MicroRNA-499 expression distinctively correlates to target genes sox6 and rod1 profiles to resolve the skeletal muscle phenotype in nile tilapia

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Systemic analysis of microarray data sets towards identifying genes regulating root development

Nitrogen and water are essential for plant growth and development. In this study, we designed experiments to produce gene expression data of poplar roots under nitrogen starvation and water deprivation conditions. We found low concentration of nitrogen led first to increased root elongation followed by lateral root proliferation and eventually increased root biomass. To identify genes regulating root growth and development under nitrogen starvation and water deprivation, we designed a series of data analysis procedures, through which, we have successfully identified biologically important genes. Differentially Expressed Genes (DEGs) analysis identified the genes that are differentially expressed under nitrogen starvation or drought. Protein domain enrichment analysis identified enriched themes (in same domains) that are highly interactive during the treatment. Gene Ontology (GO) enrichment analysis allowed us to identify biological process changed during nitrogen starvation. Based on the above analyses, we examined the local Gene Regulatory Network (GRN) and identified a number of transcription factors. After testing, one of them is a high hierarchically ranked transcription factor that affects root growth under nitrogen starvation. It is very tedious and time-consuming to analyze gene expression data. To avoid doing analysis manually, we attempt to automate a computational pipeline that now can be used for identification of DEGs and protein domain analysis in a single run. It is implemented in scripts of Perl and R.

Developmental self-construction and -configuration of functional neocortical neuronal networks.

The prenatal development of neural circuits must provide sufficient configuration to support at least a set of core postnatal behaviors. Although knowledge of various genetic and cellular aspects of development is accumulating rapidly, there is less systematic understanding of how these various processes play together in order to construct such functional networks. Here we make some steps toward such understanding by demonstrating through detailed simulations how a competitive co-operative ('winner-take-all', WTA) network architecture can arise by development from a single precursor cell. This precursor is granted a simplified gene regulatory network that directs cell mitosis, differentiation, migration, neurite outgrowth and synaptogenesis. Once initial axonal connection patterns are established, their synaptic weights undergo homeostatic unsupervised learning that is shaped by wave-like input patterns. We demonstrate how this autonomous genetically directed developmental sequence can give rise to self-calibrated WTA networks, and compare our simulation results with biological data.

DBTBS: a database of Bacillus subtilis promoters and transcription factors

With the completion of the determination of its entire genome sequence, one of the next major targets of Bacillus subtilis genomics is to clarify the whole gene regulatory network. To this end, the results of systematic experiments should be compared with the rich source of individual experimental results accumulated so far. Thus, we constructed a database of the upstream regulatory information of B.subtilis (DBTBS). The current version was constructed by surveying 291 references and contains information on 90 binding factors and 403 promoters. For each promoter, all of its known cis-elements are listed according to their positions, while these cis-elements are aligned to illustrate their consensus sequence for each transcription factor. All probable transcription factors coded in the genome were classified with the Pfam motifs. Using this database, we compared the character of B.subtilis promoters with that of Escherichia coli promoters. Our database is accessible at http://elmo.ims.u-tokyo.ac.jp/dbtbs/.

Primary sex determination of placental mammals: a modelling study uncovers dynamical developmental constraints in the formation of Sertoli and granulosa cells

Primary sex determination in placental mammals is a very well studied developmental process. Here, we aim to investigate the currently established scenario and to assess its adequacy to fully recover the observed phenotypes, in the wild type and perturbed situations. Computational modelling allows clarifying network dynamics, elucidating crucial temporal constrains as well as interplay between core regulatory modules.

