Experimental comparison and cross-validation of the Affymetrix and Illumina gene expression analysis platforms

The growth in popularity of RNA expression microarrays has been accompanied by concerns about the reliability of the data especially when comparing between different platforms. Here, we present an evaluation of the reproducibility of microarray results using two platforms, Affymetrix GeneChips and Illumina BeadArrays. The study design is based on a dilution series of two human tissues (blood and placenta), tested in duplicate on each platform. The results of a comparison between the platforms indicate very high agreement, particularly for genes which are predicted to be differentially expressed between the two tissues. Agreement was strongly correlated with the level of expression of a gene. Concordance was also improved when probes on the two platforms could be identified as being likely to target the same set of transcripts of a given gene. These results shed light on the causes or failures of agreement across microarray platforms. The set of probes we found to be most highly reproducible can be used by others to help increase confidence in analyses of other data sets using these platforms

PolyDoms: a whole genome database for the identification of non-synonymous coding SNPs with the potential to impact disease

As knowledge of human genetic polymorphisms grows, so does the opportunity and challenge of identifying those polymorphisms that may impact the health or disease risk of an individual person. A critical need is to organize large-scale polymorphism analyses and to prioritize candidate non-synonymous coding SNPs (nsSNPs) that should be tested in experimental and epidemiological studies to establish their context-specific impacts on protein function. In addition, with emerging high-resolution clinical genetics testing, new polymorphisms must be analyzed in the context of all available protein feature knowledge including other known mutations and polymorphisms. To approach this, we developed PolyDoms () as a database to integrate the results of multiple algorithmic procedures and functional criteria applied to the entire Entrez dbSNP dataset. In addition to predicting structural and functional impacts of all nsSNPs, filtering functions enable group-based identification of potentially harmful nsSNPs among multiple genes associated with specific diseases, anatomies, mammalian phenotypes, gene ontologies, pathways or protein domains. PolyDoms, thus, provides a means to derive a list of candidate SNPs to be evaluated in experimental or epidemiological studies for impact on protein functions and disease risk associations. PolyDoms will continue to be curated to improve its usefulness

Improved human disease candidate gene prioritization using mouse phenotype

<p>Abstract</p> <p>Background</p> <p>The majority of common diseases are multi-factorial and modified by genetically and mechanistically complex polygenic interactions and environmental factors. High-throughput genome-wide studies like linkage analysis and gene expression profiling, tend to be most useful for classification and characterization but do not provide sufficient information to identify or prioritize specific disease causal genes.</p> <p>Results</p> <p>Extending on an earlier hypothesis that the majority of genes that impact or cause disease share membership in any of several functional relationships we, for the first time, show the utility of mouse phenotype data in human disease gene prioritization. We study the effect of different data integration methods, and based on the validation studies, we show that our approach, ToppGene <url>http://toppgene.cchmc.org</url>, outperforms two of the existing candidate gene prioritization methods, SUSPECTS and ENDEAVOUR.</p> <p>Conclusion</p> <p>The incorporation of phenotype information for mouse orthologs of human genes greatly improves the human disease candidate gene analysis and prioritization.</p

Restricting Zap70 Expression to CD4+CD8+ Thymocytes Reveals a T Cell Receptor–dependent Proofreading Mechanism Controlling the Completion of Positive Selection

Although T cell receptor (TCR) signals are essential for intrathymic T cell–positive selection, it remains controversial whether they only serve to initiate this process, or whether they are required throughout to promote thymocyte differentiation and survival. To address this issue, we have devised a novel approach to interfere with thymocyte TCR signaling in a developmental stage-specific manner in vivo. We have reconstituted mice deficient for Zap70, a tyrosine kinase required for TCR signaling and normally expressed throughout T cell development, with a Zap70 transgene driven by the adenosine deaminase (ADA) gene enhancer, which is active in CD4+CD8+ thymocytes but inactive in CD4+ or CD8+ single-positive (SP) thymocytes. In such mice, termination of Zap70 expression impaired TCR signal transduction and arrested thymocyte development after the initiation, but before the completion, of positive selection. Arrested thymocytes had terminated Rag gene expression and up-regulated TCR and Bcl-2 expression, but failed to differentiate into mature CD4 or CD8 SP thymocytes, to be rescued from death by neglect or to sustain interleukin 7Rα expression. These observations identify a TCR-dependent proofreading mechanism that verifies thymocyte TCR specificity and differentiation choices before the completion of positive selection

