Blood metagenome
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA890573 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR29097600_1.fastq.gz | Fastqsanger.gz | |||
| SRR29097601_1.fastq.gz | Fastqsanger.gz | |||
| SRR29097602_1.fastq.gz | Fastqsanger.gz | |||
| SRR29097603_1.fastq.gz | Fastqsanger.gz | |||
| SRR29097604_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 85 |
Similar Datasets
Project description:Comparison of methods for blood pathogens detection
| PRJNA693402 | ENA
Project description:The Blood Borne Pathogen Resequencing Microarray Expanded (BBP-RMAv.2) is a platform that allows multiplex detection and identification of 80 different blood-borne pathogens in one single test, comprising 60 virus, 5 bacteria and 15 parasites. The objective is to evaluate the lowest concentration detected in blood or plasma, species discrimination and applicability of the microarray platform for testing blood donors. Human blood or plasma spiked with selected pathogens (10,000, 1,000 or 100 cells or copies/ml), including 6 viral, 2 bacterial and 5 protozoan pathogens were each tested on this platform. The nucleic acids were extracted, amplified using multiplexed sets of pooled specific primers, fragmented, labeled, and hybridized to a microarray. Finally, the detected sequences were identified using an automated genomic database alignment algorithm. The performance of the BBP-RMAv.2 demonstrated detection for most spiked protozoan pathogens at 1,000 cells/ml, 10,000 cells/ml for bacterial pathogens and as low as 100 copies/ml for viral pathogens. Coded specimens, including spiked and negative controls, were identified correctly for one blood specimen and for two plasma specimens. One negative plasma resulted in a false positive detection of a virus demonstrating the effectiveness of the platform.
2024-06-08 | E-MTAB-13972 | biostudies-arrayexpress
Project description:Bloodstream infections (BSIs), the presence of microorganisms in blood, are potentially serious conditions that can quickly develop into sepsis and life-threatening situations. When assessing proper treatment, rapid diagnosis is the key; besides clinical judgement performed by attending physicians, supporting microbiological tests typically are performed, often requiring microbial isolation and culturing steps, which increases the time required for confirming positive cases of BSI. The additional waiting time forces physicians to prescribe broad-spectrum antibiotics and empiric treatment, before determining the precise cause of the disease. Thus, alternative and more rapid cultivation-independent methods are needed to improve clinical diagnostics, supporting prompt and accurate treatment and reducing the development of antibiotic resistance. In this study, a culture-independent workflow for pathogen detection and identification in blood samples was developed, using peptide biomarkers and applying bottom-up proteomics analyses, i.e., so-called ”proteotyping”. To demonstrate the feasibility of detection of blood infectious pathogens using proteotyping, Escherichia coli and Staphylococcus aureus were included in the study, as the most prominent bacterial causes of bacteremia and sepsis, as well as Candida albicans, one of the most prominent causes of fungemia. Model systems including spiked negative blood samples, as well as positive blood cultures, without further culturing steps, were investigated. Furthermore, an experiment designed to study the incubation time needed for correct identification of the infectious pathogens in blood cultures was performed. Compared to the MALDI-TOF MS-based approaches, shotgun proteotyping demonstrated higher sensitivity and accuracy, and required shorter incubation time before detection and identification of the correct pathogen could be accomplished.
