>Magister Scientiae - MSc / Type 2 diabetes mellitus (T2DM), a metabolic disease characterized by chronic hyperglycemia, is the most prevalent form of diabetes globally, affecting approximately 95 % of the total number of people with diabetes i.e. approximately 366 million. Furthermore, it is also the most prevalent form in South Africa (SA), affecting approximately 3.5 million individuals. This disease and its adverse complications can be delayed or prevented if detected early. Standardized diagnostic tests for T2DM have a few limitations which include the inability to predict the future risk of normal glucose tolerance individuals developing T2DM, they are dependent on blood glucose concentration, its invasiveness, and they cannot specify between T1DM and T2DM. Therefore, there is a need for biomarkers which could be used as a tool for the early and specific detection of T2DM. MicroRNAs are small non-coding RNA molecules which play a key role in controlling gene expression and certain biological processes. Studies show that dysregulation of microRNAs may lead to various diseases including T2DM, and thus, may be useful biomarkers for disease detection. Therefore, identifying biomarkers like microRNAs as a tool for the early and specific detection of T2DM, have great potential for diagnostic purposes. The main focus of this investigation, therefore, is the early detection of T2DM by the identification and validation of novel biomarkers. Furthermore, based on previous studies, the aim of the investigation was to identify differentially expressed miRNAs as well as identify their potential target genes associated with the onset and progression of T2DM. An in silico approach was used to identify miRNAs found to be differentially expressed in the serum/plasma of T2DM individuals. Three publically available target prediction software were used for target gene prediction of the identified miRNA. The target genes were subjected to functional analysis using a web-based software, namely DAVID. Functions which were clustered with an enrichment score > 1.3 were considered significant. The ranked target genes mostly had gene ontologies linked with “transcription regulation”, “neuron signalling, and “metal ion binding”. The ranked target genes were then split into two lists – an up-regulated (ur) miRNA targeted gene list and a down-regulated (dr) miRNA targeted gene list. The in silico method used in this investigation produced a final total of 4 miRNAs: miR-dr-1, miR-ur-1, miR-ur-2, and miR-ur-3. Based on the bioinformatics results, miR-dr-1 and its target genes LDLR, PPARA and CAMTA1, seemed the most promising miRNA for biomarker validation, due to the function of the target genes being associated with T2DM onset and progression. The expression levels of the miRNAs were then profiled in kidney tissue of male Wistar rats that were on a high fat diet (HFD), streptozotocin (STZ)-induced T1DM, and non-diabetic control rats via qRT-PCR analysis. The hypothesis was that similar miRNA expression would be found in the HFD kidney samples compared to serum expression levels of the miRNA obtained from the two databases, since kidneys are involved in cleansing the blood from impurities. This hypothesis proved to be true for all miRNAs except for miR-ur-2. Additionally, miR-ur-1 seemed the most significant miRNA due to it having different expression ratios for T1DM and T2DM (i.e. -7.65 and 4.2 fold, respectively). Future work, therefore, include validation of the predicted target genes to the miRNAs of interest i.e. miR-dr-1: PPARA and LDLR and miR-ur-1: CACNB2, using molecular approaches such as the luciferase assays and western blots.

