Project description:Bronchoalveolar lavage specimens collected from lung cancer patients, and analyzed using mass spectrometry-based quantitative N-glycoproteomic technique.
2020-05-27 | MSV000085469 | MassIVE
Project description:Clinical metagenomic approach for pathogen recovery from bronchoalveolar lavage using MinION
Project description:<p>Studies have emphasized the importance of disease-associated microorganisms in perturbed communities, however, the protective roles of commensals are largely under recognized and poorly understood. Using acne as a model disease, we investigated the determinants of the overall virulence property of the skin microbiota when disease- and health-associated organisms coexist in the community. By ultra-deep metagenomic shotgun sequencing, we revealed higher relative abundances of propionibacteria and Propionibacterium acnes phage in healthy skin. In acne patients, the microbiome composition at the species level and at P. acnes strain level was more diverse than in healthy individuals, with enriched virulence-associated factors and reduced abundance of metabolic synthesis genes. Based on the abundance profiles of the metagenomic elements, we constructed a quantitative prediction model, which classified the clinical states of the host skin with high accuracy in both our study cohort (85%) and an independent sample set (86%). Our results suggest that the balance between metagenomic elements, not the mere presence of disease-associated strains, shapes the overall virulence property of the skin microbiota. This study provides new insights into the microbial mechanism of acne pathogenesis and suggests probiotic and phage therapies as potential acne treatments to modulate the skin microbiota and to maintain skin health.</p>
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.