Orientador: Maria Ines Tiraboschi Ferro / Resumo: Períodos prolongados de seca têm se tornado mais frequentes em algumas regiões do Brasil. Além disso, a expansão da cultura de cana-de-açúcar para regiões com deficiência hídrica prolongada torna a produção sucroenergética limitada nestes locais. A melhor forma de contornar esse problema é utilizar cultivares tolerantes a este estresse. Neste trabalho, o perfil de expressão gênica da cana-de-açúcar foi avaliado, a partir de duas cultivares com respostas contrastantes ao déficit hídrico: uma delas com comportamento considerado tolerante (SP81-3250), e a outra altamente exigente em água (RB855453), considerada sensível. Ambas foram submetidas a três potenciais hídricos do solo (controle (sem estresse hídrico), déficit hídrico moderado e déficit hídrico severo) a partir de 60 dias após o plantio. Essas plantas foram avaliadas molecular e fisiologicamente em três épocas distintas: 30, 60 e 90 dias após a aplicação dos tratamentos, sendo este um dos poucos estudos realizados até o momento sobre a resposta de plantas de cana-de-açúcar sob déficit hídrico prolongado, estresse esse que foi realizado no período conhecido como fase de formação da cana-de-açúcar, compreendendo o período mais crítico por demanda de água. A análise global da expressão gênica através da tecnologia de RNA-Seq mostrou alterações significativas em resposta ao déficit hídrico entre as duas cultivares. Os transcritos do genótipo tolerante apresentaram uma maior capacidade de reação das plantas frente ao déficit... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Extended periods of drought have become more frequent in some regions of Brazil. In addition, the expansion of sugarcane cultivation to regions with prolonged water deficiency makes sugarcane production limited at these locations. The best way around this problem is to use stress-tolerant cultivars. In this work, the sugarcane gene expression profile was evaluated from two cultivars with contrasting responses to water deficit: one of them with tolerant behavior (SP81-3250), and the other highly demanding in water (RB855453 ), considered sensitive. Both were submitted to three soil water potentials (control (without water stress), moderate water deficit and severe water deficit) from 60 days after planting. These plants were evaluated molecularly and physiologically at three different times: 30, 60 and 90 days after the application of the treatments. This is one of the few studies carried out so far on the response of sugarcane plants under prolonged water deficit. This stress was realized during the period known as sugarcane formation phase, comprising the most critical period by water demand. The overall analysis of gene expression through RNA-Seq technology showed significant changes in response to water deficit between the two cultivars. The transcripts of the tolerant genotype showed a higher reaction capacity of the plants to prolonged water deficit, while in the sensitive genotype, several plant survival mechanisms were repressed. The tolerant cultivar presented inducti... (Complete abstract click electronic access below) / Mestre

Differential expression analysis can be a powerful method to investigate expressed differences between closely related species. Our ambition is to highlight differentially expressed nuclear genes to explain the hybrid incompatibilities among the Amazonian, Centro-American and Orinocan semispecies of Drosophila paulistorum. RNA sequencing (RNA-seq) establishes the foundation of the study where we first evaluate the influence of two distinct alignment references. We discover the benefits of concatenating a de novo assembly instead of using the genome reference of a close relative. The bioinformatic pipeline handles the interesting inclusion of D. melanogaster and D. willistoni, where their contribution assists in the search for previously studied speciation genes. Among the down- and upregulated subsets we can see a diverse mix of general biological processes such as regulatory functions and transcriptional factors. In the end we uncover potential indications to why the Amazonian seems to be the least compatible semispecie to produce hybrids. This study provides a competitive working frame for comparative RNA-seq studies between closely related species.

