Blood bacterial DNAemia
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PROVIDER: PRJEB30560 | ENA |
REPOSITORIES: ENA
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Project description:Longitudinal Gene expression profiling of whole blood from critically ill influenza and bacterial pneumonia patients. In addition before vs 7 days post influenza vaccination volunteer samples are assayed. 3 groups of samples. First is bacterial pneumonia patients with 6 subjects sampled for up to 5 days. Second group is severe influenza infection with 4 subjects sampled for up to 5 days. Third group is influenza vaccination with 18 subjects sampled before and 7 days post vaccination.
2011-06-30 | E-GEOD-20346 | biostudies-arrayexpress
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.
2012-08-15 | GSE40012 | GEO
Project description:A pressing clinical challenge is identifying the etiologic basis of acute respiratory illness. Without reliable diagnostics, the uncertainty associated with this clinical entity leads to a significant, inappropriate use of antibacterials. Use of host peripheral blood gene expression data to classify individuals with bacterial infection, viral infection, or non-infection represents a complementary diagnostic approach. Patients with respiratory tract infection along with ill, non-infected controls were enrolled through the emergency department or undergraduate student health services. Whole blood was obtained to generate gene expression profiles. These profiles were then used to generate signatures of bacterial acute respiratory infection, viral acute respiratory infection, and non-infectious illness. 273 subjects were ascertained for this analysis. This included 88 patients with non-infectious illness, 115 with viral acute respiratory infection, and 70 with bacterial acute respiratory infection. Samples were obtained at the time of enrollment, which was at initial clinical presentation. Total RNA was extracted from human blood using the PAXgene Blood RNA Kit. Microarray data were generated using the GeneChip Human Genome U133A 2.0 Array. Microarrays were generated in two microarray batches with seven overlapping samples giving rise to 280 total microarray experiments.
2016-01-20 | E-GEOD-63990 | biostudies-arrayexpress
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
2012-08-14 | E-GEOD-40012 | biostudies-arrayexpress
Project description:Genome wise DNA methylation profiling of peripheral blood mononuclear cells (PBMCs) obtained from younger sedentary (Y-SED), older sedentary (O-SED) and older aerobically exercise trained (O-Ex) human subjects. The Illumina 450K methylation beadchip array was used to obtain DNA methylation profiles across approximately 450,000 CpG dinucleotide methylation loci in DNA isolated from PBMCs. Samples include 12 Y-SED subjects, 15 O-SED subjects and 11 O-Ex subjects.
2020-10-01 | GSE85311 | GEO
Project description:The aim of present study was to describe the genetic pathways activated during the community acquired bacterial meningitis (BM) by using genome-wide RNA expression profiling combined with functional annotation of transcriptional changes. We included 21 patients with BM hospitalized in 2008. The control group consisted of 18 healthy subjects. The RNA was extracted from whole blood, globin mRNA was depleted and gene expression profiling was performed with GeneChip Human Gene 1.0 ST Arrays enabling the analysis of 28,869 genes. Gene expression profile data were analyzed using Bioconductor packages and Bayesian modeling. Functional annotation of the enriched gene sets was used to define changed genetic networks. We also analyzed if the gene expression profile depends on the clinical course and outcome. In order to verify the genechip results, we chose ten functionally relevant genes with high statistical significance (CD177, IL1R2, IL18R1, IL18RAP, OLFM4, TLR5, CPA3, FCER1A, IL5RA, IL7R) and performed quantitative real-time (qRT) PCR.We identified the significant differences at p values of <0.05 in 8569 genes and after False Discovery Rate (FDR) correction, total of 5500 genes remained significant at p value of <0.01. Quantitative RT-PCR confirmed differential expression for selected genes. Functional annotation and network analysis indicated that most of the genes were related to activation of humoral and cellular immune responses (enrichment score 43). Those changes were found in adults and in children with BM compared to the healthy controls. Gene expression profile didn’t depend on the clinical outcome, but there was very strong influence by the type of the pathogen. This study demonstrates a strong functional genomic evidence of the over-active immune response during bacterial meningitis. This hyperactive response possibly explains the complicated clinical course of this disease. 22 bacterial meningitis patients and 18 healthy controls
2012-09-05 | E-GEOD-40586 | biostudies-arrayexpress
Project description:Blood serum from 160 Childhood Asthma Management Program (CAMP) subjects were microRNA profiles with 13 subjects profiled twice. Each subject has diverse clinical phenotypes notably spirometry related phenotypes.
