Leila Taher

The Genome of the Western Clawed Frog Xenopus tropicalis

Frog Genome The African clawed frog Xenopus tropicalis is the first amphibian to have its genome sequenced. Hellsten et al. (p. 633, see the cover) present an analysis of a draft assembly of the genome. The genome of the frog, which is an important model system for developmental biology, encodes over 20,000 protein-coding genes, of which more than 1700 genes have identified human disease associations. Detailed comparison of the content of protein-coding genes with other tetrapods—human and chicken—reveals extensive shared synteny, occasionally spanning entire chromosomes. Assembly, annotation, and analysis of the frog genome compares gene content and synteny with the human and chicken genomes. The western clawed frog Xenopus tropicalis is an important model for vertebrate development that combines experimental advantages of the African clawed frog Xenopus laevis with more tractable genetics. Here we present a draft genome sequence assembly of X. tropicalis. This genome encodes more than 20,000 protein-coding genes, including orthologs of at least 1700 human disease genes. Over 1 million expressed sequence tags validated the annotation. More than one-third of the genome consists of transposable elements, with unusually prevalent DNA transposons. Like that of other tetrapods, the genome of X. tropicalis contains gene deserts enriched for conserved noncoding elements. The genome exhibits substantial shared synteny with human and chicken over major parts of large chromosomes, broken by lineage-specific chromosome fusions and fissions, mainly in the mammalian lineage.

A High-Resolution Enhancer Atlas of the Developing Telencephalon

The mammalian telencephalon plays critical roles in cognition, motor function, and emotion. Though many of the genes required for its development have been identified, the distant-acting regulatory sequences orchestrating their in vivo expression are mostly unknown. Here, we describe a digital atlas of in vivo enhancers active in subregions of the developing telencephalon. We identified more than 4,600 candidate embryonic forebrain enhancers and studied the in vivo activity of 329 of these sequences in transgenic mouse embryos. We generated serial sets of histological brain sections for 145 reproducible forebrain enhancers, resulting in a publicly accessible web-based data collection comprising more than 32,000 sections. We also used epigenomic analysis of human and mouse cortex tissue to directly compare the genome-wide enhancer architecture in these species. These data provide a primary resource for investigating gene regulatory mechanisms of telencephalon development and enable studies of the role of distant-acting enhancers in neurodevelopmental disorders.

Massively parallel decoding of mammalian regulatory sequences supports a flexible organizational model

Despite continual progress in the cataloging of vertebrate regulatory elements, little is known about their organization and regulatory architecture. Here we describe a massively parallel experiment to systematically test the impact of copy number, spacing, combination and order of transcription factor binding sites on gene expression. A complex library of ∼5,000 synthetic regulatory elements containing patterns from 12 liver-specific transcription factor binding sites was assayed in mice and in HepG2 cells. We find that certain transcription factors act as direct drivers of gene expression in homotypic clusters of binding sites, independent of spacing between sites, whereas others function only synergistically. Heterotypic enhancers are stronger than their homotypic analogs and favor specific transcription factor binding site combinations, mimicking putative native enhancers. Exhaustive testing of binding site permutations suggests that there is flexibility in binding site order. Our findings provide quantitative support for a flexible model of regulatory element activity and suggest a framework for the design of synthetic tissue-specific enhancers.

Genome-wide CTCF distribution in vertebrates defines equivalent sites that aid the identification of disease-associated genes

Many genomic alterations associated with human diseases localize in noncoding regulatory elements located far from the promoters they regulate, making it challenging to link noncoding mutations or risk-associated variants with target genes. The range of action of a given set of enhancers is thought to be defined by insulator elements bound by the 11 zinc-finger nuclear factor CCCTC-binding protein (CTCF). Here we analyzed the genomic distribution of CTCF in various human, mouse and chicken cell types, demonstrating the existence of evolutionarily conserved CTCF-bound sites beyond mammals. These sites preferentially flank transcription factor–encoding genes, often associated with human diseases, and function as enhancer blockers in vivo, suggesting that they act as evolutionarily invariant gene boundaries. We then applied this concept to predict and functionally demonstrate that the polymorphic variants associated with multiple sclerosis located within the EVI5 gene impinge on the adjacent gene GFI1.

Global gene expression analysis of murine limb development.

Detailed information about stage-specific changes in gene expression is crucial for understanding the gene regulatory networks underlying development and the various signal transduction pathways contributing to morphogenesis. Here we describe the global gene expression dynamics during early murine limb development, when cartilage, tendons, muscle, joints, vasculature and nerves are specified and the musculoskeletal system of limbs is established. We used whole-genome microarrays to identify genes with differential expression at 5 stages of limb development (E9.5 to 13.5), during fore- and hind-limb patterning. We found that the onset of limb formation is characterized by an up-regulation of transcription factors, which is followed by a massive activation of genes during E10.5 and E11.5 which levels off at later time points. Among the 3520 genes identified as significantly up-regulated in the limb, we find ∼30% to be novel, dramatically expanding the repertoire of candidate genes likely to function in the limb. Hierarchical and stage-specific clustering identified expression profiles that are likely to correlate with functional programs during limb development and further characterization of these transcripts will provide new insights into specific tissue patterning processes. Here, we provide for the first time a comprehensive analysis of developmentally regulated genes during murine limb development, and provide some novel insights into the expression dynamics governing limb morphogenesis.

