Fundamental patterns underlying gene expression profiles: Simplicity from complexity

Analysis of previously published sets of DNA microarray gene expression data by singular value decomposition has uncovered underlying patterns or “characteristic modes” in their temporal profiles. These patterns contribute unequally to the structure of the expression profiles. Moreover, the essential features of a given set of expression profiles are captured using just a small number of characteristic modes. This leads to the striking conclusion that the transcriptional response of a genome is orchestrated in a few fundamental patterns of gene expression change. These patterns are both simple and robust, dominating the alterations in expression of genes throughout the genome. Moreover, the characteristic modes of gene expression change in response to environmental perturbations are similar in such distant organisms as yeast and human cells. This analysis reveals simple regularities in the seemingly complex transcriptional transitions of diverse cells to new states, and these provide insights into the operation of the underlying genetic networks.

Statistical modeling of large microarray data sets to identify stimulus-response profiles

A statistical modeling approach is proposed for use in searching large microarray data sets for genes that have a transcriptional response to a stimulus. The approach is unrestricted with respect to the timing, magnitude or duration of the response, or the overall abundance of the transcript. The statistical model makes an accommodation for systematic heterogeneity in expression levels. Corresponding data analyses provide gene-specific information, and the approach provides a means for evaluating the statistical significance of such information. To illustrate this strategy we have derived a model to depict the profile expected for a periodically transcribed gene and used it to look for budding yeast transcripts that adhere to this profile. Using objective criteria, this method identifies 81% of the known periodic transcripts and 1,088 genes, which show significant periodicity in at least one of the three data sets analyzed. However, only one-quarter of these genes show significant oscillations in at least two data sets and can be classified as periodic with high confidence. The method provides estimates of the mean activation and deactivation times, induced and basal expression levels, and statistical measures of the precision of these estimates for each periodic transcript.

Id1 regulation of cellular senescence through transcriptional repression of p16/Ink4a

The Id family of helix–loop–helix (HLH) transcriptional regulatory proteins does not possess a basic DNA-binding domain and functions as a negative regulator of basic HLH transcription factors. Id proteins coordinate cell growth and differentiation pathways within mammalian cells and have been shown to regulate G1-S cell-cycle transitions. Although much recent data has implicated Id1 in playing a critical role in modulating cellular senescence, no direct genetic evidence has been reported to substantiate such work. Here we show that Id1-null primary mouse embryo fibroblasts undergo premature senescence despite normal growth profiles at early passage. These cells possess increased expression of the tumor-suppressor protein p16/Ink4a but not p19/ARF, and have decreased cyclin-dependent kinase (cdk) 2 and cdk4 kinase activity. We also show that Id1 is able to directly inhibit p16/Ink4a but not p19/ARF promoter activity via its HLH domain, and that Id1inhibits transcriptional activation at E-boxes within the p16/Ink4a promoter. Our data provide, to our knowledge, the first genetic evidence for a role for Id1 as an inhibitor of cellular senescence and suggest that Id1 functions to delay cellular senescence through repression of p16/Ink4a. Because epigenetic and genetic abrogation of p16/Ink4a function has been implicated in the evolution of several human malignancies, we propose that transcriptional regulation of p16/Ink4a may also provide a mechanism for the dysregulation of normal cellular growth controls during the evolution of human malignancies.

Genome-wide gene expression profiles of the developing mouse hippocampus

We have analyzed the developmental molecular programs of the mouse hippocampus, a cortical structure critical for learning and memory, by means of large-scale DNA microarray techniques. Of 11,000 genes and expressed sequence tags examined, 1,926 showed dynamic changes during hippocampal development from embryonic day 16 to postnatal day 30. Gene-cluster analysis was used to group these genes into 16 distinct clusters with striking patterns that appear to correlate with major developmental hallmarks and cellular events. These include genes involved in neuronal proliferation, differentiation, and synapse formation. A complete list of the transcriptional changes has been compiled into a comprehensive gene profile database (http://BrainGenomics.Princeton.edu), which should prove valuable in advancing our understanding of the molecular and genetic programs underlying both the development and the functions of the mammalian brain.