ABSTRACT: OrR drosophila 3rd instar larvae were subjected to septic injury with a mixture of E.coli and S.aureus at 3h, 6h and 18h. Plasmatocytes were isolated afterwards and subjected to RNA-seq
Project description:Drosophila plasmatocyes expressed control or trxG RNAis under the plasmatocyte specific Hml promotor. 6h prior to plasmatocyte collection animals were split into control and septic injury groups and treated accordingly. Afterwards plasmatocytes were isolated and polyA RNA was subjected to RNA-seq.
Project description:Drosophila carrying Hml-SwitchGal4 were crossed to different uas-constructs (uas-eGFP, uas-PGRP-LC, uas-hop, uas-tkvQD). 3rd instar larvae were either kept as control or SwitchGal4 was activated by hormone feeding for 24h. Afterwards plasmatocytes were extracted and subjected to RNA-seq.
Project description:Drosophila 3rd instar larvae were subjected to septic injury. After 6h plasmatocytes were isolated, fixed and subjected to ChIP-seq.
Project description:To identify the presence of different chromatin states in homogeneous primary cells, Drosophila melanogaster plasmatocytes were isolated from 3rd instar larvae and subjected to cross-linked ChIP-seq using antibodies against a range of H3 histone modifications and PolII.
Project description:Drosophila melanogaster 3rd instar wandering larvae with a genetic construct for hemocyte ablation were collected along with genetically matched non-ablated animals, RNA was extracted from whole animals and used for RNA-seq.
Project description:This experiment sought to understand the transcriptomic changes that occur in the larval zebrafish heart following injury. 600 hearts were laser injured at 3 days post fertilisation, extracted 48 hours later and pooled into three groups of 200. RNA was extracted from the whole heart(s) and sent for sequencing along with 3 groups of 200 uninjured hearts, extracted and processed identically. RNA sequencing, quality control and alignment was performed by the commercial company GENEWIZ.
Project description: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. Expression data from 82 children with septic shock and 21 normal controls were generated using whole blood-derived RNA samples representing the first 24 hours of admission to the pediatric intensive care unit. The controls were used for normalization. Subsequently, we used the expression data from 100 class defining genes to validate the existence of pediatric septic shock subclasses having phenotypic differences.
Project description:Background: Septic shock is a heterogeneous syndrome within which probably exist several biological subclasses. Discovery and identification of septic shock subclasses could provide the foundation for the design of more specifically targeted therapies. Herein we tested the hypothesis that pediatric septic shock subclasses can be discovered through genome-wide expression profiling. Methods: Genome-wide expression profiling was conducted using whole blood-derived RNA from 98 children with septic shock, followed by a series of bioinformatic approaches targeted at subclass discovery and characterization. Results: Three putative subclasses (subclasses A, B, and C) were initially identified based on an empiric, discovery-oriented expression filter and unsupervised hierarchical clustering. Statistical comparison of the 3 putative subclasses (ANOVA, Bonferonni correction, p < 0.05) identified 6,934 differentially regulated genes. K means clustering of these 6,934 genes generated 10 coordinately regulated gene clusters corresponding to multiple signaling and metabolic pathways, all of which were differentially regulated across the 3 subclasses. Leave one out cross validation procedures indentified 100 genes having the strongest predictive values for subclass identification. Forty-four of these 100 genes corresponded to signaling pathways relevant to the adaptive immune system and glucocorticoid receptor signaling, the majority of which were repressed in subclass A patients. Subclass A patients were also characterized by repression of genes corresponding to zinc-related biology. Phenotypic analyses revealed that subclass A patients were younger, had a higher illness severity, and a higher mortality rate than patients in subclasses B and C. Conclusions: Genome-wide expression profiling can identify pediatric septic shock subclasses having clinically relevant phenotypes. Expression data from 98 children with septic shock and 32 normal controls were generated using whole blood-derived RNA samples representing the first 24 hours of admission to the pediatric intensive care unit. The controls were used for normalization. Subsequently, we used the expression data to derive expression-based subclasses of patients using discovery oriented expression and statistical filters, followed by unsupervised hierarchical clustering.