Systematic Dissection of Roles for Chromatin Regulators in a Yeast Stress Response

Systematic Dissection of Roles for Chromatin Regulators in a Yeast Stress Response:
by Assaf Weiner, Hsiuyi V. Chen, Chih Long Liu, Ayelet Rahat, Avital Klien, Luis Soares, Mohanram Gudipati, Jenna Pfeffner, Aviv Regev, Stephen Buratowski, Jeffrey A. Pleiss, Nir Friedman, Oliver J. Rando

Packaging of eukaryotic genomes into chromatin has wide-ranging effects on gene transcription. Curiously, it is commonly observed that deletion of a global chromatin regulator affects expression of only a limited subset of genes bound to or modified by the regulator in question. However, in many single-gene studies it has become clear that chromatin regulators often do not affect steady-state transcription, but instead are required for normal transcriptional reprogramming by environmental cues. We therefore have systematically investigated the effects of 83 histone mutants, and 119 gene deletion mutants, on induction/repression dynamics of 170 transcripts in response to diamide stress in yeast. Importantly, we find that chromatin regulators play far more pronounced roles during gene induction/repression than they do in steady-state expression. Furthermore, by jointly analyzing the substrates (histone mutants) and enzymes (chromatin modifier deletions) we identify specific interactions between histone modifications and their regulators. Combining these functional results with genome-wide mapping of several histone marks in the same time course, we systematically investigated the correspondence between histone modification occurrence and function. We followed up on one pathway, finding that Set1-dependent H3K4 methylation primarily acts as a gene repressor during multiple stresses, specifically at genes involved in ribosome biosynthesis. Set1-dependent repression of ribosomal genes occurs via distinct pathways for ribosomal protein genes and ribosomal biogenesis genes, which can be separated based on genetic requirements for repression and based on chromatin changes during gene repression. Together, our dynamic studies provide a rich resource for investigating chromatin regulation, and identify a significant role for the “activating” mark H3K4me3 in gene repression.

Transcription initiation by human RNA polymerase II visualized at single-molecule resolution [Research Papers]

Transcription initiation by human RNA polymerase II visualized at single-molecule resolution [Research Papers]:
Forty years of classical biochemical analysis have identified the molecular players involved in initiation of transcription by eukaryotic RNA polymerase II (Pol II) and largely assigned their functions. However, a dynamic picture of Pol II transcription initiation and an understanding of the mechanisms of its regulation have remained elusive due in part to inherent limitations of conventional ensemble biochemistry. Here we have begun to dissect promoter-specific transcription initiation directed by a reconstituted human Pol II system at single-molecule resolution using fluorescence video-microscopy. We detected several stochastic rounds of human Pol II transcription from individual DNA templates, observed attenuation of transcription by promoter mutations, observed enhancement of transcription by activator Sp1, and correlated the transcription signals with real-time interactions of holo-TFIID molecules at individual DNA templates. This integrated single-molecule methodology should be applicable to studying other complex biological processes.

Evolutionary History and Adaptation from High-Coverage Whole-Genome Sequences of Diverse African Hunter-Gatherers

Evolutionary History and Adaptation from High-Coverage Whole-Genome Sequences of Diverse African Hunter-Gatherers: Joseph Lachance, Benjamin Vernot, Clara C. Elbers, Bart Ferwerda, Alain Froment, Jean-Marie Bodo, Godfrey Lema, Wenqing Fu, Thomas B. Nyambo, Timothy R. Rebbeck, Kun Zhang, Joshua M. Akey, Sarah A. Tishkoff. To reconstruct modern human evolutionary history and identify loci that have shaped hunter-gatherer adaptation, we sequenced the whole genomes of five individuals in each of three different hunter....

Proto-genes and de novo gene birth

Proto-genes and de novo gene birth:
Proto-genes and de novo gene birth

Nature 487, 7407 (2012). doi:10.1038/nature11184

Authors: Anne-Ruxandra Carvunis, Thomas Rolland, Ilan Wapinski, Michael A. Calderwood, Muhammed A. Yildirim, Nicolas Simonis, Benoit Charloteaux, César A. Hidalgo, Justin Barbette, Balaji Santhanam, Gloria A. Brar, Jonathan S. Weissman, Aviv Regev, Nicolas Thierry-Mieg, Michael E. Cusick & Marc Vidal

Novel protein-coding genes can arise either through re-organization of pre-existing genes or de novo. Processes involving re-organization of pre-existing genes, notably after gene duplication, have been extensively described. In contrast, de novo gene birth remains poorly understood, mainly because translation of sequences devoid of genes, or ‘non-genic’ sequences, is expected to produce insignificant polypeptides rather than proteins with specific biological functions. Here we formalize an evolutionary model according to which functional genes evolve de novo through transitory proto-genes generated by widespread translational activity in non-genic sequences. Testing this model at the genome scale in Saccharomyces cerevisiae, we detect translation of hundreds of short species-specific open reading frames (ORFs) located in non-genic sequences. These translation events seem to provide adaptive potential, as suggested by their differential regulation upon stress and by signatures of retention by natural selection. In line with our model, we establish that S. cerevisiae ORFs can be placed within an evolutionary continuum ranging from non-genic sequences to genes. We identify ∼1,900 candidate proto-genes among S. cerevisiae ORFs and find that de novo gene birth from such a reservoir may be more prevalent than sporadic gene duplication. Our work illustrates that evolution exploits seemingly dispensable sequences to generate adaptive functional innovation.

