Background: Septic shock heterogeneity has important implications for the conduct of clinical trials and individual patient management. We previously addressed this heterogeneity by indentifying 3 putative subclasses of children with septic shock based on a 100-gene expression signature corresponding to adaptive immunity and glucocorticoid receptor signaling. Herein we attempted to prospectively validate the existence of these gene expression-based subclasses in a validation cohort. Methods: Gene expression mosaics were generated from the 100 class-defining genes for 82 individual patients in the validation cohort. Patients were classified into 1 of 3 subclasses (A, B, or C) based on color and pattern similarity relative to reference mosaics generated from the original derivation cohort. Separate classifications were conducted by 21 individual clinicians and a computer-based algorithm. After subclassification the clinical database was mined for clinical phenotyping. Results: In the final consensus subclassification generated by clinicians, subclass A patients had a higher illness severity, as measured by illness severity scores and maximal organ failure, relative to subclasses B and C. The k coefficient across all possible inter-evaluator comparisons was 0.633. Similar observations were made based on the computer-generated subclassification. Patients in subclass A were also characterized by repression of a large number of genes having functional annotations related to zinc biology. Conclusions: We have validated the existence of subclasses of children with septic shock based on a biologically relevant, 100-gene expression signature. The subclasses can be indentified by clinicians without formal bioinformatics training, at a clinically relevant time point, and have clinically relevant phenotypic differences.
The influence of developmental age on the early transcriptomic response of children with septic shock.
Age, Specimen part, Disease, Disease stage
View SamplesAcute myeloid leukemia (AML) continues to have the lowest survival rates of all leukemias. Therefore, new therapeutic strategies are urgently needed to improve clinical outcomes for AML patients. Here, we report a novel role for Wilms’ tumor 1-associated protein (WTAP) in pathogenesis of AML. We have performed RNA-Seq in K562 cells with knockdown of WTAP to ascertain which genes it regulates. Overall design: We have 2 replicates of total RNA for K562 cells and 2 replicates with WTAP knocked down
WTAP is a novel oncogenic protein in acute myeloid leukemia.
Subject
View SamplesThis SuperSeries is composed of the SubSeries listed below.
The transcriptional regulator Aire binds to and activates super-enhancers.
Sex, Age, Specimen part, Treatment
View SamplesMicroarray profiles of MECs from mice treated with topoisomerase inhibitors
The transcriptional regulator Aire binds to and activates super-enhancers.
Sex, Age, Treatment
View SamplesHigh quality RNA was extracted from the whole seedlings (Combined root and leaf samples) using TRI Reagent (Ambion, Inc. USA) and pooled from 12 independent stressed and non-stressed plant samples separately, and treated with DNase-I (QIAGEN GmbH, Germany). Subsequently, RNA cleanup was carried out using RNeasy Plant Mini Kit (QIAGEN GmbH, Germany) and 5 ug of total RNA from each sample in triplicates were reverse-transcribed to double stranded cDNA using the GeneChipᆴ One-Cycle cDNA Synthesis Kit. The biotin-labelled cRNA was made using the GeneChipᆴ IVT Labelling Kit (Affymetrix, CA, USA). Twenty microgram of cRNA samples was fragmented and out of which which 7.5 ug cRNA were hybridized for 16 hours at 45C to the Affymetrix GeneChipᆴ Rice Genome Array (Santa Clara, CA, USA). After washing and staining with R-phycoerythrin streptavidin in a Fluidics Station, using the Genechipᆴ Fluidics Station 450, the arrays were scanned by the Genechipᆴ 3000 Scanner. The chip images were scanned and extracted using default settings and the CEL files were produced with the Affymetrix GeneChip Operating Software (GCOS 1.2). The resulting .CEL files were imported into the GeneSpring GX 10 (Agilent Technologies Inc, Santa Clara CA) and normalized with the PLIER16 algorithm. The resulting expression values were log2-transformed. Average log signal intensity values of three technical replicates for each sample were used for advance analysis.
Comparative analysis of drought-responsive transcriptome in Indica rice genotypes with contrasting drought tolerance.
Specimen part
View SamplesVS94 gene expression at different time-points in SAPI medium in absence and presence of AI-2 was studied.
Temporal regulation of enterohemorrhagic Escherichia coli virulence mediated by autoinducer-2.
No sample metadata fields
View SamplesFor the microarray experiments, 10 g glass wool (Corning Glass Works, Corning, N.Y.) were used to form biofilms (30) in 250 mL in 1 L Erlenmeyer shake flasks which were inoculated with overnight cultures diluted that were 1:100. For EHEC with 7-hydroxyindole and isatin, 1000 mM 7-hydroxyindole in 250 mL DMF, 250 mM isatin in 250 mL DMF, or 250 mL DMF alone were added to cells grown in LB. The cells were shaken at 250 rpm and 30C for 7 hours to form biofilms on the glass wool, and RNA was isolated from the suspension cells and the biofilm.
Enterohemorrhagic Escherichia coli biofilms are inhibited by 7-hydroxyindole and stimulated by isatin.
No sample metadata fields
View SamplesE coli O157H7 (EHEC) wildtype 7 hour biofilm cells studied in LB glucose medium with and without chemicals - Epinephrine, Norepinephrine and Indole. Biofilm cells were cultured from glass wool.
Differential effects of epinephrine, norepinephrine, and indole on Escherichia coli O157:H7 chemotaxis, colonization, and gene expression.
No sample metadata fields
View SamplesThis SuperSeries is composed of the SubSeries listed below.
Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering.
No sample metadata fields
View SamplesGenome-wide identification of bona fide targets of transcription factors in mammalian cells is still a challenge. We present a novel integrated computational and experimental approach to identify direct targets of a transcription factor. This consists in measuring time-course (dynamic) gene expression profiles upon perturbation of the transcription factor under study, and in applying a novel reverse-engineering algorithm (TSNI) to rank genes according to their probability of being direct targets. Using primary keratinocytes as a model system, we identified novel transcriptional target genes of Trp63, a crucial regulator of skin development. TSNI-predicted Trp63 target genes were validated by Trp63 knockdown and by ChIP-chip to identify Trp63-bound regions in vivo. Our study revealed that short sampling times, in the order of minutes, are needed to capture the dynamics of gene expression in mammalian cells. We show that Trp63 transiently regulates a subset of its direct targets, thus highlighting the importance of considering temporal dynamics when identifying transcriptional targets. Using this approach, we uncovered a previously unsuspected transient regulation of the AP-1 complex by Trp63, through direct regulation of a subset of AP-1 components. The integrated experimental and computational approach described here is readily applicable to other transcription factors in mammalian systems and is complementary to genome-wide identification of transcription factor binding sites.
Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering.
No sample metadata fields
View Samples