An atlas of combinatorial transcriptional regulation in mouse and man

Compensatory dendritic cell development mediated by BATF-IRF interactions

The AP-1 transcription factor Batf3 is required for homeostatic development of CD8α⁺ classical dendritic cells that prime CD8 T-cell responses against intracellular pathogens. Here, we identify an alternative, Batf3-independent pathway for their development operating during infection with intracellular pathogens mediated by the cytokines IL-12 and IFN-γ. This alternative pathway results from molecular compensation for Batf3 provided by the related AP-1 factors Batf, which also functions in T and B cells, and Batf2 induced by cytokines in response to infection. Reciprocally, physiologic compensation between Batf and Batf3 also occurs in T cells for expression of IL-10 and CTLA-4. Compensation among BATF factors is based on the shared capacity of their leucine zipper domains to interact with non-AP-1 factors such as Irf4 and Irf8 to mediate cooperative gene activation. Conceivably, manipulating this alternative pathway of dendritic cell development could be of value in augmenting immune responses to vaccines.

Application of a systems approach to study developmental gene regulation

All cells in a multicellular organism contain the same genome, yet different cell types express different sets of genes. Recent advances in high throughput genomic technologies have opened up new opportunities to understand the gene regulatory network in diverse cell types in a genome-wide manner. Here, I discuss recent advances in experimental and computational approaches for the study of gene regulation in embryonic development from a systems perspective. This review is written for computational biologists who have an interest in studying developmental gene regulation through integrative analysis of gene expression, chromatin landscape, and signaling pathways. I highlight the utility of publicly available data and tools, as well as some common analysis approaches.

Cistrome plasticity and mechanisms of cistrome reprogramming

Mammalian genomes contain thousands of cis-regulatory elements for each transcription factor (TF), but TFs only occupy a relatively small subset referred to as cistrome. Recent studies demonstrate that a TF cistrome might differ among different organisms, tissue types and individuals. In a cell, a TF cistrome might differ among different physiological states, pathological stages and between physiological and pathological conditions. It is, therefore, remarkable how highly plastic these binding profiles are, and how massively they can be reprogrammed in rapid response to intra/extracellular variations and during cell identity transitions and evolution. Biologically, cistrome reprogramming events tend to be followed by changes in transcriptional outputs, thus serving as transformative mechanisms to synchronically alter the biology of the cell. In this review, we discuss the molecular basis of cistrome plasticity and attempt to integrate the different mechanisms and biological conditions associated with cistrome reprogramming. Emerging data suggest that, when altered, these reprogramming events might be linked to tumor development and/or progression, which is a radical conceptual change in our mechanistic understanding of cancer and, potentially, other diseases.

The 1-1-1 fallacy

T-cell help to B cells is a fundamental aspect of adaptive immunity and the generation of B-cell memory (memory B cells and plasma cells). Follicular helper CD4(+) T (Tfh) cells are the specialized providers of B-cell help, and Tfh cells depend on Bcl6 for their differentiation. This review discusses Tfh cell functions, transcription factors, and induction signals, with particular focus on the richness of the underlying biology and assessing the simplicity or complexity of each of these processes.

Dissecting the gene network of dietary restriction to identify evolutionarily conserved pathways and new functional genes

Dietary restriction (DR), limiting nutrient intake from diet without causing malnutrition, delays the aging process and extends lifespan in multiple organisms. The conserved life-extending effect of DR suggests the involvement of fundamental mechanisms, although these remain a subject of debate. To help decipher the life-extending mechanisms of DR, we first compiled a list of genes that if genetically altered disrupt or prevent the life-extending effects of DR. We called these DR–essential genes and identified more than 100 in model organisms such as yeast, worms, flies, and mice. In order for other researchers to benefit from this first curated list of genes essential for DR, we established an online database called GenDR (http://genomics.senescence.info/diet/). To dissect the interactions of DR–essential genes and discover the underlying lifespan-extending mechanisms, we then used a variety of network and systems biology approaches to analyze the gene network of DR. We show that DR–essential genes are more conserved at the molecular level and have more molecular interactions than expected by chance. Furthermore, we employed a guilt-by-association method to predict novel DR–essential genes. In budding yeast, we predicted nine genes related to vacuolar functions; we show experimentally that mutations deleting eight of those genes prevent the life-extending effects of DR. Three of these mutants (OPT2, FRE6, and RCR2) had extended lifespan under ad libitum, indicating that the lack of further longevity under DR is not caused by a general compromise of fitness. These results demonstrate how network analyses of DR using GenDR can be used to make phenotypically relevant predictions. Moreover, gene-regulatory circuits reveal that the DR–induced transcriptional signature in yeast involves nutrient-sensing, stress responses and meiotic transcription factors. Finally, comparing the influence of gene expression changes during DR on the interactomes of multiple organisms led us to suggest that DR commonly suppresses translation, while stimulating an ancient reproduction-related process.

Dietary restriction has been shown to extend lifespan in diverse, evolutionarily distant species, yet its underlying mechanisms remain unknown. We first constructed a database of genes essential for the life-extending effects of dietary restriction in various model organisms and then studied their interactions using a variety of network and systems biology approaches. This enabled us to predict novel genes related to dietary restriction, which we validated experimentally in yeast. By comparing large-scale data compilations (interactomes and transcriptomes) from multiple organisms, we were able to condense this -omics information to the most conserved essential elements, eliminating species-specific adaptive responses. These results lead us to the rather surprising conclusion that lifespan extension by a restricted diet commonly may exploit an ancient rejuvenation process derived from gametogenesis.

Establishment of single-cell screening system for the rapid identification of transcriptional modulators involved in direct cell reprogramming

Combinatorial interactions of transcription modulators are critical to regulate cell-specific expression and to drive direct cell reprogramming (e.g. trans-differentiation). However, the identification of key transcription modulators from myriad of candidate genes is laborious and time consuming. To rapidly identify key regulatory factors involved in direct cell reprogramming, we established a multiplex single-cell screening system using a fibroblast-to-monocyte transition model. The system implements a single-cell 'shotgun-transduction' strategy followed by nested-single-cell-polymerase chain reaction (Nesc-PCR) gene expression analysis. To demonstrate this, we simultaneously transduced 18 monocyte-enriched transcription modulators in fibroblasts followed by selection of single cells expressing monocyte-specific CD14 and HLA-DR cell-surface markers from a heterogeneous population. Highly multiplex Nesc-PCR expression analysis revealed a variety of gene combinations with a significant enrichment of SPI1 (86/86) and a novel transcriptional modulator, HCLS1 (76/86), in the CD14(+)/HLA-DR(+) single cells. We could further demonstrate the synergistic role of HCLS1 in regulating monocyte-specific gene expressions and phagocytosis in dermal fibroblasts in the presence of SPI1. This study establishes a platform for a multiplex single-cell screening of combinatorial transcription modulators to drive any direct cell reprogramming.

