Publications by authors named "Itamar Simon"

47 Publications

Chromosomal coordination and differential structure of asynchronous replicating regions.

Nat Commun 2021 02 15;12(1):1035. Epub 2021 Feb 15.

Department of Microbiology and Molecular Genetics, IMRIC, Hebrew University Medical School, Jerusalem, Israel.

Stochastic asynchronous replication timing (AS-RT) is a phenomenon in which the time of replication of each allele is different, and the identity of the early allele varies between cells. By taking advantage of stable clonal pre-B cell populations derived from C57BL6/Castaneous mice, we have mapped the genome-wide AS-RT loci, independently of genetic differences. These regions are characterized by differential chromatin accessibility, mono-allelic expression and include new gene families involved in specifying cell identity. By combining population level mapping with single cell FISH, our data reveal the existence of a novel regulatory program that coordinates a fixed relationship between AS-RT regions on any given chromosome, with some loci set to replicate in a parallel and others set in the anti-parallel orientation. Our results show that AS-RT is a highly regulated epigenetic mark established during early embryogenesis that may be used for facilitating the programming of mono-allelic choice throughout development.
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http://dx.doi.org/10.1038/s41467-021-21348-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7884787PMC
February 2021

Study of mitotic chromatin supports a model of bookmarking by histone modifications and reveals nucleosome deposition patterns.

Genome Res 2018 10 30;28(10):1455-1466. Epub 2018 Aug 30.

Department of Microbiology and Molecular Genetics, Institute of Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem 91120, Israel.

Mitosis encompasses key molecular changes including chromatin condensation, nuclear envelope breakdown, and reduced transcription levels. Immediately after mitosis, the interphase chromatin structure is reestablished and transcription resumes. The reestablishment of the interphase chromatin is probably achieved by "bookmarking," i.e., the retention of at least partial information during mitosis. To gain a deeper understanding of the contribution of histone modifications to the mitotic bookmarking process, we merged proteomics, immunofluorescence, and ChIP-seq approaches. We focused on key histone modifications and employed HeLa-S3 cells as a model system. Generally, in spite of the general hypoacetylation observed during mitosis, we observed a global concordance between the genomic organization of histone modifications in interphase and mitosis, suggesting that the epigenomic landscape may serve as a component of the mitotic bookmarking process. Next, we investigated the nucleosome that enters nucleosome depleted regions (NDRs) during mitosis. We observed that in ∼60% of the NDRs, the entering nucleosome is distinct from the surrounding highly acetylated nucleosomes and appears to have either low levels of acetylation or high levels of phosphorylation in adjacent residues (since adjacent phosphorylation may interfere with the ability to detect acetylation). Inhibition of histone deacetylases (HDACs) by the small molecule TSA reverts this pattern, suggesting that these nucleosomes are specifically deacetylated during mitosis. Altogether, by merging multiple approaches, our study provides evidence to support a model where histone modifications may play a role in mitotic bookmarking and uncovers new insights into the deposition of nucleosomes during mitosis.
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http://dx.doi.org/10.1101/gr.230300.117DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6169886PMC
October 2018

Germline DNA replication timing shapes mammalian genome composition.

Nucleic Acids Res 2018 09;46(16):8299-8310

Department of Microbiology and Molecular Genetics, IMRIC, Hebrew University-Hadassah Medical School, Jerusalem, Israel.

Mammalian DNA replication is a highly organized and regulated process. Large, Mb-sized regions are replicated at defined times along S-phase. Replication Timing (RT) is thought to play a role in shaping the mammalian genome by affecting mutation rates. Previous analyses relied on somatic RT profiles. However, only germline mutations are passed on to offspring and affect genomic composition. Therefore, germ cell RT information is necessary to evaluate the influences of RT on the mammalian genome. We adapted the RT mapping technique for limited amounts of cells, and measured RT from two stages in the mouse germline - primordial germ cells (PGCs) and spermatogonial stem cells (SSCs). RT in germline cells exhibited stronger correlations to both mutation rate and recombination hotspots density than those of RT in somatic tissues, emphasizing the importance of using correct tissues-of-origin for RT profiling. Germline RT maps exhibited stronger correlations to additional genetic features including GC-content, transposable elements (SINEs and LINEs), and gene density. GC content stratification and multiple regression analysis revealed independent contributions of RT to SINE, gene, mutation, and recombination hotspot densities. Together, our results establish a central role for RT in shaping multiple levels of mammalian genome composition.
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http://dx.doi.org/10.1093/nar/gky610DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6144785PMC
September 2018

Perturbations in the Replication Program Contribute to Genomic Instability in Cancer.

