Evidence for transcript networks composed of chimeric RNAs in human cells.

The classic organization of a gene structure has followed the Jacob and Monod bacterial gene model proposed more than 50 years ago. Since then, empirical determinations of the complexity of the transcriptomes found in yeast to human has blurred the definition and physical boundaries of genes. Using multiple analysis approaches we have characterized individual gene boundaries mapping on human chromosomes 21 and 22. Analyses of the locations of the 5' and 3' transcriptional termini of 492 protein coding genes revealed that for 85% of these genes the boundaries extend beyond the current annotated termini, most often connecting with exons of transcripts from other well annotated genes. The biological and evolutionary importance of these chimeric transcripts is underscored by (1) the non-random interconnections of genes involved, (2) the greater phylogenetic depth of the genes involved in many chimeric interactions, (3) the coordination of the expression of connected genes and (4) the close in vivo and three dimensional proximity of the genomic regions being transcribed and contributing to parts of the chimeric RNAs. The non-random nature of the connection of the genes involved suggest that chimeric transcripts should not be studied in isolation, but together, as an RNA network.

Hard-wired heterogeneity in blood stem cells revealed using a dynamic regulatory network model.

MOTIVATION: Combinatorial interactions of transcription factors with cis-regulatory elements control the dynamic progression through successive cellular states and thus underpin all metazoan development. The construction of network models of cis-regulatory elements, therefore, has the potential to generate fundamental insights into cellular fate and differentiation. Haematopoiesis has long served as a model system to study mammalian differentiation, yet modelling based on experimentally informed cis-regulatory interactions has so far been restricted to pairs of interacting factors. Here, we have generated a Boolean network model based on detailed cis-regulatory functional data connecting 11 haematopoietic stem/progenitor cell (HSPC) regulator genes. RESULTS: Despite its apparent simplicity, the model exhibits surprisingly complex behaviour that we charted using strongly connected components and shortest-path analysis in its Boolean state space. This analysis of our model predicts that HSPCs display heterogeneous expression patterns and possess many intermediate states that can act as 'stepping stones' for the HSPC to achieve a final differentiated state. Importantly, an external perturbation or 'trigger' is required to exit the stem cell state, with distinct triggers characterizing maturation into the various different lineages. By focusing on intermediate states occurring during erythrocyte differentiation, from our model we predicted a novel negative regulation of Fli1 by Gata1, which we confirmed experimentally thus validating our model. In conclusion, we demonstrate that an advanced mammalian regulatory network model based on experimentally validated cis-regulatory interactions has allowed us to make novel, experimentally testable hypotheses about transcriptional mechanisms that control differentiation of mammalian stem cells. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Regulatory elements involved in the post-transcriptional control of stage-specific gene expression in Trypanosoma cruzi: a review

Trypanosoma cruzi, a protozoan parasite that causes Chagas disease, exhibits unique mechanisms for gene expression such as constitutive polycistronic transcription of protein-coding genes, RNA editing and trans-splicing. In the absence of mechanism controlling transcription initiation, organized subsets of T. cruzi genes must be post-transcriptionally co-regulated in response to extracellular signals. The mechanisms that regulate stage-specific gene expression in this parasite have become much clearer through sequencing its whole genome as well as performing various proteomic and microarray analyses, which have demonstrated that at least half of the T. cruzi genes are differentially regulated during its life cycle. In this review, we attempt to highlight the recent advances in characterising cis and trans-acting elements in the T. cruzi genome that are involved in its post-transcriptional regulatory machinery.

Update of the FANTOM web resource: from mammalian transcriptional landscape to its dynamic regulation.

The international Functional Annotation Of the Mammalian Genomes 4 (FANTOM4) research collaboration set out to better understand the transcriptional network that regulates macrophage differentiation and to uncover novel components of the transcriptome employing a series of high-throughput experiments. The primary and unique technique is cap analysis of gene expression (CAGE), sequencing mRNA 5'-ends with a second-generation sequencer to quantify promoter activities even in the absence of gene annotation. Additional genome-wide experiments complement the setup including short RNA sequencing, microarray gene expression profiling on large-scale perturbation experiments and ChIP-chip for epigenetic marks and transcription factors. All the experiments are performed in a differentiation time course of the THP-1 human leukemic cell line. Furthermore, we performed a large-scale mammalian two-hybrid (M2H) assay between transcription factors and monitored their expression profile across human and mouse tissues with qRT-PCR to address combinatorial effects of regulation by transcription factors. These interdependent data have been analyzed individually and in combination with each other and are published in related but distinct papers. We provide all data together with systematic annotation in an integrated view as resource for the scientific community (http://fantom.gsc.riken.jp/4/). Additionally, we assembled a rich set of derived analysis results including published predicted and validated regulatory interactions. Here we introduce the resource and its update after the initial release.

