Symmetries and fixed point stability of stochastic differential equations modeling self-organized criticality

A stochastic nonlinear partial differential equation is constructed for two different models exhibiting self-organized criticality: the Bak-Tang-Wiesenfeld (BTW) sandpile model [Phys. Rev. Lett. 59, 381 (1987); Phys. Rev. A 38, 364 (1988)] and the Zhang model [Phys. Rev. Lett. 63, 470 (1989)]. The dynamic renormalization group (DRG) enables one to compute the critical exponents. However, the nontrivial stable fixed point of the DRG transformation is unreachable for the original parameters of the models. We introduce an alternative regularization of the step function involved in the threshold condition, which breaks the symmetry of the BTW model. Although the symmetry properties of the two models are different, it is shown that they both belong to the same universality class. In this case the DRG procedure leads to a symmetric behavior for both models, restoring the broken symmetry, and makes accessible the nontrivial fixed point. This technique could also be applied to other problems with threshold dynamics.

Transcriptional regulatory patterns of the myelin basic protein and malic enzyme genes by the thyroid hormone receptors alpha1 and beta1.

While there is evidence that the two ubiquitously expressed thyroid hormone (T3) receptors, TRalpha1 and TRbeta1, have distinct functional specificities, the mechanism by which they discriminate potential target genes remains largely unexplained. In this study, we demonstrate that the thyroid hormone response elements (TRE) from the malic enzyme and myelin basic protein genes (METRE and MBPTRE) respectively, are not functionally equivalent. The METRE, which is a direct repeat motif with a 4-base pair gap between the two half-site hexamers binds thyroid hormone receptor as a heterodimer with 9-cis-retinoic acid receptor (RXR) and mediates a high T3-dependent activation in response to TRalpha1 or TRbeta1 in NIH3T3 cells. In contrast, the MBPTRE, which consists of an inverted palindrome formed by two hexamers spaced by 6 base pairs, confers an efficient transactivation by TRbeta1 but a poor transactivation by TRalpha1. While both receptors form heterodimers with RXR on MBPTRE, the poor transactivation by TRalpha1 correlates also with its ability to bind efficiently as a monomer. This monomer, which is only observed with TRalpha1 bound to MBPTRE, interacts neither with N-CoR nor with SRC-1, explaining its functional inefficacy. However, in Xenopus oocytes, in which RXR proteins are not detectable, the transactivation mediated by TRalpha1 and TRbeta1 is equivalent and independent of a RXR supply, raising the question of the identity of the thyroid hormone receptor partner in these cells. Thus, in mammalian cells, the binding characteristics of TRalpha1 to MBPTRE (i.e. high monomer binding efficiency and low transactivation activity) might explain the particular pattern of T3 responsiveness of MBP gene expression during central nervous system development.

Bioinformatics analysis of biomarkers and transcriptional factor motifs in Down syndrome

In this study, biomarkers and transcriptional factor motifs were identified in order to investigate the etiology and phenotypic severity of Down syndrome. GSE 1281, GSE 1611, and GSE 5390 were downloaded from the gene expression ominibus (GEO). A robust multiarray analysis (RMA) algorithm was applied to detect differentially expressed genes (DEGs). In order to screen for biological pathways and to interrogate the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database, the database for annotation, visualization, and integrated discovery (DAVID) was used to carry out a gene ontology (GO) function enrichment for DEGs. Finally, a transcriptional regulatory network was constructed, and a hypergeometric distribution test was applied to select for significantly enriched transcriptional factor motifs. CBR1, DYRK1A, HMGN1, ITSN1, RCAN1, SON, TMEM50B, and TTC3 were each up-regulated two-fold in Down syndrome samples compared to normal samples; of these, SON and TTC3 were newly reported. CBR1, DYRK1A, HMGN1, ITSN1, RCAN1, SON, TMEM50B, and TTC3 were located on human chromosome 21 (mouse chromosome 16). The DEGs were significantly enriched in macromolecular complex subunit organization and focal adhesion pathways. Eleven significantly enriched transcription factor motifs (PAX5, EGR1, XBP1, SREBP1, OLF1, MZF1, NFY, NFKAPPAB, MYCMAX, NFE2, and RP58) were identified. The DEGs and transcription factor motifs identified in our study provide biomarkers for the understanding of Down syndrome pathogenesis and progression.

