Genetic epidemiology of gallbladder disease in Mexican Americans and cholesterol 7$\alpha$-hydroxylase gene sequence variation

Among Mexican Americans, the second largest minority group in the United States, the prevalence of gallbladder disease is markedly elevated. Previous data from both genetic admixture and family studies indicate that there is a genetic component to the occurrence of gallbladder disease in Mexican Americans. However, prior to this thesis no formal genetic analysis of gallbladder disease had been carried out nor had any contributing genes been identified.^ The results of complex segregation analysis in a sample of 232 Mexican American pedigrees documented the existence of a major gene having two alleles with age- and gender-specific effects influencing the occurrence of gallbladder disease. The estimated frequency of the allele increasing susceptibility was 0.39. The lifetime probabilities that an individual will be affected by gallbladder disease were 1.0, 0.54, and 0.00 for females of genotypes "AA", "Aa", and "aa", respectively, and 0.68, 0.30, and 0.00 for males, respectively. This analysis provided the first conclusive evidence for the existence of a common single gene having a large effect on the occurrence of gallbladder disease.^ Human cholesterol 7$\alpha$-hydroxylase is the rate-limiting enzyme in bile acid synthesis. The results of an association study in both a random sample and a matched case/control sample showed that there is a significant association between cholesterol 7$\alpha$-hydroxylase gene variation and the occurrence of gallbladder disease in Mexican Americans males but not in females. These data have implicated a specific gene, 7$\alpha$-hydroxylase, in the etiology of gallbladder disease in this population.^ Finally, I asked whether the inferred major gene from complex segregation analysis is genetically linked to the cholesterol 7$\alpha$-hydroxylase gene. Three pedigrees predicted to be informative for linkage analysis by virtue of supporting the major gene hypothesis and having parents with informative genotypes and multiple offspring were selected for this linkage analysis. In each of these pedigrees, the recombination fractions maximized at 0 with a positive, albeit low, LOD score. The results of this linkage analysis provide preliminary and suggestive evidence that the cholesterol 7$\alpha$-hydroxylase gene and the inferred gallbladder disease susceptibility gene are genetically linked. ^

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.

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.

Gene-gene interaction and gene-pathway analysis of genome-wide association for schizophrenia

Schizophrenia (SZ) is a complex disorder with high heritability and variable phenotypes that has limited success in finding causal genes associated with the disease development. Pathway-based analysis is an effective approach in investigating the molecular mechanism of susceptible genes associated with complex diseases. The etiology of complex diseases could be a network of genetic factors and within the genes, interaction may occur. In this work we argue that some genes might be of small effect that by itself are neither sufficient nor necessary to cause the disease however, their effect may induce slight changes to the gene expression or affect the protein function, therefore, analyzing the gene-gene interaction mechanism within the disease pathway would play crucial role in dissecting the genetic architecture of complex diseases, making the pathway-based analysis a complementary approach to GWAS technique. ^ In this study, we implemented three novel linkage disequilibrium based statistics, the linear combination, the quadratic, and the decorrelation test statistics, to investigate the interaction between linked and unlinked genes in two independent case-control GWAS datasets for SZ including participants of European (EA) and African (AA) ancestries. The EA population included 1,173 cases and 1,378 controls with 729,454 genotyped SNPs, while the AA population included 219 cases and 288 controls with 845,814 genotyped SNPs. We identified 17,186 interacting gene-sets at significant level in EA dataset, and 12,691 gene-sets in AA dataset using the gene-gene interaction method. We also identified 18,846 genes in EA dataset and 19,431 genes in AA dataset that were in the disease pathways. However, few genes were reported of significant association to SZ. ^ Our research determined the pathways characteristics for schizophrenia through the gene-gene interaction and gene-pathway based approaches. Our findings suggest insightful inferences of our methods in studying the molecular mechanisms of common complex diseases.^

Post-transcriptional Regulation of Mammalian Gene Expression in Non-coding Region of Target RNA