Cell-type specificity of ChIP-predicted transcription factor binding sites

BACKGROUND

Context-dependent transcription factor (TF) binding is one reason for differences in gene expression patterns between different cellular states. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) identifies genome-wide TF binding sites for one particular context-the cells used in the experiment. But can such ChIP-seq data predict TF binding in other cellular contexts and is it possible to distinguish context-dependent from ubiquitous TF binding?

RESULTS

We compared ChIP-seq data on TF binding for multiple TFs in two different cell types and found that on average only a third of ChIP-seq peak regions are common to both cell types. Expectedly, common peaks occur more frequently in certain genomic contexts, such as CpG-rich promoters, whereas chromatin differences characterize cell-type specific TF binding. We also find, however, that genotype differences between the cell types can explain differences in binding. Moreover, ChIP-seq signal intensity and peak clustering are the strongest predictors of common peaks. Compared with strong peaks located in regions containing peaks for multiple transcription factors, weak and isolated peaks are less common between the cell types and are less associated with data that indicate regulatory activity.

CONCLUSIONS

Together, the results suggest that experimental noise is prevalent among weak peaks, whereas strong and clustered peaks represent high-confidence binding events that often occur in other cellular contexts. Nevertheless, 30-40% of the strongest and most clustered peaks show context-dependent regulation. We show that by combining signal intensity with additional data-ranging from context independent information such as binding site conservation and position weight matrix scores to context dependent chromatin structure-we can predict whether a ChIP-seq peak is likely to be present in other cellular contexts.

The co-regulation mechanism of transcription factors in the human gene regulatory network

The co-regulation of transcription factors (TFs) has been widely observed in various species. Why is such a co-regulation mechanism needed for transcriptional regulation? To answer this question, the following experiments and analyses were performed. First, examination of the human gene regulatory network (GRN) indicated that co-regulation was significantly enriched in the human GRN. Second, mathematical simulation of an artificial regulatory network showed that the co-regulation mechanism was related to the biphasic dose-response patterns of TFs. Third, the relationship between the co-regulation mechanism and the biphasic dose-response pattern was confirmed using microarray experiments examining different time points and different doses of the toxicant tetrachlorodibenzodioxin. Finally, two mathematical models were constructed to mimic highly co-regulated networks (HCNs) and little co-regulated networks (LCNs), and we found that HCNs were more robust to parameter perturbation than LCNs, whereas LCNs were faster in adaptation to environmental changes than HCNs.

Integration of biological networks and pathways with genetic association studies

Millions of genetic variants have been assessed for their effects on the trait of interest in genome-wide association studies (GWAS). The complex traits are affected by a set of inter-related genes. However, the typical GWAS only examine the association of a single genetic variant at a time. The individual effects of a complex trait are usually small, and the simple sum of these individual effects may not reflect the holistic effect of the genetic system. High-throughput methods enable genomic studies to produce a large amount of data to expand the knowledge base of the biological systems. Biological networks and pathways are built to represent the functional or physical connectivity among genes. Integrated with GWAS data, the network- and pathway-based methods complement the approach of single genetic variant analysis, and may improve the power to identify trait-associated genes. Taking advantage of the biological knowledge, these approaches are valuable to interpret the functional role of the genetic variants, and to further understand the molecular mechanism influencing the traits. The network- and pathway-based methods have demonstrated their utilities, and will be increasingly important to address a number of challenges facing the mainstream GWAS.

Plenary perspective: the complexity of constitutive and inducible gene expression in mononuclear phagocytes

Monocytes and macrophages differentiate from progenitor cells under the influence of colony-stimulating factors. Genome-scale data have enabled the identification of the set of genes that distinguishes macrophages from other cell types and the ways in which thousands of genes are regulated in response to pathogen challenge. Although there has been a focus on a small subset of lineage-enriched transcription factors, such as PU.1, more than one-half of the transcription factors in the genome can be expressed in macrophage lineage cells under some state of activation, and they interact in a complex network. The network architecture is conserved across species, but many of the target genes evolve rapidly and differ between mouse and human. The data and publication deluge related to macrophage biology require the development of new analytical tools and ways of presenting information in an accessible form.

Gene network analyses of first service conception in Brangus heifers: use of genome and trait associations, hypothalamic-transcriptome information, and transcription factors

Measures of heifer fertility are economically relevant traits for beef production systems and knowledge of candidate genes could be incorporated into future genomic selection strategies. Ten traits related to growth and fertility were measured in 890 Brangus heifers (3/8 Brahman × 5/8 Angus, from 67 sires). These traits were: BW and hip height adjusted to 205 and 365 d of age, postweaning ADG, yearling assessment of carcass traits (i.e., back fat thickness, intramuscular fat, and LM area), as well as heifer pregnancy and first service conception (FSC). These fertility traits were collected from controlled breeding seasons initiated with estrous synchronization and AI targeting heifers to calve by 24 mo of age. The BovineSNP50 BeadChip was used to ascertain 53,692 SNP genotypes for ∼802 heifers. Associations of genotypes and phenotypes were performed and SNP effects were estimated for each trait. Minimally associated SNP (P < 0.05) and their effects across the 10 traits formed the basis for an association weight matrix and its derived gene network related to FSC (57.3% success and heritability = 0.06 ± 0.05). These analyses yielded 1,555 important SNP, which inferred genes linked by 113,873 correlations within a network. Specifically, 1,386 SNP were nodes and the 5,132 strongest correlations (|r| ≥ 0.90) were edges. The network was filtered with genes queried from a transcriptome resource created from deep sequencing of RNA (i.e., RNA-Seq) from the hypothalamus of a prepubertal and a postpubertal Brangus heifer. The remaining hypothalamic-influenced network contained 978 genes connected by 2,560 edges or predicted gene interactions. This hypothalamic gene network was enriched with genes involved in axon guidance, which is a pathway known to influence pulsatile release of LHRH. There were 5 transcription factors with 21 or more connections: ZMAT3, STAT6, RFX4, PLAGL1, and NR6A1 for FSC. The SNP that identified these genes were intragenic and were on chromosomes 1, 5, 9, and 11. Chromosome 5 harbored both STAT6 and RFX4. The large number of interactions and genes observed with network analyses of multiple sources of genomic data (i.e., GWAS and RNA-Seq) support the concept of FSC being a polygenic trait.

The distinct and overlapping phenotypic spectra of FOXP1 and FOXP2 in cognitive disorders

Rare disruptions of FOXP2 have been strongly implicated in deficits in language development. Research over the past decade has suggested a role in the formation of underlying neural circuits required for speech. Until recently no evidence existed to suggest that the closely related FOXP1 gene played a role in neurodevelopmental processes. However, in the last few years, novel rare disruptions in FOXP1 have been reported in multiple cases of cognitive dysfunction, including intellectual disability and autism spectrum disorder, together with language impairment. As FOXP1 and FOXP2 form heterodimers for transcriptional regulation, one may assume that they co-operate in common neurodevelopmental pathways through the co-regulation of common targets. Here we compare the phenotypic consequences of FOXP1 and FOXP2 impairment, drawing on well-known studies from the past as well as recent exciting findings and consider what these tell us regarding the functions of these two genes in neural development.

