Itamar Simon

Polycomb-mediated methylation on Lys27 of histone H3 pre-marks genes for de novo methylation in cancer

Many genes associated with CpG islands undergo de novo methylation in cancer. Studies have suggested that the pattern of this modification may be partially determined by an instructive mechanism that recognizes specifically marked regions of the genome. Using chromatin immunoprecipitation analysis, here we show that genes methylated in cancer cells are specifically packaged with nucleosomes containing histone H3 trimethylated on Lys27. This chromatin mark is established on these unmethylated CpG island genes early in development and then maintained in differentiated cell types by the presence of an EZH2-containing Polycomb complex. In cancer cells, as opposed to normal cells, the presence of this complex brings about the recruitment of DNA methyl transferases, leading to de novo methylation. These results suggest that tumor-specific targeting of de novo methylation is pre-programmed by an established epigenetic system that normally has a role in marking embryonic genes for repression.

Allelic inactivation regulates olfactory receptor gene expression

We suggest a model in which a hierarchy of controls is exerted on the family of odorant receptor genes to assure that a sensory neuron expresses a single receptor from a family of 1000 genes. We propose that a cis-regulatory element directs the stochastic expression of only one gene from a large array of linked receptor genes. Moreover, only one allelic array encoding multiple receptor genes is active in an individual neuron. We demonstrate that in a neuron expressing a given receptor, expression derives exclusively from one allele. In addition, we observe that alleles encoding the odorant receptors are replicated asynchronously, a phenomenon consistently associated with allelic inactivation. This model, involving inactivation of one allelic array and cis control of the active array, provides a mechanism such that individual neurons express one or a small number of receptors.

Serial Regulation of Transcriptional Regulators in the Yeast Cell Cycle

Genome-wide location analysis was used to determine how the yeast cell cycle gene expression program is regulated by each of the nine known cell cycle transcriptional activators. We found that cell cycle transcriptional activators that function during one stage of the cell cycle regulate transcriptional activators that function during the next stage. This serial regulation of transcriptional activators forms a connected regulatory network that is itself a cycle. Our results also reveal how the nine transcriptional regulators coordinately regulate global gene expression and diverse stage-specific functions to produce a continuous cycle of cellular events. This information forms the foundation for a complete map of the transcriptional regulatory network that controls the cell cycle.

Evidence for an instructive mechanism of de novo methylation in cancer cells

DNA methylation has a role in the regulation of gene expression during normal mammalian development but can also mediate epigenetic silencing of CpG island genes in cancer and other diseases. Many individual genes (including tumor suppressors) have been shown to undergo de novo methylation in specific tumor types, but the biological logic inherent in this process is not understood. To decipher this mechanism, we have adopted a new approach for detecting CpG island DNA methylation that can be used together with microarray technology. Genome-wide analysis by this technique demonstrated that tumor-specific methylated genes belong to distinct functional categories, have common sequence motifs in their promoters and are found in clusters on chromosomes. In addition, many are already repressed in normal cells. These results are consistent with the hypothesis that cancer-related de novo methylation may come about through an instructive mechanism.

Developmental programming of CpG island methylation profiles in the human genome

CpG island–like sequences are commonly thought to provide the sole signals for designating constitutively unmethylated regions in the genome, thus generating open chromatin domains within a sea of global repression. Using a new database obtained from comprehensive microarray analysis, we show that unmethylated regions (UMRs) seem to be formed during early embryogenesis, not as a result of CpG-ness, but rather through the recognition of specific sequence motifs closely associated with transcription start sites. This same system probably brings about the resetting of pluripotency genes during somatic cell reprogramming. The data also reveal a new class of nonpromoter UMRs that become de novo methylated in a tissue-specific manner during development, and this process may be involved in gene regulation. In short, we show that UMRs are an important aspect of genome structure that have a dynamic role in development.

Continuous representations of time-series gene expression data.

