Infectious diseases are the top leading cause of death worldwide. Identifying essential genes, genes indispensable for survival, has been proven indispensable in defining new therapeutic targets against pathogens, major elements of the minimal set genome to be harnessed in synthetic biology, and determinants of evolutionary relationships of phylogenetically distant species. Thus, essentiality studies promise valuable revenues that can decipher much of biological complexities. Taking advantage of the available microbial sequences and the essentiality studies conducted in various microbial models, we proposed a framework for the prediction of essential genes based on our experimentally verified knowledge of the pathways involved in three essential xiv functions: genetic information processing, cell wall biosynthesis, and energy metabolism. We investigated physiological pathways in Kyoto Encyclopedia of Genes and Genomes (KEGG) database and developed a bioinformatics approach to predict essential genes in 13 different microbial species. Our in silico findings matched to a high degree the experimental data derived from essentiality studies conducted on the same microbial models, providing insights about the microbial lifestyles, including energy resources, cell wall structure, and ecological preferences, but not virulence tools and mechanisms. Furthermore, we believe that essential genes have survived the evolutionary purifying selection due to their evolved capacity to re-wire genetic and protein networks in response to any emerging stress. In this sense, an environmental specificity (stress) provides a dominant determinant of an essential gene set. The new challenge was understanding the contribution of the essential genome in S. sanguinis to the coping mechanisms to different clinically relevant stress factors, namely temperature elevation (43oC) and sub-inhibitory concentration of ampicillin, an abundantly prescribed antibiotic for prophylaxis and treatment against S. sanguinis. The current project investigated the transcriptomic and proteomic profiles of essential genes and proteins, using RNA-seq and mass spectrometry respectively, under the impact of the two stressors separately, to elucidate the essential genome and proteome dynamics on a temporal basis and define “pathogenesis signatures” as potential therapeutic targets. We believe that the current findings will help characterize a bacterial model for studying the dynamics of essential genes and assist in designing evidence-based guidelines for drug prescription in clinical practice.

Beef quality in Brazil is important for both consumers and the food industry due to high demand and competitiveness in the domestic and international markets. Therefore, it is necessary to develop research to improve beef quality of Nellore cattle (Bos indicus), mainly tenderness, one of the main features to add value to meat. New-generation technologies provide accurate, rapid and inexpensive information on the entire genome, showing great advantage over conventional methods for sequencing and gene expression. However, these new technologies generate large database, which require the use of bioinformatics tools for data analyses of sequencing and for a better understanding of biological regulation mechanisms , cellular control, gene interactions, among other applications. In a previous study, samples were collected from the Longissimus dorsi muscle of 790 animals from Nellore cattle and shear force assessments were made 24 hours after slaughter, with seven and 14 days of aging. Aiming to identify differentially expressed (DE) genes, 34 samples from Nellore animals with extreme levels of estimated breeding value (EBV) for shear force (SF) were selected, sequenced by the method of RNA sequencing (RNA-Seq) (Illumina HiScanSQ). This study performed the processing of data generated by RNA-Seq using software QuasiSeq and Cuffdiff. In the QuasiSeq analysis, 22 DE genes were found, while in the Cuffdiff analysis, 113 DE genes were found. To better understand the biological process involved in meat tenderness, integrative analysis identified possible regulators that can explain the activity of transcriptional regulation in this process using partial correlation coefficient with information theory (PCIT), phenotypic impact factor (PIF) and regulatory impact factor (RIF) methods. The genes found in the PCIT analysis USP2, GBR10, ANO1 and TMBIM4; microRNAs found in RIF analysis bta-mir-133a-2 and bta-mir-22, and the genes with high PIF value MB, ENO3, CA3 could be fundamental to unravel the complex molecular mechanisms that control the meat tenderness in Nellore cattle. / A qualidade da carne bovina no Brasil é importante tanto para o consumidor, como para a indústria alimentícia devido à alta competitividade e exigência do mercado nacional e internacional. Portanto, é necessário o desenvolvimento de pesquisas para melhorar a qualidade da carne bovina da raça Nelore (Bos indicus), principalmente a maciez, que é considerada uma das principais responsáveis por agregar valor à carne. Tecnologias de nova geração proporcionam informações precisas, rápidas e baratas de todo genoma, mostrando grande vantagem em relação aos métodos convencionais de sequenciamento e de estudos de expressão gênica. Essas novas tecnologias geram um grande volume de dados, sendo necessário o uso de ferramentas de bioinformática para realizar as análises de sequenciamento e ter uma maior compreensão de mecanismos biológicos de regulação, controle celular, interações gênicas, entre outras aplicações. Em um estudo prévio, foram coletadas amostras do músculo Longissimus dorsi de 790 animais da raça Nelore e foram realizadas avaliações da força de cisalhamento 24 horas após abate, e com sete e 14 dias de maturação. Com o objetivo de identificar genes diferencialmente expressos (DE), foram selecionadas no total 34 amostras de animais da raça Nelore com valores extremos de valor genético estimado (EBV) para força de cisalhamento (SF), e sequenciados pelo método de sequenciamento de RNA (RNA-Seq) (Illumina HiScanSQ). Neste estudo foi realizado o processamento dos dados gerados pelo RNA-Seq através dos softwares QuasiSeq e Cuffdiff. Foram encontrados 22 genes DE para as análises do QuasiSeq e 113 genes DE para as análises do Cuffdiff. Para melhor compreensão dos processos biológicos envolvidos na maciez da carne, análises integrativas identificaram possíveis reguladores que podem explicar a atividade de regulação transcricional neste processo utilizando os métodos do Coeficiente de Correlação Parcial com Teoria da Informação (PCIT), Fator de Impacto Fenotípico (PIF) e Fator de Impacto Regulatório (RIF). Os genes encontrados nas análises análises do PCIT USP2, GBR10, ANO1 e TMBIM4, assim como os microRNAs encontrados nas análises do RIF, bta-mir-133a-2 e bta-mir-22 e os genes de maior valor de PIF MB, ENO3, CA3 podem ser fundamentais para desvendar os complexos mecanismos moleculares que controlam a maciez da carne na raça Nelore.

