Differential gene expression in the developing human macula: microarray analysis using rare tissue samples.
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ABSTRACT:
SUBMITTER: Kozulin P
PROVIDER: S-EPMC2816828 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:PURPOSE: The primate retina contains a specialized, cone-rich macula, which mediates high acuity and color vision. The spatial resolution provided by the neural retina at the macula is optimized by stereotyped retinal blood vessel and ganglion cell axon patterning, which radiate away from the macula and reduce shadowing of macular photoreceptors. However, the genes that mediate these specializations, and the reasons for the vulnerability of the macula to degenerative disease, remain obscure. The aim of this study was to identify novel genes that may influence retinal vascular patterning and definition of the foveal avascular area. METHODS: We used RNA from human fetal retinas at 19-20 weeks of gestation (WG; n=4) to measure differential gene expression in the macula, a region nasal to disc (nasal) and in the surrounding retina (surround) by hybridization to 12 GeneChip microarrays (HG-U133 Plus 2.0). The raw data was subjected to quality control assessment and preprocessing, using GC-RMA. We then used ANOVA analysis (Partek) Genomic Suite 6.3) and clustering (DAVID website) to identify the most highly represented genes clustered according to "biological process." The neural retina is fully differentiated at the macula at 19-20 WG, while neuronal progenitor cells are present throughout the rest of the retina. We therefore excluded genes associated with the cell cycle, and markers of differentiated neurons, from further analyses. Significantly regulated genes (p<0.01) were then identified in a second round of clustering according to molecular/reaction (KEGG) pathway. Genes of interest were verified by quantitative PCR (QRT-PCR), and 2 genes were localized by in situ hybridization. RESULTS: We generated two lists of differentially regulated genes: "macula versus surround" and "macula versus nasal." KEGG pathway clustering of the filtered gene lists identified 25 axon guidance-related genes that are differentially regulated in the macula. Furthermore, we found significant upregulation of three anti-angiogenic factors in the macula: pigment epithelium derived factor (PEDF), natriuretic peptide precurusor B (NPPB), and collagen type IValpha2. Differential expression of several members of the ephrin and semaphorin axon guidance gene families, PEDF, and NPPB was verified by QRT-PCR. Localization of PEDF and Eph-A6 mRNAs in sections of macaque retina shows expression of both genes concentrates in the ganglion cell layer (GCL) at the developing fovea, consistent with an involvement in definition of the foveal avascular area. CONCLUSIONS: Because the axons of macular ganglion cells exit the retina from around 8 WG, we suggest that the axon guidance genes highly expressed at the macula at 19-20 WG are also involved in vascular patterning, along with PEDF and NPPB. Localization of both PEDF and Eph-A6 mRNAs to the GCL of the developing fovea supports this idea. It is possible that specialization of the macular vessels, including definition of the foveal avascular area, is mediated by processes that piggyback on axon guidance mechanisms in effect earlier in development. These findings may be useful to understand the vulnerability of the macula to degeneration and to develop new therapeutic strategies to inhibit neovascularization.
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Project description:We present a powerful application of ultra high-throughput sequencing, SAGE-Seq, for the accurate quantification of normal and neoplastic mammary epithelial cell transcriptomes. We develop data analysis pipelines that allow the mapping of sense and antisense strands of mitochondrial and RefSeq genes, the normalization between libraries, and the identification of differentially expressed genes. We find that the diversity of cancer transcriptomes is significantly higher than that of normal cells. Our analysis indicates that transcript discovery plateaus at 10 million reads/sample, and suggests a minimum desired sequencing depth around 5 million reads. Comparison of SAGE-Seq and traditional SAGE on normal and cancerous breast tissues reveals higher sensitivity of SAGE-Seq to detect less abundant genes including those encoding for known breast cancer-related transcription factors and G protein-coupled receptors (GPCRs). SAGE-Seq is able to identify genes and pathways abnormally activated in breast cancer that traditional SAGE failed to call. SAGE-Seq is a powerful method for the identification of biomarkers and therapeutic targets in human disease. Global transcriptome profilings of 7 samples in normal and 7 samples in cancer from clinical breast tissue using Sage-Seq
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Project description:With the expansion of public repositories such as the Gene Expression Omnibus (GEO), we are rapidly cataloging cellular transcriptional responses to diverse experimental conditions. Methods that query these repositories based on gene expression content, rather than textual annotations, may enable more effective experiment retrieval as well as the discovery of novel associations between drugs, diseases, and other perturbations.We develop methods to retrieve gene expression experiments that differentially express the same transcriptional programs as a query experiment. Avoiding thresholds, we generate differential expression profiles that include a score for each gene measured in an experiment. We use existing and novel dimension reduction and correlation measures to rank relevant experiments in an entirely data-driven manner, allowing emergent features of the data to drive the results. A combination of matrix decomposition and p-weighted Pearson correlation proves the most suitable for comparing differential expression profiles. We apply this method to index all GEO DataSets, and demonstrate the utility of our approach by identifying pathways and conditions relevant to transcription factors Nanog and FoxO3.Content-based gene expression search generates relevant hypotheses for biological inquiry. Experiments across platforms, tissue types, and protocols inform the analysis of new datasets.
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Project description:BackgroundPrevious differential coexpression analyses focused on identification of differentially coexpressed gene pairs, revealing many insightful biological hypotheses. However, this method could not detect coexpression relationships between pairs of gene sets. Considering the success of many set-wise analysis methods for microarray data, a coexpression analysis based on gene sets may elucidate underlying biological processes provoked by the conditional changes. Here, we propose a differentially coexpressed gene sets (dCoxS) algorithm that identifies the differentially coexpressed gene set pairs between conditions.ResultsdCoxS is a two-step analysis method. In each condition, dCoxS measures the interaction score (IS), which represents the expression similarity between two gene sets using Renyi relative entropy. When estimating the relative entropy, multivariate kernel density estimation was used to model gene-gene correlation structure. Statistical tests for the conditional difference between the ISs determined the significance of differential coexpression of the gene set pair. Simulation studies supported that the IS is a representative measure of similarity between gene expression matrices. Single gene coexpression analysis of two publicly available microarray datasets detected no significant results. However, the dCoxS analysis of the datasets revealed differentially coexpressed gene set pairs related to the biological conditions of the datasets.ConclusiondCoxS identified differentially coexpressed gene set pairs not found by single gene analysis. The results indicate that set-wise differential coexpression analysis is useful for understanding biological processes induced by conditional changes.
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Project description:Retinal development occurs in mice between embryonic day E11.5 and post-natal day P8 as uncommitted neuroblasts assume retinal cell fates. The genetic pathways regulating retinal development are being identified but little is understood about the global networks that link these pathways together or the complexity of the expressed gene set required to form the retina. At E14.5, the retina contains mostly uncommitted neuroblasts and newly differentiated neurons. Here we report a sequence analysis of an E14.5 retinal cDNA library. To date, we have archived 15 268 ESTs and have annotated 9035, which represent 5288 genes. The fraction of singly occurring ESTs as a function of total EST accrual suggests that the total number of expressed genes in the library could approach 27 000. The 9035 ESTs were categorized by their known or putative functions. Representation of the genes involved in eye development was significantly higher in the retinal clone set compared with the NIA mouse 15K cDNA clone set. Screening with a microarray containing 864 cDNA clones using wild-type and brn-3b (-/-) retinal cDNA probes revealed a potential regulatory linkage between the transcription factor Brn-3b and expression of GAP-43, a protein associated with axon growth. The retinal EST database will be a valuable platform for gene expression profiling and a new source for gene discovery.
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