Molecular Diagnostics in Sepsis: from Bedside to Bench
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus Homo sapiens
PROVIDER: GSE5663 | GEO | 2006/08/29
SECONDARY ACCESSION(S): PRJNA96877
REPOSITORIES: GEO
Similar Datasets
Project description:Accuracy of sepsis prediction was obtained using cross-validation of gene expression data from 12 human spleen samples and from 16 mouse spleen samples. For blood studies, classifiers were constructed using data from a training data set of 26 microarrays. The error rate of the classifiers was estimated on seven de-identified microarrays, and then on a subsequent cross-validation for all 33 blood microarrays. Estimates of classification accuracy of sepsis in human spleen were 67.1%; in mouse spleen, 96%; and in mouse blood, 94.4% (all estimates were based on nested cross-validation). Lists of genes with substantial changes in expression between study and control groups were used to identify nine mouse common inflammatory response genes, six of which were mapped into a single pathway using contemporary pathway analysis tools. Experiment Overall Design: Exploratory studies were conducted using spleens from septic patients and from mice with abdominal sepsis. Seven patients with sepsis after injury were identified retrospectively and compared with six injured patients. C57BL/6 male mice were subjected to cecal ligation and puncture, or to IP lipopolysaccharide. Control mice had sham laparotomy or injection of IP saline, respectively. A sepsis classification model was created and tested on blood samples from septic mice.
2008-06-14 | E-GEOD-5663 | biostudies-arrayexpress
Project description:In a prospective case-control study, we identified novel transcriptional classifiers for TB among US patients and systematically compared their accuracy to other classifiers in published studies. Whole blood from US patients was collected to accurately diagnose active TB in racially and ethnically diverse populations for RNA extraction and hybridization on Affymetrix microarrays.
2015-11-23 | E-GEOD-73408 | biostudies-arrayexpress
Project description:Iron Mouse PV3
"A computational model to understand mouse iron physiology and diseases"
By Jignesh Parmar and Pedro Mendes
Base model
This is a dynamic model of iron distribution in mice, covering seven compartments: plasma, bone marrow, red blood cells (RBC), spleen, duodenum, liver, and the rest of the body . This is mostly a physiological model with regulation by hepcidin and erythropoietin, including only a minimal amount of molecular details.
This version of the model does not include the radioactive-labelled tracer iron species that were used for parameter estimation (that is included in a separate file). This model has all parameter values already set to the best estimates obtained with the model with radioactive tracer. This model is useful to study the steady state properties of the system and as a basis for various types of simulation.
Model validation was carried out with other model files that were derived from this one and where certain parameters were altered or new interventions added.
2022-09-16 | MODEL1805140003 | BioModels
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:Melioidosis is a severe infectious disease caused by Burkholderia pseudomallei, a gram-negative bacillus classified by the NIAID as a category B priority agent. Septicemia is the most common presentation of the disease with 40% mortality rate even with appropriate treatments. Faster diagnostic procedures are required to improve therapeutic response and survival rates. We have used microarray technology to generate genome-wide transcriptional profiles (>48,000 transcripts) of whole blood obtained from patients with septicemic melioidosis (n=32), patients with sepsis caused by other pathogens (n=31), and uninfected controls (n=29). Unsupervised analyses demonstrated the existence of a whole blood transcriptional signature distinguishing patients with sepsis from control subjects. The majority of changes observed were common to both septicemic melioidosis and sepsis caused by other infections, including genes related to inflammation, interferon-related genes, neutrophils, cytotoxic cells, and T cells. Finally, class prediction analysis identified a 37 transcript candidate diagnostic signature that distinguished melioidosis from sepsis caused by other organisms with 100% and 78% accuracy in training and independent test sets, respectively. This finding was confirmed by the independent validation set, which showed 80% prediction accuracy. This signature was highly enriched in genes coding for products involved in the MHC Class II antigen processing and presentation pathway. Transcriptional patterns of whole blood RNA distinguish patients with septicemic melioidosis from patients with sepsis caused by other pathogens. Once confirmed in a large scale trial this diagnostic signature might constitute the basis of a differential diagnostic assay.
2009-07-01 | GSE13015 | GEO
Project description:Melioidosis, caused by Gram negative bacteria Burkholderia pseudomallei, is a major type of community-acquired septicemia in Southeast Asia and Northern Australia with high mortality and morbidity rate. More accurate and rapid diagnosis is needed for improving the management of septicemic melioidosis. We previously identified 37-gene candidate signature to distinguish septicemic melioidosis from sepsis due to other pathogens. The aims of this current study were to independently validate our previous biomarker and consolidate gene selection from each of our microarray data set for establishing a targeted assay for the differential diagnosis of melioidosis. Blood samples were collected from patients who presented with severe inflammatory response syndromes from 3 provincial hospitals in Northeast of Thailand during September 2009 and November 2011. Only culture-confirmed sepsis were included in the study (n=166). We generated a new microarray dataset comprising of 29 patients with septicemic melioidosis and 54 patients with sepsis due to other pathogens. Validation of the 37-gene signature using this new dataset demonstrated the prediction accuracy of approximately 80% for detecting type of sepsis. In order to develop a nanoliter-scale high throughput PCR technology, we further identified additional gene signature from this new microarray dataset and by revisiting our published data. Altogether 85 genes including 6 housekeeping genes were selected. Using multi-steps iteration approach we could reduce the number of biomarkers to 12 genes while the performance is comparable to that of the full panel. The high performance (accuracy >70%) of this 12-gene signature could be validated in a second independent set of samples. The 12-gene panel identified by our study provides high performance for the differential diagnosis of septicemic melioidosis. This finding will be useful for improving the management of septicemic melioidosis in term of diagnosis, treatment and follow up. Total RNA from whole blood obtained from patients with sepsis caused by B.pseudomallei (n=29) or other pathogens (n=54) and uninfected controls (28 healthy and 27 subjects with type 2 diabetes mellitus) were collected. In order to validate the published signature, microarray data were generated from these samples. This dataset was also used for an independent selection of signature for septicemic melioidosis. The same RNA samples were used for validation by a high throughput real-time PCR technique, Fluidigm.
