RNA around the clock – regulation at the RNA level in biological timing

The circadian timing system in plants synchronizes their physiological functions with the environment. This is brought about by a global control of the gene expression program with a considerable part of the transcriptome undergoing 24-h oscillations in steady-state abundance. These circadian oscillations are driven by a set of core clock proteins that generate their own 24-h rhythm through periodic feedback on their own transcription. Here we provide an update review on molecular events at the RNA level that contribute to the 24-h rhythm of the core clock proteins and shape the circadian transcriptome. We focus on RNA-based regulation in the circadian system of the model plant Arabidopsis thaliana but also discuss selected regulatory principles in other organisms

A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single gene classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single gene classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single gene sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single gene classifiers for predicting outcome in breast cancer

Synchronization in G0/G1 enhances the mitogenic response of cells overexpressing the human insulin receptor A isoform to insulin

Evaluating mitogenic signaling specifically through the human insulin receptor (IR) is relevant for the preclinical safety assessment of developmental insulin analogs. It is known that overexpression of IR sensitizes cells to the mitogenic effects of insulin, but it is essentially unknown how mitogenic responses can be optimized to allow practical use of such recombinant cell lines for preclinical safety testing. We constitutively overexpressed the short isoform of the human insulin receptor (hIR-A, exon 11-negative) in L6 rat skeletal myoblasts. Because the mitogenic effect of growth factors such as insulin is expected to act in G0/G1, promoting S-phase entry, we developed a combined topoinhibition + serum deprivation strategy to explore the effect of G0/G1 synchronization as an independent parameter in the context of serum deprivation, the latter being routinely used to reduce background in mitogenicity assays. G0/G1 synchronization significantly improved the mitogenic responses of L6-hIR cells to insulin, measured by 3H-thymidine incorporation. Comparison with the parental L6 cells using phospho-mitogen-activated protein kinase, phospho-AKT, as well as 3H-thymidine incorporation end points supported that the majority of the mitogenic effect of insulin in L6-hIR cells was mediated by the overexpressed hIR-A. Using the optimized L6-hIR assay, we found that the X-10 insulin analog was more mitogenic than native human insulin, supporting that X-10 exhibits increased mitogenic signaling through the hIR-A. In summary, this study provides the first demonstration that serum deprivation may not be sufficient, and G0/G1 synchronization may be required to obtain optimal responsiveness of hIR-overexpressing cell lines for preclinical safety testing

Identification of genetic elements in metabolism by high-throughput mouse phenotyping.

Metabolic diseases are a worldwide problem but the underlying genetic factors and their relevance to metabolic disease remain incompletely understood. Genome-wide research is needed to characterize so-far unannotated mammalian metabolic genes. Here, we generate and analyze metabolic phenotypic data of 2016 knockout mouse strains under the aegis of the International Mouse Phenotyping Consortium (IMPC) and find 974 gene knockouts with strong metabolic phenotypes. 429 of those had no previous link to metabolism and 51 genes remain functionally completely unannotated. We compared human orthologues of these uncharacterized genes in five GWAS consortia and indeed 23 candidate genes are associated with metabolic disease. We further identify common regulatory elements in promoters of candidate genes. As each regulatory element is composed of several transcription factor binding sites, our data reveal an extensive metabolic phenotype-associated network of co-regulated genes. Our systematic mouse phenotype analysis thus paves the way for full functional annotation of the genome

Association of NIPA1 repeat expansions with amyotrophic lateral sclerosis in a large international cohort