Transcription factor co-localization patterns affect human cell type-specific gene expression

BACKGROUND

Cellular development requires the precise control of gene expression states. Transcription factors are involved in this regulatory process through their combinatorial binding with DNA. Information about transcription factor binding sites can help determine which combinations of factors work together to regulate a gene, but it is unclear how far the binding data from one cell type can inform about regulation in other cell types.

RESULTS

By integrating data on co-localized transcription factor binding sites in the K562 cell line with expression data across 38 distinct hematopoietic cell types, we developed regression models to describe the relationship between the expression of target genes and the transcription factors that co-localize nearby. With K562 binding sites identifying the predictors, the proportion of expression explained by the models is statistically significant only for monocytic cells (p-value< 0.001), which are closely related to K562. That is, cell type specific binding patterns are crucial for choosing the correct transcription factors for the model. Comparison of predictors obtained from binding sites in the GM12878 cell line with those from K562 shows that the amount of difference between binding patterns is directly related to the quality of the prediction. By identifying individual genes whose expression is predicted accurately by the binding sites, we are able to link transcription factors FOS, TAF1 and YY1 to a sparsely studied gene LRIG2. We also find that the activity of a transcription factor may be different depending on the cell type and the identity of other co-localized factors.

CONCLUSION

Our approach shows that gene expression can be explained by a modest number of co-localized transcription factors, however, information on cell-type specific binding is crucial for understanding combinatorial gene regulation.

The retinoblastoma gene undergoes rearrangements in BRCA1-deficient basal-like breast cancer

Breast tumors from BRCA1 germ line mutation carriers typically exhibit features of the basal-like molecular subtype. However, the specific genes recurrently mutated as a consequence of BRCA1 dysfunction have not been fully elucidated. In this study, we used gene expression profiling to molecularly subtype 577 breast tumors, including 73 breast tumors from BRCA1/2 mutation carriers. Focusing on the RB1 locus, we analyzed 33 BRCA1-mutated, 36 BRCA2-mutated, and 48 non-BRCA1/2-mutated breast tumors using a custom-designed high-density oligomicroarray covering the RB1 gene. We found a strong association between the basal-like subtype and BRCA1-mutated breast tumors and the luminal B subtype and BRCA2-mutated breast tumors. RB1 was identified as a major target for genomic disruption in tumors arising in BRCA1 mutation carriers and in sporadic tumors with BRCA1 promoter methylation but rarely in other breast cancers. Homozygous deletions, intragenic breaks, or microdeletions were found in 33% of BRCA1-mutant tumors, 36% of BRCA1 promoter-methylated basal-like tumors, 13% of non-BRCA1-deficient basal-like tumors, and 3% of BRCA2-mutated tumors. In conclusion, RB1 was frequently inactivated by gross gene disruption in BRCA1 hereditary breast cancer and BRCA1-methylated sporadic basal-like breast cancer but rarely in BRCA2 hereditary breast cancer and non-BRCA1-deficient sporadic breast cancers. Together, our findings show the existence of genetic heterogeneity within the basal-like breast cancer subtype that is based upon BRCA1 status.

Characterization of enhancer function from genome-wide analyses

There has been a recent surge in the use of genome-wide methodologies to identify and annotate the transcriptional regulatory elements in the human genome. Here we review some of these methodologies and the conceptual insights about transcription regulation that have been gained from the use of genome-wide studies. It has become clear that the binding of transcription factors is itself a highly regulated process, and binding does not always appear to have functional consequences. Numerous properties have now been associated with regulatory elements that may be useful in their identification. Several aspects of enhancer function have been shown to be more widespread than was previously appreciated, including the highly combinatorial nature of transcription factor binding, the postinitiation regulation of many target genes, and the binding of enhancers at early stages to maintain their competence during development. Going forward, the integration of multiple genome-wide data sets should become a standard approach to elucidate higher-order regulatory interactions.

Extensive translatome remodeling during ER stress response in mammalian cells

In this work we have described the translatome of two mammalian cell lines, NIH3T3 and Jurkat, by scoring the relative polysome association of ∼10,000 mRNA under normal and ER stress conditions. We have found that translation efficiencies of mRNA correlated poorly with transcript abundance, although a general tendency was observed so that the highest translation efficiencies were found in abundant mRNA. Despite the differences found between mouse (NIH3T3) and human (Jurkat) cells, both cell types share a common translatome composed by ∼800–900 mRNA that encode proteins involved in basic cellular functions. Upon stress, an extensive remodeling in translatomes was observed so that translation of ∼50% of mRNA was inhibited in both cell types, this effect being more dramatic for those mRNA that accounted for most of the cell translation. Interestingly, we found two subsets comprising 1000–1500 mRNA whose translation resisted or was induced by stress. Translation arrest resistant class includes many mRNA encoding aminoacyl tRNA synthetases, ATPases and enzymes involved in DNA replication and stress response such as BiP. This class of mRNA is characterized by high translation rates in both control and stress conditions. Translation inducible class includes mRNA whose translation was relieved after stress, showing a high enrichment in early response transcription factors of bZIP and zinc finger C2H2 classes. Unlike yeast, a general coordination between changes in translation and transcription upon stress (potentiation) was not observed in mammalian cells. Among the different features of mRNA analyzed, we found a relevant association of translation efficiency with the presence of upstream ATG in the 5′UTR and with the length of coding sequence of mRNA, and a looser association with other parameters such as the length and the G+C content of 5′UTR. A model for translatome remodeling during the acute phase of stress response in mammalian cells is proposed.

Reverse engineering and analysis of genome-wide gene regulatory networks from gene expression profiles using high-performance computing

Regulation of gene expression is a carefully regulated phenomenon in the cell. “Reverse-engineering” algorithms try to reconstruct the regulatory interactions among genes from genome-scale measurements of gene expression profiles (microarrays). Mammalian cells express tens of thousands of genes; hence, hundreds of gene expression profiles are necessary in order to have acceptable statistical evidence of interactions between genes. As the number of profiles to be analyzed increases, so do computational costs and memory requirements. In this work, we designed and developed a parallel computing algorithm to reverse-engineer genome-scale gene regulatory networks from thousands of gene expression profiles. The algorithm is based on computing pairwise Mutual Information between each gene-pair. We successfully tested it to reverse engineer the Mus Musculus (mouse) gene regulatory network in liver from gene expression profiles collected from a public repository. A parallel hierarchical clustering algorithm was implemented to discover “communities” within the gene network. Network communities are enriched for genes involved in the same biological functions. The inferred network was used to identify two mitochondrial proteins.