Physiological and molecular analysis of the interaction between aluminium toxicity and drought stress in common bean (Phaseolus vulgaris)

Aluminium (Al) toxicity and drought are two major factors limiting common bean (Phaseolus vulgaris) production in the tropics. Short-term effects of Al toxicity and drought stress on root growth in acid, Al-toxic soil were studied, with special emphasis on Al-drought interaction in the root apex. Root elongation was inhibited by both Al and drought. Combined stresses resulted in a more severe inhibition of root elongation than either stress alone. This result was different from the alleviation of Al toxicity by osmotic stress (-0.60 MPa polyethylene glycol) in hydroponics. However, drought reduced the impact of Al on the root tip, as indicated by the reduction of Al-induced callose formation and MATE expression. Combined Al and drought stress enhanced up-regulation of ACCO expression and synthesis of zeatin riboside, reduced drought-enhanced abscisic acid (ABA) concentration, and expression of NCED involved in ABA biosynthesis and the transcription factors bZIP and MYB, thus affecting the regulation of ABA-dependent genes (SUS, PvLEA18, KS-DHN, and LTP) in root tips. The results provide circumstantial evidence that in soil, drought alleviates Al injury, but Al renders the root apex more drought-sensitive, particularly by impacting the gene regulatory network involved in ABA signal transduction and cross-talk with other phytohormones necessary for maintaining root growth under drought.

Insights into Neural Crest Evolution

Neural crest cells are unique to vertebrates and essential to the development and evolution of the craniofacial skeleton. Using a combination of DiI cell lineage tracing, transcriptomics, and analysis of key transcription factors of the Sox Family, I examined neural crest development in the sea lamprey, Petromyzon marinus, as the most basal extant vertebrate from which it is possible to get embryos. The results have uncovered distinct cranial and trunk neural crest subpopulations along the anterior-posterior axis of the lamprey embryo, with a clear separation between the two. However, no evidence of the presence of an intermediate vagal neural crest population was uncovered. Comparing cranial neural crest genes between lamprey and chick, either by examining individual candidate genes or whole genome transcriptome analysis, reveals significant changes in the cranial neural crest gene regulatory network of lamprey compared with chick. In particular, the lamprey cranial neural crest is "missing" several gnathostome cranial crest genes. We speculate that these may underlie the evolutionary divergence of craniofacial development between jawed and jawless vertebrates. Despite the absence of vagal neural crest, DiI-labeling shows that trunk neural crest-derived cells, likely homologous to mammalian Schwann cell precursors, contribute to the lamprey enteric nervous system, potentially representing the most primitive form of neural crest cells contribution to the ENS. Finally, I characterized key members of the Sox Family (Sox B-F) due to their importance in neural crest specification in other species. In comparative studies of the SoxC genes (Sox4, Sox11, and Sox12) in both lamprey and Xenopus, I found similar expression patterns and a novel key role in early neural crest specification, suggesting a conserved role of the SoxC genes amongst vertebrates. Taken together, this work represents important progress in characterizing the early evolution of the neural crest in vertebrates and its role in the transition from jawless to jawed vertebrates.

Multiscale analysis of protein functions and stochastic modelling of gene transcriptional regulatory networks

Genomic and proteomic analyses have attracted a great deal of interests in biological research in recent years. Many methods have been applied to discover useful information contained in the enormous databases of genomic sequences and amino acid sequences. The results of these investigations inspire further research in biological fields in return. These biological sequences, which may be considered as multiscale sequences, have some specific features which need further efforts to characterise using more refined methods. This project aims to study some of these biological challenges with multiscale analysis methods and stochastic modelling approach. The first part of the thesis aims to cluster some unknown proteins, and classify their families as well as their structural classes. A development in proteomic analysis is concerned with the determination of protein functions. The first step in this development is to classify proteins and predict their families. This motives us to study some unknown proteins from specific families, and to cluster them into families and structural classes. We select a large number of proteins from the same families or superfamilies, and link them to simulate some unknown large proteins from these families. We use multifractal analysis and the wavelet method to capture the characteristics of these linked proteins. The simulation results show that the method is valid for the classification of large proteins. The second part of the thesis aims to explore the relationship of proteins based on a layered comparison with their components. Many methods are based on homology of proteins because the resemblance at the protein sequence level normally indicates the similarity of functions and structures. However, some proteins may have similar functions with low sequential identity. We consider protein sequences at detail level to investigate the problem of comparison of proteins. The comparison is based on the empirical mode decomposition (EMD), and protein sequences are detected with the intrinsic mode functions. A measure of similarity is introduced with a new cross-correlation formula. The similarity results show that the EMD is useful for detection of functional relationships of proteins. The third part of the thesis aims to investigate the transcriptional regulatory network of yeast cell cycle via stochastic differential equations. As the investigation of genome-wide gene expressions has become a focus in genomic analysis, researchers have tried to understand the mechanisms of the yeast genome for many years. How cells control gene expressions still needs further investigation. We use a stochastic differential equation to model the expression profile of a target gene. We modify the model with a Gaussian membership function. For each target gene, a transcriptional rate is obtained, and the estimated transcriptional rate is also calculated with the information from five possible transcriptional regulators. Some regulators of these target genes are verified with the related references. With these results, we construct a transcriptional regulatory network for the genes from the yeast Saccharomyces cerevisiae. The construction of transcriptional regulatory network is useful for detecting more mechanisms of the yeast cell cycle.