2021-07-20 | PXD023033 | Pride
Project description:Sepsis is a clinical syndrome that can be caused by bacteria or fungi. Early knowledge on the nature of the causative agent is a prerequisite for targeted anti-microbial therapy. Besides currently used detection methods like blood culture and PCR-based assays, the analysis of the transcriptional response of the host to infecting organisms holds great promise. In this study, we aim to examine the transcriptional footprint of infections caused by the bacterial pathogens Staphylococcus aureus and Escherichia coli and the fungal pathogens Candida albicans and Aspergillus fumigatus in a human whole-blood model. Moreover, we use the expression information to build a random forest classifier to determine if the pathogen is bacterial, fungal or neither of the two. After normalizing the transcription intensities using stably expressed reference genes, we filtered the gene set for biomarkers of bacterial or fungal blood infections. This selection is based on differential expression and an additional gene relevance measure. In this way, we identified 38 biomarker genes, including IL6, SOCS3, and IRG1 which were already associated to sepsis by other studies. Using these genes, we trained the classifier and assessed its performance. It yielded a 96% accuracy (sensitivities >93%, specificities >97%) for a 10-fold stratified cross-validation and a 92% accuracy (sensitivities and specificities >83%) for an additional dataset comprising Cryptococcus neoformans infections. Furthermore, the noise-robustness of the classifier suggests high rates of correct class predictions on datasets of new species. In conclusion, this genome-wide approach demonstrates an effective feature selection process in combination with the construction of a well-performing classification model. Further analyses of genes with pathogen-dependent expression patterns can provide insights into the systemic host responses, which may lead to new anti-microbial therapeutic advances. Analysis of innate immune activation on the basis of gene expression of whole blood cells during ex vivo whole blood infection with bacterial (Staphylococcus aureus, Escherichia coli) and fungal pathogens (Candida albicans, Aspergillus fumigatus) in comparison to mock-treated blood.
2015-03-11 | E-GEOD-65088 | biostudies-arrayexpress
Project description:The use of microbiological cultures for diagnosing bacterial infections in young febrile infants have substantial limitations, including false positive and false negative cultures, and non-ideal turn-around times. Analysis of host genomic expression patterns (“RNA biosignatures”) in response to the presence of specific pathogens, however, may provide an alternate and potentially improved diagnostic approach. This study was designed to define bacterial and non-bacterial RNA biosignatures to distinguish these infections in young febrile infants.
2016-07-27 | GSE64456 | GEO
Project description:Background: Meningitis can be caused by several viruses and bacteria. Identifying the causative pathogen as quickly as possible is crucial to initiate the most optimal therapy, as acute bacterial meningitis is associated with a significant morbidity and mortality. Bacterial meningitis requires antibiotics, as opposed to enteroviral meningitis, which only requires supportive therapy. Clinical presentation is usually not sufficient to differentiate between viral and bacterial meningitis, thereby necessitating cerebrospinal fluid (CSF) analysis by PCR and/or time-consuming bacterial cultures. However, collecting CSF in children is not always feasible and a rather invasive procedure. Methods: In 12 Belgian hospitals, we obtained acute blood samples from children with signs of meningitis (49 viral and 7 bacterial cases). (aged between 3 months and 16 years). After pathogen confirmation on CSF, the patient was asked to give a convalescent sample after recovery. 3’mRNA sequencing was performed to determine differentially expressed genes (DEGs) to create a host transcriptomic profile. Results: Enteroviral meningitis cases displayed the largest upregulated fold change enrichment in type I interferon production, response and signaling pathways. Patients with bacterial meningitis showed a significant upregulation of genes related to macrophage and neutrophil activation. We found several significantly DEGs between enteroviral and bacterial meningitis. Random forest classification showed that we were able to differentiate enteroviral from bacterial meningitis with an AUC of 0.982 on held-out samples. Conclusions: Enteroviral meningitis has an innate immunity signature with type 1 interferons as key players. Our classifier, based on blood host transcriptomic profiles of different meningitis cases, is a possible strong alternative for diagnosing enteroviral meningitis.
2019-06-27 | GSE133378 | GEO
Project description:The goal of this study was to characterize altered inducible immune networks in Systemic onset juvenile idiopathic arthritis (sJIA), an IL-1-driven autoinflammatory disease of unknown etiology. To this end, we developed a high-throughput assay that quantifies the transcriptional and protein-level responses of blood leukocytes to innate stimuli. Herein, we report transcriptional data from healthy adult blood stimulated with 16 different conditions, including TLR ligands, cytosolic receptor ligands and inflammatory cytokines. We further report blood transcriptional profiles from sJIA patients with various disease activity and treatment statuses, both ex vivo (baseline) and after in vitro stimulation with a subset of innate stimuli including heat-killed bacterial pathogens.
