A qualitative assessment of direct-labeled cDNA products prior to microarray analysis

BACKGROUND: The success of the microarray process in determining differential gene expression of thousands of genes is dependent upon the quality and integrity of the starting RNA, this being particularly true of direct labeling via a reverse transcription procedure. Furthermore, an RNA of reasonable quality still may not yield reliable hybridization data if the labeling efficiency was poor. RESULTS: Here we present a novel assay for assessing the quality of directly labeled fluorescent cDNA prior to microarray hybridization utilizing the Agilent 2100 Bioanalyzer, which employs microfluidic technology for the analysis of nucleic acids and proteins. Using varying amounts of RNase to simulate RNA degradation, we show the strength of this un-advertised assay in determining the relative amounts of cDNA obtained from a direct labeling reaction. CONCLUSION: Utilization of this method in the lab will help to prevent the costly mistake of hybridizing poor quality direct labeled products to expensive arrays

A newly-developed community microarray resource for transcriptome profiling in Brassica species enables the confirmation of Brassica-specific expressed sequences

<p>Abstract</p> <p>Background</p> <p>The <it>Brassica </it>species include an important group of crops and provide opportunities for studying the evolutionary consequences of polyploidy. They are related to <it>Arabidopsis thaliana</it>, for which the first complete plant genome sequence was obtained and their genomes show extensive, although imperfect, conserved synteny with that of <it>A. thaliana</it>. A large number of EST sequences, derived from a range of different <it>Brassica </it>species, are available in the public database, but no public microarray resource has so far been developed for these species.</p> <p>Results</p> <p>We assembled unigenes using ~800,000 EST sequences, mainly from three species: <it>B. napus</it>, <it>B. rapa </it>and <it>B. oleracea</it>. The assembly was conducted with the aim of co-assembling ESTs of orthologous genes (including homoeologous pairs of genes in <it>B. napus </it>from each of the A and C genomes), but resolving assemblies of paralogous, or paleo-homoeologous, genes (<it>i.e</it>. the genes related by the ancestral genome triplication observed in diploid <it>Brassica </it>species). 90,864 unique sequence assemblies were developed. These were incorporated into the BAC sequence annotation for the <it>Brassica rapa </it>Genome Sequencing Project, enabling the identification of cognate genomic sequences for a proportion of them. A 60-mer oligo microarray comprising 94,558 probes was developed using the unigene sequences. Gene expression was analysed in reciprocal resynthesised <it>B. napus </it>lines and the <it>B. oleracea </it>and <it>B. rapa </it>lines used to produce them. The analysis showed that significant expression could consistently be detected in leaf tissue for 35,386 unigenes. Expression was detected across all four genotypes for 27,355 unigenes, genome-specific expression patterns were observed for 7,851 unigenes and 180 unigenes displayed other classes of expression pattern. Principal component analysis (PCA) clearly resolved the individual microarray datasets for <it>B. rapa</it>, <it>B. oleracea </it>and resynthesised <it>B. napus</it>. Quantitative differences in expression were observed between the resynthesised <it>B. napus </it>lines for 98 unigenes, most of which could be classified into non-additive expression patterns, including 17 that showed cytoplasm-specific patterns. We further characterized the unigenes for which A genome-specific expression was observed and cognate genomic sequences could be identified. Ten of these unigenes were found to be <it>Brassica</it>-specific sequences, including two that originate from complex loci comprising gene clusters.</p> <p>Conclusion</p> <p>We succeeded in developing a <it>Brassica </it>community microarray resource. Although expression can be measured for the majority of unigenes across species, there were numerous probes that reported in a genome-specific manner. We anticipate that some proportion of these will represent species-specific transcripts and the remainder will be the consequence of variation of sequences within the regions represented by the array probes. Our studies demonstrated that the datasets obtained from the arrays can be used for typical analyses, including PCA and the analysis of differential expression. We have also demonstrated that <it>Brassica</it>-specific transcripts identified <it>in silico </it>in the sequence assembly of public EST database accessions are indeed reported by the array. These would not be detectable using arrays designed using <it>A. thaliana </it>sequences.</p

Dynamic Zebrafish Interactome Reveals Transcriptional Mechanisms of Dioxin Toxicity

