Leveraging two-way probe-level block design for identifying differential gene expression with high-density oligonucleotide arrays

BACKGROUND: To identify differentially expressed genes across experimental conditions in oligonucleotide microarray experiments, existing statistical methods commonly use a summary of probe-level expression data for each probe set and compare replicates of these values across conditions using a form of the t-test or rank sum test. Here we propose the use of a statistical method that takes advantage of the built-in redundancy architecture of high-density oligonucleotide arrays. RESULTS: We employ parametric and nonparametric variants of two-way analysis of variance (ANOVA) on probe-level data to account for probe-level variation, and use the false-discovery rate (FDR) to account for simultaneous testing on thousands of genes (multiple testing problem). Using publicly available data sets, we systematically compared the performance of parametric two-way ANOVA and the nonparametric Mack-Skillings test to the t-test and Wilcoxon rank-sum test for detecting differentially expressed genes at varying levels of fold change, concentration, and sample size. Using receiver operating characteristic (ROC) curve comparisons, we observed that two-way methods with FDR control on sample sizes with 2–3 replicates exhibits the same high sensitivity and specificity as a t-test with FDR control on sample sizes with 6–9 replicates in detecting at least two-fold change. CONCLUSIONS: Our results suggest that the two-way ANOVA methods using probe-level data are substantially more powerful tests for detecting differential gene expression than corresponding methods for probe-set level data

Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome.

Eukaryotic gene transcription is accompanied by acetylation and methylation of nucleosomes near promoters, but the locations and roles of histone modifications elsewhere in the genome remain unclear. We determined the chromatin modification states in high resolution along 30 Mb of the human genome and found that active promoters are marked by trimethylation of Lys4 of histone H3 (H3K4), whereas enhancers are marked by monomethylation, but not trimethylation, of H3K4. We developed computational algorithms using these distinct chromatin signatures to identify new regulatory elements, predicting over 200 promoters and 400 enhancers within the 30-Mb region. This approach accurately predicted the location and function of independently identified regulatory elements with high sensitivity and specificity and uncovered a novel functional enhancer for the carnitine transporter SLC22A5 (OCTN2). Our results give insight into the connections between chromatin modifications and transcriptional regulatory activity and provide a new tool for the functional annotation of the human genome. Activation of eukaryotic gene transcription involves the coordination of a multitude of transcription factors and cofactors on regulatory DNA sequences such as promoters and enhancers and on the chromatin structure containing these elements 1-3 . Promoters are located at the 5¢ ends of genes immediately surrounding the transcriptional start site (TSS) and serve as the point of assembly of the transcriptional machinery and initiation of transcription 4 . Enhancers contribute to the activation of their target genes from positions upstream, downstream or within a target or neighboring gene Recent investigations using chromatin immunoprecipitation (ChIP) and microarray (ChIP-chip) experiments have described the chromatin architecture of transcriptional promoters in yeast, fly and mammalian systems 9 . In a manner largely conserved across species, active promoters are marked by acetylation of various residues of histones H3 and H4 and methylation of H3K4, particularly trimethylation of this residue. Nucleosome depletion is also a general characteristic of active promoters in yeast and flies, although this feature remains to be thoroughly examined in mammalian systems. Although some studies suggest that distal regulatory elements like enhancers may be marked by similar histone modification patterns 10-13 , the distinguishing chromatin features of promoters and enhancers have yet to be determined, hindering our understanding of a predictive histone code for different classes of regulatory elements. Here, we present high-resolution maps of multiple histone modifications and transcriptional regulators in 30 Mb of the human genome, demonstrating that active promoters and enhancers are associated with distinct chromatin signatures that can be used to predict these regulatory elements in the human genome. RESULTS Chromatin architecture and transcription factor localization We performed ChIP-chip analysis 14 to determine the chromatin architecture along 44 human loci selected by the ENCODE consortium as common targets for genomic analysis 15 , totaling 30 Mb

Correction to: Cluster identification, selection, and description in Cluster randomized crossover trials: the PREP-IT trials

An amendment to this paper has been published and can be accessed via the original article

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

The original version of this article unfortunately contained a mistake

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Genome-wide mapping and analysis of mammalian promoters

Mammalian organisms such as mouse and human are characterized by large genomes of 2-3 billion base pairs. Sequencing of these genomes has revealed that only a small fraction, ̃1.5%, encodes protein-coding genes. The diversity of more than 200 cell types which make up mammals, from the zygote to the differentiated cell types which perform the functions of organs, is brought about by the coordinated expression of specific subsets of these genes. Control of gene expression is, in turn, mediated by the binding of transcription factors at non-coding genomic regulatory sequences such as promoters, enhancers, and insulators. Unraveling the control of gene expression, resulting in mammalian cell type diversity, thus entails the accurate and systematic characterization of these sequences. In this work, we describe a pilot application of chromatin immunoprecipitation with microarrays (ChIP- chip) to define active promoters in human fibroblast cells. To do this, we mapped the genomic location of components of the transcription pre-initiation complex (PIC) using microarrays tiling the entire non-repetitive human genome sequence at 100 bp resolution. The scale and novelty of this high-throughput strategy entailed significant bioinformatics challenges. In particular, we highlight our model-based approach for the accurate identification of binding sites from the data. Interestingly, this pilot identification of 10,567 active promoters revealed the extent of alternative promoter usage within a single cell type, clustering of active promoters, and classes of genes based on PIC binding and transcript expression level. We then extended our genome- wide promoter mapping strategy to characterize active promoters in mouse embryonic stem cells (mES) and adult organs. We mapped ̃24,000 promoters across these samples, including 5,153 sites validating cap-analysis of gene expression (CAGE) 5' end data in addition to 16,976 annotated mRNA 5' ends. To profile promoter usage across tissues by relative occupancy of RNA polymerase II (Pol II), we adapted a quantitative index of tissue-specificity and thus overcome limitations of "bound" or "unbound" classification. We examined the sequence and epigenetic features of tissue-specific promoters defined by this measure and discovered a subset of promoters with enriched Pol II binding in mES persistently marked by H3K4me3 in adult tissue

ChIP-chip: Data, Model, and Analysis

ChIP-chip (or ChIP-on-chip) is a technology for isolation and identification of genomic sites occupied by specific DNA binding proteins in living cells. The ChIP-chip data can be obtained over the whole genome by tiling arrays, where a peak in the signal is generally observed at a protein binding site. In this paper, we describe and present a probability theory for modelling ChIP-chip data. We then propose a model-based computational method for locating and testing peaks for the purpose of identifying potential protein binding sites

ChIP-chip: Data, Model, and Analysis

ChIP-chip (or ChIP-on-chip) is a technology for isolation and identification of genomic sites occupied by specific DNA binding proteins in living cells. The ChIP-chip data can be obtained over the whole genome by tiling arrays, where a peak in the signal is generally observed at a protein binding site. In this paper, we describe and present a probability theory for modelling ChIP-chip data. We then propose a model-based computational method for locating and testing peaks for the purpose of identifying potential protein binding sites