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.
Sex, Specimen part, Subject, Time
View SamplesInvasive 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.
Sex, Specimen part
View SamplesCarnitine 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.
Sex, Age, Specimen part
View SamplesThis SuperSeries is composed of the SubSeries listed below.
Transcriptome analysis of Wnt3a-treated triple-negative breast cancer cells.
Cell line
View SamplesFor this study, we selected, from the French Sarcoma Group (FSG) database, soft tissue sarcomas with no recurrent chromosomal translocations and for which a frozen tissue of the untreated primary tumor was available. Three hundred and ten sarcomas have been studied. They are split in two cohorts.
Validated prediction of clinical outcome in sarcomas and multiple types of cancer on the basis of a gene expression signature related to genome complexity.
Specimen part, Disease, Time
View SamplesIdentification of predictive markers of response to treatment is a major objective in breast cancer. A major problem in clinical sampling is the variability of RNA templates, requiring accurate management of tumour material and subsequent analyses for future translation in clinical practice. Our aim was to establish the feasibility and reliability of high throughput RNA analysis in a prospective trial.
Importance of pre-analytical steps for transcriptome and RT-qPCR analyses in the context of the phase II randomised multicentre trial REMAGUS02 of neoadjuvant chemotherapy in breast cancer patients.
Specimen part, Disease stage
View SamplesFor this study, we selected, from the French Sarcoma Group (FSG) database, soft tissue sarcomas with no recurrent chromosomal translocations and for which frozen tissue of the untreated primary tumor was available. One hundred and eighty-three cases were studied.
No associated publication
Specimen part, Disease
View SamplesTranscriptome analysis of 130 breast cancer samples (41 TNBC; 30 Her2; 30 Luminal B and 29 Luminal A), 11 normal breast tissue samples and 14 TNBC cell lines.
Transcriptome analysis of Wnt3a-treated triple-negative breast cancer cells.
Cell line
View SamplesTranscriptome analysis of 130 breast cancer samples (41 TNBC; 30 Her2; 30 Luminal B and 29 Luminal A), 11 normal breast tissue samples and 14 TNBC cell lines.
Transcriptome analysis of Wnt3a-treated triple-negative breast cancer cells.
Cell line
View SamplesThe distinction between primary and secondary ovarian tumors may be challenging for pathologists.
A genomic and transcriptomic approach for a differential diagnosis between primary and secondary ovarian carcinomas in patients with a previous history of breast cancer.
Specimen part, Disease stage
View Samples