Int J Mol Sci 2017 May 25;18(6). Epub 2017 May 25.

Department of Microbiology and Molecular Genetics, IMRIC, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 91120, Israel.

Cancer and genomic instability are highly impacted by the deoxyribonucleic acid (DNA) replication program. Inaccuracies in DNA replication lead to the increased acquisition of mutations and structural variations. These inaccuracies mainly stem from loss of DNA fidelity due to replication stress or due to aberrations in the temporal organization of the replication process. Here we review the mechanisms and impact of these major sources of error to the replication program.
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http://dx.doi.org/10.3390/ijms18061138DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5485962PMC
May 2017

Biases in the SMART-DNA library preparation method associated with genomic poly dA/dT sequences.

PLoS One 2017 24;12(2):e0172769. Epub 2017 Feb 24.

Department of Microbiology and Molecular Genetics, IMRIC, The Hebrew University Hadassah Medical School, Jerusalem, Israel.

Avoiding biases in next generation sequencing (NGS) library preparation is crucial for obtaining reliable sequencing data. Recently, a new library preparation method has been introduced which has eliminated the need for the ligation step. This method, termed SMART (switching mechanism at the 5' end of the RNA transcript), is based on template switching reverse transcription. To date, there has been no systematic analysis of the additional biases introduced by this method. We analysed the genomic distribution of sequenced reads prepared from genomic DNA using the SMART methodology and found a strong bias toward long (≥12bp) poly dA/dT containing genomic loci. This bias is unique to the SMART-based library preparation and does not appear when libraries are prepared with conventional ligation based methods. Although this bias is obvious only when performing paired end sequencing, it affects single end sequenced samples as well. Our analysis demonstrates that sequenced reads originating from SMART-DNA libraries are heavily skewed toward genomic poly dA/dT tracts. This bias needs to be considered when deciding to use SMART based technology for library preparation.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0172769PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5325289PMC
August 2017

Genome-wide Determination of Mammalian Replication Timing by DNA Content Measurement.

J Vis Exp 2017 01 19(119). Epub 2017 Jan 19.

Dept. of Microbiology and Molecular Genetics, IMRIC, Faculty of Medicine, Hebrew University of Jerusalem;

Replication of the genome occurs during S phase of the cell cycle in a highly regulated process that ensures the fidelity of DNA duplication. Each genomic region is replicated at a distinct time during S phase through the simultaneous activation of multiple origins of replication. Time of replication (ToR) correlates with many genomic and epigenetic features and is linked to mutation rates and cancer. Comprehending the full genomic view of the replication program, in health and disease is a major future goal and challenge. This article describes in detail the "Copy Number Ratio of S/G1 for mapping genomic Time of Replication" method (herein called: CNR-ToR), a simple approach to map the genome wide ToR of mammalian cells. The method is based on the copy number differences between S phase cells and G1 phase cells. The CNR-ToR method is performed in 6 steps: 1. Preparation of cells and staining with propidium iodide (PI); 2. Sorting G1 and S phase cells using fluorescence-activated cell sorting (FACS); 3. DNA purification; 4. Sonication; 5. Library preparation and sequencing; and 6. Bioinformatic analysis. The CNR-ToR method is a fast and easy approach that results in detailed replication maps.
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http://dx.doi.org/10.3791/55157DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5352273PMC
January 2017

Persistence to anti-cancer treatments in the stationary to proliferating transition.

Cell Cycle 2016 Dec 1;15(24):3442-3453. Epub 2016 Nov 1.

a Racah Institute of Physics, Edmond J. Safra Campus, The Hebrew University , Jerusalem , Israel.

The heterogeneous responses of clonal cancer cells to treatment is understood to be caused by several factors, including stochasticity, cell-cycle dynamics, and different micro-environments. In a tumor, cancer cells may encounter fluctuating conditions and transit from a stationary culture to a proliferating state, for example this may occur following treatment. Here, we undertake a quantitative evaluation of the response of single cancerous lymphoblasts (L1210 cells) to various treatments administered during this transition. Additionally, we developed an experimental system, a "Mammalian Mother Machine," that tracks the fate of thousands of mammalian cells over several generations under transient exposure to chemotherapeutic drugs. Using our developed system, we were able to follow the same cell under repeated treatments and continuously track many generations. We found that the dynamics of the transition between stationary and proliferative states are highly variable and affect the response to drug treatment. Using cell-cycle markers, we were able to isolate a subpopulation of persister cells with distinctly higher than average survival probability. The higher survival rate encountered with cell-cycle phase specific drugs was associated with a significantly longer time-till-division, and was reduced by a non cell-cycle specific drug. Our results suggest that the variability of transition times from the stationary to the proliferating state may be an obstacle hampering the effectiveness of drugs and should be taken into account when designing treatment regimens.
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http://dx.doi.org/10.1080/15384101.2016.1248006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5224467PMC
December 2016

The mutation spectrum in genomic late replication domains shapes mammalian GC content.