PIAS1 is a GATA4 SUMO ligase that regulates GATA4-dependent intestinal promoters independent of SUMO ligase activity and GATA4 sumoylation

GATA4 confers cell type-specific gene expression on genes expressed in cardiovascular, gastro-intestinal, endocrine and neuronal tissues by interacting with various ubiquitous and cell-type-restricted transcriptional regulators. By using yeast two-hybrid screening approach, we have identified PIAS1 as an intestine-expressed GATA4 interacting protein. The physical interaction between GATA4 and PIAS1 was confirmed in mammalian cells by coimmunoprecipitation and two-hybrid analysis. The interacting domains were mapped to the second zinc finger and the adjacent C-terminal basic region of GATA4 and the RING finger and the adjoining C-terminal 60 amino acids of PIAS1. PIAS1 and GATA4 synergistically activated IFABP and SI promoters but not LPH promoters suggesting that PIAS1 differentially activates GATA4 targeted promoters. In primary murine enterocytes PIAS1 was recruited to the GATA4-regulated IFABP promoter. PIAS1 promoted SUMO-1 modification of GATA4 on lysine 366. However, sumoylation was not required for the nuclear localization and stability of GATA4. Further, neither GATA4 sumoylation nor the SUMO ligase activity of PIAS1 was required for coactivation of IFABP promoter by GATA4 and PIAS1. Together, our results demonstrate that PIAS1 is a SUMO ligase for GATA4 that differentially regulates GATA4 transcriptional activity independent of SUMO ligase activity and GATA4 sumoylation.

Downstream and intermediate interactions of synovial sarcoma-associated fusion oncoproteins and their implication for targeted therapy

Synovial sarcoma (SS), an aggressive type of soft tissue tumor, occurs mostly in adolescents and young adults. The origin and molecular mechanism of the development of SS remain only partially known. Over 90% of SS cases are characterized by the t(X;18)(p11.2;q11.2) translocation, which results mainly in the formation of SS18-SSX1 or SS18-SSX2 fusion genes. In recent years, several reports describing direct and indirect interactions of SS18-SSX1/SSX2 oncoproteins have been published. These reports suggest that the fusion proteins particularly affect the cell growth, cell proliferation, TP53 pathway, and chromatin remodeling mechanisms, contributing to SS oncogenesis. Additional research efforts are required to fully explore the protein-protein interactions of SS18-SSX oncoproteins and the pathways that are regulated by these partnerships for the development of effective targeted therapy.

Dynamic HoxB4-regulatory network during embryonic stem cell differentiation to hematopoietic cells

Efficient in vitro generation of hematopoietic stem cells (HSCs) from embryonic stem cells (ESCs) holds great promise for cell-based therapies to treat hematologic diseases. To date, HoxB4 remains the most effective transcription factor (TF) the overexpression of which in ESCs confers long-term repopulating ability to ESC-derived HSCs. Despite its importance, the components and dynamics of the HoxB4 transcriptional regulatory network is poorly understood, hindering efforts to develop more efficient protocols for in vitro derivation of HSCs. In the present study, we performed global gene-expression profiling and ChIP coupled with deep sequencing at 4 stages of the HoxB4-mediated ESC differentiation toward HSCs. Joint analyses of ChIP/deep sequencing and gene-expression profiling unveiled several global features of the HoxB4 regulatory network. First, it is highly dynamic and gradually expands during the differentiation process. Second, HoxB4 functions as a master regulator of hematopoiesis by regulating multiple hematopoietic TFs and chromatin-modification enzymes. Third, HoxB4 acts in different combinations with 4 other hematopoietic TFs (Fli1, Meis1, Runx1, and Scl) to regulate distinct sets of pathways. Finally, the results of our study suggest that down-regulation of mitochondria and lysosomal genes by HoxB4 plays a role in the impaired lymphoid lineage development from ESC-derived HSCs.

Unveiling combinatorial regulation through the combination of ChIP information and in silico cis-regulatory module detection

Computationally retrieving biologically relevant cis-regulatory modules (CRMs) is not straightforward. Because of the large number of candidates and the imperfection of the screening methods, many spurious CRMs are detected that are as high scoring as the biologically true ones. Using ChIP-information allows not only to reduce the regions in which the binding sites of the assayed transcription factor (TF) should be located, but also allows restricting the valid CRMs to those that contain the assayed TF (here referred to as applying CRM detection in a query-based mode). In this study, we show that exploiting ChIP-information in a query-based way makes in silico CRM detection a much more feasible endeavor. To be able to handle the large datasets, the query-based setting and other specificities proper to CRM detection on ChIP-Seq based data, we developed a novel powerful CRM detection method 'CPModule'. By applying it on a well-studied ChIP-Seq data set involved in self-renewal of mouse embryonic stem cells, we demonstrate how our tool can recover combinatorial regulation of five known TFs that are key in the self-renewal of mouse embryonic stem cells. Additionally, we make a number of new predictions on combinatorial regulation of these five key TFs with other TFs documented in TRANSFAC.

Reconstruction of monocyte transcriptional regulatory network accompanies monocytic functions in human fibroblasts

Transcriptional regulatory networks (TRN) control the underlying mechanisms behind cellular functions and they are defined by a set of core transcription factors regulating cascades of peripheral genes. Here we report SPI1, CEBPA, MNDA and IRF8 as core transcription factors of monocyte TRN and demonstrate functional inductions of phagocytosis, inflammatory response and chemotaxis activities in human dermal fibroblasts. The Gene Ontology and KEGG pathway analyses also revealed notable representation of genes involved in immune response and endocytosis in fibroblasts. Moreover, monocyte TRN-inducers triggered multiple monocyte-specific genes based on the transcription factor motif response analysis and suggest that complex cellular TRNs are uniquely amenable to elicit cell-specific functions in unrelated cell types.

Deriving transcriptional programs and functional processes from gene expression databases

MOTIVATION

A system-wide approach to revealing the underlying molecular state of a cell is a long-standing biological challenge. Developed over the last decade, gene expression profiles possess the characteristics of such an assay. They have the capacity to reveal both underlying molecular events as well as broader phenotypes such as clinical outcomes. To interpret these profiles, many gene sets have been developed that characterize biological processes. However, the full potential of these gene sets has not yet been achieved. Since the advent of gene expression databases, many have posited that they can reveal properties of activities that are not evident from individual datasets, analogous to how the expression of a single gene generally cannot reveal the activation of a biological process.

RESULTS

To address this issue, we have developed a high-throughput method to mine gene expression databases for the regulation of gene sets. Given a set of genes, we scored it against each gene expression dataset by looking for enrichment of co-regulated genes relative to an empirical null distribution. After validating the method, we applied it to address two biological problems. First, we deciphered the E2F transcriptional network. We confirmed that true transcriptional targets exhibit a distinct regulatory profile across a database. Second, we leveraged the patterns of regulation across a database of gene sets to produce an automatically generated catalog of biological processes. These demonstrations revealed the power of a global analysis of the data contained within gene expression databases, and the potential for using them to address biological questions.

