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      Bioinformatic identification and characterization of human endothelial cell-restricted genes

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          Abstract

          Background

          In this study, we used a systematic bioinformatics analysis approach to elucidate genes that exhibit an endothelial cell (EC) restricted expression pattern, and began to define their regulation, tissue distribution, and potential biological role.

          Results

          Using a high throughput microarray platform, a primary set of 1,191 transcripts that are enriched in different primary ECs compared to non-ECs was identified (LCB >3, FDR <2%). Further refinement of this initial subset of transcripts, using published data, yielded 152 transcripts (representing 109 genes) with different degrees of EC-specificity. Several interesting patterns emerged among these genes: some were expressed in all ECs and several were restricted to microvascular ECs. Pathway analysis and gene ontology demonstrated that several of the identified genes are known to be involved in vasculature development, angiogenesis, and endothelial function (P < 0.01). These genes are enriched in cardiovascular diseases, hemorrhage and ischemia gene sets (P < 0.001). Most of the identified genes are ubiquitously expressed in many different tissues. Analysis of the proximal promoter revealed the enrichment of conserved binding sites for 26 different transcription factors and analysis of the untranslated regions suggests that a subset of the EC-restricted genes are targets of 15 microRNAs. While many of the identified genes are known for their regulatory role in ECs, we have also identified several novel EC-restricted genes, the function of which have yet to be fully defined.

          Conclusion

          The study provides an initial catalogue of EC-restricted genes most of which are ubiquitously expressed in different endothelial cells.

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          Most cited references33

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          Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection.

          Recent advances in cDNA and oligonucleotide DNA arrays have made it possible to measure the abundance of mRNA transcripts for many genes simultaneously. The analysis of such experiments is nontrivial because of large data size and many levels of variation introduced at different stages of the experiments. The analysis is further complicated by the large differences that may exist among different probes used to interrogate the same gene. However, an attractive feature of high-density oligonucleotide arrays such as those produced by photolithography and inkjet technology is the standardization of chip manufacturing and hybridization process. As a result, probe-specific biases, although significant, are highly reproducible and predictable, and their adverse effect can be reduced by proper modeling and analysis methods. Here, we propose a statistical model for the probe-level data, and develop model-based estimates for gene expression indexes. We also present model-based methods for identifying and handling cross-hybridizing probes and contaminating array regions. Applications of these results will be presented elsewhere.
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            Correlative memory deficits, Abeta elevation, and amyloid plaques in transgenic mice.

            Transgenic mice overexpressing the 695-amino acid isoform of human Alzheimer beta-amyloid (Abeta) precursor protein containing a Lys670 --> Asn, Met671 --> Leu mutation had normal learning and memory in spatial reference and alternation tasks at 3 months of age but showed impairment by 9 to 10 months of age. A fivefold increase in Abeta(1-40) and a 14-fold increase in Abeta(1-42/43) accompanied the appearance of these behavioral deficits. Numerous Abeta plaques that stained with Congo red dye were present in cortical and limbic structures of mice with elevated amounts of Abeta. The correlative appearance of behavioral, biochemical, and pathological abnormalities reminiscent of Alzheimer's disease in these transgenic mice suggests new opportunities for exploring the pathophysiology and neurobiology of this disease.
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              "Stemness": transcriptional profiling of embryonic and adult stem cells.

              The transcriptional profiles of mouse embryonic, neural, and hematopoietic stem cells were compared to define a genetic program for stem cells. A total of 216 genes are enriched in all three types of stem cells, and several of these genes are clustered in the genome. When compared to differentiated cell types, stem cells express a significantly higher number of genes (represented by expressed sequence tags) whose functions are unknown. Embryonic and neural stem cells have many similarities at the transcriptional level. These results provide a foundation for a more detailed understanding of stem cell biology.
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                Author and article information

                Journal
                BMC Genomics
                BMC Genomics
                BioMed Central
                1471-2164
                2010
                28 May 2010
                : 11
                : 342
                Affiliations
                [1 ]Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston MA 02215, USA
                [2 ]Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston MA 02215, USA
                [3 ]Division of Cardiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston MA 02215, USA
                [4 ]Division of Molecular and Vascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston MA 02215, USA
                [5 ]Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, 02215 USA
                Article
                1471-2164-11-342
                10.1186/1471-2164-11-342
                2887814
                20509943
                90d8c63b-2743-498c-bfb3-621753c42caa
                Copyright ©2010 Bhasin et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 25 January 2010
                : 28 May 2010
                Categories
                Research Article

                Genetics
                Genetics

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