Systematic evaluation, verification and comparison of tuberculosis-related non-coding RNA diagnostic panels

p50-associated Cyclooxygenase-2 Extragenic RNA (PACER) and Long Non-coding RNA 13 (LNC13) as potential biomarkers for monitoring tuberculosis treatment

Gaps in early and accurate diagnosis, effective drug control, and treatment monitoring are hindering the global eradication effort of tuberculosis. This infectious disease has become the deadliest worldwide before the outbreak of Covid-19. The search for new molecular biomarkers of tuberculosis will help to reverse this trend. Long non-coding RNAs (lncRNAs) have emerged as important regulators of the host immune response to infection, hence their link with the etiology and diagnosis of tuberculosis has attracted some attention from the research community. However, very little is known about their potential for the monitoring of tuberculosis treatment. This study aimed at assessing the potential of two lncRNAs: p50-associated Cyclooxygenase-2 Extragenic RNA (PACER) and Long Non-coding RNA 13 (LNC13) in the monitoring of tuberculosis treatment. This was a cross-sectional study carried out in Douala, Cameroon from December 2020 to August 2021. A quantitative real-time polymerase chain reaction followed by Cq analysis using the Livak method were performed to measure the relative expression levels of PACER and LNC13 in whole blood of healthy controls, patients with active pulmonary tuberculosis at the initiation of treatment, after two, five, and six months into treatment. Receiver Operating Characteristic curves analysis was used to assess the ability of targeted lncRNAs to discriminate among those groups. The study showed that the lncRNAs PACER and LNC13 were significantly upregulated in patients with active pulmonary tuberculosis at the initiation of treatment than in healthy controls. The expression levels of the two lncRNAs were significantly downregulated in patients during the treatment as compared to the active pulmonary tuberculosis patients. However, the expression levels of the lncRNAs PACER and LNC13 in whole blood of patients after six months of treatment were similar to those in healthy controls. Similarly, lncRNAs PACER and LNC13 showed very good performance in distinguishing between active tuberculosis patients and healthy controls as well as in differentiating between newly diagnosed active tuberculosis patients and those under treatment. Interestingly, those lncRNAs could not discriminate healthy controls from patients after six months of treatment. The lncRNAs PACER and LNC13 are therefore potential biomarkers for the monitoring of tuberculosis treatment.

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.

Validation of Serotransferrin in the Serum as Candidate Biomarkers for the Diagnosis of Pulmonary Tuberculosis by Label-Free LC/MS

This study aimed to identify secreted protein biomarkers in serum from the label-free LC/MS proteomics of neutrophils in pulmonary tuberculosis (TB) patients for the diagnosis biomarkers of TB label-free LC/MS. The proteomic profiles of neutrophils from 15 active TB patients and 15 healthy controls (HCs) were analyzed using label-free LC/MS. We identified 358 differentially expressed proteins preliminarily, including 279 up-regulated proteins and 79 down-regulated proteins. Thirty-eight differentially expressed secreted proteins involved in the progress of platelet degranulation between TB patients and HCs were focused. Of these, serotransferrin (TRF), alpha-2-macroglobulin (AMG), alpha-1-antitrypsin (AAT), alpha-1-acid glycoprotein 1 (AAG), alpha-1-acid glycoprotein 2 (AGP2), and alpha-1B-glycoprotein (A1BG) were selected for further verification in the serum of additional 134 TB patients and 138 HCs by nephelometry and ELISA in the training set. Statistically significant differences of TRF (P < 0.0001), AAT (P < 0.0001), AAG (P < 0.0001), AGP2 (P < 0.0001), and A1BG (P = 0.0003) were observed. The serum concentration of TRF was down-regulated in TB patients compared with healthy controls, which was coincident with the proteomics results. An additional validation of TRF was performed in an independent cohort of patients with active TB (n = 46), patients with lung cancer (n = 37), 20 HCs, and patients with pneumonia (n = 35) in the test set by nephelometry. The serum expression levels of TRF in the TB patients showed lower levels compared with those in patients with pneumonia (P = 0.0125), lung cancer (P = 0.0005), HCs (P < 0.0001), and the non-TB controls (P < 0.0001). Furthermore, the AUC value of TRF was 0.647 with 90.22% sensitivity and 42.86% specificity in discriminating the TB group from the pneumonia group, 0.702 with 93.48% sensitivity and 47.16% specificity in discriminating the TB group from the lung cancer group, 0.894 with 91.30% sensitivity and 71.62% specificity in discriminating the TB group from all HCs, and 0.792 with 91.30% sensitivity and 58.90% specificity in discriminating the TB group from the non-TB controls. This study obtained the proteomic profiles of neutrophils in the TB patients and HCs, which contribute to a better understanding of the pathogenesis molecules existing in the neutrophils of pulmonary tuberculosis and provide candidate biomarkers for the diagnosis of pulmonary tuberculosis.