2016-06-09 | GSE74770 | GEO
Project description:Sepsis is a clinical syndrome that can be caused by bacteria or fungi. Early knowledge on the nature of the causative agent is a prerequisite for targeted anti-microbial therapy. Besides currently used detection methods like blood culture and PCR-based assays, the analysis of the transcriptional response of the host to infecting organisms holds great promise. In this study, we aim to examine the transcriptional footprint of infections caused by the bacterial pathogens Staphylococcus aureus and Escherichia coli and the fungal pathogens Candida albicans and Aspergillus fumigatus in a human whole-blood model. Moreover, we use the expression information to build a random forest classifier to determine if the pathogen is bacterial, fungal or neither of the two. After normalizing the transcription intensities using stably expressed reference genes, we filtered the gene set for biomarkers of bacterial or fungal blood infections. This selection is based on differential expression and an additional gene relevance measure. In this way, we identified 38 biomarker genes, including IL6, SOCS3, and IRG1 which were already associated to sepsis by other studies. Using these genes, we trained the classifier and assessed its performance. It yielded a 96% accuracy (sensitivities >93%, specificities >97%) for a 10-fold stratified cross-validation and a 92% accuracy (sensitivities and specificities >83%) for an additional dataset comprising Cryptococcus neoformans infections. Furthermore, the noise-robustness of the classifier suggests high rates of correct class predictions on datasets of new species. In conclusion, this genome-wide approach demonstrates an effective feature selection process in combination with the construction of a well-performing classification model. Further analyses of genes with pathogen-dependent expression patterns can provide insights into the systemic host responses, which may lead to new anti-microbial therapeutic advances. Analysis of innate immune activation on the basis of gene expression of whole blood cells during ex vivo whole blood infection with bacterial (Staphylococcus aureus, Escherichia coli) and fungal pathogens (Candida albicans, Aspergillus fumigatus) in comparison to mock-treated blood.
2015-03-11 | E-GEOD-65088 | biostudies-arrayexpress
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.
2012-10-15 | GSE40396 | GEO
Project description:Background MicroRNA expression is frequently dysregulated in cancer and it could be used potentially as a disease classifier and a prognostic tool in cancer. It has been reported that the cancer associated specific microRNAs were stably detected in blood. The objective of this study was to discover a panel of circulating microRNAs as potential ER+/HER2- breast cancer biomarkers. Methods We compared levels of circulating microRNAs in blood samples from 11 ER+/HER2- advanced breast cancer patients with age-matched 5 control subjects by using microarray-based expression profiling. We validated the level of microRNAs by real-time quantitative polymerase cycle reaction (RT-qPCR) in 40 control subjects, 180 early breast cancer patients (EBC), and 52 metastatic breast cancer patients (MBC). Then, we assessed the association between the levels of microRNA and clinical outcomes of ER+/HER2- metastatic breast cancer. Background MicroRNA expression is frequently dysregulated in cancer and it could be used potentially as a disease classifier and a prognostic tool in cancer. It has been reported that the cancer associated specific microRNAs were stably detected in blood. The objective of this study was to discover a panel of circulating microRNAs as potential ER+/HER2- breast cancer biomarkers. Methods We compared levels of circulating microRNAs in blood samples from 11 ER+/HER2- advanced breast cancer patients with age-matched 5 control subjects by using microarray-based expression profiling. We validated the level of microRNAs by real-time quantitative polymerase cycle reaction (RT-qPCR) in 40 control subjects, 180 early breast cancer patients (EBC), and 52 metastatic breast cancer patients (MBC). Then, we assessed the association between the levels of microRNA and clinical outcomes of ER+/HER2- metastatic breast cancer.
2013-12-12 | GSE53179 | GEO