Genome-wide characterization of the Complex Trancriptome Architecture of S.cerevisiae with tiling arrays

Recent genome-wide transcriptome analysis in humans, Drosophila, Arabidopsis and yeast challenged the old notion of fundamental aspects of gene regulation, providing evidence that protein-encoding genes are not the only agents controlling cellular processes. Non-coding RNAs comprising untranslated regions of protein coding genes, antisense transcripts of annotated genes, micro RNAs and small interfering RNAs present another tier in gene regulation, enabling integration and networking of complex suites of gene activity. Sophisticated RNA signaling networks operate in higher eukaryotes, enabling gene to gene communication and regulation of chromatin structure, DNA methylation, transcription, translation, RNA silencing and stability, and coordinate multiple tasks of the whole cellular system. Fundamental mechanisms and structure of such control architecture remained largely obscure due to limitations of available approaches, such as noise in the data, strand–unspecific transcription analysis and difficulties in functional follow-up opportunities in higher eukaryotes. To address the complexity of transcriptome architecture we undertook the genome-wide transcriptome study in a simpler genome of S.cerevisiae with the help of a new tiling array. We have shown that 85% of the genome is expressed in rich media. Apart from expected transcripts, we found operon-like transcripts, transcripts from neighboring genes not separated by intergenic regions, and genes with complex transcriptional architecture where different parts of the same gene are expressed at different levels. We mapped the positions of 3' and 5' UTRs of coding genes and identified hundreds of RNA transcripts distinct from annotated genes. These non-annotated transcripts, on average, have lower sequence conservation and lower rates of deletion phenotype than protein coding genes. Many other transcripts overlap known genes in antisense orientation, and for these pairs global correlations were discovered: UTR lengths correlated with gene function, localization, and requirements for regulation; antisense transcripts overlapped 3' UTRs more than 5' UTRs; UTRs with overlapping antisense tended to be longer; and the presence of antisense associated with gene function. Overall our study revealed complexity of yeast transcriptional architecture calling for additional annotation of the genome and putting forward an important role for RNA-mediated regulation. An attractive model for the study of the genome-wide RNA-mediated regulation of gene activity in yeast is mitotic cell cycle, which has been extensively studied over past decade and is therefore a well characterized system. Mitosis is associated with important physiological changes in the cell and diverse biological events depend on this periodicity. To ensure the proper functioning of the mechanisms that maintain order during cell division about 800 genes of diverse GO categories are coordinately regulated in a periodic manner coincident with the cell cycle. This includes genes involved in DNA replication, budding, glycosylation, nuclear division, control of mRNA transcription, responsiveness to external stimuli and subcellular localization of proteins. Several genome-wide studies have been done to catalogue cell cycle-regulated genes with the help of early expression arrays. Given the high resolution of our technique, profiling genome-wide periodic expression with the tiling arrays allowed taking a step forward to prove the existence of RNA-mediated regulation of transcription. Using two methods of synchronization, I have monitored cell-cycle dependent transcription for more than 3 complete cell cycles. I have identified about ~600 periodic ORFs. In consent with previous studies on transcriptional regulation during specific mitotic phases, I have shown prevalence of periodic expression of annotated genes in three distinct periods of cell cycle progression: late G1/S transition, G2/M transition and exit of M phase of mitosis. Moreover, I have shown antisense transcription throughout the cell cycle phases. Out of ~260 antisense transcripts that we discovered, 37 display periodic patterns; half of them are expressed coincidentally with peak expression intensity of cell cycle-regulated ORFs, whereas the other half peaks at the periods of relaxation of the transcriptional machinery, which drives phase transition. Cycling antisense has been registered opposite several important cell cycle regulators. Additionally, periodic novel isolated transcripts were detected in the dataset, which may represent non-annotated ncRNAs involved in regulation of mitosis or regulated by cell cycle controlling genes

High-resolution transcription atlas of the mitotic cell cycle in budding yeast.