Bagging statistical network inference from large-scale gene expression data.

Modern biology and medicine aim at hunting molecular and cellular causes of biological functions and diseases. Gene regulatory networks (GRN) inferred from gene expression data are considered an important aid for this research by providing a map of molecular interactions. Hence, GRNs have the potential enabling and enhancing basic as well as applied research in the life sciences. In this paper, we introduce a new method called BC3NET for inferring causal gene regulatory networks from large-scale gene expression data. BC3NET is an ensemble method that is based on bagging the C3NET algorithm, which means it corresponds to a Bayesian approach with noninformative priors. In this study we demonstrate for a variety of simulated and biological gene expression data from S. cerevisiae that BC3NET is an important enhancement over other inference methods that is capable of capturing biochemical interactions from transcription regulation and protein-protein interaction sensibly. An implementation of BC3NET is freely available as an R package from the CRAN repository. © 2012 de Matos Simoes, Emmert-Streib.

Control of the master virulence regulatory gene atxA in Bacillus anthracis

Transcription of the Bacillus anthracis structural genes for the anthrax toxin proteins and biosynthetic operon for capsule are positively regulated by AtxA, a transcription regulator with unique properties. Consistent with the role of atxA in virulence factor expression, a B. anthracis atxA-null mutant is avirulent in a murine model for anthrax. In batch culture, multiple signals impact atxA transcript levels, and the timing and steady state level of atxA expression is critical for optimal toxin and capsule synthesis. Despite the apparent complex control of atxA transcription, only one trans-acting protein, the transition state regulator AbrB, has been demonstrated to directly interact with the atxA promoter. The AbrB-binding site has been described, but additional cis-acting control sequences have not been defined. Using transcriptional lacZ fusions, electrophoretic mobility shift assays, and Western blot analysis, the cis-acting elements and trans-acting factors involved in regulation of atxA in B. anthracis strains containing either both virulence plasmids, pXO1 and pXO2, or only one plasmid, pXO1, were studied. This work demonstrates that atxA transcription from the major start site P1 is dependent upon a consensus sequence for the housekeeping sigma factor SigA, and an A+T-rich upstream element (UP-element) for RNA polymerase (RNAP). In addition, the data show that a trans-acting protein(s) other than AbrB negatively impacts atxA transcription when it binds specifically to a 9-bp palindrome within atxA promoter sequences located downstream of P1. Mutation of the palindrome prevents binding of the trans-acting protein(s) and results in a corresponding increase in AtxA and anthrax toxin production in a strain- and culture-dependent manner. The identity of the trans-acting repressor protein(s) remains elusive; however, phenotypes associated with mutation of the repressor binding site have revealed that the trans-acting repressor protein(s) indirectly controls B. anthracis development. Mutation of the repressor binding site results in misregulation and overexpression of AtxA in conditions conducive for development, leading to a marked sporulation defect that is both atxA- and pXO2-61-dependent. pXO2-61 is homologous to the sensor domain of sporulation sensor histidine kinases and is proposed to titrate an activating signal away from the sporulation phosphorelay when overexpressed by AtxA. These results indicate that AtxA is not only a master virulence regulator, but also a modulator of proper B. anthracis development. Also demonstrated in this work is the impact of the developmental regulators AbrB, Spo0A, and SigH on atxA expression and anthrax toxin production in a genetically incomplete (pXO1+, pXO2-) and genetically complete (pXO1+, pXO2+) strain background. AtxA and anthrax toxin production resulting from deletion of the developmental regulators are strain-dependent suggesting that factors on pXO2 are involved in control of atxA. The only developmental deletion mutant that resulted in a prominent and consistent strain-independent increase in AtxA protein levels was an abrB-null mutant. As a result of increased AtxA levels, there is early and increased production of anthrax toxins in an abrB-null mutant. In addition, the abrB-null mutant exhibited an increase in virulence in a murine model for anthrax. In contrast, virulence of the atxA promoter mutant was unaffected in a murine model for anthrax despite the production of 5-fold more AtxA than the abrB-null mutant. These results imply that AtxA is not the only factor impacting pathogenesis in an abrB-null mutant. Overall, this work highlights the complex regulatory network that governs expression of atxA and provides an additional role for AtxA in B. anthracis development.