Tumor Suppressor Candidate 2 (TUSC2) is a novel tumor suppressor gene located in the human chromosome 3p21.3 region. TUSC2 mRNA transcripts could be detected on Northern blots in both normal lung and some lung cancer cell lines, but no endogenous TUSC2 protein could be detected in a majority of lung cancer cell lines. Mechanisms regulating TUSC2 protein expression and its inactivation in primary lung cancer cells are largely unknown. We investigated the role of the 5’- and 3’-untranslated regions (UTRs) of the TUSC2 gene in the regulation of TUSC2 protein expression. We found that two small upstream open-reading frames (uORFs) in the 5’UTR of TUSC2 could markedly inhibit the translational initiation of TUSC2 protein by interfering with the “scanning” of the ribosome initiation complexes. Site-specific stem-loop array reverse transcription-polymerase chain reaction (SLA-RT-PCR) verified several micoRNAs (miRNAs) targeted at 3’UTR and directed TUSC2 cleavage and degradation. In addition, we used the established let-7-targeted high mobility group A2 (Hmga2) mRNA as a model system to study the mechanism of regulation of target mRNA by miRNAs in mammalian cells under physiological conditions. There have been no evidence of direct link between mRNA downregulation and mRNA cleavages mediated by miRNAs. Here we showed that the endonucleolytic cleavages on mRNAs were initiated by mammalian miRNA in seed pairing style. Let-7 directed cleavage activities among the eight predicted potential target sites have varied efficiency, which are influenced by the positional and the structural contexts in the UTR. The 5’ cleaved RNA fragments were mostly oligouridylated at their 3’-termini and accumulated for delayed 5’–3’ degradation. RNA fragment oligouridylation played important roles in marking RNA fragments for delayed bulk degradation and in converting RNA degradation mode from 3’–5’ to 5’–3’ with cooperative efforts from both endonucleolytic and non-catalytic miRNA-induced silencing complex (miRISC). Our findings point to a mammalian miRNA-mediated mechanism for the regulation of mRNA that miRNA can decrease target mRNA through target mRNA cleavage and uridine addition

Functional Analysis of Drosophila Integrator Complex in snRNA 3' End Processing

Uridine-rich small nuclear RNAs (U snRNAs) play essential roles in eukaryotic gene expression by facilitating the removal of introns from mRNA precursors and the processing of the replication-dependent histone pre-mRNAs. Formation of the 3’ end of these snRNAs is carried out by a poorly characterized, twelve-membered protein complex named Integrator Complex. In the effort to understand Integrator Complex function in the formation of the snRNA 3’ end, we performed a functional RNAi screen in Drosophila S2 cells to identify protein factors required for snRNA 3’ end formation. This screen was conducted by using a fluorescence-based reporter that elicits GFP expression in response to a deficiency in snRNA processing. Besides scoring the known Integrator subunits, we identified Asunder and CG4785 as additional core members of the Integrator Complex. Additionally, we also found a conserved requirement for Cyclin C and Cdk8 in both fly and human snRNA 3’ end processing. We have further demonstrated that the kinase activity of Cdk8 is critical for snRNA 3’ end processing and is likely to function independent of its well-documented function within the Mediator Cdk8 module. Taken together, this work functionally defines the Drosophila Integrator Complex and demonstrates a novel function for Cyclin C/Cdk8 in snRNA 3’ end formation. This thesis work has also characterized an important functional interaction mediated by a microdomain within Integrator subunit 12 (IntS12) and IntS1 that is required for the activity of the Integrator Complex in processing the snRNA 3’ end. Through the development of a reporter-based functional RNAi-rescue assay in Drosophila S2 cells, we analyzed domains within IntS12 required for snRNA 3’ end formation. This analysis unexpectedly revealed that an N-terminal 30 amino acid region and not the highly conserved central PHD finger domain, is required for snRNA 3’ end cleavage. The IntS12 microdomain (1-45) functions autonomously, and is sufficient to interact and stabilize the putative scaffold protein IntS1. Our findings provide more details of the Integrator Complex for understanding the molecular mechanism of snRNA 3’ end processing. Moreover, these results lay the foundation for future studies of the complex through the identification of a novel functional domain within one subunit and the identification of additional subunits.

