David P. Kreil

Atypical RNA polymerase subunits required for RNA-directed DNA methylation

RNA-directed DNA methylation, one of several RNA interference–mediated pathways in the nucleus, has been documented in plants and in human cells. Despite progress in identifying the DNA methyltransferases, histone-modifying enzymes and RNA interference proteins needed for RNA-directed DNA methylation, the mechanism remains incompletely understood. We screened for mutants defective in RNA-directed DNA methylation and silencing of a transgene promoter in Arabidopsis thaliana and identified three drd complementation groups. DRD1 is a SNF2-like protein required for RNA-directed de novo methylation. We report here that DRD2 and DRD3 correspond to the second-largest subunit and largest subunit, respectively, of a fourth class of DNA-dependent RNA polymerase (polymerase IV) that is unique to plants. DRD3 is a functionally diversified homolog of NRPD1a or SDE4, identified in a separate screen for mutants defective in post-transcriptional gene silencing. The identical DNA methylation patterns observed in all three drd mutants suggest that DRD proteins cooperate to create a substrate for RNA-directed de novo methylation.

Involvement of Putative SNF2 Chromatin Remodeling Protein DRD1 in RNA-Directed DNA Methylation

In plants, the mechanism by which RNA can induce de novo cytosine methylation of homologous DNA is poorly understood. Cytosines in all sequence contexts become modified in response to RNA signals. Recent work has implicated the de novo DNA methyltransferases (DMTases), DRM1 and DRM2, in establishing RNA-directed methylation of the constitutive nopaline synthase promoter, as well as the DMTase MET1 and the putative histone deacetylase HDA6 in maintaining or enhancing CpG methylation induced by RNA. Despite the identification of enzymes that catalyze epigenetic modifications in response to RNA signals, it is unclear how RNA targets DNA for methylation. A screen for mutants defective in RNA-directed DNA methylation identified a novel putative chromatin-remodeling protein, DRD1. This protein belongs to a previously undefined, plant-specific subfamily of SWI2/SNF2-like proteins most similar to the RAD54/ATRX subfamily. In drd1 mutants, RNA-induced non-CpG methylation is almost eliminated at a target promoter, resulting in reactivation, whereas methylation of centromeric and rDNA repeats is unaffected. Thus, unlike the SNF2-like proteins DDM1/Lsh1 and ATRX, which regulate methylation of repetitive sequences, DRD1 is not a global regulator of cytosine methylation. DRD1 is the first SNF2-like protein implicated in an RNA-guided, epigenetic modification of the genome.

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

The concordance of RNA-sequencing (RNA-seq) with microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed using a range of chemical treatment conditions. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same liver samples of rats exposed in triplicate to varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOAs). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is linearly correlated with treatment effect size (R20.8). Furthermore, the concordance is also affected by transcript abundance and biological complexity of the MOA. RNA-seq outperforms microarray (93% versus 75%) in DEG verification as assessed by quantitative PCR, with the gain mainly due to its improved accuracy for low-abundance transcripts. Nonetheless, classifiers to predict MOAs perform similarly when developed using data from either platform. Therefore, the endpoint studied and its biological complexity, transcript abundance and the genomic application are important factors in transcriptomic research and for clinical and regulatory decision making.

A structural-maintenance-of-chromosomes hinge domain–containing protein is required for RNA-directed DNA methylation

RNA-directed DNA methylation (RdDM) is a process in which dicer-generated small RNAs guide de novo cytosine methylation at the homologous DNA region. To identify components of the RdDM machinery important for Arabidopsis thaliana development, we targeted an enhancer active in meristems for methylation, which resulted in silencing of a downstream GFP reporter gene. This silencing system also features secondary siRNAs, which trigger methylation that spreads beyond the targeted enhancer region. A screen for mutants defective in meristem silencing and enhancer methylation retrieved six dms complementation groups, which included the known factors DRD1 (ref. 3; a SNF2-like chromatin-remodeling protein) and Pol IVb subunits. Additionally, we identified a previously unknown gene DMS3 (At3g49250), encoding a protein similar to the hinge-domain region of structural maintenance of chromosomes (SMC) proteins. This finding implicates a putative chromosome architectural protein that can potentially link nucleic acids in facilitating an RNAi-mediated epigenetic modification involving secondary siRNAs and spreading of DNA methylation.