Long Non-coding RNAs in Tuberculosis: From Immunity to Biomarkers

Tuberculosis (TB) caused by Mycobacterium tuberculosis (Mtb) is the leading lethal infectious disease with 1.3 million deaths in 2020. Despite significant advances have been made in detection techniques and therapeutic approaches for tuberculosis, no suitable diagnostic tools are available for early and precise screening. Many studies have reported that Long non-coding RNAs (lncRNAs) play a regulatory role in gene expression in the host immune response against Mtb. Dysregulation of lncRNAs expression patterns associated with immunoregulatory pathways arose in mycobacterial infection. Meanwhile, host-induced lncRNAs regulate antibacterial processes such as apoptosis and autophagy to limit bacterial proliferation. In this review, we try to summarize the latest reports on how dysregulated expressed lncRNAs influence host immune response in tuberculosis infection. We also discuss their potential clinical prospects for tuberculosis diagnosis and development as molecular biomarkers.

Deciphering a TB-related DNA methylation biomarker and constructing a TB diagnostic classifier

We systemically identified tuberculosis (TB)-related DNA methylation biomarkers and further constructed classifiers for TB diagnosis. TB-related DNA methylation datasets were searched through October 3, 2020. Limma and DMRcate were employed to identify differentially methylated probes (DMPs) and regions (DMRs). Machine learning methods were used to construct classifiers. The performance of the classifiers was evaluated in discovery datasets and a prospective independent cohort. Eighty-nine DMPs and 24 DMRs were identified based on 67 TB patients and 45 healthy controls from 4 datasets. Nine and three DMRs were selected by elastic net regression and logistic regression, respectively. Among the selected DMRs, two regions (chr3: 195635643-195636243 and chr6: 29691631-29692475) were differentially methylated in the independent cohort (p = 4.19 × 10^-5 and 0.024, respectively). Among the ten classifiers, the 3-DMR logistic regression classifier exhibited the strongest performance. The sensitivity, specificity, and area under the curve were, respectively, 79.1%, 84.4%, and 0.888 in the discovery datasets and 64.5%, 90.3%, and 0.838 in the independent cohort. The differential diagnostic ability of this classifier was also assessed. Collectively, these data showed that DNA methylation might be a promising TB diagnostic biomarker. The 3-DMR logistic regression classifier is a potential clinical tool for TB diagnosis, and further validation is needed.

Non-Coding RNAs in the Etiology and Control of Major and Neglected Human Tropical Diseases

Non-coding RNAs (ncRNAs) including microRNAs (miRs) and long non-coding RNAs (lncRNAs) have emerged as key regulators of gene expression in immune cells development and function. Their expression is altered in different physiological and disease conditions, hence making them attractive targets for the understanding of disease etiology and the development of adjunctive control strategies, especially within the current context of mitigated success of control measures deployed to eradicate these diseases. In this review, we summarize our current understanding of the role of ncRNAs in the etiology and control of major human tropical diseases including tuberculosis, HIV/AIDS and malaria, as well as neglected tropical diseases including leishmaniasis, African trypanosomiasis and leprosy. We highlight that several ncRNAs are involved at different stages of development of these diseases, for example miR-26-5p, miR-132-3p, miR-155-5p, miR-29-3p, miR-21-5p, miR-27b-3p, miR-99b-5p, miR-125-5p, miR-146a-5p, miR-223-3p, miR-20b-5p, miR-142-3p, miR-27a-5p, miR-144-5p, miR-889-5p and miR-582-5p in tuberculosis; miR-873, MALAT1, HEAL, LINC01426, LINC00173, NEAT1, NRON, GAS5 and lincRNA-p21 in HIV/AIDS; miR-451a, miR-let-7b and miR-106b in malaria; miR-210, miR-30A-5P, miR-294, miR-721 and lncRNA 7SL RNA in leishmaniasis; and miR-21, miR-181a, miR-146a in leprosy. We further report that several ncRNAs were investigated as diseases biomarkers and a number of them showed good potential for disease diagnosis, including miR-769-5p, miR-320a, miR-22-3p, miR-423-5p, miR-17-5p, miR-20b-5p and lncRNA LOC152742 in tuberculosis; miR-146b-5p, miR-223, miR-150, miR-16, miR-191 and lncRNA NEAT1 in HIV/AIDS; miR-451 and miR-16 in malaria; miR-361-3p, miR-193b, miR-671, lncRNA 7SL in leishmaniasis; miR-101, miR-196b, miR-27b and miR-29c in leprosy. Furthermore, some ncRNAs have emerged as potential therapeutic targets, some of which include lncRNAs NEAT1, NEAT2 and lnr6RNA, 152742 in tuberculosis; MALAT1, HEAL, SAF, lincRNA-p21, NEAT1, GAS5, NRON, LINC00173 in HIV/AIDS; miRNA-146a in malaria. Finally, miR-135 and miR-126 were proposed as potential targets for the development of therapeutic vaccine against leishmaniasis. We also identify and discuss knowledge gaps that warrant for increased research work. These include investigation of the role of ncRNAs in the etiology of African trypanosomiasis and the assessment of the diagnostic potential of ncRNAs for malaria, and African trypanosomiasis. The potential targeting of ncRNAs for adjunctive therapy against tuberculosis, leishmaniasis, African trypanosomiasis and leprosy, as well as their targeting in vaccine development against tuberculosis, HIV/AIDS, malaria, African trypanosomiasis and leprosy are also new avenues to explore.

