>Magister Scientiae - MSc / A large volume of gene expression data exists in public repositories like the NCBI’s Gene Expression Omnibus (GEO) and the EBI’s ArrayExpress and a significant opportunity to re-use data in various combinations for novel in-silico analyses that would otherwise be too costly to perform or for which the equivalent sample numbers would be difficult to collects exists. For example, combining and re-analysing large numbers of data sets from the same cancer type would increase statistical power, while the effects of individual study-specific variability is weakened, which would result in more reliable gene expression signatures. Similarly, as the number of normal control samples associated with various cancer datasets are often limiting, datasets can be combined to establish a reliable baseline for accurate differential expression analysis. However, combining different microarray studies is hampered by the fact that different studies use different analysis techniques, microarray platforms and experimental protocols. We have developed and optimised a method which transforms gene expression measurements from continuous to discrete data points by grouping similarly expressed genes into quantiles on a per-sample basis. After cross mapping each probe on each chip to the gene it represents, thereby enabling us to integrate experiments based on genes they have in common across different platforms. We optimised the quantile discretization method on previously published prostate cancer datasets produced on two different array technologies and then applied it to a larger breast cancer dataset of 411 samples from 8 microarray platforms. Statistical analysis of the breast cancer datasets identified 1371 differentially expressed genes. Cluster, gene set enrichment and pathway analysis identified functional groups that were previously described in breast cancer and we also identified a novel module of genes encoding ribosomal proteins that have not been previously reported, but whose overall functions have been implicated in cancer development and progression. The former indicates that our integration method does not destroy the statistical signal in the original data, while the latter is strong evidence that the increased sample size increases the chances of finding novel gene expression signatures. Such signatures are also robust to inter-population variation, and show promise for translational applications like tumour grading, disease subtype classification, informing treatment selection and molecular prognostics.

Recent advances in high-throughput technique enables the generation of a large amount of omic data such as genomics, transcriptomics, proteomics, metabolomics, glycomics etc. Typically, differential expression analysis (e.g., student's t-test, ANOVA) is performed to identify biomolecules (e.g., genes, proteins, metabolites, glycans) with significant changes on individual level between biologically disparate groups (disease cases vs. healthy controls) for cancer biomarker discovery. However, differential expression analysis on independent studies for the same clinical types of patients often led to different sets of significant biomolecules and had only few in common. This may be attributed to the fact that biomolecules are members of strongly intertwined biological pathways and highly interactive with each other. Without considering these interactions, differential expression analysis could lead to biased results. Network-based methods provide a natural framework to study the interactions between biomolecules. Commonly used data-driven network models include relevance network, Bayesian network and Gaussian graphical models. In addition to data-driven network models, there are many publicly available databases such as STRING, KEGG, Reactome, and ConsensusPathDB, where one can extract various types of interactions to build knowledge-driven networks. While both data- and knowledge-driven networks have their pros and cons, an appropriate approach to incorporate the prior biological knowledge from publicly available databases into data-driven network model is desirable for more robust and biologically relevant network reconstruction. Recently, there has been a growing interest in differential network analysis, where the connection in the network represents a statistically significant change in the pairwise interaction between two biomolecules in different groups. From the rewiring interactions shown in differential networks, biomolecules that have strongly altered connectivity between distinct biological groups can be identified. These biomolecules might play an important role in the disease under study. In fact, differential expression and differential network analyses investigate omic data from two complementary perspectives: the former focuses on the change in individual biomolecule level between different groups while the latter concentrates on the change in pairwise biomolecules level. Therefore, an approach that can integrate differential expression and differential network analyses is likely to discover more reliable and powerful biomarkers. To achieve these goals, we start by proposing a novel data-driven network model (i.e., LOPC) to reconstruct sparse biological networks. The sparse networks only contains direct interactions between biomolecules which can help researchers to focus on the more informative connections. Then we propose a novel method (i.e., dwgLASSO) to incorporate prior biological knowledge into data-driven network model to build biologically relevant networks. Differential network analysis is applied based on the networks constructed for biologically disparate groups to identify cancer biomarker candidates. Finally, we propose a novel network-based approach (i.e., INDEED) to integrate differential expression and differential network analyses to identify more reliable and powerful cancer biomarker candidates. INDEED is further expanded as INDEED-M to utilize omic data at different levels of human biological system (e.g., transcriptomics, proteomics, metabolomics), which we believe is promising to increase our understanding of cancer. Matlab and R packages for the proposed methods are developed and available at Github (https://github.com/Hurricaner1989) to share with the research community. / Ph. D.