The transcriptome of syncytia induced by the cyst nematode Heterodera schachtii in Arabidopsis roots

Arabidopsis thaliana is a host for the sugar beet cyst nematode Heterodera schachtii. Juvenile nematodes invade the roots and induce the development of a syncytium, which functions as a feeding site for the nematode. Here, we report on the transcriptome of syncytia induced in the roots of Arabidopsis. Microaspiration was employed to harvest pure syncytium material, which was then used to prepare RNA for hybridization to Affymetrix GeneChips. Initial data analysis showed that the gene expression in syncytia at 5 and 15 days post-infection did not differ greatly, and so both time points were compared together with control roots. Out of a total of 21 138 genes, 18.4% (3893) had a higher expression level and 15.8% (3338) had a lower expression level in syncytia, as compared with control roots, using a multiple-testing corrected false discovery rate of below 5%. A gene ontology (GO) analysis of up- and downregulated genes showed that categories related to high metabolic activity were preferentially upregulated. A principal component analysis was applied to compare the transcriptome of syncytia with the transcriptome of different Arabidopsis organs (obtained by the AtGenExpress project), and with specific root tissues. This analysis revealed that syncytia are transcriptionally clearly different from roots (and all other organs), as well as from other root tissues.

B7-H3 is a potent inhibitor of human T-cell activation: No evidence for B7-H3 and TREML2 interaction

B7‐H3 belongs to the B7 superfamily, a group of molecules that costimulate or down‐modulate T‐cell responses. Although it was shown that B7‐H3 could inhibit T‐cell responses, several studies – most of them performed in murine systems – found B7‐H3 to act in a costimulatory manner. In this study, we have specifically addressed a potential functional dualism of human B7‐H3 by assessing the effect of this molecule under varying experimental conditions as well as on different T‐cell subsets. We show that B7‐H3 does not costimulate human T cells. In the presence of strong activating signals, B7‐H3 potently and consistently down‐modulated human T‐cell responses. This inhibitory effect was evident when analysing proliferation and cytokine production and affected naïve as well as pre‐activated T cells. Furthermore, we demonstrate that B7‐H3–T‐cell interaction is characterised by an early suppression of IL‐2 and that T‐cell inhibition can be reverted by exogenous IL‐2. Since the triggering receptor expressed on myeloid cells like transcript 2 (TREML2/TLT‐2) has been recently described as costimulatory receptor of murine B7‐H3 we have extensively analysed interaction of human B7‐H3 with TREML2/TLT‐2. In these experiments we found no evidence for such an interaction. Furthermore, our data do not point to a role for murine TREML2 as a receptor for murine B7‐H3.

Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling

Motivation: Measurement precision determines the power of any analysis to reliably identify significant signals, such as in screens for differential expression, independent of whether the experimental design incorporates replicates or not. With the compilation of large-scale RNA-Seq datasets with technical replicate samples, however, we can now, for the first time, perform a systematic analysis of the precision of expression level estimates from massively parallel sequencing technology. This then allows considerations for its improvement by computational or experimental means. Results: We report on a comprehensive study of target identification and measurement precision, including their dependence on transcript expression levels, read depth and other parameters. In particular, an impressive recall of 84% of the estimated true transcript population could be achieved with 331 million 50 bp reads, with diminishing returns from longer read lengths and even less gains from increased sequencing depths. Most of the measurement power (75%) is spent on only 7% of the known transcriptome, however, making less strongly expressed transcripts harder to measure. Consequently, <30% of all transcripts could be quantified reliably with a relative error <20%. Based on established tools, we then introduce a new approach for mapping and analysing sequencing reads that yields substantially improved performance in gene expression profiling, increasing the number of transcripts that can reliably be quantified to over 40%. Extrapolations to higher sequencing depths highlight the need for efficient complementary steps. In discussion we outline possible experimental and computational strategies for further improvements in quantification precision. Contact: rnaseq10@boku.ac.at Supplementary information: Supplementary data are available at Bioinformatics online.

