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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

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

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

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

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Fungal Dysbiosis in Post-Influenza MRSA Infection

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

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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.

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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

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