of lung injury by controlling transcription, translating into protein, and sponging microRNAs (miRNAs) and proteins. Although the study of circRNAs in lung injury caused by pulmonary toxicants is just beginning, several studies have revealed their expression patterns. The function that circRNAs perform in relation to pulmonary toxicants (severe acute respiratory distress syndrome coronavirus-2 (SARS-CoV-2), drug abuse, PM2.5, and cigarette smoke) is the main topic of this review. A variety of circRNAs can serve as potential biomarkers of lung injury. In this review, the biogenesis, properties, and biological functions of circRNAs were concluded, and the relationship between circRNAs and pulmonary toxicants was discussed. It is expected that the new ideas and potential treatment targets that circRNAs provide would be beneficial to research into the molecular mechanisms behind lung injury.

Cargoes of exosomes function as potential biomarkers for Mycobacterium tuberculosis infection

Exosomes as double-membrane vesicles contain various contents of lipids, proteins, mRNAs and non-coding RNAs, and involve in multiple physiological processes, for instance intercellular communication and immunomodulation. Currently, numerous studies found that the components of exosomal proteins, nucleic acids or lipids released from host cells are altered following infection with Mycobacterium tuberculosis. Exosomal contents provide excellent biomarkers for the auxiliary diagnosis, efficacy evaluation, and prognosis of tuberculosis. This study aimed to review the current literatures detailing the functions of exosomes in the procedure of M. tuberculosis infection, and determine the potential values of exosomes as biomarkers to assist in the diagnosis and monitoring of tuberculosis.

Circular RNA circTRAPPC6B Enhances IL-6 and IL-1β Expression and Repolarizes Mycobacteria Induced Macrophages from M2- to M1-Like Phenotype by Targeting miR-892c-3p

Mycobacterium tuberculosis (Mtb) infection elicits macrophage polarization into M2 phenotype to block the host's protective immune response. However, it remains unclear how Mtb regulates macrophage polarization. Recent studies have suggested that noncoding RNA may play a role in macrophage polarization. In this study, we investigated the potential involvement of circTRAPPC6B, a circular RNA that is downregulated in tuberculosis (TB) patients, in regulating macrophage polarization. We found that Mtb infection downregulated M1-related IL-6 and IL-1β while highly expressed M2-related CCL22 and CD163. Overexpressed circTRAPPC6B had switched Mtb-infected macrophages from M2- to M1-like phenotype, accompanied by upregulation of IL-6 and IL-1β. Meanwhile overexpressed circTRAPPC6B significantly inhibited Mtb growth in macrophages. Our findings suggest that circTRAPPC6B may regulate macrophage polarization by targeting miR-892c-3p, which is highly expressed in TB patients and M2-like macrophages. And miR-892c-3p inhibitor decreased intracellular Mtb growth in macrophages. Thus, TB-inhibited circTRAPPC6B could specifically induce IL-6 and IL-1β expression to switch/antagonize Mtb-induced macrophage polarization from M2- to M1-like phenotype by targeting miR-892c-3p, leading to enhanced host clearance of Mtb. Our results reveal a potential role for circTRAPPC6B in regulating macrophage polarization during Mtb infection and provide new insights into the molecular mechanisms underlying host defense against Mtb.

Analysis of the noncoding RNA regulatory networks of H37Rv- and H37Rv△1759c-infected macrophages

Noncoding RNAs regulate the process of Mycobacterium tuberculosis (M. tb) infecting the host, but there is no simultaneous transcriptional information of long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) and the global regulatory networks of non-coding RNA. Rv1759c, a virulence factor, is a member of protein family containing the proline-glutamic acid (PE) in M. tb, which can increase M. tb survival. To reveal the noncoding RNA regulatory networks and the effect of Rv1759c on non-coding RNA expression during M. tb infection, we collected samples of H37Rv- and H37Rv△1759c-infected macrophages and explored the full transcriptome expression profile. We found 356 mRNAs, 433 lncRNAs, 168 circRNAs, and 12 miRNAs differentially expressed during H37Rv infection, 356 mRNAs, 433 lncRNAs, 168 circRNAs, and 12 miRNAs differentially expressed during H37Rv△1759c infection. We constructed lncRNA/circRNA-miRNA-mRNA regulatory networks during H37Rv and H37Rv△1759c infection. We demonstrated the role of one of the hubs of the networks, hsa-miR-181b-3p, for H37Rv survival in macrophages. We discovered that the expression changes of 68 mRNAs, 92 lncRNAs, 26 circRNAs, and 3 miRNAs were only related to the deletion of Rv1759c by comparing the transcription profiles of H37Rv and H37Rv△1759c. Here, our study comprehensively characterizes the transcriptional profiles in THP1-derived-macrophages infected with H37Rv and H37Rv△1759c, which provides support and new directions for in-depth exploration of noncoding RNA and PE/PPE family functions during the infection process.