Topological comparison of methods for predicting transcriptional cooperativity in yeast

Background: The cooperative interaction between transcription factors has a decisive role in the control of the fate of the eukaryotic cell. Computational approaches for characterizing cooperative transcription factors in yeast, however, are based on different rationales and provide a low overlap between their results. Because the wealth of information contained in protein interaction networks and regulatory networks has proven highly effective in elucidating functional relationships between proteins, we compared different sets of cooperative transcription factor pairs (predicted by four different computational methods) within the frame of those networks. Results: Our results show that the overlap between the sets of cooperative transcription factors predicted by the different methods is low yet significant. Cooperative transcription factors predicted by all methods are closer and more clustered in the protein interaction network than expected by chance. On the other hand, members of a cooperative transcription factor pair neither seemed to regulate each other nor shared similar regulatory inputs, although they do regulate similar groups of target genes. Conclusion: Despite the different definitions of transcriptional cooperativity and the different computational approaches used to characterize cooperativity between transcription factors, the analysis of their roles in the framework of the protein interaction network and the regulatory network indicates a common denominator for the predictions under study. The knowledge of the shared topological properties of cooperative transcription factor pairs in both networks can be useful not only for designing better prediction methods but also for better understanding the complexities of transcriptional control in eukaryotes.

Stochastic models and numerical algorithms for a class of regulatory gene networks.

Regulatory gene networks contain generic modules, like those involving feedback loops, which are essential for the regulation of many biological functions (Guido et al. in Nature 439:856-860, 2006). We consider a class of self-regulated genes which are the building blocks of many regulatory gene networks, and study the steady-state distribution of the associated Gillespie algorithm by providing efficient numerical algorithms. We also study a regulatory gene network of interest in gene therapy, using mean-field models with time delays. Convergence of the related time-nonhomogeneous Markov chain is established for a class of linear catalytic networks with feedback loops.

REST: transcriptional and epigenetic regulator

Acquisition and maintenance of cell fate and potential are dependent on the complex interplay of extracellular signaling, gene regulatory networks and epigenetic states. During embryonic development, embryonic stem cells become progressively more restricted along specific lineages, ultimately giving rise to the diversity of cell types in the adult mammalian organism. Recent years have seen major advances in our understanding of the mechanisms that regulate the underlying transcriptional programmes during development. In particular, there has been a significant increase in our knowledge of how epigenetic marks on chromatin can regulate transcription by generating more or less permissive chromatin conformations. This article focuses on how a single transcription factor, repressor element-1 silencing transcription factor, can function as both a transcriptional and epigenetic regulator, controlling diverse aspects of development. We will discuss how the elucidation of repressor element-1 silencing transcription factor function in both normal and disease conditions has provided valuable insights into how the epigenome and transcriptional regulators might cooperatively orchestrate correct development.

Extensive cross-talk and global regulators identified from an analysis of the integrated transcriptional and signaling network in Escherichia coli

To understand the regulatory dynamics of transcription factors (TFs) and their interplay with other cellular components we have integrated transcriptional, protein-protein and the allosteric or equivalent interactions which mediate the physiological activity of TFs in Escherichia coli. To study this integrated network we computed a set of network measurements followed by principal component analysis (PCA), investigated the correlations between network structure and dynamics, and carried out a procedure for motif detection. In particular, we show that outliers identified in the integrated network based on their network properties correspond to previously characterized global transcriptional regulators. Furthermore, outliers are highly and widely expressed across conditions, thus supporting their global nature in controlling many genes in the cell. Motifs revealed that TFs not only interact physically with each other but also obtain feedback from signals delivered by signaling proteins supporting the extensive cross-talk between different types of networks. Our analysis can lead to the development of a general framework for detecting and understanding global regulatory factors in regulatory networks and reinforces the importance of integrating multiple types of interactions in underpinning the interrelationships between them.

