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A 5-transcript signature for discriminating viral and bacterial etiology in pediatric pneumonia

ABSTRACT: A 5-transcript signature for discriminating viral and bacterial etiology in pediatric pneumonia

PROVIDER: PRJNA1087273 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR28330200_1.fastq.gz	Fastqsanger.gz
	SRR28330200_2.fastq.gz	Fastqsanger.gz
	SRR28330300_1.fastq.gz	Fastqsanger.gz
	SRR28330300_2.fastq.gz	Fastqsanger.gz
	SRR28330201_1.fastq.gz	Fastqsanger.gz

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A 5-transcript signature for discriminating viral and bacterial etiology in pediatric pneumonia

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.

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A 5-transcript signature for discriminating viral and bacterial etiology in pediatric pneumonia.

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RNAseq data of blood samples from hospitalized children with viral and bacterial pneumonia used to validate the novel 5-transcript signature that differentiates bacterial from viral pneumonia

Project description:Pneumonia remains the leading cause of death in children under five, but existing diagnostic methods frequently lead to either insufficient or excessive treatments. Our study aims to bridge this gap by identifying host transcriptomic biomarkers in the blood of children with confirmed viral or bacterial pneumonia. Using RNA sequencing, we identified and validated in an independent cohort a 5-transcript signature that accurately differentiates bacterial from viral pneumonia. This signature has considerable potential to improve diagnostic accuracy for pediatric pneumonia, minimizing delays in diagnosis and avoiding unnecessary treatments, with the possibility of significantly impacting clinical practice. a strand specific library preparation was completed using NEBNext® Ultra™ II mRNA kit (NEB) and NEB rRNA/globin depletion probes following manufacturer’s recommendations. Individual libraries were normalized using Qubit, pooled together and diluted. The sequencing was performed using a 150 paired-end configuration in a Novaseq6000 platform. Quality control of raw data was carried out using FastQC, alignment and read counting were performed using STAR, alignment filtering was done with SAMtools and read counting was carried out using FeatureCounts. RNAseq data was processed for batch correction using control samples and COMBAT-Seq package.

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Blood transcriptome indicates different disease etiology in pediatric ANCA-associated vasculitis subtypes.

Project description:Purpose: The goal of this study was to compare gene expression profiles from whole blood RNA-Seq data from pediatric ANCA-associated vasculitis patients to identify mechanistic endotypes. Methods: Whole blood mRNA profiles were generated from pediatric and adult vasculitis patients using Illumina GAIIx or Hi Seq 2500 RNA-Seq. Fastq files were alinged to human genome version GRC38.91 using STAR v2.6 and count data was generated with HTSEQ v0.61p1. Counts that passed quality and minimum expression filteres were anlayzed by hierarchical clustering based on Euclidean distance, and then for differential expression with DESeq2. Results: Our workflow identified 14,156 transcripts consistently expressed in vasculitis patients. Distance hierarchcial clustering identified three groups within these pediatric samples, and two groups plus one outlier in the adult samples. The two largest pediatric clusters were used as groups for differential expression analysis with the DESeq2 package for R. DE analysis identified 3,809 differentially expressed genes between clusters. Between the two adult groups, 1,682 genes were differentially expressed. DE genes were anlayzed for enrichment using ReatomePA as the reference database. This analysis revealed that genes involved in T-cell mediated immunity are more highly expressed in pediatric patients that fell into one endotype and those involved in neutrophil and Toll-like receptor mediated inflammation are more highly expressed in pediatric patients that fell into the other endotype group. Similar results were also seen in adults. Conclusions: This study utilizes RNA-Seq data to investigate the etiologies of ANCA-associated vasculitis. Using this approach we have identified T-cell mediated immunity and Toll-like receptor mediated inflammation as potentially differing inflammatory mechanisms in both pediatric and adult vasculitis patients that do not fall along normal diagnostic criteria.

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