Ontology highlight
ABSTRACT:
PROVIDER: PRJNA842783 | 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:Pathogen ID by mNGS during neutropenic fever
| PRJEB54778 | ENA
Project description:This series includes microarrays from 36 patient samples and 2 cell-culture controls, used to optimize and validate the pathogen detection microarray (Wong, et. al. 2007) Keywords: viral pathogen detection
2007-05-29 | GSE7779 | GEO
Project description:To advance our understanding of cellular host-pathogen interactions, technologies that facilitate the co-capture of both host and pathogen spatial transcriptome information are needed. Here, we present an approach to simultaneously capture host and pathogen spatial gene expression information from the same formalin-fixed paraffin embedded (FFPE) tissue section using the spatial transcriptomics technology. We applied the method to COVID-19 patient lung samples and enabled the dual detection of human and SARS-CoV-2 transcriptomes at 55 µm resolution. We validated our spatial detection of SARS-CoV-2 and identified an average specificity of 94.92% in comparison to RNAScope and 82.20% in comparison to in situ sequencing (ISS). COVID-19 tissues showed an upregulation of host immune response, such as increased expression of inflammatory cytokines, lymphocyte and fibroblast markers. Our colocalization analysis revealed that SARS-CoV-2+ spots presented shifts in host RNA metabolism, autophagy, NFκB, and interferon response pathways. Future applications of our approach will enable new insights into host response to pathogen infection through the simultaneous, unbiased detection of two transcriptomes.
| EGAS00001006186 | EGA
Project description:Single-molecule read technologies allow for detection of epigenomic base modifications during routine sequencing by analysis of kinetic data during the reaction, including the duration between base incorporations at the elongation site (the "inter-pulse duration.") Methylome data associated with a closed de novo bacterial genome of Salmonella enterica subsp. enterica serovar Javiana str. CFSAN001992 was produced and submitted to the Gene Expression Omnibus. Single-sample sequencing and base modification detection of cultured isolate of a foodborne pathogen.
2013-03-15 | E-GEOD-45178 | biostudies-arrayexpress