Nucleic Acids Res 2016 05 16;44(9):4222-32. Epub 2016 Apr 16.

Department of Microbiology and Molecular Genetics, IMRIC, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel

Genome sequence compositions and epigenetic organizations are correlated extensively across multiple length scales. Replication dynamics, in particular, is highly correlated with GC content. We combine genome-wide time of replication (ToR) data, topological domains maps and detailed functional epigenetic annotations to study the correlations between replication timing and GC content at multiple scales. We find that the decrease in genomic GC content at large scale late replicating regions can be explained by mutation bias favoring A/T nucleotide, without selection or biased gene conversion. Quantification of the free dNTP pool during the cell cycle is consistent with a mechanism involving replication-coupled mutation spectrum that favors AT nucleotides at late S-phase. We suggest that mammalian GC content composition is shaped by independent forces, globally modulating mutation bias and locally selecting on functional element. Deconvoluting these forces and analyzing them on their native scales is important for proper characterization of complex genomic correlations.
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http://dx.doi.org/10.1093/nar/gkw268DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4872117PMC
May 2016

Distinguishing between stochasticity and determinism: Examples from cell cycle duration variability.

Bioessays 2016 Jan 2;38(1):8-13. Epub 2015 Dec 2.

Department of Microbiology and Molecular Genetics, IMRIC, The Hebrew University Hadassah Medical School, Jerusalem, Israel.

We describe a recent approach for distinguishing between stochastic and deterministic sources of variability, focusing on the mammalian cell cycle. Variability between cells is often attributed to stochastic noise, although it may be generated by deterministic components. Interestingly, lineage information can be used to distinguish between variability and determinism. Analysis of correlations within a lineage of the mammalian cell cycle duration revealed its deterministic nature. Here, we discuss the sources of such variability and the possibility that the underlying deterministic process is due to the circadian clock. Finally, we discuss the "kicked cell cycle" model and its implication on the study of the cell cycle in healthy and cancerous tissues.
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http://dx.doi.org/10.1002/bies.201500113DOI Listing
January 2016

The p53 C terminus controls site-specific DNA binding and promotes structural changes within the central DNA binding domain.

Mol Cell 2015 Mar;57(6):1034-1046

Department of Biological Sciences, Columbia University, New York, NY 10027, USA. Electronic address:

DNA binding by numerous transcription factors including the p53 tumor suppressor protein constitutes a vital early step in transcriptional activation. While the role of the central core DNA binding domain (DBD) of p53 in site-specific DNA binding has been established, the contribution of the sequence-independent C-terminal domain (CTD) is still not well understood. We investigated the DNA-binding properties of a series of p53 CTD variants using a combination of in vitro biochemical analyses and in vivo binding experiments. Our results provide several unanticipated and interconnected findings. First, the CTD enables DNA binding in a sequence-dependent manner that is drastically altered by either its modification or deletion. Second, dependence on the CTD correlates with the extent to which the p53 binding site deviates from the canonical consensus sequence. Third, the CTD enables stable formation of p53-DNA complexes to divergent binding sites via DNA-induced conformational changes within the DBD itself.
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http://dx.doi.org/10.1016/j.molcel.2015.02.015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6221458PMC
March 2015

Lineage correlations of single cell division time as a probe of cell-cycle dynamics.

Nature 2015 Mar 11;519(7544):468-71. Epub 2015 Mar 11.

Racah Institute of Physics, Edmond J. Safra Campus, The Hebrew University, Jerusalem 91904, Israel.