In order to generate hypotheses regarding the mechanisms by which 2,3,7,8-tetrachlorodibenzo-p-dioxin (dioxin) causes toxicity, we analyzed global gene expression changes in developing zebrafish embryos exposed to this potent toxicant in the context of a dynamic gene network. For this purpose, we also computationally inferred a zebrafish (Danio rerio) interactome based on orthologs and interaction data from other eukaryotes.Using novel computational tools to analyze this interactome, we distinguished between dioxin-dependent and dioxin-independent interactions between proteins, and tracked the temporal propagation of dioxin-dependent transcriptional changes from a few genes that were altered initially, to large groups of biologically coherent genes at later times. The most notable processes altered at later developmental stages were calcium and iron metabolism, embryonic morphogenesis including neuronal and retinal development, a variety of mitochondria-related functions, and generalized stress response (not including induction of antioxidant genes). Within the interactome, many of these responses were connected to cytochrome P4501A (cyp1a) as well as other genes that were dioxin-regulated one day after exposure. This suggests that cyp1a may play a key role initiating the toxic dysregulation of those processes, rather than serving simply as a passive marker of dioxin exposure, as suggested by earlier research.Thus, a powerful microarray experiment coupled with a flexible interactome and multi-pronged interactome tools (which are now made publicly available for microarray analysis and related work) suggest the hypothesis that dioxin, best known in fish as a potent cardioteratogen, has many other targets. Many of these types of toxicity have been observed in mammalian species and are potentially caused by alterations to cyp1a

Profiles of Global Gene Expression in Ionizing-Radiation–Damaged Human Diploid Fibroblasts Reveal Synchronization behind the G(1) Checkpoint in a G(0)-like State of Quiescence

Cell cycle arrest and stereotypic transcriptional responses to DNA damage induced by ionizing radiation (IR) were quantified in telomerase-expressing human diploid fibroblasts. Analysis of cytotoxicity demonstrated that 1.5 Gy IR inactivated colony formation by 40–45% in three fibroblast lines; this dose was used in all subsequent analyses. Fibroblasts exhibited > 90% arrest of progression from G(2) to M at 2 hr post-IR and a similarly severe arrest of progression from G(1) to S at 6 and 12 hr post-IR. Normal rates of DNA synthesis and mitosis 6 and 12 hr post-IR caused the S and M compartments to empty by > 70% at 24 hr. Global gene expression was analyzed in IR-treated cells. A microarray analysis algorithm, EPIG, identified nine IR-responsive patterns of gene expression that were common to the three fibroblast lines, including a dominant p53-dependent G(1) checkpoint response. Many p53 target genes, such as CDKN1A, GADD45, BTG2, and PLK3, were significantly up-regulated at 2 hr post-IR. Many genes whose expression is regulated by E2F family transcription factors, including CDK2, CCNE1, CDC6, CDC2, MCM2, were significantly down-regulated at 24 hr post-IR. Numerous genes that participate in DNA metabolism were also markedly repressed in arrested fibroblasts apparently as a result of cell synchronization behind the G(1) checkpoint. However, cluster and principal component analyses of gene expression revealed a profile 24 hr post-IR with similarity to that of G(0) growth quiescence. The results reveal a highly stereotypic pattern of response to IR in human diploid fibroblasts that reflects primarily synchronization behind the G(1) checkpoint but with prominent induction of additional markers of G(0) quiescence such as GAS1

Identification of Primary Transcriptional Regulation of Cell Cycle-Regulated Genes upon DNA Damage

The changes in global gene expression in response to DNA damage may derive from either direct induction or repression by transcriptional regulation or indirectly by synchronization of cells to specific cell cycle phases, such as G1 or G2. We developed a model that successfully estimated the expression levels of >400 cell cycle-regulated genes in normal human fibroblasts based on the proportions of cells in each phase of the cell cycle. By isolating effects on the gene expression associated with the cell cycle phase redistribution after genotoxin treatment, the direct transcriptional target genes were distinguished from genes for which expression changed secondary to cell synchronization. Application of this model to ionizing radiation (IR)-treated normal human fibroblasts identified 150 of 406 cycle-regulated genes as putative direct transcriptional targets of IR-induced DNA damage. Changes in expression of these genes after IR treatment derived from both direct transcriptional regulation and cell cycle synchronization

