A sigmoidal transcriptional response: cooperativity, synergy and dosage effects

Maternal Groucho and bHLH repressors amplify the dose-sensitive X chromosome signal in Drosophila sex determination

In Drosophila, XX embryos are fated to develop as females, and XY embryos as males, because the diplo-X dose of four X-linked signal element genes, XSEs, activates the Sex-lethal establishment promoter, SxlPe, whereas the haplo-X XSE dose leaves SxlPe off. The threshold response of SxlPe to XSE concentrations depends in part on the bHLH repressor, Deadpan, present in equal amounts in XX and XY embryos. We identified canonical and non-canonical DNA-binding sites for Dpn at SxlPe and found that cis-acting mutations in the Dpn-binding sites caused stronger and earlier Sxl expression than did deletion of dpn implicating other bHLH repressors in Sxl regulation. Maternal Hey encodes one such bHLH regulator but the E(spl) locus does not. Elimination of the maternal corepressor Groucho also caused strong ectopic Sxl expression in XY, and premature Sxl activation in XX embryos, but Sxl was still expressed differently in the sexes. Our findings suggest that Groucho and associated maternal and zygotic bHLH repressors define the threshold XSE concentrations needed to activate SxlPe and that they participate directly in sex signal amplification. We present a model in which the XSE signal is amplified by a feedback mechanism that interferes with Gro-mediated repression in XX, but not XY embryos.

Computational methods to dissect cis-regulatory transcriptional networks

The formation of diverse cell types from an invariant set of genes is governed by biochemical and molecular processes that regulate gene activity. A complete understanding of the regulatory mechanisms of gene expression is the major function of genomics. Computational genomics is a rapidly emerging area for deciphering the regulation of metazoan genes as well as interpreting the results of high-throughput screening. The integration of computer science with biology has expedited molecular modelling and processing of large-scale data inputs such as microarrays, analysis of genomes, transcriptomes and proteomes. Many bioinformaticians have developed various algorithms for predicting transcriptional regulatory mechanisms from the sequence, gene expression and interaction data. This review contains compiled information of various computational methods adopted to dissect gene expression pathways.

The mutations and potential targets of the forkhead transcription factor FOXL2

Mutations of FOXL2, a gene encoding a forkhead transcription factor, have been shown to cause the blepharophimosis-ptosis-epicanthus inversus syndrome (BPES). This genetic disorder is characterized by eyelid and mild craniofacial abnormalities that can appear associated with premature ovarian failure. FOXL2 is one of the earliest ovarian markers and it offers, along with its targets, an excellent model to study ovarian development and function in normal and pathological conditions. In this review we summarize recent data concerning FOXL2, its mutations and its potential targets. Indeed, many mutations have been described in the coding sequence of FOXL2. Among them, polyalanine expansions and premature nonsense mutations have been shown to induce protein aggregation. In the context of the ovary, FOXL2 has been suggested to be involved in the regulation of cholesterol and steroid metabolism, apoptosis, reactive oxygen species detoxification and inflammation processes. The elucidation of the impact of FOXL2 mutations on its function will allow a better understanding of the pathogenic mechanisms underlying the BPES phenotype.

The limits of subfunctionalization

Background

The duplication-degeneration-complementation (DDC) model has been proposed as an explanation for the unexpectedly high retention of duplicate genes. The hypothesis proposes that, following gene duplication, the two gene copies degenerate to perform complementary functions that jointly match that of the single ancestral gene, a process also known as subfunctionalization. We distinguish between subfunctionalization at the regulatory level and at the product level (e.g within temporal or spatial expression domains).

Results

In contrast to what is expected under the DDC model, we use in silico modeling to show that regulatory subfunctionalization is expected to peak and then decrease significantly. At the same time, neofunctionalization (recruitment of novel interactions) increases monotonically, eventually affecting the regulatory elements of the majority of genes. Furthermore, since this process occurs under conditions of stabilizing selection, there is no need to invoke positive selection. At the product level, the frequency of subfunctionalization is no higher than would be expected by chance, a finding that was corroborated using yeast microarray time-course data. We also find that product subfunctionalization is not necessarily caused by regulatory subfunctionalization.