Stochastic processes in cells are associated with fluctuations in mRNA, protein production and degradation, noisy partition of cellular components at division, and other cell processes. Variability within a clonal population of cells originates from such stochastic processes, which may be amplified or reduced by deterministic factors. Cell-to-cell variability, such as that seen in the heterogeneous response of bacteria to antibiotics, or of cancer cells to treatment, is understood as the inevitable consequence of stochasticity. Variability in cell-cycle duration was observed long ago; however, its sources are still unknown. A central question is whether the variance of the observed distribution originates from stochastic processes, or whether it arises mostly from a deterministic process that only appears to be random. A surprising feature of cell-cycle-duration inheritance is that it seems to be lost within one generation but to be still present in the next generation, generating poor correlation between mother and daughter cells but high correlation between cousin cells. This observation suggests the existence of underlying deterministic factors that determine the main part of cell-to-cell variability. We developed an experimental system that precisely measures the cell-cycle duration of thousands of mammalian cells along several generations and a mathematical framework that allows discrimination between stochastic and deterministic processes in lineages of cells. We show that the inter- and intra-generation correlations reveal complex inheritance of the cell-cycle duration. Finally, we build a deterministic nonlinear toy model for cell-cycle inheritance that reproduces the main features of our data. Our approach constitutes a general method to identify deterministic variability in lineages of cells or organisms, which may help to predict and, eventually, reduce cell-to-cell heterogeneity in various systems, such as cancer cells under treatment.
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http://dx.doi.org/10.1038/nature14318DOI Listing
March 2015

Integrative analysis of methylome and transcriptome reveals the importance of unmethylated CpGs in non-CpG island gene activation.

Biomed Res Int 2013 10;2013:785731. Epub 2013 Jul 10.

Department of Microbiology and Molecular Genetics, IMRIC, The Hebrew University-Hadassah Medical School, 91120 Jerusalem, Israel.

Background: Promoter methylation is associated with gene repression; however, little is known about its mechanism. It was proposed that the repression of methylated genes is achieved through the recruitment of methyl binding proteins (MBPs) that participate in closing the chromatin. An alternative mechanism suggests that methylation interferes with the binding of either site specific activators or more general activators that bind to the CpG dinucleotide. However, the relative contribution of these two mechanisms to gene repression is not known.

Results: Bioinformatics analyses of genome-wide transcriptome and methylome data support the latter hypothesis by demonstrating a strong association between transcription and the number of unmethylated CpGs at the promoter of genes lacking CpG islands.

Conclusions: Our results suggest that methylation represses gene expression mainly by preventing the binding of CpG binding activators.
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http://dx.doi.org/10.1155/2013/785731DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3722964PMC
March 2014

Systematic determination of replication activity type highlights interconnections between replication, chromatin structure and nuclear localization.

PLoS One 2012 7;7(11):e48986. Epub 2012 Nov 7.

Department of Microbiology and Molecular Genetics, Hebrew University Medical School, IMRIC, Jerusalem, Israel.

DNA replication is a highly regulated process, with each genomic locus replicating at a distinct time of replication (ToR). Advances in ToR measurement technology enabled several genome-wide profiling studies that revealed tight associations between ToR and general genomic features and a remarkable ToR conservation in mammals. Genome wide studies further showed that at the hundreds kb-to-megabase scale the genome can be divided into constant ToR regions (CTRs) in which the replication process propagates at a faster pace due to the activation of multiple origins and temporal transition regions (TTRs) in which the replication process propagates at a slower pace. We developed a computational tool that assigns a ToR to every measured locus and determines its replication activity type (CTR versus TTR). Our algorithm, ARTO (Analysis of Replication Timing and Organization), uses signal processing methods to fit a constant piece-wise linear curve to the measured raw data. We tested our algorithm and provide performance and usability results. A Matlab implementation of ARTO is available at http://bioinfo.cs.technion.ac.il/people/zohar/ARTO/. Applying our algorithm to ToR data measured in multiple mouse and human samples allowed precise genome-wide ToR determination and replication activity type characterization. Analysis of the results highlighted the plasticity of the replication program. For example, we observed significant ToR differences in 10-25% of the genome when comparing different tissue types. Our analyses also provide evidence for activity type differences in up to 30% of the probes. Integration of the ToR data with multiple aspects of chromosome organization characteristics suggests that ToR plays a role in shaping the regional chromatin structure. Namely, repressive chromatin marks, are associated with late ToR both in TTRs and CTRs. Finally, characterization of the differences between TTRs and CTRs, with matching ToR, revealed that TTRs are associated with compact chromatin and are located significantly closer to the nuclear envelope. Supplementary material is available. Raw and processed data were deposited in Geo (GSE17236).
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0048986PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3492150PMC
July 2013

Genome-wide analysis of androgen receptor targets reveals COUP-TF1 as a novel player in human prostate cancer.

PLoS One 2012 4;7(10):e46467. Epub 2012 Oct 4.

Department of Pathology and Lautenberg center for immunology, IMRIC, The Hebrew University-Hadassah Medical School, Jerusalem, Israel.