THE NATURAL AND ORTHOGONAL INTERACTION (NOIA) MODELS FOR QUANTITATIVE TRAITS (QTs) AND COMPLEX DISEASES

My dissertation focuses on developing methods for gene-gene/environment interactions and imprinting effect detections for human complex diseases and quantitative traits. It includes three sections: (1) generalizing the Natural and Orthogonal interaction (NOIA) model for the coding technique originally developed for gene-gene (GxG) interaction and also to reduced models; (2) developing a novel statistical approach that allows for modeling gene-environment (GxE) interactions influencing disease risk, and (3) developing a statistical approach for modeling genetic variants displaying parent-of-origin effects (POEs), such as imprinting. In the past decade, genetic researchers have identified a large number of causal variants for human genetic diseases and traits by single-locus analysis, and interaction has now become a hot topic in the effort to search for the complex network between multiple genes or environmental exposures contributing to the outcome. Epistasis, also known as gene-gene interaction is the departure from additive genetic effects from several genes to a trait, which means that the same alleles of one gene could display different genetic effects under different genetic backgrounds. In this study, we propose to implement the NOIA model for association studies along with interaction for human complex traits and diseases. We compare the performance of the new statistical models we developed and the usual functional model by both simulation study and real data analysis. Both simulation and real data analysis revealed higher power of the NOIA GxG interaction model for detecting both main genetic effects and interaction effects. Through application on a melanoma dataset, we confirmed the previously identified significant regions for melanoma risk at 15q13.1, 16q24.3 and 9p21.3. We also identified potential interactions with these significant regions that contribute to melanoma risk. Based on the NOIA model, we developed a novel statistical approach that allows us to model effects from a genetic factor and binary environmental exposure that are jointly influencing disease risk. Both simulation and real data analyses revealed higher power of the NOIA model for detecting both main genetic effects and interaction effects for both quantitative and binary traits. We also found that estimates of the parameters from logistic regression for binary traits are no longer statistically uncorrelated under the alternative model when there is an association. Applying our novel approach to a lung cancer dataset, we confirmed four SNPs in 5p15 and 15q25 region to be significantly associated with lung cancer risk in Caucasians population: rs2736100, rs402710, rs16969968 and rs8034191. We also validated that rs16969968 and rs8034191 in 15q25 region are significantly interacting with smoking in Caucasian population. Our approach identified the potential interactions of SNP rs2256543 in 6p21 with smoking on contributing to lung cancer risk. Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting affects several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we propose a NOIA framework for a single locus association study that estimates both main allelic effects and POEs. We develop statistical (Stat-POE) and functional (Func-POE) models, and demonstrate conditions for orthogonality of the Stat-POE model. We conducted simulations for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Regulation of {\it neu\/} gene expression by the simian virus 40 large T antigen and tumor suppressors Rb and p53

The neu gene (also c-erbB-2 or HER2) encodes a 185 kilodalton protein that is frequently overexpressed in breast, ovarian and non-small cell lung cancers. Study of the regulation of neu indicates that neu gene expression can be modulated by c-myc or by the adenovirus 5 E1a gene product. This study demonstrates that the transforming protein, large T antigen, of the simian virus 40 represses neu promoter activity. Repression of neu by large T antigen is mediated through the region $-$172 to $-$79 (relative to first ATG) of the neu promoter--unlike through $-$312 to $-$172 for c-myc or E1a. This suggests a different pathway for repression of neu by large T antigen. The 10 amino acid region of large T required for binding the tumor suppressor, retinoblastoma gene product, Rb, is not necessary for repression of neu. Moreover, the tumor suppressors, Rb and p53 can independently inhibit neu promoter activity. Rb inhibits neu through a 10 base pair G-rich enhancer (GTG element) ($-$243 to $-$234) and also through regions close to transcription initiation sites ($-$172 to $-$79). Mutant Rb unable to complex large T is able to repress the region close to transcription initiation but not the GTG enhancer. Thus, Rb inhibits the two regulatory domains of the neu gene by different mechanisms. Both Rb and p53 can repress the transforming activity of activated neu in focus forming assays. These data provide evidence that tumor suppressors regulate expression of growth stimulatory genes such as neu. Therefore, one reason for the overexpression of neu that is frequently seen in breast cancer cells may be due to functional inactivation of Rb and p53 which is also a common occurrence in breast cancer cells. ^

Cross -packing among retroviruses and characterization of the feline immunodeficiency virus (FIV) packaging signal: Implications for the development of FIV-based gene transfer systems