Le lymphome folliculaire (LF) représente le 2ème lymphome par ordre de fréquence et reste considéré à l’heure actuelle comme incurable. De nombreuses questions sur le processus de lymphomagénèse sont encore non résolues et il n’existe aucun marqueur génomique ou moléculaire unanimement reconnu permettant de prédire l’évolution des patients. Nos travaux de recherche s’inscrivent dans l’objectif de mieux comprendre l’impact des altérations moléculaires identifiées dans ces tumeurs, grâce à une approche intégrative visant à combiner des données génomiques, transcriptomiques et mutationnelles. Ce travail a permis de construire un score, basé sur l’expression d’un panel de gènes, prédictif du risque de progression de la maladie. Ce score a été confirmé sur une seconde cohorte de patients, validant son utilité potentielle en pratique clinique. Par ailleurs, nos résultats suggèrent que les cellules tumorales peuvent acquérir des propriétés évocatrices d’un profil de cellules souches et associées à un pronostic particulièrement défavorable. Une 2ème partie de notre travail a porté sur les altérations touchant le gène EZH2, muté chez 25% des patients. Nous avons démontré qu’un gain génomique au niveau du locus EZH2 pouvait également avoir des conséquences sur le profil transcriptomique et un impact pronostique, soulignant l’importance de prendre en compte l’ensemble des anomalies touchant ce gène. Enfin, nous rapportons qu’un polymorphisme constitutionnel situé dans ce gène est associé au risque de progression des patients traités par un anticorps anti-CD20. L’ensemble de ces résultats apporte un éclairage nouveau sur la biologie du LF et peut contribuer à améliorer la prise en charge des patients / Follicular Lymphoma (FL) is the 2nd most frequent lymphoma subtype and is usually considered incurable with current strategies. Several questions regarding the lymphomagenesis process are still pending, and no molecular or genomic marker has been unanimously recognized yet to predict outcome. We performed an integrative analysis combining genomic, transcriptomic and mutational data in the view to bringing new highlights in the molecular alterations acting in FL. Based on gene-expression profiling data we developed a model able to predict progression-free survival in FL patients. We confirmed its predictive value in another cohort of patients, thereby allowing its potential use in clinical practice. Furthermore, our results highlight that some tumors show a stem-cell-like gene-expression profile that was associated with highly unfavorable outcome. In the second part of our work, we focused on alterations of the gene EZH2. Although mutations have been reported in 25% of FL patients, we questioned whether genomic gains at EZH2 locus could also contribute to lymphomagenesis. We showed that such gain may impact the transcriptional profile and have a prognostic significance, thus highlighting the crucial interest of determining both kinds of alterations. Finally, we report that a germ-line polyporphism in the EZH2 gene was significantly associated with progression-free survival in patients treated by anti-CD20 therapy. Taken together, these results bring new highlights on FL biology and may help to improve the clinical management of FL patients

Ribosome profiling (RP) is a novel technique that exploits RNA sequencing and ribosome immobilization to quantify transcription and translation at different cell growth stages. Therefore, RP provides invaluable information for expression dynamics studies. Quantitative –omics studies are of crucial importance for identification of potential biomarkers of infection. An ideal parasite detection system should definitely establish the presence or absence of infection; determine the species involved; be detectable even in low concentrations; be proportional to parasite density; and determine the presence of antibiotic resistance. Here, we propose a simple workflow that attempts to identify a set of biomarkers that fulfill some of the above criteria for the ideal detection system. RP expression profiles were ranked for abundance, crosschecked with PlasmoDB for homogeneity along infection cycles and probed for availability of structural stability. The latter is of fundamental importance for the development of molecular biosensors to be give birth to rapid diagnostic kits. In addition, a simple biochemistry workflow was developed for easy production of the selected biomarkers in E. coli. Altogether, the present work provides two complementary and novel workflows that shall aid researchers to rapidly produce molecular biomarkers and develop biosensors based on antibodies or aptamers. / Tesis

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The lymphocyte-specific transcription factor Interferon (IFN) Regulatory Factor 4 (IRF4) is implicated in certain types of lymphoid and myeloid malignancies. However, the molecular mechanisms underlying its interactions with these malignancies are largely unknown. In this study, we have first profiled molecular signatures associated with IRF4 expression in associated cancers, by analyzing existing gene expression profiling datasets. Our results show that IRF4 is overexpressed in melanoma, in addition to previously reported contexts including leukemia, myeloma, and lymphoma, and that IRF4 is associated with a unique gene expression pattern in each context. A pool of important genes involved in B-cell development, oncogenesis, cell cycle regulation, and cell death including BATF, LIMD1, CFLAR, PIM2, and CCND2 are common signatures associated with IRF4 in non-Hodgkin B cell lymphomas. We confirmed the correlation of IRF4 with LIMD1 and CFLAR in a panel of cell lines derived from lymphomas. Moreover, we profiled the IRF4 transcriptome in the context of EBV latent infection, and confirmed several genes including IFI27, IFI44, GBP1, and ARHGAP18, as well as CFLAR as novel targets for IRF4. These results provide valuable information for understanding the IRF4 regulatory network, and improve our knowledge of the unique roles of IRF4 in different hematological malignancies.

