Background: There are ever increasing researches implying that noncoded RNAs (ncRNAs) specifically circular RNAs (circRNAs) and microRNAs (miRNAs) in exosomes play vital roles in respiratory disease. However, the detailed mechanisms persist to be unclear in mycobacterial infection. Methods: In order to detect circRNAs and miRNAs expression pattern and potential biological function in tuberculosis, we performed immense parallel sequencing for exosomal ncRNAs from THP-1-derived macrophages infected by Mycobacterium tuberculosis H37Ra, Mycobacterium bovis BCG and control Streptococcus pneumonia, respectively and uninfected normal cells. Besides, THP-1-derived macrophages were used to verify the validation of differential miRNAs, and monocytes from PBMCs and clinical plasma samples were used to further validate differentially expressed miR-185-5p. Results: Many exosomal circRNAs and miRNAs associated with tuberculosis infection were recognized. Extensive enrichment analyses were performed to illustrate the major effects of altered ncRNAs expression. Moreover, the miRNA–mRNA and circRNA–miRNA networks were created and expected to reveal their interrelationship. Further, significant differentially expressed miRNAs based on Exo-BCG, Exo-Ra and Exo-Control, were evaluated, and the potential target mRNAs and function were analyzed. Eventually, miR-185-5p was collected as a promising potential biomarker for tuberculosis. Conclusion: Our findings provide a new vision for exploring biological functions of ncRNAs in mycobacterial infection and screening novel potential biomarkers. To sum up, exosomal ncRNAs might represent useful functional biomarkers in tuberculosis pathogenesis and diagnosis.

The incidence of non-tuberculous mycobacteria (NTM)-related death has increased globally recently. To obtain information of the species and characterization of pathogens involved in NTM pulmonary infection in Southern-central China, we identified 160 non-tuberculous infection cases from 3995 acid-fast bacilli (AFB)-positive tuberculous suspects. We then randomly selected 101 non-tuberculous patients, isolated bacteria from their sputa and genotyped the pathogens using the 16S rRNA gene and 16S-23S rRNA internal transcribed spacer sequences. M. intracellulare (32.67%, 33/101), M. abscessus (32.67%, 33/101) and M. fortuitum (7.92%, 8/101) are identified in these isolates. Surprisingly, non-mycobacteria including Gordonia (8.91%, 9/101), Nocardia (5.94%, 6/101) and Tsukamurella (0.99%, 1/101) are also discovered, and the case of Tsukamurella pulmonis infection is first discovered in Southern-central China. Moreover, species of M. mucogenicum group, M. chubuense, M. kansasii, M. gastri, M. avium, M. porcinum and M. smegmatis are identified. In addition, nine immune compromised cases (8.91%, 9/101), including type two diabetes mellitus and HIV/AIDS are found to be infected with non-tuberculous bacteria. This study revealed the distribution and characteristics of non-tuberculous AFB pathogen infection occurred in Southern-central China, and suggested that physicians should be alert of the emerging of NTM and non-mycobacteria infection in AFB positive cases and take caution when choosing chemotherapy for tuberculosis-like pulmonary infections. Generally, this study may help with the development of new strategy for the diagnosis and treatment of mycobacterial infection.