Conclusion

Our results suggest a more complex picture of post-duplication evolution in which subfunctionalization plays only a partial role in conjunction with redundancy and neofunctionalization. We argue that this behavior is a consequence of the high evolutionary plasticity in gene networks.

Inferring transcriptional regulatory networks from high-throughput data

MOTIVATION

Inferring the relationships between transcription factors (TFs) and their targets has utmost importance for understanding the complex regulatory mechanisms in cellular systems. However, the transcription factor activities (TFAs) cannot be measured directly by standard microarray experiment owing to various post-translational modifications. In particular, cooperative mechanism and combinatorial control are common in gene regulation, e.g. TFs usually recruit other proteins cooperatively to facilitate transcriptional reaction processes.

RESULTS

In this article, we propose a novel method for inferring transcriptional regulatory networks (TRN) from gene expression data based on protein transcription complexes and mass action law. With gene expression data and TFAs estimated from transcription complex information, the inference of TRN is formulated as a linear programming (LP) problem which has a globally optimal solution in terms of L(1) norm error. The proposed method not only can easily incorporate ChIP-Chip data as prior knowledge, but also can integrate multiple gene expression datasets from different experiments simultaneously. A unique feature of our method is to take into account protein cooperation in transcription process. We tested our method by using both synthetic data and several experimental datasets in yeast. The extensive results illustrate the effectiveness of the proposed method for predicting transcription regulatory relationships between TFs with co-regulators and target genes.

Commonalities in fly embryogenesis and mammalian pituitary patterning

During embryonic development, morphogenetic gradients can specify the formation of gene expression territories. Here, we explore possible commonalities between pattern formation in the Drosophila blastoderm and murine pituitary. Shared principles include the need for positive feedback involving fate-determining genes to maintain a differentiated state, and the existence of intra- or extracellular inhibitory signals that improve spatial resolution of neighboring territories. The precision of spatial segregation is, however, limited by stochastic gene expression. Variability in gene expression at territory boundaries might give rise to a poorly differentiated pool of cells, which could harbor stem-like properties. The ideas outlined here deserve further theoretical and experimental exploration.

Genome clashes in hybrids: insights from gene expression

In interspecific hybrids, novel phenotypes often emerge from the interaction of two divergent genomes. Interactions between the two transcriptional networks are assumed to contribute to these unpredicted new phenotypes by inducing novel patterns of gene expression. Here we provide a review of the recent literature on the accumulation of regulatory incompatibilities. We review specific examples of regulatory incompatibilities reported at particular loci as well as genome-scale surveys of gene expression in interspecific hybrids. Finally, we consider and preview novel technologies that could help decipher how divergent transcriptional networks interact in hybrids between species.

Nonlinear regulation enhances the phenotypic expression of trans-acting genetic polymorphisms

BACKGROUND

Genetic variation explains a considerable part of observed phenotypic variation in gene expression networks. This variation has been shown to be located both locally (cis) and distally (trans) to the genes being measured. Here we explore to which degree the phenotypic manifestation of local and distant polymorphisms is a dynamic feature of regulatory design.

RESULTS

By combining mathematical models of gene expression networks with genetic maps and linkage analysis we find that very different network structures and regulatory motifs give similar cis/trans linkage patterns. However, when the shape of the cis-regulatory input functions is more nonlinear or threshold-like, we observe for all networks a dramatic increase in the phenotypic expression of distant compared to local polymorphisms under otherwise equal conditions.

CONCLUSION

Our findings indicate that genetic variation affecting the form of cis-regulatory input functions may reshape the genotype-phenotype map by changing the relative importance of cis and trans variation. Our approach combining nonlinear dynamic models with statistical genetics opens up for a systematic investigation of how functional genetic variation is translated into phenotypic variation under various systemic conditions.