Novel Long Non-coding RNA and LASSO Prediction Model to Better Identify Pulmonary Tuberculosis: A Case-Control Study in China

Introduction

The insufficient understanding and misdiagnosis of clinically diagnosed pulmonary tuberculosis (PTB) without an aetiological evidence is a major problem in the diagnosis of tuberculosis (TB). This study aims to confirm the value of Long non-coding RNA (lncRNA) n344917 in the diagnosis of PTB and construct a rapid, accurate, and universal prediction model.

Methods

A total of 536 patients were prospectively and consecutively recruited, including clinically diagnosed PTB, PTB with an aetiological evidence and non-TB disease controls, who were admitted to West China hospital from Dec 2014 to Dec 2017. The expression levels of lncRNA n344917 of all patients were analyzed using reverse transcriptase quantitative real-time PCR. Then, the laboratory findings, electronic health record (EHR) information and expression levels of n344917 were used to construct a prediction model through the Least Absolute Shrinkage and Selection Operator algorithm and multivariate logistic regression.

Results

The factors of n344917, age, CT calcification, cough, TBIGRA, low-grade fever and weight loss were included in the prediction model. It had good discrimination (area under the curve = 0.88, cutoff = 0.657, sensitivity = 88.98%, specificity = 86.43%, positive predictive value = 85.61%, and negative predictive value = 89.63%), consistency and clinical availability. It also showed a good replicability in the validation cohort. Finally, it was encapsulated as an open-source and free web-based application for clinical use and is available online at https://ziruinptb.shinyapps.io/shiny/.

Conclusion

Combining the novel potential molecular biomarker n344917, laboratory and EHR variables, this web-based prediction model could serve as a user-friendly, accurate platform to improve the clinical diagnosis of PTB.

Systematic evaluation, verification and comparison of tuberculosis-related non-coding RNA diagnostic panels

We systematically summarized tuberculosis (TB)‐related non‐coding RNA (ncRNA) diagnostic panels, validated and compared panel performance. We searched TB‐related ncRNA panels in PubMed, OVID and Web of Science up to 28 February 2020, and available datasets in GEO, SRA and EBI ArrayExpress up to 1 March 2020. We rebuilt models and synthesized the results of each model in validation sets by bivariate mixed models. Specificity at 90% sensitivity, area under curve (AUC) and inconsistence index (I²) were calculated. NcRNA biofunctions were analysed. Nineteen models based on 18 ncRNA panels (miRNA, lncRNA, circRNA and snoRNA panels) and 18 datasets were included. Limited available datasets only allowed to evaluate miRNA panels further. Cui 2017 and Latorre 2015 exhibited specificity >70% at 90% sensitivity and AUC >80% in all validation sets. Cui 2017 showed higher specificity at 90% sensitivity (92%) and AUC (95%) and lower heterogeneity (I² = 0%) in ethological‐confirmation validation sets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis indicated that most ncRNAs in panels involved in immune cell activation, oxidative stress, and Wnt and MAPK signalling pathway. Cui 2017 outperformed other models in both all available and aetiological‐confirmed validation sets, meeting the criteria of target product profile of WHO. This work provided a basis for clinical choice of TB‐related ncRNA diagnostic panels to a certain extent.