Environmental Impact on Direct Neuronal Reprogramming In Vivo in the Adult Brain

Direct reprogramming of non-neuronal cells to generate new neurons is a promising approach to repair damaged brains. Impact of the in vivo environment on neuronal reprogramming, however, is poorly understood. Here we show that regional differences and injury conditions have significant influence on the efficacy of reprogramming and subsequent survival of newly generated neurons in the adult rodent brain. A combination of local exposure to growth factors and retrovirus-mediated overexpression of the neurogenic transcription factor Neurogenin2 (Neurog2) can induce new neurons from non-neuronal cells in the adult neocortex and striatum where neuronal turnover is otherwise very limited. These two regions respond to growth factors and Neurog2 differently and instruct new neurons to exhibit distinct molecular phenotypes. Moreover, ischemic insult differentially affects differentiation of new neurons in these regions. These results demonstrate strong environmental impact on direct neuronal reprogramming in vivo

Engineered Human Skin Substitutes Undergo Large-Scale Genomic Reprogramming and Normal Skin-Like Maturation after Transplantation to Athymic Mice

Bioengineered skin substitutes can facilitate wound closure in severely burned patients, but deficiencies limit their outcomes compared with native skin autografts. To identify gene programs associated with their in vivo capabilities and limitations, we extended previous gene expression profile analyses to now compare engineered skin after in vivo grafting with both in vitro maturation and normal human skin. Cultured skin substitutes were grafted on full-thickness wounds in athymic mice, and biopsy samples for microarray analyses were collected at multiple in vitro and in vivo time points. Over 10,000 transcripts exhibited large-scale expression pattern differences during in vitro and in vivo maturation. Using hierarchical clustering, 11 different expression profile clusters were partitioned on the basis of differential sample type and temporal stage-specific activation or repression. Analyses show that the wound environment exerts a massive influence on gene expression in skin substitutes. For example, in vivo–healed skin substitutes gained the expression of many native skin-expressed genes, including those associated with epidermal barrier and multiple categories of cell–cell and cell–basement membrane adhesion. In contrast, immunological, trichogenic, and endothelial gene programs were largely lacking. These analyses suggest important areas for guiding further improvement of engineered skin for both increased homology with native skin and enhanced wound healing

CisMols Analyzer: identification of compositionally similar cis-element clusters in ortholog conserved regions of coordinately expressed genes

Combinatorial interactions of sequence-specific trans-acting factors with localized genomic cis-element clusters are the principal mechanism for regulating tissue-specific and developmental gene expression. With the emergence of expanding numbers of genome-wide expression analyses, the identification of the cis-elements responsible for specific patterns of transcriptional regulation represents a critical area of investigation. Computational methods for the identification of functional cis-regulatory modules are difficult to devise, principally because of the short length and degenerate nature of individual cis-element binding sites and the inherent complexity that is generated by combinatorial interactions within cis-clusters. Filtering candidate cis-element clusters based on phylogenetic conservation is helpful for an individual ortholog gene pair, but combining data from cis-conservation and coordinate expression across multiple genes is a more difficult problem. To approach this, we have extended an ortholog gene-pair database with additional analytical architecture to allow for the analysis and identification of maximal numbers of compositionally similar and phylogenetically conserved cis-regulatory element clusters from a list of user-selected genes. The system has been successfully tested with a series of functionally related and microarray profile-based co-expressed ortholog pairs of promoters and genes using known regulatory regions as training sets and co-expressed genes in the olfactory and immunohematologic systems as test sets. CisMols Analyzer is accessible via a Web interface at