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Background: Conventional wisdom holds that, owing to the dominance of features such as chromatin level control, the expression of a gene cannot be readily predicted from knowledge of promoter architecture. This is reflected, for example, in a weak or absent correlation between promoter divergence and expression divergence between paralogs. However, an inability to predict may reflect an inability to accurately measure or employment of the wrong parameters. Here we address this issue through integration of two exceptional resources: ENCODE data on transcription factor binding and the FANTOM5 high-resolution expression atlas., Results: Consistent with the notion that in eukaryotes most transcription factors are activating, the number of transcription factors binding a promoter is a strong predictor of expression breadth. In addition, evolutionarily young duplicates have fewer transcription factor binders and narrower expression. Nonetheless, we find several binders and cooperative sets that are disproportionately associated with broad expression, indicating that models more complex than simple correlations should hold more predictive power. Indeed, a machine learning approach improves fit to the data compared with a simple correlation. Machine learning could at best moderately predict tissue of expression of tissue specific genes., Conclusions: We find robust evidence that some expression parameters and paralog expression divergence are strongly predictable with knowledge of transcription factor binding repertoire. While some cooperative complexes can be identified, consistent with the notion that most eukaryotic transcription factors are activating, a simple predictor, the number of binding transcription factors found on a promoter, is a robust predictor of expression breadth.

White adipose tissue (WAT) morphology characterized by hypertrophy (i.e., fewer but larger adipocytes) associates with increased adipose inflammation, lipolysis, insulin resistance, and risk of diabetes. However, the causal relationships and the mechanisms controlling WAT morphology are unclear. Herein, we identified EBF1 as an adipocyte-expressed transcription factor with decreased expression/activity in WAT hypertrophy. In human adipocytes, the regulatory targets of EBF1 were enriched for genes controlling lipolysis and adipocyte morphology/differentiation, and in both humans and murine models, reduced EBF1 levels associated with increased lipolysis and adipose hypertrophy. Although EBF1 did not affect adipose inflammation, TNFα reduced EBF1 gene expression. High-fat diet intervention in Ebf1(+/-) mice resulted in more pronounced WAT hypertrophy and attenuated insulin sensitivity compared with wild-type littermate controls. We conclude that EBF1 is an important regulator of adipose morphology and fat cell lipolysis and may constitute a link between WAT inflammation, altered lipid metabolism, adipose hypertrophy, and insulin resistance., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Hematopoietic differentiation is governed by a complex regulatory program controlling the generation of different lineages of blood cells from multipotent hematopoietic stem cells. The transcriptional program that dictates hematopoietic cell fate and differentiation requires an epigenetic memory function provided by a network of epigenetic factors regulating DNA methylation, posttranslational histone modifications, and chromatin structure. Aberrant interactions between epigenetic factors and transcription factors cause perturbations in the blood cell differentiation program that result in various types of hematopoietic disorders. To elucidate the contributions of different epigenetic factors in human hematopoiesis, high-throughput cap analysis of gene expression was used to build transcription profiles of 199 epigenetic factors in a wide range of blood cells. Our epigenetic transcriptome analysis revealed cell type- (eg, HELLS and ACTL6A), lineage- (eg, MLL), and/or leukemia- (eg, CHD2, CBX8, and EPC1) specific expression of several epigenetic factors. In addition, we show that several epigenetic factors use alternative transcription start sites in different cell types. This analysis could serve as a resource for the scientific community for further characterization of the role of these epigenetic factors in blood development.

Enhancers control the correct temporal and cell-type-specific activation of gene expression in multicellular eukaryotes. Knowing their properties, regulatory activity and targets is crucial to understand the regulation of differentiation and homeostasis. Here we use the FANTOM5 panel of samples, covering the majority of human tissues and cell types, to produce an atlas of active, in vivo-transcribed enhancers. We show that enhancers share properties with CpG-poor messenger RNA promoters but produce bidirectional, exosome-sensitive, relatively short unspliced RNAs, the generation of which is strongly related to enhancer activity. The atlas is used to compare regulatory programs between different cells at unprecedented depth, to identify disease-associated regulatory single nucleotide polymorphisms, and to classify cell-type-specific and ubiquitous enhancers. We further explore the utility of enhancer redundancy, which explains gene expression strength rather than expression patterns. The online FANTOM5 enhancer atlas represents a unique resource for studies on cell-type-specific enhancers and gene regulation.

Regulated transcription controls the diversity, developmental pathways and spatial organization of the hundreds of cell types that make up a mammal. Using single-molecule cDNA sequencing, we mapped transcription start sites (TSSs) and their usage in human and mouse primary cells, cell lines and tissues to produce a comprehensive overview of mammalian gene expression across the human body. We find that few genes are truly 'housekeeping', whereas many mammalian promoters are composite entities composed of several closely separated TSSs, with independent cell-type-specific expression profiles. TSSs specific to different cell types evolve at different rates, whereas promoters of broadly expressed genes are the most conserved. Promoter-based expression analysis reveals key transcription factors defining cell states and links them to binding-site motifs. The functions of identified novel transcripts can be predicted by coexpression and sample ontology enrichment analyses. The functional annotation of the mammalian genome 5 (FANTOM5) project provides comprehensive expression profiles and functional annotation of mammalian cell-type-specific transcriptomes with wide applications in biomedical research.