Linking genome to epigenome

Recent epigenomic studies have identified significant differences between developmental stages and cell types. While the importance of epigenetic regulation has been increasingly recognized, it remains unclear how the global epigenetic patterns are established and maintained. Here I review a number of recent studies with the emphasis on the role of the genomic sequence in shaping the epigenetic landscape. These studies strongly suggest that the sequence information is important not just for controlling target specificity but for orchestrating the diversity of epigenetic patterns among different cell types. The epigenome is maintained by the complex network of a large number of interactions. Integrative approaches are needed to gain insights into these networks.

Mutational robustness of gene regulatory networks

Mutational robustness of gene regulatory networks refers to their ability to generate constant biological output upon mutations that change network structure. Such networks contain regulatory interactions (transcription factor – target gene interactions) but often also protein-protein interactions between transcription factors. Using computational modeling, we study factors that influence robustness and we infer several network properties governing it. These include the type of mutation, i.e. whether a regulatory interaction or a protein-protein interaction is mutated, and in the case of mutation of a regulatory interaction, the sign of the interaction (activating vs. repressive). In addition, we analyze the effect of combinations of mutations and we compare networks containing monomeric with those containing dimeric transcription factors. Our results are consistent with available data on biological networks, for example based on evolutionary conservation of network features. As a novel and remarkable property, we predict that networks are more robust against mutations in monomer than in dimer transcription factors, a prediction for which analysis of conservation of DNA binding residues in monomeric vs. dimeric transcription factors provides indirect evidence.

Detection of interacting transcription factors in human tissues using predicted DNA binding affinity

Background

Tissue-specific gene expression is generally regulated by combinatorial interactions among transcription factors (TFs) which bind to the DNA. Despite this known fact, previous discoveries of the mechanism that controls gene expression usually consider only a single TF.

Results

We provide a prediction of interacting TFs in 22 human tissues based on their DNA-binding affinity in promoter regions. We analyze all possible pairs of 130 vertebrate TFs from the JASPAR database. First, all human promoter regions are scanned for single TF-DNA binding affinities with TRAP and for each TF a ranked list of all promoters ordered by the binding affinity is created. We then study the similarity of the ranked lists and detect candidates for TF-TF interaction by applying a partial independence test for multiway contingency tables. Our candidates are validated by both known protein-protein interactions (PPIs) and known gene regulation mechanisms in the selected tissue. We find that the known PPIs are significantly enriched in the groups of our predicted TF-TF interactions (2 and 7 times more common than expected by chance). In addition, the predicted interacting TFs for studied tissues (liver, muscle, hematopoietic stem cell) are supported in literature to be active regulators or to be expressed in the corresponding tissue.

Conclusions

The findings from this study indicate that tissue-specific gene expression is regulated by one or two central regulators and a large number of TFs interacting with these central hubs. Our results are in agreement with recent experimental studies.

Barx homeobox family in muscle development and regeneration

Homeobox transcription factors are key intrinsic regulators of myogenesis. In studies spanning several years, we have characterized the homeobox factor Barx2 as a novel marker for muscle progenitor cells and an important regulator of muscle growth and repair. In this review, we place the expression and function of Barx2 and its paralogue Barx1 in context with other muscle-expressed homeobox factors in both embryonic and adult myogenesis. We also describe the structure and regulation of Barx genes and possible gene/disease associations. The functional domains of Barx proteins, their molecular interactions, and cellular functions are presented with particular emphasis on control of genes and processes involved in myogenic differentiation. Finally, we describe the patterns of Barx gene expression in vivo and the phenotypes of various Barx gene perturbation models including null mice. We focus on the Barx2 null mouse model, which has demonstrated the critical roles of Barx2 in postnatal myogenesis including muscle maintenance during aging, and regeneration of acute and chronic muscle injury.

How do you find transcription factors? Computational approaches to compile and annotate repertoires of regulators for any genome

Transcription factors (TFs) play an important role in regulating gene expression. The availability of complete genome sequences and associated functional genomic data offer excellent opportunities to understand the transcriptional regulatory system of an entire organism. To do so, however, it is essential to compile a reliable dataset of regulatory components. Here, we review computational methods and publicly accessible resources that help identify TF-coding genes in prokaryotic and eukaryotic genomes. Since the regulatory functions of most TFs remain unknown, we also discuss approaches for combining diverse genomic datasets that will help elucidate their chromosomal organisation, expression, and evolutionary conservation. These analysis methods provide a solid foundation for further investigations of the transcriptional regulatory system.

Genetic control of gene expression in whole blood and lymphoblastoid cell lines is largely independent

The degree to which the level of genetic variation for gene expression is shared across multiple tissues has important implications for research investigating the role of expression on the etiology of complex human traits and diseases. In the last few years, several studies have been published reporting the extent of overlap in expression quantitative trait loci (eQTL) identified in multiple tissues or cell types. Although these studies provide important information on the regulatory control of genes across tissues, their limited power means that they can typically only explain a small proportion of genetic variation for gene expression. Here, using expression data from monozygotic twins (MZ), we investigate the genetic control of gene expression in lymphoblastoid cell lines (LCL) and whole blood (WB). We estimate the genetic correlation that represents the combined effects of all causal loci across the whole genome and is a measure of the level of common genetic control of gene expression between the two RNA sources. Our results show that, when averaged across the genome, mean levels of genetic correlation for gene expression in LCL and WB samples are close to zero. We support our results with evidence from gene expression in an independent sample of LCL, T-cells, and fibroblasts. In addition, we provide evidence that housekeeping genes, which maintain basic cellular functions, are more likely to have high genetic correlations between the RNA sources than non-housekeeping genes, implying a relationship between the transcript function and the degree to which a gene has tissue-specific genetic regulatory control.

Identification of co-occurring transcription factor binding sites from DNA sequence using clustered position weight matrices

Accurate prediction of transcription factor binding sites (TFBSs) is a prerequisite for identifying cis-regulatory modules that underlie transcriptional regulatory circuits encoded in the genome. Here, we present a computational framework for detecting TFBSs, when multiple position weight matrices (PWMs) for a transcription factor are available. Grouping multiple PWMs of a transcription factor (TF) based on their sequence similarity improves the specificity of TFBS prediction, which was evaluated using multiple genome-wide ChIP-Seq data sets from 26 TFs. The Z-scores of the area under a receiver operating characteristic curve (AUC) values of 368 TFs were calculated and used to statistically identify co-occurring regulatory motifs in the TF bound ChIP loci. Motifs that are co-occurring along with the empirical bindings of E2F, JUN or MYC have been evaluated, in the basal or stimulated condition. Results prove our method can be useful to systematically identify the co-occurring motifs of the TF for the given conditions.

Clustered ChIP-Seq-defined transcription factor binding sites and histone modifications map distinct classes of regulatory elements

Background

Transcription factor binding to DNA requires both an appropriate binding element and suitably open chromatin, which together help to define regulatory elements within the genome. Current methods of identifying regulatory elements, such as promoters or enhancers, typically rely on sequence conservation, existing gene annotations or specific marks, such as histone modifications and p300 binding methods, each of which has its own biases.