DNA microarray normalization methods can remove bias from differential protein expression analysis of 2D difference gel electrophoresis results

MOTIVATION Two-dimensional Difference Gel Electrophoresis (DIGE) measures expression differences for thousands of proteins in parallel. In contrast to DNA microarray analysis, however, there have been few systematic studies on the validity of differential protein expression analysis, and the effects of normalization methods have not yet been investigated. To address this need, we assessed a series of same-same comparisons, evaluating how random experimental variance influenced differential expression analysis. RESULTS The strong fluctuations observed were reflected in large discrepancies between the distributions of the spot intensities for different gels. Correct normalization for pooling of multiple gels for analysis is, therefore, essential. We show that both dye-specific background levels and the differences in scale of the spot intensity distributions must be accounted for. A variance stabilizing transform that had been developed for DNA microarray analysis combined with a robust Z-score allowed the determination of gel-independent signal thresholds based on the empirical distributions from same-same comparisons. In contrast, similar thresholds holding up to cross-validation could not be proposed for data normalized using methods established in the field of proteomics. AVAILABILITY Software is available on request from the authors. SUPPLEMENTARY INFORMATION There is supplementary material available online at http://www.flychip.org.uk/kreil/pub/2dgels/

Novel insights into the unfolded protein response using Pichia pastoris specific DNA microarrays

BackgroundDNA Microarrays are regarded as a valuable tool for basic and applied research in microbiology. However, for many industrially important microorganisms the lack of commercially available microarrays still hampers physiological research. Exemplarily, our understanding of protein folding and secretion in the yeast Pichia pastoris is presently widely dependent on conclusions drawn from analogies to Saccharomyces cerevisiae. To close this gap for a yeast species employed for its high capacity to produce heterologous proteins, we developed full genome DNA microarrays for P. pastoris and analyzed the unfolded protein response (UPR) in this yeast species, as compared to S. cerevisiae.ResultsBy combining the partially annotated gene list of P. pastoris with de novo gene finding a list of putative open reading frames was generated for which an oligonucleotide probe set was designed using the probe design tool TherMODO (a thermodynamic model-based oligoset design optimizer). To evaluate the performance of the novel array design, microarrays carrying the oligo set were hybridized with samples from treatments with dithiothreitol (DTT) or a strain overexpressing the UPR transcription factor HAC1, both compared with a wild type strain in normal medium as untreated control. DTT treatment was compared with literature data for S. cerevisiae, and revealed similarities, but also important differences between the two yeast species. Overexpression of HAC1, the most direct control for UPR genes, resulted in significant new understanding of this important regulatory pathway in P. pastoris, and generally in yeasts.ConclusionThe differences observed between P. pastoris and S. cerevisiae underline the importance of DNA microarrays for industrial production strains. P. pastoris reacts to DTT treatment mainly by the regulation of genes related to chemical stimulus, electron transport and respiration, while the overexpression of HAC1 induced many genes involved in translation, ribosome biogenesis, and organelle biosynthesis, indicating that the regulatory events triggered by DTT treatment only partially overlap with the reactions to overexpression of HAC1. The high reproducibility of the results achieved with two different oligo sets is a good indication for their robustness, and underlines the importance of less stringent selection of regulated features, in order to avoid a large number of false negative results.

Independent component analysis of microarray data in the study of endometrial cancer

Gene microarray technology is highly effective in screening for differential gene expression and has hence become a popular tool in the molecular investigation of cancer. When applied to tumours, molecular characteristics may be correlated with clinical features such as response to chemotherapy. Exploitation of the huge amount of data generated by microarrays is difficult, however, and constitutes a major challenge in the advancement of this methodology. Independent component analysis (ICA), a modern statistical method, allows us to better understand data in such complex and noisy measurement environments. The technique has the potential to significantly increase the quality of the resulting data and improve the biological validity of subsequent analysis. We performed microarray experiments on 31 postmenopausal endometrial biopsies, comprising 11 benign and 20 malignant samples. We compared ICA to the established methods of principal component analysis (PCA), Cyber-T, and SAM. We show that ICA generated patterns that clearly characterized the malignant samples studied, in contrast to PCA. Moreover, ICA improved the biological validity of the genes identified as differentially expressed in endometrial carcinoma, compared to those found by Cyber-T and SAM. In particular, several genes involved in lipid metabolism that are differentially expressed in endometrial carcinoma were only found using this method. This report highlights the potential of ICA in the analysis of microarray data.