Global transcriptional response of Caulobacter crescentus to iron availability

Abstract Background In the alpha subclass of proteobacteria iron homeostasis is controlled by diverse iron responsive regulators. Caulobacter crescentus, an important freshwater α-proteobacterium, uses the ferric uptake repressor (Fur) for such purpose. However, the impact of the iron availability on the C. crescentus transcriptome and an overall perspective of the regulatory networks involved remain unknown. Results In this work we report the identification of iron-responsive and Fur-regulated genes in C. crescentus using microarray-based global transcriptional analyses. We identified 42 genes that were strongly upregulated both by mutation of fur and by iron limitation condition. Among them, there are genes involved in iron uptake (four TonB-dependent receptor gene clusters, and feoAB), riboflavin biosynthesis and genes encoding hypothetical proteins. Most of these genes are associated with predicted Fur binding sites, implicating them as direct targets of Fur-mediated repression. These data were validated by β-galactosidase and EMSA assays for two operons encoding putative transporters. The role of Fur as a positive regulator is also evident, given that 27 genes were downregulated both by mutation of fur and under low-iron condition. As expected, this group includes many genes involved in energy metabolism, mostly iron-using enzymes. Surprisingly, included in this group are also TonB-dependent receptors genes and the genes fixK, fixT and ftrB encoding an oxygen signaling network required for growth during hypoxia. Bioinformatics analyses suggest that positive regulation by Fur is mainly indirect. In addition to the Fur modulon, iron limitation altered expression of 113 more genes, including induction of genes involved in Fe-S cluster assembly, oxidative stress and heat shock response, as well as repression of genes implicated in amino acid metabolism, chemotaxis and motility. Conclusions Using a global transcriptional approach, we determined the C. crescentus iron stimulon. Many but not all of iron responsive genes were directly or indirectly controlled by Fur. The iron limitation stimulon overlaps with other regulatory systems, such as the RpoH and FixK regulons. Altogether, our results showed that adaptation of C. crescentus to iron limitation not only involves increasing the transcription of iron-acquisition systems and decreasing the production of iron-using proteins, but also includes novel genes and regulatory mechanisms.

Global transcriptional response of Caulobacter crescentus to iron availability

BACKGROUND: In the alpha subclass of proteobacteria iron homeostasis is controlled by diverse iron responsive regulators. Caulobacter crescentus, an important freshwater α-proteobacterium, uses the ferric uptake repressor (Fur) for such purpose. However, the impact of the iron availability on the C. crescentus transcriptome and an overall perspective of the regulatory networks involved remain unknown. RESULTS: In this work we report the identification of iron-responsive and Fur-regulated genes in C. crescentus using microarray-based global transcriptional analyses. We identified 42 genes that were strongly upregulated both by mutation of fur and by iron limitation condition. Among them, there are genes involved in iron uptake (four TonB-dependent receptor gene clusters, and feoAB), riboflavin biosynthesis and genes encoding hypothetical proteins. Most of these genes are associated with predicted Fur binding sites, implicating them as direct targets of Fur-mediated repression. These data were validated by β-galactosidase and EMSA assays for two operons encoding putative transporters. The role of Fur as a positive regulator is also evident, given that 27 genes were downregulated both by mutation of fur and under low-iron condition. As expected, this group includes many genes involved in energy metabolism, mostly iron-using enzymes. Surprisingly, included in this group are also TonB-dependent receptors genes and the genes fixK, fixT and ftrB encoding an oxygen signaling network required for growth during hypoxia. Bioinformatics analyses suggest that positive regulation by Fur is mainly indirect. In addition to the Fur modulon, iron limitation altered expression of 113 more genes, including induction of genes involved in Fe-S cluster assembly, oxidative stress and heat shock response, as well as repression of genes implicated in amino acid metabolism, chemotaxis and motility. CONCLUSIONS: Using a global transcriptional approach, we determined the C. crescentus iron stimulon. Many but not all of iron responsive genes were directly or indirectly controlled by Fur. The iron limitation stimulon overlaps with other regulatory systems, such as the RpoH and FixK regulons. Altogether, our results showed that adaptation of C. crescentus to iron limitation not only involves increasing the transcription of iron-acquisition systems and decreasing the production of iron-using proteins, but also includes novel genes and regulatory mechanisms

MOLECULAR MECHANISMS UNDERLYING THE TRANSCRIPTIONAL REGULATION OF T HELPER 17 AND REGULATORY T CELLS

CD4+ T helper (Th) lymphocytes are vital for integrating immune responses by orchestrating the function of other immune cell types. Naïve Th cells can differentiate into different effector subsets that are characterized by their cytokine profile and immune regulatory functions. These subsets include Th1, Th2, Th17, natural and inducible regulatory T cells (nTreg and iTreg respectively), among others. We focused our investigation on two Th lineages, Th17 and regulatory T cells, with opposing functions in the immune system. These subsets have been suggested to be reciprocally regulated since they both require TGF-b for their development. We investigated the role of the Treg-associated master transcription factor Foxp3, and found that Foxp3 inhibits Th17 cell generation by preventing the transcriptional activity of the two main Th17-specific transcription factors, nuclear orphan receptors RORa and RORgt. At the molecular level, we identified two different functional domains in Foxp3 required for such inhibition: the LQALL sequence in exon 2 and the TIP60/HDAC7 binding domain. These domains could be crucial to either prevent the association of the nuclear receptors to coactivators or to recruit histone deacetylases to RORa- or RORgt-target genes. Since TGF-b is a common cytokine required for the commitment towards both Th lineages, we determined the role of the TGF-b-dependent signaling pathway in the generation of each subset. By using mice with deficiencies in signaling molecules downstream of TGF-b, we found that while Smad2, Smad3 and Smad4 are required for the generation of iTreg cells, only Smad2 is indispensable for the induction of IL-17-producing cells, suggesting that TGF-b induces these T helper lineages through differential signaling pathways. Thus, our findings describe novel transcriptional regulatory mechanisms that control the generation of two T helper lineages with opposing functions. These findings could provide novel therapeutic targets to treat diseases where the balance of these T cells is dysregulated, such as in autoimmunity, chronic infectious diseases and cancer.

