Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1274761 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:This SuperSeries is composed of the following subset Series: GSE16180: Bone-marrow derived macrophages response to the Whipple's disease agent, Tropheryma whipplei GSE20207: Wild-type bone-marrow derived macrophages response to Escherichia coli lipopolysaccharide (LPS) GSE20208: Interleukin-16-knock-out bone-marrow derived macrophages response to Escherichia coli lipopolysaccharide (LPS) GSE20209: Interleukin-16-knock-out bone-marrow derived macrophages response to the Whipple's disease agent, Tropheryma whipplei Refer to individual Series
2010-11-08 | E-GEOD-20210 | biostudies-arrayexpress
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:In this work we analyzed the genomic diversity of several Tropheryma whipplei strains by microarray based-comparative genomic hybridization. Fifteen clinical isolates originating from biopsy samples recovered from different countries were compared with the T. whipplei Twist strain. For each isolate, the genes were defined as either present or absent/divergent using the GACK analysis software. Genomic changes were then further characterized by PCR and sequencing. Obtained results revealed a limited genetic variation between these T. whipplei isolates with at most 2.24 % of the probes exhibiting differential hybridization against the Twist strain. The main variation was found in genes encoding for the WiSP family proteins supporting the view of these membrane proteins as key actors of immune evasion. This work also evidenced a 19.2 kb-pair deletion within T. whipplei DIG15 strain. This deletion takes place in the same region as the large genomic rearrangement previously described between Twist and TW08/27 which can thus be considered as a major hot-spot for intra-specific T. whipplei differentiation. Analysis of this deleted region confirmed the role of WND-domains in generating T. whipplei diversity Keywords: Comparative genomic hybridization
2007-04-06 | GSE7453 | GEO
Project description:Tropheryma whipplei Detection by Nanopore Sequencing
| PRJNA754208 | ENA