Bacterial persistence is considered as one of the primary reason for antibiotic tolerance besides genetically acquired antibiotic resistance. Persisters are the subpopulation of a clonal bacterial population, which can survive environmental extremes and become invulnerable to stresses due to limited metabolic activities and physiological functions. Cognate toxin and antitoxin (TA) pairs, which are transcribed simultaneously from the same or different operons within the bacterial chromosomes or plasmids, play an important role for bacterial survival during stressful growth environments. Pseudomonas aeruginosa PAO1 is one of the most versatile microorganisms in the environment. Despite its ubiquitous presence, no studies have shown the differential expression pattern of its toxin-antitoxins, and persistence related genes. The purpose of the following study is to analyze differential expression of P. aeruginosa PAO1 type II toxin-antitoxins and persistence related genes under different growth conditions and to show how their stoichiometric ratio changes during different growth conditions. Differential expression analysis indicated that the toxins and antitoxin pairs behave differently under different growth conditions. In addition, the genes related to persistence presented relatively consistent differential expression pattern under different growth environment. / Master of Science

En Amérique Latine, les punaises hématophages Triatominae transmettent à l’homme le parasite Trypanosoma cruzi, responsable de la maladie de Chagas touchant actuellement 5 millions de personnes. Même si les programmes d’éradication chimique des vecteurs sont efficaces, la maladie persiste du fait de la recolonisation des habitations humaines par des vecteurs provenant d’habitats naturels. Ainsi, certaines espèces présentent une capacité d’adaptation aux anthroposystèmes (processus de domiciliation), alors que d’autres espèces apparentées ne l’ont pas. Comprendre cette capacité d’adaptation est crucial d’un point de vue épidémiologique afin de cibler les espèces présentant un risque pour l’homme. La capacité à s’adapter à un nouvel habitat pourrait être liée à l’évolution du répertoire de gènes du système chimiosensoriel, important pour la perception du milieu. Cette étude a porté sur le système chimiosensoriel des Triatominae dans le but de documenter le processus d’adaptation et donc de domiciliation des vecteurs. Des données transcriptomiques obtenues en séquençage à haut débit ont été utilisées pour annoter et répertorier les gènes chimiosensoriels ainsi que pour comparer leur expression au sein de punaises hématophages d’habitats différents. L’existence d’une relation entre les variations de ces gènes chez différentes espèces de Triatominae et leur capacité d’adaptation à un habitat a par la suite été évaluée. L’espèce T. brasiliensis en voie de domiciliation au Brésil et présentant à la fois des populations sylvatiques, péri-domiciliaires et domiciliaires, et différentes espèces du genre Rhodnius d’habitats variés, ont été étudiées, notamment les deux espèces sœurs, R. robustus, sylvatique en Amazonie et R. prolixus majoritairement domiciliée dans toute son aire de répartition. En l’absence de génomes de références suffisamment proches de T. brasiliensis et des 10 espèces de Rhodnius étudiées, leurs transcriptomes ont été assemblés de novo. Les transcriptomes des deux espèces R. prolixus et R. robustus ont été assemblés par alignement sur le génome de R. prolixus. Chez ces différentes espèces de Triatominae étudiées, l’analyse du répertoire des gènes chimiosensoriels codant les OBPs et CSPs (familles multigéniques) comparé à celui d’autres Paranéoptères a montré des expansions géniques pouvant refléter des processus adaptatifs. Par ailleurs, chez les différentes espèces du genre Rhodnius, il existe une corrélation positive entre le nombre de gènes codant les OBPs et la capacité de domiciliation, suggérant l’implication de cette famille de gènes dans l’adaptation au milieu anthropique. Les analyses d’expression différentielle concernant les différentes populations de T. brasiliensis et les espèces R. prolixus/R. robustus ont montré qu’un certain nombre de transcrits sont différentiellement exprimés selon l’environnement dans lequel ont évolué les punaises notamment des gènes chimiosensoriels (OBPs, CSPs) ainsi que des gènes impliqués dans le rythme circadien et le comportement de recherche alimentaire (Takeout), dans la réponse à des