Cette thèse regroupe des contributions méthodologiques à l'analyse statistique des données issues des technologies de séquençage du transcriptome (RNA-seq). Les difficultés de modélisation des données de comptage RNA-seq sont liées à leur caractère discret et au faible nombre d'échantillons disponibles, limité par le coût financier du séquençage. Une première partie de travaux de cette thèse porte sur la classification à l'aide de modèle de mélange. L'objectif de la classification est la détection de modules de gènes co-exprimés. Un choix naturel de modélisation des données RNA-seq est un modèle de mélange de lois de Poisson. Mais des transformations simples des données permettent de se ramener à un modèle de mélange de lois gaussiennes. Nous proposons de comparer, pour chaque jeu de données RNA-seq, les différentes modélisations à l'aide d'un critère objectif permettant de sélectionner la modélisation la plus adaptée aux données. Par ailleurs, nous présentons un critère de sélection de modèle prenant en compte des informations biologiques externes sur les gènes. Ce critère facilite l'obtention de classes biologiquement interprétables. Il n'est pas spécifique aux données RNA-seq. Il est utile à toute analyse de co-expression à l'aide de modèles de mélange visant à enrichir les bases de données d'annotations fonctionnelles des gènes. Une seconde partie de travaux de cette thèse porte sur l'inférence de réseau à l'aide d'un modèle graphique. L'objectif de l'inférence de réseau est la détection des relations de dépendance entre les niveaux d'expression des gènes. Nous proposons un modèle d'inférence de réseau basé sur des lois de Poisson, prenant en compte le caractère discret et la grande variabilité inter-échantillons des données RNA-seq. Cependant, les méthodes d'inférence de réseau nécessitent un nombre d'échantillons élevé.Dans le cadre du modèle graphique gaussien, modèle concurrent au précédent, nous présentons une approche non-asymptotique pour sélectionner des sous-ensembles de gènes pertinents, en décomposant la matrice variance en blocs diagonaux. Cette méthode n'est pas spécifique aux données RNA-seq et permet de réduire la dimension de tout problème d'inférence de réseau basé sur le modèle graphique gaussien. / This thesis gathers methodologicals contributions to the statistical analysis of next-generation high-throughput transcriptome sequencing data (RNA-seq). RNA-seq data are discrete and the number of samples sequenced is usually small due to the cost of the technology. These two points are the main statistical challenges for modelling RNA-seq data.The first part of the thesis is dedicated to the co-expression analysis of RNA-seq data using model-based clustering. A natural model for discrete RNA-seq data is a Poisson mixture model. However, a Gaussian mixture model in conjunction with a simple transformation applied to the data is a reasonable alternative. We propose to compare the two alternatives using a data-driven criterion to select the model that best fits each dataset. In addition, we present a model selection criterion to take into account external gene annotations. This model selection criterion is not specific to RNA-seq data. It is useful in any co-expression analysis using model-based clustering designed to enrich functional annotation databases.The second part of the thesis is dedicated to network inference using graphical models. The aim of network inference is to detect relationships among genes based on their expression. We propose a network inference model based on a Poisson distribution taking into account the discrete nature and high inter sample variability of RNA-seq data. However, network inference methods require a large number of samples. For Gaussian graphical models, we propose a non-asymptotic approach to detect relevant subsets of genes based on a block-diagonale decomposition of the covariance matrix. This method is not specific to RNA-seq data and reduces the dimension of any network inference problem based on the Gaussian graphical model.

L’objectif de ce travail était de mieux comprendre les mécanismes moléculaires de l’adaptation microbienne à l’environnement fromager par des approches de génomique fonctionnelle via le modèle de Brevibacterium, un genre bactérien largement utilisé en technologie fromagère, mais dont l’implantation est parfois difficile à maîtriser.L’analyse génomique comparative de 23 souches de Brevibacterium, dont 12 issues de fromages, a révélé des différences en déterminants génétiques impliqués dans la capacité à croître à la surface du fromage. Parmi ces différences, plusieurs sont corrélées à la phylogénie des souches, et d’autres résultent de transferts horizontaux, notamment dans le cas des gènes liés à l’acquisition du fer et à la biosynthèse de bactériocines. Nous avons identifié des îlots génomiques correspondant à des transferts de gènes d’acquisition du fer entre des souches fromagères de Brevibacterium et des bactéries d’affinage appartenant à d’autres genres. Nous avons également mis en évidence un transposon conjugatif codant pour la synthèse de bactériocines présent chez des souches de Brevibacterium d'origine fromagère mais aussi chez une souche fromagère du genre Corynebacterium.L’étude fonctionnelle des interactions biotiques entre Brevibacterium et Hafnia alvei, une autre bactérie d’affinage du fromage, a été menée dans un modèle fromager développé au cours de ce travail. En couplant des analyses microbiologiques, biochimiques et transcriptomiques (RNA-seq), nous avons mis en évidence l’existence de différents mécanismes d’interaction entre ces bactéries. Ceux-ci concernent notamment l’acquisition du fer, la protéolyse, la lipolyse, le métabolisme soufré et le catabolisme du D-galactonate. Nos résultats suggèrent que dans la relation mutualiste observée entre certaines souches de Brevibacterium et H. alvei, cette dernière sécrète des sidérophores qui sont utilisés par Brevibacterium pour capter le fer plus efficacement, stimulant ainsi sa croissance. En contrepartie, Brevibacterium sécrète des lipases et des protéases qui dégradent les caséines et triglycérides du fromage en constituants énergétiques favorisant la croissance de H. alvei. Ce type d’interaction est intéressant à considérer pour la formulation des ferments d'affinage car il en résulte une meilleure capacité de tous les partenaires à coloniser le fromage, et ainsi à générer les propriétés technologiques recherchées. / The objective of this study was to better understand the molecular mechanisms of microbial adaptation to the cheese habitat by functional genomic approaches using Brevibacterium as a model microorganism. This bacterium is widely used for the manufacturing of cheese but its growth on the cheese surface is sometimes difficult to control.Comparative genomic analysis of 23 Brevibacterium strains, including 12 strains isolated from cheeses, revealed differences in genetic determinants involved in the growth on the cheese surface. Some of them are correlated to strain phylogeny and others are the result of gene transfers, especially those involved in iron acquisition and bacteriocin biosynthesis. We identified genomic islands corresponding to transfers of genes involved in iron acquisition between cheese-associated Brevibacterium strains and cheese-associated strains belonging to other genera. We also detected a conjugative transposon encoding bacteriocin production, which is present in cheese-associated Brevibacterium strains as well as in a cheese-associated Corynebacterium strain.Functional study of biotic interactions between Brevibacterium and Hafnia alvei, another cheese-ripening bacterium, was performed in a model cheese developed in this study. By coupling microbial, biochemical and transcriptomic (RNA-seq) analyses, we revealed several interaction mechanisms between these bacteria. These concern, in particular, iron acquisition, proteolysis, lipolysis, sulfur metabolism and D-galactonate catabolism. Our findings suggest that in the mutualistic relationship between some Brevibacterium strains and H. alvei, the latter stimulates Brevibacterium growth by the secretion of siderophores, which can be used by Brevibacterium to capture iron more efficiently. In return, Brevibacterium secretes lipases and proteases, which degrade cheese caseins and triglycerides into energetic substrates that stimulate H. alvei growth. This type of interaction is interesting to consider in the formulation of ripening cultures because it results in a better ability of all partners to colonize the cheese, and thus to generate the desired technological properties.

