Transcription of satellite DNAs in insects

Chromatin condensation is an important regulatory mechanism of gene silencing as well as gene activation for the hundreds of functional protein genes harbored in heterochromatic regions of different insect species. Being the major heterochromatin constituents, satellite DNAs serve important roles in heterochromatin regulation in insect in general. Their expression occurs in all developmental stages, being the highest during embryogenesis. Satellite DNA transcrips range from small RNAs, corresponding in size to siRNA, and piwiRNAs, to large, a few Kb long RNAs. The long transcripts are preferentially nonpolyadenylated and remain in the nucleus. The actively regulated expression of satDNAs by cis or trans elements as well as by environmental stress, rather than constitutive transcription, speaks in favour of their involvement in differentiation, development, and environmental response

Selective maintenance of Drosophila tandemly arranged duplicated genes during evolution

Genes occurring in conserved, tandemly-arrayed clusters in Drosophila melanogaster are co-expressed to a much higher extent than other duplicated genes

Transcription factor clusters regulate genes in eukaryotic cells

Transcription is regulated through binding factors to gene promoters to activate or repress expression, however, the mechanisms by which factors find targets remain unclear. Using single-molecule fluorescence microscopy, we determined in vivo stoichiometry and spatiotemporal dynamics of a GFP tagged repressor, Mig1, from a paradigm signaling pathway of Saccharomyces cerevisiae. We find the repressor operates in clusters, which upon extracellular signal detection, translocate from the cytoplasm, bind to nuclear targets and turnover. Simulations of Mig1 configuration within a 3D yeast genome model combined with a promoter-specific, fluorescent translation reporter confirmed clusters are the functional unit of gene regulation. In vitro and structural analysis on reconstituted Mig1 suggests that clusters are stabilized by depletion forces between intrinsically disordered sequences. We observed similar clusters of a co-regulatory activator from a different pathway, supporting a generalized cluster model for transcription factors that reduces promoter search times through intersegment transfer while stabilizing gene expression

Predicting DNA-Binding Specificities of Eukaryotic Transcription Factors

Today, annotated amino acid sequences of more and more transcription factors (TFs) are readily available. Quantitative information about their DNA-binding specificities, however, are hard to obtain. Position frequency matrices (PFMs), the most widely used models to represent binding specificities, are experimentally characterized only for a small fraction of all TFs. Even for some of the most intensively studied eukaryotic organisms (i.e., human, rat and mouse), roughly one-sixth of all proteins with annotated DNA-binding domain have been characterized experimentally. Here, we present a new method based on support vector regression for predicting quantitative DNA-binding specificities of TFs in different eukaryotic species. This approach estimates a quantitative measure for the PFM similarity of two proteins, based on various features derived from their protein sequences. The method is trained and tested on a dataset containing 1 239 TFs with known DNA-binding specificity, and used to predict specific DNA target motifs for 645 TFs with high accuracy

Studying DNA Double-Strand Break Repair: An Ever-Growing Toolbox

To ward off against the catastrophic consequences of persistent DNA double-strand breaks (DSBs), eukaryotic cells have developed a set of complex signaling networks that detect these DNA lesions, orchestrate cell cycle checkpoints and ultimately lead to their repair. Collectively, these signaling networks comprise the DNA damage response (DDR). The current knowledge of the molecular determinants and mechanistic details of the DDR owes greatly to the continuous development of ground-breaking experimental tools that couple the controlled induction of DSBs at distinct genomic positions with assays and reporters to investigate DNA repair pathways, their impact on other DNA-templated processes and the specific contribution of the chromatin environment. In this review, we present these tools, discuss their pros and cons and illustrate their contribution to our current understanding of the DDR.European Research Council (ERC-2014-CoG 647344

Higher-Order Inter-chromosomal Hubs Shape 3D Genome Organization in the Nucleus

Eukaryotic genomes are packaged into a 3-dimensional structure in the nucleus. Current methods for studying genome-wide structure are based on proximity ligation. However, this approach can fail to detect known structures, such as interactions with nuclear bodies, because these DNA regions can be too far apart to directly ligate. Accordingly, our overall understanding of genome organization remains incomplete. Here, we develop split-pool recognition of interactions by tag extension (SPRITE), a method that enables genome-wide detection of higher-order interactions within the nucleus. Using SPRITE, we recapitulate known structures identified by proximity ligation and identify additional interactions occurring across larger distances, including two hubs of inter-chromosomal interactions that are arranged around the nucleolus and nuclear speckles. We show that a substantial fraction of the genome exhibits preferential organization relative to these nuclear bodies. Our results generate a global model whereby nuclear bodies act as inter-chromosomal hubs that shape the overall packaging of DNA in the nucleus

Characterization of cross-species transcription and splicing from Penicillium to Saccharomyces cerevisiae

Heterologous expression of eukaryotic gene clusters in yeast has been widely used for producing high-value chemicals and bioactive secondary metabolites. However, eukaryotic transcription cis-elements are still undercharacterized, and the cross-species expression mechanism remains poorly understood. Here we used the whole expression unit (including original promoter, terminator, and open reading frame with introns) of orotidine 5\u27-monophosphate decarboxylases from 14 Penicillium species as a showcase, and analyzed their cross-species expression in Saccharomyces cerevisiae. We found that pyrG promoters from the Penicillium species could drive URA3 expression in yeast, and that inefficient cross-species splicing of Penicillium introns might result in weak cross-species expression. Thus, this study demonstrates cross-species expression from Penicillium to yeast, and sheds light on the opportunities and challenges of cross-species expression of fungi expression units and gene clusters in yeast without refactoring for novel natural product discovery

Human pol II promoter prediction: time series descriptors and machine learning

Although several in silico promoter prediction methods have been developed to date, they are still limited in predictive performance. The limitations are due to the challenge of selecting appropriate features of promoters that distinguish them from non-promoters and the generalization or predictive ability of the machine-learning algorithms. In this paper we attempt to define a novel approach by using unique descriptors and machine-learning methods for the recognition of eukaryotic polymerase II promoters. In this study, non-linear time series descriptors along with non-linear machine-learning algorithms, such as support vector machine (SVM), are used to discriminate between promoter and non-promoter regions. The basic idea here is to use descriptors that do not depend on the primary DNA sequence and provide a clear distinction between promoter and non-promoter regions. The classification model built on a set of 1000 promoter and 1500 non-promoter sequences, showed a 10-fold cross-validation accuracy of 87% and an independent test set had an accuracy >85% in both promoter and non-promoter identification. This approach correctly identified all 20 experimentally verified promoters of human chromosome 22. The high sensitivity and selectivity indicates that n-mer frequencies along with non-linear time series descriptors, such as Lyapunov component stability and Tsallis entropy, and supervised machine-learning methods, such as SVMs, can be useful in the identification of pol II promoters