Results

Herein we show that an approach based on clustering of transcription factor peaks from high-throughput sequencing coupled with chromatin immunoprecipitation (Chip-Seq) can be used to evaluate markers for regulatory elements. We used 67 data sets for 54 unique transcription factors distributed over two cell lines to create regulatory element clusters. By integrating the clusters from our approach with histone modifications and data for open chromatin, we identified general methylation of lysine 4 on histone H3 (H3K4me) as the most specific marker for transcription factor clusters. Clusters mapping to annotated genes showed distinct patterns in cluster composition related to gene expression and histone modifications. Clusters mapping to intergenic regions fall into two groups either directly involved in transcription, including miRNAs and long noncoding RNAs, or facilitating transcription by long-range interactions. The latter clusters were specifically enriched with H3K4me1, but less with acetylation of lysine 27 on histone 3 or p300 binding.

Conclusion

By integrating genomewide data of transcription factor binding and chromatin structure and using our data-driven approach, we pinpointed the chromatin marks that best explain transcription factor association with different regulatory elements. Our results also indicate that a modest selection of transcription factors may be sufficient to map most regulatory elements in the human genome.

The NF-Y/p53 liaison: well beyond repression

NF-Y is a sequence-specific transcription factor - TF - targeting the common CCAAT promoter element. p53 is a master TF controlling the response to stress signals endangering genome integrity, often mutated in human cancers. The NF-Y/p53 - and p63, p73 - interaction results in transcriptional repression of a subset of genes within the vast NF-Y regulome under DNA-damage conditions. Recent data shows that NF-Y is also involved in pro-apoptotic activities, either directly, by mediating p53 transcriptional activation, or indirectly, by being targeted by a non coding RNA, PANDA. The picture is subverted in cells carrying Gain-of-function mutant p53, through interactions with TopBP1, a protein also involved in DNA repair and replication. In summary, the connection between p53 and NF-Y is crucial in determining cell survival or death.

Integrating human and murine anatomical gene expression data for improved comparisons

MOTIVATION

Information concerning the gene expression pattern in four dimensions (species, genes, anatomy and developmental stage) is crucial for unraveling the roles of genes through time. There are a variety of anatomical gene expression databases, but extracting information from them can be hampered by their diversity and heterogeneity.

RESULTS

aGEM 3.1 (anatomic Gene Expression Mapping) addresses the issues of diversity and heterogeneity of anatomical gene expression databases by integrating six mouse gene expression resources (EMAGE, GXD, GENSAT, Allen Brain Atlas data base, EUREXPRESS and BioGPS) and three human gene expression databases (HUDSEN, Human Protein Atlas and BioGPS). Furthermore, aGEM 3.1 provides new cross analysis tools to bridge these resources.

AVAILABILITY AND IMPLEMENTATION

aGEM 3.1 can be queried using gene and anatomical structure. Output information is presented in a friendly format, allowing the user to display expression maps and correlation matrices for a gene or structure during development. An in-depth study of a specific developmental stage is also possible using heatmaps that relate gene expression with anatomical components. http://agem.cnb.csic.es

CONTACT

natalia@cnb.csic.es

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Construction and analysis of an integrated regulatory network derived from high-throughput sequencing data

We present a network framework for analyzing multi-level regulation in higher eukaryotes based on systematic integration of various high-throughput datasets. The network, namely the integrated regulatory network, consists of three major types of regulation: TF→gene, TF→miRNA and miRNA→gene. We identified the target genes and target miRNAs for a set of TFs based on the ChIP-Seq binding profiles, the predicted targets of miRNAs using annotated 3′UTR sequences and conservation information. Making use of the system-wide RNA-Seq profiles, we classified transcription factors into positive and negative regulators and assigned a sign for each regulatory interaction. Other types of edges such as protein-protein interactions and potential intra-regulations between miRNAs based on the embedding of miRNAs in their host genes were further incorporated. We examined the topological structures of the network, including its hierarchical organization and motif enrichment. We found that transcription factors downstream of the hierarchy distinguish themselves by expressing more uniformly at various tissues, have more interacting partners, and are more likely to be essential. We found an over-representation of notable network motifs, including a FFL in which a miRNA cost-effectively shuts down a transcription factor and its target. We used data of C. elegans from the modENCODE project as a primary model to illustrate our framework, but further verified the results using other two data sets. As more and more genome-wide ChIP-Seq and RNA-Seq data becomes available in the near future, our methods of data integration have various potential applications.

The precise control of gene expression lies at the heart of many biological processes. In eukaryotes, the regulation is performed at multiple levels, mediated by different regulators such as transcription factors and miRNAs, each distinguished by different spatial and temporal characteristics. These regulators are further integrated to form a complex regulatory network responsible for the orchestration. The construction and analysis of such networks is essential for understanding the general design principles. Recent advances in high-throughput techniques like ChIP-Seq and RNA-Seq provide an opportunity by offering a huge amount of binding and expression data. We present a general framework to combine these types of data into an integrated network and perform various topological analyses, including its hierarchical organization and motif enrichment. We find that the integrated network possesses an intrinsic hierarchical organization and is enriched in several network motifs that include both transcription factors and miRNAs. We further demonstrate that the framework can be easily applied to other species like human and mouse. As more and more genome-wide ChIP-Seq and RNA-Seq data are going to be generated in the near future, our methods of data integration have various potential applications.

AnimalTFDB: a comprehensive animal transcription factor database

Transcription factors (TFs) are proteins that bind to specific DNA sequences, thereby playing crucial roles in gene-expression regulation through controlling the transcription of genetic information from DNA to RNA. Transcription cofactors and chromatin remodeling factors are also essential in the gene transcriptional regulation. Identifying and annotating all the TFs are primary and crucial steps for illustrating their functions and understanding the transcriptional regulation. In this study, based on manual literature reviews, we collected and curated 72 TF families for animals, which is currently the most complete list of TF families in animals. Then, we systematically characterized all the TFs in 50 animal species and constructed a comprehensive animal TF database, AnimalTFDB. To better serve the community, we provided detailed annotations for each TF, including basic information, gene structure, functional domain, 3D structure hit, Gene Ontology, pathway, protein–protein interaction, paralogs, orthologs, potential TF-binding sites and targets. In addition, we collected and annotated transcription cofactors and chromatin remodeling factors. AnimalTFDB has a user-friendly web interface with multiple browse and search functions, as well as data downloading. It is freely available at http://www.bioguo.org/AnimalTFDB/.