Improved Models for Transcription Factor Binding Site Identification Using Nonindependent Interactions [Gene Expression]

Improved Models for Transcription Factor Binding Site Identification Using Nonindependent Interactions [Gene Expression]:
Identifying transcription factor (TF) binding sites is essential for understanding regulatory networks. The specificity of most TFs is currently modeled using position weight matrices (PWMs) that assume the positions within a binding site contribute independently to binding affinity for any site. Extensive, high-throughput quantitative binding assays let us examine, for the first time, the independence assumption for many TFs. We find that the specificity of most TFs is well fit with the simple PWM model, but in some cases more complex models are required. We introduce a binding energy model (BEM) that can include energy parameters for nonindependent contributions to binding affinity. We show that in most cases where a PWM is not sufficient, a BEM that includes energy parameters for adjacent dinucleotide contributions models the specificity very well. Having more accurate models of specificity greatly improves the interpretation of in vivo TF localization data, such as from chromatin immunoprecipitation followed by sequencing (ChIP-seq) experiments.

Efficient Reverse-Engineering of a Developmental Gene Regulatory Network

Efficient Reverse-Engineering of a Developmental Gene Regulatory Network:
by Anton Crombach, Karl R. Wotton, Damjan Cicin-Sain, Maksat Ashyraliyev, Johannes Jaeger

Understanding the complex regulatory networks underlying development and evolution of multi-cellular organisms is a major problem in biology. Computational models can be used as tools to extract the regulatory structure and dynamics of such networks from gene expression data. This approach is called reverse engineering. It has been successfully applied to many gene networks in various biological systems. However, to reconstitute the structure and non-linear dynamics of a developmental gene network in its spatial context remains a considerable challenge. Here, we address this challenge using a case study: the gap gene network involved in segment determination during early development of Drosophila melanogaster. A major problem for reverse-engineering pattern-forming networks is the significant amount of time and effort required to acquire and quantify spatial gene expression data. We have developed a simplified data processing pipeline that considerably increases the throughput of the method, but results in data of reduced accuracy compared to those previously used for gap gene network inference. We demonstrate that we can infer the correct network structure using our reduced data set, and investigate minimal data requirements for successful reverse engineering. Our results show that timing and position of expression domain boundaries are the crucial features for determining regulatory network structure from data, while it is less important to precisely measure expression levels. Based on this, we define minimal data requirements for gap gene network inference. Our results demonstrate the feasibility of reverse-engineering with much reduced experimental effort. This enables more widespread use of the method in different developmental contexts and organisms. Such systematic application of data-driven models to real-world networks has enormous potential. Only the quantitative investigation of a large number of developmental gene regulatory networks will allow us to discover whether there are rules or regularities governing development and evolution of complex multi-cellular organisms.

Accurate whole-genome sequencing and haplotyping from 10 to 20 human cells

Accurate whole-genome sequencing and haplotyping from 10 to 20 human cells:
Accurate whole-genome sequencing and haplotyping from 10 to 20 human cells

Nature 487, 7406 (2012). doi:10.1038/nature11236

Authors: Brock A. Peters, Bahram G. Kermani, Andrew B. Sparks, Oleg Alferov, Peter Hong, Andrei Alexeev, Yuan Jiang, Fredrik Dahl, Y. Tom Tang, Juergen Haas, Kimberly Robasky, Alexander Wait Zaranek, Je-Hyuk Lee, Madeleine Price Ball, Joseph E. Peterson, Helena Perazich, George Yeung, Jia Liu, Linsu Chen, Michael I. Kennemer, Kaliprasad Pothuraju, Karel Konvicka, Mike Tsoupko-Sitnikov, Krishna P. Pant, Jessica C. Ebert, Geoffrey B. Nilsen, Jonathan Baccash, Aaron L. Halpern, George M. Church & Radoje Drmanac
Recent advances in whole-genome sequencing have brought the vision of personal genomics and genomic medicine closer to reality. However, current methods lack clinical accuracy and the ability to describe the context (haplotypes) in which genome variants co-occur in a cost-effective manner. Here we describe a