Integration of circadian and phototransduction pathways in the network controlling CAB gene transcription in Arabidopsis

The transcription of CAB genes, encoding the chlorophyll a/b-binding proteins, is rapidly induced in dark-grown Arabidopsis seedlings following a light pulse. The transient induction is followed by several cycles of a circadian rhythm. Seedlings transferred to continuous light are known to exhibit a robust circadian rhythm of CAB expression. The precise waveform of CAB expression in light–dark cycles, however, reflects a regulatory network that integrates information from photoreceptors, from the circadian clock and possibly from a developmental program. We have used the luciferase reporter system to investigate CAB expression with high time resolution. We demonstrate that CAB expression in light-grown plants exhibits a transient induction following light onset, similar to the response in dark-grown seedlings. The circadian rhythm modulates the magnitude and the kinetics of the response to light, such that the CAB promoter is not light responsive during the subjective night. A signaling pathway from the circadian oscillator must therefore antagonize the phototransduction pathways controlling the CAB promoter. We have further demonstrated that the phase of maximal CAB expression is delayed in light–dark cycles with long photoperiods, due to the entrainment of the circadian oscillator. Under short photoperiods, this pattern of entrainment ensures that dawn coincides with a phase of high light responsiveness, whereas under long photoperiods, the light response at dawn is reduced.

Computational approaches for modelling intrinsic noise and delays in genetic regulatory networks

This chapter focuses on the interactions and roles between delays and intrinsic noise effects within cellular pathways and regulatory networks. We address these aspects by focusing on genetic regulatory networks that share a common network motif, namely the negative feedback loop, leading to oscillatory gene expression and protein levels. In this context, we discuss computational simulation algorithms for addressing the interplay of delays and noise within the signaling pathways based on biological data. We address implementational issues associated with efficiency and robustness. In a molecular biology setting we present two case studies of temporal models for the Hes1 gene (Monk, 2003; Hirata et al., 2002), known to act as a molecular clock, and the Her1/Her7 regulatory system controlling the periodic somite segmentation in vertebrate embryos (Giudicelli and Lewis, 2004; Horikawa et al., 2006).

Prediction of transcriptional regulatory interactions in bacteria : a comparative genomics approach

