Gwenael Badis

Cryptic Pol II Transcripts Are Degraded by a Nuclear Quality Control Pathway Involving a New Poly(A) Polymerase

Since detection of an RNA molecule is the major criterion to define transcriptional activity, the fraction of the genome that is expressed is generally considered to parallel the complexity of the transcriptome. We show here that several supposedly silent intergenic regions in the genome of S. cerevisiae are actually transcribed by RNA polymerase II, suggesting that the expressed fraction of the genome is higher than anticipated. Surprisingly, however, RNAs originating from these regions are rapidly degraded by the combined action of the exosome and a new poly(A) polymerase activity that is defined by the Trf4 protein and one of two RNA binding proteins, Air1p or Air2p. We show that such a polyadenylation-assisted degradation mechanism is also responsible for the degradation of several Pol I and Pol III transcripts. Our data strongly support the existence of a posttranscriptional quality control mechanism limiting inappropriate expression of genetic information.

Variation in homeodomain DNA-binding revealed by high-resolution analysis of sequence preferences

Most homeodomains are unique within a genome, yet many are highly conserved across vast evolutionary distances, implying strong selection on their precise DNA-binding specificities. We determined the binding preferences of the majority (168) of mouse homeodomains to all possible 8-base sequences, revealing rich and complex patterns of sequence specificity and showing that there are at least 65 distinct homeodomain DNA-binding activities. We developed a computational system that successfully predicts binding sites for homeodomain proteins as distant from mouse as Drosophila and C. elegans, and we infer full 8-mer binding profiles for the majority of known animal homeodomains. Our results provide an unprecedented level of resolution in the analysis of this simple domain structure and suggest that variation in sequence recognition may be a factor in its functional diversity and evolutionary success.

Genome-wide analysis of ETS-family DNA-binding in vitro and in vivo

Members of the large ETS family of transcription factors (TFs) have highly similar DNA‐binding domains (DBDs)—yet they have diverse functions and activities in physiology and oncogenesis. Some differences in DNA‐binding preferences within this family have been described, but they have not been analysed systematically, and their contributions to targeting remain largely uncharacterized. We report here the DNA‐binding profiles for all human and mouse ETS factors, which we generated using two different methods: a high‐throughput microwell‐based TF DNA‐binding specificity assay, and protein‐binding microarrays (PBMs). Both approaches reveal that the ETS‐binding profiles cluster into four distinct classes, and that all ETS factors linked to cancer, ERG, ETV1, ETV4 and FLI1, fall into just one of these classes. We identify amino‐acid residues that are critical for the differences in specificity between all the classes, and confirm the specificities in vivo using chromatin immunoprecipitation followed by sequencing (ChIP‐seq) for a member of each class. The results indicate that even relatively small differences in in vitro binding specificity of a TF contribute to site selectivity in vivo.

A Library of Yeast Transcription Factor Motifs Reveals a Widespread Function for Rsc3 in Targeting Nucleosome Exclusion at Promoters

The sequence specificity of DNA-binding proteins is the primary mechanism by which the cell recognizes genomic features. Here, we describe systematic determination of yeast transcription factor DNA-binding specificities. We obtained binding specificities for 112 DNA-binding proteins representing 19 distinct structural classes. One-third of the binding specificities have not been previously reported. Several binding sequences have striking genomic distributions relative to transcription start sites, supporting their biological relevance and suggesting a role in promoter architecture. Among these are Rsc3 binding sequences, containing the core CGCG, which are found preferentially approximately 100 bp upstream of transcription start sites. Mutation of RSC3 results in a dramatic increase in nucleosome occupancy in hundreds of proximal promoters containing a Rsc3 binding element, but has little impact on promoters lacking Rsc3 binding sequences, indicating that Rsc3 plays a broad role in targeting nucleosome exclusion at yeast promoters.

TFCat: the curated catalog of mouse and human transcription factors

Unravelling regulatory programs governed by transcription factors (TFs) is fundamental to understanding biological systems. TFCat is a catalog of mouse and human TFs based on a reliable core collection of annotations obtained by expert review of the scientific literature. The collection, including proven and homology-based candidate TFs, is annotated within a function-based taxonomy and DNA-binding proteins are organized within a classification system. All data and user-feedback mechanisms are available at the TFCat portal http://www.tfcat.ca.

Hmo1 Is Required for TOR-Dependent Regulation of Ribosomal Protein Gene Transcription

ABSTRACT Ribosome biogenesis requires equimolar amounts of four rRNAs and all 79 ribosomal proteins (RP). Coordinated regulation of rRNA and RP synthesis by eukaryotic RNA polymerases (Pol) I, III, and II is a key requirement for growth control. Using a novel global genetic approach, we showed that the absence of Hmo1 becomes lethal when combined with mutations of components of either the RNA Pol II or Pol I transcription machineries, of specific RP, or of the TOR pathway. Hmo1 directly interacts with both the region transcribed by Pol I and a subset of RP gene promoters. Down-regulation of Hmo1 expression affects RP gene expression. Upon TORC1 inhibition, Hmo1 dissociates from ribosomal DNA (rDNA) and some RP gene promoters simultaneously. Finally, in the absence of Hmo1, TOR-dependent repression of RP genes is alleviated. Therefore, we show here that Saccharomyces cerevisiae Hmo1 is directly involved in coordinating rDNA transcription by Pol I and RP gene expression by Pol II under the control of the TOR pathway.

