Identification of pediatric septic shock subclasses based on genome-wide expression profiling

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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 three putative subclasses (analysis of variance, Bonferonni correction, <it>P </it>< 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 three 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.</p> <p>Conclusion</p> <p>Genome-wide expression profiling can identify pediatric septic shock subclasses having clinically relevant phenotypes.</p

RNA deep sequencing reveals differential MicroRNA expression during development of sea urchin and sea star

microRNAs (miRNAs) are small (20-23 nt), non-coding single stranded RNA molecules that act as post-transcriptional regulators of mRNA gene expression. They have been implicated in regulation of developmental processes in diverse organisms. The echinoderms, Strongylocentrotus purpuratus (sea urchin) and Patiria miniata (sea star) are excellent model organisms for studying development with well-characterized transcriptional networks. However, to date, nothing is known about the role of miRNAs during development in these organisms, except that the genes that are involved in the miRNA biogenesis pathway are expressed during their developmental stages. In this paper, we used Illumina Genome Analyzer (Illumina, Inc.) to sequence small RNA libraries in mixed stage population of embryos from one to three days after fertilization of sea urchin and sea star (total of 22,670,000 reads). Analysis of these data revealed the miRNA populations in these two species. We found that 47 and 38 known miRNAs are expressed in sea urchin and sea star, respectively, during early development (32 in common). We also found 13 potentially novel miRNAs in the sea urchin embryonic library. miRNA expression is generally conserved between the two species during development, but 7 miRNAs are highly expressed in only one species. We expect that our two datasets will be a valuable resource for everyone working in the field of developmental biology and the regulatory networks that affect it. The computational pipeline to analyze Illumina reads is available at http://www.benoslab.pitt.edu/services.html. © 2011 Kadri et al

Covariation in Plant Functional Traits and Soil Fertility within Two Species-Rich Forests

The distribution of plant species along environmental gradients is expected to be predictable based on organismal function. Plant functional trait research has shown that trait values generally vary predictably along broad-scale climatic and soil gradients. This work has also demonstrated that at any one point along these gradients there is a large amount of interspecific trait variation. The present research proposes that this variation may be explained by the local-scale sorting of traits along soil fertility and acidity axes. Specifically, we predicted that trait values associated with high resource acquisition and growth rates would be found on soils that are more fertile and less acidic. We tested the expected relationships at the species-level and quadrat-level (20×20 m) using two large forest plots in Panama and China that contain over 450 species combined. Predicted relationships between leaf area and wood density and soil fertility were supported in some instances, but the majority of the predicted relationships were rejected. Alternative resource axes, such as light gradients, therefore likely play a larger role in determining the interspecific variability in plant functional traits in the two forests studied

Antagonism between DNA and H3K27 Methylation at the Imprinted Rasgrf1 Locus

At the imprinted Rasgrf1 locus in mouse, a cis-acting sequence controls DNA methylation at a differentially methylated domain (DMD). While characterizing epigenetic marks over the DMD, we observed that DNA and H3K27 trimethylation are mutually exclusive, with DNA and H3K27 methylation limited to the paternal and maternal sequences, respectively. The mutual exclusion arises because one mark prevents placement of the other. We demonstrated this in five ways: using 5-azacytidine treatments and mutations at the endogenous locus that disrupt DNA methylation; using a transgenic model in which the maternal DMD inappropriately acquired DNA methylation; and by analyzing materials from cells and embryos lacking SUZ12 and YY1. SUZ12 is part of the PRC2 complex, which is needed for placing H3K27me3, and YY1 recruits PRC2 to sites of action. Results from each experimental system consistently demonstrated antagonism between H3K27me3 and DNA methylation. When DNA methylation was lost, H3K27me3 encroached into sites where it had not been before; inappropriate acquisition of DNA methylation excluded normal placement of H3K27me3, and loss of factors needed for H3K27 methylation enabled DNA methylation to appear where it had been excluded. These data reveal the previously unknown antagonism between H3K27 and DNA methylation and identify a means by which epigenetic states may change during disease and development

NAViGaTing the Micronome – Using Multiple MicroRNA Prediction Databases to Identify Signalling Pathway-Associated MicroRNAs

MicroRNAs are a class of small RNAs known to regulate gene expression at the transcript level, the protein level, or both. Since microRNA binding is sequence-based but possibly structure-specific, work in this area has resulted in multiple databases storing predicted microRNA:target relationships computed using diverse algorithms. We integrate prediction databases, compare predictions to in vitro data, and use cross-database predictions to model the microRNA:transcript interactome--referred to as the micronome--to study microRNA involvement in well-known signalling pathways as well as associations with disease. We make this data freely available with a flexible user interface as our microRNA Data Integration Portal--mirDIP (http://ophid.utoronto.ca/mirDIP).mirDIP integrates prediction databases to elucidate accurate microRNA:target relationships. Using NAViGaTOR to produce interaction networks implicating microRNAs in literature-based, KEGG-based and Reactome-based pathways, we find these signalling pathway networks have significantly more microRNA involvement compared to chance (p<0.05), suggesting microRNAs co-target many genes in a given pathway. Further examination of the micronome shows two distinct classes of microRNAs; universe microRNAs, which are involved in many signalling pathways; and intra-pathway microRNAs, which target multiple genes within one signalling pathway. We find universe microRNAs to have more targets (p<0.0001), to be more studied (p<0.0002), and to have higher degree in the KEGG cancer pathway (p<0.0001), compared to intra-pathway microRNAs.Our pathway-based analysis of mirDIP data suggests microRNAs are involved in intra-pathway signalling. We identify two distinct classes of microRNAs, suggesting a hierarchical organization of microRNAs co-targeting genes both within and between pathways, and implying differential involvement of universe and intra-pathway microRNAs at the disease level

Surviving Sepsis Campaign: International guidelines for management of severe sepsis and septic shock: 2008

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Deconstructing transcriptional heterogeneity in pluripotent stem cells

SUMMARY Pluripotent stem cells (PSCs) are capable of dynamic interconversion between distinct substates, but the regulatory circuits specifying these states and enabling transitions between them are not well understood. We set out to characterize transcriptional heterogeneity in PSCs by single-cell expression profiling under different chemical and genetic perturbations. Signaling factors and developmental regulators show highly variable expression, with expression states for some variable genes heritable through multiple cell divisions. Expression variability and population heterogeneity can be influenced by perturbation of signaling pathways and chromatin regulators. Strikingly, either removal of mature miRNAs or pharmacologic blockage of signaling pathways drives PSCs into a low-noise ground state characterized by a reconfigured pluripotency network, enhanced self-renewal, and a distinct chromatin state, an effect mediated by opposing miRNA families acting on the c-myc / Lin28 / let-7 axis. These data illuminate the nature of transcriptional heterogeneity in PSCs