Exponential growth of genomic data in the last two decades has made manual analyses impractical for all but trial studies. As genomic analyses have become more sophisticated, and move toward comparisons across large datasets, computational approaches have become essential. One of the most important biological questions is to understand the mechanisms underlying gene regulation. Genetic regulation is commonly investigated and modelled through the use of transcriptional regulatory network (TRN) structures. These model the regulatory interactions between two key components: transcription factors (TFs) and the target genes (TGs) they regulate. Transcriptional regulatory networks have proven to be invaluable scientific tools in Bioinformatics. When used in conjunction with comparative genomics, they have provided substantial insights into the evolution of regulatory interactions. Current approaches to regulatory network inference, however, omit two additional key entities: promoters and transcription factor binding sites (TFBSs). In this study, we attempted to explore the relationships among these regulatory components in bacteria. Our primary goal was to identify relationships that can assist in reducing the high false positive rates associated with transcription factor binding site predictions and thereupon enhance the reliability of the inferred transcription regulatory networks. In our preliminary exploration of relationships between the key regulatory components in Escherichia coli transcription, we discovered a number of potentially useful features. The combination of location score and sequence dissimilarity scores increased de novo binding site prediction accuracy by 13.6%. Another important observation made was with regards to the relationship between transcription factors grouped by their regulatory role and corresponding promoter strength. Our study of E.coli ��70 promoters, found support at the 0.1 significance level for our hypothesis | that weak promoters are preferentially associated with activator binding sites to enhance gene expression, whilst strong promoters have more repressor binding sites to repress or inhibit gene transcription. Although the observations were specific to �70, they nevertheless strongly encourage additional investigations when more experimentally confirmed data are available. In our preliminary exploration of relationships between the key regulatory components in E.coli transcription, we discovered a number of potentially useful features { some of which proved successful in reducing the number of false positives when applied to re-evaluate binding site predictions. Of chief interest was the relationship observed between promoter strength and TFs with respect to their regulatory role. Based on the common assumption, where promoter homology positively correlates with transcription rate, we hypothesised that weak promoters would have more transcription factors that enhance gene expression, whilst strong promoters would have more repressor binding sites. The t-tests assessed for E.coli �70 promoters returned a p-value of 0.072, which at 0.1 significance level suggested support for our (alternative) hypothesis; albeit this trend may only be present for promoters where corresponding TFBSs are either all repressors or all activators. Nevertheless, such suggestive results strongly encourage additional investigations when more experimentally confirmed data will become available. Much of the remainder of the thesis concerns a machine learning study of binding site prediction, using the SVM and kernel methods, principally the spectrum kernel. Spectrum kernels have been successfully applied in previous studies of protein classification [91, 92], as well as the related problem of promoter predictions [59], and we have here successfully applied the technique to refining TFBS predictions. The advantages provided by the SVM classifier were best seen in `moderately'-conserved transcription factor binding sites as represented by our E.coli CRP case study. Inclusion of additional position feature attributes further increased accuracy by 9.1% but more notable was the considerable decrease in false positive rate from 0.8 to 0.5 while retaining 0.9 sensitivity. Improved prediction of transcription factor binding sites is in turn extremely valuable in improving inference of regulatory relationships, a problem notoriously prone to false positive predictions. Here, the number of false regulatory interactions inferred using the conventional two-component model was substantially reduced when we integrated de novo transcription factor binding site predictions as an additional criterion for acceptance in a case study of inference in the Fur regulon. This initial work was extended to a comparative study of the iron regulatory system across 20 Yersinia strains. This work revealed interesting, strain-specific difierences, especially between pathogenic and non-pathogenic strains. Such difierences were made clear through interactive visualisations using the TRNDifi software developed as part of this work, and would have remained undetected using conventional methods. This approach led to the nomination of the Yfe iron-uptake system as a candidate for further wet-lab experimentation due to its potential active functionality in non-pathogens and its known participation in full virulence of the bubonic plague strain. Building on this work, we introduced novel structures we have labelled as `regulatory trees', inspired by the phylogenetic tree concept. Instead of using gene or protein sequence similarity, the regulatory trees were constructed based on the number of similar regulatory interactions. While the common phylogentic trees convey information regarding changes in gene repertoire, which we might regard being analogous to `hardware', the regulatory tree informs us of the changes in regulatory circuitry, in some respects analogous to `software'. In this context, we explored the `pan-regulatory network' for the Fur system, the entire set of regulatory interactions found for the Fur transcription factor across a group of genomes. In the pan-regulatory network, emphasis is placed on how the regulatory network for each target genome is inferred from multiple sources instead of a single source, as is the common approach. The benefit of using multiple reference networks, is a more comprehensive survey of the relationships, and increased confidence in the regulatory interactions predicted. In the present study, we distinguish between relationships found across the full set of genomes as the `core-regulatory-set', and interactions found only in a subset of genomes explored as the `sub-regulatory-set'. We found nine Fur target gene clusters present across the four genomes studied, this core set potentially identifying basic regulatory processes essential for survival. Species level difierences are seen at the sub-regulatory-set level; for example the known virulence factors, YbtA and PchR were found in Y.pestis and P.aerguinosa respectively, but were not present in both E.coli and B.subtilis. Such factors and the iron-uptake systems they regulate, are ideal candidates for wet-lab investigation to determine whether or not they are pathogenic specific. In this study, we employed a broad range of approaches to address our goals and assessed these methods using the Fur regulon as our initial case study. We identified a set of promising feature attributes; demonstrated their success in increasing transcription factor binding site prediction specificity while retaining sensitivity, and showed the importance of binding site predictions in enhancing the reliability of regulatory interaction inferences. Most importantly, these outcomes led to the introduction of a range of visualisations and techniques, which are applicable across the entire bacterial spectrum and can be utilised in studies beyond the understanding of transcriptional regulatory networks.