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The response of plants to environmental stress spans several orders of magnitude in time and space, causing system-wide changes. These changes comprise of both protective responses and adverse reactions in the plant. Stresses like water deficit or drought cause a drastic effect in crop yield, while concomitantly agriculture consumes 1/3rd of the fresh water available to us and there is widespread water scarcity around the world. It is, hence, a fundamental goal of modern biology and applied biotechnology to unravel this complex stress response in laboratory model plants like Arabidopsis and crop models like rice. Such an understanding, especially at the cellular level, will aid in informed engineering of stress tolerance in plants. We have developed and used integrative functional genomics approaches to characterize environmental stress response at various levels of organization including genes, modules and networks in Arabidopsis and rice. We have also applied these methods in problems concerning bioenergy. Since the poor knowledge of the cellular roles of a large portion of plant genes remains a fundamental barrier to using such approaches, we have further explored the problem of 'gene function prediction'. And, finally, as a contribution to the community, we have curated a large mutant resource for the crop model, rice, and established a web resource for exploratory analysis of abiotic stress in this model. All together, this work presents insights into several facets of stress response, offers numerous novel predictions for experimental validation, and provides principled analysis frameworks for systems level analysis of environmental stress response in plants. / Ph. D.

BACKGROUND: The course of disease for patients with idiopathic pulmonary fibrosis (IPF) is highly heterogeneous. Prognostic models rely on demographic and clinical characteristics and are not reproducible. Integrating data from genomic analyses may identify novel prognostic models and provide mechanistic insights into IPF. METHODS: Total RNA of peripheral blood mononuclear cells was subjected to microarray profiling in a training (45 IPF individuals) and two independent validation cohorts (21 IPF/10 controls, and 75 IPF individuals, respectively). To identify a gene set predictive of IPF prognosis, we incorporated genomic, clinical, and outcome data from the training cohort. Predictor genes were selected if all the following criteria were met: 1) Present in a gene co-expression module from Weighted Gene Co-expression Network Analysis (WGCNA) that correlated with pulmonary function (p < 0.05); 2) Differentially expressed between observed "good" vs. "poor" prognosis with fold change (FC) >1.5 and false discovery rate (FDR) < 2 %; and 3) Predictive of mortality (p < 0.05) in univariate Cox regression analysis. "Survival risk group prediction" was adopted to construct a functional genomic model that used the IPF prognostic predictor gene set to derive a prognostic index (PI) for each patient into either high or low risk for survival outcomes. Prediction accuracy was assessed with a repeated 10-fold cross-validation algorithm and independently assessed in two validation cohorts through multivariate Cox regression survival analysis. RESULTS: A set of 118 IPF prognostic predictor genes was used to derive the functional genomic model and PI. In the training cohort, high-risk IPF patients predicted by PI had significantly shorter survival compared to those labeled as low-risk patients (log rank p < 0.001). The prediction accuracy was further validated in two independent cohorts (log rank p < 0.001 and 0.002). Functional pathway analysis revealed that the canonical pathways enriched with the IPF prognostic predictor gene set were involved in T-cell biology, including iCOS, T-cell receptor, and CD28 signaling. CONCLUSIONS: Using supervised and unsupervised analyses, we identified a set of IPF prognostic predictor genes and derived a functional genomic model that predicted high and low-risk IPF patients with high accuracy. This genomic model may complement current prognostic tools to deliver more personalized care for IPF patients.