General transfer matrix formalism to calculate DNA-protein-drug binding in gene regulation: application to OR operator of phage lambda

The transfer matrix methodology is proposed as a systematic tool for the statistical-mechanical description of DNA-protein-drug binding involved in gene regulation. We show that a genetic system of several cis-regulatory modules is calculable using this method, considering explicitly the site-overlapping, competitive, cooperative binding of regulatory proteins, their multilayer assembly and DNA looping. In the methodological section, the matrix models are solved for the basic types of short- and long-range interactions between DNA-bound proteins, drugs and nucleosomes. We apply the matrix method to gene regulation at the O(R) operator of phage lambda. The transfer matrix formalism allowed the description of the lambda-switch at a single-nucleotide resolution, taking into account the effects of a range of inter-protein distances. Our calculations confirm previously established roles of the contact CI-Cro-RNAP interactions. Concerning long-range interactions, we show that while the DNA loop between the O(R) and O(L) operators is important at the lysogenic CI concentrations, the interference between the adjacent promoters P(R) and P(RM) becomes more important at small CI concentrations. A large change in the expression pattern may arise in this regime due to anticooperative interactions between DNA-bound RNA polymerases. The applicability of the matrix method to more complex systems is discussed.

SJ. Identification and characterization of a novel Schwann and outflow tract endocardial cushion lineage-restricted periostin enhancer

Periostin is a fasciclin-containing adhesive glycoprotein that facilitates the migration and differentiation of cells that have undergone epithelial-mesenchymal transformation during embryogenesis and in pathological conditions. Despite the importance of post-transformational differentiation as a general developmental mechanism, little is known how periostin's embryonic expression is regulated. To help resolve this deficiency, a 3.9-kb periostin proximal promoter was isolated and shown to drive tissue-specific expression in the neural crest-derived Schwann cell lineage and in a subpopulation of periostin-expressing cells in the cardiac outflow tract endocardial cushions. In order to identify the enhancer and associated DNA binding factor(s) responsible, in vitro promoter dissection was undertaken in a Schwannoma line. Ultimately a 304-bp(peri) enhancer was identified and shown to be capable of recapitulating 3.9 kb(peri-lacZ)in vivo spatiotemporal patterns. Further mutational and EMSA analysis helped identify a minimal 37-bp region that is bound by the YY1 transcription factor. The 37-bp enhancer was subsequently shown to be essential for in vivo 3.9 kb(peri-lacZ) promoter activity. Taken together, these studies identify an evolutionary-conserved YY1-binding 37-bp region within a 304-bp periostin core enhancer that is capable of regulating simultaneous novel tissue-specific periostin expression in the cardiac outflow-tract cushion mesenchyme and Schwann cell lineages.

A new framework for identifying combinatorial regulation of transcription factors: a case study of the yeast cell cycle

By integrating heterogeneous functional genomic datasets, we have developed a new framework for detecting combinatorial control of gene expression, which includes estimating transcription factor activities using a singular value decomposition method and reducing high-dimensional input gene space by considering genomic properties of gene clusters. The prediction of cooperative gene regulation is accomplished by either Gaussian Graphical Models or Pairwise Mixed Graphical Models. The proposed framework was tested on yeast cell cycle datasets: (1) 54 known yeast cell cycle genes with 9 cell cycle regulators and (2) 676 putative yeast cell cycle genes with 9 cell cycle regulators. The new framework gave promising results on inferring TF-TF and TF-gene interactions. It also revealed several interesting mechanisms such as negatively correlated protein-protein interactions and low affinity protein-DNA interactions that may be important during the yeast cell cycle. The new framework may easily be extended to study other higher eukaryotes.

Mathematical model of morphogen electrophoresis through gap junctions

Gap junctional communication is important for embryonic morphogenesis. However, the factors regulating the spatial properties of small molecule signal flows through gap junctions remain poorly understood. Recent data on gap junctions, ion transporters, and serotonin during left-right patterning suggest a specific model: the net unidirectional transfer of small molecules through long-range gap junctional paths driven by an electrophoretic mechanism. However, this concept has only been discussed qualitatively, and it is not known whether such a mechanism can actually establish a gradient within physiological constraints. We review the existing functional data and develop a mathematical model of the flow of serotonin through the early Xenopus embryo under an electrophoretic force generated by ion pumps. Through computer simulation of this process using realistic parameters, we explored quantitatively the dynamics of morphogen movement through gap junctions, confirming the plausibility of the proposed electrophoretic mechanism, which generates a considerable gradient in the available time frame. The model made several testable predictions and revealed properties of robustness, cellular gradients of serotonin, and the dependence of the gradient on several developmental constants. This work quantitatively supports the plausibility of electrophoretic control of morphogen movement through gap junctions during early left-right patterning. This conceptual framework for modeling gap junctional signaling -- an epigenetic patterning mechanism of wide relevance in biological regulation -- suggests numerous experimental approaches in other patterning systems.