Gene expression regulation is dynamic and specific to various factors such as developmental stages, environmental conditions, and stimulation of pathogens. Nowadays, a tremendous amount of transcriptome data sets are available from diverse species. This trend enables us to perform comparative transcriptome analysis that identifies conserved or diverged gene expression responses across species using transcriptome data. The goal of this dissertation is to develop and apply approaches of comparative transcriptomics to transfer knowledge from model species to non-model species with the hope that such an approach can contribute to the improvement of crop yield and human health. First, we presented a comprehensive method to identify cross-species modules between two plant species. We adapted the unsupervised network-based module finding method to identify conserved patterns of co-expression and functional conservation between Arabidopsis, a model species, and soybean, a crop species. Second, we compared drought-responsive genes across Arabidopsis, soybean, rice, corn, and Populus in order to explore the genomic characteristics that are conserved under drought stress across species. We identified hundreds of common gene families and conserved regulatory motifs between monocots and dicots. We also presented a BLS-based clustering method which takes into account evolutionary relationships among species to identify conserved co-expression genes. Last, we analyzed single-cell RNA-seq data from monocytes to attempt to understand regulatory mechanism of innate immune system under low-grade inflammation. We identified novel subpopulations of cells treated with lipopolysaccharide (LPS), that show distinct expression patterns from pro-inflammatory genes. The data revealed that a promising therapeutic reagent, sodium 4-phenylbutyrate, masked the effect of LPS. We inferred the existence of specific cellular transitions under different treatments and prioritized important motifs that modulate the transitions using feature selection by a random forest method. There has been a transition in genomics research from bulk RNA-seq to single-cell RNA-seq, and scRNA-seq has become a widely used approach for transcriptome analysis. With the experience we gained by analyzing scRNA-seq data, we plan to conduct comparative single-cell transcriptome analysis across multiple species. / Doctor of Philosophy / All cells in an organism have the same set of genes, but there are different cell types, tissues, organs with different functions as the organism ages or under different conditions. Gene expression regulation is one mechanism that modulates complex, dynamic, and specific changes in tissues or cell types for any living organisms. Understanding gene regulation is of fundamental importance in biology. With the rapid advancement of sequencing technologies, there is a tremendous amount of gene expression data (transcriptome) from individual species in public repositories. However, major studies have been reported from several model species and research on non-model species have relied on comparison results with a few model species. Comparative transcriptome analysis across species will help us to transform knowledge from model species to non-model species and such knowledge transfer can contribute to the improvement of crop yields and human health. The focus of my dissertation is to develop and apply approaches for comparative transcriptome analysis that can help us better understand what makes each species unique or special, and what kinds of common functions across species have been passed down from ancestors (evolutionarily conserved functions). Three research chapters are presented in this dissertation. First, we developed a method to identify groups of genes that are commonly co-expressed in two species. We chose seed development data from soybean with the hope to contribute to crop improvement. Second, we compared gene expression data across five plant species including soybean, rice, and corn to provide new perspectives about crop plants. We chose drought stress to identify conserved functions and regulatory factors across species since drought stress is one of the major stresses that negatively impact agricultural production. We also proposed a method that groups genes with evolutionary relationships from an unlimited number of species. Third, we analyzed single-cell RNA-seq data from mouse monocytes to understand the regulatory mechanism of the innate immune system under low-grade inflammation. We observed how innate immune cells respond to inflammation that could cause no symptoms but persist for a long period of time. Also, we reported an effect of a promising therapeutic reagent (sodium 4-phenylbutyrate) on chronic inflammatory diseases. The third project will be extended to comparative single-cell transcriptome analysis with multiple species.

