A comparative analysis of transcription factor expression during metazoan embryonic development

During embryonic development, a complex organism is formed from a single starting cell. These processes of growth and differentiation are driven by large transcriptional changes, which are following the expression and activity of transcription factors (TFs). This study sought to compare TF expression during embryonic development in a diverse group of metazoan animals: representatives of vertebrates (Danio rerio, Xenopus tropicalis), a chordate (Ciona intestinalis) and invertebrate phyla such as insects (Drosophila melanogaster, Anopheles gambiae) and nematodes (Caenorhabditis elegans) were sampled, The different species showed overall very similar TF expression patterns, with TF expression increasing during the initial stages of development. C2H2 zinc finger TFs were over-represented and Homeobox TFs were under-represented in the early stages in all species. We further clustered TFs for each species based on their quantitative temporal expression profiles. This showed very similar TF expression trends in development in vertebrate and insect species. However, analysis of the expression of orthologous pairs between more closely related species showed that expression of most individual TFs is not conserved, following the general model of duplication and diversification. The degree of similarity between TF expression between Xenopus tropicalis and Danio rerio followed the hourglass model, with the greatest similarity occuring during the early tailbud stage in Xenopus tropicalis and the late segmentation stage in Danio rerio. However, for Drosophila melanogaster and Anopheles gambiae there were two periods of high TF transcriptome similarity, one during the Arthropod phylotypic stage at 8-10 hours into Drosophila development and the other later at 16-18 hours into Drosophila development.Comment: ~10 pages, 50 references, 6+3 figures and 5 table

BioSAVE: display of scored annotation within a sequence context.

BACKGROUND: Visualization of sequence annotation is a common feature in many bioinformatics tools. For many applications it is desirable to restrict the display of such annotation according to a score cutoff, as biological interpretation can be difficult in the presence of the entire data. Unfortunately, many visualisation solutions are somewhat static in the way they handle such score cutoffs. RESULTS: We present BioSAVE, a sequence annotation viewer with on-the-fly selection of visualisation thresholds for each feature. BioSAVE is a versatile OS X program for visual display of scored features (annotation) within a sequence context. The program reads sequence and additional supplementary annotation data (e.g., position weight matrix matches, conservation scores, structural domains) from a variety of commonly used file formats and displays them graphically. Onscreen controls then allow for live customisation of these graphics, including on-the-fly selection of visualisation thresholds for each feature. CONCLUSION: Possible applications of the program include display of transcription factor binding sites in a genomic context or the visualisation of structural domain assignments in protein sequences and many more. The dynamic visualisation of these annotations is useful, e.g., for the determination of cutoff values of predicted features to match experimental data. Program, source code and exemplary files are freely available at the BioSAVE homepage.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

GRiP: a computational tool to simulate transcription factor binding in prokaryotes

Motivation: Transcription factors (TFs) are proteins that regulate gene activity by binding to specific sites on the DNA. Understanding the way these molecules locate their target site is of great importance in understanding gene regulation. We developed a comprehensive computational model of this process and estimated the model parameters in (N.R.Zabet and B.Adryan, submitted for publication)

The developmental expression dynamics of Drosophila melanogaster transcription factors.

BACKGROUND: Site-specific transcription factors (TFs) are coordinators of developmental and physiological gene expression programs. Their binding to cis-regulatory modules of target genes mediates the precise cell- and context-specific activation and repression of genes. The expression of TFs should therefore reflect the core expression program of each cell. RESULTS: We studied the expression dynamics of about 750 TFs using the available genomics resources in Drosophila melanogaster. We find that 95% of these TFs are expressed at some point during embryonic development, with a peak roughly between 10 and 12 hours after egg laying, the core stages of organogenesis. We address the differential utilization of DNA-binding domains in different developmental programs systematically in a spatio-temporal context, and show that the zinc finger class of TFs is predominantly early expressed, while Homeobox TFs exhibit later expression in embryogenesis. CONCLUSIONS: Previous work, dissecting cis-regulatory modules during Drosophila development, suggests that TFs are deployed in groups acting in a cooperative manner. In contrast, we find that there is rapid exchange of co-expressed partners amongst the fly TFs, at rates similar to the genome-wide dynamics of co-expression clusters. This suggests there may also be a high level of combinatorial complexity of TFs at cis-regulatory modules.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