Androgen activity plays a key role in prostate cancer progression. Androgen receptor (AR) is the main mediator of androgen activity in the prostate, through its ability to act as a transcription mediator. Here we performed a genome-wide analysis of human AR binding to promoters in the presence of an agonist or antagonist in an androgen dependent prostate cancer cell line. Many of the AR bound promoters are bound in all examined conditions while others are bound only in the presence of an agonist or antagonist. Several motifs are enriched in AR bound promoters, including the AR Response Element (ARE) half-site and recognition elements for the transcription factors OCT1 and SOX9. This suggests that these 3 factors could define a module of co-operating transcription factors in the prostate. Interestingly, AR bound promoters are preferentially located in AT rich genomic regions. Analysis of mRNA expression identified chicken ovalbumin upstream promoter-transcription factor 1 (COUP-TF1) as a direct AR target gene that is downregulated upon binding by the agonist liganded AR. COUP-TF1 immunostaining revealed nucleolar localization of COUP-TF1 in epithelium of human androgen dependent prostate cancer, but not in adjacent benign prostate epithelium. Stromal cells both in human and mouse prostate show nuclear COUP-TF1 staining. We further show that there is an inverse correlation between COUP-TF1 expression in prostate stromal cells and the rising levels of androgen with advancing puberty. This study extends the pool of recognized putative AR targets and identifies a negatively regulated target of AR - COUP-TF1 - which could possibly play a role in human prostate cancer.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0046467PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3464259PMC
June 2013

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

Nat Rev Genet 2012 Jul 18;13(8):552-64. Epub 2012 Jul 18.

Lane Center for Computational Biology and Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.

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.
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http://dx.doi.org/10.1038/nrg3244DOI Listing
July 2012

Competition between small RNAs: a quantitative view.

Biophys J 2012 Apr;102(8):1712-21

Racah Institute of Physics, The Hebrew University, Jerusalem, Israel.

Two major classes of small regulatory RNAs--small interfering RNAs (siRNAs) and microRNA (miRNAs)--are involved in a common RNA interference processing pathway. Small RNAs within each of these families were found to compete for limiting amounts of shared components, required for their biogenesis and processing. Association with Argonaute (Ago), the catalytic component of the RNA silencing complex, was suggested as the central mechanistic point in RNA interference machinery competition. Aiming to better understand the competition between small RNAs in the cell, we present a mathematical model and characterize a range of specific cell and experimental parameters affecting the competition. We apply the model to competition between miRNAs and study the change in the expression level of their target genes under a variety of conditions. We show quantitatively that the amount of Ago and miRNAs in the cell are dominant factors contributing greatly to the competition. Interestingly, we observe what to our knowledge is a novel type of competition that takes place when Ago is abundant, by which miRNAs with shared targets compete over them. Furthermore, we use the model to examine different interaction mechanisms that might operate in establishing the miRNA-Ago complexes, mainly those related to their stability and recycling. Our model provides a mathematical framework for future studies of competition effects in regulation mechanisms involving small RNAs.
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http://dx.doi.org/10.1016/j.bpj.2012.01.058DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3328701PMC
April 2012

DECOD: fast and accurate discriminative DNA motif finding.

Bioinformatics 2011 Sep 12;27(17):2361-7. Epub 2011 Jul 12.

Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Motivation: Motif discovery is now routinely used in high-throughput studies including large-scale sequencing and proteomics. These datasets present new challenges. The first is speed. Many motif discovery methods do not scale well to large datasets. Another issue is identifying discriminative rather than generative motifs. Such discriminative motifs are important for identifying co-factors and for explaining changes in behavior between different conditions.

Results: To address these issues we developed a method for DECOnvolved Discriminative motif discovery (DECOD). DECOD uses a k-mer count table and so its running time is independent of the size of the input set. By deconvolving the k-mers DECOD considers context information without using the sequences directly. DECOD outperforms previous methods both in speed and in accuracy when using simulated and real biological benchmark data. We performed new binding experiments for p53 mutants and used DECOD to identify p53 co-factors, suggesting new mechanisms for p53 activation.

Availability: The source code and binaries for DECOD are available at http://www.sb.cs.cmu.edu/DECOD CONTACT: zivbj@cs.cmu.edu

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btr412DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3157928PMC
September 2011

DNA methylation and gene expression.

Wiley Interdiscip Rev Syst Biol Med 2010 May-Jun;2(3):362-371

Department of Microbiology and Molecular Genetics, The Institute for Medical Research-Israel-Canada, The Hebrew University-Hadassah Medical School, 91120, Jerusalem, Israel.