Abiotic stress is a major constraint for crop productivity worldwide. To better understand the common biological mechanisms of abiotic stress responses in plants, we performed meta-analysis of 652 samples of RNA sequencing (RNA-Seq) data from 43 published abiotic stress experiments in Arabidopsis thaliana. These samples were categorized into eight different abiotic stresses including drought, heat, cold, salt, light and wounding. We developed a multi-step computational pipeline, which performs data downloading, preprocessing, read mapping, read counting and differential expression analyses for RNA-Seq data. We found that 5729 and 5062 genes are induced or repressed by only one type of abiotic stresses. There are only 18 and 12 genes that are induced or repressed by all stresses. The commonly induced genes are related to gene expression regulation by stress hormone abscisic acid. The commonly repressed genes are related to reduced growth and chloroplast activities. We compared stress responsive genes between any two types of stresses and found that heat and cold regulate similar set of genes. We also found that high light affects different set of genes than blue light and red light. Interestingly, ABA regulated genes are different from those regulated by other stresses. Finally, we found that membrane related genes are repressed by ABA, heat, cold and wounding but are up regulated by blue light and red light. The results from this work will be used to further characterize the gene regulatory networks underlying stress responsive genes in plants. / Master of Science / Abiotic stress is a major constraint for crop productivity worldwide. To better understand the common biological mechanisms of abiotic stress responses in plants, we performed analysis of 652 samples of RNA sequencing data from 43 published abiotic stress experiments in Arabidopsis thaliana. These samples were collected from eight different abiotic stresses including drought, heat, cold, salt, light and wounding. We identified genes that were induced or repressed by each of these stresses. We found that 5729 and 5062 genes are induced or repressed by only one type of abiotic stresses. There are only 18 and 12 genes that are induced or repressed by all stresses. The commonly induced genes are related to gene expression regulation by stress hormone. The commonly repressed genes are related to reduced growth. We compared stress responsive genes between any two types of stresses and found that heat and cold regulate similar set of genes. We also found that high light affects different set of genes than blue light and red light. Finally, we found that membrane related genes are repressed by stress hormone, heat, cold and wounding but are up regulated by blue light and red light. The results from this work will be used to further characterize the gene regulations underlying stress responsive genes in plants.

Master of Science / Statistics / Nora M. Bello / Big bluestem (Andropogon gerardii) is a wide-ranging dominant prairie grass of ecological and agricultural importance to the US Midwest while edaphic subspecies sand bluestem (A. gerardii ssp. Hallii) grows exclusively on sand dunes. Sand bluestem exhibits phenotypic divergence related to epicuticular properties and enhanced drought tolerance relative to big bluestem. Understanding the mechanisms underlying differential drought tolerance is relevant in the face of climate change. For bluestem subspecies, presence or absence of these phenotypes may be associated with RNA transcripts characterized by low number of read counts. So called low-count transcripts pose particular inferential challenges and are thus usually filtered out at early steps of data management protocols and ignored for analyses. In this study, we use a plasmode-based approach to assess the relative performance of alternative inferential strategies on RNA-seq transcripts, with special emphasis on low-count transcripts as motivated by differential bluestem phenotypes. Our dataset consists of RNA-seq read counts for 25,582 transcripts (60% of which are classified as low-count) collected from leaf tissue of 4 individual plants of big bluestem and 4 of sand bluestem. We also compare alternative ad-hoc data filtering techniques commonly used in RNA-seq pipelines and assess the performance of recently developed statistical methods for differential expression (DE) analysis, namely DESeq2 and edgeR robust. These methods attempt to overcome the inherently noisy behavior of low-count transcripts by either shrinkage or differential weighting of observations, respectively. Our results indicate that proper specification of DE methods can remove the need for ad- hoc data filtering at arbitrary expression threshold, thus allowing for inference on low-count transcripts. Practical recommendations for inference are provided when low-count RNA-seq transcripts are of interest, as is the case in the comparison of subspecies of bluestem grasses. Insights from this study may also be relevant to other applications also focused on transcripts of low expression levels.

