Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Pathogen Class-specific Transcriptional Responses Derived from PBMCs Accurately Discriminate Between Fungal, Bacterial, and Viral Infections [ValidationCohort]

ABSTRACT: Host gene expression responses can be used to determine the etiology of acute infection. PBMCs were stimulated with bacterial, viral, and fungal stimuli and then analyzed for differential gene expression utilizing microarrays to derive pathogen class-specific gene expression classifiers of infection. Validation Cohort: peripheral blood samples of human patients with acute viral, bacterial, or fungal infections

ORGANISM(S): Homo sapiens

PROVIDER: GSE277849 | GEO | 2024/09/28

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Pathogen Class-specific Transcriptional Responses Derived from PBMCs Accurately Discriminate Between Fungal, Bacterial, and Viral Infections [DiscoveryCohort]

Project description:Host gene expression responses can be used to determine the etiology of acute infection. PBMCs were stimulated with bacterial, viral, and fungal stimuli and then analyzed for differential gene expression utilizing microarrays to derive pathogen class-specific gene expression classifiers of infection. Discovery Cohort: In vitro human PBMC challenges were performed with bacteria (gram postive and gram negative), viruses (4 strains of influenza), and fungi (Cryptococcus and Candida)

2024-09-28 | GSE277848 | GEO

Host-response Transcriptional Biomarkers Accurately Discriminate Bacterial and Viral Infections by Global Pathogens

Project description:Global endemic infections, such as leptospirosis, rickettsial diseases, and dengue infections present diagnostic challenges, posing a dilemma for antibiotic stewardship worldwide. The goal of this project was to identify accurate transcriptional classifiers able to discriminate between bacterial and viral illness of global pathogens.

2023-12-19 | GSE211567 | GEO

Pathogen Class-specific Transcriptional Responses Derived from PBMCs Accurately Discriminate Between Fungal, Bacterial, and Viral Infections.

Project description:Pathogen Class-specific Transcriptional Responses Derived from PBMCs Accurately Discriminate Between Fungal, Bacterial, and Viral Infections.

| PRJNA1164321 | ENA

Pathogen Class-specific Transcriptional Responses Derived from PBMCs Accurately Discriminate Between Fungal, Bacterial, and Viral Infections [DiscoveryCohort]

Project description:Pathogen Class-specific Transcriptional Responses Derived from PBMCs Accurately Discriminate Between Fungal, Bacterial, and Viral Infections [DiscoveryCohort]

| PRJNA1164323 | ENA

Pathogen Class-specific Transcriptional Responses Derived from PBMCs Accurately Discriminate Between Fungal, Bacterial, and Viral Infections [ValidationCohort]

Project description:Pathogen Class-specific Transcriptional Responses Derived from PBMCs Accurately Discriminate Between Fungal, Bacterial, and Viral Infections [ValidationCohort]

| PRJNA1164325 | ENA

Fungal Dysbiosis in Post-Influenza MRSA Infection

Project description:A leading cause of morbidity and mortality during influenza infection is the development of a secondary bacterial pneumonia, which is appropriately treated with antibiotics. In the absence of a bacterial superinfection, prescribing antibiotics is not indicated but has nevertheless become a common clinical practice for those presenting with a respiratory viral illness. We found that antibiotic use during an antecedent influenza infection impaired the lung innate immunologic defenses toward a secondary challenge with methicillin-resistantStaphylococcus aureus(MRSA). The antibiotics perturbed the gut microbiome causing a fungal dysbiosis that drives an increase in lung eosinophils. We also demonstrate eosinophils, through the release of major basic protein, impair macrophage ability to clear MRSA. Moreover, we provide clinical evidence that eosinophils positively correlate with antibiotic use and worsened outcomes in patients hospitalized for viral infections. Altogether, our work establishes a counterproductive effect of antibiotic treatment during influenza infection that have negative immunologic consequences in the lungs thereby increasing the risk of developing a secondary bacterial infection.

2024-08-21 | GSE243542 | GEO

Host gene expression profiling of fungal (candidemia), bacterial, and viral infection, and non-infectious illness [validation cohort]

Project description:Improved diagnostics are necessary to differentiate between multipe potential etiologies of acute illness in a hospitalized patient. The peripheral blood host gene expression response can act as a supplementary diagnostic tool and better inform the full host immune response to a pathogen. We performed RNA sequencing on peripheral blood from 48 hospitalized patients with confirmed candidemia as well as patients with other acute viral, bacterial, and non-infectious illnesses and derived pathogen class-specific gene expression classifiers.

2021-06-08 | GSE176261 | GEO

Host gene expression profiling of fungal (candidemia), bacterial, and viral infection, and non-infectious illness [discovery cohort]

2021-06-08 | GSE176260 | GEO

Pathogen Class-specific Transcriptional Responses Derived from PBMCs Accurately Discriminate Between Fungal, Bacterial, and Viral Infections.

Project description:This SuperSeries is composed of the SubSeries listed below.

2024-09-28 | GSE277851 | GEO

Transcriptional Profiling is Superior to Procalcitonin to Discriminate Bacterial vs. Viral Lower Respiratory Tract Infections in Hospitalized Adults

Project description:Background: Distinguishing between bacterial and viral lower respiratory tract infections (LRTI) in hospitalized patients remains challenging. Transcriptional profiling is a promising tool for improving diagnosis in LRTI. Methods: We performed whole blood transcriptional analysis in a cohort of 118 adult patients (median [IQR] age, 61 [50-76] years) hospitalized with bacterial, viral or viral-bacterial LRTI, and 40 age-matched healthy controls (60 [46-70] years). We applied class comparisons, modular analysis and class prediction algorithms to identify distinct biosignatures for bacterial and viral LRTI, which were validated in an independent group of patients. Results: Patients were classified as bacterial (B, n=22), viral (V, n=71) and bacterial-viral LRTI (BV, n=25) based on comprehensive microbiologic testing. Compared with healthy controls statistical group comparisons (p<0.01; with multiple test corrections) identified 3,376 differentially expressed genes in patients with B-LRTI; 2,391 in V-LRTI, and 2,628 in BV-LRTI. Independent of etiologic pathogen, patients with LRTI demonstrated overexpression of innate immunity and underexpression of adaptive immunity genes. Patients with B-LRTI showed significant overexpression of inflammation (B>BV>V) and neutrophils (B>BV>V) while those with V-LRTI displayed significantly greater overexpression of interferon genes (V>BV>B). The K-Nearest Neighbors (K-NN) algorithm identified 10 classifier genes that discriminated patients with bacterial vs viral LRTI with 97% [95%CI: 84-100] sensitivity and 92% [77-98] specificity. In comparison, procalcitonin classified bacterial vs viral LRTI with 38% [18-62] sensitivity and 91% [76-98] specificity. Conclusions: Transcriptional profiling can be used as a helpful tool for the diagnosis of adults hospitalized with LRTI. 158 samples, no replicates; bacterial LRTI n=22, viral LRTI n=71, bacterial-viral coinfections n=25, and healthy controls n=40

2015-09-14 | E-GEOD-60244 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data