stress environnementaux comme des gènes de détoxification (P450, glutathione S-transférase), dans la résistance à des changements climatiques (Heat-shock protéines) et dans la protection du milieu extérieur (protéines cuticulaires). Ce travail a permis de mettre à la disposition de la communauté scientifique des outils performants pour l’étude du processus de domiciliation des vecteurs de la maladie de Chagas (transcriptome, répertoire de gènes). Il a également permis de révéler des gènes qui pourraient être impliqués dans l’adaptation et/ou la plasticité phénotypique en réponse à un changement d’habitat. La compréhension des bases moléculaires de l’adaptation des vecteurs aux habitations humaines ouvre des potentialités de développer des méthodes alternatives de lutte contre les vecteurs qui pourraient être basées sur une perturbation de la communication chimique. / In Latin America, the bloodsucking bugs (Triatominae, Hemiptera, Reduviidae) are vectors of the parasite Trypanosoma cruzi, which causes Chagas disease. More than five million people are infected. Even if chemical control campaigns are effective against vectors, the disease persists due to the recolonization of human habitations by vectors from natural habitats. Some species have the capacity to adapt to anthroposystems (domiciliation process), while other related species do not. Understanding this capacity to adapt is crucial from an epidemiological perspective to target species at risk to humans. The capacity to adapt to a new habitat could be linked to changes in the repertoire of chemosensory system genes, particularly for odorant binding proteins (OBP) and chemosensory proteins (CSP), which are important proteins to detect various odor stimuli. This study is based on the chemosensory system of Triatominae to document the adaptation process and then the domiciliation of the vectors. Transcriptomic data obtained by high-throughput sequencing were used to annotate and list the chemosensory genes and also to compare their expression in bloodsucking bugs from different habitats. The relationship between changes in these genes in different Triatominae species and their ability to adapt to a new habitat was evaluated. The species T. brasiliensis, which is in the process of domiciliation in Brazil with sylvatic, peridomiciliary and domiciliary populations, and various species of the genus Rhodnius from diverse habitats were studied, especially the two sibling species R. robustus, sylvatic in the Amazonia and R. prolixus mostly domiciliary throughout its geographical range. In the absence of a reference genome for T. brasiliensis, a reference transcriptome via de novo assembly (data 454 and Illumina) was achieved. The reference transcriptomes for 10 Rhodnius species were also established using the de novo assembly method. A genome reference based method on R. prolixus was also used to assemble the transcriptome of the two species R. prolixus and R. robustus. In the different species of the Triatominae studied, the chemosensory gene repertoire showed a high diversity and genic expansions compared to that of others Paraneoptera, which could reflect adaptive process. Furthermore, a positive correlation was shown between the number of OBP genes in Rhodnius species and their domiciliation ability, suggesting that this gene family is involved in the adaptation to anthropogenic environment. The differential expression analyses on the T. brasiliensis populations and the R. prolixus / R. robustus species showed that some transcripts are differentially expressed according to the environment in which the bugs have evolved, especially the chemosensory genes (OBP, CSP) and also genes involved in the circadian rhythm and foraging behavior (Takeout), in the response to environmental stress such as detoxification genes (P450, glutathione S-transferase), in resistance to climatic changes (heat-shock proteins) and in protection from the external environment (cuticular proteins).This work has helped make available to the scientific community powerful tools for studying the process of domiciliation of Chagas disease vectors (transcriptome, gene repertoire). It also revealed genes that could be involved in the adaptation and/or phenotypic plasticity in response to a change in habitat. Understanding the molecular basis of vector adaptation to human dwellings opens the potential to develop new tools to control the disease vectors, for example by disrupting chemical communication.