Agronomic outcomes are the product of a plant's genotype and its environment. Genomic technologies allow farmers and researchers new avenues to explore the genetic component of agriculture. These technologies can also enhance understanding of environmental effects. With a growing world population, a wide variety of tools will be necessary to increase the agronomic productivity. Here I use massively parallel, deep sequencing of RNA (RNA-Seq) to measure changes in cotton gene expression levels in response to a change in the plant's surroundings caused by conservation tillage. Conservation tillage is an environmentally friendly, agricultural practice characterized by little or no inversion of the soil prior to planting. In addition to changes in cotton gene expression and biological pathway activity, I assay the transcriptional activity of microbial symbiotes living in and around the cotton roots. I found a large degree of similarity between cotton individuals in different treatments. However, under conventional disk tillage I did find significantly greater activity of cotton phosphatase and sulfate transport genes, as well as greater abundance of the microbes Candidatus Burkholderia brachynathoides and Arthrobacter species L77. This study also includes the use of high-throughput physical mapping of DNA to examine the genomic structure of a wild cotton species, Gossypium raimondii, which is closely related to the economically significant crop species Gossypium hirsutum. This technology characterizes genomic regions by assembling large input DNA molecules labeled at restriction enzyme recognition sites. I created an efficient algorithm and generated 812 whole genome assemblies from two datasets. The best of these assemblies allowed us to detect 3,806 potential misassemblies in the current release of the G. raimondii genome sequence assembly.

No description available.

Les technologies de séquençage jouent un rôle croissant dans l'analyse de l'expression des transcrits . La méthode la plus courante de séquençage du transcriptome, RNA-Seq est une méthode d'investigation d'une population de transcrits par cisaillement aléatoire, amplification et séquençage à haut débit. Les données issues du RNA-Seq peuvent être utilisées pour la quantification des niveaux d'expression des transcrits et pour la détection des régions transcrites et demandent des approches bioinformatiques.Nous avons développé des approches statistiques pour l'estimation des niveaux de transcription et l'identification des frontières de transcription sans faire usage de l'annotation existante et pour l'analyse des différences dans l'expression entre deux conditions. La reconstruction du paysage transcriptionel est faite dans un cadre probabiliste (Chaînes de Markov Caché - HMM) ou les variations du niveau de la transcription sont prises en compte en termes de changements brusques et de dérives. Le HMM est complété par une loi d'émission qui capture la variance des comptages dans un transcrit, l'auto-corrélation de courte portée et la fraction des positions avec zéro comptages. L'estimation repose sur un algorithme de Monte Carlo Séquentiel (SMC), le Particle Gibbs, dont le temps d'exécution est plus adapté aux génomes microbiennes. L'analyse des différences dans l'expression (DE) est réalisée sans faire usage de l'annotation existante. L'estimation de DE est premièrement faite à la résolution de position et en suite les régions avec un signal DE continu sont agrégés. Deux programmes nommés Parseq et Pardiff sont disponibles à http://www.lgm.upmc.fr/parseq/. / In this thesis we address the problem of reconstructing the transcription profile from RNA-Seq reads in cases where the reference genome is available but without making use of existing annotation. In the first two chapters consist of an introduction to the biological context, high-throughput sequencing and the statistical methods that can be used in the analysis of series of counts. Then we present our contribution for the RNA-Seq read count model, the inference transcription profile by using Particle Gibbs and the reconstruction of DE regions. The analysis of several data-sets proved that using Negative Binomial distributions to model the read count emission is not generally valid. We develop a mechanistic model which accounts for the randomness generated within all RNA-Seq protocol steps. Such a model is particularly important for the assessment of the credibility intervals associated with the transcription level and coverage changes. Next, we describe a State Space Model accounting for the read count profile for observations and transcription profile for the latent variable. For the transition kernel we design a mixture model combining the possibility of making, between two adjacent positions, no move, a drift move or a shift move. We detail our approach for the reconstruction of the transcription profile and the estimation of parameters using the Particle Gibbs algorithm. In the fifth chapter we complete the results by presenting an approach for analysing differences in expression without making use of existing annotation. The proposed method first approximates these differences for each base-pair and then aggregates continuous DE regions.

