Dataset Information

Gene Array to Predict Bacterial Infection with in Respiratory Illness

ABSTRACT:

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.

PROVIDER: phs001248 | dbGaP |

SECONDARY ACCESSION(S): PRJNA353736PRJNA353737

REPOSITORIES: dbGaP

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	GapExchange_phs001248.v1.p1.xml	Xml
	dbGaPEx2.1.5.xsd	Other
	Study_Report.phs001248.Hospitalization_LRTI.v1.p1.MULTI.pdf	Pdf
	manifest_phs001248.Hospitalization_LRTI.v1.p1.c1.HMB-IRB-NPU.pdf	Pdf
	datadict_v2.xsl	Other

Items per page:

1 - 5 of 15

Similar Datasets

Project description:Casandra W. Philipson, Josep Bassaganya-Riera, Monica Viladomiu, Barbara Kronsteiner, Vida Abedi, Stefan Hoops, Pawel Michalak, Lin Kang, Stephen E. Girardin & Raquel Hontecillas. Modeling the Regulatory Mechanisms by Which NLRX1 Modulates Innate Immune Responses to Helicobacter pylori Infection. PLOS ONE 10, 9 (2015). Helicobacter pylori colonizes half of the world's population as the dominant member of the gastric microbiota resulting in a lifelong chronic infection. Host responses toward the bacterium can result in asymptomatic, pathogenic or even favorable health outcomes; however, mechanisms underlying the dual role of H. pylori as a commensal versus pathogenic organism are not well characterized. Recent evidence suggests mononuclear phagocytes are largely involved in shaping dominant immunity during infection mediating the balance between host tolerance and succumbing to overt disease. We combined computational modeling, bioinformatics and experimental validation in order to investigate interactions between macrophages and intracellular H. pylori. Global transcriptomic analysis on bone marrow-derived macrophages (BMDM) in a gentamycin protection assay at six time points unveiled the presence of three sequential host response waves: an early transient regulatory gene module followed by sustained and late effector responses. Kinetic behaviors of pattern recognition receptors (PRRs) are linked to differential expression of spatiotemporal response waves and function to induce effector immunity through extracellular and intracellular detection of H. pylori. We report that bacterial interaction with the host intracellular environment caused significant suppression of regulatory NLRC3 and NLRX1 in a pattern inverse to early regulatory responses. To further delineate complex immune responses and pathway crosstalk between effector and regulatory PRRs, we built a computational model calibrated using time-series RNAseq data. Our validated computational hypotheses are that: 1) NLRX1 expression regulates bacterial burden in macrophages; and 2) early host response cytokines down-regulate NLRX1 expression through a negative feedback circuit. This paper applies modeling approaches to characterize the regulatory role of NLRX1 in mechanisms of host tolerance employed by macrophages to respond to and/or to co-exist with intracellular H. pylori.

Project description:Introduction: Diagnosis of severe influenza pneumonia remains challenging because of the lack of correlation between presence of influenza virus and patient’s clinical status. We conducted gene expression profiling in the whole blood of critically ill patients to identify a gene signature that would allow clinicians to distinguish influenza infection from other causes of severe respiratory failure (e.g. bacterial pneumonia, non-infective systemic inflammatory response syndrome). Methods: Whole blood samples were collected from critically ill individuals and assayed on Illumina HT-12 gene expression beadarrays. Differentially expressed genes were determined by linear mixed model analysis and over-represented biological pathways determined using GeneGo MetaCore. Results: The gene expression profile of H1N1 influenza A pneumonia was distinctly different from bacterial pneumonia and systemic inflammatory response syndrome. The influenza gene expression profile is characterized by up-regulation of genes from cell cycle regulation, apoptosis and DNA-damage response pathways. In contrast, no distinctive gene-expression signature was found in patients with bacterial pneumonia or systemic inflammatory response syndrome. The gene expression profile of influenza infection persisted through five days of follow-up. Furthermore, in patients with primary H1N1 influenza A infection who subsequently developed bacterial co-infection, the influenza gene-expression signature remained unaltered, despite the presence of a super-imposed bacterial infection. Conclusions: The whole blood expression profiling data indicates that the host response to influenza pneumonia is distinctly different from that caused by bacterial pathogens. This information may speed up identification of the cause of infection in patients presenting with severe respiratory failure, allowing appropriate patient care to be undertaken more rapidly. Daily PAXgene samples for up to 5 days for; influenza A pneumonia patients (n=8), bacterial pneumonia patients (n=16), mixed bacterial and influenza A pneumonia patients (n=3), systemic inflammatory response patients (SIRS, n=13). Days 1 and 5 PAXgene samples for healthy control individuals

Dataset Information

Gene Array to Predict Bacterial Infection with in Respiratory Illness

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets