RNA-Seq vs Dual- and Single-Channel Microarray Data: Sensitivity Analysis for Differential Expression and Clustering

With the fast development of high-throughput sequencing technologies, a new generation of genome-wide gene expression measurements is under way. This is based on mRNA sequencing (RNA-seq), which complements the already mature technology of microarrays, and is expected to overcome some of the latter’s disadvantages. These RNA-seq data pose new challenges, however, as strengths and weaknesses have yet to be fully identified. Ideally, Next (or Second) Generation Sequencing measures can be integrated for more comprehensive gene expression investigation to facilitate analysis of whole regulatory networks. At present, however, the nature of these data is not very well understood. In this paper we study three alternative gene expression time series datasets for the Drosophila melanogaster embryo development, in order to compare three measurement techniques: RNA-seq, single-channel and dual-channel microarrays. The aim is to study the state of the art for the three technologies, with a view of assessing overlapping features, data compatibility and integration potential, in the context of time series measurements. This involves using established tools for each of the three different technologies, and technical and biological replicates (for RNA-seq and microarrays, respectively), due to the limited availability of biological RNA-seq replicates for time series data. The approach consists of a sensitivity analysis for differential expression and clustering. In general, the RNA-seq dataset displayed highest sensitivity to differential expression. The single-channel data performed similarly for the differentially expressed genes common to gene sets considered. Cluster analysis was used to identify different features of the gene space for the three datasets, with higher similarities found for the RNA-seq and single-channel microarray dataset.     

Genome-Scale Validation of Deep-Sequencing Libraries

Chromatin immunoprecipitation followed by high-throughput (HTP) sequencing (ChIP-seq) is a powerful tool to establish protein-DNA interactions genome-wide. The primary limitation of its broad application at present is the often-limited access to sequencers. Here we report a protocol, Mab-seq, that generates genome-scale quality evaluations for nucleic acid libraries intended for deep-sequencing. We show how commercially available genomic microarrays can be used to maximize the efficiency of library creation and quickly generate reliable preliminary data on a chromosomal scale in advance of deep sequencing. We also exploit this technique to compare enriched regions identified using microarrays with those identified by sequencing, demonstrating that they agree on a core set of clearly identified enriched regions, while characterizing the additional enriched regions identifiable using HTP sequencing.     

PKCepsilon activation induces dichotomous cardiac phenotypes and modulates PKCepsilon-RACK interactions and RACK expression

Receptors for activated C kinase (RACKs) have been shown to facilitate activation of protein kinase C (PKC). However, it is unknown whether PKC activation modulates RACK protein expression and PKC-RACK interactions. This issue was studied in two PKCepsilon transgenic lines exhibiting dichotomous cardiac phenotypes: one exhibits increased resistance to myocardial ischemia (cardioprotected phenotype) induced by a modest increase in PKCepsilon activity (228 +/- 23% of control), whereas the other exhibits cardiac hypertrophy and failure (hypertrophied phenotype) induced by a marked increase in PKCepsilon activity (452 +/- 28% of control). Our data demonstrate that activation of PKC modulates the expression of RACK isotypes and PKC-RACK interactions in a PKCepsilon activity- and dosage-dependent fashion. We found that, in mice displaying the cardioprotected phenotype, activation of PKCepsilon enhanced RACK2 expression (178 +/- 13% of control) and particulate PKCepsilon-RACK2 protein-protein interactions (178 +/- 18% of control). In contrast, in mice displaying the hypertrophied phenotype, there was not only an increase in RACK2 expression (330 +/- 33% of control) and particulate PKCepsilon-RACK2 interactions (154 +/- 14% of control) but also in RACK1 protein expression (174 +/- 10% of control). Most notably, PKCepsilon-RACK1 interactions were identified in this line. With the use of transgenic mice expressing a dominant negative PKCepsilon, we found that the changes in RACK expression as well as the attending cardiac phenotypes were dependent on PKCepsilon activity. Our observations demonstrate that RACK expression is dynamically regulated by PKCepsilon and suggest that differential patterns of PKCepsilon-RACK interactions may be important determinants of PKCepsilon-dependent cardiac phenotypes.     

Measuring and analyzing tissue specificity of human genes and protein complexes

Proteins and their interactions are essential for the survival of each human cell. Knowledge of their tissue occurrence is important for understanding biological processes. Therefore, we analyzed microarray and high-throughput RNA-sequencing data to identify tissue-specific and universally expressed genes. Gene expression data were used to investigate the presence of proteins, protein interactions and protein complexes in different tissues. Our comparison shows that the detection of tissue-specific genes and proteins strongly depends on the applied measurement technique. We found that microarrays are less sensitive for low expressed genes than high-throughput sequencing. Functional analyses based on microarray data are thus biased towards high expressed genes. This also means that previous biological findings based on microarrays might have to be re-examined using high-throughput sequencing results.     

TECHNICAL ADVANCES: A microarray for large-scale genomic and transcriptional analyses of the zebra finch (Taeniopygia guttata) and other passerines

The microarray technology has revolutionized biological research in the last decade. By monitoring the expression of many genes simultaneously, microarrays can elucidate gene function, as well as scan entire genomes for candidate genes encoding complex traits. However, because of high costs of sequencing and design, microarrays have largely been restricted to a few model species. Cross-species microarray (CSM) analyses, where microarrays are used for other species than the one they were designed for, have had varied success. We have conducted a CSM analysis by hybridizing genomic DNA from the common whitethroat (Sylvia communis) on a newly developed Affymetrix array designed for the zebra finch (Taeniopygia guttata), the Lund-zf array. The results indicate a very high potential for the zebra finch array to act as a CSM utility in other passerine birds. When hybridizing zebra finch genomic DNA, 98% of the gene representatives had higher signal intensities than the background cut-off, and for the common whitethroat, we found the equivalent proportion to be as high as 96%. This was surprising given the fact that finches and warblers diverged 25-50 million years ago, but may be explained by a relatively low sequence divergence between passerines (89-93%). Passerine birds are widely used in studies of ecology and evolution, and a zebra finch array that can be used for many species may have a large impact on future research directions.