Computer simulation of the stochastic transport equation

Trabalho apresentado no 37th Conference on Stochastic Processes and their Applications - July 28 - August 01, 2014 -Universidad de Buenos Aires

Balancing immunity and tolerance: genetic footprint of natural selection in the transcriptional regulatory region of HLA-G

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

The SUCEST-FUN regulatory network database: Designing an energy grass

Modern sugarcane cultivars are complex hybrids resulting from crosses among several Saccharum species. Traditional breeding methods have been employed extensively in different countries over the past decades to develop varieties with increased sucrose yield and resistance to pests and diseases. Conventional variety improvement, however, may be limited by the narrow pool of suitable genes. Thus, molecular genetics is seen as a promising tool to assist in the process of developing improved varieties. The SUCEST-FUN Project (http://sucest-fun.org) aims to associate function with sugarcane genes using a variety of tools, in particular those that enable the study of the sugarcane transcriptome. An extensive analysis has been conducted to characterise, phenotypically, sugarcane genotypes with regard to their sucrose content, biomass and drought responses. Through the analysis of different cultivars, genes associated with sucrose content, yield, lignin and drought have been identified. Currently, tools are being developed to determine signalling and regulatory networks in grasses, and to sequence the sugarcane genome, as well as to identify sugarcane promoters. This is being implemented through the SUCEST-FUN (http://sucest-fun.org) and GRASSIUS databases (http://grassius.org), the cloning of sugarcane promoters, the identification of cis-regulatory elements (CRE) using Chromatin Immunoprecipitation-sequencing (ChIP-Seq) and the generation of a comprehensive Signal Transduction and Transcription gene catalogue (SUCAST Catalogue).

Modeling gene expression regulatory networks with the sparse vector autoregressive model

Abstract Background To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems. Results We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets. Conclusion The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any a priori information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.

Systems Biology Approaches to Probe Gene Regulation in Bacteria

Mechanisms that allow pathogens to colonize the host are not the product of isolated genes, but instead emerge from the concerted operation of regulatory networks. Therefore, identifying components and the systemic behavior of networks is necessary to a better understanding of gene regulation and pathogenesis. To this end, I have developed systems biology approaches to study transcriptional and post-transcriptional gene regulation in bacteria, with an emphasis in the human pathogen Mycobacterium tuberculosis (Mtb). First, I developed a network response method to identify parts of the Mtb global transcriptional regulatory network utilized by the pathogen to counteract phagosomal stresses and survive within resting macrophages. As a result, the method unveiled transcriptional regulators and associated regulons utilized by Mtb to establish a successful infection of macrophages throughout the first 14 days of infection. Additionally, this network-based analysis identified the production of Fe-S proteins coupled to lipid metabolism through the alkane hydroxylase complex as a possible strategy employed by Mtb to survive in the host. Second, I developed a network inference method to infer the small non-coding RNA (sRNA) regulatory network in Mtb. The method identifies sRNA-mRNA interactions by integrating a priori knowledge of possible binding sites with structure-driven identification of binding sites. The reconstructed network was useful to predict functional roles for the multitude of sRNAs recently discovered in the pathogen, being that several sRNAs were postulated to be involved in virulence-related processes. Finally, I applied a combined experimental and computational approach to study post-transcriptional repression mediated by small non-coding RNAs in bacteria. Specifically, a probabilistic ranking methodology termed rank-conciliation was developed to infer sRNA-mRNA interactions based on multiple types of data. The method was shown to improve target prediction in Escherichia coli, and therefore is useful to prioritize candidate targets for experimental validation.

A potential role for the nuclear factor of activated T cells family of transcriptional regulatory proteins in adipogenesis

NFAT (nuclear factor of activated T cells) is a family of transcription factors implicated in the control of cytokine and early immune response gene expression. Recent studies have pointed to a role for NFAT proteins in gene regulation outside of the immune system. Herein we demonstrate that NFAT proteins are present in 3T3-L1 adipocytes and, upon fat cell differentiation, bind to and transactivate the promoter of the adipocyte-specific gene aP2. Further, fat cell differentiation is inhibited by cyclosporin A, a drug shown to prevent NFAT nuclear localization and hence function. Thus, these data suggest a role for NFAT transcription factors in the regulation of the aP2 gene and in the process of adipocyte differentiation.

Smad proteins function as co-modulators for MEF2 transcriptional regulatory proteins

An emerging theme in transforming growth factor-β (TGF-β) signalling is the association of the Smad proteins with diverse groups of transcriptional regulatory proteins. Several Smad cofactors have been identified to date but the diversity of TGF-β effects on gene transcription suggests that interactions with other co-regulators must occur. In these studies we addressed the possible interaction of Smad proteins with the myocyte enhancer-binding factor 2 (MEF2) transcriptional regulators. Our studies indicate that Smad2 and 4 (Smad2/4) complexes cooperate with MEF2 regulatory proteins in a GAL4-based one-hybrid reporter gene assay. We have also observed in vivo interactions between Smad2 and MEF2A using co-immunoprecipitation assays. This interaction is confirmed by glutathione S-transferase pull-down analysis. Immunofluorescence studies in C2C12 myotubes show that Smad2 and MEF2A co-localise in the nucleus of multinuclear myotubes during differentiation. Interestingly, phospho-acceptor site mutations of MEF2 that render it unresponsive to p38 MAP kinase signalling abrogate the cooperativity with the Smads suggesting that p38 MAP Kinase-catalysed phosphorylation of MEF2 is a prerequisite for the Smad–MEF2 interaction. Thus, the association between Smad2 and MEF2A may subserve a physical link between TGF-β signalling and a diverse array of genes controlled by the MEF2 cis element.