One of the cellular responses to temperature fluctuations in plants is the adjustment in the degree of membrane unsaturation. Glycerolipids are major constituents of cellular membranes. In higher plants, glycerolipids are synthesized via two major metabolic pathways compartmentalized in the ER and chloroplast. Adaptive responses in membrane lipids include alterations in fatty acid desaturation, proportional changes in membrane lipids as well as molecular composition of each lipid species. In this study, I systematically explored the significance of glycerolipid pathway balance in temperature induced lipid composition changes in three plant species that have distinctive modes of lipid pathway interactions through a combination of biochemical and molecular approaches including lipidomics and RNA-seq analysis. In Arabidopsis thaliana, a 16:3 plant, low temperature induces an augmented prokaryotic pathway, whereas high temperature enhances the eukaryotic pathway. Atriplex lentiformis reduces its overall lipid desaturation at high temperature and switches lipid phenotype from 16:3 to 18:3 through drastically increasing the contribution of the eukaryotic pathway as well as suppression of the prokaryotic pathway. In sync with the metabolic changes, coordinated expression of glycerolipid pathway genes, as revealed by RNA-seq also occurs. In Triticum aestivum, an 18:3 plant, low temperature leads to a reduced glycerolipid flux from ER to chloroplast. Evidence of differential trafficking of diacylglycerol (DAG) moieties from ER to chloroplast was uncovered in three plant species as another layer of metabolic adaptation under different temperatures. Taken together, this study has established a biochemical basis that highlights the predominance and prevalence of lipid pathway interactions in temperature induced lipid compositional changes.

Technological and methodological advances in the fields of medical and life-sciences have, over the last 25 years, revolutionised the way in which cellular activity is measured at the molecular level. Three such advances have provided a means of accurately and rapidly quantifying mRNA, from the development of quantitative Polymerase Chain Reaction (qPCR), to DNA microarrays, and second-generation RNA-sequencing (RNA-seq). Despite consistent improvements in measurement precision and sample throughput, the data generated continue to be a ffected by high levels of variability due to the use of biologically distinct experimental subjects, practical restrictions necessitating the use of small sample sizes, and technical noise introduced during frequently complex sample preparation and analysis procedures. A series of experiments were performed during this project to pro le sources of technical noise in each of these three techniques, with the aim of using the information to produce more accurate and more reliable results. The mechanisms for the introduction of confounding noise in these experiments are highly unpredictable. The variance structure of a qPCR experiment, for example, depends on the particular tissue-type and gene under assessment while expression data obtained by microarray can be greatly influenced by the day on which each array was processed and scanned. RNA-seq, on the other hand, produces data that appear very consistent in terms of differences between technical replicates, however there exist large differences when results are compared against those reported by microarray, which require careful interpretation. It is demonstrated in this thesis that by quantifying some of the major sources of noise in an experiment and utilising compensation mechanisms, either pre- or post-hoc, researchers are better equipped to perform experiments that are more robust, more accurate, and more consistent.

12 month embargo; Available online 9 November 2016 / SCN8A encephalopathy is a severe, early-onset epilepsy disorder resulting from de novo gain-of-function mutations in the voltage-gated sodium channel Na(v)1.6. To identify the effects of this disorder on mRNA expression, RNA-seq was performed on brain tissue from a knock-in mouse expressing the patient mutation p.Asn1768Asp (N1768D). RNA was isolated from forebrain, cerebellum, and brainstem both before and after seizure onset, and from age-matched wildtype littermates. Altered transcript profiles were observed only in forebrain and only after seizures. The abundance of 50 transcripts increased more than 3-fold and 15 transcripts decreased more than 3 fold after seizures. The elevated transcripts included two anti-convulsant neuropeptides and more than a dozen genes involved in reactive astrocytosis and response to neuronal damage. There was no change in the level of transcripts encoding other voltage-gated sodium, potassium or calcium channels. Reactive astrocytosis was observed in the hippocampus of mutant mice after seizures. There is considerable overlap between the genes affected in this genetic model of epilepsy and those altered by chemically induced seizures, traumatic brain injury, ischemia, and inflammation. The data support the view that gain-of-function mutations of SCN8A lead to pathogenic alterations in brain function contributing to encephalopathy.