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.
Biomarker-based classification of bacterial and fungal whole-blood infections in a genome-wide expression study.
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Carnitine is a water soluble quaternary amine which is essential for normal function of all tissues.
Effect of L-carnitine on the hepatic transcript profile in piglets as animal model.
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The purpose of this study was to characterize the transcriptional effects induced by intramuscular IFN-beta-1a treatment (Avonex, 30 g once weekly) in patients with relapsing-remitting form of multiple sclerosis (MS). By using Affymetrix DNA microarrays, we obtained genome-wide expression profiles of peripheral blood mononuclear cells from 24 MS patients within the first four weeks of IFN-beta administration.
Network analysis of transcriptional regulation in response to intramuscular interferon-β-1a multiple sclerosis treatment.
Human fibroblasts at different population doublings were treated with low amounts of rotenone (mild stress) and compared to untreated fibroblasts. Two different cell lines were used (MRC-5, HFF). Illumina sequencing (HiSeq2000) was applied to generate 50bp single-end reads. Jena Centre for Systems Biology of Ageing - JenAge (www.jenage.de) Overall design: 60 samples: 3 biological replicates for each group: MRC-5 cells at 4 different population doublings (PD) with and without rotenone; HFF cells at 6 different population doublings with and without rotenone
Hormetic effect of rotenone in primary human fibroblasts.
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Background. Rheumatoid arthritis (RA) is a chronic inflammatory and destructive joint disease, characterized by overexpression of pro-inflammatory/-destructive genes and other activating genes (e.g., proto-oncogenes) in the synovial membrane (SM). The gene expression in disease is often characterized by significant inter-individual variances via specific synchronization/ desynchronization of gene expression. To elucidate the contribution of the variance to the pathogenesis of disease, expression variances were tested in SM samples of RA patients, osteoarthritis (OA) patients, and normal controls (NC).
Identification of intra-group, inter-individual, and gene-specific variances in mRNA expression profiles in the rheumatoid arthritis synovial membrane.
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Two biological replicate experiments were performed to estimate the bias of the gene expression pattern of infected and non-infected HEp-2 cells. Microarrays hybridized with RNA from 2 h of non-infected HEp-2 cells were used as reference chips for the comparison with microarrays hybridized with RNA from 2 h and 4 h of eukaryotic cells exposed to wt-bacteria and .fasX-mutant. As a reference for chips hybridized with RNA prepared from 6 h p. i. and 8 h p. i. of both GAS-infected HEp-2 cells we used chips that were hybridized with RNA isolated from non-infected cells 8 h p. i. We also compared the microarray data from 2 h of non-infected HEp-2 cells with those from 8 h of non-infected HEp-2 cells to determine the influence of the extended culture on the non-infected cells. Only such genes which were differentially regulated after infection with wt-bacteria and .fasX-mutant infected cells and not differentially present in unequal amounts between the 2 h and 8 h of controls were included in the subsequent statistical analysis.
Global epithelial cell transcriptional responses reveal Streptococcus pyogenes Fas regulator activity association with bacterial aggressiveness.
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Synovial fibroblasts of 6 RA patients were treated with IL1 or PDGF-D. The aim of this study was to outline mechanism of the disease RA by a treatment with one of these cytokines.
Novel application of multi-stimuli network inference to synovial fibroblasts of rheumatoid arthritis patients.
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Comparing gene expression level by Illumina sequencing of fibroblasts after irradiation Jena Centre for Systems Biology of Ageing - JenAge (www.jenage.de) Overall design: 6 samples, 3 samples per group, 2 groups: 1) MRC-5 cells population doublings (PD) 16 and irradiation (20GY) and 2) HFF cells PD32 and irradiation (20GY)
Conserved genes and pathways in primary human fibroblast strains undergoing replicative and radiation induced senescence.
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Invasive aspergillosis (IA) is a devastating opportunistic infection and its treatment constitutes a considerable burden for the health care system. Immunocompromised patients are at an increased risk for IA, which is mainly caused by the species Aspergillus fumigatus. An early and reliable diagnosis is required to initiate the appropriate antifungal therapy. However, diagnostic sensitivity and accuracy still needs to be improved, which can be achieved at least partly by the definition of new biomarkers. Besides the direct detection of the pathogen by the current diagnostic methods, the analysis of the host response is a promising strategy towards this aim. Following this approach, we sought to identify new biomarkers for IA. For this purpose, we analyzed gene expression profiles of haematological patients and compared profiles of patients suffering from IA with non-IA patients. Based on microarray data, we applied a comprehensive feature selection using a random forest classifier. We identified the transcript coding for the S100 calcium-binding protein B (S100B) as a potential new biomarker for the diagnosis of IA. Considering the expression of this gene, we were able to classify samples from patients with IA with 82.3% sensitivity and 74.6% specificity. Moreover, we validated the expression of S100B in a real-time RT-PCR assay and we also found a down-regulation of S100B in A.fumigatus stimulated DCs. An influence on the IL1B and CXCL1 downstream levels was demonstrated by this S100B knockdown. In conclusion, this study covers an effective feature selection revealing a key regulator of the human immune response during IA. S100B may represent an additional diagnostic marker that in combination with the established techniques may improve the accuracy of IA diagnosis.
Genome-Wide Expression Profiling Reveals S100B as Biomarker for Invasive Aspergillosis.
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