2015-06-04 | E-GEOD-69528 | biostudies-arrayexpress
Project description:We have established proof of principle for the Indicator Cell Assay Platformé (iCAPé), a broadly applicable tool for blood-based diagnostics that uses specifically-selected, standardized cells as biosensors, relying on their innate ability to integrate and respond to diverse signals present in patients' blood. To develop an assay, indicator cells are exposed in vitro to serum from case or control subjects and their global differential response patterns are used to train reliable, cost-effective disease classifiers based on a small number of features. In a feasibility study, the iCAP detected pre-symptomatic disease in a murine model of amyotrophic lateral sclerosis (ALS) with 94% accuracy (p-Value=3.81E-6) and correctly identified samples from a murine Huntington's disease model as non-carriers of ALS. In a preliminary human disease assay, the iCAP detected early stage Alzheimer's disease with 72% cross-validated accuracy (p-Value=3.10E-3). For both assays, iCAP features were enriched for disease-related genes, supporting the assay's relevance for disease research.
2017-03-17 | GSE95842 | GEO
Project description:We have established proof of principle for the Indicator Cell Assay Platformé (iCAPé), a broadly applicable tool for blood-based diagnostics that uses specifically-selected, standardized cells as biosensors, relying on their innate ability to integrate and respond to diverse signals present in patientsÕ blood. To develop an assay, indicator cells are exposed in vitro to serum from case or control subjects and their global differential response patterns are used to train reliable, cost-effective disease classifiers based on a small number of features. In a feasibility study, the iCAP detected pre-symptomatic disease in a murine model of amyotrophic lateral sclerosis (ALS) with 94% accuracy (p-Value=3.81E-6) and correctly identified samples from a murine HuntingtonÕs disease model as non-carriers of ALS. In a preliminary human disease assay, the iCAP detected early stage AlzheimerÕs disease with 72% cross-validated accuracy (p-Value=3.10E-3). For both assays, iCAP features were enriched for disease-related genes, supporting the assayÕs relevance for disease research.
2017-03-17 | GSE95810 | GEO
Project description:We have established proof of principle for the Indicator Cell Assay PlatformÈ (iCAPÈ), a broadly applicable tool for blood-based diagnostics that uses specifically-selected, standardized cells as biosensors, relying on their innate ability to integrate and respond to diverse signals present in patientsê blood. To develop an assay, indicator cells are exposed in vitro to serum from case or control subjects and their global differential response patterns are used to train reliable, cost-effective disease classifiers based on a small number of features. In a feasibility study, the iCAP detected pre-symptomatic disease in a murine model of amyotrophic lateral sclerosis (ALS) with 94% accuracy (p-Value=3.81E-6) and correctly identified samples from a murine Huntingtonês disease model as non-carriers of ALS. In a preliminary human disease assay, the iCAP detected early stage Alzheimerês disease with 72% cross-validated accuracy (p-Value=3.10E-3). For both assays, iCAP features were enriched for disease-related genes, supporting the assayês relevance for disease research.
2017-03-17 | GSE95659 | GEO
Project description:In the event of a large-scale radiation exposure incident, accurate and quick assessment of radiation dose would be critical for triage and medical treatment of large numbers of potentially exposed individuals. Current methods of biodosimetry, such as the dicentric chromosome assay, are time consuming and require sophisticated equipment and highly trained personnel. Therefore, scalable biodosimetry approaches, including gene expression profiles in peripheral blood cells, are being investigated. Due to limited availability of appropriate human samples, however, biodosimetry development has relied heavily on mouse models, which are not directly applicable to human response. Therefore, to explore the feasibility of using non-human primate models to build and test a biodosimetry algorithm for use in humans, we ex vivo-irradiated peripheral blood samples from both humans and rhesus macaques to 0, 2, 5, 6, and 7 Gy, and compared the gene expression profiles 24 hours later using Agilent human microarrays. Among the dose-responsive genes in human and using non-human primate, 52 genes showed highly correlated expression patterns between the species, and were enriched in p53/DNA damage response, apoptosis, and cell cycle-related genes. When these interspecies-correlated genes were used to build biodosimetry models with using non-human primate data, the mean prediction accuracy on non-human primate samples was about 90% within 1 Gy of delivered dose in leave-one-out cross-validation. However, tests on human samples suggested that human gene expression values may need to be adjusted prior to application of the non-human primate model. A ‘multi-gene’ approach utilizing all gene values for cross-species conversion and applying the converted values on the non-human primate biodosimetry models, gave a leave-one-out cross-validation prediction accuracy for human samples highly comparable (up to 94%) to that for non-human primate. Overall, this study demonstrates that a robust NHP biodosimetry model can be built using interspecies-correlated genes, and that, by using multiple regression-based cross-species conversion of expression values, absorbed dose in human samples can be accurately predicted by the non-human primate model.
2017-08-24 | GSE102971 | GEO