Feline immunodeficiency virus (FIV)-based gene transfer systems are being seriously considered for human gene therapy as an alternative to vectors based on primate lentiviruses, a genetically complex group of retroviruses capable of infecting non-dividing cells. The greater phylogenetic distance between the feline and primate lentiviruses is thought to reduce chances of the generation of recombinant viruses. However, safety of FIV-based vector systems has not been tested experimentally. Since primate lentiviruses such as human and simian immunodeficiency viruses (HIV/SIV) can cross-package each other's genomes, we tested this trait with respect to FIV. Unexpectedly, both feline and primate lentiviruses were reciprocally able to both cross-package and propagate each other's RNA genomes. This was largely due to the recognition of viral packaging signals by the heterologous proteins. However, a simple retrovirus such as Mason-Pfizer monkey virus (MPMV) was unable to package FIV RNA. Interestingly, FIV could package MPMV RNA, but not propagate it for further steps of replication. These findings suggest that upon co-infection of the same host, cross-packaging may allow distinct retroviruses to generate chimeric variants with unknown pathogenic potential. ^ In order to understand the packaging determinants in FIV, we conducted a detailed mutational analysis of the region thought to contain FIV packaging signal. We show that the first 90–120 nt of the 5′ untranslated region (UTR) and the first 90 nt of gag were simultaneously required for efficient FIV RNA packaging. These results suggest that the primary FIV packaging signal is multipartite and discontinuous, composed of two core elements separated by 150 nt of the 5 ′UTR. ^ The above studies are being used towards the development of safer FIV-based self-inactivating (SIN) vectors. These vectors are being designed to eliminate the ability of FIV transfer vector RNAs to be mobilized by primate lentiviral proteins that may be present in the target cells. Preliminary test of the first generation of these vectors has revealed that they are incapable of being propagated by feline proteins. The inability of FIV transfer vectors to express packageable vector RNA after integration should greatly increase the safety of FIV vectors for human gene therapy. ^

Regulation of tyrosine hydroxylase gene expression by mRNA stabilization

Tyrosine hydroxylase (TH) expression increases in adrenal chromaffin cells treated with the nicotinic agonist, dimethylphenylpiperazinium (DMPP; 1 μM). We are using this response as a model of the changes in TH level that occur during increased cholinergic neural activity. Here we report a 4-fold increase in TH mRNA half-life in DMPP-treated chromaffin cells that is apparent when using a pulse-chase analysis to measure TH mRNA half-life. No increase is apparent using actinomycin D to measure half-life, indicating a requirement for ongoing transcription. Characterization of protein binding to the TH 3′UTR using RNA electro-mobility shift assays show the presence of two complexes both of which are increased by DMPP-treatment. The faster migrating complex (FMC) increases 2.5-fold and the slower migrating complex (SMC) increases 1.5-fold. Separation of UV crosslinked RNA-protein complexes on SDS polyacrylamide gels shows FMC to contain a single protein whereas SMC contains two proteins. Northwesterns yielded similar results. Transfection studies reveal an increase in expression of the full-length TH transcript due to DMPP-treatment similar to that of endogenous TH mRNA. This finding suggests the increased expression is due primarily to mRNA stabilization. Transfection of luciferase reporter constructs containing regions of the TH 3′UTR reveal only the full-length 3′UTR influenced the expression level of reporter transcripts. ^

Whole transcriptome analysis of the coral Acropora millepora reveals complex responses to CO2-driven acidification during the initiation of calcification

The impact of ocean acidification (OA) on coral calcification, a subject of intense current interest, is poorly understood in part because of the presence of symbionts in adult corals. Early life history stages of Acropora spp. provide an opportunity to study the effects of elevated CO(2) on coral calcification without the complication of symbiont metabolism. Therefore, we used the Illumina RNAseq approach to study the effects of acute exposure to elevated CO(2) on gene expression in primary polyps of Acropora millepora, using as reference a novel comprehensive transcriptome assembly developed for this study. Gene ontology analysis of this whole transcriptome data set indicated that CO(2) -driven acidification strongly suppressed metabolism but enhanced extracellular organic matrix synthesis, whereas targeted analyses revealed complex effects on genes implicated in calcification. Unexpectedly, expression of most ion transport proteins was unaffected, while many membrane-associated or secreted carbonic anhydrases were expressed at lower levels. The most dramatic effect of CO(2) -driven acidification, however, was on genes encoding candidate and known components of the skeletal organic matrix that controls CaCO(3) deposition. The skeletal organic matrix effects included elevated expression of adult-type galaxins and some secreted acidic proteins, but down-regulation of other galaxins, secreted acidic proteins, SCRiPs and other coral-specific genes, suggesting specialized roles for the members of these protein families and complex impacts of OA on mineral deposition. This study is the first exhaustive exploration of the transcriptomic response of a scleractinian coral to acidification and provides an unbiased perspective on its effects during the early stages of calcification.