Evaluation of data-dependent versus targeted shotgun proteomic approaches for monitoring transcription factor expression in breast cancer.

In breast cancer, there is a significant degree of molecular diversity among tumors. Multiple perturbations in signal transduction pathways impinge on transcriptional networks that in turn dictate malignant transformation and metastatic progression. Detailed knowledge of the sequence-specific transcription factors that become activated or repressed within a tumor and comparison of their relative levels of expression in cancer versus normal tissue should therefore provide insight into disease mechanisms, improving patient stratification and facilitating personalized treatment. While high-throughput tandem mass spectrometry methods for global proteome profiling have been developed, existing approaches have limited sensitivity and are often unable to detect low-abundance transcription factors in a complex biological specimen like a biopsy or tumor cell extract. To this end, we have undertaken a systematic comparative evaluation of three MS/MS methods for the ability to detect reference transcription factors spiked in known amounts into a cell-free breast cancer nuclear extract: Data-Dependent Acquisition (DDA), wherein precursor ion intensity dictates selection for fragmentation; Targeted Peptide Monitoring (TPM), a directed approach using successive isolation and fragmentation of predefined m/ z ratios; and Multiple Reaction Monitoring (MRM), in which specific precursor ion to product ion transitions are selectively monitored. Through a series of controlled, parallel benchmarking experiments, we have determined the relative figures-of-merit of each approach, and have established that prior knowledge of signature proteotypic peptides markedly improves overall detection sensitivity, reliability, and quantification.

Predicting the binding preference of transcription factors to individual DNA k-mers

Motivation: Recognition of specific DNA sequences is a central mechanism by which transcription factors (TFs) control gene expression. Many TF-binding preferences, however, are unknown or poorly characterized, in part due to the difficulty associated with determining their specificity experimentally, and an incomplete understanding of the mechanisms governing sequence specificity. New techniques that estimate the affinity of TFs to all possible k-mers provide a new opportunity to study DNA–protein interaction mechanisms, and may facilitate inference of binding preferences for members of a given TF family when such information is available for other family members. Results: We employed a new dataset consisting of the relative preferences of mouse homeodomains for all eight-base DNA sequences in order to ask how well we can predict the binding profiles of homeodomains when only their protein sequences are given. We evaluated a panel of standard statistical inference techniques, as well as variations of the protein features considered. Nearest neighbour among functionally important residues emerged among the most effective methods. Our results underscore the complexity of TF–DNA recognition, and suggest a rational approach for future analyses of TF families. Contact: t.hughes@utorotno.ca Supplementary information: Supplementary data are available at Bioinformatics online.

Objective sequence-based subfamily classifications of mouse homeodomains reflect their in vitro DNA-binding preferences

Classifying proteins into subgroups with similar molecular function on the basis of sequence is an important step in deriving reliable functional annotations computationally. So far, however, available classification procedures have been evaluated against protein subgroups that are defined by experts using mainly qualitative descriptions of molecular function. Recently, in vitro DNA-binding preferences to all possible 8-nt DNA sequences have been measured for 178 mouse homeodomains using protein-binding microarrays, offering the unprecedented opportunity of evaluating the classification methods against quantitative measures of molecular function. To this end, we automatically derive homeodomain subtypes from the DNA-binding data and independently group the same domains using sequence information alone. We test five sequence-based methods, which use different sequence-similarity measures and algorithms to group sequences. Results show that methods that optimize the classification robustness reflect well the detailed functional specificity revealed by the experimental data. In some of these classifications, 73–83% of the subfamilies exactly correspond to, or are completely contained in, the function-based subtypes. Our findings demonstrate that certain sequence-based classifications are capable of yielding very specific molecular function annotations. The availability of quantitative descriptions of molecular function, such as DNA-binding data, will be a key factor in exploiting this potential in the future.

Antisense transcriptional interference mediates condition-specific gene repression in budding yeast

Abstract Pervasive transcription generates many unstable non-coding transcripts in budding yeast. The transcription of such noncoding RNAs, in particular antisense RNAs (asRNAs), has been shown in a few examples to repress the expression of the associated mRNAs. Yet, such mechanism is not known to commonly contribute to the regulation of a given class of genes. Using a mutant context that stabilized pervasive transcripts, we observed that the least expressed mRNAs during the exponential phase were associated with high levels of asRNAs. These asRNAs also overlapped their corresponding gene promoters with a much higher frequency than average. Interrupting antisense transcription of a subset of genes corresponding to quiescence-enriched mRNAs restored their expression. The underlying mechanism acts in cis and involves several chromatin modifiers. Our results convey that transcription interference represses up to 30% of the 590 least expressed genes, which includes 163 genes with quiescence-enriched mRNAs. We also found that pervasive transcripts constitute a higher fraction of the transcriptome in quiescence relative to the exponential phase, consistent with gene expression itself playing an important role to suppress pervasive transcription. Accordingly, the HIS1 asRNA, normally only present in quiescence, is expressed in exponential phase upon HIS1 mRNA transcription interruption.