By coupling laser capture microdissection to nanoCAGE technology and next-generation sequencing we have identified the genome-wide collection of active promoters in the mouse Main Olfactory Epithelium (MOE). Transcription start sites (TSSs) for the large majority of Olfactory Receptors (ORs) have been previously mapped increasing our understanding of their promoter architecture. Here we show that in our nanoCAGE libraries of the mouse MOE we detect a large number of tags mapped in loci hosting Type-1 and Type-2 Vomeronasal Receptors genes (V1Rs and V2Rs). These loci also show a massive expression of Long Interspersed Nuclear Elements (LINEs). We have validated the expression of selected receptors detected by nanoCAGE with in situ hybridization, RT-PCR and qRT-PCR. This work extends the repertory of receptors capable of sensing chemical signals in the MOE, suggesting intriguing interplays between MOE and VNO for pheromone processing and positioning transcribed LINEs as candidate regulatory RNAs for VRs expression.

Recent research hints at an underappreciated complexity in pre-miRNA processing and regulation. Global profiling of pre-miRNA and its potential to increase understanding of the pre-miRNA landscape is impeded by overlap with highly expressed classes of other non coding (nc) RNA. Here, we present a data set excluding these RNA before sequencing through locked nucleic acids (LNA), greatly increasing pre-miRNA sequence counts with no discernable effect on pre-miRNA or mature miRNA sequencing. Analysis of profiles generated in total, nuclear and cytoplasmic cell fractions reveals that pre-miRNAs are subject to a wide range of regulatory processes involving loci-specific 3'- and 5'-end variation entailing complex cleavage patterns with co-occurring polyuridylation. Additionally, examination of nuclear-enriched flanking sequences of pre-miRNA, particularly those derived from polycistronic miRNA transcripts, provides insight into miRNA and miRNA-offset (moRNA) production, specifically identifying novel classes of RNA potentially functioning as moRNA precursors. Our findings point to particularly intricate regulation of the let-7 family in many ways reminiscent of DICER1-independent, pre-mir-451-like processing, introduce novel and unify known forms of pre-miRNA regulation and processing, and shed new light on overlooked products of miRNA processing pathways.

Perturbation and time-course data sets, in combination with computational approaches, can be used to infer transcriptional regulatory networks which ultimately govern the developmental pathways and responses of cells. Here, we individually knocked down the four transcription factors PU.1, IRF8, MYB and SP1 in the human monocyte leukemia THP-1 cell line and profiled the genome-wide transcriptional response of individual transcription starting sites using deep sequencing based Cap Analysis of Gene Expression. From the proximal promoter regions of the responding transcription starting sites, we derived de novo binding-site motifs, characterized their biological function and constructed a network. We found a previously described composite motif for PU.1 and IRF8 that explains the overlapping set of transcriptional responses upon knockdown of either factor.

Combinatorial interactions among transcription factors are critical to directing tissue-specific gene expression. To build a global atlas of these combinations, we have screened for physical interactions among the majority of human and mouse DNA-binding transcription factors (TFs). The complete networks contain 762 human and 877 mouse interactions. Analysis of the networks reveals that highly connected TFs are broadly expressed across tissues, and that roughly half of the measured interactions are conserved between mouse and human. The data highlight the importance of TF combinations for determining cell fate, and they lead to the identification of a SMAD3/FLI1 complex expressed during development of immunity. The availability of large TF combinatorial networks in both human and mouse will provide many opportunities to study gene regulation, tissue differentiation, and mammalian evolution., ((c) 2010 Elsevier Inc. All rights reserved.)

Protein families typically embody a range of related functions and may thus be decomposed into subfamilies with, for example, distinct substrate specificities. Detection of functionally divergent subfamilies is possible by methods for recognizing branches of adaptive evolution in a gene tree. As the number of genome sequences is growing rapidly, it is highly desirable to automatically detect subfamily function divergence. To this end, we here introduce a method for large-scale prediction of function divergence within protein families. It is called the alpha shift measure (ASM) as it is based on detecting a shift in the shape parameter (alpha [alpha]) of the substitution rate gamma distribution. Four different methods for estimating alpha were investigated. We benchmarked the accuracy of ASM using function annotation from Enzyme Commission numbers within Pfam protein families divided into subfamilies by the automatic tree-based method BETE. In a test using 563 subfamily pairs in 162 families, ASM outperformed functional site-based methods using rate or conservation shifting (rate shift measure [RSM] and conservation shift measure [CSM]). The best results were obtained using the "GZ-Gamma" method for estimating alpha. By combining ASM with RSM and CSM using linear discriminant analysis, the prediction accuracy was further improved.

The systematic accumulation of gene expression data, although revolutionary, is insufficient in itself for an understanding of system-level physiology. In the post-genomic era, the next cognitive step is linking genes to biological processes and assembling a mosaic of data into global models of biosystem function. A dynamic network of informational flows in Arabidopsis plants perturbed by sulphur depletion is presented here. With the use of an original protocol, the first biosystem response network was reconstructed from a time series of transcript and metabolite profiles, which, on the one hand, integrates complex metabolic and transcript data and, on the other hand, possesses a causal relationship. Using the informational fluxes within this reconstruction, it was possible to link system perturbation to response endpoints. Robustness and stress tolerance, as consequences of scale-free network topology, and hubs, as potential controllers of homeostasis maintenance, were revealed. Communication paths of propagating system excitement directed to physiological endpoints, such as anthocyanin accumulation and enforced root formation were dissected from the network. An auxin regulatory circuit involved in the control of a hypo-sulphur stress response was uncovered.