MiR-34a expression has an effect for lower risk of metastasis and associates with expression patterns predicting clinical outcome in breast cancer

MiR-34a acts as a candidate tumour suppressor gene, and its expression is reduced in several cancer types. We aimed to study miR-34a expression in breast cancer and its correlation with tumour characteristics and clinical outcome, and regulatory links with other genes. We analysed miR-34a expression in 1,172 breast tumours on TMAs. 25% of the tumours showed high, 43% medium and 32% low expression of miR-34a. High miR-34a expression associated with poor prognostic factors for breast cancer: positive nodal status (p = 0.006), high tumour grade (p<0.0001), ER-negativity (p = 0.0002), HER2-positivity (p = 0.0002), high proliferation rate (p<0.0001), p53-positivity (p<0.0001), high cyclin E (p<0.0001) and γH2AX (p<0.0001). However, multivariate analysis adjusting for conventional prognostic factors indicated that high miR-34a expression in fact associated with a lower risk of recurrence or death from breast cancer (HR = 0.63, 95% CI = 0.41–0.96, p = 0.031). Gene expression analysis by differential miR-34a expression revealed an expression signature with an effect on both the 5-year and 10-year survival of the patients (p<0.001). Functional genomic analysis highlighted a novel regulatory role of the transcription factor MAZ, apart from the known control by p53, on the expression of miR-34a and a number of miR-34a targets. Our findings suggest that while miR-34a expression activation is a marker of aggressive breast tumour phenotype it exerts an independent effect for a lower risk of recurrence or death from breast cancer. We also present an expression signature of 190 genes associated with miR-34a expression. Our analysis for regulatory loops suggest that MAZ and p53 transcription factors co-operate in modulating miR-34a, as well as miR-34a targets involved in several cellular pathways. Taken together, these results suggest that the network of genes co-regulated with and targeted by miR-34a form a group of down-stream effectors that maybe of use in predicting clinical outcome, and that highlight novel regulatory mechanisms in breast cancer.

NF-Y and the transcriptional activation of CCAAT promoters

The CCAAT box promoter element and NF-Y, the transcription factor (TF) that binds to it, were among the first cis-elements and trans-acting factors identified; their interplay is required for transcriptional activation of a sizeable number of eukaryotic genes. NF-Y consists of three evolutionarily conserved subunits: a dimer of NF-YB and NF-YC which closely resembles a histone, and the "innovative" NF-YA. In this review, we will provide an update on the functional and biological features that make NF-Y a fundamental link between chromatin and transcription. The last 25 years have witnessed a spectacular increase in our knowledge of how genes are regulated: from the identification of cis-acting sequences in promoters and enhancers, and the biochemical characterization of the corresponding TFs, to the merging of chromatin studies with the investigation of enzymatic machines that regulate epigenetic states. Originally identified and studied in yeast and mammals, NF-Y - also termed CBF and CP1 - is composed of three subunits, NF-YA, NF-YB and NF-YC. The complex recognizes the CCAAT pentanucleotide and specific flanking nucleotides with high specificity (Dorn et al., 1997; Hatamochi et al., 1988; Hooft van Huijsduijnen et al, 1987; Kim & Sheffery, 1990). A compelling set of bioinformatics studies clarified that the NF-Y preferred binding site is one of the most frequent promoter elements (Suzuki et al., 2001, 2004; Elkon et al., 2003; Mariño-Ramírez et al., 2004; FitzGerald et al., 2004; Linhart et al., 2005; Zhu et al., 2005; Lee et al., 2007; Abnizova et al., 2007; Grskovic et al., 2007; Halperin et al., 2009; Häkkinen et al., 2011). The same consensus, as determined by mutagenesis and SELEX studies (Bi et al., 1997), was also retrieved in ChIP-on-chip analysis (Testa et al., 2005; Ceribelli et al., 2006; Ceribelli et al., 2008; Reed et al., 2008). Additional structural features of the CCAAT box - position, orientation, presence of multiple Transcriptional Start Sites - were previously reviewed (Dolfini et al., 2009) and will not be considered in detail here.

Systems analysis of ATF3 in stress response and cancer reveals opposing effects on pro-apoptotic genes in p53 pathway

Stress-inducible transcription factors play a pivotal role in cellular adaptation to environment to maintain homeostasis and integrity of the genome. Activating transcription factor 3 (ATF3) is induced by a variety of stress and inflammatory conditions and is over-expressed in many kinds of cancer cells. However, molecular mechanisms underlying pleiotropic functions of ATF3 have remained elusive. Here we employed systems analysis to identify genome-wide targets of ATF3 that is either induced by an alkylating agent methyl methanesulfonate (MMS) or over-expressed in a prostate tumour cell line LNCaP. We show that stress-induced and cancer-associated ATF3 is recruited to 5,984 and 1,423 targets, respectively, in the human genome, 89% of which are common. Notably, ATF3 targets are highly enriched for not only ATF/CRE motifs but also binding sites of several other stress-inducible transcription factors indicating an extensive network of stress response factors in transcriptional regulation of target genes. Further analysis of effects of ATF3 knockdown on these targets revealed that stress-induced ATF3 regulates genes in metabolic pathways, cell cycle, apoptosis, cell adhesion, and signalling including insulin, p53, Wnt, and VEGF pathways. Cancer-associated ATF3 is involved in regulation of distinct sets of genes in processes such as calcium signalling, Wnt, p53 and diabetes pathways. Notably, stress-induced ATF3 binds to 40% of p53 targets and activates pro-apoptotic genes such as TNFRSF10B/DR5 and BBC3/PUMA. Cancer-associated ATF3, by contrast, represses these pro-apoptotic genes in addition to CDKN1A/p21. Taken together, our data reveal an extensive network of stress-inducible transcription factors and demonstrate that ATF3 has opposing, cell context-dependent effects on p53 target genes in DNA damage response and cancer development.

Transcriptional networks in epithelial-mesenchymal transition

Backround

Epithelial-mesenchymal transition (EMT) changes polarized epithelial cells into migratory phenotypes associated with loss of cell-cell adhesion molecules and cytoskeletal rearrangements. This form of plasticity is seen in mesodermal development, fibroblast formation, and cancer metastasis.

Methods and Findings

Here we identify prominent transcriptional networks active during three time points of this transitional process, as epithelial cells become fibroblasts. DNA microarray in cultured epithelia undergoing EMT, validated in vivo, were used to detect various patterns of gene expression. In particular, the promoter sequences of differentially expressed genes and their transcription factors were analyzed to identify potential binding sites and partners. The four most frequent cis-regulatory elements (CREs) in up-regulated genes were SRY, FTS-1, Evi-1, and GC-Box, and RNA inhibition of the four transcription factors, Atf2, Klf10, Sox11, and SP1, most frequently binding these CREs, establish their importance in the initiation and propagation of EMT. Oligonucleotides that block the most frequent CREs restrain EMT at early and intermediate stages through apoptosis of the cells.

Conclusions

Our results identify new transcriptional interactions with high frequency CREs that modulate the stability of cellular plasticity, and may serve as targets for modulating these transitional states in fibroblasts.

