Nick Schurch

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

RNA-seq is now the technology of choice for genome-wide differential gene expression experiments, but it is not clear how many biological replicates are needed to ensure valid biological interpretation of the results or which statistical tools are best for analyzing the data. An RNA-seq experiment with 48 biological replicates in each of two conditions was performed to answer these questions and provide guidelines for experimental design. With three biological replicates, nine of the 11 tools evaluated found only 20%-40% of the significantly differentially expressed (SDE) genes identified with the full set of 42 clean replicates. This rises to >85% for the subset of SDE genes changing in expression by more than fourfold. To achieve >85% for all SDE genes regardless of fold change requires more than 20 biological replicates. The same nine tools successfully control their false discovery rate at ≲5% for all numbers of replicates, while the remaining two tools fail to control their FDR adequately, particularly for low numbers of replicates. For future RNA-seq experiments, these results suggest that at least six biological replicates should be used, rising to at least 12 when it is important to identify SDE genes for all fold changes. If fewer than 12 replicates are used, a superior combination of true positive and false positive performances makes edgeR and DESeq2 the leading tools. For higher replicate numbers, minimizing false positives is more important and DESeq marginally outperforms the other tools.

PTEN Protein Phosphatase Activity Correlates with Control of Gene Expression and Invasion, a Tumor-Suppressing Phenotype, But Not with AKT Activity

The lipid and protein phosphatase activities of PTEN are both required for glioma cell invasion and many of its effects on gene expression. Self-Directed Phosphatase The lipid and protein phosphatase PTEN, which acts as a tumor suppressor, is known for its ability to dephosphorylate phosphatidylinositol 3,4,5-trisphosphate and thereby antagonize mitogenic signaling by phosphoinositide 3-kinase and downstream effectors, such as AKT. The role of PTEN’s protein phosphatase activity, however, is less clear. Tibarewal et al. used PTEN mutants to isolate its protein phosphatase activity from its lipid phosphatase activity and found that both appeared to be required on the same molecule for PTEN to inhibit glioma cell invasion in vitro. In addition, both its lipid and its protein phosphatase activities were required to mediate many of PTEN’s largest effects on gene expression. Various lines of evidence indicated that PTEN dephosphorylated a residue in its own C-terminal tail and implicated dephosphorylation of this residue in limiting invasion independently of any effects on AKT, leading the authors to propose that autodephosphorylation may enable targeting PTEN’s lipid phosphatase activity to a particular locale. The authors identified a PTEN mutant from a human lung cancer cell line that retained the ability to decrease AKT’s phosphorylation and catalytic activity but lacked protein phosphatase activity and failed to suppress invasion, suggesting that PTEN’s protein phosphatase activity may contribute to its tumor suppressor function. The tumor suppressor phosphatase and tensin homolog deleted on chromosome 10 (PTEN) has a well-characterized lipid phosphatase activity and a poorly characterized protein phosphatase activity. We show that both activities are required for PTEN to inhibit cellular invasion and to mediate most of its largest effects on gene expression. PTEN appears to dephosphorylate itself at threonine 366, and mutation of this site makes lipid phosphatase activity sufficient for PTEN to inhibit invasion. We propose that the dominant role for PTEN’s protein phosphatase activity is autodephosphorylation-mediated regulation of its lipid phosphatase activity. Because PTEN’s regulation of invasion and these changes in gene expression required lipid phosphatase activity, but did not correlate with the total cellular abundance of its phosphatidylinositol 3,4,5-trisphosphate (PIP3) lipid substrate or AKT activity, we propose that localized PIP3 signaling may play a role in those PTEN-mediated processes that depend on both its protein and lipid phosphatase activities. Finally, we identified a tumor-derived PTEN mutant selectively lacking protein phosphatase activity, indicating that in some circumstances the regulation of invasion and not that of AKT can correlate with PTEN-mediated tumor suppression.

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

Background Atopic 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. Objective We 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. Methods We 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. Results Two 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. Conclusion These 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.

Re-replication induced by geminin depletion occurs from G2 and is enhanced by checkpoint activation.

To prevent re-replication of DNA in a single cell cycle, the licensing of replication origins by Mcm2-7 is prevented during S and G2 phases. Animal cells achieve this by cell-cycle-regulated proteolysis of the essential licensing factor Cdt1 and inhibition of Cdt1 by geminin. Here we investigate the consequences of ablating geminin in synchronised human U2OS cells. Following geminin loss, cells complete an apparently normal S phase, but a proportion arrest at the G2–M boundary. When Cdt1 accumulates in these cells, DNA re-replicates, suggesting that the key role of geminin is to prevent re-licensing in G2. If cell cycle checkpoints are inhibited in cells lacking geminin, cells progress through mitosis and less re-replication occurs. Checkpoint kinases thereby amplify re-replication into an all-or-nothing response by delaying geminin-depleted cells in G2. Deep DNA sequencing revealed no preferential re-replication of specific genomic regions after geminin depletion. This is consistent with the observation that cells in G2 have lost their replication timing information. By contrast, when Cdt1 is overexpressed or is stabilised by the neddylation inhibitor MLN4924, re-replication can occur throughout S phase.

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

Motivation: 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. Results: 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. Availability and implementation: RNA-seq data have been submitted to ENA archive with project ID PRJEB5348. Contact: g.j.barton@dundee.ac.uk

High Energy spectra of Seyferts and Unification schemes

The Unified Model of AGN predicts the sole difference between Seyfert 1 and Seyfert 2 nuclei is the viewing angle with respect to an obscuring structure around the nucleus. High energy photons above 20 keV are not affected by this absorption if the column is Compton thin, so their 30--100 keV spectra should be the same. However, the observed spectra at high energies appear to show a systematic difference, with Seyfert 1s having

Major transcriptome re-organisation and abrupt changes in signalling, cell cycle and chromatin regulation at neural differentiation in vivo

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The impact of KLF2 modulation on the transcriptional program and function of CD8 T cells.

2.1 whereas Seyfert 2s are harder with

Improved Annotation of 3′ Untranslated Regions and Complex Loci by Combination of Strand-Specific Direct RNA Sequencing, RNA-Seq and ESTs

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Identifying differential isoform abundance with RATs: a universal tool and a warning

1.9. We estimate the mass and accretion rate of Seyferts detected in these high energy samples and show that they span a wide range in