Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1095911 | ENA |
REPOSITORIES: ENA
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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.
2024-01-08 | GSE252118 | GEO
Project description:Filoviruses infect a wide range of cell types with the exception of lymphocytes. The intracellular proteins cathepsin B and L, two-pore channel 1 and 2, and bona fide receptor Niemann–Pick Disease C1 (NPC1) are essential for the endosomal phase of cell entry. However, earlier steps of filoviral infection remain poorly characterized. Numerous plasma membrane proteins have been implicated in attachment but it is still unclear which ones are sufficient for productive entry. To define a minimal set of host factors required for filoviral glycoprotein-driven cell entry, we screened twelve cell lines and identified the nonlymphocytic cell line SH-SY5Y to be specifically resistant to filovirus infection. Heterokaryons of SH-SY5Y cells fused to susceptible cells were susceptible to filoviruses, indicating that SH-SY5Y cells do not express a restriction factor but lack an enabling factor critical for filovirus entry. However, all tested cell lines expressed functional intracellular factors. Global gene expression profiling of known cell surface entry factors and protein expression levels of analyzed attachment factors did not reveal any correlation between susceptibility and expression of a specific host factor. Using binding assays with recombinant filovirus glycoprotein, we identified cell attachment as the step impaired in filovirus entry in SH-SY5Y cells. Individual overexpression of attachment factors T-cell immunoglobulin and mucin domain 1 (TIM-1), Axl, Mer, or dendritic cell-specific intercellular adhesion molecule-3-grabbing non-integrin (DC-SIGN) rendered SH-SY5Y cells susceptible to filovirus glycoprotein-driven transduction. Our study reveals that a lack of attachment factors limits filovirus entry and provides direct experimental support for a model of filoviral cell attachment where host factor usage at the cell surface is highly promiscuous.
2019-03-31 | GSE104008 | GEO