Protein networks as logic functions in development and cancer

Many biological and clinical outcomes are based not on single proteins, but on modules of proteins embedded in protein networks. A fundamental question is how the proteins within each module contribute to the overall module activity. Here, we study the modules underlying three representative biological programs related to tissue development, breast cancer metastasis, or progression of brain cancer, respectively. For each case we apply a new method, called Network-Guided Forests, to identify predictive modules together with logic functions which tie the activity of each module to the activity of its component genes. The resulting modules implement a diverse repertoire of decision logic which cannot be captured using the simple approximations suggested in previous work such as gene summation or subtraction. We show that in cancer, certain combinations of oncogenes and tumor suppressors exert competing forces on the system, suggesting that medical genetics should move beyond cataloguing individual cancer genes to cataloguing their combinatorial logic.

Biological outcomes are often determined by modules of proteins working in combination. In classic biological studies, these modules have been shown to encode a diverse repertoire of logic functions which provide the means to express complex regulatory programs using a limited number of proteins. Here, we integrate gene expression profiles and physical protein interaction maps to provide a systematic and global view of combinatorial network modules underlying representative developmental and cancer programs. We develop a new method that associates decision trees with concise network regions to identify network decision modules predictive of biological or clinical outcome. The resulting network signatures prove robust across different sample cohorts and capture causal mechanisms of development or disease. Furthermore, we find that the most predictive network decision functions rely on both coherent and opposing gene activities. Notably, in cancer progression the predictive gene associations often map to physical interactions between known oncogenes and tumor suppressors, where the combined activity of these genes determines disease outcome.

Transposon-mediated rewiring of gene regulatory networks contributed to the evolution of pregnancy in mammals

A fundamental challenge in biology is explaining the origin of novel phenotypic characters such as new cell types; the molecular mechanisms that give rise to novelties are unclear. We explored the gene regulatory landscape of mammalian endometrial cells using comparative RNA-Seq and found that 1,532 genes were recruited into endometrial expression in placental mammals, indicating that the evolution of pregnancy was associated with a large-scale rewiring of the gene regulatory network. About 13% of recruited genes are within 200 kb of a Eutherian-specific transposable element (MER20). These transposons have the epigenetic signatures of enhancers, insulators and repressors, directly bind transcription factors essential for pregnancy and coordinately regulate gene expression in response to progesterone and cAMP. We conclude that the transposable element, MER20, contributed to the origin of a novel gene regulatory network dedicated to pregnancy in placental mammals, particularly by recruiting the cAMP signaling pathway into endometrial stromal cells.

The long noncoding RNA Six3OS acts in trans to regulate retinal development by modulating Six3 activity

BACKGROUND

Thousands of different long non-coding RNAs are expressed during embryonic development, but the function of these molecules remains largely unexplored.

RESULTS

Here we characterize the expression and function of Six3OS, a long non-coding RNA that is transcribed from the distal promoter region of the gene encoding the homeodomain transcription factor Six3. Overexpression and knockdown analysis of Six3OS reveals that it plays an essential role in regulating retinal cell specification. We further observe that Six3OS regulates Six3 activity in developing retina, but does not do so by modulating Six3 expression. Finally, we show that Six3OS binds directly to Ezh2 and Eya family members, indicating that Six3OS can act as a molecular scaffold to recruit histone modification enzymes to Six3 target genes.

CONCLUSIONS

Our findings demonstrate a novel mechanism by which promoter-associated long non-coding RNAs can modulate the activity of their associated protein coding genes, and highlight the importance of this diverse class of molecules in the control of neural development.

Key role of ATF3 in p53-dependent DR5 induction upon DNA damage of human colon cancer cells

Stress response gene ATF3 is one of the p53 target genes and has a tumor suppressor role in cancer. However, the biological role of p53-ATF3 pathway is not well understood. Death receptor 5 (DR5) is a death domain-containing transmembrane receptor that triggers cell death upon binding to its ligand TRAIL (tumor necrosis factor-related apoptosis-inducing ligand), and a combination of TRAIL and agents that increase the expression of DR5 is expected as a novel anticancer therapy. In this report, we demonstrate that ATF3 is required for efficient DR5 induction upon DNA damage by camptothecin (CPT) in colorectal cancer cells. In the absence of ATF3, induction of DR5 messenger RNA and protein is remarkably abrogated, and this is associated with reduced cell death by TRAIL and CPT. By contrast, exogenous expression of ATF3 causes more rapid and elevated expression of DR5, resulting in enhanced sensitivity to apoptotic cell death by TRAIL/CPT. Reporter assay and DNA affinity precipitation assay demonstrate that at least three ATF/CRE motifs at the proximal promoter of the human DR5 gene are involved in the activation of DNA damage-induced DR5 gene transcription. Furthermore, ATF3 is shown to interact with p53 to form a complex on the DR5 gene by Re-chromatin immunoprecipitation assay. Taken together, our results provide a novel insight into the role of ATF3 as an essential co-transcription factor for p53 upon DNA damage, and this may represent a useful biomarker for TRAIL-based anticancer therapy.

Conversion of Sox17 into a pluripotency reprogramming factor by reengineering its association with Oct4 on DNA

Very few proteins are capable to induce pluripotent stem (iPS) cells and their biochemical uniqueness remains unexplained. For example, Sox2 cooperates with other transcription factors to generate iPS cells, but Sox17, despite binding to similar DNA sequences, cannot. Here, we show that Sox2 and Sox17 exhibit inverse heterodimerization preferences with Oct4 on the canonical versus a newly identified compressed sox/oct motif. We can swap the cooperativity profiles of Sox2 and Sox17 by exchanging single amino acids at the Oct4 interaction interface resulting in Sox2KE and Sox17EK proteins. The reengineered Sox17EK now promotes reprogramming of somatic cells to iPS, whereas Sox2KE has lost this potential. Consistently, when Sox2KE is overexpressed in embryonic stem cells it forces endoderm differentiation similar to wild-type Sox17. Together, we demonstrate that strategic point mutations that facilitate Sox/Oct4 dimer formation on variant DNA motifs lead to a dramatic swap of the bioactivities of Sox2 and Sox17.

A guide to stem cell identification: progress and challenges in system-wide predictive testing with complex biomarkers

We have developed a first generation tool for the unbiased identification and characterization of human pluripotent stem cells, termed PluriTest. This assay utilizes all the information contained on a microarray and abandons the conventional stem cell marker concept. Stem cells are defined by the ability to replenish themselves and to differentiate into more mature cell types. As differentiation potential is a property that cannot be directly proven in the stem cell state, biologists have to rely on correlative measurements in stem cells associated with differentiation potential. Unfortunately, most, if not all, of those markers are only valid within narrow limits of specific experimental systems. Microarray technologies and recently next-generation sequencing have revolutionized how cellular phenotypes can be characterized on a systems-wide level. Here we discuss the challenges PluriTest and similar global assays need to address to fulfill their enormous potential for industrial, diagnostic and therapeutic applications.