We present algorithms for time-series gene expression analysis that permit the principled estimation of unobserved time points, clustering, and dataset alignment. Each expression profile is modeled as a cubic spline (piecewise polynomial) that is estimated from the observed data and every time point influences the overall smooth expression curve. We constrain the spline coefficients of genes in the same class to have similar expression patterns, while also allowing for gene specific parameters. We show that unobserved time points can be reconstructed using our method with 10-15% less error when compared to previous best methods. Our clustering algorithm operates directly on the continuous representations of gene expression profiles, and we demonstrate that this is particularly effective when applied to nonuniformly sampled data. Our continuous alignment algorithm also avoids difficulties encountered by discrete approaches. In particular, our method allows for control of the number of degrees of freedom of the warp through the specification of parameterized functions, which helps to avoid overfitting. We demonstrate that our algorithm produces stable low-error alignments on real expression data and further show a specific application to yeast knock-out data that produces biologically meaningful results.

Program-Specific Distribution of a Transcription Factor Dependent on Partner Transcription Factor and MAPK Signaling

Specialized gene expression programs are induced by signaling pathways that act on transcription factors. Whether these transcription factors can function in multiple developmental programs through a global switch in promoter selection is not known. We have used genome-wide location analysis to show that the yeast Ste12 transcription factor, which regulates mating and filamentous growth, is bound to distinct program-specific target genes dependent on the developmental condition. This condition-dependent distribution of Ste12 requires concurrent binding of the transcription factor Tec1 during filamentation and is differentially regulated by the MAP kinases Fus3 and Kss1. Program-specific distribution across the genome may be a general mechanism by which transcription factors regulate distinct gene expression programs in response to signaling.In the unactivated Limulus sperm, a 60-µm-long bundle of actin filaments crosslinked by the protein scruin is bent and twisted into a coil around the base of the nucleus. At fertilization, the bundle uncoils and fully extends in five seconds to support a finger of membrane known as the acrosomal process. This biological spring is powered by stored elastic energy and does not require the action of motor proteins or actin polymerization1. In a 9.5-Å electron cryomicroscopic structure of the extended bundle, we show that twist, tilt and rotation of actin–scruin subunits deviate widely from a ‘standard’ F-actin filament. This variability in structural organization allows filaments to pack into a highly ordered and rigid bundle in the extended state and suggests a mechanism for storing and releasing energy between coiled and extended states without disassembly.

Studying and modelling dynamic biological processes using time-series gene expression data

Biological processes are often dynamic, thus researchers must monitor their activity at multiple time points. The most abundant source of information regarding such dynamic activity is time-series gene expression data. These data are used to identify the complete set of activated genes in a biological process, to infer their rates of change, their order and their causal effects and to model dynamic systems in the cell. In this Review we discuss the basic patterns that have been observed in time-series experiments, how these patterns are combined to form expression programs, and the computational analysis, visualization and integration of these data to infer models of dynamic biological systems.

A new approach to analyzing gene expression time series data

We present algorithms for time-series gene expression analysis that permit the principled estimation of unobserved time-points, clustering, and dataset alignment. Each expression profile is modeled as a cubic spline (piecewise polynomial) that is estimated from the observed data and every time point influences the overall smooth expression curve. We constrain the spline coefficients of genes in the same class to have similar expression patterns, while also allowing for gene specific parameters. We show that unobserved time-points can be reconstructed using our method with 10-15% less error when compared to previous best methods. Our clustering algorithm operates directly on the continuous representations of gene expression profiles, and we demonstrate that this is particularly effective when applied to non-uniformly sampled data. Our continuous alignment algorithm also avoids difficulties encountered by discrete approaches. In particular, our method allows for control of the number of degrees of freedom of the warp through the specification of parameterized functions, which helps to avoid overfitting. We demonstrate that our algorithm produces stable low-error alignments on real expression data and further show a specific application to yeast knockout data that produces biologically meaningful results.

Modulation of the Vitamin D3 Response by Cancer-Associated Mutant p53

The p53 gene is mutated in many human tumors. Cells of such tumors often contain abundant mutant p53 (mutp53) protein, which may contribute actively to tumor progression via a gain-of-function mechanism. We applied ChIP-on-chip analysis and identified the vitamin D receptor (VDR) response element as overrepresented in promoter sequences bound by mutp53. We report that mutp53 can interact functionally and physically with VDR. Mutp53 is recruited to VDR-regulated genes and modulates their expression, augmenting the transactivation of some genes and relieving the repression of others. Furthermore, mutp53 increases the nuclear accumulation of VDR. Importantly, mutp53 converts vitamin D into an antiapoptotic agent. Thus, p53 status can determine the biological impact of vitamin D on tumor cells.