Domains with transcriptional regulatory activity within the ALL1 and AF4 proteins involved in acute leukemia.

The ALLI gene, located at chromosome band 11q23, is involved in acute leukemia through a series of chromosome translocations and fusion to a variety of genes, most frequently to A4 and AF9. The fused genes encode chimeric proteins proteins. Because the Drosophila homologue of ALL1, trithorax, is a positive regulator of homeotic genes and acts at the level of transcription, it is conceivable that alterations in ALL1 transcriptional activity may underlie its action in malignant transformation. To begin studying this, we examined the All1, AF4, AF9, and AF17 proteins for the presence of potential transcriptional regulatory domains. This was done by fusing regions of the proteins to the yeast GAL4 DNA binding domain and assaying their effect on transcription of a reporter gene. A domain of 55 residues positioned at amino acids 2829-2883 of ALL1 was identified as a very strong activator. Further analysis of this domain by in vitro mutagenesis pointed to a core of hydrophobic and acidic residues as critical for the activity. An ALL1 domain that repressed transcription of the reporter gene coincided with the sequence homologous to a segment of DNA methyltransferase. An AF4 polypeptide containing residues 480-560 showed strong activation potential. The C-terminal segment of AF9 spanning amino acids 478-568 transactivated transcription of the reporter gene in HeLa but not in NIH 3T3 cells. These results suggest that ALL1, AF4, and probably AF9 interact with the transcriptional machinery of the cell.

Conservation and function of the transcriptional regulatory protein Runt.

A phylogenetic approach was used to identify conserved regions of the transcriptional regulator Runt. Alignment of the deduced protein sequences from Drosophila melanogaster, Drosophila pseudoobscura, and Drosophila virilis revealed eight blocks of high sequence homology separated by regions with little or no homology. The largest conserved block contains the Runt domain, a DNA and protein binding domain conserved in a small family of mammalian transcription factors. The functional properties of the Runt domain from the D. melanogaster gene and the human AML1 (acute myeloid leukemia 1) gene were compared in vitro and in vivo. Electrophoretic mobility-shift assays with Runt/AML1 chimeras demonstrated that the different DNA binding properties of Runt and AML1 are due to differences within their respective Runt domains. Ectopic expression experiments indicated that proteins containing the AML1 Runt domain function in Drosophila embryos and that sequences outside of this domain are important in vivo.

Bistability and switching in the lysis/lysogeny genetic regulatory network of bacteriophage lambda

Bistability and switching are two important aspects of the genetic regulatory network of phage. Positive and negative feedbacks are key regulatory mechanisms in this network. By the introduction of threshold values, the developmental pathway of A phage is divided into different stages. If the protein level reaches a threshold value, positive or negative feedback will be effective and regulate the process of development. Using this regulatory mechanism, we present a quantitative model to realize bistability and switching of phage based on experimental data. This model gives descriptions of decisive mechanisms for different pathways in induction. A stochastic model is also introduced for describing statistical properties of switching in induction. A stochastic degradation rate is used to represent intrinsic noise in induction for switching the system from the lysogenic pathway to the lysis pathway. The approach in this paper represents an attempt to describe the regulatory mechanism in genetic regulatory network under the influence of intrinsic noise in the framework of continuous models. (C) 2003 Elsevier Ltd. All rights reserved.

The prion protein gene: Identifying regulatory signals using marsupial sequence

The function of the prion protein gene (PRNP) and its normal product PrPC is elusive. We used comparative genomics as a strategy to understand the normal function of PRNP. As the reliability of comparisons increases with the number of species and increased evolutionary distance, we isolated and sequenced a 66.5 kb BAC containing the PRNP gene from a distantly related mammal, the model Australian marsupial Macropus eugenii (tammar wallaby). Marsupials are separated from eutherians such as human and mouse by roughly 180 million years of independent evolution. We found that tammar PRNP, like human PRNP, has two exons. Prion proteins encoded by the tammar wallaby and a distantly related marsupial, Monodelphis domestica (Brazilian opossum) PRNP contain proximal PrP repeats with a distinct, marsupial-specific composition and a variable number. Comparisons of tammar wallaby PRNP with PRNPs from human, mouse, bovine and ovine allowed us to identify non-coding gene regions conserved across the marsupial-eutherian evolutionary distance, which are candidates for regulatory regions. In the PRNP 3' UTR we found a conserved signal for nuclear-specific polyadenylation and the putative cytoplasmic polyadenylation element (CPE), indicating that post-transcriptional control of PRNP mRNA activity is important. Phylogenetic footprinting revealed conserved potential binding sites for the MZF-1 transcription factor in both upstream promoter and intron/intron 1, and for the MEF2, MyTI, Oct-1 and NFAT transcription factors in the intron(s). The presence of a conserved NFAT-binding site and CPE indicates involvement of PrPC in signal transduction and synaptic plasticity. (c) 2004 Elsevier B.V. All rights reserved.