Methylation of cytosines is the key epigenetic modification of DNA in eukaryotes and is associated with a repressed chromatin state and inhibition of gene expression. The methylation pattern in mammalian genomes is bimodal, with most of the genomes methylated except for short DNA stretches called CpG islands (CGIs), which are generally protected from methylation. Recent technical advances have made it possible to map DNA methylation patterns on a large scale. Several genomic studies have made significant progress in unraveling the intricate relationships between DNA methylation, chromatin structure, and gene expression. What is emerging is a more dynamic and complex association between DNA methylation and expression than previously known. Here we highlight several recent genomic studies with an emphasis on what new information is gained from these studies and what conclusions can be reached about the role of DNA methylation in controlling gene expression.
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http://dx.doi.org/10.1002/wsbm.64DOI Listing
January 2011

Combination of genomic approaches with functional genetic experiments reveals two modes of repression of yeast middle-phase meiosis genes.

BMC Genomics 2010 Aug 17;11:478. Epub 2010 Aug 17.

Department of Microbiology and Molecular Genetics, The Institute for Medical Research-Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel.

Background: Regulation of meiosis and sporulation in Saccharomyces cerevisiae is a model for a highly regulated developmental process. Meiosis middle phase transcriptional regulation is governed by two transcription factors: the activator Ndt80 and the repressor Sum1. It has been suggested that the competition between Ndt80 and Sum1 determines the temporal expression of their targets during middle meiosis.

Results: Using a combination of ChIP-on-chip and expression profiling, we characterized a middle phase transcriptional network and studied the relationship between Ndt80 and Sum1 during middle and late meiosis. While finding a group of genes regulated by both factors in a feed forward loop regulatory motif, our data also revealed a large group of genes regulated solely by Ndt80. Measuring the expression of all Ndt80 target genes in various genetic backgrounds (WT, sum1Delta and MK-ER-Ndt80 strains), allowed us to dissect the exact transcriptional network regulating each gene, which was frequently different than the one inferred from the binding data alone.

Conclusion: These results highlight the need to perform detailed genetic experiments to determine the relative contribution of interactions in transcriptional regulatory networks.
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http://dx.doi.org/10.1186/1471-2164-11-478DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3091674PMC
August 2010

Comparative analysis of DNA replication timing reveals conserved large-scale chromosomal architecture.

PLoS Genet 2010 Jul 1;6(7):e1001011. Epub 2010 Jul 1.

Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.

Recent evidence suggests that the timing of DNA replication is coordinated across megabase-scale domains in metazoan genomes, yet the importance of this aspect of genome organization is unclear. Here we show that replication timing is remarkably conserved between human and mouse, uncovering large regions that may have been governed by similar replication dynamics since these species have diverged. This conservation is both tissue-specific and independent of the genomic G+C content conservation. Moreover, we show that time of replication is globally conserved despite numerous large-scale genome rearrangements. We systematically identify rearrangement fusion points and demonstrate that replication time can be locally diverged at these loci. Conversely, rearrangements are shown to be correlated with early replication and physical chromosomal proximity. These results suggest that large chromosomal domains of coordinated replication are shuffled by evolution while conserving the large-scale nuclear architecture of the genome.
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http://dx.doi.org/10.1371/journal.pgen.1001011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2895651PMC
July 2010

Modulation of the vitamin D3 response by cancer-associated mutant p53.

Cancer Cell 2010 Mar;17(3):273-85

Department of Molecular Cell Biology, The Weizmann Institute of Science, Rehovot 76100, Israel.

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.
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http://dx.doi.org/10.1016/j.ccr.2009.11.025DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2882298PMC
March 2010

Integrating multiple evidence sources to predict transcription factor binding in the human genome.

Genome Res 2010 Apr 10;20(4):526-36. Epub 2010 Mar 10.

Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Information about the binding preferences of many transcription factors is known and characterized by a sequence binding motif. However, determining regions of the genome in which a transcription factor binds based on its motif is a challenging problem, particularly in species with large genomes, since there are often many sequences containing matches to the motif but are not bound. Several rules based on sequence conservation or location, relative to a transcription start site, have been proposed to help differentiate true binding sites from random ones. Other evidence sources may also be informative for this task. We developed a method for integrating multiple evidence sources using logistic regression classifiers. Our method works in two steps. First, we infer a score quantifying the general binding preferences of transcription factor binding at all locations based on a large set of evidence features, without using any motif specific information. Then, we combined this general binding preference score with motif information for specific transcription factors to improve prediction of regions bound by the factor. Using cross-validation and new experimental data we show that, surprisingly, the general binding preference can be highly predictive of true locations of transcription factor binding even when no binding motif is used. When combined with motif information our method outperforms previous methods for predicting locations of true binding.
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http://dx.doi.org/10.1101/gr.096305.109DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2847756PMC
April 2010

Genome-wide analysis of the replication program in mammals.