Functional genomics analysis of the fibroblast growth factor Branchless in the fruitfly Drosophila melanogaster

Die Embryonalentwicklung der Tracheen der Fruchtfliege Drosophila stellt ein Modell für genetische und molekulare Analysen der Verzweigungsmorphogenese dar. Durch die Arbeiten vieler Laboratorien konnten BRANCHLESS (BNL, ein Homolog des Fibroblastenwachstumsfaktors) und BREATHLESS (BTL, sein Rezeptor) als essentielle Faktoren für viele Schritte in der Entwicklung des Tracheensystems identifiziert werden. Die Zielgene der BNL/BTL-Signalkaskade waren bisher zumeist unbekannt. Für die vorliegende Arbeit wurden Embryonen nach Verlust von BNL-Aktivität als auch solche nach gewebespezifischer trachealer Überexpression von BNL morphologisch und physiologisch charakterisiert. Ebenso wurden Transkriptionsprofile für die funktionell-genomische Analyse erstellt. Durch im Rahmen der vorliegenden Arbeit neu entwickelte bioinformatische Methoden konnten bisher unbekannte Gene identifiziert werden, die von BNL abhängig sind und deren tracheale Expression durch in situ-Hybridisierung validiert werden konnte. Ebenso war es möglich, durch die Projektion der Messdaten auf genetische Interaktionsgraphen putative Wechselwirkungen der BNL/BTL-Signalkaskade mit anderen Signalwegen aufzuzeigen, und eventuelle Aktivierungsmechanismen beispielsweise für unter Überexpression von BNL auftretende Immunfaktoren zu erkennen. Es konnte gezeigt werden, dass der Signalweg über Stickstoffmonoxid von BNL unabhängig in den Tracheen agiert, wohingegen sich eine Interaktion mit der JAK/STAT-Signalkaskade andeutet.The embryonic development of the Drosophila tracheal system resembles a model for genetic and molecular analyses of branching morphogenesis. Efforts of many laboratories have identified BRANCHLESS (BNL, a homologue of the fibroblast growth factor) and BREATHLESS (BTL, its receptor) as essential factors for many steps in the development of the tracheal system. However, the transcriptional targets of the BNL/BTL signalling cascade remain mostly unkown. This thesis presents the characterisation of BNL gain- and loss-of-function on the level of morphology and physiology. Transcriptional profiles of wildtype, bnl mutant and bnl over-expressing embryos were obtained. Computational methods (projection of expression data onto chromosomal position, functional Gene Ontology-annotation and to graphs of protein or genetic interaction) were developed in order to identify novel BNL-dependent genes, which are expressed in the tracheal system. It was possible to suggest putative cross-talk between the BNL/BTL signalling cascade and other pathways, which may explain the expression of genes of the innate immune system in response to ectopic dosages of BNL. Signalling of nitric oxide in the trachea appears to be independent from BNL, whereas there is evidence for an interaction with the JAK/STAT signalling cascade

Homotypic clusters of transcription factor binding sites: A model system for understanding the physical mechanics of gene expression.