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.

Weed resistance to control measures, particularly herbicides, is a growing problem in agriculture. In the case of herbicides, resistance is sometimes connected to genetic changes that directly affect the target site of the herbicide. Other cases are less straightforward where resistance arises without such a clear-cut mechanism. Understanding the genetic and gene regulatory mechanisms that may lead to the rapid evolution of resistance in weedy species is critical to securing our food supply. To study this phenomenon, we exposed young Arabidopsis plants to sublethal levels of one of four weed management stressors, glyphosate herbicide, trifloxysulfuron herbicide, mechanical clipping, and shading. To evaluate responses to these stressors we collected data on gene expression and regulation via epigenetic modification (methylation) and small RNA (sRNA). For all of the treatments except shade, the stress was limited in duration, and the plants were allowed to recover until flowering, to identify changes that persist to reproduction. At flowering, DNA for methylation bisulfite sequencing, RNA, and sRNA were extracted from newly formed rosette leaf tissue. Analyzing the individual datasets revealed many differential responses when compared to the untreated control for gene expression, methylation, and sRNA expression. All three measures showed increases in differential abundance that were unique to each stressor, with very little overlap between stressors. Herbicide treatments tended to exhibit the largest number of significant differential responses, with glyphosate treatment most often associated with the greatest differences and contributing to overlap. To evaluate how large datasets from methylation, gene expression, and sRNA analyses could be connected and mined to link regulatory information with changes in gene expression, the information from each dataset and for each gene was united in a single large matrix and mined with classification algorithms. Although our models were able to differentiate patterns in a set of simulated data, the raw datasets were too noisy for the models to consistently identify differentially expressed genes. However, by focusing on responses at a local level, we identified several genes with differential expression, differential sRNA, and differential methylation. While further studies will be needed to determine whether these epigenetic changes truly influence gene expression at these sites, the changes detected at the treatment level could prime the plants for future incidents of stress, including herbicides. / Doctor of Philosophy / Growing resistance to herbicides, particularly glyphosate, is one of the many problems facing agriculture. The rapid rise of resistance across herbicide classes has caused some to wonder if there is a mechanism of adaptation that does not involve mutations. Epigenetics is the study of changes in the phenotype that cannot be attributed to changes in the genotype. Typically, studies revolve around two features of the chromosomes: cytosine methylation and histone modifications. The former can influence how proteins interact with DNA, and the latter can influence protein access to DNA. Both can affect each other in self-reinforcing loops. They can affect gene expression, and DNA methylation can be directed by small RNA (sRNA), which can also influence gene expression through other pathways. To study these processes and their role in abiotic stress response, we aimed to analyze sRNA, RNA, and DNA from Arabidopsis thaliana plants under stress. The stresses applied were sublethal doses of the herbicides, glyphosate and trifloxysulfuron, as well as mechanical clipping and shade to represent other weed management stressors. The focus of the project was to analyze these responses individually and together to find epigenetic responses to stresses routinely encountered by weeds. We tested RNA for gene expression changes under our stress conditions and identified many, including some pertaining to DNA methylation regulation. The herbicide treatments were associated with upregulated defense genes and downregulated growth genes. Shade treated plants had many downregulated defense and other stress response genes. We also detected differential methylation and sRNA responses when compared to the control plants. Changes to methylation and sRNA only accounted for about 20% of the variation in gene expression. While attempting to link the epigenetic process of methylation to gene expression, we connected all the data sets and developed computer programs to try to make correlations. While these methods worked on a simulated dataset, we did not detect broad patterns of changes to epigenetic pathways that correlated strongly with gene expression in our experiment's data. There are many factors that can influence gene expression that could create noise that would hinder the algorithms' abilities to detect differentially expressed genes. This does not, however, rule out the possibility of epigenetic influence on gene expression in local contexts. Through scoring the traits of individual genes, we found several that interest us for future studies.

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