2017-09-19 | GSE103500 | GEO
Project description:Introduction: Diagnosis of severe influenza pneumonia remains challenging because of the lack of correlation between presence of influenza virus and patient’s clinical status. We conducted gene expression profiling in the whole blood of critically ill patients to identify a gene signature that would allow clinicians to distinguish influenza infection from other causes of severe respiratory failure (e.g. bacterial pneumonia, non-infective systemic inflammatory response syndrome). Methods: Whole blood samples were collected from critically ill individuals and assayed on Illumina HT-12 gene expression beadarrays. Differentially expressed genes were determined by linear mixed model analysis and over-represented biological pathways determined using GeneGo MetaCore. Results: The gene expression profile of H1N1 influenza A pneumonia was distinctly different from bacterial pneumonia and systemic inflammatory response syndrome. The influenza gene expression profile is characterized by up-regulation of genes from cell cycle regulation, apoptosis and DNA-damage response pathways. In contrast, no distinctive gene-expression signature was found in patients with bacterial pneumonia or systemic inflammatory response syndrome. The gene expression profile of influenza infection persisted through five days of follow-up. Furthermore, in patients with primary H1N1 influenza A infection who subsequently developed bacterial co-infection, the influenza gene-expression signature remained unaltered, despite the presence of a super-imposed bacterial infection. Conclusions: The whole blood expression profiling data indicates that the host response to influenza pneumonia is distinctly different from that caused by bacterial pathogens. This information may speed up identification of the cause of infection in patients presenting with severe respiratory failure, allowing appropriate patient care to be undertaken more rapidly.
2012-08-15 | GSE40012 | GEO
Project description:Introduction: Diagnosis of severe influenza pneumonia remains challenging because of the lack of correlation between presence of influenza virus and patient’s clinical status. We conducted gene expression profiling in the whole blood of critically ill patients to identify a gene signature that would allow clinicians to distinguish influenza infection from other causes of severe respiratory failure (e.g. bacterial pneumonia, non-infective systemic inflammatory response syndrome). Methods: Whole blood samples were collected from critically ill individuals and assayed on Illumina HT-12 gene expression beadarrays. Differentially expressed genes were determined by linear mixed model analysis and over-represented biological pathways determined using GeneGo MetaCore. Results: The gene expression profile of H1N1 influenza A pneumonia was distinctly different from bacterial pneumonia and systemic inflammatory response syndrome. The influenza gene expression profile is characterized by up-regulation of genes from cell cycle regulation, apoptosis and DNA-damage response pathways. In contrast, no distinctive gene-expression signature was found in patients with bacterial pneumonia or systemic inflammatory response syndrome. The gene expression profile of influenza infection persisted through five days of follow-up. Furthermore, in patients with primary H1N1 influenza A infection who subsequently developed bacterial co-infection, the influenza gene-expression signature remained unaltered, despite the presence of a super-imposed bacterial infection. Conclusions: The whole blood expression profiling data indicates that the host response to influenza pneumonia is distinctly different from that caused by bacterial pathogens. This information may speed up identification of the cause of infection in patients presenting with severe respiratory failure, allowing appropriate patient care to be undertaken more rapidly. Daily PAXgene samples for up to 5 days for; influenza A pneumonia patients (n=8), bacterial pneumonia patients (n=16), mixed bacterial and influenza A pneumonia patients (n=3), systemic inflammatory response patients (SIRS, n=13). Days 1 and 5 PAXgene samples for healthy control individuals
2012-08-14 | E-GEOD-40012 | biostudies-arrayexpress
Project description:To explore new aspects of Chronic non-bacterial osteomyelitis (CNO) based on whole blood RNA sequencing (> 6Gb per sample)
2021-11-05 | GSE187429 | GEO