Mutations in cardiac T-Box Factor gene TBX20 are associated with diverse cardiac pathologies, including defects of septation and valvulogenesis and cardiomyopathy

The T-box family transcription factor gene TBX20 acts in a conserved regulatory network, guiding heart formation and patterning in diverse species. Mouse Tbx20 is expressed in cardiac progenitor cells, differentiating cardiomyocytes, and developing valvular tissue, and its deletion or RNA interference-mediated knockdown is catastrophic for heart development. TBX20 interacts physically, functionally, and genetically with other cardiac transcription factors, including NKX2-5, GATA4, and TBX5, mutations of which cause congenital heart disease (CHD). Here, we report nonsense (Q195X) and missense (I152M) germline mutations within the T-box DNA-binding domain of human TBX20 that were associated with a family history of CHD and a complex spectrum of developmental anomalies, including defects in septation, chamber growth, and valvulogenesis. Biophysical characterization of wild-type and mutant proteins indicated how the missense mutation disrupts the structure and function of the TBX20 T-box. Dilated cardiomyopathy was a feature of the TBX20 mutant phenotype in humans and mice, suggesting that mutations in developmental transcription factors can provide a sensitized template for adult-onset heart disease. Our findings are the first to link TBX20 mutations to human pathology. They provide insights into how mutation of different genes in an interactive regulatory circuit lead to diverse clinical phenotypes, with implications for diagnosis, genetic screening, and patient follow-up.

Computational interrogation of cis-regulatory elements of genes that are common targets of luteotropin and luteolysin in the primate corpus luteum

The rapid recent increase in microarray-based gene expression studies in the corpus luteum (CL) utilizing macaque models gathered increasing volume of data in publically accessible microarray expression databases. Examining gene pathways in different functional states of CL may help to understand the factors that control luteal function and hence human fertility. Co-regulation of genes in microarray experiments may imply common transcriptional regulation by sequence-specific DNA-binding transcriptional factors. We have computationally analyzed the transcription factor binding sites (TFBS) in a previously reported macaque luteal microarray gene set (n = 15) that are common targets of luteotropin (luteinizing hormone (LH) and human chorionic gonadotropin (hCG)) and luteolysin (prostaglandin (PG) F-2 alpha). This in silico approach can reveal transcriptional networks that control these important genes which are representative of the interplay between luteotropic and luteolytic factors in the control of luteal function. Our computational analyses revealed 6 matrix families whose binding sites are significantly over-represented in promoters of these genes. The roles of these factors are discussed, which might help to understand the transcriptional regulatory network in the control of luteal function. These factors might be promising experimental targets for investigation of human luteal insufficiency. (C) 2012 Elsevier B.V. All rights reserved.