Background: Failure to early detect multidrug-resistant tuberculosis (MDR-TB) results in treatment failure and poor clinical outcomes, and highlights the need to rapidly detect resistance to rifampicin (RIF) and isoniazid (INH) Methods: In Multi-Fluorescence quantitative Real-Time PCR (MF-qRT-PCR) assay, 10 probes labeled with 4 kinds of fluorophores were designed to detect the mutations in regions of rpoB, katG, mabA-inhA, oxyR-ahpC and rrs. The efficiency of MF-qRT-PCR assay was tested using 261 bacterial isolates and 33 clinical sputum specimens. Among these samples, 227 Mycobacterium tuberculosis isolates were analyzed using drug susceptibility testing (DST), DNA sequencing and MF-qRT-PCR assay. Results: Compared with DST, MF-qRT-PCR sensitivity and specificity for RIF-resistance were 94.6% and 100%, respectively. And the detection sensitivity and specificity for INH-resistance were 85.9% and 95.3%, respectively. Compared with DNA sequencing, the sensitivity and specificity of our assay were 97.2% and 100% for RIF-resistance and 97.9% and 96.4% for INH-resistance. Compared with Phenotypic strain identification, MF-qRT-PCR can distinguish 227 Mycobacterium tuberculosis complexes (MTC) from 34 Non-tuberculous mycobacteria (NTM) isolates with 100% accuracy rate. Conclusions: MF-qRT-PCR assay was an efficient, accurate, reliable and easy-operated method for detection of RIF and INH-resistance, and distinction of MTC and NTM of clinical isolates.

Background: For early screening and diagnosis of non-small cell lung cancer (NSCLC), a robust model based on plasma proteomics and metabolomics is required for accurate and accessible non-invasive detection. Here we aim to combine TMT-LC-MS/MS and machine-learning algorithms to establish models with high specificity and sensitivity, and summarize a generalized model building scheme. Methods: TMT-LC-MS/MS was used to discover the differentially expressed proteins (DEPs) in the plasma of NSCLC patients. Plasma proteomics-guided metabolites were selected for clinical evaluation in 110 NSCLC patients who were going to receive therapies, 108 benign pulmonary diseases (BPD) patients, and 100 healthy controls (HC). The data were randomly split into training set and test set in a ratio of 80:20. Three supervised learning algorithms were applied to the training set for models fitting. The best performance models were evaluated with the test data set. Results: Differential plasma proteomics and metabolic pathways analyses revealed that the majority of DEPs in NSCLC were enriched in the pathways of complement and coagulation cascades, cholesterol and bile acids metabolism. Moreover, 10 DEPs, 14 amino acids, 15 bile acids, as well as 6 classic tumor biomarkers in blood were quantified using clinically validated assays. Finally, we obtained a high-performance screening model using logistic regression algorithm with AUC of 0.96, sensitivity of 92%, and specificity of 89%, and a diagnostic model with AUC of 0.871, sensitivity of 86%, and specificity of 78%. In the test set, the screening model achieved accuracy of 90%, sensitivity of 91%, and specificity of 90%, and the diagnostic model achieved accuracy of 82%, sensitivity of 77%, and specificity of 86%. Conclusions: Integrated analysis of DEPs, amino acid, and bile acid features based on plasma proteomics-guided metabolite profiling, together with classical tumor biomarkers, provided a much more accurate detection model for screening and differential diagnosis of NSCLC. In addition, this new mathematical modeling based on plasma proteomics-guided metabolite profiling will be used for evaluation of therapeutic efficacy and long-term recurrence prediction of NSCLC.

With mycobacteriosis increasing, the study of non-tuberculous mycobacteria is imperative for clinical therapy and management. Non-tuberculous mycobacteria are naturally resistant to most anti-tuberculosis drugs. Accordingly, it is important to decipher the biology of the novel non-tuberculous mycobacteria through complete genomic analysis of novel pathogenic mycobacteria. We describe Mycobacterium sinense JDM601, a novel, slow-growing mycobacterium of the Mycobacterium terrae complex resistant to nine antibiotics, by clinical presentation, cultural and biochemical characteristics, minimal inhibitory concentrations, and genome-sequencing analysis. JDM601 is closest to Mycobacterium nonchromogenicum according to mycolic acid composition, but closest to Mycobacterium algericum sp. nov according to 16S rDNA. JDM601 is resistant to isoniazid, streptomycin, rifampin, euteropas, protionamide, capromycin, ciprofloxacin, amikacin and levofloxacin but not ethambutol. The clinical information, mycolic acid composition, and virulence genes indicate that JDM601 is an opportunistic pathogen.