Filaggrin-stratified transcriptomic analysis of pediatric skin identifies mechanistic pathways in patients with atopic dermatitis

BackgroundAtopic dermatitis (AD; eczema) is characterized by a widespread abnormality in cutaneous barrier function and propensity to inflammation. Filaggrin is a multifunctional protein and plays a key role in skin barrier formation. Loss-of-function mutations in the gene encoding filaggrin (FLG) are a highly significant risk factor for atopic disease, but the molecular mechanisms leading to dermatitis remain unclear.ObjectiveWe sought to interrogate tissue-specific variations in the expressed genome in the skin of children with AD and to investigate underlying pathomechanisms in atopic skin.MethodsWe applied single-molecule direct RNA sequencing to analyze the whole transcriptome using minimal tissue samples. Uninvolved skin biopsy specimens from 26 pediatric patients with AD were compared with site-matched samples from 10 nonatopic teenage control subjects. Cases and control subjects were screened for FLG genotype to stratify the data set.ResultsTwo thousand four hundred thirty differentially expressed genes (false discovery rate, P < .05) were identified, of which 211 were significantly upregulated and 490 downregulated by greater than 2-fold. Gene ontology terms for “extracellular space” and “defense response” were enriched, whereas “lipid metabolic processes” were downregulated. The subset of FLG wild-type cases showed dysregulation of genes involved with lipid metabolism, whereas filaggrin haploinsufficiency affected global gene expression and was characterized by a type 1 interferon–mediated stress response.ConclusionThese analyses demonstrate the importance of extracellular space and lipid metabolism in atopic skin pathology independent of FLG genotype, whereas an aberrant defense response is seen in subjects with FLG mutations. Genotype stratification of the large data set has facilitated functional interpretation and might guide future therapy development

Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment

High-throughput RNA sequencing (RNA-seq) is now the standard method to determine differential gene expression. Identifying differentially expressed genes crucially depends on estimates of read count variability. These estimates are typically based on statistical models such as the negative binomial distribution, which is employed by the tools edgeR, DESeq and cuffdiff. Until now, the validity of these models has usually been tested on either low-replicate RNA-seq data or simulations. A 48-replicate RNA-seq experiment in yeast was performed and data tested against theoretical models. The observed gene read counts were consistent with both log-normal and negative binomial distributions, while the mean-variance relation followed the line of constant dispersion parameter of ~0.01. The high-replicate data also allowed for strict quality control and screening of bad replicates, which can drastically affect the gene read-count distribution. RNA-seq data have been submitted to ENA archive with project ID PRJEB5348.Comment: 15 pages 6 figure

XMM-Newton view of the ultra-luminous X-ray sources in M51

(Abridged) We present results based on XMM-Newton observation of the nearby spiral galaxy M51 (NGC5194 and NGC5195). Two ULXs in NGC5194 show evidence for short-term variability, and all but two ULXs vary on long time scales (over a baseline of 2.5 years), providing strong evidence that these are accreting sources. One ULX in NGC5194, source 69, shows possible periodic behavior in its X-ray flux. We derive a period of 5925\pm200s at a confidence level of 95%, based on three cycles. This period is lower than the period of 7620\pm500s derived from a Chandra observation in 2000. The higher effective area of XMM-Newton enables us to identify multiple components in the spectra of ULXs. Most ULXs require at least two components -- a power law and a soft X-ray excess component which is modeled by an optically thin plasma or multicolor disk blackbody (MCD). However, the soft excess emission, inferred from all ULXs except source 69, are unlikely to be physically associated with the ULXs as their strengths are comparable to that of the surrounding diffuse emission. The soft excess emission of source 69 is well described either by a two temperature mekal plasma or a single temperature mekal plasma kT~690eV) and an MCD (kT~170eV). The MCD component suggests a cooler accretion disks compared to that in Galactic X-ray binaries and consistent with that expected for intermediate mass black holes (IMBHs). An iron line (EW 700eV) or K absorption edge at 7.1keV is present in the EPIC PN spectrum of source 26. The spectrum of the ULX in NGC5195, source 12, is consistent with a simple power law.Comment: 20 pages, To appear in Ap

How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?

An RNA-seq experiment with 48 biological replicates in each of 2 conditions was performed to determine the number of biological replicates ($n_r$) required, and to identify the most effective statistical analysis tools for identifying differential gene expression (DGE). When $n_r=3$, seven of the nine tools evaluated give true positive rates (TPR) of only 20 to 40 percent. For high fold-change genes ($|log_{2}(FC)|\gt2$) the TPR is $\gt85$ percent. Two tools performed poorly; over- or under-predicting the number of differentially expressed genes. Increasing replication gives a large increase in TPR when considering all DE genes but only a small increase for high fold-change genes. Achieving a TPR $\gt85$% across all fold-changes requires $n_r\gt20$. For future RNA-seq experiments these results suggest $n_r\gt6$, rising to $n_r\gt12$ when identifying DGE irrespective of fold-change is important. For $6 \lt n_r \lt 12$, superior TPR makes edgeR the leading tool tested. For $n_r \ge12$, minimizing false positives is more important and DESeq outperforms the other tools.Comment: 21 Pages and 4 Figures in main text. 9 Figures in Supplement attached to PDF. Revision to correct a minor error in the abstrac

Active and adaptive plasticity in a changing climate

Better understanding of the mechanistic basis of plant plasticity will enhance efforts to breed crops resilient to predicted climate change. However, complexity in plasticity's conceptualisation and measurement may hinder fruitful crossover of concepts between disciplines that would enable such advances. We argue active adaptive plasticity is particularly important in shaping the fitness of wild plants, representing the first line of a plant's defence to environmental change. Here, we define how this concept may be applied to crop breeding, suggest appropriate approaches to measure it in crops, and propose a refocussing on active adaptive plasticity to enhance crop resilience. We also discuss how the same concept may have wider utility, such as in ex situ plant conservation and reintroductions.ISSN:1360-1385ISSN:1878-437

The origin and evolution of the surfactant system in fish: Insights into the evolution of lungs and swim bladders

Copyright © 2004 by The University of Chicago. All rights reserved.Christopher B. Daniels; Sandra Orgeig; Lucy C. Sullivan; Nicholas Ling; Michael B. Bennett; Samuel Schurch; Adalberto Luis Val; Colin J. Braune

Comparison of annotations for <i>SLFN5</i> gene locus.

<p>Comparison of annotations for <i>SLFN5</i> gene locus.</p