Statistical epistasis is a generic feature of gene regulatory networks

Functional dependencies between genes are a defining characteristic of gene networks underlying quantitative traits. However, recent studies show that the proportion of the genetic variation that can be attributed to statistical epistasis varies from almost zero to very high. It is thus of fundamental as well as instrumental importance to better understand whether different functional dependency patterns among polymorphic genes give rise to distinct statistical interaction patterns or not. Here we address this issue by combining a quantitative genetic model approach with genotype-phenotype models capable of translating allelic variation and regulatory principles into phenotypic variation at the level of gene expression. We show that gene regulatory networks with and without feedback motifs can exhibit a wide range of possible statistical genetic architectures with regard to both type of effect explaining phenotypic variance and number of apparent loci underlying the observed phenotypic effect. Although all motifs are capable of harboring significant interactions, positive feedback gives rise to higher amounts and more types of statistical epistasis. The results also suggest that the inclusion of statistical interaction terms in genetic models will increase the chance to detect additional QTL as well as functional dependencies between genetic loci over a broad range of regulatory regimes. This article illustrates how statistical genetic methods can fruitfully be combined with nonlinear systems dynamics to elucidate biological issues beyond reach of each methodology in isolation.

Molecular karyotyping and aneuploidy detection in Arabidopsis thaliana using quantitative fluorescent polymerase chain reaction

Certain cellular processes are sensitive to changes in gene dosage. Aneuploidy is deleterious because of an imbalance of gene dosage on a chromosomal scale. Identification, classification and characterization of aneuploidy are therefore important for molecular, population and medical genetics and for a deeper understanding of the mechanisms underlying dosage sensitivity. Notwithstanding recent progress in genomic technologies, limited means are available for detecting and classifying changes in chromosome dose. The development of an inexpensive and scalable karyotyping method would allow rapid detection and characterization of both simple and complex aneuploid types. In addition to the problem of karyotyping, genomic and molecular genetic studies of aneuploids and polyploids are complicated by multiple heterozygous combinations possible at loci present in more than two copies. Quantitative scoring of allele genotypes would enable large-scale population genetic experiments in polyploids, and permit genetic analyses on bulked populations in diploid species. Here, we demonstrate that quantitative fluorescent-polymerase chain reaction (QF-PCR) can be used to simultaneously genotype and karyotype aneuploid and polyploid Arabidopsis thaliana. Comparison of QF-PCR with flow cytometric determination of nuclear DNA content indicated near perfect agreement between the methods, but complete karyotype resolution was only possible using QF-PCR. A complex karyotype, determined by QF-PCR, was validated by comparative genomic hybridization to microarrays. Finally, we screened the progeny of tetraploid individuals and found that more than 25% were aneuploid and that our artificially induced tetraploid strain produced fewer aneuploid individuals than a tetraploid strain isolated from nature.

Adaptively inferring human transcriptional subnetworks

Although the human genome has been sequenced, progress in understanding gene regulation in humans has been particularly slow. Many computational approaches developed for lower eukaryotes to identify cis-regulatory elements and their associated target genes often do not generalize to mammals, largely due to the degenerate and interactive nature of such elements. Motivated by the switch-like behavior of transcriptional responses, we present a systematic approach that allows adaptive determination of active transcriptional subnetworks (cis-motif combinations, the direct target genes and physiological processes regulated by the corresponding transcription factors) from microarray data in mammals, with accuracy similar to that achieved in lower eukaryotes. Our analysis uncovered several new subnetworks active in human liver and in cell-cycle regulation, with similar functional characteristics as the known ones. We present biochemical evidence for our predictions, and show that the recently discovered G2/M-specific E2F pathway is wider than previously thought; in particular, E2F directly activates certain mitotic genes involved in hepatocellular carcinomas. Additionally, we demonstrate that this method can predict subnetworks in a condition-specific manner, as well as regulatory crosstalk across multiple tissues. Our approach allows systematic understanding of how phenotypic complexity is regulated at the transcription level in mammals and offers marked advantage in systems where little or no prior knowledge of transcriptional regulation is available.