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.BACKGROUND: Extensive transcription of non-coding RNAs has been detected in eukaryotic genomes and is thought to constitute an additional layer in the regulation of gene expression. Despite this role, their transcription through the cell cycle has not been studied; genome-wide approaches have only focused on protein-coding genes. To explore the complex transcriptome architecture underlying the budding yeast cell cycle, we used 8 bp tiling arrays to generate a 5 minute-resolution, strand-specific expression atlas of the whole genome. RESULTS: We discovered 523 antisense transcripts, of which 80 cycle or are located opposite periodically expressed mRNAs, 135 unannotated intergenic non-coding RNAs, of which 11 cycle, and 109 cell-cycle-regulated protein-coding genes that had not previously been shown to cycle. We detected periodic expression coupling of sense and antisense transcript pairs, including antisense transcripts opposite of key cell-cycle regulators, like FAR1 and TAF2. CONCLUSIONS: Our dataset presents the most comprehensive resource to date on gene expression during the budding yeast cell cycle. It reveals periodic expression of both protein-coding and non-coding RNA and profiles the expression of non-annotated RNAs throughout the cell cycle for the first time. This data enables hypothesis-driven mechanistic studies concerning the functions of non-coding RNAs

Bayesian correlated clustering to integrate multiple datasets

Motivation: The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct – but often complementary – information. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as MDI (Multiple Dataset Integration). MDI can integrate information from a wide range of different datasets and data types simultaneously (including the ability to model time series data explicitly using Gaussian processes). Each dataset is modelled using a Dirichlet-multinomial allocation (DMA) mixture model, with dependencies between these models captured via parameters that describe the agreement among the datasets. Results: Using a set of 6 artificially constructed time series datasets, we show that MDI is able to integrate a significant number of datasets simultaneously, and that it successfully captures the underlying structural similarity between the datasets. We also analyse a variety of real S. cerevisiae datasets. In the 2-dataset case, we show that MDI’s performance is comparable to the present state of the art. We then move beyond the capabilities of current approaches and integrate gene expression, ChIP-chip and protein-protein interaction data, to identify a set of protein complexes for which genes are co-regulated during the cell cycle. Comparisons to other unsupervised data integration techniques – as well as to non-integrative approaches – demonstrate that MDI is very competitive, while also providing information that would be difficult or impossible to extract using other methods

GermOnline 4.0 is a genomics gateway for germline development, meiosis and the mitotic cell cycle

GermOnline 4.0 is a cross-species database portal focusing on high-throughput expression data relevant for germline development, the meiotic cell cycle and mitosis in healthy versus malignant cells. It is thus a source of information for life scientists as well as clinicians who are interested in gene expression and regulatory networks. The GermOnline gateway provides unlimited access to information produced with high-density oligonucleotide microarrays (3′-UTR GeneChips), genome-wide protein–DNA binding assays and protein–protein interaction studies in the context of Ensembl genome annotation. Samples used to produce high-throughput expression data and to carry out genome-wide in vivo DNA binding assays are annotated via the MIAME-compliant Multiomics Information Management and Annotation System (MIMAS 3.0). Furthermore, the Saccharomyces Genomics Viewer (SGV) was developed and integrated into the gateway. SGV is a visualization tool that outputs genome annotation and DNA-strand specific expression data produced with high-density oligonucleotide tiling microarrays (Sc_tlg GeneChips) which cover the complete budding yeast genome on both DNA strands. It facilitates the interpretation of expression levels and transcript structures determined for various cell types cultured under different growth and differentiation conditions

A pre-initiation complex at the 3′-end of genes drives antisense transcription independent of divergent sense transcription

The precise nature of antisense transcripts in eukaryotes such as Saccharomyces cerevisiae remains elusive. Here we show that the 3′ regions of genes possess a promoter architecture, including a pre-initiation complex (PIC), which mirrors that at the 5′ region and which is much more pronounced at genes with a defined antisense transcript. Remarkably, for genes with an antisense transcript, average levels of PIC components at the 3′ region are ∼60% of those at the 5′ region. Moreover, at these genes, average levels of nascent antisense transcription are ∼45% of sense transcription. We find that this 3′ promoter architecture persists for highly transcribed antisense transcripts where there are only low levels of transcription in the divergent sense direction, suggesting that the 3′ regions of genes can drive antisense transcription independent of divergent sense transcription. To validate this, we insert short 3′ regions into the middle of other genes and find that they are capable of both initiating antisense transcripts and terminating sense transcripts. Our results suggest that antisense transcription can be regulated independently of divergent sense transcription in a PIC-dependent manner and we propose that regulated production of antisense transcripts represents a fundamental and widespread component of gene regulation