Gene expression response in target organ and whole blood varies as a function of target organ injury phenotype

Histopathology, clinical chemistry, hematology and gene expression data were collected from the rat liver and blood after treatment with eight known hepatotoxins

Glucocorticoids with different chemical structures but similar glucocorticoid receptor potency regulate subsets of common and unique genes in human trabecular meshwork cells

<p>Abstract</p> <p>Background</p> <p>In addition to their well-documented ocular therapeutic effects, glucocorticoids (GCs) can cause sight-threatening side-effects including ocular hypertension presumably via morphological and biochemical changes in trabecular meshwork (TM) cells. In the present study, we directly compared the glucocorticoid receptor (GR) potency for dexamethasone (DEX), fluocinolone acetonide (FA) and triamcinolone acetonide (TA), examined the expression of known GRα and GRβ isoforms, and used gene expression microarrays to compare the effects of DEX, FA, and TA on the complete transcriptome in two primary human TM cell lines.</p> <p>Methods</p> <p>GR binding affinity for DEX, FA, and TA was measured by a cell-free competitive radio-labeled GR binding assay. GR-mediated transcriptional activity was assessed using the GeneBLAzer beta-lactamase reporter gene assay. Levels of GRα and GRβ isoforms were assessed by Western blot. Total RNA was extracted from TM 86 and TM 93 cells treated with 1 μM DEX, FA, or TA for 24 hr and used for microarray gene expression analysis. The microarray experiments were repeated three times. Differentially expressed genes were identified by Rosetta Resolver Gene Expression Analysis System.</p> <p>Results</p> <p>The GR binding affinity (IC₅₀) for DEX, FA, and TA was 5.4, 2.0, and 1.5 nM, respectively. These values are similar to the GR transactivation EC₅₀of 3.0, 0.7, and 1.5 nM for DEX, FA, and TA, respectively. All four GRα translational isoforms (A-D) were expressed in TM 86 and TM 93 total cell lysates, however, the C and D isoforms were more highly expressed relative to A and B. All four GRβ isoforms (A-D) were also detected in TM cells, although GRβ-D isoform expression was lower compared to that of the A, B, or C isoforms. Microarray analysis revealed 1,968 and 1,150 genes commonly regulated by DEX, FA, and TA in TM 86 and TM 93, respectively. These genes included RGC32, OCA2, ANGPTL7, MYOC, FKBP5, SAA1 and ZBTB16. In addition, each GC specifically regulated a unique set of genes in both TM cell lines. Using Ingenuity Pathway Analysis (IPA) software, analysis of the data from TM 86 cells showed that DEX significantly regulated transcripts associated with RNA post-transcriptional modifications, whereas FA and TA modulated genes involved in lipid metabolism and cell morphology, respectively. In TM 93 cells, DEX significantly regulated genes implicated in histone methylation, whereas FA and TA altered genes associated with cell cycle and cell adhesion, respectively.</p> <p>Conclusion</p> <p>Human trabecular meshwork cells in culture express all known GRα and GRβ translational isoforms, and GCs with similar potency but subtly different chemical structure are capable of regulating common and unique gene subsets and presumably biologic responses in these cells. These GC structure-dependent effects appear to be TM cell-lineage dependent.</p

Transcriptional effects of transfection: the potential for misinterpretation of gene expression data generated from transiently transfected cells

Transfection is used to introduce a gene of interest into a cell. To interpret the downstream results, understanding which effects are the true biological responses to the gene and which, if any, are off-target effects can be difficult. In order to discriminate true biological effects from off-target effects, we transfected a breast cancer cell line, MCF7, with a vector encoding either a reporter gene or the identical vector without the reporter gene insert. Both resulted in similar numbers of differentially expressed transcripts, suggesting that very few of the responses were directly due to the introduction of the reporter gene. We postulate that many differentially expressed transcripts are the result of the introduction of foreign DNA, as the biological processes associated with these genes are primarily associated with an immune response to a viral infection. Interestingly, different transfection reagents resulted in >10-fold difference in the number of differentially expressed transcripts. This suggests the importance of testing multiple reagents and selecting the best transfection reagent along with the appropriate vector within the context of the experimental model system to ensure that the majority of the observed responses are biological effects of the gene of interest and not based on a particular transfection process used