Search

Motivation: For any time-course microarray data in which the gene interactions and the associated paired patterns are dependent, the proposed pattern recognition (PARE) approach can infer time-lagged genetic interactions, a challenging task due to the small number of time points and large number of genes. PARE utilizes a non-linear score to identify subclasses of gene pairs with different time lags. In each subclass, PARE extracts non-linear characteristics of paired gene-expression curves and learns weights of the decision score applying an optimization algorithm to microarray gene-expression data (MGED) of some known interactions, from biological experiments or published literature. Namely, PARE integrates both MGED and existing knowledge via machine learning, and subsequently predicts the other genetic interactions in the subclass. Results: PARE, a time-lagged correlation approach and the latest advance in graphical Gaussian models were applied to predict 112 (132) pairs of TC/TD (transcriptional regulatory) interactions. Checked against qRT-PCR results (published literature), their true positive rates are 73% (77%), 46% (51%), and 52% (59%), respectively. The false positive rates of predicting TC and TD (AT and RT) interactions in the yeast genome are bounded by 13 and 10% (10 and 14%), respectively. Several predicted TC/TD interactions are shown to coincide with existing pathways involving Sgs1, Srs2 and Mus81. This reinforces the possibility of applying genetic interactions to predict pathways of protein complexes. Moreover, some experimentally testable gene interactions involving DNA repair are predicted. Availability: Supplementary data and PARE software are available at http://www.stat.sinica.edu.tw/~gshieh/pare.htm. Contact: gshieh@stat.sinica.edu.tw

Somatic stem cell transplantation holds great promise in regenerative medicine. The best-characterized adult stem cells are mesenchymal stem cells (MSCs), neural stem cells (NSCs), and CD133+ hematopoietic stem cells (HSCs). The applications of HSCs are hampered since these cells are difficult to maintain in an undifferentiated state in vitro. Understanding genes responsible for stem cell properties and their interactions will help on this issue. The construction of stem cell genetic networks will also help to develop rational strategies to revert somatic cells back to a stem-like state. We performed a systemic study on human CD133+ HSCs, NSCs, MSCs, and embryonic stem cells and two different progenies of CD133+ HSCs, microvascular endothelial cells (MVECs) and peripheral blood mononuclear cells. Genes abundant in each or in all three somatic stem cells were identified. We also observed complex genetic networks functioning in postnatal stem cells, in which several genes, such as PTPN11 and DHFR, acted as hubs to maintain the stability and connectivity of the whole genetic network. Eighty-seven HSC genes, including ANGPT1 and GATA2, were independently identified by comparing CD34+CD33−CD38− hematopoietic stem cells with CD34+ precursors and various matured progenies. Introducing GATA2 into MVECs resulted in dedifferentiation-like transcriptome reprogramming, with HSC genes (such as ANGPT1) being up and endothelial genes (such as EPHB2) being down. This study provides a foundation for a more detailed understanding of human somatic stem cells. Expressing the newly discovered stem cell genes in matured cells might lead to a global reversion of somatic transcriptome to a stem-like status. Disclosure of potential conflicts of interest is found at the end of this article.

In vitro analyses of plant GATA transcription factors have implicated some proteins in light-mediated and circadian-regulated gene expression, and, more recently, the analysis of mutants has uncovered further diverse roles for plant GATA factors. To facilitate function discovery for the 29 GATA genes in Arabidopsis (Arabidopsis thaliana), we have experimentally verified gene structures and determined expression patterns of all family members across adult tissues and suspension cell cultures, as well as in response to light and signals from the circadian clock. These analyses have identified two genes that are strongly developmentally light regulated, expressed predominantly in photosynthetic tissue, and with transcript abundance peaking before dawn. In contrast, several GATA factor genes are light down-regulated. The products of these light-regulated genes are candidates for those proteins previously implicated in light-regulated transcription. Coexpression of these genes with well-characterized light-responsive transcripts across a large microarray data set supports these predictions. Other genes show additional tissue-specific expression patterns suggesting novel and unpredicted roles. Genome-wide analysis using coexpression scatter plots for paralogous gene pairs reveals unexpected differences in cocorrelated gene expression profiles. Clustering the Arabidopsis GATA factor gene family by similarity of expression patterns reveals that genes of recent descent do not uniformly show conserved current expression profiles, yet some genes showing more distant evolutionary origins have acquired common expression patterns. In addition to defining developmental and environmental dynamics of GATA transcript abundance, these analyses offer new insights into the evolution of gene expression profiles following gene duplication events.

The Arabidopsis Co-expression Tool, ACT, ranks the genes across a large microarray dataset according to how closely their expression follows the expression of a query gene. A database stores pre-calculated co-expression results for approximately 21,800 genes based on data from over 300 arrays. These results can be corroborated by calculation of co-expression results for user-defined sub-sets of arrays or experiments from the NASC/GARNet array dataset. Clique Finder (CF) identifies groups of genes which are consistently co-expressed with each other across a user-defined co-expression list. The parameters can be altered easily to adjust cluster size and the output examined for optimal inclusion of genes with known biological roles. Alternatively, a Scatter Plot tool displays the correlation coefficients for all genes against two user-selected queries on a scatter plot which can be useful for visual identification of clusters of genes with similar r-values. User-input groups of genes can be highlighted on the scatter plots. Inclusion of genes with known biology in sets of genes identified using CF and Scatter Plot tools allows inferences to be made about the roles of the other genes in the set and both tools can therefore be used to generate short lists of genes for further characterization. ACT is freely available at www.Arabidopsis.leeds.ac.uk/ACT.