Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Transcription profiling of human patients suffering FROM bacterial infections TO investigate plasticity OF THE systemic inflammatory response TO acute infection during critical illness

ABSTRACT: Diagnosis of acute infection in the critically ill remains a challenge. Changes in physiologic parameters and existing molecular diagnostics are not specific and microbiologic confirmation of infection can take days. As cellular first responders, preclinical studies indicate that circulating leukocytes are activated in response to bacterial infection, manifesting infection-specific transcriptional programs. We hypothesized that circulating leukocyte transcriptional profiles can be used to diagnose infection and monitor response to therapy in the critically ill. Experiment Overall Design: A translational research approach was employed. Fifteen mice underwent intratracheal (i.t.) injections of live P. aeruginosa, P. aeruginosa endotoxin, live S. pneumoniae, or normal saline. At 24 hours after injury, GeneChip microarray analysis of circulating buffy coat RNA identified 219 genes that distinguished between the pulmonary insults and differences in 7-day mortality. Similarly, buffy coat microarray expression profiles were generated from 20 mechanically ventilated patients every two days for up to three weeks; 11 of these patients developed pneumonia. Principal components analysis of the expression levels of the human orthologs for the mouse genes resulted in gene expression trajectories that describe the cohort of 11 patients as they develop, are treated for, and convalesce from ventilator-associated pneumonia (VAP). When the VAP microarray profiles were analyzed independently, 85 genes were identified with consistent changes in abundance during the seven days bracketing the diagnosis of VAP. The estimated prediction accuracy in this small patient cohort was 91%. The abundance of select transcripts was validated using real-time PCR. Principal components analysis of the average microarray-measured abundances of these genes defined a general trajectory (riboleukogram) for the onset of acute infection in this patient population. Individual riboleukograms demonstrated that the onset and course of infection are specific for each individual, but as patients recover from infection the riboleukograms converge. The gene ontologies of the co-expression networks indicate that leukocyte chemokines and granule maturation are especially informative for pneumonia diagnostics.

ORGANISM(S): Homo sapiens

SUBMITTER: J Perren Cobb

PROVIDER: E-GEOD-6377 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:INTRO: The ongoing challenge of accurately diagnosing infection in the ICU motivates a search for novel molecular diagnostics. In the current study, we tested the hypothesis that the informational content of circulating leukocytes differs, thereby allowing one to rank leukocyte populations on their potential to contribute to RNA diagnostics for pneumonia. METHODS: Sixteen patients (10 male, 6 female) at risk for VAP were entered into our IRB-approved study that collects blood and clinical data every 48 hours for up to 21 days. Four of the sixteen patients developed VAP as diagnosed and treated by the attending ICU physician. Previously reported blood protocols were used to isolate buffy coat, enriched neutrophil, and enriched monocyte populations by using negative selection. Cellular purity was assessed by FACS for one of the 4 VAP patients. Genome-wide expression analysis was performed on RMA-normalized signal from Affymetrix U133 2.0 Plus GeneChips. EDGE software (FDR=0.10) was used to determine changes in mRNA abundance over time for each cell population. RESULTS: During the 5-day window in which each of the four patients (all males) developed VAP, significant changes in gene expression were observed (Table 2), but the information content (number of genes altered) varied across leukocyte populations. These differences were not due to signal variance (coefficient of variation, CV) or differences in the number of samples available for analysis. Moreover, only 0.6% of the monocyte gene list overlaps with the neutrophil list, arguing that neutrophil contamination of monocyte populations is insufficient to explain the 40-fold difference in gene number. CONCLUSIONS: Enriched monocyte populations from circulating blood provide far more genes for study of the host response to VAP than the other 2 cell populations studied. This information should be considered when designing blood gene expression profiling experiments in patients at risk for sepsis. Keywords: time course Four patients over several timepoints measured three different cell types for VAP signal

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

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Transcription profiling of human patients suffering FROM bacterial infections TO investigate plasticity OF THE systemic inflammatory response TO acute infection during critical illness

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