Project description:Recent progress in unbiased metagenomic next-generation sequencing (mNGS) allows simultaneous examination of microbial and host genetic material in a single test. Leveraging affordable bronchoalveolar lavage fluid (BALF) mNGS data, we employed machine learning to create a diagnostic approach distinguishing lung cancer from pulmonary infections, conditions prone to misdiagnosis in clinical settings. This prospective study analyzed BALF-mNGS data from lung cancer and pulmonary infection patients, delineating differences in DNA/RNA microbial composition, bacteriophage abundances, and host responses, including gene expression, transposable element levels, immune cell composition, and tumor fraction derived from copy number variation (CNV). Integrating these metrics into a host/microbe metagenomics-driven machine learning model (Model VI) demonstrated robustness, achieving an AUC of 0.87 (95% CI = 0.857-0.883), sensitivity = 73.8%, and specificity = 84.5% in the training cohort, and an AUC of 0.831 (95% CI = 0.819-0.843), sensitivity = 67.1%, and specificity = 94.4% in the validation cohort for distinguishing lung cancer from pulmonary infections. The application of a rule-in and rule-out strategy-based composite predictive model significantly enhances accuracy (ACC) in distinguishing between lung cancer and tuberculosis (ACC=0.913), fungal infection (ACC=0.955), and bacterial infection (ACC=0.836). These findings highlight the potential of cost-effective mNGS-based analysis as a valuable tool for early differentiation between lung cancer and pulmonary infections, offering significant benefits through a single comprehensive testing.
Project description:<p>Despite improved diagnostics, pulmonary pathogens in immunocompromised children frequently evade detection, leading to significant mortality. In this study, we performed RNA and DNA-based metagenomic next generation sequencing (mNGS) on 41 lower respiratory samples collected from 34 children. We identified a rich cross-domain pulmonary microbiome containing bacteria, fungi, RNA viruses, and DNA viruses in each patient. Potentially pathogenic bacteria were ubiquitous among samples but could be distinguished as possible causes of disease by parsing for outlier organisms. Potential pathogens were detected in half of samples previously negative by clinical diagnostics. Ongoing investigation is needed to determine the pathogenic significance of outlier microbes in the lungs of immunocompromised children with pulmonary disease. Metatranscriptomic (RNA) sequencing libraries are reported in the manuscript and are included for this release.</p>
| phs001684 | dbGaP
Project description:Detection of Mycobacterium tuberculosis complex by metagenomic next-generation sequencing
Project description:This study aims to identify the specific miRNA of mycobacterium tuberculosis (M.tb) infected THP-1 by next-generation sequencing, and further to explore the role of miRNA in innate immunity against M.tb infection.Comprehensive analysis of the next-generation sequencing results showed that the expression of miR-99a-5p was significantly lower in the MTB infected THP-1 cells.
Project description:Primary human monocytes were isolated from four healthy donors. Monocytes were differentiated into macrophages, infected with virulent Mycobacterium tuberculosis (Mtb), strain H37Rv, for 24 or 48 hours, at a multiplicity of infection of 5 or 10. Following infection, infected cells and time-matched uninfected controls were harvested, total RNA including small RNAs was isolated and used for next-generation small RNA sequencing. Small RNA sequencing data was processed using miRge2.0, including a novel miRge2.0-based tRF detection tool. Processed data was used to determine differential expression of microRNAs and differential production of tRNA-derived fragments (tRFs) during infection with Mtb.