Aging is a degenerative process characterized by a decline in physiological functions and cellular activities. Environmental and pharmacological interventions affecting longevity pathways have been extensively studied in model organisms. This study investigated the effect of chronic mild intermittent hypoxia (4 mg O2/L) or mild mitochondrial uncoupling with three doses of 0 (control), 0.1, 1, and 5 μM of 2,4-Dinitrophenol (DNP), on life history and gene expression in four clones of Daphnia magna. Interestingly, clones from intermittent ponds displayed better tolerance to hypoxia and DNP. Although neither treatments extended longevity, hypoxia increased fecundity and body size, and decreased food consumption and respiration rate. We uncovered 12 candidate genes that were differentially expressed in hypoxia-tolerant and sensitive clones in response to hypoxia. Unexpectedly, DNP increased fecundity and mitochondrial membrane potential without affecting food intake. This work opens up an opportunity for genomic determination of the potentially important phenotypes in a model organism.

In this study we build solutions to three common challenges in the fields of bioinformatics through utilizing statistical methods and developing computational approaches. First, we address a common problem in genome wide association studies, which is linking genotype features within organisms of the same species to their phenotype characteristics. We specifically studied FHA domain genes in Arabidopsis thaliana distributed within Eurasian regions by clustering those plants that share similar genotype characteristics and comparing that to the regions from which they were taken. Second, we also developed a tool for calculating transposable element density within different regions of a genome. The tool is built to utilize the information provided by other transposable element annotation tools and to provide the user with a number of options for calculating the density for various genomic elements such as genes, piRNA and miRNA or for the whole genome. It also provides a detailed calculation of densities for each family and subamily of the transposable elements. Finally, we address the problem of mapping multi reads in the genome and their effects on gene expression. To accomplish this, we implemented methods to determine the statistical significance of expression values within the genes utilizing both a unique and multi-read weighting scheme. We believe this approach provides a much more accurate measure of gene expression than existing methods such as discarding multi reads completely or assigning them randomly to a set of best assignments, while also providing a better estimation of the proper mapping locations of ambiguous reads. Overall, the solutions we built in these studies provide researchers with tools and approaches that aid in solving some of the common challenges that arise in the analysis of high throughput sequence data.

No description available.

Polyclonal plantings of Populus deltoides are expected to display increased site resource use, productivity, and tolerance to stress through plasticity changes leading to niche differentiation (i.e changes to crown/canopy structures). In the present study, P. deltoides Clones S7C8, 110412, and polyclonal plots were tested for differentially expressed genes and enriched biological pathways between planting schemes. Transcriptomic analysis of leaves revealed upregulation of an active growth gene and gene family members that play important roles in plant stress and stress tolerance in polyclonal plantings. A gene associated with oxidative stress was upregulated in polyclonal plantings across all treatments. Secondary metabolic pathways including arginine and proline metabolism were upregulated in monoclonal plantings and downregulated in polyclonal plantings. Phenotypic results displayed greater aboveground biomass in polyclonal plantings. Results suggested a potential increased tolerance in polyclonal plantings to water and heat stress, including increased productivity and resource usage.

Indiana University-Purdue University Indianapolis (IUPUI) / Triple-negative breast cancers (TNBCs) are negative for the expression of estrogen (ER), progesterone (PR), and HER-2 receptors. TNBC accounts for 15% of all breast cancers and results in disproportionally higher mortality compared to ER & HER2-positive tumours. Moreover, there is a paucity of therapies for this subtype of breast cancer resulting primarily from an inadequate understanding of the transcriptional differences that differentiate TNBC from normal breast. To this end, we embarked on a comprehensive examination of the transcriptomes of TNBCs and normal breast tissues using next-generation whole transcriptome sequencing (RNA-Seq). By comparing RNA-seq data from these tissues, we report the presence of differentially expressed coding and non-coding genes, novel transcribed regions, and mutations not previously reported in breast cancer. From these data we have identified two major themes. First, BRCA1 mutations are well known to be associated with development of TNBC. From these data we have identified many genes that work in concert with BRCA1 that are dysregulated suggesting a role of BRCA1 associated genes with sporadic TNBC. In addition, we observe a mutational profile in genes also associated with BRCA1 and DNA repair that lend more evidence to its role. Second, we demonstrate that using microdissected normal epithelium maybe an optimal comparator when searching for novel therapeutic targets for TNBC. Previous studies have used other controls such as reduction mammoplasties, adjacent normal tissue, or other breast cancer subtypes, which may be sub-optimal and have lead to identifying ineffective therapeutic targets. Our data suggests that the comparison of microdissected ductal epithelium to TNBC can identify potential therapeutic targets that may lead to be better clinical efficacy. In summation, with these data, we provide a detailed transcriptional landscape of TNBC and normal breast that we believe will lead to a better understanding of this complex disease.