The organization of binding sites in cis-regulatory elements (CREs) can influence gene expression through a combination of physical mechanisms, ranging from direct interactions between TF molecules to DNA looping and transient chromatin interactions. The study of simple and common building blocks in promoters and other CREs allows us to dissect how all of these mechanisms work together. Many adjacent TF binding sites for the same TF species form homotypic clusters, and these CRE architecture building blocks serve as a prime candidate for understanding interacting transcriptional mechanisms. Homotypic clusters are prevalent in both bacterial and eukaryotic genomes, and are present in both promoters as well as more distal enhancer/silencer elements. Here, we review previous theoretical and experimental studies that show how the complexity (number of binding sites) and spatial organization (distance between sites and overall distance from transcription start sites) of homotypic clusters influence gene expression. In particular, we describe how homotypic clusters modulate the temporal dynamics of TF binding, a mechanism that can affect gene expression, but which has not yet been sufficiently characterized. We propose further experiments on homotypic clusters that would be useful in developing mechanistic models of gene expression.This is the published version of the manuscript. It was first published by Elsevier in Computational and Structural Biotechnology Journal here: http://www.sciencedirect.com/science/article/pii/S2001037014000142

Reliable scaling of position weight matrices for binding strength comparisons between transcription factors.

BACKGROUND: Scoring DNA sequences against Position Weight Matrices (PWMs) is a widely adopted method to identify putative transcription factor binding sites. While common bioinformatics tools produce scores that can reflect the binding strength between a specific transcription factor and the DNA, these scores are not directly comparable between different transcription factors. Other methods, including p-value associated approaches (Touzet H, Varré J-S. Efficient and accurate p-value computation for position weight matrices. Algorithms Mol Biol. 2007;2(1510.1186):1748-7188), provide more rigorous ways to identify potential binding sites, but their results are difficult to interpret in terms of binding energy, which is essential for the modeling of transcription factor binding dynamics and enhancer activities. RESULTS: Here, we provide two different ways to find the scaling parameter λ that allows us to infer binding energy from a PWM score. The first approach uses a PWM and background genomic sequence as input to estimate λ for a specific transcription factor, which we applied to show that λ distributions for different transcription factor families correspond with their DNA binding properties. Our second method can reliably convert λ between different PWMs of the same transcription factor, which allows us to directly compare PWMs that were generated by different approaches. CONCLUSION: These two approaches provide computationally efficient ways to scale PWM scores and estimate the strength of transcription factor binding sites in quantitative studies of binding dynamics. Their results are consistent with each other and previous reports in most of cases

Determining Physical Mechanisms of Gene Expression Regulation from Single Cell Gene Expression Data.

Many genes are expressed in bursts, which can contribute to cell-to-cell heterogeneity. It is now possible to measure this heterogeneity with high throughput single cell gene expression assays (single cell qPCR and RNA-seq). These experimental approaches generate gene expression distributions which can be used to estimate the kinetic parameters of gene expression bursting, namely the rate that genes turn on, the rate that genes turn off, and the rate of transcription. We construct a complete pipeline for the analysis of single cell qPCR data that uses the mathematics behind bursty expression to develop more accurate and robust algorithms for analyzing the origin of heterogeneity in experimental samples, specifically an algorithm for clustering cells by their bursting behavior (Simulated Annealing for Bursty Expression Clustering, SABEC) and a statistical tool for comparing the kinetic parameters of bursty expression across populations of cells (Estimation of Parameter changes in Kinetics, EPiK). We applied these methods to hematopoiesis, including a new single cell dataset in which transcription factors (TFs) involved in the earliest branchpoint of blood differentiation were individually up- and down-regulated. We could identify two unique sub-populations within a seemingly homogenous group of hematopoietic stem cells. In addition, we could predict regulatory mechanisms controlling the expression levels of eighteen key hematopoietic transcription factors throughout differentiation. Detailed information about gene regulatory mechanisms can therefore be obtained simply from high throughput single cell gene expression data, which should be widely applicable given the rapid expansion of single cell genomics.This work was supported by: Royal Society Research Fellowship, Marshall Scholarship, Medical Research Council, the Leukemia and Lymphoma Society and core support grants from the Wellcome Trust to the Cambridge Institute for Medical Research and the Wellcome Trust and MRC Cambridge Stem Cell Institute.This is the final version of the article. It first appeared from the Public Library of Science via http://dx.doi.org/10.1371/journal.pcbi.100507

Biases in Drosophila melanogaster protein trap screens.