MEDVIR: 3D visual interface applied to gene profile analisys.

The origins for this work arise in response to the increasing need for biologists and doctors to obtain tools for visual analysis of data. When dealing with multidimensional data, such as medical data, the traditional data mining techniques can be a tedious and complex task, even to some medical experts. Therefore, it is necessary to develop useful visualization techniques that can complement the expert’s criterion, and at the same time visually stimulate and make easier the process of obtaining knowledge from a dataset. Thus, the process of interpretation and understanding of the data can be greatly enriched. Multidimensionality is inherent to any medical data, requiring a time-consuming effort to get a clinical useful outcome. Unfortunately, both clinicians and biologists are not trained in managing more than four dimensions. Specifically, we were aimed to design a 3D visual interface for gene profile analysis easy in order to be used both by medical and biologist experts. In this way, a new analysis method is proposed: MedVir. This is a simple and intuitive analysis mechanism based on the visualization of any multidimensional medical data in a three dimensional space that allows interaction with experts in order to collaborate and enrich this representation. In other words, MedVir makes a powerful reduction in data dimensionality in order to represent the original information into a three dimensional environment. The experts can interact with the data and draw conclusions in a visual and quickly way.

Structure Variation And Dynamics of the Nuclear Ribosomal Intergenic Spacer Region in Key Gibberella Fujikuroi Complex Species

The intergenic spacer (IGS) region of the ribosomal DNA was cloned and sequenced in eight species within the Gibberella fujikuroi species complex with anamorphs in the genus Fusarium , a group that includes the most relevant toxigenic species. DNA sequence analyses revealed two categories of repeated elements: long repeats and short repeats of 125 and 8 bp, respectively. Long repeats were present in two copies and were conserved in all the species analyzed, whereas different numbers of short repeat elements were observed, leading to species-specific IGS sequences with different length. In Fusarium subglutinans and Fusarium nygamai , these differences seemed to be the result of duplication and deletion events. Here, we propose a model based on unequal crossing over that can explain these processes. The partial IGS sequence of 22 Fusarium proliferatum isolates was also obtained to study variation at the intraspecific level. The results revealed no differences in terms of number or pattern of repeated elements and detected frequent gene conversion events. These results suggest that the homogenization observed at the intraspecific level might not be achieved primarily by unequal crossing-over events but rather by processes associated with recombination such as gene conversion events.

CsRAV1: a candidate gene for improving lignocellulosic biomass yield

Fast-growing tree species of Populus spp.,Salix spp. and Eucalyptus spp. are cultivated to produce wood in a short time. Poplars are cultivated with cycles of 15-18 years to obtain saw timber and peeler logs, but when grown as short -rotation coppice(SRC) to produce biomass, planting density increases and rotation is considerably reduced (3-5 years). In this regard, research efforts are focused in the identification of traits and loci that allow the generation of improved SRC biomass-yielding genotypes. Biomass yield is a highly complex trait as it is the combined outcome of many other complex traits, each under separate polygenic control. Among profitable biomass yield-related traits are the amount of sylleptic branching and the length of winter dormancy. In poplar and in a few other Salicaceae species some lateral buds grow out sylleptically, the same season in which they form without the need of an intervening rest period. Sylleptic branching in poplar increases branch number, leaf area and general growth of the tree in its early years, and is a reasonable predictor of coppice yield. On the other hand, the length of winter dormancy determines the extent of the growth period. Our group has characterized the RAV1 gene of Castanea sativa (CsRAV1), encoding a transcription factor of the subfamily RAV (Related to ABI3/VP1). CsRAV1 expression shows a marked seasonal pattern, being higher in autumn and winter both in stems and buds. We generated transgenic lines of the hybrid clone Populus tremulax P. alba INRA 717 1B4 constitutively expressing CsRAV 1. These CsRAV1-expressing poplars develop sylleptic branches only a few weeks after potting. In addition to the sylleptic branching phenotype, these trees show phenological features that could give rise to an extended growth period. We are currently assessing the phenotype and behavior of these transgenic trees in a field trial, and ultimately, we will evaluate the impact on lignocellulosic biomass quality and production.