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

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

PROVIDER: PRJNA1164321 | ENA |

REPOSITORIES: ENA

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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

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

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. Validation Cohort: peripheral blood samples of human patients with acute viral, bacterial, or fungal infections

2024-09-28 | GSE277849 | GEO

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

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

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

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

| PRJNA870919 | ENA

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

Host-response transcriptional biomarkers accurately discriminate bacterial and viral infections of global relevance.

Project description:Diagnostic limitations challenge management of clinically indistinguishable acute infectious illness globally. Gene expression classification models show great promise distinguishing causes of fever. We generated transcriptional data for a 294-participant (USA, Sri Lanka) discovery cohort with adjudicated viral or bacterial infections of diverse etiology or non-infectious disease mimics. We then derived and cross-validated gene expression classifiers including: 1) a single model to distinguish bacterial vs. viral (Global Fever-Bacterial/Viral [GF-B/V]) and 2) a two-model system to discriminate bacterial and viral in the context of noninfection (Global Fever-Bacterial/Viral/Non-infectious [GF-B/V/N]). We then translated to a multiplex RT-PCR assay and independent validation involved 101 participants (USA, Sri Lanka, Australia, Cambodia, Tanzania). The GF-B/V model discriminated bacterial from viral infection in the discovery cohort an area under the receiver operator curve (AUROC) of 0.93. Validation in an independent cohort demonstrated the GF-B/V model had an AUROC of 0.84 (95% CI 0.76-0.90) with overall accuracy of 81.6% (95% CI 72.7-88.5). Performance did not vary with age, demographics, or site. Host transcriptional response diagnostics distinguish bacterial and viral illness across global sites with diverse endemic pathogens.

| S-EPMC10728077 | biostudies-literature

Influence of catalytic and non-catalytic functions of class I HDACs on the cellular transcriptome

Project description:We generated a cellular class I HDAC toolbox allowing to discriminate between catalytic and non-catalytic roles of class I HDACs. Our study reveals a comprehensive, functional analysis of class I HDACs.

2022-08-14 | GSE193885 | GEO

Gene expression patterns in blood leukocytes discriminate patients with acute infections

Project description:Each infectious agent represents a unique combination of pathogen-associated molecular patterns that interact with specific pattern-recognition receptors expressed on immune cells. Therefore, we surmised that the blood immune cells of individuals with different infections might bear discriminative transcriptional signatures. Gene expression profiles were obtained for 131 peripheral blood samples from pediatric patients with acute infections caused by influenza A virus, Gram-negative (Escherichia coli) or Gram-positive (Staphylococcus aureus and Streptococcus pneumoniae) bacteria. Thirty-five genes were identified that best discriminate patients with influenza A virus infection from patients with either E coli or S pneumoniae infection. These genes classified with 95% accuracy (35 of 37 samples) an independent set of patients with either influenza A, E coli, or S pneumoniae infection. A different signature discriminated patients with E coli versus S aureus infections with 85% accuracy (34 of 40). Furthermore, distinctive gene expression patterns were observed in patients presenting with respiratory infections of different etiologies. Thus, microarray analyses of patient peripheral blood leukocytes might assist in the differential diagnosis of infectious diseases. Keywords: expression analysis

2007-03-16 | GSE6269 | GEO

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