Emerging roles of circular RNAs in tuberculosis

Tuberculosis (TB) remains a major global health issue, resulting in around 1.5 million people deaths each year. Better diagnostic and therapeutic tools are urgently needed. Circular RNAs (circRNAs) are a new class of noncoding RNAs with a covalently closed structure, and exhibit a tissue-, cell-, and developmental stage-specific expression pattern. Recently, circRNAs were thought to be regulatory molecules implicated in the onset and progression of a series of human diseases including tuberculosis. In tuberculosis, circRNAs have been shown to regulate host anti-TB immune responses, such as decreasing monocyte apoptosis, enhancing autophagy and promoting macrophage polarization. Importantly, circRNAs are physically stable and abundant in several types of body fluids. Therefore they are considered as promising minimally-invasive biomarkers. In this review, we focus on the recent advances in the immune regulatory roles of circRNAs, as well as their potential diagnostic value in TB.

Promising Roles of Circular RNAs as Biomarkers and Targets for Potential Diagnosis and Therapy of Tuberculosis

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb) infection, remains one of the most threatening infectious diseases worldwide. A series of challenges still exist for TB prevention, diagnosis and treatment, which therefore require more attempts to clarify the pathological and immunological mechanisms in the development and progression of TB. Circular RNAs (circRNAs) are a large class of non-coding RNA, mostly expressed in eukaryotic cells, which are generated by the spliceosome through the back-splicing of linear RNAs. Accumulating studies have identified that circRNAs are widely involved in a variety of physiological and pathological processes, acting as the sponges or decoys for microRNAs and proteins, scaffold platforms for proteins, modulators for transcription and special templates for translation. Due to the stable and widely spread characteristics of circRNAs, they are expected to serve as promising prognostic/diagnostic biomarkers and therapeutic targets for diseases. In this review, we briefly describe the biogenesis, classification, detection technology and functions of circRNAs, and, in particular, outline the dynamic, and sometimes aberrant changes of circRNAs in TB. Moreover, we further summarize the recent progress of research linking circRNAs to TB-related pathogenetic processes, as well as the potential roles of circRNAs as diagnostic biomarkers and miRNAs sponges in the case of Mtb infection, which is expected to enhance our understanding of TB and provide some novel ideas about how to overcome the challenges associated TB in the future.

Identification of Key CircRNAs Related to Pulmonary Tuberculosis Based on Bioinformatics Analysis

Pulmonary tuberculosis (TB) is a chronic infectious disease that is caused by respiratory infections, principally Mycobacterium tuberculosis. Increasingly, studies have shown that circular (circ)RNAs play regulatory roles in different diseases through different mechanisms. However, their roles and potential regulatory mechanisms in pulmonary TB remain unclear. In this study, we analyzed circRNA sequencing data from adjacent normal and diseased tissues from pulmonary TB patients and analyzed the differentially expressed genes. We then constructed machine learning models and used single-factor analysis to identify hub circRNAs. We downloaded the pulmonary TB micro (mi)RNA (GSE29190) and mRNA (GSE83456) gene expression datasets from the Gene Expression Omnibus database and performed differential expression analysis to determine the differentially expressed miRNAs and mRNAs. We also constructed a circRNA–miRNA–mRNA interaction network using Cytoscape. Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were used to predict the biological functions of the identified RNAs and determine hub genes. Then, the STRING database and cytoHubba were used to construct protein-protein interaction networks. The results showed 125 differentially expressed circRNAs in the adjacent normal and diseased tissues of pulmonary TB patients. Among them, we identified three hub genes associated with the development of pulmonary TB: hsa_circ_0007919 (upregulated), hsa_circ_0002419 (downregulated), and hsa_circ_0005521 (downregulated). Through further screening, we determined 16 mRNAs of potential downstream genes for hsa-miR-409-5p and hsa_circ_0005521 and established an interaction network. This network may have important roles in the occurrence and development of pulmonary TB. We constructed a model with 100% prediction accuracy by machine learning and single-factor analysis. We constructed a protein-protein interaction network among the top 50 hub mRNAs, with FBXW7 scoring the highest and SOCS3 the second highest. These results may provide a new reference for the identification of candidate markers for the early diagnosis and treatment of pulmonary TB.