The robustness of the transcriptional response to alterations in morphogenetic gradients

Sharp sigmoidal transitions of transcription in response to morphogenetic gradients are often involved in the generation of boundaries during development. Here we explore two simple models of transcription that generate sigmoidal outputs. Specifically, we analyse the case of a promoter responding to a single type of activator. We then consider a model where an activator and an inhibitor, distributed as two gradients with opposite slopes, act competitively on the same promoter. This system can produce a sharper response than that obtained with a promoter with the same number of binding sites responding to a similar gradient of activator alone. We also explore how these systems buffer the effect of variations in the dosage of the morphogens. Our results highlight how non-linearities can be a source of robustness and the crucial role of the inhibitor in the generation of a sharp and robust response.

Compensatory cis-trans evolution and the dysregulation of gene expression in interspecific hybrids of Drosophila

Hybrids between species are often characterized by novel gene-expression patterns. A recent study on allele-specific gene expression in hybrids between species of Drosophila revealed cases in which cis- and trans-regulatory elements within species had coevolved in such a way that changes in cis-regulatory elements are compensated by changes in trans-regulatory elements. We hypothesized that such coevolution should often lead to gene misexpression in the hybrid. To test this hypothesis, we estimated allele-specific expression and overall expression levels for 31 genes in D. melanogaster, D. simulans, and their F1 hybrid. We found that 13 genes with cis-trans compensatory evolution are in fact misexpressed in the hybrid. These represent candidate genes whose dysregulation might be the consequence of coevolution of cis- and trans-regulatory elements within species. Using a mathematical model for the regulation of gene expression, we explored the conditions under which cis-trans compensatory evolution can lead to misexpression in interspecific hybrids.

Stochasticity or the fatal ‘imperfection’ of cloning

The concept of clone is analysed with the aim of exploring the limits to which a phenotype can be said to be determined geneticaly. First of all, mutations that result from the replication, topological manipulation or lesion of DNA introduce a source of heritable variation in an otherwise identical genetic background. But more important, stochastic effects in many biological processes may superimpose a phenotypic variation which is not encoded in the genome. The source of stochasticity ranges from the random selection of alleles or whole chromosomes to be expressed in small cell populations, to fluctuations in processes such as gene expression, due to limiting amounts of the players involved. The picture emerging is that the term clone is a statistical over-simplification representing a series of individuals having essentially the same genome but capable of exhibiting wide phenotypic variation. Finally, to what extent fluctuations in biological processes, usually thought of as noise, are in fact signal is also discussed.

The synergistic effect of dexamethasone and all-trans-retinoic acid on hepatic phosphoenolpyruvate carboxykinase gene expression involves the coactivator p300

Activation of phosphoenolpyruvate carboxykinase (PEPCK) gene transcription in response to all-trans-retinoic acid (RA) or a glucocorticoid such as dexamethasone (Dex) requires a distinct arrangement of DNA-response elements and their cognate transcription activators on the gene promoter. Two of the accessory factor-binding elements involved in the Dex response (gAF1 and gAF3) coincide with the DNA-response elements involved in the RA response. We demonstrate here that the combination of Dex/RA has a synergistic effect on endogenous PEPCK gene expression in rat hepatocytes and H4IIE hepatoma cells. Reporter gene studies show that the gAF3 element and one of the two glucocorticoid receptor-binding elements (GR1) are most important for this effect. Chromatin immunoprecipitation assays revealed that when H4IIE cells were treated with Dex/RA, ligand-activated retinoic acid receptors (retinoic acid receptor/retinoid X receptor) and glucocorticoid receptors are recruited to this gene promoter, as are the transcription coregulators p300, CREB-binding protein, p/CIP, and SRC-1. Notably, the recruitment of p300 and RNA polymerase II to the PEPCK promoter is increased by the combined Dex/RA treatment compared with Dex or RA treatment alone. The functional importance of p300 in the Dex/RA response is illustrated by the observation that selective reduction of this coactivator, but not that of CREB-binding protein, abolishes the synergistic effect in H4IIE cells.