NIPA1 (nonimprinted in Prader-Willi/Angelman syndrome 1) mutations are known to cause hereditary spastic paraplegia type 6, a neurodegenerative disease that phenotypically overlaps to some extent with amyotrophic lateral sclerosis (ALS). Previously, a genomewide screen for copy number variants found an association with rare deletions in NIPA1 and ALS, and subsequent genetic analyses revealed that long (or expanded) polyalanine repeats in NIPA1 convey increased ALS susceptibility. We set out to perform a large-scale replication study to further investigate the role of NIPA1 polyalanine expansions with ALS, in which we characterized NIPA1 repeat size in an independent international cohort of 3955 patients with ALS and 2276 unaffected controls and combined our results with previous reports. Meta-analysis on a total of 6245 patients with ALS and 5051 controls showed an overall increased risk of ALS in those with expanded (>8) GCG repeat length (odds ratio = 1.50, p = 3.8×10-5). Together with previous reports, these findings provide evidence for an association of an expanded polyalanine repeat in NIPA1 and ALS

CrossValidationTool

Full code to ACES

U133A_combat.h5

<p>We compiled a large cohort of breast cancer samples from NCBI's Gene Expression Omnibus (GEO) (see Table <a href="http://journal.frontiersin.org/article/10.3389/fgene.2013.00289/full#T1">1</a>) as it was suggested in (<a href="http://journal.frontiersin.org/article/10.3389/fgene.2013.00289/full#B8">Györffy and Schäfer, 2009</a>). We only took samples from the U133A platform into account and removed duplicate samples, that is, samples that occur in several studies under the same GEO id. Array quality checks were executed for all samples belonging to the same study by the R package<code>arrayQualityMetrics</code>. Due to high memory demands of this package, studies containing more than 400 samples had to be divided into two parts. Samples that were classified as outliers in the RLE or NUSE analysis were discarded. Finally, all samples across all studies were normalized together using R's <code>justRMA</code> function yielding for each sample and each probe a log(intensity) value. This normalization also included a quantile normalization step. Subsequently, probe intensities were mean centered, yielding for each sample and each probe<i>p</i> a log(intensityμ(intensityp))<math><mrow><mi>log</mi><mo stretchy="false">(</mo><mrow>intensity</mrow><mrow><mi>μ</mi><mo stretchy="false">(</mo><msub><mrow>intensity</mrow><mi>p</mi></msub><mo stretchy="false">)</mo></mrow><mo stretchy="false">)</mo></mrow></math> value.</p><p>We found batch effects within single studies, where samples have been collected from different locations and batch effects between studies. Specifically for breast cancer, samples also form batches according to the five subtypes of breast cancer: luminal A, luminal B, Her2 enriched, normal like and basal like. To account for these effects we employed R's <code>combat</code>, where the cancer subtype was modeled as an additional covariate to maintain the variance associated with the subtypes. To do so we needed to stratify the patients according to the subtype. Since this variable is not always available in the annotation of the patients, we predict the subtype employing the PAM50 marker genes as documented in R's <code>genefu</code> package.</p><p>Principal component analysis of the batch corrected data revealed pairs of samples with a very high correlation (>0.9). Those pairs were regarded as replicate samples. For each pair of replicate samples one sample was removed randomly. Affymetrix probe IDs were mapped to Entrez Gene IDs via the mapping files provided by Affymetrix. Only probes that mapped to exactly one Gene ID were taken into account and probes starting with AFFX were discarded. If an Entrez Gene ID mapped to several Affymetrix probe IDs, probes were considered in the following order according to their suffix (<a href="http://journal.frontiersin.org/article/10.3389/fgene.2013.00289/full#B7">Gohlmann and Talloen, 2010</a>): “_at,” “s_at,” “x_at,” “i_at,” and “a_at.” When there were still several probes valid for one Gene ID, the Affymetrix probe with the higher variance of expression values was chosen.</p><p>The patients' class labels corresponding to recurrence free or distant metastasis free survival were calculated with respect to a 5-year threshold. The final cohort is shown in Table <a href="http://journal.frontiersin.org/article/10.3389/fgene.2013.00289/full#T1">1</a>. We derived two data sets: one labeled according to recurrence free survival (RFS) and one labeled according to distant metastasis free survival (DMFS). Note, that the DMFS data set is a subset of the RFS data set.</p><div></div

epd7.3.1_clean.tgz

Clean EP