BACKGROUND: The ability to localise or follow endogenous proteins in real time in vivo is of tremendous utility for cell biology or systems biology studies. Protein trap screens utilise the random genomic insertion of a transposon-borne artificial reporter exon (e.g. encoding the green fluorescent protein, GFP) into an intron of an endogenous gene to generate a fluorescent fusion protein. Despite recent efforts aimed at achieving comprehensive coverage of the genes encoded in the Drosophila genome, the repertoire of genes that yield protein traps is still small. RESULTS: We analysed the collection of available protein trap lines in Drosophila melanogaster and identified potential biases that are likely to restrict genome coverage in protein trap screens. The protein trap screens investigated here primarily used P-element vectors and thus exhibit some of the same positional biases associated with this transposon that are evident from the comprehensive Drosophila Gene Disruption Project. We further found that protein trap target genes usually exhibit broad and persistent expression during embryonic development, which is likely to facilitate better detection. In addition, we investigated the likely influence of the GFP exon on host protein structure and found that protein trap insertions have a significant bias for exon-exon boundaries that encode disordered protein regions. 38.8% of GFP insertions land in disordered protein regions compared with only 23.4% in the case of non-trapping P-element insertions landing in coding sequence introns (p < 10(-4)). Interestingly, even in cases where protein domains are predicted, protein trap insertions frequently occur in regions encoding surface exposed areas that are likely to be functionally neutral. Considering the various biases observed, we predict that less than one third of intron-containing genes are likely to be amenable to trapping by the existing methods. CONCLUSION: Our analyses suggest that the utility of P-element vectors for protein trap screens has largely been exhausted, and that approximately 2,800 genes may still be amenable using piggyBac vectors. Thus protein trap strategies based on current approaches are unlikely to offer true genome-wide coverage. We suggest that either transposons with reduced insertion bias or recombineering-based targeting techniques will be required for comprehensive genome coverage in Drosophila.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

Genome-Wide Screens for In Vivo Tinman Binding Sites Identify Cardiac Enhancers with Diverse Functional Architectures

The NK homeodomain factor Tinman is a crucial regulator of early mesoderm patterning and, together with the GATA factor Pannier and the Dorsocross T-box factors, serves as one of the key cardiogenic factors during specification and differentiation of heart cells. Although the basic framework of regulatory interactions driving heart development has been worked out, only about a dozen genes involved in heart development have been designated as direct Tinman target genes to date, and detailed information about the functional architectures of their cardiac enhancers is lacking. We have used immunoprecipitation of chromatin (ChIP) from embryos at two different stages of early cardiogenesis to obtain a global overview of the sequences bound by Tinman in vivo and their linked genes. Our data from the analysis of ~50 sequences with high Tinman occupancy show that the majority of such sequences act as enhancers in various mesodermal tissues in which Tinman is active. All of the dorsal mesodermal and cardiac enhancers, but not some of the others, require tinman function. The cardiac enhancers feature diverse arrangements of binding motifs for Tinman, Pannier, and Dorsocross. By employing these cardiac and non-cardiac enhancers in machine learning approaches, we identify a novel motif, termed CEE, as a classifier for cardiac enhancers. In vivo assays for the requirement of the binding motifs of Tinman, Pannier, and Dorsocross, as well as the CEE motifs in a set of cardiac enhancers, show that the Tinman sites are essential in all but one of the tested enhancers; although on occasion they can be functionally redundant with Dorsocross sites. The enhancers differ widely with respect to their requirement for Pannier, Dorsocross, and CEE sites, which we ascribe to their different position in the regulatory circuitry, their distinct temporal and spatial activities during cardiogenesis, and functional redundancies among different factor binding sites