Transcriptional Regulation of Enhancers Active in Protodomains of the Developing Cerebral Cortex

Elucidating the genetic control of cerebral cortical (pallial) development is essential for understanding function, evolution, and disorders of the brain. Transcription factors (TFs) that embryonically regulate pallial regionalization are expressed in gradients, raising the question of how discrete domains are generated. We provide evidence that small enhancer elements active in protodomains integrate broad transcriptional information. CreER(T2) and GFP expression from 14 different enhancer elements in stable transgenic mice allowed us to define a comprehensive regional fate map of the pallium. We explored transcriptional mechanisms that control the activity of the enhancers using informatics, in vivo occupancy by TFs that regulate cortical patterning (CoupTFI, Pax6, and Pbx1), and analysis of enhancer activity in Pax6 mutants. Overall, the results provide insights into how broadly expressed patterning TFs regulate the activity of small enhancer elements that drive gene expression in pallial protodomains that fate map to distinct cortical regions.

A machine learning approach for identifying novel cell type-specific transcriptional regulators of myogenesis.

Transcriptional enhancers integrate the contributions of multiple classes of transcription factors (TFs) to orchestrate the myriad spatio-temporal gene expression programs that occur during development. A molecular understanding of enhancers with similar activities requires the identification of both their unique and their shared sequence features. To address this problem, we combined phylogenetic profiling with a DNA–based enhancer sequence classifier that analyzes the TF binding sites (TFBSs) governing the transcription of a co-expressed gene set. We first assembled a small number of enhancers that are active in Drosophila melanogaster muscle founder cells (FCs) and other mesodermal cell types. Using phylogenetic profiling, we increased the number of enhancers by incorporating orthologous but divergent sequences from other Drosophila species. Functional assays revealed that the diverged enhancer orthologs were active in largely similar patterns as their D. melanogaster counterparts, although there was extensive evolutionary shuffling of known TFBSs. We then built and trained a classifier using this enhancer set and identified additional related enhancers based on the presence or absence of known and putative TFBSs. Predicted FC enhancers were over-represented in proximity to known FC genes; and many of the TFBSs learned by the classifier were found to be critical for enhancer activity, including POU homeodomain, Myb, Ets, Forkhead, and T-box motifs. Empirical testing also revealed that the T-box TF encoded by org-1 is a previously uncharacterized regulator of muscle cell identity. Finally, we found extensive diversity in the composition of TFBSs within known FC enhancers, suggesting that motif combinatorics plays an essential role in the cellular specificity exhibited by such enhancers. In summary, machine learning combined with evolutionary sequence analysis is useful for recognizing novel TFBSs and for facilitating the identification of cognate TFs that coordinate cell type–specific developmental gene expression patterns.

AGenDA: homology-based gene prediction

We present a www server for homology-based gene prediction. The user enters a pair of evolutionary related genomic sequences, for example from human and mouse. Our software system uses CHAOS and DIALIGN to calculate an alignment of the input sequences and then searches for conserved splicing signals and start/stop codons around regions of local sequence similarity. This way, candidate exons are identified that are used, in turn, to calculate optimal gene models. The server returns the constructed gene model by email, together with a graphical representation of the underlying genomic alignment.

Effects of HMGN variants on the cellular transcription profile

High mobility group N (HMGN) is a family of intrinsically disordered nuclear proteins that bind to nucleosomes, alters the structure of chromatin and affects transcription. A major unresolved question is the extent of functional specificity, or redundancy, between the various members of the HMGN protein family. Here, we analyze the transcriptional profile of cells in which the expression of various HMGN proteins has been either deleted or doubled. We find that both up- and downregulation of HMGN expression altered the cellular transcription profile. Most, but not all of the changes were variant specific, suggesting limited redundancy in transcriptional regulation. Analysis of point and swap HMGN mutants revealed that the transcriptional specificity is determined by a unique combination of a functional nucleosome-binding domain and C-terminal domain. Doubling the amount of HMGN had a significantly larger effect on the transcription profile than total deletion, suggesting that the intrinsically disordered structure of HMGN proteins plays an important role in their function. The results reveal an HMGN-variant-specific effect on the fidelity of the cellular transcription profile, indicating that functionally the various HMGN subtypes are not fully redundant.

Variable locus length in the human genome leads to ascertainment bias in functional inference for non-coding elements

Motivation: Several functional gene annotation databases have been developed in the recent years, and are widely used to infer the biological function of gene sets, by scrutinizing the attributes that appear over- and underrepresented. However, this strategy is not directly applicable to the study of non-coding DNA, as the non-coding sequence span varies greatly among different gene loci in the human genome and longer loci have a higher likelihood of being selected purely by chance. Therefore, conclusions involving the function of non-coding elements that are drawn based on the annotation of neighboring genes are often biased. We assessed the systematic bias in several particular Gene Ontology (GO) categories using the standard hypergeometric test, by randomly sampling non-coding elements from the human genome and inferring their function based on the functional annotation of the closest genes. While no category is expected to occur significantly over- or underrepresented for a random selection of elements, categories such as ‘cell adhesion’, ‘nervous system development’ and ‘transcription factor activities’ appeared to be systematically overrepresented, while others such as ‘olfactory receptor activity’—underrepresented. Results: Our results suggest that functional inference for non-coding elements using gene annotation databases requires a special correction. We introduce a set of correction coefficients for the probabilities of the GO categories that accounts for the variability in the length of the non-coding DNA across different loci and effectively eliminates the ascertainment bias from the functional characterization of non-coding elements. Our approach can be easily generalized to any other gene annotation database. Contact: ovcharei@ncbi.nlm.nih.gov Supplementary information: Supplementary data are available at Bioinformatics Online.