Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1203379 | 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:Streptococcus suis infection diagnosed by mNGS
| PRJEB35373 | ENA
Project description:Previously, we reported that mice made transgenic for a picornaviral RdRP â the 3Dpol protein of Theilerâs murine encephalomyelitis virus (TMEV) â suppress infection by diverse viral families. How the picornaviral RdRP transgene exerted antiviral protection in vivo was not known. To investigate the molecular mechanism, we determined gene expression profiles in spinal cords of WT and RdRP transgenic mice prior to (baseline) and after (2 days) infection with Encephalomyocarditis Virus (EMCV). Spinal cords from adult age-matched WT mice were harvested prior to (baseline) viral infection and RdRP transgenic spinal cords were harvested after (2 days) infection with Encephalomyocarditis Virus (EMCV). Total RNA was isolated (Qiagen RNeasy kit) and used as a template to synthesize biotinylated cRNA which was then hybridized to the HT Mouse Genome 430 2.0 GeneChip Array (Affymetrix).
2015-11-08 | E-GEOD-62757 | biostudies-arrayexpress
Project description:Purpose: The goal of this study was to determine the effect of an upper-respiratory infection on changes in RNA transcription occuring in the cerebellum and spinal cord. post infection. Methods: Gender matched eight week old C57BL/6 mice were inoculated saline or with Influenza A (Puerto Rico/8/34; PR8, 1.0 HAU) by intranasal route and transcriptomic changes in the cerebellum and spinal cord tissues were evaluated by RNA-seq (100bp paired end reads) at days 0 (non-infected), 4 and 8 Results: After trimming and excluding multi-mappeing reads an average of 92.07% (cerebellum) and 91.71% (spinal cord) of genes were uniquely mapped to a gene. The average number of single end reads per sample was 36.42 and 37 million for the spinal cord and cerebellum respectively. Infection caused significant changes to the transcriptome of each tissue, which was most prominent at day 8 post infection. Conclusion: This study represents the first to use RNA-seq tecnology to evaluate the effect of peripheral influenza a infection on changes in gene expression of the cerebellum and spinal cord.
2017-06-14 | GSE96870 | GEO