Chromosome Res 2010 Jan;18(1):115-25

Department of Microbiology and Molecular Genetics, The Institute for Medical Research-Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel.

Microarray technology has facilitated the research of eukaryotic DNA replication on a genome-wide scale. Recent studies have shed light on the association between time of replication and chromosome structure, on the organization principles of the replication program, and on the correlation between replication timing and transcription. In this review, we summarize various genomic measurement approaches and the biological insights achieved through applying them in the study of the mammalian replication program.
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http://dx.doi.org/10.1007/s10577-009-9091-5DOI Listing
January 2010

A combined expression-interaction model for inferring the temporal activity of transcription factors.

J Comput Biol 2009 Aug;16(8):1035-49

Machine Learning Department, School of Computer Science, Carnegie Mellon University , Pittsburgh, PA 15213, USA.

Methods suggested for reconstructing regulatory networks can be divided into two sets based on how the activity level of transcription factors (TFs) is inferred. The first group of methods relies on the expression levels of TFs, assuming that the activity of a TF is highly correlated with its mRNA abundance. The second treats the activity level as unobserved and infers it from the expression of the genes that the TF regulates. While both types of methods were successfully applied, each suffers from drawbacks that limit their accuracy. For the first set, the assumption that mRNA levels are correlated with activity is violated for many TFs due to post-transcriptional modifications. For the second, the expression level of a TF which might be informative is completely ignored. Here we present the post-transcriptional modification model (PTMM) that, unlike previous methods, utilizes both sources of data concurrently. Our method uses a switching model to determine whether a TF is transcriptionally or post-transcriptionally regulated. This model is combined with a factorial HMM to reconstruct the interactions in a dynamic regulatory network. Using simulated and real data, we show that PTMM outperforms the other two approaches discussed above. Using real data, we also show that PTMM can recover meaningful TF activity levels and identify post-transcriptionally modified TFs, many of which are supported by other sources. Supporting website: www.sb.cs.cmu.edu/PTMM/PTMM.html.
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http://dx.doi.org/10.1089/cmb.2009.0024DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3154398PMC
August 2009

Backup in gene regulatory networks explains differences between binding and knockout results.

Mol Syst Biol 2009 16;5:276. Epub 2009 Jun 16.

Computer Science Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

The complementarity of gene expression and protein-DNA interaction data led to several successful models of biological systems. However, recent studies in multiple species raise doubts about the relationship between these two datasets. These studies show that the overwhelming majority of genes bound by a particular transcription factor (TF) are not affected when that factor is knocked out. Here, we show that this surprising result can be partially explained by considering the broader cellular context in which TFs operate. Factors whose functions are not backed up by redundant paralogs show a fourfold increase in the agreement between their bound targets and the expression levels of those targets. In addition, we show that incorporating protein interaction networks provides physical explanations for knockout effects. New double knockout experiments support our conclusions. Our results highlight the robustness provided by redundant TFs and indicate that in the context of diverse cellular systems, binding is still largely functional.
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http://dx.doi.org/10.1038/msb.2009.33DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2710864PMC
September 2009

Developmental programming of CpG island methylation profiles in the human genome.

Nat Struct Mol Biol 2009 May 19;16(5):564-71. Epub 2009 Apr 19.

Department of Cellular Biochemistry and Human Genetics, The Hebrew University-Hadassah Medical School, Jerusalem, Israel.

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.
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http://dx.doi.org/10.1038/nsmb.1594DOI Listing
May 2009

Chromatin immunoprecipitation-on-chip reveals stress-dependent p53 occupancy in primary normal cells but not in established cell lines.

Cancer Res 2008 Dec;68(23):9671-7

Department of Molecular Biology, Microarray Service Laboratory, The Core Research Facility, and Lautenberg Center for General and Tumor Immunology, Hebrew University Medical School, Jerusalem, Israel.

