Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA295138 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Diagnosis of childhood bacterial and viral infection using host RNA expression [validation set]
| PRJNA295137 | ENA
Project description:Diagnosis of childhood bacterial and viral infection using host RNA expression
| PRJNA295175 | ENA
Project description:Diagnosis of childhood bacterial and viral infection using host RNA expression - Inflammatory validation cohort
| PRJNA318887 | ENA
Project description:Diagnosis of childhood bacterial and viral infection using host RNA expression - Meningococcal validation cohort
| PRJNA319053 | ENA
Project description:Multisystem inflammatory syndrome in children (MIS-C) occurs in some children approximately 2-6 weeks following infection with SARS-CoV-2. Clinical symptoms are highly overlapping with Kawasaki disease (KD) and bacterial (DB) and viral (DV) infections, making diagnosis particularly challenging. Host whole blood transcriptomics can reveal specific combinations of host genes whose expression patterns can distinguish between disease groups of interest. We performed whole blood RNA-Sequencing of individuals with MIS-C, KD, bacterial and viral infections to identify a number of host genes that, when combined, could be used to diagnose MIS-C. This data contains processed data only. Raw data will be available via a controlled access archive.
2023-06-27 | E-MTAB-12793 | biostudies-arrayexpress
Project description:<p>Accurate tests for microbiologic diagnosis of lower respiratory tract infections (LRTI) are needed. Gene expression profiling of whole blood represents a powerful new approach for analysis of the host response during respiratory infection that can be used to supplement pathogen detection testing. Using qPCR, we prospectively validated the differential expression of 10 genes previously shown to discriminate bacterial and non-bacterial LRTI confirming the utility of this approach. In addition, a novel approach using RNAseq analysis identified 141 genes differentially expressed in LRTI subjects with bacterial infection. Using "pathway-informed" dimension reduction, we identified a novel 11 gene set (selected from lymphocyte, α-linoleic acid metabolism, and IGF regulation pathways) and demonstrated a predictive accuracy for bacterial LRTI (nested CV-AUC=0.87). RNAseq represents a new and an unbiased tool to evaluate host gene expression for the diagnosis of LRTI.</p>
| phs001248 | dbGaP
Project description:Pneumonia stands as the primary cause of death among children under five, with its onset attributed to a broad spectrum of microorganisms. Diagnosis poses an ongoing challenge, relying on clinical or microbiological criteria, often resulting in delayed and inaccurate treatment and unnecessary therapy. Our research focuses on identifying host transcriptomic biomarkers in the blood of children affected by viral and bacterial pneumonia, alongside healthy controls. The main goal is to establish a gene-expression signature enhancing disease diagnosis and management. We conducted an analysis of a total of 192 whole blood samples, comprising 38 controls and 154 viral and bacterial pneumonia patients recruited through the EUCLIDS clinical network. Our investigation identified 5,486 differentially expressed genes (DEGs) when comparing blood RNA from pneumonia patients with healthy controls. Functional enrichment analysis highlighted pathways related to the immune system response, encompassing neutrophil degranulation, humoral immune response, and various inflammatory pathways. In the comparative analysis of gene expression between viral and bacterial pneumonia patients, we identified 272 DEGs. Gene set analysis revealed a significant difference in pathway enrichment for the immune response, contingent on the over-regulation of gene sets in viral or bacterial pneumonias. Furthermore, we identified a 5-transcript host signature specifically designed to distinguish between viral and bacterial pediatric pneumonia (FAM20A, BAG3, TDRD9, MXRA7 and KLF14; AUC: 0.95 [0.88–1.00]), pseudo-validated in a cohort including probable bacterial and viral patients (AUC: 0.87 [0.77–0.97]). This signature holds the potential to enhance the accuracy of previously described general transcript-based signatures for viral and bacterial infections.
2024-12-09 | GSE261482 | GEO
Project description:Viral infections are among the most common causes for fever without an apparent source (FWS) in young children; however, many febrile children are treated with antibiotics despite the absence of bacterial infection. Adenovirus, human herpesvirus 6 (HHV-6) and enterovirus are detected in children with FWS more often than other viral species. Virus and bacteria interact with pattern recognition receptors in circulating blood leukocytes and trigger specific host transcriptional programs that mediate immune response, and unique transcriptional signatures may be ascertained to discriminate between viral and bacterial causes for children with FWS. Microarray analyses were conducted on peripheral blood samples obtained from 51 pediatric patients with confirmed adenovirus, human herpesvirus 6 (HHV-6), enterovirus or bacterial infection. Whole blood transcriptional profiles could clearly distinguish febrile children from healthy controls, and febrile children with viral infections from afebrile children carrying the same virus. Molecular pathways regulating host immune response were the most affected in febrile children with infection. Pattern recognition programs were prominently activated in all febrile children with infection, while differential activation of transcriptional programs was observed among viral species. Interferon signaling pathway was uniquely activated in children with febrile viral infection, while a different set of pathways was uniquely activated in children with bacterial infection. Transcriptional signatures were identified and classified febrile children with viral or bacterial infection with 87% overall accuracy, an improvement from the current clinical practice of deducing from white blood cell (WBC) count status. Similar degree of accuracy was observed when we validated the signature probes on data sets from an independent study with different microarray platforms. The current study confirms the clinical utility of blood transcriptional analysis, suggests the composition of transcriptional signatures which can be used to ascertain the infectious etiology of febrile young children without an apparent source, thus limit the overuse of antibiotics on febrile children presenting with this common clinical complaint. Total RNA samples extracted from whole blood of young children were processed for hybridization onto Illumina Human-HT12 version 4 beadchips, and differential expression of the transcripts was analyzed between sick children with either viral or bacterial infection and healthy children.
2012-10-15 | E-GEOD-40396 | biostudies-arrayexpress
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
Project description:To elucidate key pathways in the host transcriptome of patients infected with SARS-CoV-2, we used RNA sequencing (RNA Seq) to analyze nasopharyngeal (NP) swab and whole blood (WB) samples from 333 COVID-19 patients and controls, including patients with other viral and bacterial infections. Analyses of differentially expressed genes (DEGs) and pathways was performed relative to other infections (e.g. influenza, other seasonal coronaviruses, bacterial sepsis) in both NP swabs and WB. Comparative COVID-19 host responses between NP swabs and WB were examined. Both hospitalized patients and outpatients exhibited upregulation of interferon-associated pathways, although heightened and more robust inflammatory and immune responses were observed in hospitalized patients with more clinically severe disease. A two-layer machine learning-based classifier, run on an independent test set of NP swab samples, was able to discriminate between COVID-19 and non-COVID-19 infectious or non-infectious acute respiratory illness using complete (>1,000 genes), medium (<100) and small (<20) gene biomarker panels with 85.1%-86.5% accuracy, respectively. These findings demonstrate that SARS-CoV-2 infection has a distinct biosignature that differs between NP swabs and WB and can be leveraged for differential diagnosis of COVID-19 disease.
2020-12-15 | GSE163151 | GEO