The ribosome as novel target for stress-induced small regulatory RNAs

Small non-protein-coding RNA (ncRNA) molecules represent major contributors to regulatory networks in controlling gene expression in a highly efficient manner. All of the recently discovered regulatory ncRNAs that act on translation (e.g. microRNAs, siRNAs or antisense RNAs) target the mRNA rather than the ribosome. To address the question, whether small ncRNA regulators exist that are capable of modulating the rate of protein production by directly interacting with the ribosome, we have analyzed the small ncRNA interactomes of ribosomes Deep-sequencing and subsequent bioinformatic analyses revealed thousands of putative ribosome-associated ncRNAs in various model organisms (1,2). For a subset of these ncRNA candidates we have gathered experimental evidence that they associate with ribosomes in a stress-dependent manner and are capable of regulating gene expression by fine-tuning the rate of protein biosynthesis (3,4). Many of the investigated ribosome-bound small ncRNA appear to be processing products from larger functional RNAs, such as tRNAs (2,3) or mRNAs (3). Post-transcriptional cleavage of RNA molecules to generate smaller fragments is a widespread mechanism that enlarges the structural and functional complexity of cellular RNomes. Our data reveal the ribosome as a target for small regulatory ncRNAs and demonstrate the existence of a yet unknown mechanism of translation regulation. Ribosome-associated ncRNAs (rancRNAs) are found in all domains of life and represent a prevalent but so far largely unexplored class of regulatory molecules (5). Future work on the small ncRNA interactomes of ribosomes in a variety of model systems will allow deeper insight into the conservation and functional repertoire of this emerging class of regulatory ncRNA molecules.

Inhibition of the association of RNA polymerase II with the preinitiation complex by a viral transcriptional repressor.

Transcriptional repression is an important component of regulatory networks that govern gene expression. In this report, we have characterized the mechanisms by which the immediate early protein 2 (IE2 or IE86), a master transcriptional regulator of human cytomegalovirus, down-regulates its own expression. In vitro transcription and DNA binding experiments demonstrate that IE2 blocks specifically the association of RNA polymerase II with the preinitiation complex. Although, to our knowledge, this is the first report to describe a eukaryotic transcriptional repressor that selectively impedes RNA polymerase II recruitment, we present data that suggest that this type of repression might be widely used in the control of transcription by RNA polymerase II.

Gene Expression Complex Networks: Synthesis, Identification, and Analysis

Thanks to recent advances in molecular biology, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as cDNA microarrays and RNA-Seq. Particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. Methods have been developed for gene networks modeling and identification from expression profiles. However, an important open problem regards how to validate such approaches and its results. This work presents an objective approach for validation of gene network modeling and identification which comprises the following three main aspects: (1) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (2) a computational method for gene network identification from the simulated data, which is founded on a feature selection approach where a target gene is fixed and the expression profile is observed for all other genes in order to identify a relevant subset of predictors; and (3) validation of the identified AGN-based network through comparison with the original network. The proposed framework allows several types of AGNs to be generated and used in order to simulate temporal expression data. The results of the network identification method can then be compared to the original network in order to estimate its properties and accuracy. Some of the most important theoretical models of complex networks have been assessed: the uniformly-random Erdos-Renyi (ER), the small-world Watts-Strogatz (WS), the scale-free Barabasi-Albert (BA), and geographical networks (GG). The experimental results indicate that the inference method was sensitive to average degree k variation, decreasing its network recovery rate with the increase of k. The signal size was important for the inference method to get better accuracy in the network identification rate, presenting very good results with small expression profiles. However, the adopted inference method was not sensible to recognize distinct structures of interaction among genes, presenting a similar behavior when applied to different network topologies. In summary, the proposed framework, though simple, was adequate for the validation of the inferred networks by identifying some properties of the evaluated method, which can be extended to other inference methods.

Noncoding RNA's in mammals

Transcripts that lack any protein-coding potential represent at least half of the identified elements transcriptome. We review the evidence for the existence of such transcripts in the mammalian transcriptome, and argue that there may be many more noncoding RNAs (ncRNAs) still to be discovered. Relatively few ncRNA “genes” have been ascribed a function based upon mutation analysis. The review discusses possible roles of ncRNAs as cis-acting and trans-acting elements in epigenetic transcriptional control, including monoallelic gene silencing and imprinting. We also consider the evidence that the production of ncRNAs is a common feature of transcriptional enhancers.