Ontology highlight
ABSTRACT:
PROVIDER: PRJNA971352 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| Other | ||||
| SRR24571100.fastq.gz | Fastqsanger.gz | |||
| SRR24571100_1.fastq.gz | Fastqsanger.gz | |||
| SRR24571100_2.fastq.gz | Fastqsanger.gz | |||
| SRR24571101.fastq.gz | Fastqsanger.gz |
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Project description:Objectives: Central nervous system (CNS) infections are common causes of morbidity and mortality worldwide. We aimed to discover protein biomarkers that could rapidly and accurately identify the likely cause of the infections, essential for clinical management and improving outcome. Methods: We applied liquid chromatography tandem mass-spectrometry on 45 cerebrospinal fluid (CSF) samples from a cohort of adults with/without CNS infections to discover potential diagnostic biomarkers. We then validated the diagnostic performance of a selected biomarker candidate in an independent cohort of 364 consecutively treated adults with CNS infections admitted to a referral hospital in Vietnam. Results: In the discovery cohort, we identified lipocalin 2 (LCN2) as a potential biomarker of bacterial meningitis (BM) other than tuberculous meningitis. The analysis of the validation cohort showed that LCN2 could discriminate BM from other CNS infections (including tuberculous meningitis, cryptococcal meningitis and viral/antibody-mediated encephalitis), with the sensitivity: 0.88 (95% confident interval (CI): 0.77-0.94), the specificity: 0.91 (95%CI: 0.88-0.94) and the diagnostic odd ratio: 73.8 (95%CI: 31.8-171.4)). LCN2 outperformed other CSF markers (leukocytes, glucose, protein and lactate) commonly used in routine care worldwide. The combination of LCN2, CSF leukocytes, glucose, protein and lactate resulted in the highest diagnostic performance for BM (area under receiver-operating-characteristic-curve 0.96; 95%CI: 0.93-0.99). Conclusions: Our results suggest that LCN2 is a sensitive and specific biomarker for discriminating BM from a broad spectrum of other CNS infections. A prospective study is needed to assess the diagnostic utility of LCN2 in the diagnosis and management of CNS infections..
2020-08-20 | PXD020510 | Pride
Project description:Systemic inflammatory reactions mediated by chronic infections activate microglia in the central nervous system (CNS) and have been postulated to exacerbate neurodegenerative diseases. We now demonstrate in vivo that repeated systemic challenge of mice with bacterial lipopolysaccharides (LPS) maintains an elevated microglial inflammatory response and triggers neurodegeneration. Repeated chronic intraperitoneal application of LPS over four consecutive days induced loss of dopaminergic neurons in the substantia nigra, a process that was accompanied by decreased levels of dopamine in the striatum. In contrast, total cumulative LPS dose given intraperitoneally within a single acute application did not induce a decrease in dopamine levels nor neurodegeneration. Mice that received repeated systemic LPS application showed increased microglial activation, elevated production of proinflammatory cytokines and activation of the classical complement and its associated phagosome pathway in the brain. Loss of dopaminergic neurons induced by repeated systemic LPS application was rescued in complement C3 deficient mice, confirming an involvement of the complement system in neurodegeneration. Thus, our data demonstrate that repeated systemic exposure to bacterial LPS induces a microglial phagosomal inflammatory response, leading to complement-dependent damage of dopaminergic neurons.
2014-07-11 | E-MTAB-1770 | biostudies-arrayexpress
Project description:Dengue virus is the most common arbovirus worldwide and represents a significant public health concern. To date, chronic Dengue infections have not been previously reported. While investigating the etiology of central nervous system (CNS) disease in a patient presenting with progressive dementia, we elucidated a chronic dengue infection within the CNS. Comprehensive viral immune responses in both serum and cerebrospinal fluid (CSF) were profiled by a phage-display assay (VirScan). Enrichment of Dengue viral antibodies were detected in the CSF as compared to the serum. No virus was detected in serum or CSF, but post-mortem analysis confirmed Dengue virus in the brain by quantitative polymerase chain reaction (PCR), immunohistochemistry, RNAscope and sequencing. Dengue virus was detectable by PCR and sequencing from brain biopsy tissue collected 33 months ante-mortem, confirming a chronic infection. Comprehensive antibody profiling assays can aid in the diagnosis of encephalitis of unknown etiologies. Our findings suggest that Dengue virus infections may persist in the CNS and should be considered in patients with progressive dementia in endemic regions or with relevant travel history.