Global modeling of transcriptional responses in interaction networks

Motivation: Cell-biological processes are regulated through a complex network of interactions between genes and their products. The processes, their activating conditions, and the associated transcriptional responses are often unknown. Organism-wide modeling of network activation can reveal unique and shared mechanisms between physiological conditions, and potentially as yet unknown processes. We introduce a novel approach for organism-wide discovery and analysis of transcriptional responses in interaction networks. The method searches for local, connected regions in a network that exhibit coordinated transcriptional response in a subset of conditions. Known interactions between genes are used to limit the search space and to guide the analysis. Validation on a human pathway network reveals physiologically coherent responses, functional relatedness between physiological conditions, and coordinated, context-specific regulation of the genes. Availability: Implementation is freely available in R and Matlab at http://netpro.r-forge.r-project.orgComment: 19 pages, 13 figure

Cell-Cycle Dependence of Transcription Dominates Noise in Gene Expression

The large variability in mRNA and protein levels found from both static and dynamic measurements in single cells has been largely attributed to random periods of transcription, often occurring in bursts. The cell cycle has a pronounced global role in affecting transcriptional and translational output, but how this influences transcriptional statistics from noisy promoters is unknown and generally ignored by current stochastic models. Here we show that variable transcription from the synthetic tetO promoter in S. cerevisiae is dominated by its dependence on the cell cycle. Real-time measurements of fluorescent protein at high expression levels indicate tetO promoters increase transcription rate ~2-fold in S/G2/M similar to constitutive genes. At low expression levels, where tetO promoters are thought to generate infrequent bursts of transcription, we observe random pulses of expression restricted to S/G2/M, which are correlated between homologous promoters present in the same cell. The analysis of static, single-cell mRNA measurements at different points along the cell cycle corroborates these findings. Our results demonstrate that highly variable mRNA distributions in yeast are not solely the result of randomly switching between periods of active and inactive gene expression, but instead largely driven by differences in transcriptional activity between G1 and S/G2/M.GM095733BBBE 103316MIT Startup Fun

Separation of DNA Replication from the Assembly of Break-Competent Meiotic Chromosomes

The meiotic cell division reduces the chromosome number from diploid to haploid to form gametes for sexual reproduction. Although much progress has been made in understanding meiotic recombination and the two meiotic divisions, the processes leading up to recombination, including the prolonged pre-meiotic S phase (meiS) and the assembly of meiotic chromosome axes, remain poorly defined. We have used genome-wide approaches in Saccharomyces cerevisiae to measure the kinetics of pre-meiotic DNA replication and to investigate the interdependencies between replication and axis formation. We found that replication initiation was delayed for a large number of origins in meiS compared to mitosis and that meiotic cells were far more sensitive to replication inhibition, most likely due to the starvation conditions required for meiotic induction. Moreover, replication initiation was delayed even in the absence of chromosome axes, indicating replication timing is independent of the process of axis assembly. Finally, we found that cells were able to install axis components and initiate recombination on unreplicated DNA. Thus, although pre-meiotic DNA replication and meiotic chromosome axis formation occur concurrently, they are not strictly coupled. The functional separation of these processes reveals a modular method of building meiotic chromosomes and predicts that any crosstalk between these modules must occur through superimposed regulatory mechanisms

Stressful situation if CENP-A not front and CENter

The exclusive localization of the histone H3 variant CENP-A to centromeres is essential for accurate chromosome segregation. Ubiquitin-mediated proteolysis helps to ensure that CENP-A does not mislocalize to euchromatin, which can lead to genomic instability. Consistent with this, overexpression of the budding yeast CENP-A(Cse4) is lethal in cells lacking Psh1, the E3 ubiquitin ligase that targets CENP-A(Cse4) for degradation. To identify additional mechanisms that prevent CENP-A(Cse4) misincorporation and lethality, we analyzed the genome-wide mislocalization pattern of overexpressed CENP-A(Cse4) in the presence and absence of Psh1 by chromatin immunoprecipitation followed by high throughput sequencing. We found that ectopic CENP-A(Cse4) is enriched at promoters that contain histone H2A.Z(Htz1) nucleosomes, but that H2A.Z(Htz1) is not required for CENP-A(Cse4) mislocalization. Instead, the INO80 complex, which removes H2A.Z(Htz1) from nucleosomes, promotes the ectopic deposition of CENP-A(Cse4). Transcriptional profiling revealed gene expression changes in the psh1Δ cells overexpressing CENP-A(Cse4). The down-regulated genes are enriched for CENP-A(Cse4) mislocalization to promoters, while the up-regulated genes correlate with those that are also transcriptionally up-regulated in an htz1Δ strain. Together, these data show that regulating centromeric nucleosome localization is not only critical for maintaining centromere function, but also for ensuring accurate promoter function and transcriptional regulation