Advances of Long Non-Coding RNAs as Potential Biomarkers for Tuberculosis: New Hope for Diagnosis?

Long non-coding RNA transcripts in Mycobacterium tuberculosis-host interactions

Tuberculosis (TB) persists as a significant health threat, affecting millions of people all over the world. Despite years of control measures, the elimination of TB has become a difficult task as morbidity and mortality rates remain unaffected for several years. Developing new diagnostics and therapeutics is paramount to keeping TB under control. However, it largely depends upon understanding the pathogenic mechanisms of Mycobacterium tuberculosis (Mtb), the causative agent of TB. Mtb is an intracellular pathogen capable of subverting the defensive functions of the immune cells, and it can survive and multiply within humans' mononuclear phagocytes. Emerging evidence indicates that long non-coding RNAs (lncRNAs), a class of RNA molecules with limited coding potential, are critical players in this intricate game as they regulate gene expression at transcriptional and post-transcriptional levels and also by chromatin modification. Moreover, they have been shown to regulate cellular processes by controlling the function of other molecules, such as DNA, RNA, and protein, through binding with them. Recent studies have shown that lncRNAs are differentially regulated in the tissues of TB patients and cells infected in vitro with Mtb. Some dysregulated lncRNAs are associated with essential roles in modulating immune response, apoptosis, and autophagy in the host cells, adding a new dimension to TB pathogenesis. In this article, we provide a comprehensive review of the recent literature in this field and the impact of lncRNAs in unraveling pathogenic mechanisms in TB. We also discuss how the studies involving lncRNAs provide insight into TB pathogenesis, especially Mtb-host interactions.

Comparative Transcriptomics Reveal Differential Expression of Coding and Non-Coding RNAs in Clinical Strains of Mycobacterium tuberculosis

Coding and non-coding RNAs (ncRNAs) are potential novel markers that can be exploited for TB diagnostics in the fight against Mycobacterium tuberculosis. The current study investigated the mechanisms of transcript regulation and ncRNA signatures through Total RNA Seq and small (smRNA) RNA Seq followed by Bioinformatics analysis in Beijing and F15/LAM4/KZN (KZN) clinical strains compared to the laboratory strain. Total RNA Seq revealed differential regulation of RNA transcripts in Beijing (n = 1095) and KZN (n = 856) strains compared to the laboratory H37Rv strain. The KZN vs. H37Rv coding transcripts uniquely enriched fatty acids, steroid degradation, fructose, and mannose metabolism as well as a bacterial secretion system. In contrast, Tuberculosis and biosynthesis of siderophores KEGG pathways were enriched by the Beijing vs. H37Rv-specific transcripts. Novel sense and antisense ncRNAs, as well as the expression of these transcripts, were observed, and these targeted RNA transcripts are involved in cell wall synthesis and bacterial metabolism in a strain-specific manner. RNA transcripts identified in the current study offer insights into gene regulation of transcripts involved in the growth and metabolism of the clinically relevant KZN and Beijing strains compared to the laboratory H37Rv strain and thus can be exploited in the fight against Tuberculosis.

Host Long Noncoding RNAs as Key Players in Mycobacteria-Host Interactions

Mycobacterial infections, caused by various species within the Mycobacterium genus, remain one of the main challenges to global health across the world. Understanding the complex interplay between the host and mycobacterial pathogens is essential for developing effective diagnostic and therapeutic strategies. Host long noncoding RNAs (lncRNAs) have emerged as key regulators in cellular response to bacterial infections within host cells. This review provides an overview of the intricate relationship between mycobacterial infections and host lncRNAs in the context of Mycobacterium tuberculosis and non-tuberculous mycobacterium (NTM) infections. Accumulation of evidence indicates that host lncRNAs play a critical role in regulating cellular response to mycobacterial infection within host cells, such as macrophages, the primary host cells for mycobacterial intracellular survival. The expression of specific host lncRNAs has been implicated in the pathogenesis of mycobacterial infections, providing potential targets for the development of novel host-directed therapies and biomarkers for TB diagnosis. In summary, this review aims to highlight the current state of knowledge regarding the involvement of host lncRNAs in mycobacterial infections. It also emphasizes their potential application as novel diagnostic biomarkers and therapeutic targets.

The significance of long chain non-coding RNA signature genes in the diagnosis and management of sepsis patients, and the development of a prediction model

Background

Sepsis is a life-threatening organ dysfunction condition produced by dysregulation of the host response to infection. It is now characterized by a high clinical morbidity and mortality rate, endangering patients' lives and health. The purpose of this study was to determine the value of Long chain non-coding RNA (LncRNA) RP3_508I15.21, RP11_295G20.2, and LDLRAD4_AS1 in the diagnosis of adult sepsis patients and to develop a Nomogram prediction model.