The p53 tumor suppressor protein is a transcription factor that plays a key role in the cellular response to stress and cancer prevention. Upon activation, p53 regulates a large variety of genes causing cell cycle arrest, apoptosis, or senescence. We have developed a p53-focused array, which allows us to investigate, simultaneously, p53 interactions with most of its known target sequences using the chromatin immunoprecipitation (ChIP)-on-chip methodology. Applying this technique to multiple cell types under various growth conditions revealed a profound difference in p53 activity between primary cells and established cell lines. We found that, in peripheral blood mononuclear cells, p53 exists in a form that binds only a small subset of its target regions. Upon exposure to genotoxic stress, the extent of targets bound by p53 significantly increased. By contrast, in established cell lines, p53 binds to essentially all of its targets irrespective of stress and cellular fate (apoptosis or arrest). Analysis of gene expression in these established lines revealed little correlation between DNA binding and the induction of gene expression. Our results suggest that nonactivated p53 has limited binding activity, whereas upon activation it binds to essentially all its targets. Additional triggers are most likely required to activate the transcriptional program of p53.
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http://dx.doi.org/10.1158/0008-5472.CAN-08-0865DOI Listing
December 2008

A probabilistic generative model for GO enrichment analysis.

Nucleic Acids Res 2008 Oct 1;36(17):e109. Epub 2008 Aug 1.

Computer Science Department, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213 USA.

The Gene Ontology (GO) is extensively used to analyze all types of high-throughput experiments. However, researchers still face several challenges when using GO and other functional annotation databases. One problem is the large number of multiple hypotheses that are being tested for each study. In addition, categories often overlap with both direct parents/descendents and other distant categories in the hierarchical structure. This makes it hard to determine if the identified significant categories represent different functional outcomes or rather a redundant view of the same biological processes. To overcome these problems we developed a generative probabilistic model which identifies a (small) subset of categories that, together, explain the selected gene set. Our model accommodates noise and errors in the selected gene set and GO. Using controlled GO data our method correctly recovered most of the selected categories, leading to dramatic improvements over current methods for GO analysis. When used with microarray expression data and ChIP-chip data from yeast and human our method was able to correctly identify both general and specific enriched categories which were overlooked by other methods.
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http://dx.doi.org/10.1093/nar/gkn434DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2553574PMC
October 2008

Global organization of replication time zones of the mouse genome.

Genome Res 2008 Oct 30;18(10):1562-70. Epub 2008 Jul 30.

Department of Molecular Biology, Hebrew University Medical School Jerusalem 91120, Israel.

The division of genomes into distinct replication time zones has long been established. However, an in-depth understanding of their organization and their relationship to transcription is incomplete. Taking advantage of a novel synchronization method ("baby machine") and of genomic DNA microarrays, we have, for the first time, mapped replication times of the entire mouse genome at a high temporal resolution. Our data revealed that although most of the genome has a distinct time of replication either early, middle, or late S phase, a significant portion of the genome is replicated asynchronously. Analysis of the replication map revealed the genomic scale organization of the replication time zones. We found that the genomic regions between early and late replication time zones often consist of extremely large replicons. Analysis of the relationship between replication and transcription revealed that early replication is frequently correlated with the transcription potential of a gene and not necessarily with its actual transcriptional activity. These findings, along with the strong conservation found between replication timing in human and mouse genomes, emphasize the importance of replication timing in transcription regulation.
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http://dx.doi.org/10.1101/gr.079566.108DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2556267PMC
October 2008

Genome-wide transcriptional analysis of the human cell cycle identifies genes differentially regulated in normal and cancer cells.

Proc Natl Acad Sci U S A 2008 Jan 14;105(3):955-60. Epub 2008 Jan 14.

Department of Computer Science, School of Computer Science and Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Characterization of the transcriptional regulatory network of the normal cell cycle is essential for understanding the perturbations that lead to cancer. However, the complete set of cycling genes in primary cells has not yet been identified. Here, we report the results of genome-wide expression profiling experiments on synchronized primary human foreskin fibroblasts across the cell cycle. Using a combined experimental and computational approach to deconvolve measured expression values into "single-cell" expression profiles, we were able to overcome the limitations inherent in synchronizing nontransformed mammalian cells. This allowed us to identify 480 periodically expressed genes in primary human foreskin fibroblasts. Analysis of the reconstructed primary cell profiles and comparison with published expression datasets from synchronized transformed cells reveals a large number of genes that cycle exclusively in primary cells. This conclusion was supported by both bioinformatic analysis and experiments performed on other cell types. We suggest that this approach will help pinpoint genetic elements contributing to normal cell growth and cellular transformation.
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http://dx.doi.org/10.1073/pnas.0704723105DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2242708PMC
January 2008