2019-08-28 | GSE123125 | GEO
Project description:DNA methylation profiling has emerged as a valuable tool for tumor classification, exemplified by the German Cancer Research Center's creation of online classifiers for CNS tumors and sarcomas. Identification of rare molecular events, such as TRIO::TERT fusion in undifferentiated sarcomas, through DNA methylation profiling and transcriptome analysis aims to define distinct molecular subgroups within sarcomas of uncertain diagnosis, potentially improving classification and treatment strategies.In this study, we present 8 cases of sarcomas characterized by TRIO::TERT fusion, establishing it as a distinct molecular subtype of sarcomas. This fusion represents a consistent molecular feature across all analyzed tumors, suggesting its pivotal role in sarcomatogenesis. Identifying TRIO::TERT transcript sarcoma as a new tumor type may enhance diagnostic strategies for improved patient management.
2024-05-31 | GSE261891 | GEO
Project description:Background In childhood acute lymphoblastic leukemia (ALL), central nervous system (CNS) involvement is rare at diagnosis (1-4%), but more frequent at relapse (~30%). Minimal residual disease diagnostics predict most bone marrow (BM) relapses, but likely cannot predict isolated CNS relapses. Consequently, CNS relapses may become relatively more important. Because of the significant late sequelae of CNS treatment, early identification of patients at risk of CNS relapse is crucial. Methods Gene expression profiles of ALL cells from cerebrospinal fluid (CSF) and ALL cells from BM were compared and differences were confirmed by real-time quantitative PCR. For a selected set of overexpressed genes, protein expression levels of ALL cells in CSF at relapse and of ALL cells in diagnostic BM samples were evaluated by 8-color flow cytometry. Results CSF-derived ALL cells showed a clearly different gene expression profile than BM-derived ALL cells, with differentially-expressed genes (including SCD and OPN) involved in survival and apoptosis pathways and linked to the JAK-STAT pathway. Flowcytometric analysis showed that a subpopulation of ALL cells (>1%) with a â??CNS signatureâ?? (SCD positivity and increased OPN expression) was already present in BM at diagnosis in ALL patients who later developed a CNS relapse, but was <1% or absent in virtually all other patients. Conclusions The presence of a subpopulation of ALL cells with a â??CNS signatureâ?? at diagnosis may predict isolated CNS relapse. Such information can be used to design new diagnostic and treatment strategies that aim at prevention of CNS relapse with reduced toxicity. We compared gene expression profiles of ALL cells obtained from cerebrospinal fluid (n=8) with profiles of ALL cells obtained from bone marrow at diagnosis (n=22) or at relapse (n=20)
2015-12-31 | E-GEOD-60926 | biostudies-arrayexpress
Project description:BACKGROUND. Lower respiratory tract infection (LRTI) is a leading cause of death in children worldwide. LRTI diagnosis is challenging since non-infectious respiratory illnesses appear clinically similar and existing microbiologic tests are often falsely negative or detect incidentally-carried microbes common in children. These challenges result in antimicrobial overuse and adverse patient outcomes. Lower airway metagenomics has the potential to detect host and microbial signatures of LRTI. Whether it can be applied at scale and in a pediatric population to enable improved diagnosis and precision treatment remains unclear. METHODS. We used tracheal aspirate RNA-sequencing to profile host gene expression and respiratory microbiota in 261 children with acute respiratory failure. We developed a random forest gene expression classifier for LRTI by training on patients with an established diagnosis of LRTI (n=117) or of non-infectious respiratory failure (n=50). We then developed a classifier that integrates the: i) host LRTI probability, ii) abundance of respiratory viruses, and iii) dominance in the lung microbiome of bacteria/fungi considered pathogenic by a rules-based algorithm. RESULTS. The host classifier achieved a median AUC of 0.967 by 5-fold cross-validation, driven by activation markers of T cells, alveolar macrophages and the interferon response. The integrated classifier achieved a median AUC of 0.986 and significantly increased the confidence of patient classifications. When applied to patients with an uncertain diagnosis (n=94), the integrated classifier indicated LRTI in 52% of cases and nominated likely causal pathogens in 98% of those. CONCLUSIONS. Lower airway metagenomics enables accurate LRTI diagnosis and pathogen identification in a heterogeneous cohort of critically ill children through integration of host, pathogen, and microbiome features.
2022-12-06 | GSE212532 | GEO
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:Metagenomic diagnosis of viral infections - RAPID project
| PRJNA33495 | ENA