Methods

We screened adult sepsis microarray datasets GSE57065 and GSE95233 from the GEO database and performed differentially expressed genes (DEGs), weighted gene co-expression network analysis (WGCNA), and machine learning methods to find the genes by random forest (Random Forest), least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM), respectively, with GSE95233 as the training set and GSE57065 as the validation set. Differentially expressed genes (DEGs), weighted gene co-expression network analysis (WGCNA), boxplot statistical analysis, and ROC analysis by Random Forest, Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine (SVM) machine learning methods were used to identify characteristic genes and build the Nomogram Prediction model.

Results

GSE95233 yielded a total of 1069 genes, 102 of which were sepsis-related and 22 of which were non-sepsis controls. GSE57065 yielded a total of 899 genes, with 467 up-regulated and 432 down-regulated, including 82 sepsis-related genes and 25 non-sepsis control genes. WGCNA analysis excluded outlier samples, leaving 2,029 genes for relationship analysis between sepsis- and non-sepsis patient-associated LncRNA network representation modules, as well as Wein plots of differential genes versus genes in key modules of weighted co-expression network analysis to analyze gene intersections. Machine Learning found the sepsis-related characteristic LncRNAs RP3-508I15.21, RP11-295G20.2, LDLRAD4-AS1, and CTD-2542L18.1. The datasets GSE95233 and GSE57065 were analyzed using Boxplot against the screened genes listed above, respectively. The p-value between the sepsis and non-sepsis groups was less than 0.05, indicating that anomalies were statistically significant. CTD-2542L18.1 in dataset GSE57065 had an AUC value of 0.638, which was less than 0.7 and did not indicate diagnostic significance, but RP3-508I15.21, RP11-295G20.2, and LDLRAD4-AS1 had AUC values more than 0.7 after ROC analysis. All four sepsis-associated LncRNA ROC analyses in dataset GSE95233 exhibited AUC values more than 0.7, indicating diagnostic significance.

Conclusion

LncRNAs RP3_508I15.21, RP11_295G20.2, and LDLRAD4_AS1 have some utility in the diagnosis and treatment of adult sepsis patients, as well as some reference importance in guiding the diagnosis and treatment of clinical sepsis.

Review on Long Non-Coding RNAs as Biomarkers and Potentially Therapeutic Targets for Bacterial Infections

The confrontation between humans and bacteria is ongoing, with strategies for combating bacterial infections continually evolving. With the advancement of RNA sequencing technology, non-coding RNAs (ncRNAs) associated with bacterial infections have garnered significant attention. Recently, long ncRNAs (lncRNAs) have been identified as regulators of sterile inflammatory responses and cellular defense against live bacterial pathogens. They are involved in regulating host antimicrobial immunity in both the nucleus and cytoplasm. Increasing evidence indicates that lncRNAs are critical for the intricate interactions between host and pathogen during bacterial infections. This paper emphatically elaborates on the potential applications of lncRNAs in clinical hallmarks, cellular damage, immunity, virulence, and drug resistance in bacterial infections in greater detail. Additionally, we discuss the challenges and limitations of studying lncRNAs in the context of bacterial infections and highlight clear directions for this promising field.

Identification of important modules and biomarkers in tuberculosis based on WGCNA

Background

Tuberculosis (TB) is a significant public health concern, particularly in China. Long noncoding RNAs (lncRNAs) can provide abundant pathological information regarding etiology and could include candidate biomarkers for diagnosis of TB. However, data regarding lncRNA expression profiles and specific lncRNAs associated with TB are limited.

Methods

We performed ceRNA-microarray analysis to determine the expression profile of lncRNAs in peripheral blood mononuclear cells (PBMCs). Weighted gene co-expression network analysis (WGCNA) was then conducted to identify the critical module and genes associated with TB. Other bioinformatics analyses, including Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and co-expression networks, were conducted to explore the function of the critical module. Finally, real-time quantitative polymerase chain reaction (qPCR) was used to validate the candidate biomarkers, and receiver operating characteristic analysis was used to assess the diagnostic performance of the candidate biomarkers.

Results

Based on 8 TB patients and 9 healthy controls (HCs), a total of 1,372 differentially expressed lncRNAs were identified, including 738 upregulated lncRNAs and 634 downregulated lncRNAs. Among all lncRNAs and mRNAs in the microarray, the top 25% lncRNAs (3729) and top 25% mRNAs (2824), which exhibited higher median expression values, were incorporated into the WGCNA. The analysis generated 16 co-expression modules, among which the blue module was highly correlated with TB. GO and KEGG analyses showed that the blue module was significantly enriched in infection and immunity. Subsequently, considering module membership values (>0.85), gene significance values (>0.90) and fold-change value (>2 or < 0.5) as selection criteria, the top 10 upregulated lncRNAs and top 10 downregulated lncRNAs in the blue module were considered as potential biomarkers. The candidates were then validated in an independent validation sample set (31 TB patients and 32 HCs). The expression levels of 8 candidates differed significantly between TB patients and HCs. The lncRNAs ABHD17B (area under the curve [AUC] = 1.000) and ENST00000607464.1 (AUC = 1.000) were the best lncRNAs in distinguishing TB patients from HCs.

Conclusion

This study characterized the lncRNA profiles of TB patients and identified a significant module associated with TB as well as novel potential biomarkers for TB diagnosis.