Computational integration of homolog and pathway gene module expression reveals general stemness signatures

The stemness hypothesis states that all stem cells use common mechanisms to regulate self-renewal and multi-lineage potential. However, gene expression meta-analyses at the single gene level have failed to identify a significant number of genes selectively expressed by a broad range of stem cell types. We hypothesized that stemness may be regulated by modules of homologs. While the expression of any single gene within a module may vary from one stem cell type to the next, it is possible that the expression of the module as a whole is required so that the expression of different, yet functionally-synonymous, homologs is needed in different stem cells. Thus, we developed a computational method to test for stem cell-specific gene expression patterns from a comprehensive collection of 49 murine datasets covering 12 different stem cell types. We identified 40 individual genes and 224 stemness modules with reproducible and specific up-regulation across multiple stem cell types. The stemness modules included families regulating chromatin remodeling, DNA repair, and Wnt signaling. Strikingly, the majority of modules represent evolutionarily related homologs. Moreover, a score based on the discovered modules could accurately distinguish stem cell-like populations from other cell types in both normal and cancer tissues. This scoring system revealed that both mouse and human metastatic populations exhibit higher stemness indices than non-metastatic populations, providing further evidence for a stem cell-driven component underlying the transformation to metastatic disease.     

RNA sequencing reveals two major classes of gene expression levels in metazoan cells

The expression level of a gene is often used as a proxy for determining whether the protein or RNA product is functional in a cell or tissue. Therefore, it is of fundamental importance to understand the global distribution of gene expression levels, and to be able to interpret it mechanistically and functionally. Here we use RNA sequencing (RNA‐seq) of mouse Th2 cells, coupled with a range of other techniques, to show that all genes can be separated, based on their expression abundance, into two distinct groups: one group comprised of lowly expressed and putatively non‐functional mRNAs, and the other of highly expressed mRNAs with active chromatin marks at their promoters. These observations are confirmed in many other microarray and RNA‐seq data sets of metazoan cell types.     

Variance Component Estimation for Mixed Model Analysis of cDNA Microarray Data

Microarrays provide a valuable tool for the quantification of gene expression. Usually, however, there is a limited number of replicates leading to unsatisfying variance estimates in a gene‐wise mixed model analysis. As thousands of genes are available, it is desirable to combine information across genes. When more than two tissue types or treatments are to be compared it might be advisable to consider the array effect as random. Then information between arrays may be recovered, which can increase accuracy in estimation. We propose a method of variance component estimation across genes for a linear mixed model with two random effects. The method may be extended to models with more than two random effects. We assume that the variance components follow a log‐normal distribution. Assuming that the sums of squares from the gene‐wise analysis, given the true variance components, follow a scaled χ²‐distribution, we adopt an empirical Bayes approach. The variance components are estimated by the expectation of their posterior distribution. The new method is evaluated in a simulation study. Differentially expressed genes are more likely to be detected by tests based on these variance estimates than by tests based on gene‐wise variance estimates. This effect is most visible in studies with small array numbers. Analyzing a real data set on maize endosperm the method is shown to work well. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

miRNA Regulation of Gene Expression: A Predictive Bioinformatics Analysis in the Postnatally Developing Monkey Hippocampus

Regulation of gene expression in the postnatally developing hippocampus might contribute to the emergence of selective memory function. However, the mechanisms that underlie the co-regulation of expression of hundreds of genes in different cell types at specific ages in distinct hippocampal regions have yet to be elucidated. By performing genome-wide microarray analyses of gene expression in distinct regions of the monkey hippocampal formation during early postnatal development, we identified one particular group of genes exhibiting a down-regulation of expression, between birth and six months of age in CA1 and after one year of age in CA3, to reach expression levels observed at 6-12 years of age. Bioinformatics analyses using NCBI, miRBase, TargetScan, microRNA.org and Affymetrix tools identified a number of miRNAs capable of regulating the expression of these genes simultaneously in different cell types, i.e., in neurons, astrocytes and oligodendrocytes. Interestingly, sixty-five percent of these miRNAs are conserved across species, from rodents to humans; whereas thirty-five percent are specific to primates, including humans. In addition, we found that some genes exhibiting greater down-regulation of their expression were the predicted targets of a greater number of these miRNAs. In sum, miRNAs may play a fundamental role in the co-regulation of gene expression in different cell types. This mechanism is partially conserved across species, and may thus contribute to the similarity of basic hippocampal characteristics across mammals. This mechanism also exhibits a phylogenetic diversity that may contribute to more subtle species differences in hippocampal structure and function observed at the cellular level.